Search results for: numerical validation

Authors: Maddalena Arigoni, Maria Luisa Ratto, Raffaele A. Calogero, Luca Alessandri

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The presence of tumor heterogeneity, where distinct cancer cells exhibit diverse morphological and phenotypic profiles, including gene expression, metabolism, and proliferation, poses challenges for molecular prognostic markers and patient classification for targeted therapies. Understanding the causes and progression of cancer requires research efforts aimed at characterizing heterogeneity, which can be facilitated by evolving single-cell sequencing technologies. However, analyzing single-cell data necessitates computational methods that often lack objective validation. Therefore, the establishment of benchmarking datasets is necessary to provide a controlled environment for validating bioinformatics tools in the field of single-cell oncology. Benchmarking bioinformatics tools for single-cell experiments can be costly due to the high expense involved. Therefore, datasets used for benchmarking are typically sourced from publicly available experiments, which often lack a comprehensive cell annotation. This limitation can affect the accuracy and effectiveness of such experiments as benchmarking tools. To address this issue, we introduce omics benchmark experiments designed to evaluate bioinformatics tools to depict the heterogeneity in single-cell tumor experiments. We conducted single-cell RNA sequencing on six lung cancer tumor cell lines that display resistant clones upon treatment of EGFR mutated tumors and are characterized by driver genes, namely ROS1, ALK, HER2, MET, KRAS, and BRAF. These driver genes are associated with downstream networks controlled by EGFR mutations, such as JAK-STAT, PI3K-AKT-mTOR, and MEK-ERK. The experiment also featured an EGFR-mutated cell line. Using 10XGenomics platform with cellplex technology, we analyzed the seven cell lines together with a pseudo-immunological microenvironment consisting of PBMC cells labeled with the Biolegend TotalSeq™-B Human Universal Cocktail (CITEseq). This technology allowed for independent labeling of each cell line and single-cell analysis of the pooled seven cell lines and the pseudo-microenvironment. The data generated from the aforementioned experiments are available as part of an online tool, which allows users to define cell heterogeneity and generates count tables as an output. The tool provides the cell line derivation for each cell and cell annotations for the pseudo-microenvironment based on CITEseq data by an experienced immunologist. Additionally, we created a range of pseudo-tumor tissues using different ratios of the aforementioned cells embedded in matrigel. These tissues were analyzed using 10XGenomics (FFPE samples) and Curio Bioscience (fresh frozen samples) platforms for spatial transcriptomics, further expanding the scope of our benchmark experiments. The benchmark experiments we conducted provide a unique opportunity to evaluate the performance of bioinformatics tools for detecting and characterizing tumor heterogeneity at the single-cell level. Overall, our experiments provide a controlled and standardized environment for assessing the accuracy and robustness of bioinformatics tools for studying tumor heterogeneity at the single-cell level, which can ultimately lead to more precise and effective cancer diagnosis and treatment.

Keywords: single cell omics, benchmark, spatial transcriptomics, CITEseq

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Authors: Michelangelo Sofo, Giuseppe Labianca

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In recent years, the scientific community's interest in the exploratory analysis of biomedical data has increased exponentially. Considering the field of research of nutritional biologists, the curative process, based on the analysis of clinical data, is a very delicate operation due to the fact that there are multiple solutions for the management of pathologies in the food sector (for example can recall intolerances and allergies, management of cholesterol metabolism, diabetic pathologies, arterial hypertension, up to obesity and breathing and sleep problems). In this regard, in this research work a system was created capable of evaluating various dietary regimes for specific patient pathologies. The system is founded on a mathematical-numerical model and has been created tailored for the real working needs of an expert in human nutrition using the human-centered design (ISO 9241-210), therefore it is in step with continuous scientific progress in the field and evolves through the experience of managed clinical cases (machine learning process). DietAdhoc® is a decision support system nutrition specialists for patients of both sexes (from 18 years of age) developed with an agile methodology. Its task consists in drawing up the biomedical and clinical profile of the specific patient by applying two algorithmic optimization approaches on nutritional data and a symbolic solution, obtained by transforming the relational database underlying the system into a deductive database. For all three solution approaches, particular emphasis has been given to the explainability of the suggested clinical decisions through flexible and customizable user interfaces. Furthermore, the system has multiple software modules based on time series and visual analytics techniques that allow to evaluate the complete picture of the situation and the evolution of the diet assigned for specific pathologies.

Keywords: medical decision support, physiological data extraction, data driven diagnosis, human centered AI, symbiotic AI paradigm

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Authors: R. Sekula

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Epoxy composites are broadly used as an electrical insulation for the high voltage applications since only such materials can fulfill particular mechanical, thermal, and dielectric requirements. However, properties of the final product are strongly dependent on proper manufacturing process with minimized material failures, as too large shrinkage, voids and cracks. Therefore, application of proper materials (epoxy, hardener, and filler) and process parameters (mold temperature, filling time, filling velocity, initial temperature of internal parts, gelation time), as well as design and geometric parameters are essential features for final quality of the produced components. In this paper, an approach for three-dimensional modeling of all molding stages, namely filling, curing and post-curing is presented. The reactive molding simulation tool is based on a commercial CFD package, and include dedicated models describing viscosity and reaction kinetics that have been successfully implemented to simulate the reactive nature of the system with exothermic effect. Also a dedicated simulation procedure for stress and shrinkage calculations, as well as simulation results are presented in the paper. Second part of the paper is dedicated to recent developments on formulations of functional composites for electrical insulation applications, focusing on thermally conductive materials. Concepts based on filler modifications for epoxy electrical composites have been presented, including the results of the obtained properties. Finally, having in mind tough environmental regulations, in addition to current process and design aspects, an approach for product re-design has been presented focusing on replacement of epoxy material with the thermoplastic one. Such “design-for-recycling” method is one of new directions associated with development of new material and processing concepts of electrical products and brings a lot of additional research challenges. For that, one of the successful products has been presented to illustrate the presented methodology.

Keywords: curing, epoxy insulation, numerical simulations, recycling

Procedia PDF Downloads 271

Authors: Mehdi Behbahani, Sebastian Rible, Charles Moulinec, Yvan Fournier, Mike Nicolai, Paolo Crosetto

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Computational Fluid Dynamics blood-flow simulations are increasingly used to develop and validate blood-contacting medical devices. This study shows that numerical simulations can provide additional and accurate estimates of relevant hemodynamic indicators (e.g., recirculation zones or wall shear stresses), which may be difficult and expensive to obtain from in-vivo or in-vitro experiments. The most recent FDA (Food and Drug Administration) benchmark consisted of a simplified centrifugal blood pump model that contains fluid flow features as they are commonly found in these devices with a clear focus on highly turbulent phenomena. The FDA centrifugal blood pump study is composed of six test cases with different volumetric flow rates ranging from 2.5 to 7.0 liters per minute, pump speeds, and Reynolds numbers ranging from 210,000 to 293,000. Within the frame of this study different turbulence models were tested including RANS models, e.g. k-omega, k-epsilon and a Reynolds Stress Model (RSM) and, LES. The partitioners Hilbert, METIS, ParMETIS and SCOTCH were used to create an unstructured mesh of 76 million elements and compared in their efficiency. Computations were performed on the JUQUEEN BG/Q architecture applying the highly parallel flow solver Code SATURNE and typically using 32768 or more processors in parallel. Visualisations were performed by means of PARAVIEW. Different turbulence models including all six flow situations could be successfully analysed and validated against analytical considerations and from comparison to other data-bases. It showed that an RSM represents an appropriate choice with respect to modeling high-Reynolds number flow cases. Especially, the Rij-SSG (Speziale, Sarkar, Gatzki) variant turned out to be a good approach. Visualisation of complex flow features could be obtained and the flow situation inside the pump could be characterized.

Keywords: blood flow, centrifugal blood pump, high performance computing, scalability, turbulence

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Authors: R. Meier, M. Pander

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In accordance with the industry 4.0 concept, manufacturing process steps as well as the materials themselves are going to be more and more digitalized within the next years. The “digital twin” representing the simulated and measured dataset of the (semi-finished) product can be used to control and optimize the individual processing steps and help to reduce costs and expenditure of time in product development, manufacturing, and recycling. In the present work, two material characterization methods based on Lamb waves were evaluated and compared. For demonstration purpose, both methods were shown at a standard industrial product - copper ribbons, often used in photovoltaic modules as well as in high-current microelectronic devices. By numerical approximation of the Rayleigh-Lamb dispersion model on measured phase velocities second order elastic constants (Young’s modulus, Poisson’s ratio) were determined. Furthermore, the effective third order elastic constants were evaluated by applying elastic, “non-destructive”, mechanical stress on the samples. In this way, small microstructural variations due to mechanical preconditioning could be detected for the first time. Both methods were compared with respect to precision and inline application capabilities. Microstructure of the samples was systematically varied by mechanical loading and annealing. Changes in the elastic ultrasound transport properties were correlated with results from microstructural analysis and mechanical testing. In summary, monitoring the elastic material properties of plate-like structures using Lamb waves is valuable for inline and non-destructive material characterization and manufacturing process control. Second order elastic constants analysis is robust over wide environmental and sample conditions, whereas the effective third order elastic constants highly increase the sensitivity with respect to small microstructural changes. Both Lamb wave based characterization methods are fitting perfectly into the industry 4.0 concept.

Keywords: lamb waves, industry 4.0, process control, elasticity, acoustoelasticity, microstructure

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Authors: Adenike Adesanya, Nonthaphat Wong, Xiang-Yun Lan, Shea Ping Yip, Chien-Ling Huang

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Background: The most challenging mutation of the oncokinase BCR-ABL protein T315I, which is commonly known as the “gatekeeper” mutation and is notorious for its strong resistance to almost all tyrosine kinase inhibitors (TKIs), especially imatinib. Therefore, this study aims to identify T315I-dependent downstream microRNA (miRNA) pathways associated with drug resistance in chronic myeloid leukemia (CML) for prognostic and therapeutic purposes. Methods: T315I-carrying K562 cell clones (K562-T315I) were generated by the CRISPR-Cas9 system. Imatinib-treated K562-T315I cells were subjected to small RNA library preparation and next-generation sequencing. Putative lncRNA-miRNA-mRNA networks were analyzed with (i) DESeq2 to extract differentially expressed miRNAs, using Padj value of 0.05 as cut-off, (ii) STarMir to obtain potential miRNA response element (MRE) binding sites of selected miRNAs on lncRNA H19, (iii) miRDB, miRTarbase, and TargetScan to predict mRNA targets of selected miRNAs, (iv) IntaRNA to obtain putative interactions between H19 and the predicted mRNAs, (v) Cytoscape to visualize putative networks, and (vi) several pathway analysis platforms – Enrichr, PANTHER and ShinyGO for pathway enrichment analysis. Moreover, mitochondria isolation and transcript quantification were adopted to determine the new mechanism involved in T315I-mediated resistance of CML treatment. Results: Verification of the CRISPR-mediated mutagenesis with digital droplet PCR detected the mutation abundance of ≥80%. Further validation showed the viability of ≥90% by cell viability assay, and intense phosphorylated CRKL protein band being detected with no observable change for BCR-ABL and c-ABL protein expressions by Western blot. As reported by several investigations into hematological malignancies, we determined a 7-fold increase of H19 expression in K562-T315I cells. After imatinib treatment, a 9-fold increment was observed. DESeq2 revealed 171 miRNAs were differentially expressed K562-T315I, 112 out of these miRNAs were identified to have MRE binding regions on H19, and 26 out of the 112 miRNAs were significantly downregulated. Adopting the seed-sequence analysis of these identified miRNAs, we obtained 167 mRNAs. 6 hub miRNAs (hsa-let-7b-5p, hsa-let-7e-5p, hsa-miR-125a-5p, hsa-miR-129-5p, and hsa-miR-372-3p) and 25 predicted genes were identified after constructing hub miRNA-target gene network. These targets demonstrated putative interactions with H19 lncRNA and were mostly enriched in pathways related to cell proliferation, senescence, gene silencing, and pluripotency of stem cells. Further experimental findings have also shown the up-regulation of mitochondrial transcript and lncRNA MALAT1 contributing to the lncRNA-miRNA-mRNA networks induced by BCR-ABL T315I mutation. Conclusions: Our results have indicated that lncRNA-miRNA regulators play a crucial role not only in leukemogenesis but also in drug resistance, considering the significant dysregulation and interactions in the K562-T315I cell model generated by CRISPR-Cas9. In silico analysis has further shown that lncRNAs H19 and MALAT1 bear several complementary miRNA sites. This implies that they could serve as a sponge, hence sequestering the activity of the target miRNAs.

Keywords: chronic myeloid leukemia, imatinib resistance, lncRNA-miRNA-mRNA, T315I mutation

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Authors: Mohamed F. Othman

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Manchester, Britain has become the destination of immigrants from different parts of the world. As a result, it is currently home to over 150 different ethnic languages. The present study investigates minority language policy and planning at the micro-level of the city. In order to get an in-depth investigation of such a policy, it was decided to cover it from two angles: the first is the policy making process. This was aimed at getting insights on how decisions regarding the provision of government services in minority languages are taken and what criteria are employed. The second angle is the service provider; i.e. the different departments in Manchester City Council (MCC), the NHS, the courts, and police, etc., to obtain information on the actual provisions of services. Data was collected through semi-structured interviews with different personnel representing different departments in MCC, solicitors, interpreters, etc.; through the internet, e.g. the websites of MCC, NHS, courts, and police, etc.; and via personal observation of provisions of community languages in government services. The results show that Manchester’s language policy is formulated around two concepts that work simultaneously: one is concerned with providing services in community languages in order to help minorities manage their life until they acquire English, and the other with helping the integration of minorities through encouraging them to learn English. In this regard, different government services are provided in community languages, though to varying degrees, depending on the numerical strength of each individual language. Thus, it is concluded that there is awareness in MCC and other government agencies working in Manchester of the linguistic diversity of the city and there are serious attempts to meet this diversity in their services. It is worth mentioning here that providing such services in minority languages are not meant to support linguistic diversity, but rather to maintain the legal right to equal opportunities among the residents of Manchester and to avoid any misunderstanding that may result due to the language barrier, especially in such areas as hospitals, courts, and police. There is actually no explicitly-mentioned language policy regarding minorities in Manchester; rather, there is an implied or covert policy resulting from factors that are not explicitly documented. That is, there are guidelines from the central government, which emphasize the principle of equal opportunities; then the implementation of such guidelines requires providing services in the different ethnic languages.

Keywords: community language, covert language policy, micro-language policy and planning, minority language

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Authors: Ho-Won Jung, Donghun Lee, Hyung-Jin Kim

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Predictive analytics (data analysis) uses a subset of measurements (the features, predictor, or independent variable) to predict another measurement (the outcome, target, or dependent variable) on a single person or unit. It applies empirical methods in statistics, operations research, and machine learning to predict the future, or otherwise unknown events or outcome on a single or person or unit, based on patterns in data. Most analyses of metabolic syndrome are not predictive analytics but statistical explanatory studies that build a proposed model (theory building) and then validate metabolic syndrome predictors hypothesized (theory testing). A proposed theoretical model forms with causal hypotheses that specify how and why certain empirical phenomena occur. Predictive analytics and explanatory modeling have their own territories in analysis. However, predictive analytics can perform vital roles in explanatory studies, i.e., scientific activities such as theory building, theory testing, and relevance assessment. In the context, this study is to demonstrate how to use our predictive analytics to support theory building (i.e., hypothesis generation). For the purpose, this study utilized a big data predictive analytics platform TM based on a co-occurrence graph. The co-occurrence graph is depicted with nodes (e.g., items in a basket) and arcs (direct connections between two nodes), where items in a basket are fully connected. A cluster is a collection of fully connected items, where the specific group of items has co-occurred in several rows in a data set. Clusters can be ranked using importance metrics, such as node size (number of items), frequency, surprise (observed frequency vs. expected), among others. The size of a graph can be represented by the numbers of nodes and arcs. Since the size of a co-occurrence graph does not depend directly on the number of observations (transactions), huge amounts of transactions can be represented and processed efficiently. For a demonstration, a total of 13,254 metabolic syndrome training data is plugged into the analytics platform to generate rules (potential hypotheses). Each observation includes 31 predictors, for example, associated with sociodemographic, habits, and activities. Some are intentionally included to get predictive analytics insights on variable selection such as cancer examination, house type, and vaccination. The platform automatically generates plausible hypotheses (rules) without statistical modeling. Then the rules are validated with an external testing dataset including 4,090 observations. Results as a kind of inductive reasoning show potential hypotheses extracted as a set of association rules. Most statistical models generate just one estimated equation. On the other hand, a set of rules (many estimated equations from a statistical perspective) in this study may imply heterogeneity in a population (i.e., different subpopulations with unique features are aggregated). Next step of theory development, i.e., theory testing, statistically tests whether a proposed theoretical model is a plausible explanation of a phenomenon interested in. If hypotheses generated are tested statistically with several thousand observations, most of the variables will become significant as the p-values approach zero. Thus, theory validation needs statistical methods utilizing a part of observations such as bootstrap resampling with an appropriate sample size.

Keywords: explanatory modeling, metabolic syndrome, predictive analytics, theory building

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Authors: Xiaobin Liang, Wei Liang, Laibin Zhang, Xiaoyan Guo

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Pipelines pass through coal mined gobs inevitably in the mining area, the stability of which has great influence on the safety of pipelines. After extensive literature study and field research, it was found that there are a few risk assessment methods for coal mined gob pipelines, and there is a lack of data on the gob sites. Therefore, the fuzzy comprehensive evaluation method is widely used based on expert opinions. However, the subjective opinions or lack of experience of individual experts may lead to inaccurate evaluation results. Hence the accuracy of the results needs to be further improved. This paper presents a comprehensive approach to achieve this purpose by combining bow-tie model and cloud inference. The specific evaluation process is as follows: First, a bow-tie model composed of a fault tree and an event tree is established to graphically illustrate the probability and consequence indicators of pipeline failure. Second, the interval estimation method can be scored in the form of intervals to improve the accuracy of the results, and the censored mean algorithm is used to remove the maximum and minimum values of the score to improve the stability of the results. The golden section method is used to determine the weight of the indicators and reduce the subjectivity of index weights. Third, the failure probability and failure consequence scores of the pipeline are converted into three numerical features by using cloud inference. The cloud inference can better describe the ambiguity and volatility of the results which can better describe the volatility of the risk level. Finally, the cloud drop graphs of failure probability and failure consequences can be expressed, which intuitively and accurately illustrate the ambiguity and randomness of the results. A case study of a coal mine gob pipeline carrying natural gas has been investigated to validate the utility of the proposed method. The evaluation results of this case show that the probability of failure of the pipeline is very low, the consequences of failure are more serious, which is consistent with the reality.

Keywords: bow-tie model, natural gas pipeline, coal mine gob, cloud inference

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Authors: David Bakker, Nikki Rickard

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Smartphone apps hold immense potential as mental health and wellbeing tools. Support can be made easily accessible and can be used in real-time while users are experiencing distress. Furthermore, data can be collected to enable machine learning and automated tailoring of support to users. While many apps have been developed for mental health purposes, few have adhered to evidence-based recommendations and even fewer have pursued experimental validation. This paper details the development and experimental evaluation of an app, MoodMission, that aims to provide support for low moods and anxiety, help prevent clinical depression and anxiety disorders, and serve as an adjunct to professional clinical supports. MoodMission was designed to deliver cognitive behavioural therapy for specifically reported problems in real-time, momentary interactions. Users report their low moods or anxious feelings to the app along with a subjective units of distress scale (SUDS) rating. MoodMission then provides a choice of 5-10 short, evidence-based mental health strategies called Missions. Users choose a Mission, complete it, and report their distress again. Automated tailoring, gamification, and in-built data collection for analysis of effectiveness was also included in the app’s design. The development process involved construction of an evidence-based behavioural plan, designing of the app, building and testing procedures, feedback-informed changes, and a public launch. A randomized controlled trial (RCT) was conducted comparing MoodMission to two other apps and a waitlist control condition. Participants completed measures of anxiety, depression, well-being, emotional self-awareness, coping self-efficacy and mental health literacy at the start of their app use and 30 days later. At the time of submission (November 2016) over 300 participants have participated in the RCT. Data analysis will begin in January 2017. At the time of this submission, MoodMission has over 4000 users. A repeated-measures ANOVA of 1390 completed Missions reveals that SUDS (0-10) ratings were significantly reduced between pre-Mission ratings (M=6.20, SD=2.39) and post-Mission ratings (M=4.93, SD=2.25), F(1,1389)=585.86, p < .001, np2=.30. This effect was consistent across both low moods and anxiety. Preliminary analyses of the data from the outcome measures surveys reveal improvements across mental health and wellbeing measures as a result of using the app over 30 days. This includes a significant increase in coping self-efficacy, F(1,22)=5.91, p=.024, np2=.21. Complete results from the RCT in which MoodMission was evaluated will be presented. Results will also be presented from the continuous outcome data being recorded by MoodMission. MoodMission was successfully developed and launched, and preliminary analysis suggest that it is an effective mental health and wellbeing tool. In addition to the clinical applications of MoodMission, the app holds promise as a research tool to conduct component analysis of psychological therapies and overcome restraints of laboratory based studies. The support provided by the app is discrete, tailored, evidence-based, and transcends barriers of stigma, geographic isolation, financial limitations, and low health literacy.

Keywords: anxiety, app, CBT, cognitive behavioural therapy, depression, eHealth, mission, mobile, mood, MoodMission

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Authors: Brigita Mieziene, Arunas Emeljanovas, Vida Cesnaitiene, Ingunn Fjortoft, Lise Kjonniksen

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Background. Health-related physical fitness refers to the favorable health status, i.e. ability to perform daily activities with vigor, as well as capacities that are associated with a low risk for development of chronic diseases and premature death. However, in school-aged children it is constantly declining, while some aspects of fitness declined as much as 50 percent during the last two decades, which prognosticates increasingly earlier onset of health problems, decreasing the quality of life of the population and financial burden for the society. Therefore, the goal of the current study was to indicate nationally representative age- and gender-specific reference values of anthropometric measures, musculoskeletal, motor and cardiorespiratory fitness in Lithuanian primary school children from 6 to 10 years. Methods. The study included 3556 students in total, from 73 randomly selected schools. Ethics approval for research by the Kaunas Regional Ethics Committee (No. BE-2-42) was obtained. Physical fitness was measured by the 9-item test battery, developed by Fjørtoft and colleagues. Height and weight were measured and body mass index calculated. Smoothed centile charts were derived using the LMS method. Results. The numerical age- and gender-specific percentile values (3rd, 10th, 25th, 50th, 75th, 90th, and 97th percentile) for anthropometric measures, musculoskeletal, motor and cardiorespiratory fitness were provided. The equivalent smoothed LMS curves were performed. The study indicated 12.5 percent of overweight and 5 percent of obese children in accordance with international gender and age specific norms of body mass index. This data could be used in clinical and educational settings in order to identify the level of individual physical fitness within its different components.

Keywords: fitness, overweight, primary school children, reference values, smoothed percentile curves

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Authors: Vladislav Grigorovitch, Marina Grigorovitch, David Pearlmutter, Erez Gal

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The concept of “electric island” is involved with achieving the balance between the self-power generation ability of each educational institution and energy consumption demand. Photo-Voltaic (PV) solar system installed on the roofs of educational buildings is a common way to absorb the available solar energy and generate electricity for self-consumption and even for returning to the grid. The main objective of this research is to develop and implement an “electric island” monitoring infrastructure for promoting energy efficiency in educational buildings. A microscale monitoring methodology will be developed to provide a platform to estimate energy consumption performance classified by rooms and subspaces rather than the more common macroscale monitoring of the whole building. The monitoring platform will be established on the experimental sites, enabling an estimation and further analysis of the variety of environmental and physical conditions. For each building, separate measurement configurations will be applied taking into account the specific requirements, restrictions, location and infrastructure issues. The direct results of the measurements will be analyzed to provide deeper understanding of the impact of environmental conditions and sustainability construction standards, not only on the energy demand of public building, but also on the energy consumption habits of the children that study in those schools and the educational and administrative staff that is responsible for providing the thermal comfort conditions and healthy studying atmosphere for the children. A monitoring methodology being developed in this research is providing online access to real-time data of Interferential Therapy (IFTs) from any mobile phone or computer by simply browsing the dedicated website, providing powerful tools for policy makers for better decision making while developing PV production infrastructure to achieve “electric islands” in educational buildings. A detailed measurement configuration was technically designed based on the specific conditions and restriction of each of the pilot buildings. A monitoring and analysis methodology includes a large variety of environmental parameters inside and outside the schools to investigate the impact of environmental conditions both on the energy performance of the school and educational abilities of the children. Indoor measurements are mandatory to acquire the energy consumption data, temperature, humidity, carbon dioxide and other air quality conditions in different parts of the building. In addition to that, we aim to study the awareness of the users to the energy consideration and thus the impact on their energy consumption habits. The monitoring of outdoor conditions is vital for proper design of the off-grid energy supply system and validation of its sufficient capacity. The suggested outcomes of this research include: 1. both experimental sites are designed to have PV production and storage capabilities; 2. Developing an online information feedback platform. The platform will provide consumer dedicated information to academic researchers, municipality officials and educational staff and students; 3. Designing an environmental work path for educational staff regarding optimal conditions and efficient hours for operating air conditioning, natural ventilation, closing of blinds, etc.

Keywords: sustainability, electric island, IOT, smart building

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Authors: Vijay Shankar

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In the era of water scarcity, effective use of water via irrigation requires good methods for determining crop water needs. Implementation of irrigation scheduling programs requires an accurate estimate of water use by the crop. Moisture depletion from the root zone represents the consequent crop evapotranspiration (ET). A numerical model for simulating soil water depletion in the root zone has been developed by taking into consideration soil physical properties, crop and climatic parameters. The governing differential equation for unsaturated flow of water in the soil is solved numerically using the fully implicit finite difference technique. The water uptake by plants is simulated by using three different sink functions. The non-linear model predictions are in good agreement with field data and thus it is possible to schedule irrigations more effectively. The present paper describes irrigation scheduling based on moisture depletion from the different layers of the root zone, obtained using different sink functions for three cash, oil and forage crops: cotton, safflower and barley, respectively. The soil is considered at a moisture level equal to field capacity prior to planting. Two soil moisture regimes are then imposed for irrigated treatment, one wherein irrigation is applied whenever soil moisture content is reduced to 50% of available soil water; and other wherein irrigation is applied whenever soil moisture content is reduced to 75% of available soil water. For both the soil moisture regimes it has been found that the model incorporating a non-linear sink function which provides best agreement of computed root zone moisture depletion with field data, is most effective in scheduling irrigations. Simulation runs with this moisture uptake function result in saving 27.3 to 45.5% & 18.7 to 37.5%, 12.5 to 25% % &16.7 to 33.3% and 16.7 to 33.3% & 20 to 40% irrigation water for cotton, safflower and barley respectively, under 50 & 75% moisture depletion regimes over other moisture uptake functions considered in the study. Simulation developed can be used for an optimized irrigation planning for different crops, choosing a suitable soil moisture regime depending upon the irrigation water availability and crop requirements.

Keywords: irrigation water, evapotranspiration, root uptake models, water scarcity

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Authors: U. Ali Rahoma, Nabil Esawy, Fawzia Ibrahim Moursy, A. H. Hassan, Samy A. Khalil, Ashraf S. Khamees

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The design of any solar energy conversion system requires the knowledge of solar radiation data obtained over a long period. Satellite data has been widely used to estimate solar energy where no ground observation of solar radiation is available, yet there are limitations on the temporal coverage of satellite data. Reanalysis is a “retrospective analysis” of the atmosphere parameters generated by assimilating observation data from various sources, including ground observation, satellites, ships, and aircraft observation with the output of NWP (Numerical Weather Prediction) models, to develop an exhaustive record of weather and climate parameters. The evaluation of the performance of reanalysis datasets (ERA-5) for North Africa against high-quality surface measured data was performed using statistical analysis. The estimation of global solar radiation (GSR) distribution over six different selected locations in North Africa during ten years from the period time 2011 to 2020. The root means square error (RMSE), mean bias error (MBE) and mean absolute error (MAE) of reanalysis data of solar radiation range from 0.079 to 0.222, 0.0145 to 0.198, and 0.055 to 0.178, respectively. The seasonal statistical analysis was performed to study seasonal variation of performance of datasets, which reveals the significant variation of errors in different seasons—the performance of the dataset changes by changing the temporal resolution of the data used for comparison. The monthly mean values of data show better performance, but the accuracy of data is compromised. The solar radiation data of ERA-5 is used for preliminary solar resource assessment and power estimation. The correlation coefficient (R2) varies from 0.93 to 99% for the different selected sites in North Africa in the present research. The goal of this research is to give a good representation for global solar radiation to help in solar energy application in all fields, and this can be done by using gridded data from European Centre for Medium-Range Weather Forecasts ECMWF and producing a new model to give a good result.

Keywords: solar energy, solar radiation, ERA-5, potential energy

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Authors: Pinel Sebastien, Bourrin François, De Madron Du Rieu Xavier, Ludwig Wolfgang, Arnau Pedro

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Heavy precipitation constitutes a major meteorological threat in the western Mediterranean. Research has investigated the relationship between the states of the Mediterranean Sea and the atmosphere with the precipitation for short temporal windows. However, at a larger temporal scale, the precursor signals of heavy rainfall in the sea and atmosphere have drawn little attention. Moreover, despite ongoing improvements in numerical weather prediction, the medium-term forecasting of rainfall events remains a difficult task. Here, we aim to investigate the influence of early-spring environmental parameters on the following autumnal heavy precipitations. Hence, we develop a machine learning model to predict extreme autumnal rainfall with a 6-month lead time over the Spanish Catalan coastal area, based on i) the sea pattern (main current-LPC and Sea Surface Temperature-SST) at the mesoscale scale, ii) 4 European weather teleconnection patterns (NAO, WeMo, SCAND, MO) at synoptic scale, and iii) the hydrological regime of the main local river (Rhône River). The accuracy of the developed model classifier is evaluated via statistical analysis based on classification accuracy, logarithmic and confusion matrix by comparing with rainfall estimates from rain gauges and satellite observations (CHIRPS-2.0). Sensitivity tests are carried out by changing the model configuration, such as sea SST, sea LPC, river regime, and synoptic atmosphere configuration. The sensitivity analysis suggests a negligible influence from the hydrological regime, unlike SST, LPC, and specific teleconnection weather patterns. At last, this study illustrates how public datasets can be integrated into a machine learning model for heavy rainfall prediction and can interest local policies for management purposes.

Keywords: extreme hazards, sensitivity analysis, heavy rainfall, machine learning, sea-atmosphere modeling, precipitation forecasting

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Authors: Gaurav Prabhudesai, Gaetan Brill

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The sun's energy source comes from a hydrogen-to-helium thermonuclear reaction, generating a temperature of about 5760 K on its outer layer. On account of this high temperature, energy is radiated by the sun, a part of which reaches the earth. This sunlight, even after losing part of its energy en-route to scattering and absorption, provides a time and space averaged solar flux of 174.7 W/m^2 striking the earth’s surface. According to one study, the solar energy striking earth’s surface in one and a half hour is more than the energy consumption that was recorded in the year 2001 from all sources combined. Thus, technology for extraction of solar energy holds much promise for solving energy crisis. Of the many technologies developed in this regard, Concentrating Solar Power (CSP) plants with central solar tower and receiver system are very impressive because of their capability to provide a renewable energy that can be stored in the form of heat. One design of central receiver towers is an open cavity where sunlight is concentrated into by using mirrors (also called heliostats). This concentrated solar flux produces high temperature inside the cavity which can be utilized in an energy conversion process. The amount of energy captured is reduced by losses occurring at the cavity through all three modes viz., radiation to the atmosphere, conduction to the adjoining structure and convection. This study investigates the natural convection losses to the environment from the receiver. Computational fluid dynamics were used to simulate the fluid flow and heat transfer of the receiver; since no analytical solution can be obtained and no empirical correlations exist for the given geometry. The results provide guide lines for predicting natural convection losses for hexagonal and circular shaped open cavities. Additionally, correlations are given for various inclination angles and aspect ratios. These results provide methods to minimize natural convection through careful design of receiver geometry and modification of the inclination angle, and aspect ratio of the cavity.

Keywords: concentrated solar power (CSP), central receivers, natural convection, CFD, open cavities

Procedia PDF Downloads 282

Authors: Akhila Vasudev, Himanshu Kaushik, Tadikonda Venkata Bharat

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A large number of mine tailings are produced every year as part of the extraction process of phosphates, gold, copper, and other materials. Mine tailings are high in water content and have very slow dewatering behavior. The efficient design of tailings dam and economical disposal of these slurries requires the knowledge of tailings consolidation behavior. The large-strain consolidation theory closely predicts the self-weight consolidation of these slurries as the theory considers the conservation of mass and momentum conservation and considers the hydraulic conductivity as a function of void ratio. Classical laboratory techniques, such as settling column test, seepage consolidation test, etc., are expensive and time-consuming for the estimation of hydraulic conductivity variation with void ratio. Inverse estimation of the constitutive relationships from the measured settlement versus time curves is explored. In this work, inverse analysis based on metaheuristics techniques will be explored for predicting the hydraulic conductivity parameters for mine tailings from the base excess pore water pressure dissipation curve and the initial conditions of the mine tailings. The proposed inverse model uses particle swarm optimization (PSO) algorithm, which is based on the social behavior of animals searching for food sources. The finite-difference numerical solution of the forward analytical model is integrated with the PSO algorithm to solve the inverse problem. The method is tested on synthetic data of base excess pore pressure dissipation curves generated using the finite difference method. The effectiveness of the method is verified using base excess pore pressure dissipation curve obtained from a settling column experiment and further ensured through comparison with available predicted hydraulic conductivity parameters.

Keywords: base excess pore pressure, hydraulic conductivity, large strain consolidation, mine tailings

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Authors: Domenico Carmelo Mongelli, Laura Carnieletto, Michele De Carli, Filippo Busato

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Starting from the current context in which the Russian invasion in Ukraine has highlighted Europe's dependence on natural gas imports for heating buildings, this study proposes solutions to resolve this dependency and evaluates related scenarios in the near future. In the first part of this work the methodologies and results of the economic impact are indicated by simulating a massive replacement of boilers powered by fossil fuels with electrically powered hightemperature air-water heat pumps for heating residential buildings in different European climates, without changing the current energy mix. For each individual European region, the costs for the purchase and installation of heat pumps for all residential buildings have been determined. Again for each individual European region, the economic savings during the operation phase that would be obtained in this future scenario of energy transition from fossil fuels to the electrification of domestic heating were calculated. For the European regions for which the economic savings were identified as positive, the payback times of the economic investments were analysed. In the second part of the work, hypothesizing different scenarios for a possible greater use of renewable energy sources and therefore with different possible future scenarios of the energy mix, the methodologies and results of the simulations on the economic analysis and on the environmental analysis are reported which have allowed us to evaluate the future effects of the energy transition from boilers to heat pumps for each European region. In the third part, assuming a rapid short-term diffusion of cooling for European residential buildings, the penetration shares in the cooling market and future projections of energy needs for cooling for each European region have been identified. A database was created where the results of this research relating to 38 European Nations divided into 179 regions were reported. Other previous works on the topics covered were limited to analyzing individual European nations, without ever going into detail about the individual regions within each nation, while the original contribution of the present work lies in the fact that the results achieved allow a specific numerical analysis and punctual for every single European region.

Keywords: buildings, energy, Europe, future

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Authors: L. R. Valencia Pérez, J. M. Peña Aguilar, A. Lamadrid Álvarez, A. Pastrana Palma, H. F. Valencia Pérez, M. Vivanco Vargas

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This work proposes a Cooperation-Competitive (Coopetitive) approach that allows coordinated work among the Secretary of Public Education (SEP), the Autonomous University of Querétaro (UAQ) and government funds from National Council for Science and Technology (CONACYT) or some other international organizations. To work on an overall knowledge transfer strategy with e-learning over the Cloud, where experts in junior high and high school education, working in multidisciplinary teams, perform analysis, evaluation, design, production, validation and knowledge transfer at large scale using a Cloud Computing platform. Allowing teachers and students to have all the information required to ensure a homologated nationally knowledge of topics such as mathematics, statistics, chemistry, history, ethics, civism, etc. This work will start with a pilot test in Spanish and initially in two regional dialects Otomí and Náhuatl. Otomí has more than 285,000 speaking indigenes in Queretaro and Mexico´s central region. Náhuatl is number one indigenous dialect spoken in Mexico with more than 1,550,000 indigenes. The phase one of the project takes into account negotiations with indigenous tribes from different regions, and the Information and Communication technologies to deliver the knowledge to the indigenous schools in their native dialect. The methodology includes the following main milestones: Identification of the indigenous areas where Otomí and Náhuatl are the spoken dialects, research with the SEP the location of actual indigenous schools, analysis and inventory or current schools conditions, negotiation with tribe chiefs, analysis of the technological communication requirements to reach the indigenous communities, identification and inventory of local teachers technology knowledge, selection of a pilot topic, analysis of actual student competence with traditional education system, identification of local translators, design of the e-learning platform, design of the multimedia resources and storage strategy for “Cloud Computing”, translation of the topic to both dialects, Indigenous teachers training, pilot test, course release, project follow up, analysis of student requirements for the new technological platform, definition of a new and improved proposal with greater reach in topics and regions. Importance of phase one of the project is multiple, it includes the proposal of a working technological scheme, focusing in the cultural impact in Mexico so that indigenous tribes can improve their knowledge about new forms of crop improvement, home storage technologies, proven home remedies for common diseases, ways of preparing foods containing major nutrients, disclose strengths and weaknesses of each region, communicating through cloud computing platforms offering regional products and opening communication spaces for inter-indigenous cultural exchange.

Keywords: Mexicans indigenous tribes, education, knowledge transfer, cloud computing, otomi, Náhuatl, language

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Authors: Narottam Puri, Bishnu Panigrahi, Narayan Pendse

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Background: Clinical Outcomes are the globally agreed upon, evidence-based measurable changes in health or quality of life resulting from the patient care. Reporting of outcomes and its continuous monitoring provides an opportunity for both assessing and improving the quality of patient care. In 2012, International Consortium Of HealthCare Outcome Measurement (ICHOM) was founded which has defined global Standard Sets for measuring the outcome of various treatments. Method: Monitoring of Clinical Outcomes was identified as a pillar of Fortis’ core value of Patient Centricity. The project was started as an in-house developed Clinical Outcomes Reporting Portal by the Fortis Medical IT team. Standard sets of Outcome measurement developed by ICHOM were used. A pilot was run at Fortis Escorts Heart Institute from Aug’13 – Dec’13.Starting Jan’14, it was implemented across 11 hospitals of the group. The scope was hospital-wide and major clinical specialties: Cardiac Sciences, Orthopedics & Joint Replacement were covered. The internally developed portal had its limitations of report generation and also capturing of Patient related outcomes was restricted. A year later, the company provisioned for an ICHOM Certified Software product which could provide a platform for data capturing and reporting to ensure compliance with all ICHOM requirements. Post a year of the launch of the software; Fortis Healthcare has become the 1st Healthcare Provider in Asia to publish Clinical Outcomes data for the Coronary Artery Disease Standard Set comprising of Coronary Artery Bypass Graft and Percutaneous Coronary Interventions) in the public domain. (Jan 2016). Results: This project has helped in firmly establishing a culture of monitoring and reporting Clinical Outcomes across Fortis Hospitals. Given the diverse nature of the healthcare delivery model at Fortis Network, which comprises of hospitals of varying size and specialty-mix and practically covering the entire span of the country, standardization of data collection and reporting methodology is a huge achievement in itself. 95% case reporting was achieved with more than 90% data completion at the end of Phase 1 (March 2016). Post implementation the group now has one year of data from its own hospitals. This has helped identify the gaps and plan towards ways to bridge them and also establish internal benchmarks for continual improvement. Besides the value created for the group includes: 1. Entire Fortis community has been sensitized on the importance of Clinical Outcomes monitoring for patient centric care. Initial skepticism and cynicism has been countered by effective stakeholder engagement and automation of processes. 2. Measuring quality is the first step in improving quality. Data analysis has helped compare clinical results with best-in-class hospitals and identify improvement opportunities. 3. Clinical fraternity is extremely pleased to be part of this initiative and has taken ownership of the project. Conclusion: Fortis Healthcare is the pioneer in the monitoring of Clinical Outcomes. Implementation of ICHOM standards has helped Fortis Clinical Excellence Program in improving patient engagement and strengthening its commitment to its core value of Patient Centricity. Validation and certification of the Clinical Outcomes data by an ICHOM Certified Supplier adds confidence to its claim of being leaders in this space.

Keywords: clinical outcomes, healthcare delivery, patient centricity, ICHOM

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Authors: Nicola Colaninno, Eugenio Morello

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The urban environment affects local-to-global climate and, in turn, suffers global warming phenomena, with worrying impacts on human well-being, health, social and economic activities. Physic-morphological features of the built-up space affect urban air temperature, locally, causing the urban environment to be warmer compared to surrounding rural. This occurrence, typically known as the Urban Heat Island (UHI), is normally assessed by means of air temperature from fixed weather stations and/or traverse observations or based on remotely sensed Land Surface Temperatures (LST). The information provided by ground weather stations is key for assessing local air temperature. However, the spatial coverage is normally limited due to low density and uneven distribution of the stations. Although different interpolation techniques such as Inverse Distance Weighting (IDW), Ordinary Kriging (OK), or Multiple Linear Regression (MLR) are used to estimate air temperature from observed points, such an approach may not effectively reflect the real climatic conditions of an interpolated point. Quantifying local UHI for extensive areas based on weather stations’ observations only is not practicable. Alternatively, the use of thermal remote sensing has been widely investigated based on LST. Data from Landsat, ASTER, or MODIS have been extensively used. Indeed, LST has an indirect but significant influence on air temperatures. However, high-resolution near-surface air temperature (NSAT) is currently difficult to retrieve. Here we have experimented Geographically Weighted Regression (GWR) as an effective approach to enable NSAT estimation by accounting for spatial non-stationarity of the phenomenon. The model combines on-site measurements of air temperature, from fixed weather stations and satellite-derived LST. The approach is structured upon two main steps. First, a GWR model has been set to estimate NSAT at low resolution, by combining air temperature from discrete observations retrieved by weather stations (dependent variable) and the LST from satellite observations (predictor). At this step, MODIS data, from Terra satellite, at 1 kilometer of spatial resolution have been employed. Two time periods are considered according to satellite revisit period, i.e. 10:30 am and 9:30 pm. Afterward, the results have been downscaled at 30 meters of spatial resolution by setting a GWR model between the previously retrieved near-surface air temperature (dependent variable), the multispectral information as provided by the Landsat mission, in particular the albedo, and Digital Elevation Model (DEM) from the Shuttle Radar Topography Mission (SRTM), both at 30 meters. Albedo and DEM are now the predictors. The area under investigation is the Metropolitan City of Milan, which covers an area of approximately 1,575 km2 and encompasses a population of over 3 million inhabitants. Both models, low- (1 km) and high-resolution (30 meters), have been validated according to a cross-validation that relies on indicators such as R2, Root Mean Squared Error (RMSE) and Mean Absolute Error (MAE). All the employed indicators give evidence of highly efficient models. In addition, an alternative network of weather stations, available for the City of Milano only, has been employed for testing the accuracy of the predicted temperatures, giving and RMSE of 0.6 and 0.7 for daytime and night-time, respectively.

Keywords: urban climate, urban heat island, geographically weighted regression, remote sensing

Procedia PDF Downloads 189

Authors: Rami Faraj, Cezary Graczykowski, Błażej Popławski, Grzegorz Mikułowski, Rafał Wiszowaty

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Progress in the field of sensors, electronics and computing results in more and more often applications of adaptive techniques for dynamic response mitigation. When it comes to systems excited with mechanical impacts, the control system has to take into account the significant limitations of actuators responsible for system adaptation. The paper provides a comprehensive discussion of the problem of appropriate design and implementation of adaptation techniques and mechanisms. Two case studies are presented in order to compare completely different adaptation schemes. The first example concerns a double-chamber pneumatic shock absorber with a fast piezo-electric valve and parameters corresponding to the suspension of a small unmanned aerial vehicle, whereas the second considered system is a safety air cushion applied for evacuation of people from heights during a fire. For both systems, it is possible to ensure adaptive performance, but a realization of the system’s adaptation is completely different. The reason for this is technical limitations corresponding to specific types of shock-absorbing devices and their parameters. Impact mitigation using a pneumatic shock absorber corresponds to much higher pressures and small mass flow rates, which can be achieved with minimal change of valve opening. In turn, mass flow rates in safety air cushions relate to gas release areas counted in thousands of sq. cm. Because of these facts, both shock-absorbing systems are controlled based on completely different approaches. Pneumatic shock-absorber takes advantage of real-time control with valve opening recalculated at least every millisecond. In contrast, safety air cushion is controlled using the semi-passive technique, where adaptation is provided using prediction of the entire impact mitigation process. Similarities of both approaches, including applied models, algorithms and equipment, are discussed. The entire study is supported by numerical simulations and experimental tests, which prove the effectiveness of both adaptive impact mitigation techniques.

Keywords: adaptive control, adaptive system, impact mitigation, pneumatic system, shock-absorber

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Authors: Riccardo Cacciotti, Jiri Frankl, Benjamin Wolf, Michael Machacek

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Moisture fluctuations govern the occurrence of fungi-related problems in buildings, which may impose significant health risks for users and even lead to structural failures. Several numerical engineering models attempt to capture the complexity of mold growth on building materials. From real life observations, in cases with suppressed daily variations of boundary conditions, e.g. in crawlspaces, mold growth model predictions well correspond with the observed mold growth. On the other hand, in cases with substantial diurnal variations of boundary conditions, e.g. in the ventilated cavity of a cold flat roof, mold growth predicted by the models is significantly overestimated. This study, founded by the Grant Agency of the Czech Republic (GAČR 20-12941S), aims at gaining a better understanding of mold growth behavior on solid wood, under varying boundary conditions. In particular, the experimental investigation focuses on the response of mold to changing conditions in the boundary layer and its influence on heat and moisture transfer across the surface. The main results include the design and construction at the facilities of ITAM (Prague, Czech Republic) of an innovative device allowing for the simulation of changing environmental conditions in buildings. It consists of a square section closed circuit with rough dimensions 200 × 180 cm and cross section roughly 30 × 30 cm. The circuit is thermally insulated and equipped with an electric fan to control air flow inside the tunnel, a heat and humidity exchange unit to control the internal RH and variations in temperature. Several measuring points, including an anemometer, temperature and humidity sensor, a loading cell in the test section for recording mass changes, are provided to monitor the variations of parameters during the experiments. The research is ongoing and it is expected to provide the final results of the experimental investigation at the end of 2022.

Keywords: moisture, mold growth, testing, wood

Procedia PDF Downloads 123

Authors: Jayson Cheyne, David Butler, Iain Bomphray

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In recent years, lithium-ion batteries have been used increasingly for electric vehicles and large energy storage systems due to their high-power density and long lifespan. Despite this, thermal runaway remains a significant safety problem because of its uncontrollable and irreversible nature - which can lead to fires and explosions. In large-scale lithium-ion packs and modules, thermal runaway propagation between cells can escalate fire hazards and cause significant damage. Thus, safety measures are required to mitigate thermal runaway propagation. The current research explores composite phase change materials (PCM) containing expanded graphite (EG) for thermal runaway mitigation. PCMs are an area of significant interest for battery thermal management due to their ability to absorb substantial quantities of heat during phase change. Moreover, the introduction of EG can support heat transfer from the cells to the PCM (owing to its high thermal conductivity) and provide shape stability to the PCM during phase change. During the research, a thermal model was established for an array of 16 cylindrical cells to simulate heat dissipation with and without the composite PCM. Two conditions were modeled, including the behavior during charge/discharge cycles (i.e., throughout regular operation) and thermal runaway. Furthermore, parameters including cell spacing, composite PCM thickness, and EG weight percentage (WT%) were varied to establish the optimal material parameters for enabling thermal runaway mitigation and effective thermal management. Although numerical modeling is still ongoing, initial findings suggest that a 3mm PCM containing 15WT% EG can effectively suppress thermal runaway propagation while maintaining shape stability. The next step in the research is to validate the model through controlled experimental tests. Additionally, with the perceived fire safety concerns relating to PCM materials, fire safety tests, including UL-94 and Limiting Oxygen Index (LOI), shall be conducted to explore the flammability risk.

Keywords: battery safety, electric vehicles, phase change materials, thermal management, thermal runaway

Procedia PDF Downloads 132

Authors: Chengfeng Fang, Mohamed Ali Sadakkathulla, Abdul Sheikh

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Ultra-high-performance fiber reinforced concrete (UHPFRC) is a specially formulated cement-based composite characterized with an ultra-high compressive strength (f_c^’ = 240 MPa) and a low water-cement ratio (W/B= 0.2). With such material characteristics, UHPFRC is favored for the design and constructions of structures required high structural performance and slender geometries. Unlike conventional concrete, the structural performance of members manufactured with UHPFRC has not yet been fully studied, particularly, for UHPFRC columns with high slenderness. In this study, the behaviors of slender UHPFRC columns under concentric or eccentric load will be investigated both experimentally and numerically. Four slender UHPFRC columns were tested under eccentric loads with eccentricities, of 0 mm, 35 mm, 50 mm, and 85 mm, respectively, and one UHPFRC beam was tested under four-point bending. Finite element (FE) analysis was conducted with concrete damage plasticity (CDP) modulus to simulating the load-middle height or middle span deflection relationships and damage patterns of all UHPFRC members. Simulated results were compared against the experimental results and observation to gain the confidence of FE model, and this model was further extended to conduct parametric studies, which aim to investigate the effects of slenderness regarding failure modes and load-moment interaction relationships. Experimental results showed that the load bearing capacities of the slender columns reduced with an increase in eccentricity. Comparisons between load-middle height and middle span deflection relationships as well as damage patterns of all UHPFRC members obtained both experimentally and numerically demonstrated high accuracy of the FE simulations. Based on the available FE model, the following parametric study indicated that a further increase in the slenderness of column resulted in significant decreases in the load-bearing capacities, ductility index, and flexural bending capacities.

Keywords: eccentric loads, ductility index, RC column, slenderness, UHPFRC

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Authors: Mohammad Shajid Rahman, Tarik Kaya, Edgar Matida

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Electronic cigarettes, also known as e-cigarettes, may have become a tool to improve smoking cessation due to their ability to provide nicotine at a selected rate. Unlike traditional cigarettes, which produce toxic elements from tobacco combustion, e-cigarettes generate aerosols by heating a liquid solution (commonly a mixture of propylene glycol, vegetable glycerin, nicotine and some flavoring agents). However, caution still needs to be taken when using e-cigarettes due to the presence of addictive nicotine and some harmful substances produced from the heating process. Particle size distribution (PSD) and associated velocities generated by e-cigarettes have significant influence on aerosol deposition in different regions of human respiratory tracts. On another note, low actuation power is beneficial in aerosol generating devices since it exhibits a reduced emission of toxic chemicals. In case of e-cigarettes, lower heating powers can be considered as powers lower than 10 W compared to a wide range of powers (0.6 to 70.0 W) studied in literature. Due to the importance regarding inhalation risk reduction, deeper understanding of particle size characteristics of e-cigarettes demands thorough investigation. However, comprehensive study on PSD and velocities of e-cigarettes with a standard testing condition at relatively low heating powers is still lacking. The present study aims to measure particle number count and size distribution of undiluted aerosols of a latest fourth-generation e-cigarette at low powers, within 6.5 W using real-time particle counter (time-of-flight method). Also, temporal and spatial evolution of particle size and velocity distribution of aerosol jets are examined using phase Doppler anemometry (PDA) technique. To the authors’ best knowledge, application of PDA in e-cigarette aerosol measurement is rarely reported. In the present study, preliminary results about particle number count of undiluted aerosols measured by time-of-flight method depicted that an increase of heating power from 3.5 W to 6.5 W resulted in an enhanced asymmetricity in PSD, deviating from log-normal distribution. This can be considered as an artifact of rapid vaporization, condensation and coagulation processes on aerosols caused by higher heating power. A novel mathematical expression, combining exponential, Gaussian and polynomial (EGP) distributions, was proposed to describe asymmetric PSD successfully. The value of count median aerodynamic diameter and geometric standard deviation laid within a range of about 0.67 μm to 0.73 μm, and 1.32 to 1.43, respectively while the power varied from 3.5 W to 6.5 W. Laser Doppler velocimetry (LDV) and PDA measurement suggested a typical centerline streamwise mean velocity decay of aerosol jet along with a reduction of particle sizes. In the final submission, a thorough literature review, detailed description of experimental procedure and discussion of the results will be provided. Particle size and turbulent characteristics of aerosol jets will be further examined, analyzing arithmetic mean diameter, volumetric mean diameter, volume-based mean diameter, streamwise mean velocity and turbulence intensity. The present study has potential implications in PSD simulation and validation of aerosol dosimetry model, leading to improving related aerosol generating devices.

Keywords: E-cigarette aerosol, laser doppler velocimetry, particle size distribution, particle velocity, phase Doppler anemometry

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Authors: Mohamed Khiatine, Amal Medjnoun, Ramdane Bahar

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The North Algeria is a highly seismic zone, as evidenced by the historical seismicity. During the past two decades, it has experienced several moderate to strong earthquakes. Therefore, the geotechnical engineering problems that involve dynamic loading of soils and soil-structure interaction system requires, in the presence of saturated loose sand formations, liquefaction studies. Bejaia city, located in North-East of Algiers, Algeria, is a part of the alluvial plain which covers an area of approximately 750 hectares. According to the Algerian seismic code, it is classified as moderate seismicity zone. This area had not experienced in the past urban development because of the different hazards identified by hydraulic and geotechnical studies conducted in the region. The low bearing capacity of the soil, its high compressibility and the risk of liquefaction and flooding are among these risks and are a constraint on urbanization. In this area, several cases of structures founded on shallow foundations have suffered damages. Hence, the soils need treatment to reduce the risk. Many field and laboratory investigations, core drilling, pressuremeter test, standard penetration test (SPT), cone penetrometer test (CPT) and geophysical down hole test, were performed in different locations of the area. The major part of the area consists of silty fine sand , sometimes heterogeneous, has not yet reached a sufficient degree of consolidation. The ground water depth changes between 1.5 and 4 m. These investigations show that the liquefaction phenomenon is one of the critical problems for geotechnical engineers and one of the obstacles found in design phase of projects. This paper presents an analysis to evaluate the liquefaction potential, using the empirical methods based on Standard Penetration Test (SPT), Cone Penetration Test (CPT) and shear wave velocity and numerical analysis. These liquefaction assessment procedures indicate that liquefaction can occur to considerable depths in silty sand of harbor zone of Bejaia.

Keywords: earthquake, modeling, liquefaction potential, laboratory investigations

Procedia PDF Downloads 349

Authors: Heng Ma

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Third-party warehousing logistics has an important role in the development of external logistics. At present, the third-party logistics in our country is still a new industry, the accounting system has not yet been established, the current financial accounting system of third-party warehousing logistics is mainly in the traditional way of thinking, and only able to provide the total cost information of the entire enterprise during the accounting period, unable to reflect operating indirect cost information. In order to solve the problem of third-party logistics industry cost information distortion, improve the level of logistics cost management, the paper combines theoretical research and case analysis method to reflect cost allocation by building third-party logistics costing model using Time-Driven Activity-Based Costing(TDABC), and takes S company as an example to account and control the warehousing logistics cost. Based on the idea of “Products consume activities and activities consume resources”, TDABC put time into the main cost driver and use time-consuming equation resources assigned to cost objects. In S company, the objects focuses on three warehouse, engaged with warehousing and transportation (the second warehouse, transport point) service. These three warehouse respectively including five departments, Business Unit, Production Unit, Settlement Center, Security Department and Equipment Division, the activities in these departments are classified by in-out of storage forecast, in-out of storage or transit and safekeeping work. By computing capacity cost rate, building the time-consuming equation, the paper calculates the final operation cost so as to reveal the real cost. The numerical analysis results show that the TDABC can accurately reflect the cost allocation of service customers and reveal the spare capacity cost of resource center, verifies the feasibility and validity of TDABC in third-party logistics industry cost accounting. It inspires enterprises focus on customer relationship management and reduces idle cost to strengthen the cost management of third-party logistics enterprises.

Keywords: third-party logistics enterprises, TDABC, cost management, S company

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Authors: Defne Uz

Abstract:

Horizontal Tail of helicopter is exposed to repeated oscillatory loading generated by aerodynamic and inertial loads, and bending moments depending on operating conditions and maneuvers of the helicopter. In order to ensure that maximum stress levels do not exceed certain fatigue limit of the material and to prevent damage, a numerical analysis approach can be utilized through the Finite Element Method. Therefore, in this paper, fatigue analysis of the Horizontal Tail model is studied numerically to predict high-cycle and low-cycle fatigue life related to defined loading. The analysis estimates the stress field at stress concentration regions such as around fastener holes where the maximum principal stresses are considered for each load case. Critical element identification of the main load carrying structural components of the model with rivet holes is performed as a post-process since critical regions with high-stress values are used as an input for fatigue life calculation. Once the maximum stress is obtained at the critical element and the related mean and alternating components, it is compared with the endurance limit by applying Soderberg approach. The constant life straight line provides the limit for several combinations of mean and alternating stresses. The life calculation based on S-N (Stress-Number of Cycles) curve is also applied with fully reversed loading to determine the number of cycles corresponds to the oscillatory stress with zero means. The results determine the appropriateness of the design of the model for its fatigue strength and the number of cycles that the model can withstand for the calculated stress. The effect of correctly determining the critical rivet holes is investigated by analyzing stresses at different structural parts in the model. In the case of low life prediction, alternative design solutions are developed, and flight hours can be estimated for the fatigue safe operation of the model.

Keywords: fatigue analysis, finite element method, helicopter horizontal tail, life prediction, stress concentration

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Authors: Masato Saeki

Abstract:

Presented herein is a novel algorithms for calculating the damping performance of particle dampers. The particle damper is a passive vibration control technique and has many practical applications due to simple design. It consists of granular materials constrained to move between two ends in the cavity of a primary vibrating system. The damping effect results from the exchange of momentum during the impact of granular materials against the wall of the cavity. This damping has the advantage of being independent of the environment. Therefore, particle damping can be applied in extreme temperature environments, where most conventional dampers would fail. It was shown experimentally in many papers that the efficiency of the particle dampers is high in the case of resonant vibration. In order to use the particle dampers effectively, it is necessary to solve the equations of motion for each particle, considering the granularity. The discrete element method (DEM) has been found to be effective for revealing the dynamics of particle damping. In this method, individual particles are assumed as rigid body and interparticle collisions are modeled by mechanical elements as springs and dashpots. However, the computational cost is significant since the equation of motion for each particle must be solved at each time step. In order to improve the computational efficiency of the DEM, the new algorithms are needed. In this study, new algorithms are proposed for implementing the high performance DEM. On the assumption that behaviors of the granular particles in the each divided area of the damper container are the same, the contact force of the primary system with all particles can be considered to be equal to the product of the divided number of the damper area and the contact force of the primary system with granular materials per divided area. This convenience makes it possible to considerably reduce the calculation time. The validity of this calculation method was investigated and the calculated results were compared with the experimental ones. This paper also presents the results of experimental studies of the performance of particle dampers. It is shown that the particle radius affect the noise level. It is also shown that the particle size and the particle material influence the damper performance.

Keywords: particle damping, discrete element method (DEM), granular materials, numerical analysis, equivalent noise level

Procedia PDF Downloads 450