Search results for: thermal cycling machine

Authors: Jirod Chaisan, Somchai Maneewan, Chantana Punlek, Ninnart Rachapradit, Surapong Chirarattananon, Pattana Rakkwamsuk

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In this paper, simulation modeling of heat transfer, air flow and distribution emitted from CO2 was performed in a regenerated air. The study room was divided in 3 types: common room, small lecture room and large lecture room under evaluated condition in two case: released and unreleased CO2 including of used hybrid air ventilation system for regenerated air under Thailand climate conditions. The carbon dioxide was located on the center of the room and released rate approximately 900-1200 ppm corresponded with indoor air quality standard (IAQs). The indoor air in the thermal comfort zone was calculated and simulated with the numerical method that using real data from the handbook guideline. The results of the study showed that in the case of hybrid air ventilation system explained thermal and CO2 distribution due to the system was adapted significantly in the comfort zone. The results showed that when CO2 released on the center of the other room, the CO2 high concentration in comfort zone so used hybrid air ventilation that decreased CO2 with regeneration air including of reduced temperature indoor. However, the study is simulation modeling and guideline only so the future should be the experiment of hybrid air ventilation system for evaluated comparison of the systems.

Keywords: air ventilation, indoor air quality, thermal comfort, thermoelectric, photovoltaic, dehumidify

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Authors: Pouyan Jahanbin, Mahmoud Houshmand, Omid Fatahi Valilai

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A flexible manufacturing system (FMS) is a manufacturing system having the capability of handling the variations of products features that is the result of ever-changing customer demands. The flexibility of the manufacturing systems help to utilize the resources in a more effective manner. However, the control of such systems would be complicated and challenging. FMS needs CNC machines and robots and other resources for establishing the flexibility and enhancing the efficiency of the whole system. Also it needs to integrate the resources to reach required efficiency and flexibility. In order to reach this goal, an integrator framework is proposed in which the machining data of CNC machine tools is received through a STEP-NC file. The interoperability of the system is achieved by the information system. This paper proposes an information system that its data model is designed based on object oriented approach and is implemented through a knowledge-based system. The framework is connected to a database which is filled with robot’s control commands. The framework programs the robots by rules embedded in its knowledge based system. It also controls the interactions of CNC machine tools for loading and unloading actions by robot. As a result, the proposed framework improves the integration of manufacturing resources in Flexible Manufacturing Systems.

Keywords: CNC machine tools, industrial robots, knowledge-based systems, manufacturing recourses integration, flexible manufacturing system (FMS), object-oriented data model

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Authors: Ahmed Abdulla Ashlam, Atta Badii, Frederic Stahl

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In recent years, SQL injection attacks have been identified as being prevalent against web applications. They affect network security and user data, which leads to a considerable loss of money and data every year. This paper presents the use of classification algorithms in machine learning using a method to classify the login data filtering inputs into "SQLi" or "Non-SQLi,” thus increasing the reliability and accuracy of results in terms of deciding whether an operation is an attack or a valid operation. A method Web-App auto-generated twin data structure replication. Shielding against SQLi attacks (WebAppShield) that verifies all users and prevents attackers (SQLi attacks) from entering and or accessing the database, which the machine learning module predicts as "Non-SQLi" has been developed. A special login form has been developed with a special instance of data validation; this verification process secures the web application from its early stages. The system has been tested and validated, up to 99% of SQLi attacks have been prevented.

Keywords: SQL injection, attacks, web application, accuracy, database

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Authors: P. Crosby, Shiva Krishna Pavuluri, S. Rajkumar

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Purpose: Occupied built-up space can be broadly classified as air-conditioned and naturally ventilated. Regardless of the building type, the objective of all occupied built-up space is to provide a thermally acceptable environment for human occupancy. Considering this aspect, air-conditioned spaces allow a greater degree of flexibility to control and modulate the comfort parameters during the operation phase. However, in the case of naturally ventilated space, a number of design features favoring indoor thermal comfort should be mandatorily conceptualized starting from the design phase. One such primary design feature that requires to be prioritized is, selection of building envelope material, as it decides the flow of energy from outside environment to occupied spaces. Research Methodology: In India and many countries across globe, the standardized material used for building envelope is re-enforced concrete (i.e. M-25 grade concrete). The comfort inside the RC built environment for warm & humid climate (i.e. mid-day temp of 30-35˚C, diurnal variation of 5-8˚C & RH of 70-90%) is unsatisfying to say the least. This study is mainly focused on reviewing the impact of mix design of conventional M25 grade concrete on inside thermal comfort. In this mix design, air entrainment in the range of 2000 to 2100 kg/m3 is introduced to reduce the density of M-25 grade concrete. Thermal performance parameters & indoor comfort indices are analyzed for the proposed mix and compared in relation to the conventional M-25 grade. There are diverse methodologies which govern indoor comfort calculation. In this study, three varied approaches specifically a) Indian Adaptive Thermal comfort model, b) Tropical Summer Index (TSI) c) Air temperature less than 33˚C & RH less than 70% to calculate comfort is adopted. The data required for the thermal comfort study is acquired by field measurement approach (i.e. for the new mix design) and simulation approach by using design builder (i.e. for the conventional concrete grade). Findings: The analysis points that the Tropical Summer Index has a higher degree of stringency in determining the occupant comfort band whereas also providing a leverage in thermally tolerable band over & above other methodologies in the context of the study. Another important finding is the new mix design ensures a 10% reduction in indoor air temperature (IAT) over the outdoor dry bulb temperature (ODBT) during the day. This translates to a significant temperature difference of 6 ˚C IAT and ODBT.

Keywords: Indian adaptive thermal comfort, indoor air temperature, thermal comfort, tropical summer index

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Authors: Hira Lal Gope, Hidekazu Fukai

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The aim of this study is to develop a system which can identify and sort peaberries automatically at low cost for coffee producers in developing countries. In this paper, the focus is on the classification of peaberries and normal coffee beans using image processing and machine learning techniques. The peaberry is not bad and not a normal bean. The peaberry is born in an only single seed, relatively round seed from a coffee cherry instead of the usual flat-sided pair of beans. It has another value and flavor. To make the taste of the coffee better, it is necessary to separate the peaberry and normal bean before green coffee beans roasting. Otherwise, the taste of total beans will be mixed, and it will be bad. In roaster procedure time, all the beans shape, size, and weight must be unique; otherwise, the larger bean will take more time for roasting inside. The peaberry has a different size and different shape even though they have the same weight as normal beans. The peaberry roasts slower than other normal beans. Therefore, neither technique provides a good option to select the peaberries. Defect beans, e.g., sour, broken, black, and fade bean, are easy to check and pick up manually by hand. On the other hand, the peaberry pick up is very difficult even for trained specialists because the shape and color of the peaberry are similar to normal beans. In this study, we use image processing and machine learning techniques to discriminate the normal and peaberry bean as a part of the sorting system. As the first step, we applied Deep Convolutional Neural Networks (CNN) and Support Vector Machine (SVM) as machine learning techniques to discriminate the peaberry and normal bean. As a result, better performance was obtained with CNN than with SVM for the discrimination of the peaberry. The trained artificial neural network with high performance CPU and GPU in this work will be simply installed into the inexpensive and low in calculation Raspberry Pi system. We assume that this system will be used in under developed countries. The study evaluates and compares the feasibility of the methods in terms of accuracy of classification and processing speed.

Keywords: convolutional neural networks, coffee bean, peaberry, sorting, support vector machine

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Authors: Mahdi Allaoua Seklab, Süleyman BaşTürk

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In the quest to enhance coastal environmental health, this study introduces a fully autonomous beach cleaning machine, a breakthrough in leveraging green energy and advanced artificial intelligence for ecological preservation. Designed to operate independently, the machine is propelled by a solar-powered system, underscoring a commitment to sustainability and the use of renewable energy in autonomous robotics. The vehicle's autonomous navigation is achieved through a sophisticated integration of LIDAR and a camera system, utilizing an SSD MobileNet V2 object detection model for accurate and real-time trash identification. The SSD framework, renowned for its efficiency in detecting objects in various scenarios, is coupled with the lightweight and precise highly MobileNet V2 architecture, making it particularly suited for the computational constraints of on-board processing in mobile robotics. Training of the SSD MobileNet V2 model was conducted on Google Colab, harnessing cloud-based GPU resources to facilitate a rapid and cost-effective learning process. The model was refined with an extensive dataset of annotated beach debris, optimizing the parameters using the Adam optimizer and a cross-entropy loss function to achieve high-precision trash detection. This capability allows the machine to intelligently categorize and target waste, leading to more effective cleaning operations. This paper details the design and functionality of the beach cleaning machine, emphasizing its autonomous operational capabilities and the novel application of AI in environmental robotics. The results showcase the potential of such technology to fill existing gaps in beach maintenance, offering a scalable and eco-friendly solution to the growing problem of coastal pollution. The deployment of this machine represents a significant advancement in the field, setting a new standard for the integration of autonomous systems in the service of environmental stewardship.

Keywords: autonomous beach cleaning machine, renewable energy systems, coastal management, environmental robotics

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Authors: Natascha C. D'Amico, Enzo Grossi, Giovanni Valbusa, Ala Malasevschi, Gianpiero Cardone, Sergio Papa

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Purpose: To characterize, through a radiomic approach, the nature of areas classified PI-RADS (Prostate Imaging Reporting and Data System) 3/5, recognized in multiparametric prostate magnetic resonance with T2-weighted (T2w), diffusion and perfusion sequences with paramagnetic contrast. Methods and Materials: 24 cases undergoing multiparametric prostate MR and biopsy were admitted to this pilot study. Clinical outcome of the PI-RADS 3/5 was found through biopsy, finding 8 malignant tumours. The analysed images were acquired with a Philips achieva 1.5T machine with a CE- T2-weighted sequence in the axial plane. Semi-automatic tumour segmentation was carried out on MR images using 3DSlicer image analysis software. 45 shape-based, intensity-based and texture-based features were extracted and represented the input for preprocessing. An evolutionary algorithm (a TWIST system based on KNN algorithm) was used to subdivide the dataset into training and testing set and select features yielding the maximal amount of information. After this pre-processing 20 input variables were selected and different machine learning systems were used to develop a predictive model based on a training testing crossover procedure. Results: The best machine learning system (three-layers feed-forward neural network) obtained a global accuracy of 90% ( 80 % sensitivity and 100% specificity ) with a ROC of 0.82. Conclusion: Machine learning systems coupled with radiomics show a promising potential in distinguishing benign from malign tumours in PI-RADS 3/5 areas.

Keywords: machine learning, MR prostate, PI-Rads 3, radiomics

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Authors: Willy Mbenza, Sho Kenjiro

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Increasing efforts are directed towards a better understanding and foreknowledge of extreme precipitation likelihood, given the adverse effects associated with their occurrence. This knowledge plays a crucial role in long-term planning and the formulation of effective emergency response. However, predicting extreme events reliably presents a challenge to conventional empirical/statistics due to the involvement of numerous variables spanning different time and space scales. In the recent time, Machine Learning has emerged as a promising tool for predicting the dynamics of extreme precipitation. ML techniques enables the consideration of both local and regional physical variables that have a strong influence on the likelihood of extreme precipitation. These variables encompasses factors such as air temperature, soil moisture, specific humidity, aerosol concentration, among others. In this study, we develop an ML model that incorporates both local and regional variables while establishing a robust relationship between physical variables and precipitation during the downscaling process. Furthermore, the model provides valuable information on the frequency and duration of a given intensity of precipitation.

Keywords: machine learning (ML), predictions, rainfall events, regional variables

Procedia PDF Downloads 80

Authors: Jiahui Song

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The use of very high electric fields (~ 100kV/cm or higher) with pulse durations in the nanosecond range has been a recent development. The electric pulses have been used as tools to generate electroporation which has many biomedical applications. Most of the studies of electroporation have ignored possible thermal effects because of the small duration of the applied voltage pulses. However, it has been predicted membrane temperature gradients ranging from 0.2×109 to 109 K/m. This research focuses on thermal gradients that drives for electroporative enhancements, even though the actual temperature values might not have changed appreciably from their equilibrium levels. The dynamics of pore formation with the application of an externally applied electric field is studied on the basis of molecular dynamics (MD) simulations using the GROMACS package. Different temperatures are assigned to various regions to simulate the appropriate temperature gradients. The GROMACS provides the force fields for the lipid membranes, which is taken to comprise of dipalmitoyl-phosphatidyl-choline (DPPC) molecules. The water model mimicks the aqueous environment surrounding the membrane. Velocities of water and membrane molecules are generated randomly at each simulation run according to a Maxwellian distribution. For statistical significance, a total of eight MD simulations are carried out with different starting molecular velocities for each simulation. MD simulation shows no pore is formed in a 10-ns snapshot for a DPPC membrane set at a uniform temperature of 295 K after a 0.4 V/nm electric field is applied. A nano-sized pore is clearly seen in a 10-ns snapshot on the same geometry but with the top and bottom membrane surfaces kept at temperatures of 300 and 295 K, respectively. For the same applied electric field, the formation of nanopores is clearly demonstrated, but only in the presence of a temperature gradient. MD simulation results show enhanced electroporative effects arising from thermal gradients. The study suggests the temperature gradient is a secondary driver, with the electric field being the primary cause for electroporation.

Keywords: nanosecond, electroporation, thermal effects, molecular dynamics

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Authors: M. M. Atashi, P. Malekzadeh

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A first known formulation for the out-of-plane free vibration analysis of functionally graded (FG) circular curved beams in thermal environment and with temperature dependent material properties is presented. The formulation is based on the first order shear deformation theory (FSDT), which includes the effects of shear deformation and rotary inertia due to both torsional and flexural vibrations. The material properties are assumed to be temperature dependent and graded in the direction normal to the plane of the beam curvature. The equations of motion and the related boundary conditions, which include the effects of initial thermal stresses, are derived using the Hamilton’s principle. Differential quadrature method (DQM), as an efficient and accurate numerical method, is adopted to solve the thermoelastic equilibrium equations and the equations of motion. The fast rate of convergence of the method is investigated and the formulations are validated by comparing the results in the limit cases with the available solutions in the literature for isotropic circular curved beams. In addition, for FG circular curved beams with soft simply supported edges, the results are compared with the obtained exact solutions. Then, the effects of temperature rise, boundary conditions, material and geometrical parameters on the natural frequencies are investigated.

Keywords: out of plane, free vibration, curved beams, functionally graded, thermal environment

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Authors: Nilufar Chowdhury, Fereydoun Najafian Jazi, Omid Ghasemi-Fare

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The thermal effect on soil properties induces significant variations in hydraulic conductivity, which is attributable to temperature-dependent transitions in soil properties. With the elevation of temperature, there can be a notable increase in intrinsic permeability due to the degeneration of bound water molecules into a free state facilitated by thermal energy input. Conversely, thermal consolidation may cause a reduction in intrinsic permeability as soil particles undergo densification. This thermal response of soil permeability exhibits pronounced heterogeneity across different soil types. Furthermore, this temperature-induced disruption of the bound water within clay matrices can enhance the mineral-to-mineral contact, initiating irreversible deformation within the clay structure. This indicates that when soil undergoes heating-cooling cycles, plastic strain can develop, which needs to be investigated for every soil type to understand the thermo-hydro mechanical behavior of clay properly. This research aims to study the effect of the heating-cooling cycle on the intrinsic permeability and time-dependent evaluation of thermal volume change of sodium Bentonite clay. A temperature-controlled triaxial permeameter cell is used in this study. The selected temperature is 20° C, 40° C, 40° C and 80° C. The hydraulic conductivity of Bentonite clay under 100 kPa confining stresses was measured. Hydraulic conductivity analysis was performed on a saturated sample for a void ratio e = 0.9, corresponding to a dry density of 1.2 Mg/m3. Different hydraulic gradients were applied between the top and bottom of the sample to obtain a measurable flow through the sample. The hydraulic gradient used for the experiment was 4000. The diameter and thickness of the sample are 101. 6 mm, and 25.4 mm, respectively. Both for heating and cooling, the hydraulic conductivity at each temperature is measured after the flow reaches the steady state condition to make sure the volume change due to thermal loading is stabilized. Thus, soil specimens were kept at a constant temperature during both the heating and cooling phases for at least 10-18 days to facilitate the equilibration of hydraulic transients. To assess the influence of temperature-induced volume changes of Bentonite clay, the evaluation of void ratio change during this time period has been monitored. It is observed that the intrinsic permeability increases by 30-40% during the heating cycle. The permeability during the cooling cycle is 10-12% lower compared to the permeability observed during the heating cycle at a particular temperature. This reduction in permeability implies a change in soil fabric due to the thermal effect. An initial increase followed by a rapid decrease in void ratio was observed, representing the occurrence of possible osmotic swelling phenomena followed by thermal consolidation. It has been observed that after a complete heating-cooling cycle, there is a significant change in the void ratio compared to the initial void ratio of the sample. The results obtained suggest that Bentonite clay’s microstructure can change subject to a complete heating-cooling process, which regulates macro behavior such as the permeability of Bentonite clay.

Keywords: bentonite, permeability, temperature, thermal volume change

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Authors: Chaitanya R. Mali, V. Vinod, Ashwin W. Patwardhan

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Design of long vertical steam generator (SG) tubes in nuclear power plant involves an understanding of different aspects of flow boiling phenomenon such as flow instabilities, flow regimes, dry out, critical heat flux, pressure drop, etc. The knowledge of the prediction of local thermal hydraulic characteristics is necessary to understand these aspects. For this purpose, the methodology has been developed which covers all the flow boiling regimes to model full range flow boiling phenomenon. In this methodology, the vertical tube is divided into four sections based on vapor fraction value at the end of each section. Different modeling strategies have been applied to the different sections of the vertical tube. Computational fluid dynamics simulations have been performed on a vertical SG tube of 0.0126 m inner diameter and 23 m length. The thermal hydraulic parameters such as vapor fraction, liquid temperature, heat transfer coefficient, pressure drop, heat flux distribution have been analyzed for different designed heat duties (1.1 MW (20%) to 3.3 MW (60%)) and flow conditions (10 % to 80 %). The sensitivity of different boiling parameters such as bubble departure diameter, nucleation site density, bubble departure frequency on the thermal hydraulic parameters was also studied. Flow instability has been observed at 20 % designed heat duty and 20 % flow conditions.

Keywords: thermal hydraulics, boiling, vapor fraction, sensitivity

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Authors: Vishal Das, Tianyi Mao, Zhicheng Geng, Carmen Flores, Diego Pelloso, Fang Wang

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Accurate sell-in and sell-out forecasting is a ubiquitous problem in the retail industry. It is an important element of any demand planning activity. As a global food and beverage company, Nestlé has hundreds of products in each geographical location that they operate in. Each product has its sell-in and sell-out time series data, which are forecasted on a weekly and monthly scale for demand and financial planning. To address this challenge, Nestlé Chilein collaboration with Amazon Machine Learning Solutions Labhas developed their in-house solution of using machine learning models for forecasting. Similar products are combined together such that there is one model for each product category. In this way, the models learn from a larger set of data, and there are fewer models to maintain. The solution is scalable to all product categories and is developed to be flexible enough to include any new product or eliminate any existing product in a product category based on requirements. We show how we can use the machine learning development environment on Amazon Web Services (AWS) to explore a set of forecasting models and create business intelligence dashboards that can be used with the existing demand planning tools in Nestlé. We explored recent deep learning networks (DNN), which show promising results for a variety of time series forecasting problems. Specifically, we used a DeepAR autoregressive model that can group similar time series together and provide robust predictions. To further enhance the accuracy of the predictions and include domain-specific knowledge, we designed an ensemble approach using DeepAR and XGBoost regression model. As part of the ensemble approach, we interlinked the sell-out and sell-in information to ensure that a future sell-out influences the current sell-in predictions. Our approach outperforms the benchmark statistical models by more than 50%. The machine learning (ML) pipeline implemented in the cloud is currently being extended for other product categories and is getting adopted by other geomarkets.

Keywords: sell-in and sell-out forecasting, demand planning, DeepAR, retail, ensemble machine learning, time-series

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Authors: Faisal Saleem, Kui Zhang, Adam Harvey

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The role of N₂ carrier gas towards the conversion of tar analogue was studied in a non-thermal plasma dielectric barrier discharge (DBD) reactor. The important parameters such as power (5-40W), residence time (1.41-4.23 s), concentration (20-82 g/Nm³), and temperature (Ambient-400°C) were explored. The present study demonstrated that plasma power and residence time played a key role in the decomposition of toluene, and almost complete removal of toluene was observed at 40w and 4.23 s. H₂ is obtained as a major gaseous product with a maximum selectivity of 40% along with some lighter hydrocarbons (5.5%). The removal efficiency of toluene slightly decreases with increasing the concentration of toluene from 20 g/Nm³ to 82 g/Nm³. The solid residue formation takes place inside the plasma reactor. The selectivity of LHC (lower hydrocarbons) increased up to 15% by increasing the temperature to 400°C. Introducing H₂ to the gas at elevated temperature opens up new reaction routes to raise the selectivity to lower hydrocarbons. The selectivity to methane reaches to 42% using 35% H₂ at 400°C and total selectivity of LHC increases to 57%.

Keywords: biomass gasification tar, non-thermal plasma, dielectric barrier discharge, residence time

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Authors: Hisham Elmouazen, Xiaobing Zhang

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In regenerative-cooled rocket engines, understanding the coolant behaviour within cooling channels is essential to enhance engine performance and maintain chamber walls at low temperatures. However, modelling and testing the rocket engine's cooling channels is challenging due to the high temperature of the chamber walls, supercritical flow, and high Reynolds number. Therefore, a numerical analysis of five different V-rib cross-sections to optimize rocket engine cooling channels' performance is developed and validated in this work. Three-dimensional CFD simulations are employed by the Shear Stress Transport (k- ω) turbulent model at Reynolds number 42,500. The study findings illustrate that the V-ribbed channel performance is optimized by 59.5% relative to the plain/flat channel. Additionally, the chamber wall temperature is decreased to 726.4 K, and the right-angle trapezoidal V-rib (Case 4) improves thermal augmentation up to 74.3 % with a slightly high friction factor.

Keywords: computational fluid dynamics CFD, regenerative-cooled system, thermal performance, V-rib cross-sections

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Authors: Huei Chu Weng

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This paper presents a study on the effect of second-order slip on forced convection through a long isoflux heated or cooled planar microchannel. The fully developed solutions of flow and thermal fields are analytically obtained on the basis of the second-order Maxwell-Burnett slip and local heat flux boundary conditions. Results reveal that when the average flow velocity increases or the wall heat flux amount decreases, the role of thermal creep becomes more insignificant, while the effect of second-order slip becomes larger. The second-order term in the Deissler slip boundary condition is found to contribute a positive velocity slip and then to lead to a lower pressure drop as well as a lower temperature rise for the heated-wall case or to a higher temperature rise for the cooled-wall case. These findings are contrary to predictions made by the Karniadakis slip model.

Keywords: microfluidics, forced convection, thermal creep, second-order boundary conditions

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Authors: Philipp Burger, Jonas Sommer, Haneen Daoud, Franz Hilmer, Uwe Glatzel

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Press-hardened structural components made of coated high-strength steel are an essential part of the automotive industry when it comes to weight reduction, safety, and durability. Alternative heat treatment processes, such as contact heating, have been developed to improve the efficiency of this process. However, contact heating of the steel sheets often results in cracking within the Al-Si-coated layer. Therefore, this paper will address the development of alternative coating compositions based on Al-Si-X, suitable for contact heating. For this purpose, robot-assisted thermal arc spray was applied to coat the high-strength steel sheets. This ensured high reproducibility as well as effectiveness. The influence of the coating parameters and the variation of the nozzle geometry on the microstructure of the developed coatings will be discussed. Finally, the surface and mechanical properties after contact heating and press hardening will be presented.

Keywords: press hardening, hot stamping, thermal spraying, arc spraying, coating compositions

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Authors: Yongjun Ahn, Sung Gyu Choi, Seung-Yeop Kwak

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Selective removal of free fatty acid (FFA) and chlorophyll in extra virgin olive oil (EVOO) is necessary to enhance the thermal stability in the condition of the deep frying. In this work, we demonstrated improving the thermal stability of EVOO by selective removal of free fatty acid and chlorophyll using (3-Aminopropyl)trimethoxysilane (APTMS) functionalized mesoporous silica with controlled pore size. The adsorption kinetics of free fatty acid and chlorophyll into the mesoporous silica were quantitatively analyzed by Freundlich and Langmuir model. The highest chlorophyll adsorption efficiency was shown in the pore size at 5 nm, suggesting that the interaction between the silica and the chlorophyll could be optimized at this point. The amino-functionalized mesoporous silica showed drastically improved removal efficiency of FFA than the bare silica. Moreover, beneficial compounds like tocopherol and phenolic compounds maintained even after adsorptive removal. Extra virgin olive oil treated by aminopropyl-functionalized silica had a smoke point high enough to be used as commercial frying oil. Based on these results, it is expected to attract the considerable amount of interest toward facile adsorptive refining process of EVOO using pore size controlled and amino-functionalized mesoporous silica.

Keywords: mesoporous silica, extra virgin olive oil, selective adsorption, thermal stability

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Authors: Feng Yang

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Machine learning (ML), especially deep learning (DL), has been extensively applied to many applications in recently years and gained great success in solving different problems, including scientific problems. However, conventional ML/DL methodologies are purely data-driven which have the limitations, such as need of ample amount of labelled training data, lack of consistency to physical principles, and lack of generalizability to new problems/domains. Recently, there is a growing consensus that ML models need to further take advantage of prior knowledge to deal with these limitations. Physics-informed machine learning, aiming at integration of physics/domain knowledge into ML, has been recognized as an emerging area of research, especially in the recent 2 to 3 years. In this work, physics-informed ML, specifically physics-informed neural network (NN), is employed and implemented to estimate the displacements at x, y, z directions in a solid mechanics problem that is controlled by equilibrium equations with boundary conditions. By incorporating the physics (i.e. the equilibrium equations) into the learning process of NN, it is showed that the NN can be trained very efficiently with a small set of labelled training data. Experiments with different settings of the NN model and the amount of labelled training data were conducted, and the results show that very high accuracy can be achieved in fulfilling the equilibrium equations as well as in predicting the displacements, e.g. in setting the overall displacement of 0.1, a root mean square error (RMSE) of 2.09 × 10−4 was achieved.

Keywords: deep learning, neural network, physics-informed machine learning, solid mechanics

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Authors: An Guo, Hieyong Jeong, Tianyi Wang, Na Li, Yuko Ohno

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Acupuncture, known as sensory simulation, has been used to treat various disorders for thousands of years. However, present studies had not addressed approaches for noninvasive measurement in order to evaluate therapeutic effect of acupuncture. The purpose of this study is to propose a noninvasive method to evaluate acupuncture by measuring facial temperature through thermal image. Three human subjects were recruited in this study. Each subject received acupuncture therapy for 30 mins. Acupuncture needles (Ø0.16 x 30 mm) were inserted into Baihui point (DU20), Neiguan points (PC6) and Taichong points (LR3), acupuncture needles (Ø0.18 x 39 mm) were inserted into Tanzhong point (RN17), Zusanli points (ST36) and Yinlingquan points (SP9). Facial temperature was recorded by an infrared thermometer. Acupuncture therapeutic effect was compared pre- and post-acupuncture. Experiment results demonstrated that facial temperature changed according to acupuncture therapeutic effect. It was concluded that proposed method showed high potential to evaluate acupuncture by noninvasive measurement of facial temperature.

Keywords: acupuncture, facial temperature, noninvasive evaluation, thermal image

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Authors: Sarka Keprdova, Jiri Bydzovsky

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This article describes to what extent the addition of energy by-products into the structures of the technical hemp filling materials influence their properties. The article focuses on the changes in physical-mechanical and thermal technical properties of materials after the addition of ash or FBC ash or slag in the binding component of material. Technical hemp filling materials are made of technical hemp shives bonded by the mixture of cement and dry hydrate lime. They are applicable as fillers of vertical or horizontal structures or roofs. The research used eight types of energy by-products of power or heating plants in the Czech Republic. Secondary energy products were dispensed in three different percentage ratios as a replacement of cement in the binding component. Density, compressive strength and determination of the coefficient of thermal conductivity after 28, 60 and 90 days of curing in a laboratory environment were determined and subsequently evaluated on the specimens produced.

Keywords: ash, binder, cement, energy by-product, FBC ash (fluidized bed combustion ash), filling materials, shives, slag, technical hemp

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Authors: Akshat Kumar, Vidushi

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This paper identifies 27 KOIs, 26 of which are currently classified as candidates and one as false positives that have a high probability of being confirmed. For this purpose, 11 machine learning algorithms were implemented on the cumulative kepler dataset sourced from the NASA exoplanet archive; it was observed that the best-performing model was HistGradientBoosting and XGBoost with a test accuracy of 93.5%, and the lowest-performing model was Gaussian NB with a test accuracy of 54%, to test model performance F1, cross-validation score and RUC curve was calculated. Based on the learned models, the significant characteristics for confirm exoplanets were identified, putting emphasis on the object’s transit and stellar properties; these characteristics were namely koi_count, koi_prad, koi_period, koi_dor, koi_ror, and koi_smass, which were later considered to filter out the potential KOIs. The paper also calculates the Earth similarity index based on the planetary radius and equilibrium temperature for each KOI identified to aid in their classification.

Keywords: Kepler objects of interest, exoplanets, space exploration, machine learning, earth similarity index, transit photometry

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Authors: Ahmed Ramadhan Al-Obaidi, Jassim Alhamid

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In this investigation, the flow pattern, characteristics of thermal-hydraulic, and improvement of heat transfer performance are evaluated using a numerical technique in three dimensions corrugated pipe heat exchanger. The modification was made under different corrugated pipe geometrical parameters, including corrugated ring angle (CRA), distance between corrugated ring (DBCR), and corrugated diameter (CD), the range of Re number from 2000 to 12000. The numerical results are validated with available experimental data. The numerical outcomes reveal that there is an important change in flow field behaviour and a significant increase in friction factor and improvement in heat transfer performance owing to the use of the corrugated shape in the heat exchanger pipe as compared to the conventional smooth pipe. Using corrugated pipe with different configurations makes the flow more turbulence, flow separation, boundary layer distribution, flow mixing, and that leads to augmenting the performance of heat transfer. Moreover, the value of pressure drop, and the Nusselt number increases as the corrugated pipe geometrical parameters increase. Furthermore, the corrugation configuration shapes have an important influence on the thermal evaluation performance factor, and the maximum value was more than 1.3. Numerical simulation can be performed to predict the various geometrical configurations effects on fluid flow, thermal performance, and heat transfer enhancement.

Keywords: corrugated ring angle, corrugated diameter, Nusselt number, heat transfer

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Authors: Cagri Mollamahmutoglu, Ali Mercan, Aykut Levent

Abstract:

Studies concerning the microstructures are becoming more important as the utilization of various micro-electro mechanical systems (MEMS) are increasing. Thus in recent years, thermal buckling and vibration analysis of microstructures have been subject to many investigations that are utilizing different numerical methods. In this study, thermal effects on mechanical response of a functionally graded (FG) Timoshenko micro-beam are presented in the framework of a mixed finite element formulation. Size effects are taken into consideration via modified couple stress theory. The mixed formulation is based on a function which in turn is derived via Gateaux Differential scientifically. After the resolution of all field equations of the beam, a potential operator is carefully constructed. Then this operator is used for the manufacturing of the functional. Usual procedures of finite element approximation are utilized for the derivation of the mixed finite element equations once the potential is obtained. Resulting finite element formulation allows usage of C₀ type simple linear shape functions and avoids shear-locking phenomena, which is a common shortcoming of the displacement-based formulations of moderately thick beams. The developed numerical scheme is used to obtain the effects of thermal loads on the static bending, free vibration and buckling of FG Timoshenko micro-beams for different power-law parameters, aspect ratios and boundary conditions. The versatility of the mixed formulation is presented over other numerical methods such as generalized differential quadrature method (GDQM). Another attractive property of the formulation is that it allows direct calculation of the contribution of micro effects on the overall mechanical response.

Keywords: micro-beam, functionally graded materials, thermal effect, mixed finite element method

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Authors: Liaqat Ali, Furqan Ahmad

Abstract:

The heat losses should be controlled during the high temperature processes from energy conservation point of view. For this purpose, refractories with low thermal conductivity, high porosity and good mechanical strength along with low price are desirable. In this work, various combinations of naturally occurring, locally available, cheap raw materials, namely, clay, rice husk and saw dust were used. Locally produced insulating firebricks (IFBs) cannot be used at higher than a few hundred °C and possess low strength as well. Various process parameters were studied and the refractories with desirable properties were produced, which can be used up to 1200 °C.

Keywords: firebricks, mechanical strength, thermal conductivity, refractory bricks

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Authors: Aulia Fitri

Abstract:

This aims to seek which part of the linguistics with the common mistakes occurred between Instagram translation and human translation. Instagram is a social media account that is widely used by people in the world. Everyone with the Instagram account can consume the captions and pictures that are shared by their friends, celebrity, and public figures across countries. Instagram provides the machine translation under its caption space that will assist users to understand the language of their non-native. The researcher takes samples from an Indonesian public figure whereas the account is followed by many followers. The public figure tries to help her followers from other countries understand her posts by putting up the English version after the Indonesian version. However, the research on Instagram account has not been done yet even though the account is widely used by the worldwide society. There are 20 samples that will be analysed on the perspective of translation quality and linguistics tools. As the MT, Instagram tends to give a literal translation without regarding the topic meant. On the other hand, the human translation tends to exaggerate the translation which leads a different meaning in English. This is an interesting study to discuss when the human nature and robotic-system influence the translation result.

Keywords: human translation, machine translation (MT), translation quality, linguistic tool

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Authors: Samaneh Yavari, Christiane Pferrer, Elisabeth Engelke, Alexander Starke, Juergen Rehage

Abstract:

Background: Three nociceptive thresholds of thermal, electrical, and mechanical thresholds commonly use to evaluate the local anesthesia in many species, for instance, cow, horse, cat, dog, rabbit, and so on. Due to the lack of investigations to evaluate and/or validate such those nociceptive thresholds, our plan was the comparison of two-foot local anesthesia methods of Intravenous Regional Anesthesia (IVRA) and our modified four-point Nerve Block Anesthesia (NBA). Materials and Methods: Eight healthy nonpregnant nondairy Holstein Frisian cows in a cross-over study design were selected for this study. All cows divided into two different groups to receive two local anesthesia techniques of IVRA and our modified four-point NBA. Three thermal, electrical, and mechanical force and pinpricks were applied to evaluate the quality of local anesthesia methods before and after local anesthesia application. Results: The statistical evaluation demonstrated that our four-point NBA has a qualification to select as a standard foot local anesthesia. However, the recorded results of our study revealed no significant difference between two groups of local anesthesia techniques of IVRA and modified four-point NBA related to quality and duration of anesthesia stimulated by electrical, mechanical and thermal nociceptive stimuli. Conclusion and discussion: All three nociceptive threshold stimuli of electrical, mechanical and heat nociceptive thresholds can be applied to measure and evaluate the efficacy of foot local anesthesia of dairy cows. However, our study revealed no superiority of those three nociceptive methods to evaluate the duration and quality of bovine foot local anesthesia methods. Veterinarians to investigate the duration and quality of their selected anesthesia method can use any of those heat, mechanical, and electrical methods.

Keywords: mechanical, thermal, electrical threshold, IVRA, NBA, hind limb, dairy cow

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Authors: Ahmed Aisa, Tariq Iqbal

Abstract:

This letter talks about the ready-to-use design of a solar water heating system because, in Canada, the average consumption of hot water per person is approximately 50 to 75 L per day and the average Canadian household uses 225 L. Therefore, this paper will demonstrate the method of designing a solar water heating system with thermal storage. It highlights the renewable hybrid power system, allowing you to obtain a reliable, independent system with the optimization of the ingredient size and at an improved capital cost. The system can provide hot water for a big building. The main power for the system comes from solar panels. Solar Advisory Model (SAM) and HOMER are used. HOMER and SAM are design models that calculate the consumption of hot water and cost for a house. Some results, obtained through simulation, were for monthly energy production, annual energy production, after tax cash flow, the lifetime of the system and monthly energy usage represented by three types of energy. These are system energy, electricity load electricity and net metering credit.

Keywords: water heating, thermal storage, capital cost solar, consumption

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Authors: L. F. Tamele Jr., G. Buonocore, H. F. Muiambo

Abstract:

Asphalt binders play an essential role in the performance and properties of asphalt mixtures. The increase in heavy loads, greater traffic volume, and high tire pressure, combined with a substantial variation in daily and seasonal pavement temperatures, are the main responsible for the failure of asphalt pavements. To avoid or mitigate these failures, the present research proposes the use of thermoplastic polymers, HDPE and LLDPE, and nanoclay Dellite 43B for modification of asphalt in order to improve its thermomechanical and rheological properties. The nanocomposites were prepared by the solution intercalation method in a high shear mixer for a mixing time of 2 h, at 180℃ and 5000 rpm. The addition of Dellite 43B improved the physical, rheological, and thermal properties of asphalt, either separated or in the form of polymer/bitumen blends. The results of the physical characterization showed a decrease in penetration and an increase in softening point, thermal susceptibility, viscosity, and stiffness. On the other hand, thermal characterization showed that the nanocomposites have greater stability at higher temperatures by exhibiting greater amounts of residues and improved initial and final decomposition temperatures. Thus, the modification of asphalt by polymers and nanoclays seems to be a suitable solution for road pavement in countries which experiment with high temperatures combined with long heavy rain seasons.

Keywords: asphalt, nanoclay dellite 43B, polymer modified asphalt, thermal and rheological properties

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Authors: Hanbey Hazar, Hakan Gul

Abstract:

In this study; piston, exhaust, and suction valves of a diesel engine were coated in 300 mm thickness with Tungsten Carbide (WC) by using the HVOF coating method. Mathematical modeling of a coated and uncoated (standardized) engine was performed by using ANN (Artificial Neural Networks). The purpose was to decrease the number of repetitions of tests and reduce the test cost through mathematical modeling of engines by using ANN. The results obtained from the tests were entered in ANN and therefore engines' values at all speeds were estimated. Results obtained from the tests were compared with those obtained from ANN and they were observed to be compatible. It was also observed that, with thermal barrier coating, hydrocarbon (HC), carbon monoxide (CO), and smoke density values of the diesel engine decreased; but nitrogen oxides (NOx) increased. Furthermore, it was determined that results obtained through mathematical modeling by means of ANN reduced the number of test repetitions. Therefore, it was understood that time, fuel and labor could be saved in this way.

Keywords: Artificial Neural Network, Diesel Engine, Mathematical Modelling, Thermal Barrier Coating

Procedia PDF Downloads 524