Search results for: ANN backpropagation modelling

Authors: Laura Pytel, Georg Baumann, Gregor Gstrein, Corina Klug

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Premium cars often coat the instrument panels with a bilaminate consisting of a soft foam and a plastic skin. The coating is torn open during the passenger airbag deployment under high strain rates. Characterizing and simulating the top coat layer is crucial for predicting the attenuation that delays the airbag deployment, effecting the design of the restrain system and to reduce the demand of simulation adjustments through expensive physical component testing.Up to now, bilaminates used within cars either have been modelled by using a two-dimensional shell formulation for the whole coating system as one which misses out the interaction of the two layers or by combining a three-dimensional formulation foam layer with a two-dimensional skin layer but omitting the foam in the significant parts like the expected tear line area and the hinge where high compression is expected. In both cases, the properties of the coating causing the attenuation are not considered. Further, at present, the availability of material information, as there are failure dependencies of the two layers, as well as the strain rate of up to 200 1/s, are insufficient. The velocity of the passenger airbag flap during an airbag shot has been measured with about 11.5 m/s during first ripping; the digital image correlation evaluation showed resulting strain rates of above 1500 1/s. This paper provides a high strain rate material characterization of a bilaminate consisting of a thin polypropylene foam and a thermoplasctic olefins (TPO) skin and the creation of validated material models. With the help of a Split Hopkinson tension bar, strain rates of 1500 1/s were within reach. The experimental data was used to calibrate and validate a more physical modelling approach of the forced ripping of the bilaminate. In the presented model, the three-dimensional foam layer is continuously tied to the two-dimensional skin layer, allowing failure in both layers at any possible position. The simulation results show a higher agreement in terms of the trajectory of the flaps and its velocity during ripping. The resulting attenuation of the airbag deployment measured by the contact force between airbag and flaps increases and serves usable data for dimensioning modules of an airbag system.

Keywords: bilaminate ripping behavior, High strain rate material characterization and modelling, induced material failure, TPO and foam

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Authors: Nicolaas Unland, John Webb

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The acidification and mobilisation of metals during the oxidation of acid sulfate soils exposed during lake bed drying is an increasingly common phenomenon under climate scenarios with reduced rainfall. In order to assess the risk of generating high concentrations of acidity and dissolved metals, chromium suite analysis are fundamental, but sometimes limited in characterising the potential risks they pose. This study combines such fundamental test work, along with incubation tests and 1D modelling to investigate the risks associated with the drying of Third Reedy Lake in South East Australia. Core samples were collected from a variable depth of 0.5 m below the lake bed, at 19 locations across the lake’s footprint, using a boat platform. Samples were subjected to a chromium suite of analysis, including titratable actual acidity, chromium reducible sulfur and acid neutralising capacity. Concentrations of reduced sulfur up to 0.08 %S and net acidities up to 0.15 %S indicate that acid sulfate soils have formed on the lake bed during permanent inundation over the last century. A further sub-set of samples were prepared in 7 columns and subject to accelerated heating, drying and wetting over a period of 64 days in laboratory. Results from the incubation trial indicate that while pyrite oxidation proceeded, minimal change to soil pH or the acidity of leachate occurred, suggesting that the internal buffering capacity of lake bed sediments was sufficient to neutralise a large proportion of the acidity produced. A 1D mass balance model was developed to assess potential changes in lake water quality during drying based on the results of chromium suite and incubation tests. Results from the above test work and modelling suggest that acid sulfate soils pose a moderate to low risk to the Third Reedy Lake system. Further, the risks can be effectively managed during the initial stages of lake drying via flushing with available mildly alkaline water. The study finds that while test work such as chromium suite analysis are fundamental in characterizing acid sulfate soil environments, they can the overestimate risks associated with the soils. Subsequent incubation test work may more accurately characterise such soils and lead to better-informed management strategies.

Keywords: acid sulfate soil, incubation, management, model, risk

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Authors: Rodrigo Pacheco-Ruiz, Jonathan Adams, Felix Pedrotti

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This paper introduces the applicability of underwater photogrammetric survey within challenging conditions as the main tool to enhance and enrich the process of documenting archaeological excavation through the creation of 4D models. Photogrammetry was being attempted on underwater archaeological sites at least as early as the 1970s’ and today the production of traditional 3D models is becoming a common practice within the discipline. Photogrammetry underwater is more often implemented to record exposed underwater archaeological remains and less so as a dynamic interpretative tool.  Therefore, it tends to be applied in bright environments and when underwater visibility is > 1m, reducing its implementation on most submerged archaeological sites in more turbid conditions. Recent years have seen significant development of better digital photographic sensors and the improvement of optical technology, ideal for darker environments. Such developments, in tandem with powerful processing computing systems, have allowed underwater photogrammetry to be used by this research as a standard recording and interpretative tool. Using multi-source photogrammetry (5, GoPro5 Hero Black cameras) this paper presents the accumulation of daily (4D) underwater surveys carried out in the Early Bronze Age (3,300 BC) to Late Ottoman (17th Century AD) archaeological site of Ropotamo in the Bulgarian Black Sea under challenging conditions (< 0.5m visibility). It proves that underwater photogrammetry can and should be used as one of the main recording methods even in low light and poor underwater conditions as a way to better understand the complexity of the underwater archaeological record.

Keywords: 4D modelling, Black Sea Maritime Archaeology Project, multi-source photogrammetry, low visibility underwater survey

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Authors: Maria Amodeo, Pasquale Arpaia, Marco Buzio, Vincenzo Di Capua, Francesco Donnarumma

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Different deep neural network architectures have been compared and tested to predict magnetic hysteresis in the context of pulsed electromagnets for experimental physics applications. Modelling quasi-static or dynamic major and especially minor hysteresis loops is one of the most challenging topics for computational magnetism. Recent attempts at mathematical prediction in this context using Preisach models could not attain better than percent-level accuracy. Hence, this work explores neural network approaches and shows that the architecture that best fits the measured magnetic field behaviour, including the effects of hysteresis and eddy currents, is the nonlinear autoregressive exogenous neural network (NARX) model. This architecture aims to achieve a relative RMSE of the order of a few 100 ppm for complex magnetic field cycling, including arbitrary sequences of pseudo-random high field and low field cycles. The NARX-based architecture is compared with the state-of-the-art, showing better performance than the classical operator-based and differential models, and is tested on a reference quadrupole magnetic lens used for CERN particle beams, chosen as a case study. The training and test datasets are a representative example of real-world magnet operation; this makes the good result obtained very promising for future applications in this context.

Keywords: deep neural network, magnetic modelling, measurement and empirical software engineering, NARX

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Authors: A. Ascenso, C. Silveira, B. Augusto, S. Rafael, S. Coelho, J. Ferreira, A. Monteiro, P. Roebeling, A. I. Miranda

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Innovative nature-based solutions (NBSs) can provide answers to the challenges that urban areas are currently facing due to urban densification and extreme weather conditions. The effects of NBSs are recognized and include improved quality of life, mental and physical health and improvement of air quality, among others. Part of the work developed in the scope of the UNaLab project, which aims to guide cities in developing and implementing their own co-creative NBSs, intends to assess the impacts of NBSs on air quality, using Eindhoven city as a case study. The state-of-the-art online air quality modelling system WRF-CHEM was applied to simulate meteorological and concentration fields over the study area with a spatial resolution of 1 km2 for the year 2015. The baseline simulation (without NBSs) was validated by comparing the model results with monitored data retrieved from the Eindhoven air quality database, showing an adequate model performance. In addition, land use changes were applied in a set of simulations to assess the effects of different types of NBSs. Finally, these simulations were compared with the baseline scenario and the impacts of the NBSs were assessed. Reductions on pollutant concentrations, namely for NOx and PM, were found after the application of the NBSs in the Eindhoven study area. The present work is particularly important to support public planners and decision makers in understanding the effects of their actions and planning more sustainable cities for the future.

Keywords: air quality, modelling approach, nature based solutions, urban area

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Authors: Vineeth Siripuram, Abhineet Nigam

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A bioreactor is an engineered system that supports a biologically active environment. Along the years, the advancements in bioreactors have been widely accepted all over the world for varied applications ranging from sewage treatment to tissue cloning. Driven by tissue and organ shortage, tissue engineering has emerged as an alternative to transplantation for the reconstruction of lost or damaged organs. In this study, Computational fluid dynamics (CFD) has been used to model porous medium flow in scaffolds (taken from the literature) with different flow patterns. A detailed analysis of different scaffold geometries and their influence on cell deposition in the perfusion system is been carried out using Computational fluid dynamics (CFD). Considering the fact that, the scaffold should mimic the organs or tissues structures in a three-dimensional manner, certain assumptions were made accordingly. The research on scaffolds has been extensively carried out in different bioreactors. However, there has been less focus on the morphology of the scaffolds and the flow patterns in which the perfusion system is laid upon. The objective of this paper is to employ a computational approach using CFD simulation to determine the optimal morphology and the anisotropic measurements of the various samples of scaffolds. Using predictive computational modelling approach, variables which exert dominant effects on the cell deposition within the scaffold were prioritised and corresponding changes in morphology of scaffold and flow patterns in the perfusion systems are made. A Eulerian approach was carried on in multiple CFD simulations, and it is observed that the morphological and topological changes in the scaffold perfusion system are of great importance in the commercial applications of scaffolds.

Keywords: cell seeding, CFD, flow patterns, modelling, perfusion systems, scaffold

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Authors: Yiannis Vacanas, Kyriacos Themistocleous, Athos Agapiou, Diofantos Hadjimitsis

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Building Information Modelling (BIM) technology is considered by construction professionals as a very valuable process in modern design, procurement and project management. Construction professionals of all disciplines can use a single 3D model which BIM technology provides, to design a project accurately and furthermore monitor the progress of construction works effectively and efficiently. Unmanned Aerial Vehicles (UAVs), a technology initially developed for military applications, is now without any difficulty accessible and has already been used by commercial industries, including the construction industry. UAV technology has mainly been used for collection of images that allow visual monitoring of building and civil engineering projects conditions in various circumstances. UAVs, nevertheless, have undergone significant advances in equipment capabilities and now have the capacity to acquire high-resolution imagery from many angles in a cost effective manner, and by using photogrammetry methods, someone can determine characteristics such as distances, angles, areas, volumes and elevations of an area within overlapping images. In order to examine the potential of using a combination of BIM and UAV technologies in construction project management, this paper presents the results of a case study of a typical road construction project where the combined use of the two technologies was used in order to achieve efficient and accurate as-built data collection of the works progress, with outcomes such as volumes, and production of sections and 3D models, information necessary in project progress monitoring and efficient project management.

Keywords: BIM, project management, project monitoring, UAV

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Authors: Dhruv Mehta, Alexander van Zuijlen, Hester Bijl

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Large Eddy Simulation (LES) numerically resolves the large energy-containing eddies of a turbulent flow, while modelling the small dissipative eddies. On a wind farm, these large scales carry the energy wind turbines extracts and are also responsible for transporting the turbines’ wakes, which may interact with downstream turbines and certainly with the atmospheric boundary layer (ABL). In this situation, it is important to conserve the energy that these wake’s carry and which could be altered artificially through numerical dissipation brought about by the schemes used for the spatial discretisation and temporal integration. Numerical dissipation has been reported to cause the premature recovery of turbine wakes, leading to an over prediction in the power produced by wind farms.An energy-conserving scheme is free from numerical dissipation and ensures that the energy of the wakes is increased or decreased only by the action of molecular viscosity or the action of wind turbines (body forces). The aim is to create an LES package with energy-conserving schemes to simulate wind turbine wakes correctly to gain insight into power-production, wake meandering etc. Such knowledge will be useful in designing more efficient wind farms with minimal wake interaction, which if unchecked could lead to major losses in energy production per unit area of the wind farm. For their research, the authors intend to use the Energy-Conserving Navier-Stokes code developed by the Energy Research Centre of the Netherlands.

Keywords: energy-conserving schemes, modelling turbulence, Large Eddy Simulation, atmospheric boundary layer

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Authors: Yulii D. Shikhmurzaev

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The modelling of the non-isothermal free-surface flows coupled with the solidification process has become the topic of intensive research with the advent of additive manufacturing, where complex 3-dimensional structures are produced by successive deposition and solidification of microscopic droplets of different materials. The issue is that both the spreading of liquids over solids and the propagation of the solidification front into the fluid and along the solid substrate pose fundamental difficulties for their mathematical modelling. The first of these processes, known as ‘dynamic wetting’, leads to the well-known ‘moving contact-line problem’ where, as shown recently both experimentally and theoretically, the contact angle formed by the free surfac with the solid substrate is not a function of the contact-line speed but is rather a functional of the flow field. The modelling of the propagating solidification front requires generalization of the classical Stefan problem, which would be able to describe the onset of the process and the non-equilibrium regime of solidification. Furthermore, given that both dynamic wetting and solification occur concurrently and interactively, they should be described within the same conceptual framework. The present work addresses this formidable problem and presents a mathematical model capable of describing the key element of additive manufacturing in a self-consistent and singularity-free way. The model is illustrated simple examples highlighting its main features. The main idea of the work is that both dynamic wetting and solidification, as well as some other fluid flows, are particular cases in a general class of flows where interfaces form and/or disappear. This conceptual framework allows one to derive a mathematical model from first principles using the methods of irreversible thermodynamics. Crucially, the interfaces are not considered as zero-mass entities introduced using Gibbsian ‘dividing surface’ but the 2-dimensional surface phases produced by the continuum limit in which the thickness of what physically is an interfacial layer vanishes, and its properties are characterized by ‘surface’ parameters (surface tension, surface density, etc). This approach allows for the mass exchange between the surface and bulk phases, which is the essence of the interface formation. As shown numerically, the onset of solidification is preceded by the pure interface formation stage, whilst the Stefan regime is the final stage where the temperature at the solidification front asymptotically approaches the solidification temperature. The developed model can also be applied to the flow with the substrate melting as well as a complex flow where both types of phase transition take place.

Keywords: dynamic wetting, interface formation, phase transition, solidification

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Authors: Jefri Draup, Antoine Ambard, Chi-Toan Nguyen

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Zirconium alloys exhibit solid-state phase transformations under thermal loading. These can lead to a significant evolution of the microstructure and associated mechanical properties of materials used in nuclear fuel cladding structures. Therefore, the ability to capture effects of phase transformation on the material constitutive behavior is of interest during conditions of severe transient thermal loading. Whilst typical Avrami, or Johnson-Mehl-Avrami-Kolmogorov (JMAK), type models for phase transformations have been shown to have a good correlation with the behavior of Zircaloy-4 under constant heating rates, the effects of variable and fast heating rates are not fully explored. The present study utilises the results of in-situ high energy synchrotron X-ray diffraction (SXRD) measurements in order to validate the phase transformation models for Zircaloy-4 under fast variable heating rates. These models are used to assess the performance of fuel cladding structures under loss of coolant accident (LOCA) scenarios. The results indicate that simple Avrami type models can provide a reasonable indication of the phase distribution in experimental test specimens under variable fast thermal loading. However, the accuracy of these models deteriorates under the faster heating regimes, i.e., 100Cs⁻¹. The studies highlight areas for improvement of simple Avrami type models, such as the inclusion of temperature rate dependence of the JMAK n-exponent.

Keywords: accident, fuel, modelling, zirconium

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Authors: U. Datta

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The main objective of this study is to find a suitable approach to monitor the land infrastructure growth over a period of time using multispectral satellite images. Bi-temporal change detection method is unable to indicate the continuous change occurring over a long period of time. To achieve this objective, the approach used here estimates a statistical model from series of multispectral image data over a long period of time, assuming there is no considerable change during that time period and then compare it with the multispectral image data obtained at a later time. The change is estimated pixel-wise. Statistical composite hypothesis technique is used for estimating pixel based change detection in a defined region. The generalized likelihood ratio test (GLRT) is used to detect the changed pixel from probabilistic estimated model of the corresponding pixel. The changed pixel is detected assuming that the images have been co-registered prior to estimation. To minimize error due to co-registration, 8-neighborhood pixels around the pixel under test are also considered. The multispectral images from Sentinel-2 and Landsat-8 from 2015 to 2018 are used for this purpose. There are different challenges in this method. First and foremost challenge is to get quite a large number of datasets for multivariate distribution modelling. A large number of images are always discarded due to cloud coverage. Due to imperfect modelling there will be high probability of false alarm. Overall conclusion that can be drawn from this work is that the probabilistic method described in this paper has given some promising results, which need to be pursued further.

Keywords: co-registration, GLRT, infrastructure growth, multispectral, multitemporal, pixel-based change detection

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Authors: K. Rana, H.A.Saeed, S. Zahir

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Inter Laminar Shear Stress (ILSS) is a main key parameter which quantify the properties of composite materials. These properties can ascertain the use of material for a specific purpose like aerospace, automotive etc. A modelling approach for determination of ILSS is presented in this paper. Geometric modelling of composite material is performed in TEXGEN software where reinforcement, cured matrix and their interfaces are modelled separately as per actual geometry. Mechanical properties of matrix and reinforcements are modelled separately which incorporated anisotropy in the real world composite material. ASTM D2344 is modelled in ANSYS for ILSS. In macroscopic analysis model approximates the anisotropy of the material and uses orthotropic properties by applying homogenization techniques. Shear Stress analysis in that case does not show the actual real world scenario and rather approximates it. In this paper actual geometry and properties of reinforcement and matrix are modelled to capture the actual stress state during the testing of samples as per ASTM standards. Testing of samples is also performed in order to validate the results. Fibre volume fraction of yarn is determined by image analysis of manufactured samples. Fibre volume fraction data is incorporated into the numerical model for correction of transversely isotropic properties of yarn. A comparison between experimental and simulated results is presented.

Keywords: ILSS, FEA, micromechanical, fibre volume fraction, image analysis

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Authors: Venkat S. Somayajula

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Ground surface temperature history prediction model plays a vital role in determining standards for international nuclear waste management. International standards for borehole based nuclear waste disposal require paleoclimate cycle predictions on scale of a million forward years for the place of waste disposal. This research focuses on developing a paleoclimate cycle prediction model using Bayesian long-short term memory (LSTM) neural architecture operated on accumulated borehole temperature history data. Bayesian models have been previously used for paleoclimate cycle prediction based on Monte-Carlo weight method, but due to limitations pertaining model coupling with certain other prediction networks, Bayesian models in past couldn’t accommodate prediction cycle’s over 1000 years. LSTM has provided frontier to couple developed models with other prediction networks with ease. Paleoclimate cycle developed using this process will be trained on existing borehole data and then will be coupled to surface temperature history prediction networks which give endpoints for backpropagation of LSTM network and optimize the cycle of prediction for larger prediction time scales. Trained LSTM will be tested on past data for validation and then propagated for forward prediction of temperatures at borehole locations. This research will be beneficial for study pertaining to nuclear waste management, anthropological cycle predictions and geophysical features

Keywords: Bayesian long-short term memory neural network, borehole temperature, ground surface temperature history, paleoclimate cycle

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Authors: Audrius Dedele, Aukse Miskinyte

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Air pollution is a major problem, especially in large cities, causing a variety of environmental issues and a risk to human health effects. In order to observe air quality, to reduce and control air pollution in the city, municipalities are responsible for the creation of air quality management plans, air quality monitoring and emission inventories. Atmospheric dispersion modelling systems, along with monitoring, are powerful tools, which can be used not only for air quality management, but for the assessment of human exposure to air pollution. These models are widely used in epidemiological studies, which try to determine the associations between exposure to air pollution and the adverse health effects. The purpose of this study was to determine the concentration of particulate matter smaller than 10 μm (PM10) in different districts of Kaunas city during winter season. ADMS-Urban dispersion model was used for the simulation of PM10 pollution. The inputs of the model were the characteristics of stationary, traffic and domestic sources, emission data, meteorology and background concentrations were entered in the model. To assess the modelled concentrations of PM10 in Kaunas districts, geographic information system (GIS) was used. More detailed analysis was made using Spatial Analyst tools. The modelling results showed that the average concentration of PM10 during winter season in Kaunas city was 24.8 µg/m3. The highest PM10 levels were determined in Zaliakalnis and Aleksotas districts with are the highest number of individual residential properties, 32.0±5.2 and 28.7±8.2 µg/m3, respectively. The lowest pollution of PM10 was modelled in Petrasiunai district (18.4 µg/m3), which is characterized as commercial and industrial neighbourhood.

Keywords: air pollution, dispersion model, GIS, Particulate matter

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Authors: Luiz Carlos Wrobel, Matjaz Hribersek, Jure Marn, Jurij Iljaz

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Dynamic thermography has been clinically proven to be a valuable diagnostic technique for skin tumor detection as well as for other medical applications such as breast cancer diagnostics, diagnostics of vascular diseases, fever screening, dermatological and other applications. Thermography for medical screening can be done in two different ways, observing the temperature response under steady-state conditions (passive or static thermography), and by inducing thermal stresses by cooling or heating the observed tissue and measuring the thermal response during the recovery phase (active or dynamic thermography). The numerical modelling of heat transfer phenomena in biological tissue during dynamic thermography can aid the technique by improving process parameters or by estimating unknown tissue parameters based on measured data. This paper presents a nonlinear numerical model of multilayer skin tissue containing a skin tumor, together with the thermoregulation response of the tissue during the cooling-rewarming processes of dynamic thermography. The model is based on the Pennes bioheat equation and solved numerically by using a subdomain boundary element method which treats the problem as axisymmetric. The paper includes computational tests and numerical results for Clark II and Clark IV tumors, comparing the models using constant and temperature-dependent thermophysical properties, which showed noticeable differences and highlighted the importance of using a local thermoregulation model.

Keywords: boundary element method, dynamic thermography, static thermography, skin tumor diagnostic

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Authors: Jiří. J. Urbánek, Hana Malachová, Josef Krahulec, Jitka Johanidisová

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The paper deals with crisis situations investigation and modelling within the organizations of critical infrastructure. Every crisis situation has an origin in the emergency event occurrence in the organizations of energetic critical infrastructure especially. Here, the emergency events can be both the expected events, then crisis scenarios can be pre-prepared by pertinent organizational crisis management authorities towards their coping or the unexpected event (Black Swan effect) – without pre-prepared scenario, but it needs operational coping of crisis situations as well. The forms, characteristics, behaviour and utilization of crisis scenarios have various qualities, depending on real critical infrastructure organization prevention and training processes. An aim is always better organizational security and continuity obtainment. This paper objective is to find and investigate critical/ crisis zones and functions in critical situations models of critical infrastructure organization. The DYVELOP (Dynamic Vector Logistics of Processes) method is able to identify problematic critical zones and functions, displaying critical interfaces among actors of crisis situations on the DYVELOP maps named Blazons. Firstly, for realization of this ability is necessary to derive and create identification algorithm of critical interfaces. The locations of critical interfaces are the flags of crisis situation in real organization of critical infrastructure. Conclusive, the model of critical interface will be displayed at real organization of Czech energetic crisis infrastructure subject in Black Out peril environment. The Blazons need live power Point presentation for better comprehension of this paper mission.

Keywords: algorithm, crisis, DYVELOP, infrastructure

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Authors: H. M. Ferreira, H. Cockings, D. F. Gordon

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Climate change is driving a transition in the energy sector, with low-carbon energy sources such as hydrogen (H2) emerging as an alternative to fossil fuels. However, the successful implementation of a hydrogen economy requires an expansion of hydrogen production, transportation and storage capacity. The costs associated with this transition are high but can be partly mitigated by adapting the current oil and natural gas networks, such as pipeline, an important component of the hydrogen infrastructure, to transport pure or blended hydrogen. Steel pipelines are designed to withstand fatigue, one of the most common causes of pipeline failure. However, it is well established that some materials, such as steel, can fail prematurely in service when exposed to hydrogen-rich environments. Therefore, it is imperative to evaluate how defects (e.g. inclusions, dents, and pre-existing cracks) will interact with hydrogen under cyclic loading and, ultimately, to what extent hydrogen induced failure will limit the service conditions of steel pipelines. This presentation will explore how the exposure of API 5L X65 to a hydrogen-rich environment and cyclic loads will influence its susceptibility to hydrogen induced failure. That evaluation will be performed by a combination of several techniques such as hydrogen permeation testing (ISO 17081:2014), fatigue crack growth (FCG) testing (ISO 12108:2018 and AFGROW modelling), combined with microstructural and fractographic analysis. The development of a FCG test setup coupled with an electrochemical cell will be discussed, along with the advantages and challenges of measuring crack growth rates in electrolytic hydrogen environments. A detailed assessment of several electrolytic charging conditions will also be presented, using hydrogen permeation testing as a method to correlate the different charging settings to equivalent hydrogen concentrations and effective diffusivity coefficients, not only on the base material but also on the heat affected zone and weld of the pipelines. The experimental work is being complemented with AFGROW, a useful FCG modelling software that has helped inform testing parameters and which will also be developed to ultimately help industry experts perform structural integrity analysis and remnant life characterisation of pipeline steels under representative conditions. The results from this research will allow to conclude if there is an acceleration of the crack growth rate of API 5L X65 under the influence of a hydrogen-rich environment, an important aspect that needs to be rectified instandards and codes of practice on pipeline integrity evaluation and maintenance.

Keywords: AFGROW, electrolytic hydrogen charging, fatigue crack growth, hydrogen, pipeline, steel

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Authors: Leyla Noroozbabaee, David Nickerson

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We introduce a modular, consistent, reusable bond graph model of the renal nephron’s proximal convoluted tubule (PCT), which can reproduce biological behaviour. In this work, we focus on ion and volume transport in the proximal convoluted tubule of the renal nephron. Modelling complex systems requires complex modelling problems to be broken down into manageable pieces. This can be enabled by developing models of subsystems that are subsequently coupled hierarchically. Because they are based on a graph structure. In the current work, we define two modular subsystems: the resistive module representing the membrane and the capacitive module representing solution compartments. Each module is analyzed based on thermodynamic processes, and all the subsystems are reintegrated into circuit theory in network thermodynamics. The epithelial transport system we introduce in the current study consists of five transport membranes and four solution compartments. Coupled dissipations in the system occur in the membrane subsystems and coupled free-energy increasing, or decreasing processes appear in solution compartment subsystems. These structural subsystems also consist of elementary thermodynamic processes: dissipations, free-energy change, and power conversions. We provide free and open access to the Python implementation to ensure our model is accessible, enabling the reader to explore the model through setting their simulations and reproducibility tests.

Keywords: Bond Graph, Epithelial Transport, Water Transport, Mathematical Modeling

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Authors: Cheng Sheng, Yuemin Zhao, Chenlong Duan

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Air dense medium fluidized bed is a typical application of fluidization techniques for coal particle separation in arid areas, where it is costly to implement wet coal preparation technologies. In the last three decades, air dense medium fluidized bed, as an efficient dry coal separation technique, has been studied in many aspects, including energy and mass transfer, hydrodynamics, bubbling behaviors, etc. Despite numerous researches have been published, the fluidization features, especially dual dense medium fluidization features have been rarely reported. In dual dense medium fluidized beds, different combinations of different dense mediums play a significant role in fluidization quality variation, thus influencing coal separation efficiency. Moreover, to what extent different dense mediums mix and to what extent the two-component particulate mixture affects the fluidization performance and quality have been in suspense. The proposed work attempts to reveal underlying mechanisms of generation and evolution of two-component particulate mixture in the fluidization process. Based on computational fluid dynamics methods and discrete particle modelling, movement and evolution of dual dense mediums in air dense medium fluidized bed have been simulated. Dual dense medium fluidization experiments have been conducted. Electrical capacitance tomography was employed to investigate the distribution of two-component mixture in experiments. Underlying mechanisms involving two-component particulate fluidization are projected to be demonstrated with the analysis and comparison of simulation and experimental results.

Keywords: air dense medium fluidized bed, particle separation, computational fluid dynamics, discrete particle modelling

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Authors: Lucas Peries, Rolla Monib

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The construction industry plays a vital role in the global economy, contributing billions of dollars annually. However, the industry has been struggling with persistently low productivity levels for years, unlike other sectors that have shown significant improvements. Modular and prefabricated construction methods have been identified as potential solutions to boost productivity in the construction industry. These methods offer time advantages over traditional construction methods. Despite their potential benefits, modular and prefabricated construction face hindrances and limitations that are not present in traditional building systems. Building information modelling (BIM) has the potential to address some of these hindrances, but barriers are preventing its widespread adoption in the construction industry. This research aims to enhance understanding of the shortcomings of modular and prefabricated building systems and develop BIM-based solutions to alleviate or eliminate these hindrances. The research objectives include identifying and analysing key issues hindering the use of modular and prefabricated building systems, investigating the current state of BIM adoption in the construction industry and factors affecting its successful implementation, proposing BIM-based solutions to address the issues associated with modular and prefabricated building systems, and assessing the effectiveness of the developed solutions in removing barriers to their use. The research methodology involves conducting a critical literature review to identify the key issues and challenges in modular and prefabricated construction and BIM adoption. Additionally, an online questionnaire will be used to collect primary data from construction industry professionals, allowing for feedback and evaluation of the proposed BIM-based solutions. The data collected will be analysed to evaluate the effectiveness of the solutions and their potential impact on the adoption of modular and prefabricated building systems. The main findings of the research indicate that the identified issues from the literature review align with the opinions of industry professionals, and the proposed BIM-based solutions are considered effective in addressing the challenges associated with modular and prefabricated construction. However, the research has limitations, such as a small sample size and the need to assess the feasibility of implementing the proposed solutions. In conclusion, this research contributes to enhancing the understanding of modular and prefabricated building systems' limitations and proposes BIM-based solutions to overcome these limitations. The findings are valuable to construction industry professionals and BIM software developers, providing insights into the challenges and potential solutions for implementing modular and prefabricated construction systems in future projects. Further research should focus on addressing the limitations and assessing the feasibility of implementing the proposed solutions from technical and legal perspectives.

Keywords: building information modelling, modularisation, prefabrication, technology

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Authors: Daniel Pinheiro Da Silva Junior, Aline Paes, Daniel De Oliveira, Christiano Lacerda Ghuerren, Marcio Duran

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The Courts of Accounts are institutions responsible for overseeing and point out irregularities of Public Administration expenses. They have a high demand for processes to be analyzed, whose decisions must be grounded on severity laws. Despite the existing large amount of processes, there are several cases reporting similar subjects. Thus, previous decisions on already analyzed processes can be a precedent for current processes that refer to similar topics. Identifying similar topics is an open, yet essential task for identifying similarities between several processes. Since the actual amount of topics is considerably large, it is tedious and error-prone to identify topics using a pure manual approach. This paper presents a tool based on Machine Learning and Natural Language Processing to assists in building a labeled dataset. The tool relies on Topic Modelling with Latent Dirichlet Allocation to find the topics underlying a document followed by Jensen Shannon distance metric to generate a probability of similarity between documents pairs. Furthermore, in a case study with a corpus of decisions of the Rio de Janeiro State Court of Accounts, it was noted that data pre-processing plays an essential role in modeling relevant topics. Also, the combination of topic modeling and a calculated distance metric over document represented among generated topics has been proved useful in helping to construct a labeled base of similar and non-similar document pairs.

Keywords: courts of accounts, data labelling, document similarity, topic modeling

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Authors: Laheeb M. Ibrahim, Ibrahim A. Salih

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Biometrics is a technology that has been widely used in many official and commercial identification applications. The increased concerns in security during recent years (especially during the last decades) have essentially resulted in more attention being given to biometric-based verification techniques. Here, a novel fusion approach of palmprint, dental traits has been suggested. These traits which are authentication techniques have been employed in a range of biometric applications that can identify any postmortem PM person and antemortem AM. Besides improving the accuracy, the fusion of biometrics has several advantages such as increasing, deterring spoofing activities and reducing enrolment failure. In this paper, a first unimodel biometric system has been made by using (palmprint and dental) traits, for each one classification applying an artificial neural network and a hybrid technique that combines swarm intelligence and neural network together, then attempt has been made to combine palmprint and dental biometrics. Principally, the fusion of palmprint and dental biometrics and their potential application has been explored as biometric identifiers. To address this issue, investigations have been carried out about the relative performance of several statistical data fusion techniques for integrating the information in both unimodal and multimodal biometrics. Also the results of the multimodal approach have been compared with each one of these two traits authentication approaches. This paper studies the features and decision fusion levels in multimodal biometrics. To determine the accuracy of GAR to parallel system decision-fusion including (AND, OR, Majority fating) has been used. The backpropagation method has been used for classification and has come out with result (92%, 99%, 97%) respectively for GAR, while the GAR) for this algorithm using hybrid technique for classification (95%, 99%, 98%) respectively. To determine the accuracy of the multibiometric system for feature level fusion has been used, while the same preceding methods have been used for classification. The results have been (98%, 99%) respectively while to determine the GAR of feature level different methods have been used and have come out with (98%).

Keywords: back propagation neural network BP ANN, multibiometric system, parallel system decision-fusion, practical swarm intelligent PSO

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Authors: Stefan Peters, Phillip Roetman

Abstract:

The global loss of biodiversity is threatening the benefits nature provides to human populations and has become a more pressing issue than climate change and requires immediate attention. While there have been successful global agreements for environmental protection, such as the Montreal Protocol, these are rare, and we cannot rely on them solely. Thus, it is crucial to take national and local actions to support biodiversity. Australia is one of the 17 countries in the world with a high level of biodiversity, and its cities are vital habitats for endangered species, with more of them found in urban areas than in non-urban ones. However, the protection of biodiversity in metropolitan Adelaide has been inadequate, with over 130 species disappearing since European colonization in 1836. In this research project we conceptualized, developed and implemented a framework for wildlife Habitat Hotspots and Habitat Corridor modelling in an urban context using geographic data and GIS modelling and analysis. We used detailed topographic and other geographic data provided by a local council, including spatial and attributive properties of trees, parcels, water features, vegetated areas, roads, verges, traffic, and census data. Weighted factors considered in our raster-based Habitat Hotspot model include parcel size, parcel shape, population density, canopy cover, habitat quality and proximity to habitats and water features. Weighted factors considered in our raster-based Habitat Corridor model include habitat potential (resulting from the Habitat Hotspot model), verge size, road hierarchy, road widths, human density, and presence of remnant indigenous vegetation species. We developed a GIS model, using Python scripting and ArcGIS-Pro Model-Builder, to establish an automated reproducible and adjustable geoprocessing workflow, adaptable to any study area of interest. Our habitat hotspot and corridor modelling framework allow to determine and map existing habitat hotspots and wildlife habitat corridors. Our research had been applied to the study case of Burnside, a local council in Adelaide, Australia, which encompass an area of 30 km2. We applied end-user expertise-based category weightings to refine our models and optimize the use of our habitat map outputs towards informing local strategic decision-making.

Keywords: biodiversity, GIS modeling, habitat hotspot, wildlife corridor

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Authors: A. K. Borah, A. K. Singh

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In this paper we deal building blocks of the computer simulation of the multiphase flows. Whole simulation procedure can be viewed as two super procedures; The implementation of VOF method and the solution of Navier Stoke’s Equation. Moreover, a sequential code for a Navier Stoke’s solver has been studied.

Keywords: Bi-conjugate gradient stabilized (Bi-CGSTAB), ILUT function, krylov subspace, multifluid flows preconditioner, simple algorithm

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Authors: Jadon Matthiasa, Cindy Donga, Ali Al Jibouria, Hsin Kuo

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The environmental impact of emissions from the Covanta Waste-to-Energy facility in Burnaby, BC, was comprehensively evaluated, focusing on the dispersion of air pollutants and the subsequent assessment of heavy metal contamination in surrounding soils. A Gaussian Plume Model, implemented in MATLAB, was utilized to simulate the dispersion of key pollutants to understand their atmospheric behaviour and potential deposition patterns. The MATLAB code developed for this study enhanced the accuracy of pollutant concentration predictions and provided capabilities for visualizing pollutant dispersion in 3D plots. Furthermore, the code could predict the maximum concentration of pollutants at ground level, eliminating the need to use the Ranchoux model for predictions. Complementing the modelling approach, empirical soil sampling and analysis were conducted to evaluate heavy metal concentrations in the vicinity of the facility. This integrated methodology underscored the importance of computational modelling in air pollution assessment and highlighted the necessity of soil analysis to obtain a holistic understanding of environmental impacts. The findings emphasized the effectiveness of current emissions controls while advocating for ongoing monitoring to safeguard public health and environmental integrity.

Keywords: air emissions, Gaussian Plume Model, MATLAB, soil contamination, air pollution monitoring, waste-to-energy, pollutant dispersion visualization, heavy metal analysis, environmental impact assessment, emission control effectiveness

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Authors: Akan C. Offong, E. J. Anthony, Vasilije Manovic

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A modified steady-state numerical model is developed for the electrochemical reduction of CO₂ to formic acid. The numerical model achieves a CD (current density) (~60 mA/cm²), FE-faradaic efficiency (~98%) and conversion (~80%) for CO₂ electro-reduction to formic acid in a microfluidic cell. The model integrates charge and species transport, mass conservation, and momentum with electrochemistry. Specifically, the influences of Bi-Sn based nanoparticle catalyst (on the cathode surface) at different mole fractions and 1-ethyl-3-methyl imidazolium tetra-fluoroborate ([EMIM][BF₄]) electrolyte, on CD, FE and CO₂ conversion to formic acid is studied. The reaction is carried out at a constant concentration of electrolyte (85% v/v., [EMIM][BF₄]). Based on the mass transfer characteristics analysis (concentration contours), mole ratio 0.5:0.5 Bi-Sn catalyst displays the highest CO₂ mole consumption in the cathode gas channel. After validating with experimental data (polarisation curves) from literature, extensive simulations reveal performance measure: CD, FE and CO₂ conversion. Increasing the negative cathode potential increases the current densities for both formic acid and H₂ formations. However, H₂ formations are minimal as a result of insufficient hydrogen ions in the ionic liquid electrolyte. Moreover, the limited hydrogen ions have a negative effect on formic acid CD. As CO₂ flow rate increases, CD, FE and CO₂ conversion increases.

Keywords: carbon dioxide, electro-chemical reduction, ionic liquids, microfluidics, modelling

Procedia PDF Downloads 148

Authors: Chuan Yang, Jing Bie, Panagiotis Psimoulis, Zhong Wang

Abstract:

Modelling and understanding vehicle volume distribution over the urban network are essential for urban design and transport planning. The space syntax approach was widely applied as the main conceptual and methodological framework for contemporary vehicle volume models with the help of the statistical method of multiple regression analysis (MRA). However, the MRA model with space syntax variables shows a limitation in vehicle volume predicting in accounting for the crossed effect of the urban configurational characters and socio-economic factors. The aim of this paper is to construct models by interacting with the combined impact of the street network structure and socio-economic factors. In this paper, we present a multilevel linear (ML) and an agent-based (AB) vehicle volume model at an urban scale interacting with space syntax theoretical framework. The ML model allowed random effects of urban configurational characteristics in different urban contexts. And the AB model was developed with the incorporation of transformed space syntax components of the MRA models into the agents’ spatial behaviour. Three models were implemented in the same urban environment. The ML model exhibit superiority over the original MRA model in identifying the relative impacts of the configurational characters and macro-scale socio-economic factors that shape vehicle movement distribution over the city. Compared with the ML model, the suggested AB model represented the ability to estimate vehicle volume in the urban network considering the combined effects of configurational characters and land-use patterns at the street segment level.

Keywords: space syntax, vehicle volume modeling, multilevel model, agent-based model

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Authors: Repaul Kanji, Rajat Agrawal

Abstract:

In this era where issues like climate change and disasters are the topics of discussion at national and international forums, it is very often that humanity questions the causative role of corporates in such events. It is beyond any doubt that rapid industrialisation and development has taken a toll in the form of climate change and even disasters, in some case. Thus, demanding to fulfill a corporate's responsibilities in the form of rescue and relief in times of disaster, rehabilitation and even mitigation and preparedness to adapt to the oncoming changes is obvious. But how can the responsibilities of the corporates be channelised to ensure all this, i.e., develop a resilient society? More than that, which factors, when emphasised upon, can lead to the holistic development of the society. To answer this query, an extensive literature review was done to identify several enablers like legislations of a nation, the role of brand and reputation, ease of doing Corporate Social Responsibility, mission and vision of an organisation, religion and culture, etc. as a tool for building disaster resilience. A questionnaire survey, interviews with experts and academicians followed by interpretive structural modelling (ISM) were used to construct a multi-hierarchy model depicting the contextual relationship among the identified enablers. The study revealed that culture and religion are the most powerful driver, which affects other enablers either directly or indirectly. Taking cognisance of the fact that an idea of separation between religion and workplace (business) resides subconsciously within the society, the study tries to interpret the outcome of the ISM through the lenses of past researches (The Integrating Box) and explores how it can be leveraged to build a resilient society.

Keywords: corporate social responsibility, interpretive structural modelling, disaster resilience and risk reduction, the integration box (TIB)

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Authors: Muhamad Aris Burhanudin, Angga Firmansyas, Bagus Jaya Santosa

Abstract:

Earthquakes are natural hazard that can trigger the most dangerous hazard, tsunami. 26 December 2004, a giant earthquake occurred in north-west Andalas Island. It made giant tsunami which crushed Sumatra, Bangladesh, India, Sri Lanka, Malaysia and Singapore. More than twenty thousand people dead. The occurrence of earthquake and tsunami can not be avoided. But this hazard can be mitigated by earthquake forecasting. Early preparation is the key factor to reduce its damages and consequences. We aim to investigate quantitatively on pattern of earthquake. Then, we can know the trend. We study about earthquake which has happened in Andalas island, Indonesia one last decade. Andalas is island which has high seismicity, more than a thousand event occur in a year. It is because Andalas island is in tectonic subduction zone of Hindia sea plate and Eurasia plate. A tsunami forecasting is needed to mitigation action. Thus, a Tsunami Forecasting Method is presented in this work. Neutral Network has used widely in many research to estimate earthquake and it is convinced that by using Backpropagation Method, earthquake can be predicted. At first, ANN is trained to predict Tsunami 26 December 2004 by using earthquake data before it. Then after we get trained ANN, we apply to predict the next earthquake. Not all earthquake will trigger Tsunami, there are some characteristics of earthquake that can cause Tsunami. Wrong decision can cause other problem in the society. Then, we need a method to reduce possibility of wrong decision. Fuzzy TOPSIS is a statistical method that is widely used to be decision seconder referring to given parameters. Fuzzy TOPSIS method can make the best decision whether it cause Tsunami or not. This work combines earthquake prediction using neural network method and using Fuzzy TOPSIS to determine the decision that the earthquake triggers Tsunami wave or not. Neural Network model is capable to capture non-linear relationship and Fuzzy TOPSIS is capable to determine the best decision better than other statistical method in tsunami prediction.

Keywords: earthquake, fuzzy TOPSIS, neural network, tsunami

Procedia PDF Downloads 498

Authors: Sinchan Ghosh, Fahad Al Basir, Santanu Ray, Sabyasachi Bhattacharya

Abstract:

The cooperatively breeding birds have two major ranks for the sexually mature birds. The breeders mate and produce offspring while the non-breeding helpers increase the chick production rate through help in mate-finding and allo-parenting. However, the chicks also cooperate to raise their younger siblings through warming, defending and food sharing. Although, the existing literatures describes the evolution of allo-parenting in birds but do not differentiate the significance of allo-parenting in sexually immature and mature helpers separately. This study addresses the significance of both immature and mature helpers’ contribution to the total sustainable bird population in a breeding site using Blue-tailed bee-eater as a test-bed species. To serve this purpose, a mathematical model has been built considering each social status and chicks as separate but interactive compartments. Also, to observe the dynamics of each social status with changing prey abundance, a prey population has been introduced as an additional compartment. The model was analyzed for stability condition and was validated using field-data. A simulation experiment was then performed to observe the change in equilibria with a varying helping rate from both the helpers. The result from the simulation experiment suggest that the cooperative breeding population changes its population sizes significantly with a change in helping rate from the sexually immature helpers. On the other hand, the mature helpers do not contribute to the stability of the population equilibrium as much as the immature helpers.

Keywords: Blue-tailed bee eater, Altruism, Mathematical Ethology, Behavioural modelling

Procedia PDF Downloads 162