Numerical Modeling Techniques Research Articles

SYNTHETIC DATA GENERATION FOR ANN MODELING OF THE HYDRODYNAMIC PROCESSES OF IN-SITU LEACHING

The work presents an approach to enhance the forecasting capabilities of In-Situ Leaching processes during both the production stage and early prognosis. ISL, a crucial method for resource extraction, demands rapid on-site forecasting to guide the deployment of new technological blocks. Traditional modeling techniques, though effective, are hindered by their computational demands and network throughput requirements, particularly when dealing with substantial datasets or remote computing needs. The integration of AI technologies, specifically neural networks, offers a promising opportunity for expedited calculations by leveraging the power of forward propagation through pretrained neural models. However, a critical challenge lies in transforming conventional numerical datasets into a format suitable for neural modeling. Furthermore, the scarcity of training data during the production phase, where vital parameters are concealed underground, poses an additional challenge in training AI models for In-Situ Leaching processes. This research addresses these challenges by proposing a methodology for generating training data tailored to the most resource-intensive Computational Fluid Dynamics problems encountered during modeling. Traditional numerical modeling techniques are harnessed to construct training datasets comprising input and corresponding expected output data, with a particular focus on varying well network patterns. Subsequent efforts are directed at the conversion of the acquired data into a format compatible with neural networks. The data is normalized to align with the data ranges stipulated by the activation functions employed within the neural network architecture. This preprocessing step ensures that the neural model can effectively learn from the generated data, facilitating accurate forecasting of In-Situ Leaching processes. An advantage of proposed technique lies in provision of large, reliable datasets to train neural network to predict hydrodynamic properties based on technological regimes currently active or expected on ISL site. A major implication of this approach lies in applicability of pre-trained AI technologies to forecast future or determine current hydrodynamic regime in the stratum circumventing cost deterministic simulations currently deployed at mining sites. Hence, innovative approach outlined in this paper holds promise for optimizing forecasting, allowing for quicker and more efficient decision-making in resource extraction operations while getting around the computational barriers associated with traditional methods.

SpAbundance: An R package for single‐species and multi‐species spatially explicit abundance models

Abstract Numerous modelling techniques exist to estimate abundance of plant and animal populations. The most accurate methods account for multiple complexities found in ecological data, such as observational biases, spatial autocorrelation, and species correlations. There is, however, a lack of user‐friendly and computationally efficient software to implement the various models, particularly for large data sets. We developed the spAbundance R package for fitting spatially explicit Bayesian single‐species and multi‐species hierarchical distance sampling models, N‐mixture models, and generalized linear mixed models. The models within the package can account for spatial autocorrelation using Nearest Neighbour Gaussian Processes and accommodate species correlations in multi‐species models using a latent factor approach, which enables model fitting for data sets with large numbers of sites and/or species. We provide three vignettes and three case studies that highlight spAbundance functionality. We used spatially explicit multi‐species distance sampling models to estimate density of 16 bird species in Florida, USA, an N‐mixture model to estimate black‐throated blue warbler (Setophaga caerulescens) abundance in New Hampshire, USA, and a spatial linear mixed model to estimate forest above‐ground biomass across the continental USA. spAbundance provides a user‐friendly, formula‐based interface to fit a variety of univariate and multivariate spatially explicit abundance models. The package serves as a useful tool for ecologists and conservation practitioners to generate improved inference and predictions on the spatial drivers of abundance in populations and communities.

Simulation of controlled modulus column installation using a large deformation finite element analysis

Controlled modulus columns (CMC) are widely used in ground improvement techniques where grout columns are constructed by penetrating an auger into the ground using the displacement approach. Numerical simulation of this problem is challenging owing to the large soil deformations associated with installation. Small strain-based finite element analysis procedures available in commercial finite element modeling software are unsuitable for simulating this problem because they cannot simulate large soil deformations around the auger during column installation. Hence, this study presents a numerical approach based on the finite element method to simulate the installation of CMCs. The numerical modeling technique adopted in this study considers large soil deformations around columns during penetration, and the proposed approach is based on remeshing and interpolation techniques. This method was developed using the Python development environment (PDE) within the ABAQUS/standard finite element program. It can penetrate columns below the ground surface without causing large mesh distortions. In this study, silty sand-type soil was considered; hence, the constitutive behavior was simulated using the Mohr–Coulomb criteria. The load-carrying capacity of a controlled modulus column was predicted and compared with an empirical equation based on the cone penetration resistance. The influence zone and failure mechanism of the soil during installation were determined by considering the stress distribution and soil flow pattern around the column.

Preface

GeoShanghai 2024 International Conference Volume 5: Advances in Soil Dynamics and Geohazards Shanghai, 26-29 May, 2024 GeoShanghai 2024 International ConferenceCollege of Civil Engineering, Tongji University, 2024All Rights ReservedGeoShanghai is a series of international conferences on geotechnical engineering held in Shanghai quadrennially. The conference was inaugurated in 2006 and was successfully held in 2010, 2014 and 2018, with more than 1600 participants in total. Since the last conference, the geotechnical community has witnessed many advances both in fundamental understanding and engineering practices. To demonstrate the latest developments and promote collaborations in geotechnical engineering and related fields, we launch the 5th GeoShanghai International Conference to be held in May 2024.This volume provides a comprehensive exploration of the behavior of soils when exposed to complex environmental conditions and their effects. This topic focuses on original research that delves into these natural hazards from different perspectives including understanding their mechanisms, predicting their occurrences, monitoring their activities in real time, and implementing effective mitigation strategies. It is worth noting that recent advancements in technology have significantly contributed to this field of study. The integration of coupled numerical modeling techniques has allowed researchers to simulate complex scenarios more accurately than ever before. Additionally, artificial intelligence (AI) and machine learning algorithms have played an instrumental role in analyzing vast amounts of data collected from various sources such as sensors or images. These cutting-edge technologies have opened up new avenues for research in geohazards by providing insights into patterns and trends that were previously difficult to discern.This volume received a total of 74 papers from countries such as China, United States, Japan, and others, totaling 68 papers accepted. All papers were reviewed and the accepted papers will be submitted to IOP Conference Series: Earth and Environmental Science indexed by Scopus.Cordially yours,Tong Qiu, Chen Wang, Katerina Ziotopoulou, Lijun Deng, Xuefei WangThe editors of Volume 5: Advances in Soil Dynamics and GeohazardsList of Tracks, Conference Steering Committee, International Advisory Committee, Technical Committee, Organizing Committee are avialble in this pdf.

Sustainability of underground infrastructure – Part 1: Digitalisation-based carbon assessment and baseline for TBM tunnelling

Alongside the transition towards sustainable and renewable infrastructure and energy production, tunnelling projects that adopt tunnel boring machines (TBM) continue to increase in size and complexity. Life Cycle Assessment (LCA) methodologies have a significant impact on the Architecture, Engineering and Construction (AEC) industry, with a shift in focus to cost and carbon management across the infrastructure lifecycle through the use of digitalisation techniques and sustainability rating systems. This paper aims to develop a systematic framework to enhance the sustainability of underground infrastructure in the early design and construction stages, aiming for “build clever and efficiently”. This framework integrates digitalisation, carbon assessment standards, numerical modelling, and optimisation techniques to assess carbon emissions and establish benchmarks for construction products and processes. The approach uses parametric models to define tunnelling-related entities such as geological formations, TBM, and segments, designed with dimensional constraints that allow for adaptive modelling and interactions. Enabled by building information modelling (BIM) through parametric models enriched with domain properties, module-based carbon accounting is adopted to evaluate the footprint of material- and asset-level key emitters with functional units. Leveraging established databases, baselines for segmental lining designs and carbon reduction strategies are established. Through project-driven case studies, the correlations among geotechnical conditions, tunnel dimensions, and TBM operating parameters are developed to quantify TBM carbon emissions. A prototype programme demonstrates the framework's application on typical metro and road tunnels at different levels of details (LoD). This paper serves as a guideline for conducting embodied carbon assessments specifically tailored to TBM tunnelling, acknowledging the substantial contribution of the TBM construction process, which is controlled by geotechnical conditions.

Small-scale airblast testing for the assessment of multiple perimeter wall barriers on shock wave propagation

Assessment of the impact of structural systems on blast wave propagation has traditionally been explored using experimental, analytical, and/or numerical methods. Though numerical modeling techniques have grown in popularity because of their lower cost and high accuracy, experimental testing remains necessary for verifying these models. Issues of scale, equipment capacity, and the availability of research funding continue to limit full-scale testing of subassemblies or complete structures under blast loads. In addition, full-scale blast testing can be time-consuming, allowing only a handful of tests to be conducted. Small-scale airblast experiments offer an economic alternative to full-scale testing, in terms of material and labor requirements. Additionally, these small-scale experiments can be performed rapidly with repeatable results. The use of a novel reusable tabletop small-scale airblast experiment framework is presented for repeatable, cost-effective, quick turnaround testing of airblast scenarios. This novel setup also allows for reflective surfaces to be included as part of the investigation of the behavior of the shockwave-interaction with structures. The goal is to understand the behavior of the blast shockwave interacting with multiple barriers and the effect of pressure due to those barriers; therefore, the scaled structure remains rigid in the experimental and numerical analysis. Three different wall configurations were used to investigate how the presence of multiple barriers affects the shockwave and subsequent observed pressures around and behind the barriers. The explosives used were hemispherical and elevated spherical C4 charges. Pressure gauges were used to read pressures in front of, between, and behind the barriers on the tabletop. The airblast effects such as pressure and impulse, from the scaled experiments are presented and compared to the analytical predictions using a hydrocode model. It was found that multiple walls are measurably effective in reducing the pressure shockwave compared to no walls. The use of single and double walls reduced the pressure by 50% and 25%, respectively, compared to having no walls at the same scaled distance. The hydrocode model results were generally in agreement with the experimental data. The results can allow engineers and decision makers to understand the impact that properly distanced multiple barriers can have on protection from blast events.

The Three-Dimensional Wave-Induced Current Field: An Analytical Model

Wave-induced currents play a critical role in coastal dynamics, influencing sediment transport and shaping bottom topography. Traditionally, long- and cross-shore currents in coastal zones were analyzed independently, often with two-dimensional models for longshore currents and undertow being used. The introduction of quasi-three-dimensional models marked a significant advancement toward a more holistic understanding. Despite recent proposals for fully three-dimensional models, none have achieved widespread acceptance, primarily due to challenges in accurately capturing depth-dependent radiation stress. This paper presents an innovative analytical model advocating for comprehensive three-dimensional approaches in coastal hydrodynamics. The model, based on novel simplification rules, refines relationships governing turbulent stress tensors and provides valuable insights into wave-induced stresses. It offers analytical solutions for both homogeneous and general coastal zones, laying the foundation for future advancements in numerical modeling techniques.

A machine learning-driven approach to predicting thermo-elasto-hydrodynamic lubrication in journal bearings

Traditional methods of evaluating the performance of journal bearings, for example thermal-elastic-hydrodynamic- lubrication theory, are limited to simplified conditions that often fail to accurately model real-world components. Numerical models that include additional phenomena such as cavitation and fully coupled effects like deformation, temperature, pressure and viscosity can be more accurate but require a large amount of computational overhead, making analysis slower and more costly. To address this limitation, a novel machine learning-driven approach is developed to predict the 2D distribution of surface deformation, film thickness, temperature, and pressure across the bearing surface as a function of design variables such as load and speed. The training dataset, generated using a fully coupled Reynolds’ Equation solver implemented in OpenFOAM, contains a significantly extended range of conditions than in previous studies with approximately 39000000 points encompassing 4925 different test cases. Modelled bearing speeds range from 2000 to 10000rpm, while load values are varied between 1 and 30kN. Predicting surface deformation, film thickness, temperature and pressure across the bearing surface results in a mean absolute percentage error below 0.4% or better. The work also demonstrates that the trained models have a strong ability to generalise the prediction beyond the original training data range with only a 1% error at up to 200% of the highest trained speed. This work also demonstrates that machine learning-based processes are a practical alternative to physics-based numerical modelling, especially in cases where rapid performance evaluation is desired as real-time calculation is possible with significantly reduced computational cost. This has the potential to enable development of rapid design optimisation tools and real-time performance monitoring at high resolution and with low latency. Using consumer hardware, it is found that the neural network-based approach is faster than the existing numerical modelling technique by a factor of over 10000, enabling real-time predictions of lubrication systems.

An Integrated Technique for Natural Gas Numerical PVT Modelling: Case Study of Retrograde Condensate Gas with Python Programming

After the discovery of a gas field, PVT analyses are performed in laboratory to understand the behavior of the reservoir f luid when pressure, temperature, or volume changes. Thereafter, physical or numerical representations of the fluid, called PVT fluid models, are designed to simulate the real fluid behavior when pressure, volume or temperature conditions change. This study aims to propose an integrated technique for natural gas numerical PVT modelling that will help petroleum engineers in determining the mol fractions and compositions of the vapor and liquid phases of hydrocarbon systems as function of pressure and temperature, as well as their trends in some pressure and temperature conditions. A case study has been performed on a historical retrograde condensate gas. The results show that over the production, when the reservoir pressure declines, the gas becomes much dryer and will therefore free less condensate into the reservoir. During its motion from the reservoir to the well head, the liquid phase mol fraction becomes lower so that the condensate-to-gas ratio (CGR) will be low. At pressures under 1000 psia, the decline rate of liquid phase mol fraction becomes higher. At the standard temperature, the more is pressure applied to the gas, the more the liquid phase proportion is. As a result, in the gas storage tanks, the more the gas is compressed, the more the condensate (liquid phase) deposit will be noticed. While transporting the gas through pipeline, the higher the pressure undergone by the gas, the more is the amount of condensate to be counted in pipelines. Moreover, at the standard temperature, the condensate proportion unit variation as function of pressure unit variation is higher when the pressure is under 20 bar.

Breach Analysis of Earthen Dam

Failure of a levee or dam breach can result in severe economic losses and loss of human life. To minimize these consequences, it's imperative to assess the risks associated with levee breaches. This involves accurately predicting the volume of water released during a breach and simulating the propagation of flood waves across the floodplain to create inundation maps. Despite extensive observation and research into representing dam breaches and deriving crucial breach parameters like width, height, and side slope, there is no universally accepted method for determining these parameters. This study aims to evaluate existing methodologies for dam breach analyses, highlighting their advantages, limitations, and the challenges that lie ahead in this field. Earthen dams play a crucial role in water resource management and flood control. However, breaches in these structures pose significant risks to surrounding communities and infrastructure. This study explores the causes of earthen dam breaches, focusing on hydraulic, geotechnical, and structural factors. Through a comprehensive analysis of historical case studies and numerical modeling techniques, we elucidate the underlying mechanisms leading to dam failures. Furthermore, this research investigates effective mitigation strategies to enhance the resilience of earthen dams against breach incidents. By identifying key vulnerabilities and implementing targeted interventions, we aim to minimize the likelihood and impact of future breaches, ensuring the safety and sustainability of earthen dam infrastructure. Keywords— Breach analysis; Geotechnical Engineering; Earthen dam and Gangapur Dam

Air–Water Two-Phase Flow Dynamics Analysis in Complex U-Bend Systems through Numerical Modeling

This study aims to provide insights into the intricate interactions between gas and liquid phases within flow components, which are pivotal in various industrial sectors such as nuclear reactors, oil and gas pipelines, and thermal management systems. Employing the Eulerian–Eulerian approach, our computational model incorporates interphase relations, including drag and non-drag forces, to analyze phase distribution and velocities within a complex U-bend system. Comprising two horizontal-to-vertical bends and one vertical 180-degree elbow, the U-bend system’s behavior concerning bend geometry and airflow rates is scrutinized, highlighting their significant impact on multiphase flow dynamics. The study not only presents a detailed exposition of the numerical modeling techniques tailored for this complex geometry but also discusses the results obtained. Detailed analyses of local void fraction and phase velocities for each phase are provided. Furthermore, experimental validation enhances the reliability of our computational findings, with close agreement observed between computational and experimental results. Overall, the study underscores the efficacy of the Eulerian approach with interphase relations in capturing the complex behavior of the multiphase flow in U-bend systems, offering valuable insights for hydraulic system design and optimization in industrial applications.

Characterisation of the lightning strike damage mechanics onto composite materials and discussion on its numerical modelling techniques: review

Characterisation of the lightning strike damage mechanics onto composite materials and discussion on its numerical modelling techniques: review

Quantifying Shape Transition in Anisotropic Plasmonic Nanoparticles through Geometric Inversion. Application to Gold Bipyramids.

Unraveling the nuanced interplay between the morphology and the optical properties of plasmonic nanoparticles is crucial for targeted applications. Managing the relationship becomes significantly complex when dealing with anisotropic nanoparticles that defy a simple description using parameters like length, width, or aspect ratio. This complexity requires computationally intensive numerical modeling and advanced imaging techniques. To address these challenges, we propose a detailed structural parameter determination of gold nanoparticles using their two-dimensional projections (e.g., micrographs). Employing gold bipyramids (AuBPs) as a model morphology, we can determine their three-dimensional geometry and extract optical features computationally for comparison with the experimental data. To validate our inversion model's effectiveness, we apply it to derive the structural parameters of AuBPs undergoing shape modification through oxidative etching. In summary, our findings allow for the precise characterization of structural parameters for plasmonic nanoparticles during shape transitions, potentially enhancing the comprehension of nanocrystal growth and optimizing plasmonic material design for various applications.

Creasing and folding of paper-based sandwich material–Phenomena and modelling

Creasing and folding are fundamental steps in many manufacturing processes of multi-material paperboard packaging. The complex structure of these materials, which comprise layers of cellulose fibres, aluminium, and polyethylene, coupled with the growing complexity of packaging designs, make these process operations essential to ensure the required structural integrity for packaging as well as their functionality in daily life. This paper introduces an approach for modelling damage in paper-based sandwich materials by integrating fibre-based and cohesive numerical modelling techniques. The results prove the effectiveness of the proposed methodology, opening new possibilities for process design and optimization in packaging manufacturing.

A Study of Thermal Runaway Mechanisms in Lithium-Ion Batteries and Predictive Numerical Modeling Techniques

While thermal runaway characterization and prediction is an important aspect of lithium-ion battery engineering and development, it is a requirement to ensure that a battery system can be safe under normal operations and during failure events. This study investigated the current existing literature regarding lithium-ion battery thermal runaway characterization and predictive modeling methods. A thermal model for thermal runaway prediction was adapted from the literature and is presented in this paper along with a comparison of empirical data and predicted data using the model. Empirical data were collected from a Samsung 30Q 18650 cylindrical cell and from a large 20 Ah pouch cell format using accelerated rate calorimetry. The predictive model was executed in a macro-enabled Microsoft Excel workbook for simplicity and accessibility for the public. The primary purpose of using more primitive modeling software was to provide an accurate model that was generally accessible without the purchase of or training in a specific modeling software package. The modes of heat transfer during the thermal runaway event were studied and are reported in this work, along with insights on thermal management during a thermal runaway failure event.

Numerical modeling techniques for noise emission of free railway wheels

AbstractIn this article, we consider the numerical prediction of the noise emission from a wheelset in laboratory conditions. We focus on the fluid–structure interaction leading to sound emission in the fluid domain by analyzing three different methods to account for acoustic sources. These are a discretized baffled piston using the discrete calculation method (DCM), a closed cylindrical volume using the boundary element method (BEM) and radiating elastic disks in a cubic enclosure solved with the finite element method (FEM). We provide the validation of the baffled piston and the BEM using measurements of the noise emission of a railway wheel by considering ground reflections in the numerical models. Selected space-resolved waveforms are compared with experimental results as well as with a fluid–structure interaction finite element model. The computational advantage of a discretized disk mounted on a baffle and BEM compared to FEM is highlighted, and the baffled pistons limitations caused by a lack of edge radiation effects are investigated.

Forecasting tropical cyclone coastal and marine hazards and impacts

The report highlights the significant progress over various regions with respect to understanding of coastal hazards, numerical modeling techniques and the generation & dissemination of coastal hazard warnings and products. The developments over various regions in the globe during 2014–18 have been discussed in this report as presented during 10th Session of International Workshop on Tropical Cyclones (IWTC-X) at Bali, Indonesia. More specifically, various regions have started to confront the uncertainty that cannot be removed from TC analyses and forecasts and further communicate those hazards within the context of risk [probabilistic] based information. Progress also includes impact-based forecasts such as communicating coastal inundation information relative to total water level instead of storm surge, specifically (i.e., anomaly from astronomical tide and waves). Lastly, updates to model grid configuration, model resolution, and coupled dynamical systems continue to resolve the costal hazards more effectively. Those approaches have likely helped reduce loss of life relative to historical standards. However, regions agree that the generation and dissemination of coastal hazard information still need to be improved in view of growing population along the coast and thus increased exposure of life to coastal hazard.

Optimization of the mechanical and vibrational damping properties of a hybrid rubber particle and glass fibre railway sleeper composite

In the locomotive industry, the railway structural material is frequently subjected to harsh loads and vibration, and the current durability and vibration-resilience capabilities of the sleepers are insufficient. The aim of this paper is to fabricate a railway sleeper composites with enhanced mechanical and vibrational properties. A full factorial experimental design was followed in the composite fabrication with the rubber particles and fibreglass volume fraction ratio varied between 5 and 20% and 5 to 8% respectively. Modelling and optimisation of the mix design was then carried out using numerical modelling techniques. ANOVA tests were carried out to show the model’s accuracy in predicting tensile strength, compression strength, flexural strength, and vibrational damping, as shown by R2 values of 60.69%, 86.60%, 60.05% and 81.41%, respectively. However, the model was not reliable for the composite hardness which had an R2 value of 37.87%. The optimisation model developed in the study indicated that rubber particles of size of 150 μm at volume fraction of 7.48% and fibreglass volume fraction of 8% gave the optimum mechanical and vibrational properties.

IMPROVING THE MASS PERFECTION OF COMPOSITE CYLINDRICAL SHELLS OF ROCKET FUEL TANKS

Nowadays, computing capabilities are gaining high performance. Software development has a very positive impact on the СAE of a system. A modern engineer always has tools in his arsenal that allow him not only to design but also to calculate a structure. Predict where the potential destruction will be, etc. Improving the mass sophistication of fuel tanks in rocket and space technology (RST) is an important task to ensure the safety and reliability of space missions. Fuel tanks are critical components of rocket and space technology, as they provide fuel storage and supply during launch and flight. Due to the high technical requirements for these systems, it is important to continuously improve their design and production to ensure high quality and reliability. This article highlights the main aspects of improving the mass sophistication of fuel tanks in RCTs by comparing the classical manufacturing of a wafer tank structure and a composite combination tank. First of all, it discusses the key requirements and standards that are defined for fuel tanks in order to ensure safety and efficiency.The article goes on to analyze the various methods and technologies used to manufacture and control the quality of fuel tanks, including the manufacture of tanks using traditional methods (wafer structures) and composite fuel tank winding. It analyzes the strength and durability of both designs and provides practical recommendations for the development of composite products. Finally, it highlights potential challenges and prospects for development in this area, such as the introduction of new materials, improved manufacturing processes, and the introduction of the latest numerical modeling techniques. Analysis and research of these issues can further improve the mass sophistication of fuel tanks in rocket and space technology and increase the overall reliability of space missions

Overview of the research work of Prof. Koyanagi and the composite materials laboratory

Materials at the quantum scale are highly unpredictable, making it impossible to predict certain measurements. There are also limitations to numerical simulations and modelling techniques for understanding advanced materials at the molecular scale. Professor Jun Koyanagi, Koyanagi Laboratory, Tokyo University of Science, Japan, is working to establish a quantitative link that connects the nanoscale simulations to the metre scale. This involves developing methods and techniques that accurately relate the behaviour observed at the molecular level to the macroscopic properties of the material. In order to do this, a comprehensive understanding of the underlying physics and mechanics at each scale is required. Koyanagiâ–™s team is hoping to apply multiscale numerical simulations that will help to ensure the long-term reliability of a range of advanced materials. A key focus of the research is carbon fibre reinforced plastic (CFRP) and Koyanagi wants to pave the way for the widespread use of CFRP in aerovehicles in the near future. The team will conduct multiscale numerical simulations and advanced material development to ensure the reliability and long-term durability of CFRP. This will allow them to confidently incorporate CFRP into the construction of aerovehicles and these will be more sustainable and environmentally friendly as the lightweight nature of CFRP will significantly contribute to enhancing fuel efficiency and reducing emissions. The researchers are using analytical and experimental methods to evaluate the thermal and mechanical properties of composite materials.