Model Simplification Research Articles

Highly accurate simplification of physics-based electrochemical model of all-solid-state battery for online state of charge estimation

Abstract All-solid-state lithium batteries offer superior energy density and safety features, making them highly attractive for electric vehicles and wearable devices. Original physics-based electrochemical model can effectively simulate the internal electrochemical reactions, but they are difficult to be applied to embedded battery management systems. To facilitate the development of real-time applications based on physical models, this paper proposes a SOC prediction method based on a simplified electrochemical model (SEM). First, the transcendental transfer function is converted to third-order transfer functions using the Padé approximation. The electric field in the mass transfer overpotential is then solved for using average numerical integration method. Then, the proposed SEM method under variable load conditions is verified by comparing to the results from original partial differential equations. The results show that the developed SEM strikes a balance between high fidelity and computational efficiency. By taking into account the concentration of Li+ ions in the solid electrolyte, the estimation accuracy of SOC in the range of 0.1 to 0.3 is significantly improved, compared with prior studies. Strong support is provided for the advanced control design of smart management systems of ASSBs.

Dual-Grained Lightweight Strategy.

Removing redundant parameters and computations before the model training has attracted a great interest as it can effectively reduce the storage space of the model, speed up the training and inference of the model, and save energy consumption during the running of the model. In addition, the simplification of deep neural network models can enable high-performance network models to be deployed to resource-constrained edge devices, thus promoting the development of the intelligent world. However, current pruning at initialization methods exhibit poor performance at extreme sparsity. In order to improve the performance of the model under extreme sparsity, this paper proposes a dual-grained lightweight strategy-TEDEPR. This is the first time that TEDEPR has used tensor theory in the pruning at initialization method to optimize the structure of a sparse sub-network model and improve its performance. Specifically, first, at the coarse-grained level, we represent the weight matrix or weight tensor of the model as a low-rank tensor decomposition form and use multi-step chain operations to enhance the feature extraction capability of the base module to construct a low-rank compact network model. Second, unimportant weights are pruned at a fine-grained level based on the trainability of the weights in the low-rank model before the training of the model, resulting in the final compressed model. To evaluate the superiority of TEDEPR, we conducted extensive experiments on MNIST, UCF11, CIFAR-10, CIFAR-100, Tiny-ImageNet and ImageNet datasets with LeNet, LSTM, VGGNet, ResNet and Transformer architectures, and compared with state-of-the-art methods. The experimental results show that TEDEPR has higher accuracy, faster training and inference, and less storage space than other pruning at initialization methods under extreme sparsity.

Investigation of Tool Cooling and Heat Transfer Using Computational Fluid Dynamics Simulations

Thermal errors remain one of the biggest challenges for the precision of cutting machine tools. Aside from optimizations in the machine tool design and behaviour, optimal cutting process parameters and targeted usage of cutting fluid or alternative methods of tool cooling are required for improved process efficiency with minimal energy demand and maximal tool life. A simulation-based study is presented which compares both different methods of tool cooling, specifically air cooling, flooded cooling and minimum quantity lubrication and also different simulation methods and models. Using a case study, which models an existing thermal test stand comprised of motor spindle, tool holder, tool and coolant nozzle, different cooling scenarios were tested and compared. The simulations were performed in ANSYS CFX. Comparisons were made between simulations with and without buoyancy, with and without tool rotation, transient and steady-state, with laminar flow and with different turbulence models and between the different cooling scenarios. Some insight on different time step sizes and the resulting increase in simulation time and precision was also gained. These results will make future studies on the thermal behaviour of both tool and cutting process easier by showing suitable simulation techniques and viable model simplifications.

Transient Stability-Based Fast Power System Contingency Screening and Ranking

Today’s power systems are operated closer to their stability limits due to the continuously growing load demands, interface to open markets, and integration of more renewable energies. In order to provide operators with clear insight on the current system situation, near real-time power systems dynamic security assessment tools are required. One of the core elements of near real-time dynamic security assessment tools is contingency screening and ranking. Most of the commercially available tools screen and rank contingencies by using the traditional numerical integration or Transient Energy Functions (TEFs) or hybrid methods. The traditional numerical integration method is accurate but computationally intensive and has a slow assessment speed which makes it difficult to identify any insecure contingency before it happens. Despite the TEF method of transient stability analysis being relatively fast, it develops less accurate results due to models simplification and assumptions. This paper introduces transient stability based on fast and robust contingency screening and ranking using an Adaptive step-size Differential Transformation (AsDTM) method. Based on the most current snapshot from Supervisory Control and Data Accusation (SCADA) data, the proposed method triggers AsDTM-based transient stability simulation for each credible contingency and evaluates Transient Stability Indices (TSI) as the normalized weighted sum of squares of errors derived from state variables and complex bus voltages at every simulation time step. Finally, contingencies are ranked based on these TSI and the worst contingency is identified for the next detail assessment. The method is tested on IEEE 9 bus and 39 bus test systems. Test results reveal that the proposed method is faster, robust, and can be used in near real-time dynamic security assessment sessions.

Adaptive Bounded Bilinear Control of a Parallel‐Flow Heat Exchanger

ABSTRACTThis work investigates the adaptive constrained control design for a parallel‐flow heat exchanger represented by a system of two coupled linear first‐order hyperbolic partial differential equations (PDEs). This system incorporates structured uncertainty involving unknown in‐domain parameters that characterize neglected dynamics in the heat exchanger model. These parameters may encompass unmodeled heat transfer phenomena, variations in fluid properties, and modeling simplifications. The objective is to regulate the internal fluid outlet temperature to track a desired reference trajectory by adjusting the external fluid velocity. Due to inherent physical constraints, this manipulated variable is upper and lower‐bounded. Accordingly, the control problem is bounded and bilinear. Using the set‐invariance principle and an energy‐like framework, we first develop a bounded state‐feedback controller. Then, since the measurements are considered only at the boundaries, we propose an adaptive boundary observer using a swapping scheme and a recursive least squares identifier. The proposed adaptive observer provides online estimates of the distributed state and the unknown parameters. Next, the state‐feedback controller is associated with the boundary observer and parameter identifier, and the exponential stability of the closed‐loop system is guaranteed using Lyapunov's stability theory. Finally, we provide numerical simulations to demonstrate the efficiency of the proposed control scheme.

RVE Simulations of short fiber reinforced polyamide: Direct and inverse matrix parameter identification in view of the semi-crystalline polymer structure

This paper investigates the modeling capabilities of computational homogenization for the mechanical behavior of short fiber reinforced polyamide. Simulations on a representative volume element (RVE) with elastic fibers and an elastic–plastic matrix are compared to tensile experiments on specimens taken in parallel and transversal direction from injection molded plates. In view of the semi-crystalline polymer structure, focus is put on identifying the matrix parameters through two alternative methods:First, the matrix parameters are identified directly using tensile experiments on a non-nucleated and a nucleated unreinforced polyamide. In the RVE computations based on the non-nucleated grade, the composite stress–strain behavior is somewhat underestimated, and with the nucleated grade, the behavior is slightly overestimated. To explain this, the semi-crystalline polymer structure is studied. Polarized light microscopy images reveal that the non-nucleated grade has a coarser and the nucleated grade a finer spherulite structure, compared to the matrix present in the composite. However, the degree of crystallinity measured by differential scanning calorimetry is in a similar range.As an alternative, the matrix parameters are identified inversely by fitting the RVE model to the composite tensile experiments. As uncertainties with respect to the matrix material as well as possibly remaining simplifications in the micromechanical model are compensated for, this reverse engineering approach allows for a very good fit.

VR-empowered interior design: Enhancing efficiency and quality through immersive experiences

Traditional interior design methods often struggle to meet the increasingly diverse needs of modern users. As a result, virtual reality (VR) technology has emerged as an innovative solution. This study explores the application of VR technology in interior design (ID), with a focus on enhancing efficiency and quality in the design process. To achieve this, we developed an ID platform based on VR technology and employed Level of Detail (LOD) techniques for 3D model simplification, significantly improving rendering efficiency and effectively reducing rendering times. Performance testing results indicate that the average rendering times decreased from 721.50 ms, 811.20 ms, and 748.10 ms to 335.80 ms, 431.10 ms, and 371.30 ms, respectively, while the frame rates (FPS) increased from 100.80, 85.00, and 93.40 to 107.50, 91.00, and 100.20. These experimental results demonstrate that VR-based design not only accelerates the design process but also significantly enhances the visual quality of interior spaces, providing users with a more immersive experience.

Viability of Flax Fiber-Reinforced Salt Cores for Aluminum High-Pressure Die Casting in Experiment and Simulation

AbstractParts with undercuts or hollow sections exploit the maximum lightweight potential due to efficient material usage. However, such geometries are often challenging to produce with ordinary tooling technology, especially in aluminum high-pressure die casting (HPDC). In order to close this gap, this paper investigates flax fiber-reinforced salt made by wet compression molding as a new lost core material that can be removed with water. Three-point bending tests and HPDC experiments characterized the material. The 2D and 3D simulations with aluminum melt and compressible air were carried out in ANSYS Fluent 2023R1. The outlet vent boundary condition is characterized separately to address the geometric features of the outlet vent. Combined with a two-phase flow filling simulation, it allows assessing the actual loads on the lost core material. The simulations show an excellent agreement between the proposed one-dimensional, analytical outlet model and the computational fluid dynamics (CFD) results. The 2D filling simulations are helpful to prove mesh convergence and model simplifications but overestimate the loads. A 3D simulation predicts stress peaks up to 33 MPa for an ingate speed of 64 m/s. Conventional, brittle salt cores with a bending strength of 15 MPa fail under these conditions in the HPDC experiment. In contrast, fiber-reinforced salt cores with bending strengths between 11 and 37 MPa are viable thanks to their toughness, which was demonstrated by a eight to 31 times higher energy absorption than the unreinforced benchmark in the three-point bending tests. With the new robust lost core material, a foundry gains a technology advantage that opens up new markets, e.g., in the mobility sector.

An Anti-Interference Control Algorithm for Continuum Robot Arm

The large number of joints in a continuum manipulator complicates its dynamic modeling, making model simplification inevitable for practical motion control. However, due to external disturbances and internal noise, a controller based on the simplified dynamic model often struggles to meet the desired dynamic performance. To address this issue, this paper proposes an anti-interference control algorithm for continuum manipulators, designed to compensate for parameter uncertainties, external disturbances, and measurement noise. At the same time, the parameters of the algorithm are obtained in the form of solvability of linear matrix inequalities (LMIs). The simulation results show that the algorithm proposed in the paper provides better transient performance and is not affected by the entire disturbance. Experimental results further confirm the effectiveness and robustness of the algorithm.

Mobile Augmented Reality Application to Evaluate the River Flooding Impact in Coimbra

The downtown area of the city of Coimbra, Portugal, is at low altitude and has historically suffered floods that have caused serious economic losses. The present research proposes a mobile augmented reality (MAR) application aimed at visualising the effect of possible scenarios of flooding in an area of higher risk in the city. A realistic 3D model of the city was created, using data extracted with BLosm and processed through Blender, followed by its integration into Unity with Vuforia for AR visualisation. The methodology encompasses the extraction and simplification of 3D models, mapping real-world coordinates in Unity, analysing several datasets, obtaining a model through regression and implementing a workflow to manage interactions between various Unity objects. The MAR application enables users to visualise potential flood impacts on buildings, utilising colour-coded indicators to represent different levels of water contact. The system’s efficacy was evaluated by simulating various use-case scenarios, demonstrating the application’s capability to provide real-time, interactive flood risk assessments. The results underline the potential of integrating AR and machine learning for enhancing urban flood management and prevention.

Regionalized ensemble estimation of wave periods for assessing wave energy resources across Canada. Part I: Improved wave-period modelling methodology

Large-scale estimations of wave periods are desired for wave energy assessment, ocean engineering, and wave climate research. Long-term global wave data from satellite altimeters are routinely applied to the estimations. However, this is challenged by uncertainties in wave-period models (WPMs), inaccuracies in data, and simplifications in modeling. Additionally, there exists a gap in the comprehensive examination of the variational mechanisms governing wave periods or model performances. As an effort to address them, we innovate a macroscale regionalized ensemble wave-period modeling (MREWPM) method by optimizing four wave-period models, driven by enhanced altimeter-based REWS (regionalized ensemble wave simulation) estimates of wave heights and wind speeds, within a regionalization framework in macroscale water environments. Results show that MREWPM driven by REWS dataset outperforms existing methods and performs better at larger scales (e.g., in eliminating local-scale overestimation). WPMs are more accurate over remote, deep, and windy regions in cool seasons under metrics-, scale- and data-dependent variations of performances with driving factors (mainly geographical features). This study serves as a foundational contribution towards the enhancement of wave-period simulations, the advancement of understanding wave-period dynamics, and the scientific evaluation of wave energy at macroscales.

Modeling PFAS Subsurface Transport in the Presence of Groundwater Table Fluctuations: The Impact on Source‐Zone Leaching and Exploration of Model Simplifications

AbstractAir–water interfacial adsorption represents a major source of retention for many per‐ and poly‐fluoroalkyl substances (PFAS). Therefore, transient hydrological fluxes that dynamically change the amount of air–water interfaces are expected to strongly influence PFAS retention in their source zones in the vadose zone. We employ mathematical modeling to study how seasonal groundwater table (GWT) fluctuations affect PFAS source‐zone leaching. The results suggest that, by periodically collapsing air–water interfaces, seasonal GWT fluctuations can lead to strong temporal variations in groundwater concentration and significantly enhance PFAS leaching in the vadose zone. The enhanced leaching is more pronounced for longer‐chain PFAS, coarser‐textured porous media, drier climates, and greater amplitudes of fluctuations. GWT fluctuations and lateral migration above the GWT introduce a downgradient persistent secondary source zone for longer‐chain PFAS. However, the enhanced leaching and the secondary source zone are greatly reduced when subsurface heterogeneity is present. In highly heterogeneous source zones, GWT fluctuations may even lead to overall slower leaching due to lateral flow (in the GWT fluctuation zone and above the GWT) moving PFAS into local regions with greater retention capacities. Model simplification analyses suggest that the enhanced source‐zone leaching due to GWT fluctuations may be approximated using a static but shallower GWT. Additionally, while vertical 1D models underestimate source‐zone leaching due to not representing lateral migration, they can be revised to account for lateral migration and provide lower‐ and upper‐bound estimates of PFAS source‐zone leaching under GWT fluctuations. Overall, our study suggests that representing GWT fluctuations is critical for quantifying source‐zone leaching of PFAS, especially the more interfacially active longer‐chain compounds.

Effect of Time-varying X-Ray Emission from Stellar Flares on the Ionization of Protoplanetary Disks

X-rays have significant impacts on cold, weakly ionized protoplanetary disks by increasing the ionization rate and driving chemical reactions. Stellar flares are explosions that emit intense X-rays and are the unique source of hard X-rays with an energy of ≳10 keV in the protoplanetary disk systems. Hard X-rays should be carefully taken into account in models as they can reach the disk midplane as a result of scattering in the disk atmospheres. However, previous models are insufficient to predict the hard X-ray spectra because of simplifications in flare models. We develop a model of X-ray spectra of stellar flares based on observations and flare theories. The flare temperature and nonthermal electron emissions are modeled as functions of flare energy, which allows us to better predict the hard X-ray photon flux than before. Using our X-ray model, we conduct radiative transfer calculations to investigate the impact of flare hard X-rays on disk ionization, with a particular focus on the protoplanetary disk around a T Tauri star. We demonstrate that for a flare with an energy of 1035 erg, X-ray photons with ≳5 keV increase the ionization rates more than galactic cosmic rays down to z ≈ 0.1R. The contribution of flare X-rays to the ionization at the midplane depends on the disk parameters such as disk mass and dust settling. We also find that the 10 yr averaged X-rays from multiple flares could certainly contribute to the ionization. These results emphasize the importance of stellar flares on the disk evolution.

Maximizing vertex rescue: investigating the firefighter problem with edge subdivision

The firefighter problem is a game played on a connected graph where a fire breaks out at a vertex. In each round, a firefighter chooses a vertex to protect, and the fire then spreads to all unprotected neighbors of the burning vertex. This sequence continues until the fire can no longer spread. The objective is to maximize the number of saved vertices, necessitating a strategic approach by the firefighter. This problem serves as a simplistic model for scenarios such as disease spread in a network or people’s movement under specific circumstances. To simulate a scenario with limited resources, we introduce a new firefighter method, involving the protection of one vertex and subdividing one edge. This method, achieved by adding a new vertex to the edge, effectively delays the burning of the vertex connected to the edge, providing additional time for the firefighter to protect it. Our investigation delves into the firefighter problem with subdividing edges, focusing on specific graphs. Notably, we present an optimal strategy for a graph with a maximum degree at most three.

Monte Carlo damage models of different complexity levels predict similar trends in radiation induced DNA damage

Ion therapies have an increased relative biological effectiveness (RBE) compared to X-rays, but this remains poorly quantified across different radiation qualities. Mechanistic models that simulate DNA damage and repair after irradiation could be used to help better quantify RBE. However, there is large variation in model design with the simulation detail and number of parameters required to accurately predict key biological endpoints remaining unclear. This work investigated damage models with varying detail to determine how different model features impact the predicted DNA damage.

Methods: Damage models of reducing detail were designed in TOPAS-nBio and Medras investigating the inclusion of chemistry, realistic nuclear geometries, single strand break damage, and track structure. The nucleus models were irradiated with 1 Gy of protons across a range of linear energy transfers (LETs). Damage parameters in the models with reduced levels of simulation detail were fit to proton double strand break (DSB) yield predicted by the most detailed model. Irradiation of the optimised models with a range of radiation qualities was then simulated, before undergoing repair in the Medras biological response model.

Results: Simplified damage models optimised to proton exposures predicted similar trends in DNA damage across radiation qualities. On average across radiation qualities, the simplified models experienced an 8% variation in double strand break (DSB) yield but a larger 28% variation in chromosome aberrations. Aberration differences became more prominent at higher LETs, with model features having an increasing impact on the distribution and therefore misrepair of DSBs. However, overall trends remained similar with better agreement likely achievable through repair model optimisation. 

Conclusion: Several model simplifications could be made without compromising key damage yield predictions, although changes in damage complexity and distribution were observed. This suggests simpler, more efficient models may be sufficient for initial radiation damage comparisons, if validated against experimental data.&#xD.

Terrain parameter identification for wheeled mobile robots using deep neural networks

This paper introduces an innovative approach to soil parameter estimation using deep artificial neural networks (ANN) tailored to estimate a wide range of soil types. Previous research in this field has relied on limited datasets and oversimplified assumptions in semi-empirical models. In this work, the sensitivity of stress distribution is analyzed with respect to soil parameters, revealing significant errors resulting from the simplifications in semi-empirical models and emphasizing the need for more accurate estimation methods with minimal reliance on simplifying assumptions. Training an ANN requires a comprehensive dataset, which has been a challenge due to limited access to diverse soil samples. Addressing this issue, the paper conducts simulations of a single wheel’s movement across a wide range of soil parameter combinations to generate the required dataset. These simulations are done using a dynamic motion-compliant semi-empirical model detailed in the article. Subsequently, the network’s hyperparameters are fine-tuned through a grid search, followed by extensive training of the optimized ANN model. In the end, the ANN demonstrates promising performance in accurately estimating soil parameters, validated through simulation results of both single wheel and Mars rover motion.

Prediction of Temperature‐Dependent Stress in 4H‐SiC Using In Situ Nondestructive Raman Spectroscopy Characterization

Abstract4H‐SiC is widely used in power electronics owing to its superior physical properties. However, temperature‐induced stresses compromise the reliability of 4H‐SiC power devices in high‐temperature applications, warranting precise, and nondestructive stress characterization responsive to temperature variations. Herein, a temperature‐dependent predictive model is proposed for analyzing the Raman shift–stress in 4H‐SiC. The 4H‐SiC epitaxial samples prepared via chemical vapor deposition are characterized using in situ variable‐temperature Raman spectroscopy, resulting in a temperature correction factor of approximately −0.021 cm−1 K−1, which is integrated into the conventional Raman shift–stress relationship to assess stress variations induced by temperature variations. The elastic modulus tensor of 4H‐SiC at various temperatures determined using molecular dynamics simulations indicates a linear reduction in modulus with increasing temperature. This variable temperature modulus is incorporated into the Raman shift–stress relationship. Furthermore, a finite element method is used for model simplification to perform stress calculations in three axial directions. The experimental results confirm the consistency between calculated and experimental values with a 10% error range under the uniaxial stress condition. The study findings provide valuable insights into assessing stress evolution in 4H‐SiC under temperature variations based on Raman spectroscopy, thereby advancing the application of spectroscopic techniques in material stress detection.

An assessment of electroneutrality implementations for accurate electrochemical ion transport models

During the diffusion and migration of ions in electrolytes, the electrodynamic ion-ion interactions prevent charge separation despite different ionic mobilities, ultimately enforcing electroneutrality in the bulk electrolyte. To model ion transport accurately, a method to enforce electroneutrality must be implemented. In this study, four strategies to implement electroneutrality are discussed and evaluated. The ion distributions that result from a transport model with the different electroneutrality implementations are calculated, considering various electrolytes and sets of electrochemical parameters. The meaningfulness and applicability of each implementation are assessed through spatial charge accumulations, transference numbers, and experimental data from the literature. Combining the electrochemical ion transport models with the electroneutrality constraint for all ions is shown to result in an overdetermined system of equations if the driving forces are calculated under neglection of diffusion potentials. The often-reported model simplification of using the electroneutrality constraint to resolve the transport of one specific species explicitly results in non-physically correct mass transport. A practical approach to precisely describe the measured physicochemical ion movements is obtained by equilibrating spatial charges with the ion conduction for every time step in the ion transport model, which is reasonably applicable to multi-ion systems in three-dimensional frameworks. This comprehensive assessment aims to guide readers in selecting an appropriate electroneutrality implementation framework for ion transport models.

Groundwater flow-controlled migration of dissolved microbial gas in the Eastern Hungarian Pannonian Basin

The formation of commercial microbial gas accumulations requires specific migration and accumulation conditions which can be effectively provided by groundwater flow systems. Namely, if microbial methane can be gathered and transported in aqueous solution over large lateral distances, great amounts of free gas can be exsolved where flows turn upward, causing a decrease in solubility. Based on this concept and the flow pattern of regional scale topography-driven groundwater flow systems, the present study built a simplistic 2D model in MS Excel including a methane solubility database with 32 000 data points. The model can calculate the required flow pathway length to saturate groundwater with methane, the amount of free gas that can be released in a potential accumulation zone and the necessary time interval for the whole process. Application of the model was demonstrated based on the geological and hydrogeological conditions of a study area in the Central Pannonian Basin (Hungary), focusing on the Hajdúszoboszló gas field. In light of the upscaled results of the 2D model, the known microbial gas accumulations of the study area could be charged according to the model. In addition, parameter sensitivity analysis also provided valuable insights into the complex mechanisms of microbial gas migration.

Scotch yoke structure inspired piezoelectric-electromagnetic hybrid harvester for wave energy harvesting

This paper presents a piezoelectric-electromagnetic hybrid harvester for wave energy harvesting inspired by a scotch yoke structure for obtaining energy from irregular and low-frequency waves. Through simplification of the energy harvester model, the kinematic equations of the scotch yoke are established. One of the advantages of the harvester is that the wave energy can be harvested by an up-conversion mechanism, The piezoelectric transducer (PZT) and electromagnetic generator (EMG) transform the wave excitation into electricity, and by utilizing the frequency up-conversion mechanism, the PZTs’ harvesting efficiency is increased, the influencing factors of the output power of the harvester are explored by simulation and experimental studies, The results of the experiment demonstrate that this harvester could generate an ultimate output power of 286.36 mW at ultra-low frequency (1.4 Hz) operating frequency. This study offers a practical solution for harvesting low-frequency and disordered wave energy and makes significant progress in harvesting low-frequency and irregular wave energy.