The rapid development of evolutionary deep learning has led to the emergence of various Neural Architecture Search(NAS)algorithms designed to optimize neural network structures.However,these algorithms often face sign...The rapid development of evolutionary deep learning has led to the emergence of various Neural Architecture Search(NAS)algorithms designed to optimize neural network structures.However,these algorithms often face significant computational costs due to the time-consuming process of training neural networks and evaluating their performance.Traditional NAS approaches,which rely on exhaustive evaluations and large training datasets,are inefficient for solving complex image classification tasks within limited time frames.To address these challenges,this paper proposes a novel NAS algorithm that integrates a hierarchical evaluation strategy based on Surrogate models,specifically using supernet to pre-trainweights and randomforests as performance predictors.This hierarchical framework combines rapid Surrogate model evaluations with traditional,precise evaluations to balance the trade-off between performance accuracy and computational efficiency.The algorithm significantly reduces the time required for model evaluation by predicting the fitness of candidate architectures using a random forest Surrogate model,thus alleviating the need for full training cycles for each architecture.The proposed method also incorporates evolutionary operations such as mutation and crossover to refine the search process and improve the accuracy of the resulting architectures.Experimental evaluations on the CIFAR-10 and CIFAR-100 datasets demonstrate that the proposed hierarchical evaluation strategy reduces the search time and costs compared to traditional methods,while achieving comparable or even superior model performance.The results suggest that this approach can efficiently handle resourceconstrained tasks,providing a promising solution for accelerating the NAS process without compromising the quality of the generated architectures.展开更多
In order to obtain better torque performance of high-speed interior permanent magnet motor(HSIPMM) and solve the problem that electromagnetic optimization design is seriously limited by its mechanical strength, a comp...In order to obtain better torque performance of high-speed interior permanent magnet motor(HSIPMM) and solve the problem that electromagnetic optimization design is seriously limited by its mechanical strength, a complete optimization design method is proposed in this paper. The object of optimization design is a 15 kW、20000 r/min HSIPMM whose permanent magnets in rotor is segmented. Eight structural dimensions are selected as its optimization variables. After design of experiment(DOE), multiple surrogate models are fitted, a set of surrogate models with minimum error is selected by using error evaluation indexes to optimize, the NSGA-II algorithm is used to get the optimal solution. The optimal solution is verified by load test on a 15 kW, 20000 r/min HSIPMM prototype. This paper can be used as a reference for the optimization design of HSIPMM.展开更多
This study delineates the development of the optimization framework for the preliminary design phase of Floating Offshore Wind Turbines(FOWTs),and the central challenge addressed is the optimization of the FOWT platfo...This study delineates the development of the optimization framework for the preliminary design phase of Floating Offshore Wind Turbines(FOWTs),and the central challenge addressed is the optimization of the FOWT platform dimensional parameters in relation to motion responses.Although the three-dimensional potential flow(TDPF)panel method is recognized for its precision in calculating FOWT motion responses,its computational intensity necessitates an alternative approach for efficiency.Herein,a novel application of varying fidelity frequency-domain computational strategies is introduced,which synthesizes the strip theory with the TDPF panel method to strike a balance between computational speed and accuracy.The Co-Kriging algorithm is employed to forge a surrogate model that amalgamates these computational strategies.Optimization objectives are centered on the platform’s motion response in heave and pitch directions under general sea conditions.The steel usage,the range of design variables,and geometric considerations are optimization constraints.The angle of the pontoons,the number of columns,the radius of the central column and the parameters of the mooring lines are optimization constants.This informed the structuring of a multi-objective optimization model utilizing the Non-dominated Sorting Genetic Algorithm Ⅱ(NSGA-Ⅱ)algorithm.For the case of the IEA UMaine VolturnUS-S Reference Platform,Pareto fronts are discerned based on the above framework and delineate the relationship between competing motion response objectives.The efficacy of final designs is substantiated through the time-domain calculation model,which ensures that the motion responses in extreme sea conditions are superior to those of the initial design.展开更多
Demand Response(DR)is a critical strategy for managing the integration of renewable energy sources into the power grid,addressing the challenges posed by their intermittent and unpredictable nature.This study introduc...Demand Response(DR)is a critical strategy for managing the integration of renewable energy sources into the power grid,addressing the challenges posed by their intermittent and unpredictable nature.This study introduces a rapid evaluation method for assessing the DR potential of large-scale Heating,Ventilation,and Air Conditioning(HVAC)systems,focusing on the significant role these systems play in energy consumption and grid flexibility.Firstly,the methodology involves constructing a simulation model library that encompasses three dimensions including room type,room location,and internal heat gain mode to reflect the dynamic characteristics of cooling load.Additionally,batch simulations generate DR profiles under various typical weather conditions,and surrogate models are trained for each simulation model,leveraging feature engineering and cross-validation to enhance accuracy.The Multi-Layer Perceptron(MLP)surrogate models achieve high accuracy in predicting DR potential under various scenarios,with R^(2) values exceeding 0.95.This study provides a robust framework that enables load aggregators to accurately estimate the demand response potential of large-scale HVAC systems.It supports the quantification of response capabilities and facilitates participation in bidding processes.Furthermore,it highlights the potential of data-driven models to enable rapid and scalable energy management.展开更多
Solid-state hydrides can reversibly absorb and desorb H_(2) under comparatively mild temperature and pressure conditions,making them promising candidates for H_(2) storage in renewable energy applications.The underlyi...Solid-state hydrides can reversibly absorb and desorb H_(2) under comparatively mild temperature and pressure conditions,making them promising candidates for H_(2) storage in renewable energy applications.The underlying gas-solid interactions are complex and involve multiple intermediary steps.Because they occur in series,by fitting experimental data employing several proposed models,it is possible to identify the rate-limiting step of the reaction,driving the development of new catalysts and the design of H_(2)-storage systems.The corresponding state-of-the-art method for model identification is the reduced-time method(RTM),which is time-consuming and often yields inconclusive results.To overcome these limitations and to facilitate automatization,this work proposes a framework with 12 unsupervised neural networks(NNs)which are trained using simulated curves from selected kinetic models.These networks are applied to a dataset of 144 experimental kinetic curves of an AB_(2) hydride-forming alloy as a blueprint material.Each NN attempts to reconstruct the input data,and the model with the lowest reconstruction loss is selected.The machine learning algorithm achieved a match of 97%and 91%for the absorption/desorption curves compared to the benchmark.Both reactions follow predominantly the Avrami-Erofeyev model with exponents(n)between 0.8 and 0.9.The kinetic constants(k)derived from the assigned model are used to simulate kinetic curves,showing excellent agreement with experimental data and RTM results.The proposed method provides an advantageous approach that can be applied to most gas-solid or even solid-solid reactions.展开更多
Building simulation based on physical modeling is commonly adopted for performance prediction,but the high time costs hinder its application in the early design stage of buildings.Data-driven surrogate models have bee...Building simulation based on physical modeling is commonly adopted for performance prediction,but the high time costs hinder its application in the early design stage of buildings.Data-driven surrogate models have been proposed as a means to replicate computationally expensive simulation models.However,existing surrogate models for sustainable residential block design are limited in scope,focusing either on individual buildings or on specific cases within multi-block projects.This study leverages graph neural networks to develop optimal surrogate models incorporating inter-building effects to predict multiple indicators of sustainable performance for residential blocks at a region level.A graph schema is proposed to represent the general geometric features and relations among buildings in residential block design.A regional dataset is generated for model training and testing,using real residential zones in Hong Kong.The surrogate models are developed and evaluated,using two kinds of architectures(individual architectures for specific indicators and an integrative architecture)and three different neural networks(graph attention network(GAT),graph convolutional network,and artificial neural network).The results showed that the surrogate models using the individual architectures and GAT outperform the models using other architectures and neural networks.These surrogate models achieve a high prediction accuracy with CV(RMSE)s of 11.79%,7.63%,and 8.00%in terms of energy consumption,indoor thermal comfort,and daylighting,respectively,on the regional test set.Moreover,they enable a significant acceleration of the performance evaluation,reducing the calculation time from 6.346 min to 1.565 ms(243,297 times)per case compared to physics-based simulations.展开更多
The roller is one of the fundamental elements of ore belt conveyor systems since it supports,guides,and directs material on the belt.This component comprises a body(the external tube)that rotates around a fixed shaft ...The roller is one of the fundamental elements of ore belt conveyor systems since it supports,guides,and directs material on the belt.This component comprises a body(the external tube)that rotates around a fixed shaft supported by easels.The external tube and shaft of rollers used in ore conveyor belts are mostly made of steel,resulting in high mass,hindering maintenance and replacement.Aiming to achieve mass reduction,we conducted a structural optimization of a roller with a polymeric external tube(hereafter referred to as a polymeric roller),seeking the optimal values for two design parameters:the inner diameter of the external tube and the shaft diameter.The optimization was constrained by admissible values for maximum stress,maximum deflection and misalignment angle between the shaft and bearings.A finite element model was built in Ansys Workbench to obtain the structural response of the system.The roller considered is composed of an external tube made of high-density polyethylene(HDPE),bearing seats of polyamide 6(PA6),and a steel shaft.To characterize the polymeric materials(HDPE and PA6),stress relaxation tests were conducted,and the data on shear modulus variation over time were inserted into the model to calculate Prony series terms to account for viscoelastic effects.The roller optimization was performed using surrogate modeling based on radial basis functions,with the Globalized Bounded Nelder-Mead(GBNM)algorithm as the optimizer.Two optimization cases were conducted.In the first case,concerning the roller’s initial material settings,the designs found violated the constraints and could not reduce mass.In the second case,by using PA6 in both bearing seats and the tube,a design configuration was found that respected all constraints and reduced the roller mass by 15.5%,equivalent to 5.15 kg.This study is among the first to integrate experimentally obtained viscoelastic data into the surrogate-based optimization of polymeric rollers,combining methodological innovation with industrial relevance.展开更多
To increase the efficiency and robustness of stability-based transition prediction in flow simulations, simplified methods are introduced to substitute direct stability analyses for rapid disturbance growth prediction...To increase the efficiency and robustness of stability-based transition prediction in flow simulations, simplified methods are introduced to substitute direct stability analyses for rapid disturbance growth prediction. For low-speed boundary layers, these methods are mainly established based on self-similar assumptions, which are not applicable to non-similar boundary layers in hypersonic flows. The objective of this article is to investigate the application of surrogate models to stability analysis of non-similar flows over blunt cones, focused on parameterization of boundary-layer (BL) profiles. Firstly, correlations between BL edge and profile parameters are analyzed, along with self-similar flow parameters and discrete points on BL profiles, which present four groups of BL characteristic parameters. Secondly, using these parameters as inputs, surrogate models are built for disturbance growth prediction over an MF-1 blunt cone. Results show that, surrogate models using four BL edge parameters and a BL shape factor {Ue, Te, ρe, ηe, H12} for stability analysis can achieve comparable accuracy with those using 16 discrete BL profile parameters, which are more precise than those using merely self-similar parameters or BL edge parameters. Thirdly, the established surrogate models are validated by stability analysis and transition prediction over the MF-1 blunt cone in flight experiments at the instants of t = 17 s ~ 22 s. Compared with direct linear stability analyses, the mean relative error of predicted disturbance growth rates by surrogate models is 8.0% and the maximum relative error of N factor envelopes is 6.6%, which indicates feasible applications of surrogate models to stability analysis and transition prediction of non-similar boundary layers in hypersonic flows.展开更多
The building stock is responsible for a large share of global energy consumption and greenhouse gas emissions,therefore,it is critical to promote building retrofit to achieve the proposed carbon and energy neutrality ...The building stock is responsible for a large share of global energy consumption and greenhouse gas emissions,therefore,it is critical to promote building retrofit to achieve the proposed carbon and energy neutrality goals.One of the policies implemented in recent years was the Energy Performance Certificate(EPC)policy,which proposes building stock benchmarking to identify buildings that require rehabilitation.However,research shows that these mechanisms fail to engage stakeholders in the retrofit process because it is widely seen as a mandatory and complex bureaucracy.This study makes use of an EPC database to integrate machine learning techniques with multi-objective optimization and develop an interface capable of(1)predicting a building’s,or household’s,energy needs;and(2)providing the user with optimum retrofit solutions,costs,and return on investment.The goal is to provide an open-source,easy-to-use interface that guides the user in the building retrofit process.The energy and EPC prediction models show a coefficient of determination(R2)of 0.84 and 0.79,and the optimization results for one case study EPC with a 2000€budget limit inÉvora,Portugal,show decreases of up to 60%in energy needs and return on investments of up to 7 in 3 years.展开更多
Coalbed methane(CBM)is a vital unconventional energy resource,and predicting its spatiotemporal pressure dynamics is crucial for efficient development strategies.This paper proposes a novel deep learningebased data-dr...Coalbed methane(CBM)is a vital unconventional energy resource,and predicting its spatiotemporal pressure dynamics is crucial for efficient development strategies.This paper proposes a novel deep learningebased data-driven surrogate model,AxialViT-ConvLSTM,which integrates AxialAttention Vision Transformer,ConvLSTM,and an enhanced loss function to predict pressure dynamics in CBM reservoirs.The results showed that the model achieves a mean square error of 0.003,a learned perceptual image patch similarity of 0.037,a structural similarity of 0.979,and an R^(2) of 0.982 between predictions and actual pressures,indicating excellent performance.The model also demonstrates strong robustness and accuracy in capturing spatialetemporal pressure features.展开更多
Deep neural networks are efficient methods to achieve real-time visualization of physics fields.The main concerns that prevented deep learning from being implemented in the field of energy conversion were the risks of...Deep neural networks are efficient methods to achieve real-time visualization of physics fields.The main concerns that prevented deep learning from being implemented in the field of energy conversion were the risks of overfitting and the lack of data.Therefore,it is necessary to evaluate different kinds of surrogate modeling methods and provide guidelines for designers to choose models.In this study,three conventional models(Artificial Neural Network,Radial Bias Function,and Kriging),and two deep learning-based models(Convolutional Neural Network and Conditional Generative Adversarial Neural Network)were established to predict the flow and heat transfer performance of a U-bend with variable geometries.The models were detailly compared in terms of the single-point prediction accuracy,response accuracy,sensitivity to sample size,and other characteristics of interest.Results showed that the conventional models had slightly higher single point accuracy and the relative error of pressure loss and heat transfer were within±6.6%and±5.7%respectively,while those of the deep learning-based models were within±8.0%and±6.3%respectively.Nevertheless,the deep learning-based models had higher response accuracy and could reconstruct the distributions of surface pressure and wall heat flux with the pixel-wise absolute error within±2.0 Pa and±45 W/m^(2) respectively.The results indicated that deep learning was a promising surrogate modeling approach due to its acceptable prediction error and ability to reconstruct physical fields.This effort was expected to serve as a guide for establishing more reliable data-driven surrogate models for energy conversion and heat transfer problems.展开更多
Fast machine learning-based surrogate models are trained to emulate slow,high-fidelity engineering simulation models to accelerate engineering design tasks.This introduces uncertainty as the surrogate is only an appro...Fast machine learning-based surrogate models are trained to emulate slow,high-fidelity engineering simulation models to accelerate engineering design tasks.This introduces uncertainty as the surrogate is only an approxi-mation of the original model.Bayesian methods can quantify that uncertainty,and deep learning models exist that follow the Bayesian paradigm.These models,namely Bayesian neural networks and Gaussian process models,enable us to give predic-tions together with an estimate of the model’s uncertainty.As a result we can derive uncertainty-aware surrogate models that can automatically identify unseen design samples that may cause large emulation errors.For these samples the high-fidelity model can be queried instead.This paper outlines how the Bayesian paradigm allows us to hybridize fast but approximate and slow but accurate models.In this paper,we train two types of Bayesian models,dropout neural networks and stochastic variational Gaussian Process models,to emulate a complex high dimensional building energy performance simulation problem.The surrogate model processes 35 building design parameters(inputs)to estimate 12 annual building energy perfor-mance metrics(outputs).We benchmark both approaches,prove their accuracy to be competitive,and show that errors can be reduced by up to 30%when the 10%of samples with the highest uncertainty are transferred to the high-fidelity model.展开更多
Physical models carry quantitative and explainable expert knowledge.However,they have not been introduced into gas face seal diagnosis tasks because of the unacceptable computational cost of inferring the input fault ...Physical models carry quantitative and explainable expert knowledge.However,they have not been introduced into gas face seal diagnosis tasks because of the unacceptable computational cost of inferring the input fault parameters for the observed output or solving the inverse problem of the physical model.The presented work develops a surrogate-model-assisted method for solving the nonlinear inverse problem in limited physical model evaluations.The method prepares a small initial database on sites generated with a Latin hypercube design and then performs an iterative routine that benefits from the rapidity of the surrogate models and the reliability of the physical model.The method is validated on simulated and experimental cases.Results demonstrate that the method can effectively identify the parameters that induce the abnormal signal output with limited physical model evaluations.The presented work provides a quantitative,explainable,and feasible approach for identifying the cause of gas face seal contact.It is also applicable to mechanical devices that face similar difficulties.展开更多
This study introduces a novel approach to addressing the challenges of high-dimensional variables and strong nonlinearity in reservoir production and layer configuration optimization.For the first time,relational mach...This study introduces a novel approach to addressing the challenges of high-dimensional variables and strong nonlinearity in reservoir production and layer configuration optimization.For the first time,relational machine learning models are applied in reservoir development optimization.Traditional regression-based models often struggle in complex scenarios,but the proposed relational and regression-based composite differential evolution(RRCODE)method combines a Gaussian naive Bayes relational model with a radial basis function network regression model.This integration effectively captures complex relationships in the optimization process,improving both accuracy and convergence speed.Experimental tests on a multi-layer multi-channel reservoir model,the Egg reservoir model,and a real-field reservoir model(the S reservoir)demonstrate that RRCODE significantly reduces water injection and production volumes while increasing economic returns and cumulative oil recovery.Moreover,the surrogate models employed in RRCODE exhibit lightweight characteristics with low computational overhead.These results highlight RRCODE's superior performance in the integrated optimization of reservoir production and layer configurations,offering more efficient and economically viable solutions for oilfield development.展开更多
Stratospheric airships are lighter-than-air vehicles capable of continuous flying for months.The energy balance of the airship is the key to long-duration flights.The stratospheric airship is entirely powered by the s...Stratospheric airships are lighter-than-air vehicles capable of continuous flying for months.The energy balance of the airship is the key to long-duration flights.The stratospheric airship is entirely powered by the solar array.It is necessary to accurately predict the output power of the array for any flight state.Because of the uneven solar radiation received by the solar array,the traditional model based on components has a slow simulation speed.In this study,a data-driven surrogate modeling approach for prediction the output power of the solar array is proposed.The surrogate model is trained using the samples obtained from the high-accuracy simulation model.By using the input parameter preprocessor,the accuracy of the surrogate model in predicting the output power of the solar array is improved to 98.65%.In addition,the predictive speed of the surrogate model is ten million times faster than the traditional simulation model.Finally,the surrogate model is used to predict the energy balance of stratospheric airships flying throughout the year under actual global wind fields.展开更多
The authors regret that the original publication of this paper did not include Jawad Fayaz as a co-author.After further discussions and a thorough review of the research contributions,it was agreed that his significan...The authors regret that the original publication of this paper did not include Jawad Fayaz as a co-author.After further discussions and a thorough review of the research contributions,it was agreed that his significant contributions to the foundational aspects of the research warranted recognition,and he has now been added as a co-author.展开更多
This paper proposed a RIME-VMD-BiLSTM surrogate model to rapidly and precisely predict the seismic response of a nonlinear vehicle-track-bridge(VTB)system.The surrogate model employs the RIME algorithm to optimize the...This paper proposed a RIME-VMD-BiLSTM surrogate model to rapidly and precisely predict the seismic response of a nonlinear vehicle-track-bridge(VTB)system.The surrogate model employs the RIME algorithm to optimize the variational mode decomposition(VMD)parameters(k and α)and the architecture and hyperparameter of the bidirectional long-and short-term memory network(BiLSTM).After comparing different combinations and optimization algorithms,the surrogate model was trained and used to analyze a typical 9-span 32-m high-speed railway simply supported bridge system.A series of numerical examples considering the vehicle speed,bridge damping,seismic intensity,and training strategy on the prediction effect of the surrogate model were conducted on the extended OpenSees platform.The results show that the BiLSTM model performed better than the LSTM model,whereas the prediction effects of the single-LSTM and BiLSTM models were relatively poor.With the introduction of the VMD and RIME optimization techniques,the prediction effect of the proposed RIME-VMD-BiLSTM model was excellent.The abovementioned factors had a significant influence on the seismic response of a VTB system but little impact on the prediction effect of the surrogate model.The proposed surrogate model exhibits notable transferability and robustness for predicting the VTB’s nonlinear seismic response.展开更多
Quantum optimal control(QOC)relies on accurately modeling system dynamics and is often challenged by unknown or inaccessible interactions in real systems.Taking an unknown collective spin system as an example,this wor...Quantum optimal control(QOC)relies on accurately modeling system dynamics and is often challenged by unknown or inaccessible interactions in real systems.Taking an unknown collective spin system as an example,this work introduces a machine-learning-based,data-driven scheme to overcome the challenges encountered,with a trained neural network(NN)assuming the role of a surrogate model that captures the system’s dynamics and subsequently enables QOC to be performed on the NN instead of on the real system.The trained NN surrogate proves effective for practical QOC tasks and is further demonstrated to be adaptable to different experimental conditions,remaining robust across varying system sizes and pulse durations.展开更多
In the realm of subsurface flow simulations,deep-learning-based surrogate models have emerged as a promising alternative to traditional simulation methods,especially in addressing complex optimization problems.However...In the realm of subsurface flow simulations,deep-learning-based surrogate models have emerged as a promising alternative to traditional simulation methods,especially in addressing complex optimization problems.However,a significant challenge lies in the necessity of numerous high-fidelity training simulations to construct these deep-learning models,which limits their application to field-scale problems.To overcome this limitation,we introduce a training procedure that leverages transfer learning with multi-fidelity training data to construct surrogate models efficiently.The procedure begins with the pre-training of the surrogate model using a relatively larger amount of data that can be efficiently generated from upscaled coarse-scale models.Subsequently,the model parameters are finetuned with a much smaller set of high-fidelity simulation data.For the cases considered in this study,this method leads to about a 75%reduction in total computational cost,in comparison with the traditional training approach,without any sacrifice of prediction accuracy.In addition,a dedicated well-control embedding model is introduced to the traditional U-Net architecture to improve the surrogate model's prediction accuracy,which is shown to be particularly effective when dealing with large-scale reservoir models under time-varying well control parameters.Comprehensive results and analyses are presented for the prediction of well rates,pressure and saturation states of a 3D synthetic reservoir system.Finally,the proposed procedure is applied to a field-scale production optimization problem.The trained surrogate model is shown to provide excellent generalization capabilities during the optimization process,in which the final optimized net-present-value is much higher than those from the training data ranges.展开更多
Accurate acquisition of the rock stress is crucial for various rock engineering applications.The hollow inclusion (HI) technique is widely used for measuring in-situ rock stress.This technique calculates the stress te...Accurate acquisition of the rock stress is crucial for various rock engineering applications.The hollow inclusion (HI) technique is widely used for measuring in-situ rock stress.This technique calculates the stress tensor by measuring strain using an HI strain cell.However,existing analytical solutions for stress calculation based on an HI strain cell in a double-layer medium are not applicable when an HI strain cell is used in a three-layer medium,leading to erroneous stress calculations.To address this issue,this paper presents a method for calculating stress tensors in a three-layer medium using numerical simulations,specifically by obtaining a constitutive matrix that relates strain measurements to stress tensors in a three-layer medium.Furthermore,using Latin hypercube sampling (LHS) and orthogonal experimental design strategies,764 groups of numerical models encompassing various stress measurement scenarios have been established and calculated using FLAC^(3D)software.Finally,a surrogate model based on artificial neural network (ANN) was developed to predict constitutive matrices,achieving a goodness of fit (R^(2)) of 0.999 and a mean squared error (MSE) of 1.254.A software program has been developed from this surrogate model for ease of use in practical engineering applications.The method’s accuracy was verified through numerical simulations,analytical solution and laboratory experiment,demonstrating its effectiveness in calculating stress in a three-layer medium.The surrogate model was applied to calculate mining-induced stress in the roadway roof rock of a coal mine,a typical case for stress measurement in a three-layer medium.Errors in stress calculations arising from the use of existing analytical solutions were corrected.The study also highlights the significant errors associated with using double-layer analytical solutions in a three-layer medium,which could lead to inappropriate engineering design.展开更多
文摘The rapid development of evolutionary deep learning has led to the emergence of various Neural Architecture Search(NAS)algorithms designed to optimize neural network structures.However,these algorithms often face significant computational costs due to the time-consuming process of training neural networks and evaluating their performance.Traditional NAS approaches,which rely on exhaustive evaluations and large training datasets,are inefficient for solving complex image classification tasks within limited time frames.To address these challenges,this paper proposes a novel NAS algorithm that integrates a hierarchical evaluation strategy based on Surrogate models,specifically using supernet to pre-trainweights and randomforests as performance predictors.This hierarchical framework combines rapid Surrogate model evaluations with traditional,precise evaluations to balance the trade-off between performance accuracy and computational efficiency.The algorithm significantly reduces the time required for model evaluation by predicting the fitness of candidate architectures using a random forest Surrogate model,thus alleviating the need for full training cycles for each architecture.The proposed method also incorporates evolutionary operations such as mutation and crossover to refine the search process and improve the accuracy of the resulting architectures.Experimental evaluations on the CIFAR-10 and CIFAR-100 datasets demonstrate that the proposed hierarchical evaluation strategy reduces the search time and costs compared to traditional methods,while achieving comparable or even superior model performance.The results suggest that this approach can efficiently handle resourceconstrained tasks,providing a promising solution for accelerating the NAS process without compromising the quality of the generated architectures.
基金supported by the National Natural Science Foundation of China (51907129)Project Supported by Department of Science and Technology of Liaoning Province (2021-MS-236)。
文摘In order to obtain better torque performance of high-speed interior permanent magnet motor(HSIPMM) and solve the problem that electromagnetic optimization design is seriously limited by its mechanical strength, a complete optimization design method is proposed in this paper. The object of optimization design is a 15 kW、20000 r/min HSIPMM whose permanent magnets in rotor is segmented. Eight structural dimensions are selected as its optimization variables. After design of experiment(DOE), multiple surrogate models are fitted, a set of surrogate models with minimum error is selected by using error evaluation indexes to optimize, the NSGA-II algorithm is used to get the optimal solution. The optimal solution is verified by load test on a 15 kW, 20000 r/min HSIPMM prototype. This paper can be used as a reference for the optimization design of HSIPMM.
基金financially supported by the National Natural Science Foundation of China(Grant No.52371261)the Science and Technology Projects of Liaoning Province(Grant No.2023011352-JH1/110).
文摘This study delineates the development of the optimization framework for the preliminary design phase of Floating Offshore Wind Turbines(FOWTs),and the central challenge addressed is the optimization of the FOWT platform dimensional parameters in relation to motion responses.Although the three-dimensional potential flow(TDPF)panel method is recognized for its precision in calculating FOWT motion responses,its computational intensity necessitates an alternative approach for efficiency.Herein,a novel application of varying fidelity frequency-domain computational strategies is introduced,which synthesizes the strip theory with the TDPF panel method to strike a balance between computational speed and accuracy.The Co-Kriging algorithm is employed to forge a surrogate model that amalgamates these computational strategies.Optimization objectives are centered on the platform’s motion response in heave and pitch directions under general sea conditions.The steel usage,the range of design variables,and geometric considerations are optimization constraints.The angle of the pontoons,the number of columns,the radius of the central column and the parameters of the mooring lines are optimization constants.This informed the structuring of a multi-objective optimization model utilizing the Non-dominated Sorting Genetic Algorithm Ⅱ(NSGA-Ⅱ)algorithm.For the case of the IEA UMaine VolturnUS-S Reference Platform,Pareto fronts are discerned based on the above framework and delineate the relationship between competing motion response objectives.The efficacy of final designs is substantiated through the time-domain calculation model,which ensures that the motion responses in extreme sea conditions are superior to those of the initial design.
基金supported by the State Grid Headquarters Science and Technology Fund(5400-202340383A-2-3-XG).
文摘Demand Response(DR)is a critical strategy for managing the integration of renewable energy sources into the power grid,addressing the challenges posed by their intermittent and unpredictable nature.This study introduces a rapid evaluation method for assessing the DR potential of large-scale Heating,Ventilation,and Air Conditioning(HVAC)systems,focusing on the significant role these systems play in energy consumption and grid flexibility.Firstly,the methodology involves constructing a simulation model library that encompasses three dimensions including room type,room location,and internal heat gain mode to reflect the dynamic characteristics of cooling load.Additionally,batch simulations generate DR profiles under various typical weather conditions,and surrogate models are trained for each simulation model,leveraging feature engineering and cross-validation to enhance accuracy.The Multi-Layer Perceptron(MLP)surrogate models achieve high accuracy in predicting DR potential under various scenarios,with R^(2) values exceeding 0.95.This study provides a robust framework that enables load aggregators to accurately estimate the demand response potential of large-scale HVAC systems.It supports the quantification of response capabilities and facilitates participation in bidding processes.Furthermore,it highlights the potential of data-driven models to enable rapid and scalable energy management.
文摘Solid-state hydrides can reversibly absorb and desorb H_(2) under comparatively mild temperature and pressure conditions,making them promising candidates for H_(2) storage in renewable energy applications.The underlying gas-solid interactions are complex and involve multiple intermediary steps.Because they occur in series,by fitting experimental data employing several proposed models,it is possible to identify the rate-limiting step of the reaction,driving the development of new catalysts and the design of H_(2)-storage systems.The corresponding state-of-the-art method for model identification is the reduced-time method(RTM),which is time-consuming and often yields inconclusive results.To overcome these limitations and to facilitate automatization,this work proposes a framework with 12 unsupervised neural networks(NNs)which are trained using simulated curves from selected kinetic models.These networks are applied to a dataset of 144 experimental kinetic curves of an AB_(2) hydride-forming alloy as a blueprint material.Each NN attempts to reconstruct the input data,and the model with the lowest reconstruction loss is selected.The machine learning algorithm achieved a match of 97%and 91%for the absorption/desorption curves compared to the benchmark.Both reactions follow predominantly the Avrami-Erofeyev model with exponents(n)between 0.8 and 0.9.The kinetic constants(k)derived from the assigned model are used to simulate kinetic curves,showing excellent agreement with experimental data and RTM results.The proposed method provides an advantageous approach that can be applied to most gas-solid or even solid-solid reactions.
文摘Building simulation based on physical modeling is commonly adopted for performance prediction,but the high time costs hinder its application in the early design stage of buildings.Data-driven surrogate models have been proposed as a means to replicate computationally expensive simulation models.However,existing surrogate models for sustainable residential block design are limited in scope,focusing either on individual buildings or on specific cases within multi-block projects.This study leverages graph neural networks to develop optimal surrogate models incorporating inter-building effects to predict multiple indicators of sustainable performance for residential blocks at a region level.A graph schema is proposed to represent the general geometric features and relations among buildings in residential block design.A regional dataset is generated for model training and testing,using real residential zones in Hong Kong.The surrogate models are developed and evaluated,using two kinds of architectures(individual architectures for specific indicators and an integrative architecture)and three different neural networks(graph attention network(GAT),graph convolutional network,and artificial neural network).The results showed that the surrogate models using the individual architectures and GAT outperform the models using other architectures and neural networks.These surrogate models achieve a high prediction accuracy with CV(RMSE)s of 11.79%,7.63%,and 8.00%in terms of energy consumption,indoor thermal comfort,and daylighting,respectively,on the regional test set.Moreover,they enable a significant acceleration of the performance evaluation,reducing the calculation time from 6.346 min to 1.565 ms(243,297 times)per case compared to physics-based simulations.
基金funded by Vale S.A.company(www.vale.com)and the Institute of Technology Vale(ITV—www.itv.org),grant number SAP 4600048682.
文摘The roller is one of the fundamental elements of ore belt conveyor systems since it supports,guides,and directs material on the belt.This component comprises a body(the external tube)that rotates around a fixed shaft supported by easels.The external tube and shaft of rollers used in ore conveyor belts are mostly made of steel,resulting in high mass,hindering maintenance and replacement.Aiming to achieve mass reduction,we conducted a structural optimization of a roller with a polymeric external tube(hereafter referred to as a polymeric roller),seeking the optimal values for two design parameters:the inner diameter of the external tube and the shaft diameter.The optimization was constrained by admissible values for maximum stress,maximum deflection and misalignment angle between the shaft and bearings.A finite element model was built in Ansys Workbench to obtain the structural response of the system.The roller considered is composed of an external tube made of high-density polyethylene(HDPE),bearing seats of polyamide 6(PA6),and a steel shaft.To characterize the polymeric materials(HDPE and PA6),stress relaxation tests were conducted,and the data on shear modulus variation over time were inserted into the model to calculate Prony series terms to account for viscoelastic effects.The roller optimization was performed using surrogate modeling based on radial basis functions,with the Globalized Bounded Nelder-Mead(GBNM)algorithm as the optimizer.Two optimization cases were conducted.In the first case,concerning the roller’s initial material settings,the designs found violated the constraints and could not reduce mass.In the second case,by using PA6 in both bearing seats and the tube,a design configuration was found that respected all constraints and reduced the roller mass by 15.5%,equivalent to 5.15 kg.This study is among the first to integrate experimentally obtained viscoelastic data into the surrogate-based optimization of polymeric rollers,combining methodological innovation with industrial relevance.
基金National Numerical Wind Tunnel Project(No.NNW2018-ZT1A03)National Natural Science Foundation of China(No.12072285 and No.11972305).
文摘To increase the efficiency and robustness of stability-based transition prediction in flow simulations, simplified methods are introduced to substitute direct stability analyses for rapid disturbance growth prediction. For low-speed boundary layers, these methods are mainly established based on self-similar assumptions, which are not applicable to non-similar boundary layers in hypersonic flows. The objective of this article is to investigate the application of surrogate models to stability analysis of non-similar flows over blunt cones, focused on parameterization of boundary-layer (BL) profiles. Firstly, correlations between BL edge and profile parameters are analyzed, along with self-similar flow parameters and discrete points on BL profiles, which present four groups of BL characteristic parameters. Secondly, using these parameters as inputs, surrogate models are built for disturbance growth prediction over an MF-1 blunt cone. Results show that, surrogate models using four BL edge parameters and a BL shape factor {Ue, Te, ρe, ηe, H12} for stability analysis can achieve comparable accuracy with those using 16 discrete BL profile parameters, which are more precise than those using merely self-similar parameters or BL edge parameters. Thirdly, the established surrogate models are validated by stability analysis and transition prediction over the MF-1 blunt cone in flight experiments at the instants of t = 17 s ~ 22 s. Compared with direct linear stability analyses, the mean relative error of predicted disturbance growth rates by surrogate models is 8.0% and the maximum relative error of N factor envelopes is 6.6%, which indicates feasible applications of surrogate models to stability analysis and transition prediction of non-similar boundary layers in hypersonic flows.
基金supported by Fundação para a Ciência e Tecnologia(FCT)through IN+UIDP/EEA/50009/2020-IST-ID,through CERIS UIDB/04625/2020Ph.D.grant under the contract of FCT 2021.04849.BD.Project C-TECH-Climate Driven Technologies for Low Carbon Cities,grant number POCI-01-0247-FEDER-045919,LISBOA-01-0247-FEDER-045919,co-financed by the ERDF-European Regional Development Fund through the Operational Program for Competitiveness and Internationalization-COMPETE 2020,the Lisbon Portugal Regional Operational Program-LISBOA 2020 and by the FCT under MIT Portugal Program.
文摘The building stock is responsible for a large share of global energy consumption and greenhouse gas emissions,therefore,it is critical to promote building retrofit to achieve the proposed carbon and energy neutrality goals.One of the policies implemented in recent years was the Energy Performance Certificate(EPC)policy,which proposes building stock benchmarking to identify buildings that require rehabilitation.However,research shows that these mechanisms fail to engage stakeholders in the retrofit process because it is widely seen as a mandatory and complex bureaucracy.This study makes use of an EPC database to integrate machine learning techniques with multi-objective optimization and develop an interface capable of(1)predicting a building’s,or household’s,energy needs;and(2)providing the user with optimum retrofit solutions,costs,and return on investment.The goal is to provide an open-source,easy-to-use interface that guides the user in the building retrofit process.The energy and EPC prediction models show a coefficient of determination(R2)of 0.84 and 0.79,and the optimization results for one case study EPC with a 2000€budget limit inÉvora,Portugal,show decreases of up to 60%in energy needs and return on investments of up to 7 in 3 years.
基金the National Natural Science Foundation of China(No.52474068)the Major Collab-orative Innovation Project of Prospecting Breakthrough Stra-tegic Action in Guizhou Province(No.[2022]ZD001-003).
文摘Coalbed methane(CBM)is a vital unconventional energy resource,and predicting its spatiotemporal pressure dynamics is crucial for efficient development strategies.This paper proposes a novel deep learningebased data-driven surrogate model,AxialViT-ConvLSTM,which integrates AxialAttention Vision Transformer,ConvLSTM,and an enhanced loss function to predict pressure dynamics in CBM reservoirs.The results showed that the model achieves a mean square error of 0.003,a learned perceptual image patch similarity of 0.037,a structural similarity of 0.979,and an R^(2) of 0.982 between predictions and actual pressures,indicating excellent performance.The model also demonstrates strong robustness and accuracy in capturing spatialetemporal pressure features.
基金supported by the National Science Foundation of China No.51906139the State Key Laboratory of Aerodynamics(SKLA-20190108)the Shanghai Sailing Program(19YF1423200).
文摘Deep neural networks are efficient methods to achieve real-time visualization of physics fields.The main concerns that prevented deep learning from being implemented in the field of energy conversion were the risks of overfitting and the lack of data.Therefore,it is necessary to evaluate different kinds of surrogate modeling methods and provide guidelines for designers to choose models.In this study,three conventional models(Artificial Neural Network,Radial Bias Function,and Kriging),and two deep learning-based models(Convolutional Neural Network and Conditional Generative Adversarial Neural Network)were established to predict the flow and heat transfer performance of a U-bend with variable geometries.The models were detailly compared in terms of the single-point prediction accuracy,response accuracy,sensitivity to sample size,and other characteristics of interest.Results showed that the conventional models had slightly higher single point accuracy and the relative error of pressure loss and heat transfer were within±6.6%and±5.7%respectively,while those of the deep learning-based models were within±8.0%and±6.3%respectively.Nevertheless,the deep learning-based models had higher response accuracy and could reconstruct the distributions of surface pressure and wall heat flux with the pixel-wise absolute error within±2.0 Pa and±45 W/m^(2) respectively.The results indicated that deep learning was a promising surrogate modeling approach due to its acceptable prediction error and ability to reconstruct physical fields.This effort was expected to serve as a guide for establishing more reliable data-driven surrogate models for energy conversion and heat transfer problems.
基金This research was supported by grant funding from CANARIE via the BESOS project(CANARIE RS-327).
文摘Fast machine learning-based surrogate models are trained to emulate slow,high-fidelity engineering simulation models to accelerate engineering design tasks.This introduces uncertainty as the surrogate is only an approxi-mation of the original model.Bayesian methods can quantify that uncertainty,and deep learning models exist that follow the Bayesian paradigm.These models,namely Bayesian neural networks and Gaussian process models,enable us to give predic-tions together with an estimate of the model’s uncertainty.As a result we can derive uncertainty-aware surrogate models that can automatically identify unseen design samples that may cause large emulation errors.For these samples the high-fidelity model can be queried instead.This paper outlines how the Bayesian paradigm allows us to hybridize fast but approximate and slow but accurate models.In this paper,we train two types of Bayesian models,dropout neural networks and stochastic variational Gaussian Process models,to emulate a complex high dimensional building energy performance simulation problem.The surrogate model processes 35 building design parameters(inputs)to estimate 12 annual building energy perfor-mance metrics(outputs).We benchmark both approaches,prove their accuracy to be competitive,and show that errors can be reduced by up to 30%when the 10%of samples with the highest uncertainty are transferred to the high-fidelity model.
基金This work was supported by the National Key R&D Program of China(Grant No.2020YFB2010000)the National Natural Science Foundation of China(Grant No.U1737209).None of the funding bodies influenced the study at any stage.
文摘Physical models carry quantitative and explainable expert knowledge.However,they have not been introduced into gas face seal diagnosis tasks because of the unacceptable computational cost of inferring the input fault parameters for the observed output or solving the inverse problem of the physical model.The presented work develops a surrogate-model-assisted method for solving the nonlinear inverse problem in limited physical model evaluations.The method prepares a small initial database on sites generated with a Latin hypercube design and then performs an iterative routine that benefits from the rapidity of the surrogate models and the reliability of the physical model.The method is validated on simulated and experimental cases.Results demonstrate that the method can effectively identify the parameters that induce the abnormal signal output with limited physical model evaluations.The presented work provides a quantitative,explainable,and feasible approach for identifying the cause of gas face seal contact.It is also applicable to mechanical devices that face similar difficulties.
基金supported by the National Natural Science Foundation of China under Grant 52325402,52274057,and 52074340the National Key R&D Program of China under Grant 2023YFB4104200+2 种基金the Major Scientific and Technological Projects of CNOOC under Grant CCL2022RCPS0397RSN111 Project under Grant B08028China Scholarship Council under Grant 202306450108.
文摘This study introduces a novel approach to addressing the challenges of high-dimensional variables and strong nonlinearity in reservoir production and layer configuration optimization.For the first time,relational machine learning models are applied in reservoir development optimization.Traditional regression-based models often struggle in complex scenarios,but the proposed relational and regression-based composite differential evolution(RRCODE)method combines a Gaussian naive Bayes relational model with a radial basis function network regression model.This integration effectively captures complex relationships in the optimization process,improving both accuracy and convergence speed.Experimental tests on a multi-layer multi-channel reservoir model,the Egg reservoir model,and a real-field reservoir model(the S reservoir)demonstrate that RRCODE significantly reduces water injection and production volumes while increasing economic returns and cumulative oil recovery.Moreover,the surrogate models employed in RRCODE exhibit lightweight characteristics with low computational overhead.These results highlight RRCODE's superior performance in the integrated optimization of reservoir production and layer configurations,offering more efficient and economically viable solutions for oilfield development.
基金supported by the National Natural Science Foundation of China(Nos.51775021,52302511)the Fundamental Research Funds for the Central Universities,China(Nos.YWF-23-JC-01,YWF-23-JC-04,YWF-23-JC-09)。
文摘Stratospheric airships are lighter-than-air vehicles capable of continuous flying for months.The energy balance of the airship is the key to long-duration flights.The stratospheric airship is entirely powered by the solar array.It is necessary to accurately predict the output power of the array for any flight state.Because of the uneven solar radiation received by the solar array,the traditional model based on components has a slow simulation speed.In this study,a data-driven surrogate modeling approach for prediction the output power of the solar array is proposed.The surrogate model is trained using the samples obtained from the high-accuracy simulation model.By using the input parameter preprocessor,the accuracy of the surrogate model in predicting the output power of the solar array is improved to 98.65%.In addition,the predictive speed of the surrogate model is ten million times faster than the traditional simulation model.Finally,the surrogate model is used to predict the energy balance of stratospheric airships flying throughout the year under actual global wind fields.
文摘The authors regret that the original publication of this paper did not include Jawad Fayaz as a co-author.After further discussions and a thorough review of the research contributions,it was agreed that his significant contributions to the foundational aspects of the research warranted recognition,and he has now been added as a co-author.
基金Project(52108433)supported by the National Natural Science Foundation of ChinaProject(HSR202004)supported by the Open Foundation of National Engineering Research Center of High-Speed Railway Construction Technology(CSU),China+3 种基金Projects(2024RC3170,2021RC4031)supported by the Science and Technology Innovation Program of Hunan Province,ChinaProjects(2024JJ5018,2024JJ5427)supported by the Hunan Provincial Natural Science Foundation,ChinaProject(KQ2402027)supported by the Changsha City Natural Science Foundation,ChinaProjects(2021-Special-08,2022-Special-09)supported by the Science and Technology Research and Development Program Project of China Railway Group Limited。
文摘This paper proposed a RIME-VMD-BiLSTM surrogate model to rapidly and precisely predict the seismic response of a nonlinear vehicle-track-bridge(VTB)system.The surrogate model employs the RIME algorithm to optimize the variational mode decomposition(VMD)parameters(k and α)and the architecture and hyperparameter of the bidirectional long-and short-term memory network(BiLSTM).After comparing different combinations and optimization algorithms,the surrogate model was trained and used to analyze a typical 9-span 32-m high-speed railway simply supported bridge system.A series of numerical examples considering the vehicle speed,bridge damping,seismic intensity,and training strategy on the prediction effect of the surrogate model were conducted on the extended OpenSees platform.The results show that the BiLSTM model performed better than the LSTM model,whereas the prediction effects of the single-LSTM and BiLSTM models were relatively poor.With the introduction of the VMD and RIME optimization techniques,the prediction effect of the proposed RIME-VMD-BiLSTM model was excellent.The abovementioned factors had a significant influence on the seismic response of a VTB system but little impact on the prediction effect of the surrogate model.The proposed surrogate model exhibits notable transferability and robustness for predicting the VTB’s nonlinear seismic response.
基金supported by the Innovation Program for Quantum Science and Technology(Grant No.2021ZD0302100)the National Natural Science Foundation of China(Grant Nos.12361131576,92265205,and 92476205).
文摘Quantum optimal control(QOC)relies on accurately modeling system dynamics and is often challenged by unknown or inaccessible interactions in real systems.Taking an unknown collective spin system as an example,this work introduces a machine-learning-based,data-driven scheme to overcome the challenges encountered,with a trained neural network(NN)assuming the role of a surrogate model that captures the system’s dynamics and subsequently enables QOC to be performed on the NN instead of on the real system.The trained NN surrogate proves effective for practical QOC tasks and is further demonstrated to be adaptable to different experimental conditions,remaining robust across varying system sizes and pulse durations.
基金funding support from the National Natural Science Foundation of China(No.52204065,No.ZX20230398)supported by a grant from the Human Resources Development Program(No.20216110100070)of the Korea Institute of Energy Technology Evaluation and Planning(KETEP)。
文摘In the realm of subsurface flow simulations,deep-learning-based surrogate models have emerged as a promising alternative to traditional simulation methods,especially in addressing complex optimization problems.However,a significant challenge lies in the necessity of numerous high-fidelity training simulations to construct these deep-learning models,which limits their application to field-scale problems.To overcome this limitation,we introduce a training procedure that leverages transfer learning with multi-fidelity training data to construct surrogate models efficiently.The procedure begins with the pre-training of the surrogate model using a relatively larger amount of data that can be efficiently generated from upscaled coarse-scale models.Subsequently,the model parameters are finetuned with a much smaller set of high-fidelity simulation data.For the cases considered in this study,this method leads to about a 75%reduction in total computational cost,in comparison with the traditional training approach,without any sacrifice of prediction accuracy.In addition,a dedicated well-control embedding model is introduced to the traditional U-Net architecture to improve the surrogate model's prediction accuracy,which is shown to be particularly effective when dealing with large-scale reservoir models under time-varying well control parameters.Comprehensive results and analyses are presented for the prediction of well rates,pressure and saturation states of a 3D synthetic reservoir system.Finally,the proposed procedure is applied to a field-scale production optimization problem.The trained surrogate model is shown to provide excellent generalization capabilities during the optimization process,in which the final optimized net-present-value is much higher than those from the training data ranges.
基金funding support from the National Natural Science Foundation of China (Nos. 42477208 and 52079134)the Natural Science Foundation of Hubei Province, China (No. 2024AFA072)+2 种基金the Youth Innovation Promotion Association CAS (No. 2022332)the National Key R&D Program of China (No. 2024YFF0508203)the Open Research Fund of State Key Laboratory of Geomechanics and Geotechnical Engineering Safety (Nos. SKLGME-JBGS2402 and SKLGME022022)。
文摘Accurate acquisition of the rock stress is crucial for various rock engineering applications.The hollow inclusion (HI) technique is widely used for measuring in-situ rock stress.This technique calculates the stress tensor by measuring strain using an HI strain cell.However,existing analytical solutions for stress calculation based on an HI strain cell in a double-layer medium are not applicable when an HI strain cell is used in a three-layer medium,leading to erroneous stress calculations.To address this issue,this paper presents a method for calculating stress tensors in a three-layer medium using numerical simulations,specifically by obtaining a constitutive matrix that relates strain measurements to stress tensors in a three-layer medium.Furthermore,using Latin hypercube sampling (LHS) and orthogonal experimental design strategies,764 groups of numerical models encompassing various stress measurement scenarios have been established and calculated using FLAC^(3D)software.Finally,a surrogate model based on artificial neural network (ANN) was developed to predict constitutive matrices,achieving a goodness of fit (R^(2)) of 0.999 and a mean squared error (MSE) of 1.254.A software program has been developed from this surrogate model for ease of use in practical engineering applications.The method’s accuracy was verified through numerical simulations,analytical solution and laboratory experiment,demonstrating its effectiveness in calculating stress in a three-layer medium.The surrogate model was applied to calculate mining-induced stress in the roadway roof rock of a coal mine,a typical case for stress measurement in a three-layer medium.Errors in stress calculations arising from the use of existing analytical solutions were corrected.The study also highlights the significant errors associated with using double-layer analytical solutions in a three-layer medium,which could lead to inappropriate engineering design.
