Meta databases of steel frame buildings for surrogate modelling and machine learning-based feature importance analysis

Traditionally,nonlinear time history analysis(NLTHA)is used to assess the performance of structures under fu-ture hazards which is necessary to develop effective disaster risk management strategies.However,this method is computationally intensive and not suitable for analyzing a large number of structures on a city-wide scale.Surrogate models offer an efficient and reliable alternative and facilitate evaluating the performance of multiple structures under different hazard scenarios.However,creating a comprehensive database for surrogate mod-elling at the city level presents challenges.To overcome this,the present study proposes meta databases and a general framework for surrogate modelling of steel structures.The dataset includes 30,000 steel moment-resisting frame buildings,representing low-rise,mid-rise and high-rise buildings,with criteria for connections,beams,and columns.Pushover analysis is performed and structural parameters are extracted,and finally,incorporating two different machine learning algorithms,random forest and Shapley additive explanations,sensitivity and explain-ability analyses of the structural parameters are performed to identify the most significant factors in designing steel moment resisting frames.The framework and databases can be used as a validated source of surrogate modelling of steel frame structures in order for disaster risk management.

DeepSurNet-NSGA II:Deep Surrogate Model-Assisted Multi-Objective Evolutionary Algorithm for Enhancing Leg Linkage in Walking Robots

This research paper presents a comprehensive investigation into the effectiveness of the DeepSurNet-NSGA II(Deep Surrogate Model-Assisted Non-dominated Sorting Genetic Algorithm II)for solving complex multiobjective optimization problems,with a particular focus on robotic leg-linkage design.The study introduces an innovative approach that integrates deep learning-based surrogate models with the robust Non-dominated Sorting Genetic Algorithm II,aiming to enhance the efficiency and precision of the optimization process.Through a series of empirical experiments and algorithmic analyses,the paper demonstrates a high degree of correlation between solutions generated by the DeepSurNet-NSGA II and those obtained from direct experimental methods,underscoring the algorithm’s capability to accurately approximate the Pareto-optimal frontier while significantly reducing computational demands.The methodology encompasses a detailed exploration of the algorithm’s configuration,the experimental setup,and the criteria for performance evaluation,ensuring the reproducibility of results and facilitating future advancements in the field.The findings of this study not only confirm the practical applicability and theoretical soundness of the DeepSurNet-NSGA II in navigating the intricacies of multi-objective optimization but also highlight its potential as a transformative tool in engineering and design optimization.By bridging the gap between complex optimization challenges and achievable solutions,this research contributes valuable insights into the optimization domain,offering a promising direction for future inquiries and technological innovations.

An improved interval model updating method via adaptive Kriging models

Interval model updating(IMU)methods have been widely used in uncertain model updating due to their low requirements for sample data.However,the surrogate model in IMU methods mostly adopts the one-time construction method.This makes the accuracy of the surrogate model highly dependent on the experience of users and affects the accuracy of IMU methods.Therefore,an improved IMU method via the adaptive Kriging models is proposed.This method transforms the objective function of the IMU problem into two deterministic global optimization problems about the upper bound and the interval diameter through universal grey numbers.These optimization problems are addressed through the adaptive Kriging models and the particle swarm optimization(PSO)method to quantify the uncertain parameters,and the IMU is accomplished.During the construction of these adaptive Kriging models,the sample space is gridded according to sensitivity information.Local sampling is then performed in key subspaces based on the maximum mean square error(MMSE)criterion.The interval division coefficient and random sampling coefficient are adaptively adjusted without human interference until the model meets accuracy requirements.The effectiveness of the proposed method is demonstrated by a numerical example of a three-degree-of-freedom mass-spring system and an experimental example of a butted cylindrical shell.The results show that the updated results of the interval model are in good agreement with the experimental results.

Quantitative Identification of Delamination Damage in Composite Structure Based on Distributed Optical Fiber Sensors and Model Updating

Delamination is a prevalent type of damage in composite laminate structures.Its accumulation degrades structural performance and threatens the safety and integrity of aircraft.This study presents a method for the quantitative identification of delamination identification in composite materials,leveraging distributed optical fiber sensors and a model updating approach.Initially,a numerical analysis is performed to establish a parameterized finite element model of the composite plate.Then,this model subsequently generates a database of strain responses corresponding to damage of varying sizes and locations.The radial basis function neural network surrogate model is then constructed based on the numerical simulation results and strain responses captured from the distributed fiber optic sensors.Finally,a multi-island genetic algorithm is employed for global optimization to identify the size and location of the damage.The efficacy of the proposed method is validated through numerical examples and experiment studies,examining the correlations between damage location,damage size,and strain responses.The findings confirm that the model updating technique,in conjunction with distributed fiber optic sensors,can precisely identify delamination in composite structures.

ROBUST OPTIMIZATION OF AERODYNAMIC DESIGN USING SURROGATE MODEL

To reduce the high computational cost of the uncertainty analysis, a procedure is proposed for the aerodynamic optimization under uncertainties, in which the surrogate model is used to simplify the computation of the uncertainty analysis. The surrogate model is constructed by using the Latin Hypercube design and the Kriging model. The random parameters are used to account for the small manufacturing errors and the variations of operating conditions. Based on the surrogate model, an uncertainty analysis approach, called the Monte Carlo simulation, is used to compute the mean value and the variance of the predicated performance. The robust optimization for aerodynamic design is formulated, and solved by the genetic algorithm. And then, an airfoil optimization problem is used to test the proposed procedure. Results show that the optimal solutions obtained from the uncertainty-based optimization formulation are less sensitive to uncertainties. And the design constraints are still satisfied under the uncertainties.

Parametric Geometric Model and Hydrodynamic Shape Optimization of A Flying-Wing Structure Underwater Glider

Combining high precision numerical analysis methods with optimization algorithms to make a systematic exploration of a design space has become an important topic in the modern design methods. During the design process of an underwater glider＇s flying-wing structure, a surrogate model is introduced to decrease the computation time for a high precision analysis. By these means, the contradiction between precision and efficiency is solved effectively. Based on the parametric geometry modeling, mesh generation and computational fluid dynamics analysis, a surrogate model is constructed by adopting the design of experiment （DOE） theory to solve the multi-objects design optimization problem of the underwater glider. The procedure of a surrogate model construction is presented, and the Gaussian kernel function is specifically discussed. The Particle Swarm Optimization （PSO） algorithm is applied to hydrodynamic design optimization. The hydrodynamic performance of the optimized flying-wing structure underwater glider increases by 9.1%.

Modeling of turbulent,isothermal and cryogenic cavitation under attached conditions

Cavitation is often triggered when the fluid pres- sure is lower than the vapor pressure at a local thermo- dynamic state. The present article reviews recent progress made toward developing modeling and computational strat- egies for cavitation predictions under both isothermal and cryogenic conditions, with an emphasis on the attached cav- ity. The review considers alternative cavitation models along Reynolds-averaged Navier-Stokes and very lager eddy simu- lation turbulence approaches to ensure that the computational tools can handle flows of engineering interests. Observing the substantial uncertainties associated with both modeling and experimental information, surrogate modeling strategies are reviewed to assess the implications and relative impor- tance of the various modeling and materials parameters. The exchange between static and dynamic pressures under the influence of the viscous effects can have a noticeable impact on the effective shape of a solid object, which can impact the cavitation structure. The thermal effect with respect to evaporation and condensation dynamics is examined to shed light on the fluid physics associated with cryogenic cav- itation. The surrogate modeling techniques are highlighted in the context of modeling sensitivity assessment. Keywords

Hybrid Data-Driven and Mechanistic Modeling Approaches for Multiscale Material and Process Design

The world’s increasing population requires the process industry to produce food,fuels,chemicals,and consumer products in a more efficient and sustainable way.Functional process materials lie at the heart of this challenge.Traditionally,new advanced materials are found empirically or through trial-and-error approaches.As theoretical methods and associated tools are being continuously improved and computer power has reached a high level,it is now efficient and popular to use computational methods to guide material selection and design.Due to the strong interaction between material selection and the operation of the process in which the material is used,it is essential to perform material and process design simultaneously.Despite this significant connection,the solution of the integrated material and process design problem is not easy because multiple models at different scales are usually required.Hybrid modeling provides a promising option to tackle such complex design problems.In hybrid modeling,the material properties,which are computationally expensive to obtain,are described by data-driven models,while the well-known process-related principles are represented by mechanistic models.This article highlights the significance of hybrid modeling in multiscale material and process design.The generic design methodology is first introduced.Six important application areas are then selected:four from the chemical engineering field and two from the energy systems engineering domain.For each selected area,state-ofthe-art work using hybrid modeling for multiscale material and process design is discussed.Concluding remarks are provided at the end,and current limitations and future opportunities are pointed out.

Optimization on the Crosswind Stability of Trains Using Neural Network Surrogate Model

Under the influence of crosswinds,the running safety of trains will decrease sharply,so it is necessary to optimize the suspension parameters of trains.This paper studies the dynamic performance of high-speed trains under cross-wind conditions,and optimizes the running safety of train.A computational fluid dynamics simulation was used to determine the aerodynamic loads and moments experienced by a train.A series of dynamic models of a train,with different dynamic parameters were constructed,and analyzed,with safety metrics for these being determined.Finally,a surrogate model was built and an optimization algorithm was used upon this surrogate model,to find the minimum possible values for:derailment coefficient,vertical wheel-rail contact force,wheel load reduction ratio,wheel lateral force and overturning coefficient.There were 9 design variables,all associated with the dynamic parameters of the bogie.When the train was running with the speed of 350 km/h,under a crosswind speed of 15 m/s,the benchmark dynamic model performed poorly.The derailment coefficient was 1.31.The vertical wheel-rail contact force was 133.30 kN.The wheel load reduction rate was 0.643.The wheel lateral force was 85.67 kN,and the overturning coefficient was 0.425.After optimization,under the same running conditions,the metrics of the train were 0.268,100.44 kN,0.474,34.36 kN,and 0.421,respectively.This paper show that by combining train aerodynamics,vehicle system dynamics and many-objective optimization theory,a train’s stability can be more comprehensively analyzed,with more safety metrics being considered.

Multi-objective optimisation of a vehicle energy absorption structure based on surrogate model

In order to optimize the crashworthy characteristic of energy-absorbing structures, the surrogate models of specific energy absorption （SEA） and ratio of SEA to initial peak force （REAF） with respect to the design parameters were respectively constructed based on surrogate model optimization methods （polynomial response surface method （PRSM） and Kriging method （KM））. Firstly, the sample data were prepared through the design of experiment （DOE）. Then, the test data models were set up based on the theory of surrogate model, and the data samples were trained to obtain the response relationship between the SEA ＆amp; REAF and design parameters. At last, the structure optimal parameters were obtained by visual analysis and genetic algorithm （GA）. The results indicate that the KM, where the local interpolation method is used in Gauss correlation function, has the highest fitting accuracy and the structure optimal parameters are obtained as： the SEA of 29.8558 kJ/kg （corresponding toa=70 mm andt= 3.5 mm） and REAF of 0.2896 （corresponding toa=70 mm andt=1.9615 mm）. The basis function of the quartic PRSM with higher order than that of the quadratic PRSM, and the mutual influence of the design variables are considered, so the fitting accuracy of the quartic PRSM is higher than that of the quadratic PRSM.

Random dynamic analysis of vertical train–bridge systems under small probability by surrogate model and subset simulation with splitting

The response of the train–bridge system has an obvious random behavior.A high traffic density and a long maintenance period of a track will result in a substantial increase in the number of trains running on a bridge,and there is small likelihood that the maximum responses of the train and bridge happen in the total maintenance period of the track.Firstly,the coupling model of train–bridge systems is reviewed.Then,an ensemble method is presented,which can estimate the small probabilities of a dynamic system with stochastic excitations.The main idea of the ensemble method is to use the NARX(nonlinear autoregressive with exogenous input)model to replace the physical model and apply subset simulation with splitting to obtain the extreme distribution.Finally,the efficiency of the suggested method is compared with the direct Monte Carlo simulation method,and the probability exceedance of train responses under the vertical track irregularity is discussed.The results show that when the small probability of train responses under vertical track irregularity is estimated,the ensemble method can reduce both the calculation time of a single sample and the required number of samples.

Uncertain Multidisciplinary Design Optimization on Next Generation Subsea Production System by Using Surrogate Model and Interval Method

The innovative Next Generation Subsea Production System(NextGen SPS)concept is a newly proposed petroleum development solution in ultra-deep water areas.The definition of NextGen SPS involves several disciplines,which makes the design process difficult.In this paper,the definition of NextGen SPS is modeled as an uncertain multidisciplinary design optimization(MDO)problem.The deterministic optimization model is formulated,and three concerning disciplines—cost calculation,hydrodynamic analysis and global performance analysis are presented.Surrogate model technique is applied in the latter two disciplines.Collaborative optimization(CO)architecture is utilized to organize the concerning disciplines.A deterministic CO framework with two disciplinelevel optimizations is proposed firstly.Then the uncertainties of design parameters and surrogate models are incorporated by using interval method,and uncertain CO frameworks with triple loop and double loop optimization structure are established respectively.The optimization results illustrate that,although the deterministic MDO result achieves higher reduction in objective function than the uncertain MDO result,the latter is more reliable than the former.

Fluid Analysis and Structure Optimization of Impeller Based on Surrogate Model

The surrogate model technology has a good performance in solving black-box optimization problems,which is widely used in multi-domain engineering optimization problems.The adaptive surrogate model is the mainstream research direction of surrogate model technology,which can realize model fitting and global optimization of engineering problems by infilling criteria.Based on the idea of the adaptive surrogate model,this paper proposes an efficient global optimization algorithm based on the local remodeling method(EGO-LR),which aims at improving the accuracy and optimization efficiency of the model.The proposed algorithm firstly constructs the expectation improvement(EI)function in the local area and optimizes it to get the update points.Secondly,the obtained update points are added to the global region until the global accuracy of the model meets the requirements.Then the differential evolution algorithm is used for global optimization.Sixteen benchmark functions are used to compare the EGO-LR algorithm with the existing algorithms.The results show that the EGO-LR algorithm can quickly converge to the accuracy requirements of the model and find the optimal value efficiently when facing complex problems with many local extrema and large variable spaces.The proposed algorithm is applied to the optimization design of the structural parameter of the impeller,and the outflow field analysis of the impeller is realized through finite element analysis.The optimization with the maximum fluid pressure(MP value)of the impeller as the objective function is completed,which effectively reduces the pressure value of the impeller under load.

A machine learning approach to TCAD model calibration for MOSFET

Machine learning-based surrogate models have significant advantages in terms of computing efficiency. In this paper, we present a pilot study on fast calibration using machine learning techniques. Technology computer-aided design(TCAD) is a powerful simulation tool for electronic devices. This simulation tool has been widely used in the research of radiation effects.However, calibration of TCAD models is time-consuming. In this study, we introduce a fast calibration approach for TCAD model calibration of metal–oxide–semiconductor field-effect transistors(MOSFETs). This approach utilized a machine learning-based surrogate model that was several orders of magnitude faster than the original TCAD simulation. The desired calibration results were obtained within several seconds. In this study, a fundamental model containing 26 parameters is introduced to represent the typical structure of a MOSFET. Classifications were developed to improve the efficiency of the training sample generation. Feature selection techniques were employed to identify important parameters. A surrogate model consisting of a classifier and a regressor was built. A calibration procedure based on the surrogate model was proposed and tested with three calibration goals. Our work demonstrates the feasibility of machine learning-based fast model calibrations for MOSFET. In addition, this study shows that these machine learning techniques learn patterns and correlations from data instead of employing domain expertise. This indicates that machine learning could be an alternative research approach to complement classical physics-based research.

Surrogate model-assisted interactive genetic algorithms with individual’s fuzzy and stochastic fitness

We propose a surrogate model-assisted algorithm by using a directed fuzzy graph to extract a user’s cognition on evaluated individuals in order to alleviate user fatigue in interactive genetic algorithms with an individual’s fuzzy and stochastic fitness. We firstly present an approach to construct a directed fuzzy graph of an evolutionary population according to individuals’ dominance relations, cut-set levels and interval dominance probabilities, and then calculate an individual’s crisp fitness based on the out-degree and in-degree of the fuzzy graph. The approach to obtain training data is achieved using the fuzzy entropy of the evolutionary system to guarantee the credibilities of the samples which are used to train the surrogate model. We adopt a support vector regression machine as the surrogate model and train it using the sampled individuals and their crisp fitness. Then the surrogate model is optimized using the traditional genetic algorithm for some generations, and some good individuals are submitted to the user for the subsequent evolutions so as to guide and accelerate the evolution. Finally, we quantitatively analyze the performance of the presented algorithm in alleviating user fatigue and increasing more opportunities to find the satisfactory individuals, and also apply our algorithm to a fashion evolutionary design system to demonstrate its efficiency.

Multi-objective optimization of the cathode catalyst layer micro-composition of polymer electrolyte membrane fuel cells using a multi-scale,two-phase fuel cell model and data-driven surrogates

Polymer electrolyte membrane fuel cells(PEMFCs)are considered a promising alternative to internal combustion engines in the automotive sector.Their commercialization is mainly hindered due to the cost and effectiveness of using platinum(Pt)in them.The cathode catalyst layer(CL)is considered a core component in PEMFCs,and its composition often considerably affects the cell performance(V_(cell))also PEMFC fabrication and production(C_(stack))costs.In this study,a data-driven multi-objective optimization analysis is conducted to effectively evaluate the effects of various cathode CL compositions on Vcelland Cstack.Four essential cathode CL parameters,i.e.,platinum loading(L_(Pt)),weight ratio of ionomer to carbon(wt_(I/C)),weight ratio of Pt to carbon(wt_(Pt/c)),and porosity of cathode CL(ε_(cCL)),are considered as the design variables.The simulation results of a three-dimensional,multi-scale,two-phase comprehensive PEMFC model are used to train and test two famous surrogates:multi-layer perceptron(MLP)and response surface analysis(RSA).Their accuracies are verified using root mean square error and adjusted R^(2).MLP which outperforms RSA in terms of prediction capability is then linked to a multi-objective non-dominated sorting genetic algorithmⅡ.Compared to a typical PEMFC stack,the results of the optimal study show that the single-cell voltage,Vcellis improved by 28 m V for the same stack price and the stack cost evaluated through the U.S department of energy cost model is reduced by$5.86/k W for the same stack performance.

Optimization Design of High-speed Interior Permanent Magnet Motor with High Torque Performance Based on Multiple Surrogate Models

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.

Optimal Design of Electrical Machines Assisted by Hybrid Surrogate Model Based Algorithm

In this paper,for design of large-scale electromagnetic problems,a novel robust global optimization algorithm based on surrogate models is presented.The proposed algorithm can automatically select a proper meta-model technique among multiple alternatives.In this paper,three representative meta-modeling techniques including ordinary Kriging,universal Kriging,and response surface method with multi-quadratic radial basis functions are applied.In each optimization iteration,the above three models are used for parallel calculation.The proposed hybrid surrogate model optimization algorithm synthesizes advantages of these different meta-models.Without verification of a specific meta-model,a suitable one for the engineering problem to be analyzed is automatically selected.Therefore,the proposed algorithm intends to make a better trade-off between numerical efficiency and searching accuracy for solving engineering problems,which are characterized by stronger non-linearity,higher complexity,non-convex feasible region,and expensive performance analysis.

Deep Learning-Based Surrogate Model for Flight Load Analysis

Flight load computations(FLC)are generally expensive and time-consuming.This paper studies deep learning(DL)-based surrogate models of FLC to provide a reliable basis for the strength design of aircraft structures.We mainly analyze the influence of Mach number,overload,angle of attack,elevator deflection,altitude,and other factors on the loads of key monitoring components,based on which input and output variables are set.The data used to train and validate the DL surrogate models are derived using aircraft flight load simulation results based on wind tunnel test data.According to the FLC features,a deep neural network(DNN)and a random forest(RF)are proposed to establish the surrogate models.The DNN meets the FLC accuracy requirement using rich data sources in the FLC;the RF can alleviate overfitting and evaluate the importance of flight parameters.Numerical experiments show that both the DNN-and RF-based surrogate models achieve high accuracy.The input variables importance analysis demonstrates that vertical overload and elevator deflection have a significant influence on the FLC.We believe that synthetic applications of these DL-based surrogate methods show a great promise in the field of FLC.

Evolutionary Algorithm with Ensemble Classifier Surrogate Model for Expensive Multiobjective Optimization

For many real-world multiobjective optimization problems,the evaluations of the objective functions are computationally expensive.Such problems are usually called expensive multiobjective optimization problems(EMOPs).One type of feasible approaches for EMOPs is to introduce the computationally efficient surrogates for reducing the number of function evaluations.Inspired from ensemble learning,this paper proposes a multiobjective evolutionary algorithm with an ensemble classifier(MOEA-EC)for EMOPs.More specifically,multiple decision tree models are used as an ensemble classifier for the pre-selection,which is be more helpful for further reducing the function evaluations of the solutions than using single inaccurate model.The extensive experimental studies have been conducted to verify the efficiency of MOEA-EC by comparing it with several advanced multiobjective expensive optimization algorithms.The experimental results show that MOEA-EC outperforms the compared algorithms.