Metamodeling Approach Research Articles

Numerical evaluation of wear parameters using meta-models

AbstractWear parameters identified by linear friction tester (LFT) experiments overestimate the wear mass loss when applied on a laboratory abrasion & skid tester 100 (LAT100) simulation. On the other hand, the identification of wear parameters directly from the LAT100 experiment can be challenging since variables such as the contact area and the slip velocity are not measured experimentally and need to be assumed. To improve the identification process, numerical calibration is used as a more suitable approach. This approach relies on meta-models as a target function for minimization. The meta-models are generated using a sample of wear parameters applied to an LAT100 finite element modeling (FEM) to calculate the corresponding wear mass.. In this model, a transport velocity is defined for the rolling simulation, and an arbitrary lagrangian–eulerian (ALE) adaptive meshing approach is adopted for the wear modeling. For the wear model, a combination of archard’s wear model and schallamach’s abrasion law is used. This model contains a pair of wear parameters to be identified. The ALE adaptive meshing technique moves the nodes independently of the material. Since the mesh topology remains the same, failure of the simulation occurs if the wear volume loss exceeds that of the element. Meta-models are created to extend wear modeling beyond this failure. Once the meta-models are created, they are used as a target function for the minimization algorithm. The minimization algorithm aims to find the optimal wear parameters by minimizing the difference between experimentally observed and numerically produced wear mass loss. The minimization algorithm inputs a set of wear parameters into the meta-models which in turn yield a prediction of the wear mass loss. The process is carried out until an optimum parameter set is identified. Such an approach has a lower accuracy if the parameters are identified directly from the experiment using assumptions regarding the contact shear stress and the sliding velocity. Nonetheless, the main advantage of parameters identified using the meta-model approach is the usability of these parameters in an LAT100 model.

A physics-informed neural network enhanced importance sampling (PINN-IS) for data-free reliability analysis

Reliability analysis of highly sensitive structures is crucial to prevent catastrophic failures and ensure safety. Therefore, these safety-critical systems are to be designed for extremely rare failure events. Accurate statistical quantification of these events associated with a very low probability of occurrence requires millions of evaluations of the limit state function (LSF) involving computationally expensive numerical simulations. Variance reduction techniques like importance sampling (IS) reduce such repetitions to a few thousand. The use of a data-centric metamodel can further cut it down to a few hundred. In data-centric metamodeling approaches, the actual complex numerical analysis is performed at a few points to train the metamodel for approximating the structural response. On the other hand, a physics-informed neural network (PINN) can predict the structural response based on the governing differential equation describing the physics of the problem, without a single evaluation of the complex numerical solver, i.e., data-free. However, the existing PINN models for reliability analysis have been effective only in estimating a large range of failure probabilities (10-1∼10-3). To address this issue, the present study develops a PINN-based data-free reliability analysis for low failure probabilities (<10-5). In doing so, a two-stage PINN integrated with IS (PINN-IS) is proposed. The first stage is employed to approximate the most probable failure point (MPP) appropriately while the second stage enhances the accuracy of LSF predictions at the IS population centred on the approximated MPP. The effectiveness of the proposed approach is numerically illustrated by three structural reliability analysis examples.

Corn yield response to phosphorus fertilizer in Michigan: a metamodeling approach for phosphorus management policies

Phosphorus (P) pollution from nonpoint sources is a persistent agricultural externality. P fertilizer use in excess of crop removal leads to soil P accumulation and increases chance of P runoff from agricultural fields. For fertilizer-intensive crops such as corn, effective nutrient management can partly mitigate the externality associated with fertilizer use. In states such as Michigan with abundant freshwater resources that are vulnerable to P pollution and where corn is grown intensively, understanding yield response is instrumental for P management policies. We analyze corn yield response to P fertilizer and control for soil P. We use farmer survey data from 1650 corn farmers in Michigan and a crop-simulation model to obtain data in a metamodeling approach to estimate yield function. Using the estimated function in profit maximization set up, we calculate agronomically and economically optimal profit-maximizing P application rates for Michigan, across agricultural regions, and across different soil types. We find that corn yield responds to P only in low soil P (0–5 ppm or mg kg−1) fields and the economically optimal P rate for Michigan is [63, 68] kg ha−1 or [53, 61] lbs acre−1 for low soil P fields and would be zero for fields with adequate soil P. For existing soil P levels in Michigan, the self-reported P application of Michigan corn farmers exceeds the profit-maximizing P application rate. We compare our analysis results with self-reported survey data to demonstrate excess P applications. Since many fields in Michigan have high soil P, additional P application in excess of crop removal increases risks of eutrophication. We also find that 10 % increase in P prices faced by farmers would lead to 3.1 % reduction in the economically optimal P application rate. Based on these results, we discuss suitable policy options such as fertilizer “usage fee” and tailored fertilizer recommendations.

IAT/ML: a metamodel and modelling approach for discourse analysis

Language technologies are gaining momentum as textual information saturates social networks and media outlets, compounded by the growing role of fake news and disinformation. In this context, approaches to represent and analyse public speeches, news releases, social media posts and other types of discourses are becoming crucial. Although there is a large body of literature on text-based machine learning, it tends to focus on lexical and syntactical issues rather than semantic or pragmatic. Being useful, these advances cannot tackle the nuanced and highly context-dependent problems of discourse evaluation that society demands. In this paper, we present IAT/ML, a metamodel and modelling approach to represent and analyse discourses. IAT/ML focuses on semantic and pragmatic issues, thus tackling a little researched area in language technologies. It does so by combining three different modelling approaches: ontological, which focuses on what the discourse is about; argumentation, which deals with how the text justifies what it says; and agency, which provides insights into the speakers’ beliefs, desires and intentions. Together, these three modelling approaches make IAT/ML a comprehensive solution to represent and analyse complex discourses towards their understanding, evaluation and fact checking.

Managing low–acuity patients in an Emergency Department through simulation–based multiobjective optimization using a neural network metamodel

This paper deals with Emergency Department (ED) fast-tracks for low-acuity patients, a strategy often adopted to reduce ED overcrowding. We focus on optimizing resource allocation in minor injuries units, which are the ED units that can treat low-acuity patients, with the aim of minimizing patient waiting times and ED operating costs. We formulate this problem as a general multiobjective simulation-based optimization problem where some of the objectives are expensive black-box functions that can only be evaluated through a time-consuming simulation. To efficiently solve this problem, we propose a metamodeling approach that uses an artificial neural network to replace a black-box objective function with a suitable model. This approach allows us to obtain a set of Pareto optimal points for the multiobjective problem we consider, from which decision-makers can select the most appropriate solutions for different situations. We present the results of computational experiments conducted on a real case study involving the ED of a large hospital in Italy. The results show the reliability and effectiveness of our proposed approach, compared to the standard approach based on derivative-free optimization.

A reliability-based design against post-buckling load drop in spherical shell cap with stochastic imperfections

Elastic buckling is a critical design consideration for deep spherical shell caps. Significant reduction is observed in the experimental critical load from the linearized eigen-buckling analysis, for reasons extensively discussed in the past and relooked recently through the energy barrier concepts. The Knock Down Factors (KDFs) were proposed to account for such a reduction. The highly conservative KDFs constitute the lower bound of a large experimental dataset; that can be reconciled by nonlinear stability analysis of the imperfect shell. This study models the KDFs as Uncertain-but-Bounded (UBB) variables to present a Reliability-Based Design (RBD) for spherical shell caps having random geometric imperfections in radius (R) and thickness (t). The RBD furnishes the pertinent design variables ((R/t) ratio and the dome height-to-base radius (H/r0) ratio) for a target reliability index (β) and the respective KDFs. A metamodelling approach is employed to reduce the computational burden of the proposed RBD for a thin spherical shell cap having zero-mean, stationary, homogeneous, gaussian stochastic geometric imperfections. The Riks algorithm is employed in the commercial code ABAQUS for the finite element-based nonlinear stability analysis of shells. The KDFs and the reliability bounds are presented for varying degrees of imperfections. Remarkably, the margin of conservatism in the KDFs is shown to be narrowed down by incorporating the dependency on (H/r0).

IoT Security Model for Smart Cities based on a Metamodeling Approach

IoT Security Model for Smart Cities based on a Metamodeling Approach

Efficient MAPoD via Least Angle Regression based Polynomial Chaos Expansion Metamodel for Eddy Current NDT

In this article, a metamodeling approach based on non-intrusive polynomial chaos expansion (PCE) with least angle regression (LAR) method is used in boundary element analysis for a model-assisted probability of detection (MAPoD) study of eddy current nondestructive testing (NDT) systems. The LAR-PCE metamodel represents the NDT system model responses by a set of coefficients with the polynomial basis functions in lieu of pure kernel degeneration accelerated boundary element method (KD-BEM) based physical model. Both the computational accuracy and efficiency of the LAR-PCE metamodel over the ordinary least squares (OLS) based PCE metamodel are demonstrated by testing the 3D eddy current NDT benchmarks with different system setups, flaw lengths and widths. The simulation results show two digits accuracy of the PoD metrics compared with the ones achieved by the KD-BEM based physical model as the benchmark. The LAR-PCE metamodel has remarkable improvements in computational efficiency over the OLS-PCE metamodel and accelerates the MAPoD study.

Design and validation of a new ring hoop plane strain test for characterizing the anisotropic plastic behavior of tubular materials

The accurate characterization of the plastic behavior of tubular materials is challenging due to the difficulties in acquiring proper specimens for mechanical testing. In this study, we propose a novel testing technique, namely the ring hoop plane strain Test (RHPST), as a supplementary test for characterizing materials experiencing distinct strain paths compared to standard uniaxial tensile tests. To design a suitable ring specimen’s geometry that satisfies the necessary plane strain conditions, we developed a meta-modelling approach merging response surface method (RSM) and the finite element method (FEM). Experimental tests are conducted on RHPST specimens, and 3D digital image correlation (DIC) technique is used to monitor the strain fields within the specimen's gauge section. However, observations indicate the presence of edge effects on the gauge specimen. These edge effects significantly influence and limit the homogeneity of the plane strain field. To tackle this limitation, a correction procedure is developed for separating the authentic plane strain area, constituting approximately 78% of the RHPST specimen's width. Furthermore, we successfully fabricated a novel grooved ring specimen (GRHPST), which exhibits a wider region of the plane strain state encompassing up to 90% of the gauge width, while minimizing the impact of edge effects. The true stress-true strain curve obtained from the GRHPST specimen exhibits exceptional agreement with the curve predicted using the Hill48 yield criterion. Notably, no additional corrections are required for the measured true stress-true strain curve, thereby affirming the efficacy of the GRHPST specimen as an adept test for characterizing the anisotropic plastic behavior of AA6063 tubes.

Consistent meta-modelling approach for a less-conservative reliability based design of shells against buckling

Consistent meta-modelling approach for a less-conservative reliability based design of shells against buckling

Optimization of a Tapered Specimen Geometry for Short-Term Dynamic Tensile Testing of Continuous Fiber Reinforced Thermoplastics

Continuous fiber reinforced thermoplastics (cFRTP) are one of the most promising lightweight materials. For their use in structural components, reproducible and comparable material values have to be evaluated, especially at high strain rates. Due to their high stiffness and outstanding strength properties, the evaluation of the material behavior at high strain rates is complex. In the presented work, a new tensile specimen geometry for strain rate testing is virtually optimized using a metamodel approach with an artificial neural network. The final specimen design is experimentally validated and compared with rectangular specimen results for a carbon fiber reinforced polycarbonate (CF-PC). The optimized specimen geometry leads to 100% valid test results in experimental validation of cross-ply laminates and reaches 9% higher tensile strength values than the rectangle geometry with applied end tabs at a strain rate of 40 s−1. Through the optimization, comparable material parameters can be efficiently generated for a successful cFRTP strain rate characterization.

Metamodeling on uncertainty quantification in the behavior of the tire/road interaction of vehicles

In recent years, the automotive industry has been developing applied research to meet customer’s needs; considering safety, vehicle comfort and energetic efficiency. In particular, automotive tires have a prominent position in this research area, ensuring good vehicle handling, comfort and safety. In the vehicle dynamics performance, excellent gripping and reduced rolling resistance in the tires are crucial to maximize the energetic efficiency in a reliable way. However, to ensure such a level of reliability in vehicle operation, the inherent uncertainties of tires, as well as other factors subject to variability must be taken into account in the vehicle design. In this way, the present paper analyzes in detail the effect of variations in some parameters such as ambient temperature, ground conditions, vertical load, speed and tire inflation on the analysis of vehicle dynamics. A Metamodeling approach associated with the Monte Carlo Simulation was employed to develop the mathematical models to analyze the effect of uncertain parameters on the tire rolling resistance; traction, centripetal and lateral forces, using experimental data from the literature, in the longitudinal and lateral vehicle dynamics. Therefore, the present research brings as an innovation an integrated approach to the input parameters of the system with the rolling resistance through the developed metamodels. There was a substantial variability of up to 15% both up and down in the Maximum Traction Force of a vehicle in response to variations in the vehicle’s weight and the coefficient of tire rolling resistance. In contrast, the Lateral Force exhibited a greater variability, with a 25 downward and 10% upward variation associated with the weight and friction coefficient variability of the vehicle. Further investigations into the sensitivity analysis highlight the significant influence of the friction coefficient and temperature on the Traction Forces of the vehicle.

A Shrinkage Approach to Improve Direct Bootstrap Resampling Under Input Uncertainty

Discrete-event simulation models generate random variates from input distributions and compute outputs according to the simulation logic. The input distributions are typically fitted to finite real-world data and thus are subject to estimation errors that can propagate to the simulation outputs: an issue commonly known as input uncertainty (IU). This paper investigates quantifying IU using the output confidence intervals (CIs) computed from bootstrap quantile estimators. The standard direct bootstrap method has overcoverage due to convolution of the simulation error and IU; however, the brute-force way of washing away the former is computationally demanding. We present two new bootstrap methods to enhance direct resampling in both statistical and computational efficiencies using shrinkage strategies to down-scale the variabilities encapsulated in the CIs. Our asymptotic analysis shows how both approaches produce tight CIs accounting for IU under limited input data and simulation effort along with the simulation sample-size requirements relative to the input data size. We demonstrate performances of the shrinkage strategies with several numerical experiments and investigate the conditions under which each method performs well. We also show advantages of nonparametric approaches over parametric bootstrap when the distribution family is misspecified and over metamodel approaches when the dimension of the distribution parameters is high. History: Accepted by Bruno Tuffin, Area Editor for Simulation. Funding: This work was supported by the National Science Foundation [CAREER CMMI-1834710, CAREER CMMI-2045400, DMS-1854659, and IIS-1849280]. Supplemental Material: The software that supports the findings of this study is available within the paper and its Supplemental Information ( https://pubsonline.informs.org/doi/suppl/10.1287/ijoc.2022.0044 ) as well as from the IJOC GitHub software repository ( https://github.com/INFORMSJoC/2022.0044 ). The complete IJOC Software and Data Repository is available at https://informsjoc.github.io/ .

Structuring and organizing database security domain from big data perspective using meta-modeling approach

Database security is an area focused on safeguarding databases against harmful access. It involves ensuring data accuracy, blocking unauthorized entry, and preventing harmful code within the database. Although various security models and methods exist, they often don't comprehensively cover all aspects of database security. This leads to a diverse and unclear understanding of database security among experts. This study proposes a unified framework, the Database Security Meta-model (DBSM), which acts as a standard language in this field. The DBSM, comprising twelve main elements, is thoroughly vetted to align with security needs and offers guidelines for practitioners to create specific security solutions.

Meta-modeling the effects of anodal left prefrontal transcranial direct current stimulation on working memory performance

Abstract Numerous studies have demonstrated the beneficial effects of anodal prefrontal transcranial direct current stimulation (tDCS) on working memory. However, a large variability exists in the applied tDCS parameters and working memory outcome measures. Using a meta-modeling approach, we investigated the relationship between tDCS electric fields in the left prefrontal cortex and improvements in working memory performance. Using this approach, a vector of outcome measures is correlated with the tDCS-related electric fields across several studies. These performance-electric field correlations (PEC) are calculated for each spatial location of the grey matter. Extracting 354 data points from 67 studies, we compared the spatial maps of tDCS effects on I) working memory accuracy and speed (regardless of working memory type and time of assessment), II) verbal and visuospatial working memory (regardless of performance measurement and time of assessment), and III) performance during and after stimulation (regardless of performance measurement and working memory type). We found that accuracy improves when anodal tDCS is applied to inferior frontal regions (Brodmann area 47) while working memory speed benefits from stimulation to dorsolateral and anterior prefrontal areas (Brodmann areas 9/10). Furthermore, the beneficial effects of left prefrontal tDCS are exclusive to verbal working memory, with no improvements in visuospatial working memory. We also observed region-specific effects only for task performance during, but not after, stimulation. The results of this study elucidate the causal involvement of prefrontal regions in working memory and can help guide tDCS placement for therapeutic application in disorders that involve working memory deficits.

Development of a novel multi-fidelity meta modeling approach for robust multi-objective optimization of a natural gas-hydrogen/diesel dual fuel engine

Multi-fidelity modeling (MFM) is an evolving field that matches low-fidelity models (LFM) and high-fidelity models (HFM) to get better solutions with low computational cost. However, improving the duality between accuracy and computational cost remain challenging, particularly for complex problems such as dual fuel engines. This paper contributes to the MF modeling cost-effectiveness improvement by proposing a new approach to solve large-dimensional multi-objective optimization problems. The first step is to build a meta-model based on the LF model, which will be subjected to a comet-governed analysis to detect potential areas where the uncertainty on the LF model is relatively high. Then, a design of experiment (DOE) will be developed based on the results of this analysis to construct an initial HF model. Finally, an iterative loop will be activated to improve the accuracy of the MF model using a well-weighed combination of the details delivered by the LF model correction via the HF model and the HF meta-model. The developed approach is validated on four different mathematical benchmarks with different difficulties, compared with four different MF modeling strategies. This validation shows that the proposed MF modeling is competitive and can produce solutions as accurate as the HF model while reducing significantly the overall computation time by up to 50%. As an engineering application, the operating conditions in a natural gas-hydrogen/diesel dual fuel engine in terms of compression ratio, pilot injection timing, and EGR are optimized. A reduction of 46%, 68%, and 96% was achieved for HC, NOx, and the knocking index, respectively, while an increase in thermal efficiency of about 5% was obtained.

Stochastic Day-Ahead Scheduling of Distributed Energy Resources: A Meta Modeling Approach

Stochastic Day-Ahead Scheduling of Distributed Energy Resources: A Meta Modeling Approach

Securing Smart Agriculture: Proposed Hybrid Meta-Model and Certificate-based Cyber Security Approaches

The Internet of Things is a decentralized network of physically connected devices that communicate with other systems and devices over the internet. As the number of IoT-based devices continues to grow at an exponential rate, this technology has the potential to improve nearly every aspect of daily life, from smart networks and transportation to home automation and agriculture. However, the absence of adequate security measures on all levels of the IoT poses a significant security risk, with the potential for cyber-attacks and data theft. While scholars have suggested various security measures, there are still gaps that need to be addressed. In this study, we analyzed previous research and proposed metamodels for security, IoT, and machine learning. We then proposed a new IoT-based smart agriculture model with integrated security measures to mitigate cyber- attacks and increase agricultural output. Our model takes into account the unique features of the smart farming domain and offers a framework for securing IoT devices in this specific application area. Moreover, in order to mitigate a range of cyber security attacks across various layers of IoT, we introduced two certificate-based schemes named CBHA and SCKA for smart agriculture. A comparative analysis of their security with existing literature demonstrates their superior robustness against diverse attacks. Additionally, security testing utilizing scyther affirms the resilience and security of both CBHA and SCKA, establishing them as viable options for ensuring security in smart agriculture

Partial safety factor calibration using surrogate models: An application for running safety of ballasted high-speed railway bridges

Traditionally, regulations employ semi-probabilistic methods with partial safety factors to control design limits. Calibrating these partial safety factors involves estimating the target reliability level and optimizing the partial safety factor values in order to minimize the deviation of the safety index between the considered design scenarios and the target value. This procedure necessitates performing a demanding amount of reliability analyses and is often carried out for simplified design situations. Therefore, high computational costs must be accepted for design problems formulated with complex computational models. This study implements a meta-modeling approach based on active learning in the partial safety calibration procedure, enabling its application to computationally intensive problems. Subsequently, the approach is applied to the running safety of ballasted high-speed railway bridges. This limit state implicitly accounts for the phenomenon of ballast destabilization, the occurrence of which disturbs the load path from the rail level to the bridge structure. The dramatic increase in train operating speeds in recent decades has increased the possibility of this design limit state being violated due to resonance. Despite the evident safety concerns, the adopted safety factors appear to be solely based on engineering judgments rather than calibration through higher-level reliability analysis. Therefore, the proposed calibration method is employed to determine the corresponding partial safety factors for various maximum allowable operating train speeds. The newly calibrated partial safety factors allow for a permissible maximum vertical acceleration of the bridge deck approximately 25% higher than the conventional design approaches. Therefore, incorporating these factors into the design procedure may lead to the construction of lighter bridges.

An Adaptive Meta-Modelling Approach for Multi-Dimensional Correlated Flow Field Responses

ABSTRACT It has become common practice to build inexpensive surrogate models to supplant time-consuming numerical simulations. By gradually increasing the number of training samples, the efficiency of model construction can be significantly improved. This study proposes an adaptive meta-modelling approach for multi-dimensional correlated responses within the framework of proper orthogonal decomposition (POD) and the Kriging model. The algorithm begins with the adaptive sampling algorithm for each reduced-dimension response, which integrates the prediction variance, distances between samples, and sensitivity indicator of each parameter. The adaptive sampling criterion for each reduced-dimension response is weighted by the energy of the modal, forming the adaptive sampling algorithm for multi-dimensional correlated responses. Tests on an analytical function and M6 wing simulation show that, under the same number of training samples, the proposed adaptive algorithm results in a model with lower prediction error than the random sampling algorithm, offering a more efficient model for flow field prediction.