Variable Fidelity Research Articles

Laser Welding Process Parameters Optimization Using Variable-Fidelity Metamodel and NSGA-II

An optimization methodology based on variable-fidelity (VF) metamodels and nondominated sorting genetic algorithm II (NSGA-II) for laser bead-on-plate welding of stainless steel 316L is presented. The relationships between input process parameters (laser power, welding speed and laser focal position) and output responses (weld width and weld depth) are constructed by VF metamodels. In VF metamodels, the information from two levels fidelity models are integrated, in which the low-fidelity model (LF) is finite element simulation model that is used to capture the general trend of the metamodels, and high-fidelity (HF) model which from physical experiments is used to ensure the accuracy of metamodels. The accuracy of the VF metamodel is verified by actual experiments. To slove the optimization problem, NSGA-II is used to search for multi-objective Pareto optimal solutions. The results of verification experiments show that the obtained optimal parameters are effective and reliable.

Experiment / simulation integrated shape optimization using variable fidelity Kriging model approach

Experiment / simulation integrated shape optimization using variable fidelity Kriging model approach

A variable fidelity information fusion method based on radial basis function

Radial basis function (RBF) model has been widely used in complex engineering design process to replace the computational-intensive simulation models. This paper proposes a variable-fidelity metamodeling (VFM) approach based on RBF, in which different levels fidelity information can be integrated and fully exploited. In the proposed VFM approach, a RBF metamodel is constructed for the low-fidelity (LF) model as a start. Then by taking the constructed LF metamodel as a prior-knowledge and mapping the output space of the LF metamodel to that of the studied high-fidelity (HF) model, a variable fidelity (VF) metamodel is created to approximate the relationships between the design variables and corresponding output responses. A numerical illustrative example is adopted to make a detailed comparison between the VFM approach developed in this research and three existing scaling function based VFM approaches, considering different sample sizes and sample noises. Results illustrate that the proposed VFM approach outperforms the scaling function based VFM approaches both in global and local accuracy. Then the proposed VFM approach is applied to two engineering problems, modeling aerodynamic data for a three-dimensional aircraft and the prediction of weld bead profile in laser welding, to illustrate its ability in support of complex engineering design.

Multi-fidelity modelling of mixed convection based on experimental correlations and numerical simulations

For thermal mixed-convection flows, the Nusselt number is a function of Reynolds number, Grashof number and the angle between the forced- and natural-convection directions. We consider flow over a heated cylinder for which there is no universal correlation that accurately predicts Nusselt number as a function of these parameters, especially in opposing-convection flows, where the natural convection is against the forced convection. Here, we revisit this classical problem by employing modern tools from machine learning to develop a general multi-fidelity framework for constructing a stochastic response surface for the Nusselt number. In particular, we combine previously developed experimental correlations (low-fidelity model) with direct numerical simulations (high-fidelity model) using Gaussian process regression and autoregressive stochastic schemes. In this framework the high-fidelity model is sampled only a few times, while the inexpensive empirical correlation is sampled at a very high rate. We obtain the mean Nusselt number directly from the stochastic multi-fidelity response surface, and we also propose an improved correlation. This new correlation seems to be consistent with the physics of this problem as we correct the vectorial addition of forced and natural convection with a pre-factor that weighs differently the forced convection. This, in turn, results in a new definition of the effective Reynolds number, hence accounting for the ‘incomplete similarity’ between mixed convection and forced convection. In addition, due to the probabilistic construction, we can quantify the uncertainty associated with the predictions. This information-fusion framework is useful for elucidating the physics of the flow, especially in cases where anomalous transport or interesting dynamics may be revealed by contrasting the variable fidelity across the models. While in this paper we focus on the thermal mixed convection, the multi-fidelity framework provides a new paradigm that could be used in many different contexts in fluid mechanics including heat and mass transport, but also in combining various levels of fidelity of models of turbulent flows.

A VF-SLP framework using least squares hybrid scaling for RBDO

Using computationally cheap low-fidelity (LF) model and more accurate but expensive high-fidelity (HF) model, variable fidelity (VF) model has been widely used in engineering design to replace the actual computationally expensive experiments or computer simulations. To further extend the application of VF to reliability-based design optimization (RBDO), a new framework based on sequential linear programming (SLP) is proposed in this paper. Combining the advantages of additive scaling method and multiplicative scaling method, a hybrid scaling method based on least squares (LSHS) is developed. In LSHS method, the VF model is introduced to replace the implicit performance function in RBDO by using the HF function values and gradient values at all evaluated points around the current design. With the failure probability and its gradient calculated by Monte Carlo Simulation (MCS) at current design, SLP is adopted to calculate the next design. A novel method which considers the target reliability index and the influence domain at the current design is also developed to determine the step size in every sub-optimization problem. Two numerical examples and the shape optimization problem of a curved beam are analyzed in order to demonstrate the performance of the proposed methodology. The comparison results show that the proposed method is very accurate and efficient.

Adaptive Variable-Fidelity Analysis and Design Using Dynamic Fidelity Indicators

The purpose of the current study is to develop a decision-making model of a dynamic fidelity indicator (DFI) for adaptive variable-fidelity (VF) analysis and to apply it to practical engineering problems, including computational design optimization. The biggest advantage is considerable computation efficiency while satisfying solution accuracy. Bayesian-based DFI and difference-based DFI are formulated as probabilistic model validation metrics to quantify model-form uncertainties attributed to the VF analysis. Two adaptive VF surrogate models of the VF kriging with regression and sample reinterpolation and the simple VF kriging with an additive bridge function are developed. The VF kriging models are integrated with the DFIs into an efficient global optimization design framework for the design problems, which was further improved by the multipoint and multi-objective infill sampling criteria. Validation of the developed framework is carried out with two analytic functions for solution optimality and computational efficiency. As a practical design application, the NLR 7301 multi-element airfoil with a flap is designed for high lift while the model-from uncertainty is defined by different flow solutions of the Euler and Reynolds-averaged Navier–Stokes equations. The results show that the design optimization becomes more efficient with great accuracy by the adaptive VF analysis with the DFIs.

A modified BLISCO method and its combination with variable fidelity metamodel for engineering design

Purpose – The purpose of this paper is to present a modified bi-level integrated system collaborative optimization (BLISCO) to avoid the non-separability of the original BLISCO. Besides, to mitigate the computational burden caused by expensive simulation codes and employ both efficiently simplified and expensively detailed information in multidisciplinary design optimization (MDO), an effective framework combining variable fidelity metamodels (VFM) and modified BLISCO (MBLISCO) (VFM-MBLISCO) is proposed. Design/methodology/approach – The concept of the quasi-separable MDO problems is introduced to limit range of applicability about the BLISCO method and then based on the quasi-separable MDO form, the modification of BLISCO method without any derivatives is presented to solve the problems of BLISCO. Besides, an effective framework combining VFM-MBLISCO is presented. Findings – One mathematical problem conforms to the quasi-separable MDO form is tested and the overall results illustrate the feasibility and robustness of the MBLISCO. The design of a Small Waterplane Area Twin Hull catamaran demonstrates that the proposed VFM-MBLISCO framework is a feasible and efficient design methodology in support of design of engineering products. Practical implications – The proposed approach exhibits great capability for MDO problems with tremendous computational costs. Originality/value – A MBLISCO is proposed which can avoid the non-separability of the original BLISCO and an effective framework combining VFM-MBLISCO is presented to efficiently integrate the different fidelities information in MDO.

Construction of nested maximin designs based on successive local enumeration and modified novel global harmony search algorithm

ABSTRACTEngineering design often involves different types of simulation, which results in expensive computational costs. Variable fidelity approximation-based design optimization approaches can realize effective simulation and efficiency optimization of the design space using approximation models with different levels of fidelity and have been widely used in different fields. As the foundations of variable fidelity approximation models, the selection of sample points of variable-fidelity approximation, called nested designs, is essential. In this article a novel nested maximin Latin hypercube design is constructed based on successive local enumeration and a modified novel global harmony search algorithm. In the proposed nested designs, successive local enumeration is employed to select sample points for a low-fidelity model, whereas the modified novel global harmony search algorithm is employed to select sample points for a high-fidelity model. A comparative study with multiple criteria and an engineering application are employed to verify the efficiency of the proposed nested designs approach.

An active learning metamodeling approach by sequentially exploiting difference information from variable-fidelity models

Complex system engineering design optimization based on simulation is a very time-consuming, even computationally prohibitive process. To relieve the computational burden, metamodels are commonly used to replace the computation-intensive simulations. In this paper, an active learning variable fidelity (VF) metamodeling approach (AL-VFM) is proposed for the purpose of integrating information from both low-fidelity (LF) and high-fidelity (HF) models. In AL-VFM, Kriging metamodel is adopted to map the difference between the HF and LF models aiming to approach the HF model on the entire domain. Besides, a general active learning strategy is introduced in AL-VFM to make full use of the already-acquired information to guide the VF metamodeling. The already-acquired information represents the location of regions where the differences between the HF and LF models are multi-model, non-smooth and have abrupt changes. Several numerical and engineering cases with different degrees of difficulty verify the applicability of the proposed VF metamodeling approach.

An active learning variable-fidelity metamodelling approach based on ensemble of metamodels and objective-oriented sequential sampling

ABSTRACTComputational simulation models with different fidelity have been widely used in complex systems design. However, running the high-fidelity (HF) simulation models tends to be very time-consuming, while incorporating low-fidelity (LF), inexpensive models into the design process may result in inaccurate design alternatives. To make a trade-off between high accuracy and low expense, an active learning variable-fidelity (VF) metamodelling approach aiming to integrate information from both LF and HF models is proposed. In the proposed VF metamodelling approach, a model fusion technology based on ensemble of metamodels is employed to map the difference between the HF and LF models. Furthermore, an active learning strategy based on a generalised objective-oriented sequential sampling strategy is introduced to make full use of the already-acquired information of difference characteristics between the HF and LF models. Several numerical and engineering cases verify the applicability of the proposed VF metamodelling approach. Different types of test cases, sample sizes, and metamodel performance evaluation measures including accuracy and robustness are considered.

Quality control mechanisms of protein biogenesis: proteostasis dies hard

The biosynthesis of proteins entails a complex series of chemical reactions that transform the information stored in the nucleic acid sequence into a polypeptide chain that needs to properly fold and reach its functional location in or outside the cell. It is of no surprise that errors might occur that alter the polypeptide sequence leading to a non-functional proteins or that impede delivery of proteins at the appropriate site of activity. In order to minimize such mistakes and guarantee the synthesis of the correct amount and quality of the proteome, cells have developed folding, quality control, degradation and transport mechanisms that ensure and tightly regulate protein biogenesis. Genetic mutations, harsh environmental conditions or attack by pathogens can subvert the cellular quality control machineries and perturb cellular proteostasis leading to pathological conditions. This review summarizes basic concepts of the flow of information from DNA to folded and active proteins and to the variable fidelity (from incredibly high to quite sloppy) characterizing these processes. We will give particular emphasis on events that maintain or recover the homeostasis of the endoplasmic reticulum (ER), a major site of proteins synthesis and folding in eukaryotic cells. Finally, we will report on how cells can adapt to stressful conditions, how perturbation of ER homeostasis may result in diseases and how these can be treated.

Variable fidelity robust optimization of pulsed laser orbital debris removal under epistemic uncertainty

A variable fidelity robust optimization method for pulsed laser orbital debris removal (LODR) under uncertainty is proposed. Dempster–shafer theory of evidence (DST), which merges interval-based and probabilistic uncertainty modeling, is used in the robust optimization. The robust optimization method optimizes the performance while at the same time maximizing its belief value. A population based multi-objective optimization (MOO) algorithm based on a steepest descent like strategy with proper orthogonal decomposition (POD) is used to search robust Pareto solutions. Analytical and numerical lifetime predictors are used to evaluate the debris lifetime after the laser pulses. Trust region based fidelity management is designed to reduce the computational cost caused by the expensive model. When the solutions fall into the trust region, the analytical model is used to reduce the computational cost. The proposed robust optimization method is first tested on a set of standard problems and then applied to the removal of Iridium 33 with pulsed lasers. It will be shown that the proposed approach can identify the most robust solutions with minimum lifetime under uncertainty.

An Edge-Preserved Image Denoising Algorithm Based on Local Adaptive Regularization

Image denoising methods are often based on the minimization of an appropriately defined energy function. Many gradient dependent energy functions, such as Potts model and total variation denoising, regard image as piecewise constant function. In these methods, some important information such as edge sharpness and location is well preserved, but some detailed image feature like texture is often compromised in the process of denoising. For this reason, an image denoising method based on local adaptive regularization is proposed in this paper, which can adaptively adjust denoising degree of noisy image by adding spatial variable fidelity term, so as to better preserve fine scale features of image. Experimental results show that the proposed denoising method can achieve state-of-the-art subjective visual effect, and the signal-noise-ratio (SNR) is also objectively improved by 0.3–0.6 dB.

Aerodynamic design of high-aspect-ratio flying wing based on criteria

To improve aerodynamic performance of flying wing from design reality,aerodynamic design and analysis of high-aspect-ratio flying wing unmanned aerial vehicle( UAV) were conducted. In design and analysis process,according to features of flying wing,aerodynamic design criteria have been proposed. Based on design criteria,a design optimization framework,using updated design strategies,was built which combined variable fidelity numerical simulation and surrogated model optimization method. Flying wing UAV was carried out with parametric expression,automatic mesh generation of transfinite interpolation( TFI),and multi-round optimization. And then recommended configuration was obtained and validated for detailed aerodynamic performance by γ-Reθttransition model method. The results show that design configuration of flying wing UAV agrees well with design criteria,cruise lift-to-drag ratio of UAV increases 14% compared to the original configuration,γ-Reθttransition model method can analyze the detailed aerodynamic characteristics of high-aspect-ratio flying wing UAV.

An adaptive global variable fidelity metamodeling strategy using a support vector regression based scaling function

Computational simulation models with variable fidelity have been widely used in complex systems design. However, running the most accurate simulation models tends to be very time-consuming and can therefore only be used sporadically, while incorporating less accurate, inexpensive models into the design process may result in inaccurate design alternatives. To make a trade-off between high accuracy and low expense, variable fidelity (VF) metamodeling approaches that aim to integrate information from both low-fidelity (LF) and high-fidelity (HF) models have gained increasing popularity. In this paper, an adaptive global VF metamodeling approach named difference adaptive decreasing variable-fidelity metamodeling (DAD-VFM) is proposed, in which the one-shot VF metamodeling process is transformed into an iterative process to utilize the already-acquired information of difference characteristics between the HF and LF models. In DAD-VFM, support vector regression (SVR) is adopted to map the difference between the HF and LF models. Besides, a generalized objective-oriented sampling strategy is introduced to adaptively probe and sample more points in the interesting regions where the differences between the HF and LF models are multi-model, non-smooth and have abrupt changes. Several numerical cases and a long cylinder pressure vessel optimization design problem verify the applicability of the proposed VF metamodeling approach.

Simulation-driven design of low-speed wind tunnel contraction

A low-speed wind tunnel is developed for conducting research on the flow past micro air vehicles. The tunnel is of open suction type and is composed of a square inlet with a honeycomb and turbulence screens, settling chamber, contraction, experimental section housing, diffuser, and axial fan. In this paper, we describe the details of the design optimization procedure of the contraction, which is key to getting a high quality flow in the experimental section. A high-fidelity computational fluid dynamic (CFD) flow solver is used to capture the nonlinear flow physics. Due to the high computational expense of the CFD simulations, surrogate-based optimization (SBO) is used to accelerate the design process. The SBO approach replaces direct optimization of the high-fidelity (accurate but computationally expensive) model by iterative optimization of a properly corrected low-fidelity model. Here, we exploit variable–fidelity CFD simulations, as well as a simple multiplicative response correction technique to construct the surrogate model of the wind tunnel contraction, allowing us to optimize its shape at a low computational cost. To our knowledge, it is the first application of variable–fidelity surrogate modeling to wind tunnel contraction design. The optimum nozzle design is verified using a high-fidelity CFD simulation, as well as by experimental measurements of the fabricated wind tunnel. Experimental validation confirms the correctness of the numerical optimization procedures utilized to design the contraction.

A Comprehensive Probabilistic Framework to Learn Air Data from Surface Pressure Measurements

Use of probabilistic techniques has been demonstrated to learn air data parameters from surface pressure measurements. Integration of numerical models with wind tunnel data and sequential experiment design of wind tunnel runs has been demonstrated in the calibration of a flush air data sensing anemometer system. Development and implementation of a metamodeling method, Sequential Function Approximation (SFA), are presented which lies at the core of the discussed probabilistic framework. SFA is presented as a tool capable of nonlinear statistical inference, uncertainty reduction by fusion of data with physical models of variable fidelity, and sequential experiment design. This work presents the development and application of these tools in the calibration of FADS for a Runway Assisted Landing Site (RALS) control tower. However, the multidisciplinary nature of this work is general in nature and is potentially applicable to a variety of mechanical and aerospace engineering problems.

Improving surrogate-assisted variable fidelity multi-objective optimization using a clustering algorithm

Surrogate-assisted evolutionary optimization has proved to be effective in reducing optimization time, as surrogates, or meta-models can approximate expensive fitness functions in the optimization run. While this is a successful strategy to improve optimization efficiency, challenges arise when constructing surrogate models in higher dimensional function space, where the trade space between multiple conflicting objectives is increasingly complex. This complexity makes it difficult to ensure the accuracy of the surrogates. In this article, a new surrogate management strategy is presented to address this problem. A k-means clustering algorithm is employed to partition model data into local surrogate models. The variable fidelity optimization scheme proposed in the author's previous work is revised to incorporate this clustering algorithm for surrogate model construction. The applicability of the proposed algorithm is illustrated on six standard test problems. The presented algorithm is also examined in a three-objective stiffened panel optimization design problem to show its superiority in surrogate-assisted multi-objective optimization in higher dimensional objective function space. Performance metrics show that the proposed surrogate handling strategy clearly outperforms the single surrogate strategy as the surrogate size increases.

Habitat use and selection of black scoters in southern New England and siting of offshore wind energy facilities

ABSTRACTThe southern New England continental shelf is an important region for black scoters (Melanitta americana) during winter and migratory staging periods and a priority area for developing offshore wind energy facilities. However, little is known about the migration phenology and habitat use of black scoters in this portion of their range and this information is necessary to assess potential risks to black scoters during the marine spatial planning process. In this regional black scoter study over 2 winters, we used satellite telemetry and spatial modeling techniques to estimate migratory timing and length of stay, quantify winter home range size and site fidelity between winters, examine key habitat characteristics associated with core‐use areas, and map relative probabilities of use across a 3,800‐km2 marine spatial planning area for 2 proposed offshore renewable energy facilities. Black scoters spent nearly 5 months in southern New England, with wide variation among individuals in the size of winter utilization distributions (range 16–12,367 km2). Approximately 50% of the tagged birds returned to southern New England during the subsequent winter and had variable fidelity to core‐use areas occupied the previous winter. During both winters, black scoter core‐use areas were located closer to shore, at shallower water depths, with coarser sediment grain size and higher probability of hard‐bottom occurrence relative to available areas. Resource selection functions classified the majority of a nearshore 5‐turbine, 34‐km2 renewable energy zone off Block Island as high probability of use by black scoters, whereas an offshore 200‐turbine, 667‐km2 federal lease block zone was classified as low to medium‐low probability of selection. Wind energy facilities, such as the Block Island site, constructed in relatively shallow (<20 m deep), nearshore habitats (<5 km) over hard‐bottomed or coarse‐sand substrate could displace some foraging black scoters wintering in this region, whereas the larger federal lease block zone located farther offshore is more likely to affect scoters dispersing among core‐use areas and during migration between wintering and breeding grounds. © 2014 The Wildlife Society.

Variable fidelity of tissue‐marking dyes in surgical pathology

Pathology specimens often contain important margins that must be identified from gross examination of specimens through to microscopic examination. Commonly, unique colours of tissue-marking dye (TMD) are applied to each margin, which facilitates both macroscopic and microscopic identification. Various techniques have been described, but the colour endurance and fidelity of TMDs following special tissue processing have not been addressed. The aim of this study was to evaluate the performance of various TMDs through decalcification and immunohistochemistry (IHC) protocols. Samples of TMDs from two manufacturers and acrylic artists' inks were obtained in seven colours and applied to excess non-diagnostic surgical pathology tissue. Tissues were subjected to a decalcification protocol or directly processed in a routine fashion. The presence and colour of TMD or ink were assessed on routine H&E sections and following IHC. Of the colours that reliably survived routine processing, loss of colour and colour change following decalcification and IHC protocols were seen with one manufacturer's product. TMD may lose or change its colour during special tissue processing. This previously unreported artefact may lead to potentially serious errors in margin assessment and reporting. Laboratories should evaluate TMDs and inks through routine processing, decalcification, and IHC protocols, to ensure colour endurance and fidelity.