Metamodels instead of computer simulations are often adopted to reduce the computational cost in the uncertainty-based multilevel optimization. However, metamodel techniques may bring prediction discrepancy, which is ...
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Metamodels instead of computer simulations are often adopted to reduce the computational cost in the uncertainty-based multilevel optimization. However, metamodel techniques may bring prediction discrepancy, which is defined as metamodeling uncertainty, due to the limited training data. An unreliable solution will be obtained when the metamodeling uncertainty is ignored, while an overly conservative solution, which contradicts the original intension of the design, may be got when both parametric and metamodeling uncertainty are treated concurrently. Hence, an adaptive sequential sampling framework is developed for the metamodeling uncertainty reduction of multilevel systems to obtain a solution that approximates the true solution. Based on the Kriging model for the probabilistic analytical target cascading (ATC), the proposed framework establishes a revised objective-oriented sampling criterion and sub-model selection criterion, which can realize the location of additional samples and the selection of subsystem requiring sequential samples. Within the sampling criterion, the metamodeling uncertainty is decomposed by the Karhunen-Loeve expansion into a set of stochastic variables, and then polynomial chaos expansion (PCE) is used for uncertainty quantification (UQ). The polynomial coefficients are encoded and integrated in the selection criterion to obtain subset sensitivity indices for the sub-model selection. The effectiveness of the developed framework for metamodeling uncertainty reduction is demonstrated on a mathematical example and an application.
Biomimetic practice requires a diverse set of knowledge from both biology and engineering. Several researchers have been supporting the integration of biologists within biomimetic design teams in order to meet those b...
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Biomimetic practice requires a diverse set of knowledge from both biology and engineering. Several researchers have been supporting the integration of biologists within biomimetic design teams in order to meet those biological requirements and improve the effectiveness of biomimetic processes. However, interdisciplinarity practices create well-known communication challenges. Based on functional representations (like SAPPhIRE or function behavior structure (FBS)), several approaches to model biological information have been investigated in the literature. Nonetheless, actual communication processes within interdisciplinary biomimetic design teams are yet to be studied. Following this research axis, this publication focuses on communication noises and wonders if a shared framework of reference can be defined to improve communication between biologists and engineers? Through the comparison of processes and graphic representations between biology and engineering design, a set of guidelines is defined to structure a shared framework of reference. Within this framework, a new tool referred to as LINKAGE is then proposed to assist interdisciplinary communication during the biomimetic process.
Multi-objective optimization (MOO) problems with computationally expensive constraints are commonly seen in real-world engineering design. However, metamodel-based designoptimization (MBDO) approaches for MOO are oft...
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Multi-objective optimization (MOO) problems with computationally expensive constraints are commonly seen in real-world engineering design. However, metamodel-based designoptimization (MBDO) approaches for MOO are often not suitable for high-dimensional problems and often do not support expensive constraints. In this work, the situational adaptive Kreisselmeier and Steinhauser (SAKS) method was combined with a new multi-objective trust region optimizer (MTRO) strategy to form the SAKS-MTRO method for MOO problems with expensive black-box constraint functions. The SAKS method is an approach that hybridizes the modeling and aggregation of expensive constraints and adds an adaptive strategy to control the level of hybridization. The MTRO strategy uses a combination of objective decomposition and K-means clustering to handle MOO problems. SAKS-MTRO was benchmarked against four popular multi-objective optimizers and demonstrated superior performance on average. SAKS-MTRO was also applied to optimize the design of a semiconductor substrate and the design of an industrial recessed impeller.
Sequential sampling methods have gained significant attention due to their ability to iteratively construct surrogate models by sequentially inserting new samples based on existing ones. However, efficiently and accur...
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Sequential sampling methods have gained significant attention due to their ability to iteratively construct surrogate models by sequentially inserting new samples based on existing ones. However, efficiently and accurately creating surrogate models for high-dimensional, nonlinear, and multimodal problems is still a challenging task. This paper proposes a new sequential sampling method for surrogate modeling based on a hybrid metric, specifically making the following three contributions: (1) a hybrid metric is developed by integrating the leave-one-out cross-validation error, the local nonlinearity, and the relative size of Voronoi regions using the entropy weights, which well considers both the global exploration and local exploitation of existing samples;(2) a Pareto-TOPSIS strategy is proposed to first filter out unnecessary regions and then efficiently identify the sensitive region within the remaining regions, thereby improving the efficiency of sensitive region identification;and (3) a prediction-error-and-variance (PE&V) learning function is proposed based on the prediction error and variance of the intermediate surrogate models to identify the new sample to be inserted in the sensitive region, ultimately improving the efficiency of the sequential sampling process and the accuracy of the final surrogate model. The proposed sequential sampling method is compared with four state-of-the-art sequential sampling methods for creating Kriging surrogate models in seven numerical cases and one real-world engineering case of a cutterhead of a tunnel boring machine. The results show that compared with the other four methods, the proposed sequential sampling method can more quickly and robustly create an accurate surrogate model using a smaller number of samples.
The micro air vehicle is a growing field of new aircraft and its control is a challenging key technology in the MAV research. Summarizing MAV, this paper presents a survey of the current situation and development of M...
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The micro air vehicle is a growing field of new aircraft and its control is a challenging key technology in the MAV research. Summarizing MAV, this paper presents a survey of the current situation and development of MAV flight controller, and analyzes some key tech problems in the design of MAV flight controller, which includes the scheme of flight control, on-board sensors, new control device, flight control algorithms, and controller design methods.
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