Robust design of Mars entry micro-probe with evidence theory and multi-fidelity strategies. Issue 6 (29th July 2014)

• Record Type: Journal Article
• Authors: Massimiliano Vasile, Dr Edmondo Minisci and Dr Domenico Quagliarella, Professor; Liqiang, Hou; Yuanli, Cai; Rongzhi, Zhang; Hengnian, Li; Jisheng, Li
• Is Part Of: Engineering computations. Volume 31:Issue 6(2014)
• Page Start: 1052
• Page End: 1073
• Publication Date: 2014-07-29
• Subjects: Computer-aided engineering -- Periodicals; Computer graphics -- Periodicals; 620.00285
• DOI: 10.1108/EC-08-2012-0188
• Languages: English
• ISSNs: 0264-4401

Abstract

<abstract> <title> <x content-type="archive" xml:space="preserve">Abstract</x> </title> <sec> <title content-type="abstract-heading">Purpose</title> <p> – A multi-disciplinary robust design optimization method for micro Mars entry probe (no more than 0.8 m in diameter) is proposed. The purpose of this paper is to design a Mars entry probe, not only the geometric configuration, but the trajectory and thermal protection system (TPS). In the design optimization, the uncertainties of atmospheric and aerodynamic parameters are taken into account. The probability distribution information of the uncertainties are supposed to be unknown in the design. To ensure accuracy levels, time-consuming numerical models are coupled in the optimization. Multi-fidelity approach is designed for model management to balance the computational cost and accuracy. </p> </sec> <sec> <title content-type="abstract-heading">Design/methodology/approach</title> <p> – Uncertainties which cannot defined by usual Gaussian probability distribution are modeled with degree of belief, and optimized through with multiple-objective optimization method. The optimization objectives are set to be the thermal performance of the probe TPS and the corresponding belief values. Robust Pareto front is obtained by an improved multi-objective density estimator algorithm. Multi-fidelity management is performed with an Artificial Neural Network (ANN) surrogate model. Analytical model is used first, and then with the improvement of accuracy, rather complex numerical models are activated. ANN updates the database during the optimization, and makes the solutions finally converge to a high-level accuracy. </p> </sec> <sec> <title content-type="abstract-heading">Findings</title> <p> – The optimization method provides a way for conducting complex design optimization involving multi-discipline and multi-fidelity models. Uncertainty effects are analyzed and optimized through multi-disciplinary robust design. Because of the micro size, and uncertain impacts of aerodynamic and atmospheric parameters, simulation results show the performance trade-off by the uncertainties. Therefore an effective robust design is necessary for micro entry probe, particularly when details of model parameter are not available. </p> </sec> <sec> <title content-type="abstract-heading">Originality/value</title> <p> – The optimization is performed through a new developed multi-objective density estimator algorithm. Affinity propagation algorithm partitions adaptively the samples by passing and analyzing messages between data points. Local principle component techniques are employed to resample and reproduce new individuals in each cluster. A strategy similar to NSGA-II selects data with better performance, and converges to the Pareto front. Models with different fidelity levels are incorporated in the multi-disciplinary design via ANN surrogate model. Database of aerodynamic coefficients is updated in an online manner. The computational time is greatly reduced while keeping nearly the same accuracy level of high fidelity model.</p> </sec> </abstract>