Multi-fidelity and multi-objective optimization of low-thrust transfers with control strategy for all-electric geostationary satellites

Shi, Renhe; Li, Teng; Baoyin, Hexi

doi:10.1016/j.actaastro.2020.08.013

Cited by 15 publications

(5 citation statements)

References 30 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The iterative multidisciplinary analysis (MDA) further increases the aerospace system optimization cost [2,3]. Note that the aerospace system design is also a multiobjective optimization problem (e.g., optimizing the satellite structure to minimize the structural mass and maximize the natural frequencies), which needs to make a trade-off among different system indexes [4]. In that case, the optimized result of the aerospace system is a set of nondominated solutions (i.e., Pareto frontier) instead of a unique design.…”

Section: A Research Background and Literature Reviewmentioning

confidence: 99%

“…A linear regression model of displacement damage dose is used to predict the radiation damage. More details of the multi-fidelity transfer models are given by [4].…”

Section: A Low-thrust Geo Transfer Trajectory Multi-objective Optimiz...mentioning

confidence: 99%

“…is the number of design variables; () i k x and () j k x are respectively the i -th and j -th components of the k -th sample point; k  is the correlation parameter to be estimated. The correlation parameter k  are optimized by maximizing the likelihood function in(4) …”

mentioning

confidence: 99%

See 2 more Smart Citations

Dynamic Co-Kriging Method Using Probability of Multi-Objective Constrained Improvement for Aerospace System Optimization

Zhang,

Dong,

Shi

2023

IEEE Access

Self Cite

View full text Add to dashboard Cite

To effectively solve the aerospace system multi-objective optimization problems with expensive black-box constraints, a novel dynamic Co-Kriging method (DCK) is proposed in this paper. Via taking full advantages of multi-fidelity simulation models, Co-Kriging metamodels are constructed for multiobjective optimization to efficiently obtain the pseudo Pareto frontier. And a probability of multi-objective constrained improvement (PMCI) function is developed to measure the potential improvements of optimality, feasibility, and uniformity for the Pareto frontier. During the optimization process, the pseudo Pareto frontier points with maximum PMCI are identified as the infill multi-fidelity samples to dynamically refine the Co-Kriging metamodels, which leads the search to the true Pareto frontier rapidly. A two-dimensional numerical example is employed to demonstrate the optimization capacity of DCK. Finally, a low-thrust GEO transfer trajectory optimization problem and a satellite structure optimization problem are investigated. The optimization results indicate that DCK performs better than the competitive algorithms in both computational cost and Pareto frontier exploration performance, which illustrates the effectiveness and practicality of the proposed method for solving real-world aerospace system multi-objective optimization problems.

show abstract

Section: A Research Background and Literature Reviewmentioning

confidence: 99%

“…A linear regression model of displacement damage dose is used to predict the radiation damage. More details of the multi-fidelity transfer models are given by [4].…”

Section: A Low-thrust Geo Transfer Trajectory Multi-objective Optimiz...mentioning

confidence: 99%

See 1 more Smart Citation

Dynamic Co-Kriging Method Using Probability of Multi-Objective Constrained Improvement for Aerospace System Optimization

Zhang,

Dong,

Shi

2023

IEEE Access

Self Cite

View full text Add to dashboard Cite

show abstract

“…In this class of methodologies, it is often that a Pareto front approximation is found with the help of a standard multi-objective optimizer (like the NSGA-II or MOPSO) while the objectives are modeled by multi-fidelity metamodels. Then different criteria are used to better resolve the Pareto front approximation and choose the solver fidelity for each iteration [38][39][40]. Similarly, Kontogiannis et al [41] used the Expected Improvement (EI) of each objective to find a non-dominated front of EIs with the help of a population-based multi-objective algorithm.…”

Section: Multi-fidelity Multi-objective Acquisition Functionmentioning

confidence: 99%

Multi-fidelity Multi-Objective Optimization of a High-Altitude Propeller

Mourousias

Malim

Marinus

et al. 2023

AIAA AVIATION 2023 Forum

View full text Add to dashboard Cite

show abstract

“…Ye et al 45 presented a Co-Kriging-based space reduction method for design optimization of large scale high-voltage devices. Multifidelity Kriging metamodels can thus make a trade-off between high prediction accuracy and low computational cost 46 by augmenting the small number of expensive HF samples with a number of cheap LF simulations.…”

Section: Introductionmentioning

confidence: 99%

Real‐time hybrid simulation with multi‐fidelity Co‐Kriging for global response prediction under structural uncertainties

Chen

Yang

Hou

et al. 2022

Earthq Engng Struct Dyn

View full text Add to dashboard Cite

Real‐time hybrid simulation (RTHS) provides an effective and efficient experimental technique to enable large‐ or full‐scale experiments to account for rate‐dependent behavior in size limited laboratories. Traditional practice of RTHS assumes deterministic substructure properties therefore could not account for structural uncertainties in global response prediction. This study explores the use of Co‐Kriging metamodeling for global response prediction under the presence of structural uncertainties. RTHS in laboratory is used as high‐fidelity (HF) modeling while computational simulation of the prototype structure under investigation is used as low‐fidelity (LF) modeling. Multifidelity modeling is integrated through Co‐Kriging to render accurate response prediction over the entire sample space of structural uncertainties. An entropy‐based adaptive strategy is used to sequentially determine the sampling points for HF RTHS and LF simulation. The proposed multifidelity Co‐Kriging approach is experimentally evaluated through RTHS of a single‐degree‐of‐freedom structure with a self‐centering viscous damper. The Co‐Kriging predicted maximum structural responses are further compared with validation tests. It is demonstrated that the Co‐Kriging can effectively reduce the number of RTHS tests in laboratory and significantly improve the metamodel accuracy for global prediction of structural response under uncertainties. The presented study presents an innovative way to further expand the capacity of RTHS for uncertainty quantification toward seismic hazard mitigation.

show abstract

Multi-fidelity and multi-objective optimization of low-thrust transfers with control strategy for all-electric geostationary satellites

Cited by 15 publications

References 30 publications

Dynamic Co-Kriging Method Using Probability of Multi-Objective Constrained Improvement for Aerospace System Optimization

Dynamic Co-Kriging Method Using Probability of Multi-Objective Constrained Improvement for Aerospace System Optimization

Multi-fidelity Multi-Objective Optimization of a High-Altitude Propeller

Real‐time hybrid simulation with multi‐fidelity Co‐Kriging for global response prediction under structural uncertainties

Contact Info

Product

Resources

About