Multi-objective optimization and decision analysis of a system based on biomass fueled SOFC using couple method of entropy/VIKOR

The US Government is the world’s de facto provider of space object cataloging data, but it is challenged to maintain pace in an increasingly complex space environment. This work advances a multi-disciplinary approach to better understand and evaluate an underexplored solution recommended by national policy in which current collection capabilities are augmented with non-traditional sensors. System architecting techniques and extant literature identified likely needs, performance measures, and potential contributors to a conceptualized Augmented Network (AN). Multiple hypothetical architectures of ground- and space-based telescopes with representative capabilities were modeled and simulated on four separate days throughout the year, then evaluated against performance measures and constraints using Multi-Objective Optimization. Decision analysis and Pareto optimality identified a small, diverse set of high-performing architectures while preserving design flexibility. Should decision-makers adopt the AN approach, this research effort indicates (1) a threefold increase in average capacity, (2) a 55% improvement in coverage, and (3) a 2.5-h decrease in the average maximum time a space object goes unobserved.

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An Effective Couple Method for Reliability-Based Multi-Objective Optimization of Truss Structures with Static and Dynamic Constraints

International Journal of Computational Methods ◽

10.1142/s0219876219500166 ◽

2019 ◽

Vol 17 (06) ◽

pp. 1950016 ◽

Cited By ~ 4

Author(s):

T. Vo-Duy ◽

D. Duong-Gia ◽

V. Ho-Huu ◽

T. Nguyen-Thoi

Keyword(s):

Cross Section ◽

Optimization Problems ◽

Computational Cost ◽

Probabilistic Constraints ◽

Truss Structures ◽

Multi Objective Optimization ◽

Nsga Ii ◽

Dynamic Constraints ◽

Multi Objective ◽

Couple Method

This paper proposes an effective couple method for solving reliability-based multi-objective optimization problems of truss structures with static and dynamic constraints. The proposed coupling method integrates a single-loop deterministic method (SLDM) into the nondominated sorting genetic algorithm II (NSGA-II) algorithm to give the so-called SLDM-NSGA-II. Thanks to the advantage of SLDM, the probabilistic constraints are treated as approximating deterministic constraints. And therefore the reliability-based multi-objective optimization problems can be transformed into the deterministic multi-objective optimization problems of which the computational cost is reduced significantly. In these reliability-based multi-objective optimization problems, the conflicting objective functions are to minimize the weight and the displacements of the truss. The design variables are cross-section areas of the bars and contraints include static and dynamic constraints. For reliability analysis, the effect of uncertainty of parameters such as force, added mass in the nodes, material properties and cross-section areas of the bars are taken into account. The effectiveness and reliability of the proposed method are demonstrated through three benchmark-type truss structures including a 10-bar planar truss, a 72-bar spatial truss and a 200-bar planar truss. Moreover, the influence of parameters on the reliability-based Pareto optimal fronts is also carried out.

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Augmenting the space domain awareness ground architecture via decision analysis and multi-objective optimization

Journal of Defense Analytics and Logistics ◽

10.1108/jdal-11-2020-0023 ◽

2021 ◽

Vol ahead-of-print (ahead-of-print) ◽

Author(s):

Albert Vasso ◽

Richard Cobb ◽

John Colombi ◽

Bryan Little ◽

David Meyer

Keyword(s):

Decision Analysis ◽

Performance Measures ◽

Low Cost ◽

Fold Increase ◽

Multi Objective Optimization ◽

Space Object ◽

Content Type ◽

Multi Objective ◽

Maximum Time ◽

Average Maximum

Purpose The US Government is challenged to maintain pace as the world’s de facto provider of space object cataloging data. Augmenting capabilities with nontraditional sensors present an expeditious and low-cost improvement. However, the large tradespace and unexplored system of systems performance requirements pose a challenge to successful capitalization. This paper aims to better define and assess the utility of augmentation via a multi-disiplinary study. Design/methodology/approach Hypothetical telescope architectures are modeled and simulated on two separate days, then evaluated against performance measures and constraints using multi-objective optimization in a heuristic algorithm. Decision analysis and Pareto optimality identifies a set of high-performing architectures while preserving decision-maker design flexibility. Findings Capacity, coverage and maximum time unobserved are recommended as key performance measures. A total of 187 out of 1017 architectures were identified as top performers. A total of 29% of the sensors considered are found in over 80% of the top architectures. Additional considerations further reduce the tradespace to 19 best choices which collect an average of 49–51 observations per space object with a 595–630 min average maximum time unobserved, providing redundant coverage of the Geosynchronous Orbit belt. This represents a three-fold increase in capacity and coverage and a 2 h (16%) decrease in the maximum time unobserved compared to the baseline government-only architecture as-modeled. Originality/value This study validates the utility of an augmented network concept using a physics-based model and modern analytical techniques. It objectively responds to policy mandating cataloging improvements without relying solely on expert-derived point solutions.

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