Computational Process Management for Aircraft Conceptual Design

Balachandran, L. K.; Fantini, Paolo; Guenov, Marin D.

doi:10.2514/6.2007-7817

Cited by 4 publications

(3 citation statements)

References 3 publications

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“…In Chen et al 8 , it was demonstrated that the standard deviation of an input variable of a default workflow (original sequencing) can be swapped with one of the standard deviations of the original output variables. This technique is based on a computational workflow management (CWM) method, developed by Balachandran et al 17 and Guenov et al 18 , where the user is able to specify the variables he or she wants to swap, after which the reversed workflow is created. The CWM method enables the automatic formulation of the reversed workflow into an optimization problem and it is solved using a Gauss-Newton algorithm.…”

Section: Workflow Reversalmentioning

confidence: 99%

Interactive Uncertainty Allocation and Trade-off at Early-stage Aircraft Computational Design

Molina-Cristóbal

Guenov

Riaz

et al. 2018

2018 AIAA Non-Deterministic Approaches Conference

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A common probabilistic approach to uncertainty allocation is to assign acceptable variability in the sources of uncertainty, such that pre-specified probabilities of meeting performance constraints are satisfied. However, the computational cost of obtaining the associated trade-offs increases significantly when more sources of uncertainty and more outputs are considered. Consequently, visualizing and exploring the trade-off space becomes increasingly difficult, which, in turn, makes the decision-making process cumbersome for practicing designers. To tackle this problem, proposed is a parameterization of the input probability distribution functions, to account for several statistical moments. This, combined with efficient uncertainty propagation and inverse computation techniques, results in a computational system which performs order(s) of magnitude faster, compared with a combination of Monte Carlo Simulation and optimization techniques. Also, to aid decisionmaking regarding the potential combinations of uncertainty allocation, enablers for visualizing the trade space are proposed. The combined approach is demonstrated by means of a representative aircraft thermal system integration example.

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Section: Workflow Reversalmentioning

confidence: 99%

Interactive Uncertainty Allocation and Trade-off at Early-stage Aircraft Computational Design

Molina-Cristóbal

Guenov

Riaz

et al. 2018

2018 AIAA Non-Deterministic Approaches Conference

View full text Add to dashboard Cite

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“…1. A brief summary of the flow chart steps is given below while a detailed description of the associated techniques is presented in Guenov et al [9], Balachandran et al [1] and Balachandran [2].…”

Section: Computational Workflow Managementmentioning

confidence: 99%

“…The methods follow the tracks laid by the NBI, PP-based and NC methods, combining the various approaches and the knowledge gained from them. Its formulation is as follows: min f(x) subject to K inequality constraints: g k (x) £ 0, k = 1,2,…,K P equality constraints: h p (x) = 0, p = 1,2,…,P subject to the additional (1) where v j = (l j / l i ) e l -e j for j ¹ l, v l = m l e l / l l -m, e j ∀j are the base vectors of the coordinate system, l is the unit vector orthogonal to the utopia plane, n c is a fraction of the Euclidean distance between two contiguous utopia plane points, m Î  M is a vector such that m i = 1 ∀i and m l = 0 and , L U x x are the lower and upper bounds for the input variables, respectively. The formulation is similar to the NC method, where M -1 constraints are used for building M -1 hyper-planes which confine the solution to a region of the criterion space.…”

Section: Multiobjective Optimization -Generating a Well-distributed Smentioning

confidence: 99%

Multidisciplinary Design Optimization Framework for the Pre Design Stage

Guenov

Fantini

Balachandran

et al. 2010

J Intell Robot Syst

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Presented is a novel framework for performing flexible computational design studies at preliminary design stage. It incorporates a workflow management device (WMD) and a number of advanced numerical treatments, including multi-objective optimization, sensitivity analysis and uncertainty management with emphasis on design robustness. The WMD enables the designer to build, understand, manipulate and share complex processes and studies. Results obtained after applying the WMD on various test cases, showed a significant reduction of the iterations required for the convergence of the computational system. The tests results also demonstrated the capabilities of the advanced treatments as follows:-The novel procedure for global multi-objective optimization has the unique ability to generate well-distributed Pareto points on both local and global Pareto fronts simultaneously.-The global sensitivity analysis procedure is able to identify input variables whose range of variation does not have significant effect on the objectives and constraints. It was demonstrated that fixing such variables can greatly reduce the computational time while retaining a satisfactory quality of the resulting Pareto front.-The novel derivative-free method for uncertainty propagation, which was proposed for enabling multi-objective robust optimization, delivers a higher accuracy compared to the one based on function linearization, without altering significantly the cost of the single optimization step.The work demonstrated for the first time that such capabilities can be used in a coordinated way to enhance the efficiency of the computational process and the effectiveness of the decision making at preliminary design stage.

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Composition, Management, and Exploration of Computational Studies at Early Design Stage

Guenov¹,

Nunez²,

Molina-Cristóbal³

et al. 2014

Computational Intelligence in Aerospace Sciences

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I. INTRODUCTIONExploring the design space with the aim of finding feasible, let alone optimal solutions is not trivial. Essentially, this involves finding a solution to an inverse problem. That is, a relatively small number of (presumably) known requirements and performance characteristics need to be mapped onto a much larger number (space) of unknown design parameters, subject to constraints. The inverse problem, of course, is generic and forms parts of the study of complex systems, from ecology [1,2] to engineering design [3], where the integration of an expanding set of (validated) numerical methods and models is used to investigate scenarios and to predict outcomes.The work presented here lies within this context, with a particular emphasis on computational intelligence methods and tools for interactive design space exploration. The scope is restricted (but not limited) to conceptual computational design where a complex product, for example aircraft, ship, and so on, is described by a large number of computational models related to geometry parameterization, performance, cost, and so forth. It is assumed that the computational models are black-boxes (e.g., compiled code) which contain low-fidelity code (e.g., parametric/empirical equations) and/or surrogate models. This assumption reflects the realities of the commercial world in which the content of a model is usually a closely guarded intellectual property.There are a number of challenges associated with such complex and relatively little studied computational systems:

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Computational Process Management for Aircraft Conceptual Design

Cited by 4 publications

References 3 publications

Interactive Uncertainty Allocation and Trade-off at Early-stage Aircraft Computational Design

Interactive Uncertainty Allocation and Trade-off at Early-stage Aircraft Computational Design

Multidisciplinary Design Optimization Framework for the Pre Design Stage

Composition, Management, and Exploration of Computational Studies at Early Design Stage

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