A Comparison between Monte Carlo and Evidence Theory Approaches for Technology Portfolio Planning

Akram, Farooq; Prior, Matthew A.; Mavris, Dimitri N.

doi:10.2514/6.2011-1412

Cited by 3 publications

(3 citation statements)

References 4 publications

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“…An even more general method than D-S theory is Dezer-Smarandache (DSm) theory, which uses new models for the FD and new rules of combination to take into account paradoxical and uncertain information. 108 Akram and Mavris use D-S theory for a technology valuation process / technology portfolio analysis, [109][110][111][112][113] and conclude it is better capable of providing insight into epistemic uncertainties, as compared to deterministic or probability theory methods. Additionally they state: "It reduces the number of assumptions during the elicitation process, when experts are forced to assign probability distributions to their opinions without sufficient knowledge."…”

Section: B Uncertainty Modelingmentioning

confidence: 99%

“…In a comparison with Monte Carlo approaches it results that both methods scale similarly, but D-S theory is an order of magnitude quicker. 110 Furthermore, D-S theory can be used for the quantification of model form uncertainty and model discrepancy. 93,114 Comparing the theories presented, it can be concluded that probability theory provides the least conservative results.…”

Section: B Uncertainty Modelingmentioning

confidence: 99%

See 1 more Smart Citation

Uncertainty-Based Design Optimization and Technology Evaluation: A Review

Roelofs

Vos

2018

2018 AIAA Aerospace Sciences Meeting

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Evaluation and assessment of novel technologies for aerospace applications is essential for business strategy and decision making regarding development efforts. Since technology is evaluated in the conceptual design phase and little is known about the technology, large uncertainty is present. This uncertainty needs to be accurately assessed and managed. To investigate the research efforts that have been performed to perform technology evaluation under uncertainty, a literature review was conducted, focusing on methods and modeling approaches to assign and quantify these uncertainties. It is found that probability theory is still the most popular theory for representing uncertainty. Polynomial Chaos Expansions and Stochastic Collocation methods are gaining popularity for propagating uncertainty through a modeling environment, but Monte Carlo Simulations are still widely used. Commonly, surrogate models are used to reduce computational effort. Other efforts focus on the use of multifidelity approaches to reduce computational effort when high-fidelity methods are required. Four issues that may need to be addressed in future research were identified.

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Section: B Uncertainty Modelingmentioning

confidence: 99%

Section: B Uncertainty Modelingmentioning

confidence: 99%

Uncertainty-Based Design Optimization and Technology Evaluation: A Review

Roelofs

Vos

2018

2018 AIAA Aerospace Sciences Meeting

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“…Even though all of the mentioned research uses probability theory, there are alternatives: Bayesian theory, possibility theory [14], interval analysis [15,16], and Dempster-Schafer (D-S) theory [17][18][19]. Akram et al [20][21][22][23][24] use D-S theory for a technology valuation process-technology portfolio analysis. While there are strong arguments against theories other than (subjective) probability theory [25,26], the most relevant argument in the current context is that only Bayesian theory supports the inverse problem, besides probability theory.…”

Section: Selecting Technologies In Aircraft Conceptualmentioning

confidence: 99%

Selecting Technologies in Aircraft Conceptual Design Using Probabilistic Inversion

Roelofs

Kurowicka

Vos

2021

Journal of Aircraft

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A collocation interval analysis method for interval structural parameters and stochastic excitation

Qiu

2011

Sci. China Phys. Mech. Astron.

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Uncertainty propagation, one of the structural engineering problems, is receiving increasing attention owing to the fact that most significant loads are random in nature and structural parameters are typically subject to variation. In the study, the collocation interval analysis method based on the first class Chebyshev polynomial approximation is presented to investigate the least favorable responses and the most favorable responses of interval-parameter structures under random excitations. Compared with the interval analysis method based on the first order Taylor expansion, in which only information including the function value and derivative at midpoint is used, the collocation interval analysis method is a non-gradient algorithm using several collocation points which improve the precision of results owing to better approximation of a response function. The pseudo excitation method is introduced to the solving procedure to transform the random problem into a deterministic problem. To validate the procedure, we present numerical results concerning a building under seismic ground motion and aerofoil under continuous atmosphere turbulence to show the effectiveness of the collocation interval analysis method. uncertainty propagation, interval analysis, Taylor series expansion, Chebyshev polynomial, Pseudo excitation method PACS number(s): 02.30.Mv, 02.50Ey, 02.60Gf, 02.70.Jn Citation:Qi W C, Qiu Z P. A collocation interval analysis method for interval structural parameters and stochastic excitation.

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A Comparison between Monte Carlo and Evidence Theory Approaches for Technology Portfolio Planning

Cited by 3 publications

References 4 publications

Uncertainty-Based Design Optimization and Technology Evaluation: A Review

Uncertainty-Based Design Optimization and Technology Evaluation: A Review

Selecting Technologies in Aircraft Conceptual Design Using Probabilistic Inversion

A collocation interval analysis method for interval structural parameters and stochastic excitation

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