Robust Design Simulation: A Probabilistic Approach to Multidisciplinary Design

Mavris, Dimitri N.; Bandte, Oliver; DeLaurentis, Daniel A.

doi:10.2514/2.2437

Cited by 117 publications

(52 citation statements)

References 5 publications

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“…Modeling and simulation in the financial sector has enabled real-time evaluation of economic performance measures using a mathematical model of the particular business sector to predict future performance and to optimize financial return [5]. In the aerospace industry, modeling and simulation is used to design new airframes, which eliminates the need for multiple physical prototypes constructed at intermediate points during design and reduces the time from concept to production [6]. In both cases, mathematical modeling and simulation provide a quantitative framework to capture our conceptual understanding of the modeled process and interpret heterogeneous data acquired from the process.…”

Section: Introductionmentioning

confidence: 99%

A Quantitative Systems Pharmacology Perspective on Cancer Immunology

Byrne-Hoffman

Klinke

2015

Processes

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Abstract:The return on investment within the pharmaceutical industry has exhibited an exponential decline over the last several decades. Contemporary analysis suggests that the rate-limiting step associated with the drug discovery and development process is our limited understanding of the disease pathophysiology in humans that is targeted by a drug. Similar to other industries, mechanistic modeling and simulation has been proposed as an enabling quantitative tool to help address this problem. Moreover, immunotherapies are transforming the clinical treatment of cure cancer and are becoming a major segment of the pharmaceutical research and development pipeline. As the clinical benefit of these immunotherapies seems to be limited to subset of the patient population, identifying the specific defect in the complex network of interactions associated with host immunity to a malignancy is a major challenge for expanding the clinical benefit. Understanding the interaction between malignant and immune cells is inherently a systems problem, where an engineering perspective may be helpful. The objective of this manuscript is to summarize this quantitative systems perspective, particularly with respect to developing immunotherapies for the treatment of cancer. OPEN ACCESSProcesses 2015, 3 236

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Section: Introductionmentioning

confidence: 99%

A Quantitative Systems Pharmacology Perspective on Cancer Immunology

Byrne-Hoffman

Klinke

2015

Processes

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“…The aim of the present work is to analyze the combined effects of considering several disciplines under uncertainty in ship design problems, developing a MRDO procedure that utilizes efficient methods for uncertainty analysis and encompasses the features of the MDO framework. Theory and applications of MDO subject to uncertainty may be found in, e.g., , Chen (2000a, b, 2002), Giassi et al (2004), Mavris et al (1999), Smith and Mahadevan (2005), and Sues et al (1995).…”

Section: Introductionmentioning

confidence: 99%

Hydroelastic optimization of a keel fin of a sailing boat: a multidisciplinary robust formulation for ship design

Diez

Peri

Fasano

et al. 2012

Struct Multidisc Optim

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The paper presents a formulation for multidisciplinary design optimization of vessels, subject to uncertain operating conditions. The formulation couples the multidisciplinary design analysis with the Bayesian approach to decision problems affected by uncertainty. In the present context, the design specifications are no longer given in terms of a single operating design point, but in terms of probability density function of the operating scenario. The optimal configuration is that which maximizes the performance expectation over the uncertain parameters variation. In this sense, the optimal solution is "robust" within the stochastic scenario assumed. Theoretical and numerical issues are addressed and numerical results in the hydroelastic optimization of a keel fin of a sailing yacht are presented.

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“…the sensitivity of the design. [6,7,23] In a robust optimisation problem, a design with maximum/minimum 'mean on target' and 'minimised variance' under uncertainties is sought for. Basically, the variance of the structural performance can be roughly described by its standard deviation (SD) or RSD.…”

Section: Objective Functionmentioning

confidence: 99%

“…Robust optimisation, which is one of the methods for solving a probabilistic design problem, [6] is an optimisation theory that addresses optimisation problems in which a certain measure of robustness is sought against uncertainty that can be represented as deterministic variability in the values of the parameters of the problem itself and/or its solution. That is, the optimisation process considers uncertainties in the evaluation of the objective and constraint functions.…”

Section: Introductionmentioning

confidence: 99%

Robust optimisation of railway crossing geometry

Wan

Markine

Dollevoet

2016

Vehicle System Dynamics

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This paper presents a methodology for improving the crossing (frog) geometry through the robust optimisation approach, wherein the variability of the design parameters within a prescribed tolerance is included in the optimisation problem. Here, the crossing geometry is defined by parameterising the B-spline represented crosssectional shape and the longitudinal height profile of the nose rail. The dynamic performance of the crossing is evaluated considering the variation of wheel profiles and track alignment. A multipoint approximation method (MAM) is applied in solving the optimisation problem of minimising the contact pressure during the wheel-rail contact and constraining the location of wheel transition at the crossing. To clarify the difference between the robust optimisation and the normal deterministic optimisation approaches, the optimisation problems are solved in both approaches. The results show that the deterministic optimum fails under slight change of the design variables; the robust optimum, however, has improved and robust performance. ARTICLE HISTORY

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Robust Design Simulation: A Probabilistic Approach to Multidisciplinary Design

Cited by 117 publications

References 5 publications

A Quantitative Systems Pharmacology Perspective on Cancer Immunology

A Quantitative Systems Pharmacology Perspective on Cancer Immunology

Hydroelastic optimization of a keel fin of a sailing boat: a multidisciplinary robust formulation for ship design

Robust optimisation of railway crossing geometry

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