Efficient sampling technique for optimization under uncertainty

Diwekar, Urmila M.; Kalagnanam, Jayant

doi:10.1002/aic.690430217

Cited by 232 publications

(103 citation statements)

References 32 publications

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“…It also provides a more refined approach compared to other optimisation methods such as desirability functions [9]and Shifted-Hammersley sampling [10].…”

Section: Multi-objective Genetic Algorithmmentioning

confidence: 99%

Optimal Design of Opening Ventilation Shaft by Kriging Metamodel Assisted Multi-objective Genetic Algorithm

Mukhtar¹,

Ng²,

Yusoff³

2017

IJMO

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Abstract-Ventilation shaft designs are the most effective devices used for ventilating underground shelter. A Kriging Metamodel assisted Multi-Objective Genetic Algorithm (MOGA) was utilised for the evaluation of an optimal design for the opening ventilation shaft, which improved the ventilation rate of a naturally-ventilated underground shelter. Computational Fluid Dynamics (CFD) was employed as a simulation tool, and the result was validated with experimental data from the previous literature. For the optimisation, three parameters were considered for the effectiveness of the ventilation rate. The generated results found an excellent performance of the strength correlation between parameters and the recommended optimised design. This revealed that an equal opening area has a better ventilation rate for naturally-ventilated underground shelters. Overall, these results can provide support selecting ventilation shaft opening areas in relation to the design of ventilation systems.

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“…It also provides a more refined approach compared to other optimisation methods such as desirability functions [9]and Shifted-Hammersley sampling [10].…”

Section: Multi-objective Genetic Algorithmmentioning

confidence: 99%

Optimal Design of Opening Ventilation Shaft by Kriging Metamodel Assisted Multi-objective Genetic Algorithm

Mukhtar¹,

Ng²,

Yusoff³

2017

IJMO

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“…Twenty-five data points were generated for each dimensionless group through HSS. 32,33 Table 3 gives details of the data. This was implemented in MATLAB.…”

Section: Problem Descriptionmentioning

confidence: 99%

“…32 Four types of spacefilling designs have been used more often in the literature: orthogonal arrays, Latin hypercube designs, Hammersley sequences, and uniform designs. 18 Hammersley sequence sampling (HSS) has been found to provide better uniformity than Latin hypercube designs for a multi dimensional 18,33 It is an example of a low-discrepancy sequence. The concept of discrepancy is applied to choose the appropriate quasi-Monte Carlo sequence, of which the Hammersley sequence is an example.…”

Section: Problem Descriptionmentioning

confidence: 99%

Development of Artificial Neural Network Based Metamodels for Inactivation of Anthrax Spores in Ventilated Spaces Using Computational Fluid Dynamics

Hoque

Farouk

Haas

2011

Journal of the Air & Waste Management Association

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Linear, quadratic, and artificial neural network (ANN)-based metamodels were developed for predicting the extent of anthrax spore inactivation by chlorine dioxide in a ventilated three-dimensional space over time from computational fluid dynamics model (CFD) simulation data. Dimensionless groups were developed to define the design space of the problem scenario. The Hammersley sequence sampling (HSS) method was used to determine the sampling points for the numerical experiments within the design space. A CFD model, comprised of multiple submodels, was applied to conduct the numerical experiments. Large eddy simulation (LES) with the Smagorinsky subgridscale model was applied to compute the airflow. Anthrax spores were modeled as a dispersed solid phase using the Lagrangian treatment. The disinfectant transport was calculated by solving a mass transport equation. Kinetic decay constants were included for spontaneous decay of the disinfectant and for the reaction of the disinfectant with the surfaces of the three-dimensional space. To enhance the mixing of the disinfectant with the room air, a momentum source was included in the simulation. An inactivation rate equation accounted for the reaction between the spores and the disinfectant. The ANN-based metamodels were most successful in predicting the number of viable bioaerosols remaining in an arbitrary enclosed space. Sensitivity analysis showed that the mass fraction of the disinfectant, inactivation rate constant, and contact time had the most influence on the inactivation of the spores.

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“…Diwekar and Kalagnanam (1997) proposed this low-discrepancy sequence that can easily be expanded to higher dimensions. They provide a description of an algorithm that constructs the sequence, which has been implemented in this work.…”

Section: Brazilian Journal Of Chemical Engineeringmentioning

confidence: 99%

Supporting chemical process design under uncertainty

Wechsung

Oldenburg

et al. 2010

Braz. J. Chem. Eng.

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-A major challenge in chemical process design is to make design decisions based on partly incomplete or imperfect design input data. Still, process engineers are expected to design safe, dependable and cost-efficient processes under these conditions. The complexity of typical process models limits intuitive engineering estimates to judge the impact of uncertain parameters on the proposed design. In this work, an approach to quantify the effect of uncertainty on a process design in order to enhance comparisons among different designs is presented. To facilitate automation, a novel relaxation-based heuristic to differentiate between numerical and physical infeasibility when simulations do not converge is introduced. It is shown how this methodology yields more details about limitations of a studied process design.

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Efficient sampling technique for optimization under uncertainty

Cited by 232 publications

References 32 publications

Optimal Design of Opening Ventilation Shaft by Kriging Metamodel Assisted Multi-objective Genetic Algorithm

Optimal Design of Opening Ventilation Shaft by Kriging Metamodel Assisted Multi-objective Genetic Algorithm

Development of Artificial Neural Network Based Metamodels for Inactivation of Anthrax Spores in Ventilated Spaces Using Computational Fluid Dynamics

Supporting chemical process design under uncertainty

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