Q2S2: Merging Qualitative Information in Sequential DOE

2007 ◽  
Author(s):  
Rahul Rai ◽  
Matthew I. Campbell
Keyword(s):  
Sequential Sampling ◽  
Sampling Strategy ◽  
Simulation Models ◽  
Test Functions ◽  
Qualitative Information ◽  
Decision Variables ◽  
Multiple Fidelity ◽  
Computer Simulation Models ◽  
User Knowledge ◽  
Different Sources

Sequential sampling refers to a set of design of experiment (DOE) methods where the next sample point is determined by information from previous experiments. This paper introduces a qualitative and quantitative sequential sampling (Q2S2) technique, in which optimization and user knowledge is used to guide the efficient choice of sample points. This method combines information from multiple fidelity sources including computer simulation models of the product, first principals involved in design, and designer’s qualitative intuitions about the design. Both quantitative and qualitative information from different sources are merged together to arrive at a new sampling strategy. This is accomplished by introducing the concept of a confidence function, C, which is represented as a field that is a function of the decision variables, x, and the performance parameter, f. We compare the sampling plans generated by Q2S2 to previously known sample plans on five test functions using various metrics. In each case, the performance of Q2S2 is highly encouraging.

scholarly journals Qualitative and Quantitative Sequential Sampling

2006 ◽  
Author(s):  
Rahul Rai ◽  
Matthew I. Campbell
Keyword(s):  
Design Of Experiments ◽  
Sequential Sampling ◽  
Sampling Strategy ◽  
Simulation Models ◽  
Decision Variables ◽  
Previous Sample ◽  
Sample Points ◽  
Computer Simulation Models ◽  
User Knowledge ◽  
Different Sources

This paper introduces a method for sequentially determining experiments in a “design of experiments” where optimization and user knowledge are used to guide the efficient choice of sample points. Typical approaches to the design of experiments involves determining the sample points all at once prior to any experimentation, or sequentially based on the results of previous sample points. This method combines information from multiple fidelity sources including actual physical experiment, computer simulation models of the product, first principals involved in design and designer’s qualitative intuitions about the design. Both quantitative and qualitative information from different sources are merged together to arrive at new sampling strategy. This is accomplished by introducing the concept of confidence, C, which is represented as a field that is a function of the decision variables, x, and the performance parameter, f. The advantages of the approach are demonstrated using different example cases.

Q2S2: A New Methodology for Merging Quantitative and Qualitative Information in Experimental Design

2008 ◽  
Vol 130 (3) ◽  
Author(s):  
Rahul Rai ◽  
Matthew Campbell
Keyword(s):  
Experimental Design ◽  
Mechanical System ◽  
Sequential Sampling ◽  
Sampling Strategy ◽  
Simulation Models ◽  
Qualitative Information ◽  
Multiple Sources ◽  
Physical Experiments ◽  
Computer Simulation Models ◽  
User Knowledge

Sequential sampling refers to a set of experimental design methods where the next sample point is determined by information from previous experiments. This paper introduces a new sequential sampling method where optimization and user knowledge are used to guide the efficient choice of sample points. This method combines information from multiple sources of varying fidelity including actual physical experiments, computer simulation models of the product, and first principles involved in design and designer’s qualitative intuition about the design. Both quantitative and qualitative information from different sources are merged together to arrive at a new sampling strategy. This is accomplished by introducing the concept of a confidence function C, which is represented as a field that is a function of the decision variables x and the performance parameter f. The advantages of the approach are demonstrated using different example cases. The examples include design of a bistable microelectro mechanical system switch, a complex and relevant mechanical system.

Laboratory study and computer model development related to gross solids' movement in combined sewers

1996 ◽  
Vol 33 (9) ◽  
pp. 39-47 ◽  
Author(s):  
John W. Davies ◽  
Yanli Xu ◽  
David Butler
Keyword(s):  
Computer Simulation ◽  
Pipe Flow ◽  
Model Development ◽  
Simulation Models ◽  
Laboratory Studies ◽  
Pilot Model ◽  
Sewer Systems ◽  
Flow Quality ◽  
Computer Simulation Models ◽  
Theoretical Considerations

Significant problems in sewer systems are caused by gross solids, and there is a strong case for their inclusion in computer simulation models of sewer flow quality. The paper describes a project which considered methods of modelling the movement of gross solids in combined sewers. Laboratory studies provided information on advection and deposition of typical gross solids in part-full pipe flow. Theoretical considerations identified aspects of models for gross solids that should differ from those for dissolved and fine suspended pollutants. The proposed methods for gross solids were incorporated in a pilot model, and their effects on simple simulations were considered.

scholarly journals Investigating molecular mechanisms of ageing cartilage using computer simulation models

2014 ◽  
Vol 22 ◽  
pp. S57-S58
Author(s):  
W. Hui ◽  
D.A. Young ◽  
A.D. Rowan ◽  
T.E. Cawston ◽  
C.J. Proctor
Keyword(s):  
Computer Simulation ◽  
Molecular Mechanisms ◽  
Simulation Models ◽  
Computer Simulation Models
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