scholarly journals Qualitative and Quantitative Sequential Sampling

Author(s):  
Rahul Rai ◽  
Matthew I. Campbell

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.

Author(s):  
Rahul Rai ◽  
Matthew I. Campbell

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.


2008 ◽  
Vol 130 (3) ◽  
Author(s):  
Rahul Rai ◽  
Matthew Campbell

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.


2017 ◽  
Vol 34 (8) ◽  
pp. 2547-2564 ◽  
Author(s):  
Leshi Shu ◽  
Ping Jiang ◽  
Li Wan ◽  
Qi Zhou ◽  
Xinyu Shao ◽  
...  

Purpose Metamodels are widely used to replace simulation models in engineering design optimization to reduce the computational cost. The purpose of this paper is to develop a novel sequential sampling strategy (weighted accumulative error sampling, WAES) to obtain accurate metamodels and apply it to improve the quality of global optimization. Design/methodology/approach A sequential single objective formulation is constructed to adaptively select new sample points. In this formulation, the optimization objective is to select a sample point with the maximum weighted accumulative predicted error obtained by analyzing data from previous iterations, and a space-filling criterion is introduced and treated as a constraint to avoid generating clustered sample points. Based on the proposed sequential sampling strategy, a two-step global optimization approach is developed. Findings The proposed WAES approach and the global optimization approach are tested in several cases. A comparison has been made between the proposed approach and other existing approaches. Results illustrate that WAES approach performs the best in improving metamodel accuracy and the two-step global optimization approach has a great ability to avoid local optimum. Originality/value The proposed WAES approach overcomes the shortcomings of some existing approaches. Besides, the two-step global optimization approach can be used for improving the optimization results.


1996 ◽  
Vol 33 (9) ◽  
pp. 39-47 ◽  
Author(s):  
John W. Davies ◽  
Yanli Xu ◽  
David Butler

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.


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

Sign in / Sign up

Export Citation Format

Share Document