An accelerated experimental design algorithm

1982 ◽  
pp. 267-275
Author(s):  
T. G. Robertazzi ◽  
S. C. Schwartz
Keyword(s):  
Experimental Design ◽  
Design Algorithm

scholarly journals Accelerating Kinetic Parameter Identification by Extracting Information from Transient Data: A Hydroprocessing Study Case

Catalysts ◽  
2020 ◽  
Vol 10 (4) ◽  
pp. 361
Author(s):  
Ngoc-Yen-Phuong Cao ◽  
Benoit Celse ◽  
Denis Guillaume ◽  
Isabelle Guibard ◽  
Joris W. Thybaut
Keyword(s):  
Steady State ◽  
Experimental Design ◽  
Kinetic Model ◽  
Kinetic Modeling ◽  
Sufficient Data ◽  
Design Algorithm ◽  
State Data ◽  
Transient Data ◽  
Study Case ◽  
The One

Hydroprocessing reactions require several days to reach steady-state, leading to long experimentation times for collecting sufficient data for kinetic modeling purposes. The information contained in the transient data during the evolution toward the steady-state is, at present, not used for kinetic modeling since the stabilization behavior is not well understood. The present work aims at accelerating kinetic model construction by employing these transient data, provided that the stabilization can be adequately accounted for. A comparison between the model obtained against the steady-state data and the one after accounting for the transient information was carried out. It was demonstrated that by accounting for the stabilization, combined with an experimental design algorithm, a more robust and faster manner was obtained to identify kinetic parameters, which saves time and cost. An application was presented in hydrodenitrogenation, but the proposed methodology can be extended to any hydroprocessing reaction.

Stag Monotone Experimental Design Algorithm (SMEDAL)

1973 ◽  
Vol 20 (2) ◽  
pp. 214-220
Author(s):  
Alexander Morgan
Keyword(s):  
Experimental Design ◽  
Design Algorithm

scholarly journals Bayesian Experimental Design of Cyber-Physical Tests for Hydrodynamic Model Calibration

2021 ◽  
Author(s):  
Giuseppe Abbiati ◽  
Thomas Sauder
Keyword(s):  
Experimental Design ◽  
Transfer Functions ◽  
Low Frequency ◽  
Mooring System ◽  
Empirical Estimation ◽  
Design Algorithm ◽  
Frequency Wave ◽  
Laboratory Setup ◽  
Hydrodynamic Damping ◽  
Physical Tests

An application of cyber-physical testing to the empirical estimation of difference-frequency quadratic transfer functions is presented. As an alternative to today's procedure based on hydrodynamic tests with broad-banded or realistic (e.g., JONSWAP) wave spectra, tests in bichromatic waves are considered. The laboratory setup is the one developed by Sauder \& Tahchiev (2020) that enables magnifying the sensitivity of the floater response to the low-frequency wave loading by adjusting the stiffness and damping parameters of a virtual soft mooring system. Bayesian experimental design is proposed to optimize the selection of the control variables (frequencies in the bichromatic wave and properties of the virtual mooring system) for a batch of cyber-physical tests. The experimental design algorithm is based on the recent work of Huan \& Marzouk (2013). In a virtual yet realistic case study using an uncertain parametric quadratic transfer function, we demonstrate how the uncertainty of its describing parameters and other calibration parameters (low-frequency added mass and hydrodynamic damping) can be reduced. Results indicate that the proposed procedure has the potential for reducing experimental cost for calibration of hydrodynamic models.

Optimal Experimental Design of Human Appraisals for Modeling Consumer Preferences in Engineering Design

2008 ◽  
Author(s):  
Christopher Hoyle ◽  
Wei Chen ◽  
Bruce Ankenman ◽  
Nanxin Wang
Keyword(s):  
Experimental Design ◽  
Engineering Design ◽  
Model Building ◽  
Consumer Preferences ◽  
Information Matrix ◽  
Product Attributes ◽  
Design Algorithm ◽  
Split Plot ◽  
Demographic Attributes ◽  
Plot Structure

Human appraisals are becoming increasingly important in the design of engineering systems to link engineering design attributes to customer preferences. Human appraisals are used to assess consumers’ opinions of a given product design, and are unique in that the experiment response is a function of both the product attributes and the respondents’ demographic attributes. The design of a human appraisal is characterized as a split-plot design, in which the respondent demographic attributes form the whole-plot factors while the product attributes form the split-plot factors. The experiments are also characterized by random block effects, in which the design configurations evaluated by a single respondent form a block. An experimental design algorithm is needed for human appraisal experiments because standard experimental designs often do not meet the needs of these experiments. In this work, an algorithmic approach to identify the optimal design for a human appraisal experiment is developed, which considers the effects of respondent fatigue and the block and split-plot structure of such a design. The developed algorithm seeks to identify the experimental design which maximizes the determinant of the Fisher Information Matrix, labeled as the D-criterion of a given design. The algorithm is derived assuming an ordered logit model will be used to model the rating responses. The advantages of this approach over competing approaches for minimizing the number of appraisal experiments and model-building efficiency are demonstrated using an automotive interior package human appraisal as an example.

scholarly journals Bayesian Experimental Design of Cyber-Physical Tests for Hydrodynamic Model Calibration

2021 ◽  
Author(s):  
Giuseppe Abbiati ◽  
Thomas Sauder
Keyword(s):  
Experimental Design ◽  
Transfer Functions ◽  
Low Frequency ◽  
Mooring System ◽  
Empirical Estimation ◽  
Design Algorithm ◽  
Frequency Wave ◽  
Laboratory Setup ◽  
Hydrodynamic Damping ◽  
Physical Tests

Abstract An application of cyber-physical testing to the empirical estimation of difference-frequency quadratic transfer functions is presented. As an alternative to today’s procedure based on hydrodynamic tests with broad-banded or realistic (e.g., JONSWAP) wave spectra, tests in bichromatic waves are considered. The laboratory setup is the one developed by Sauder & Tahchiev (2020) that enables magnifying the sensitivity of the floater response to the low-frequency wave loading by adjusting the stiffness and damping parameters of a virtual soft mooring system. Bayesian experimental design is proposed to optimize the selection of the control variables (frequencies in the bichromatic wave and properties of the virtual mooring system) for a batch of cyber-physical tests. The experimental design algorithm is based on the recent work of Huan & Marzouk (2013). In a virtual yet realistic case study using an uncertain parametric quadratic transfer function, we demonstrate how the uncertainty of its describing parameters and other calibration parameters (low-frequency added mass and hydrodynamic damping) can be reduced. Results indicate that the proposed procedure has the potential for reducing experimental cost for calibration of hydrodynamic models.

Experimental design: Electrical resistivity data sets that provide optimum subsurface information

Geophysics ◽  
2004 ◽  
Vol 69 (1) ◽  
pp. 120-139 ◽  
Author(s):  
Peter Stummer ◽  
Hansruedi Maurer ◽  
Alan G. Green
Keyword(s):  
Electrical Resistivity ◽  
Experimental Design ◽  
Design Procedure ◽  
Data Sets ◽  
Electrode Arrays ◽  
Design Algorithm ◽  
Comprehensive Data ◽  
Data Points ◽  
Depth Ranges ◽  
2D Resistivity

Although multielectrode electrical‐resistivity systems have been commercially available for more than a decade, resistivity imaging of the subsurface continues to be based on data sets recorded using one or more of the standard electrode arrays (e.g., the Wenner or conventional dipole‐dipole array). To exploit better the full capabilities of multielectrode acquisition systems, we have developed an experimental design procedure to identify suites of electrode configurations that provide subsurface information according to predefined optimization criteria. The experimental design algorithm includes a goodness function that ranks the sensitivity of every possible electrode configuration to changes in the subsurface parameters. To examine the potential and limitations of the new algorithm, comprehensive data sets that included data from all standard and nonstandard electrode configurations were (a) generated for a complex 2D resistivity model and (b) recorded across a well‐studied test site in Switzerland. Images determined from the resultant comprehensive data sets were used as benchmarks against which the images derived from the optimized data sets were assessed. Images from relatively small optimized data sets, containing 265–282 data points, provided more information than did those from standard data sets of equal size. By far the best images, comparable to those determined from the much larger comprehensive data sets, were obtained from optimized data sets with 1000–6000 data points. These images supplied reliable information over depth ranges that were three times greater than the depth ranges resolved by the standard images. The first ∼600 electrode configurations selected by the experimental design procedure were nonstandard dipole‐dipole‐type arrays, whereas the following ∼4800 electrode configurations were an approximately equal mix of nonstandard dipole‐dipole‐type arrays and nested configurations (i.e., mostly gradient and other nonstandard arrays).

Optimal Experimental Design of Human Appraisals for Modeling Consumer Preferences in Engineering Design

2009 ◽  
Vol 131 (7) ◽  
Author(s):  
Christopher Hoyle ◽  
Wei Chen ◽  
Bruce Ankenman ◽  
Nanxin Wang
Keyword(s):  
Experimental Design ◽  
Engineering Design ◽  
Model Building ◽  
Consumer Preferences ◽  
Information Matrix ◽  
Product Attributes ◽  
Design Algorithm ◽  
Split Plot ◽  
Random Block ◽  
Human Attributes

Human appraisals are becoming increasingly important in the design of engineering systems to link engineering design attributes to customer preferences. Human appraisals are used to assess consumers’ opinions of a given product design, and are unique in that the experiment response is a function of both the product attributes and the respondents’ human attributes. The design of a human appraisal is characterized as a split-plot design, in which the respondents’ human attributes form the whole-plot factors while the product attributes form the split-plot factors. The experiments are also characterized by random block effects, in which the design configurations evaluated by a single respondent form a block. An experimental design algorithm is needed for human appraisal experiments because standard experimental designs often do not meet the needs of these experiments. In this work, an algorithmic approach to identify the optimal design for a human appraisal experiment is developed, which considers the effects of respondent fatigue and the blocked and split-plot structures of such a design. The developed algorithm seeks to identify the experimental design, which maximizes the determinant of the Fisher information matrix. The algorithm is derived assuming an ordered logit model will be used to model the rating responses. The advantages of this approach over competing approaches for minimizing the number of appraisal experiments and model-building efficiency are demonstrated using an automotive interior package human appraisal as an example.

Identifying suboptimalities with factorial model comparison

2018 ◽  
Vol 41 ◽  
Author(s):  
Wei Ji Ma
Keyword(s):  
Experimental Design ◽  
Model Comparison ◽  
Process Models ◽  
Multiple Forms ◽  
Factorial Experimental Design ◽  
Factorial Model ◽  
Multiple Components ◽  
The Many

AbstractGiven the many types of suboptimality in perception, I ask how one should test for multiple forms of suboptimality at the same time – or, more generally, how one should compare process models that can differ in any or all of the multiple components. In analogy to factorial experimental design, I advocate for factorial model comparison.

Combining integrated systems-biology approaches with intervention-based experimental design provides a higher-resolution path forward for microbiome research

2019 ◽  
Vol 42 ◽  
Author(s):  
J. Alfredo Blakeley-Ruiz ◽  
Carlee S. McClintock ◽  
Ralph Lydic ◽  
Helen A. Baghdoyan ◽  
James J. Choo ◽  
...  
Keyword(s):  
Systems Biology ◽  
High Resolution ◽  
Experimental Design ◽  
Integrated Systems ◽  
Microbiome Research ◽  
Constructive Criticism

Abstract The Hooks et al. review of microbiota-gut-brain (MGB) literature provides a constructive criticism of the general approaches encompassing MGB research. This commentary extends their review by: (a) highlighting capabilities of advanced systems-biology “-omics” techniques for microbiome research and (b) recommending that combining these high-resolution techniques with intervention-based experimental design may be the path forward for future MGB research.

Sign in / Sign up

Export Citation Format

Share Document