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Bell System Technical Journal, 41: 2. March 1962 pp 723-744. The Design and Analysis of Pattern Recognition Experiments. (Highleyman, W.H.)

Bell System Technical Journal, 41: 2. March 1962 pp 723-744. The Design and Analysis of Pattern Recognition Experiments. (Highleyman, W.H.)

Bell System Technical Journal, 41: 2. March 1962 pp 723-744. The Design and Analysis of Pattern Recognition Experiments. (Highleyman, W.H.)

Bell System Technical Journal, 41: 2. March 1962 pp 723-744. The Design and Analysis of Pattern Recognition Experiments. (Highleyman, W.H.)

Bell System Technical Journal, 41: 2. March 1962 pp 723-744. The Design and Analysis of Pattern Recognition Experiments. (Highleyman, W.H.)

This research concerns the design and analysis of different Side-Looking Sonar experiments in order to satisfy different operational requirements. The different designs and analysis have been done via computer simulation. Side-Looking Sonar (also known as side-scan sonar) is known for very high quality, high resolution, ocean bottom imaging. Hence, it is used for bathymetric surveys, commonly called seafloor mapping. It is able to rapidly survey large ocean areas for bottom and suspended sea-mines or other kinds of threats. Another operational aspect of these systems is that they allow autonomous underwater vehicles (AUVs) to conduct operations, mostly in shallow water and near land. Thus Side-Looking Sonar can be a very useful device in littoral warfare operations. This research has defined the basic parameters that rule the operation of a Side-Looking Sonar and, furthermore, analyzed various aspects that affect the performance of these parameters. Special focus was given to the various operational requirements and conditions that a designer or a user may encounter in realistic situations. Toward that end, many numerical examples are presented. Moreover, the research has tried to indicate the various problems that may arise when a Side-Looking Sonar operates in its near-field region and suggests certain solutions. The active sonar equation and its factors were explained and were evaluated for a realistic example of mine detection as well.

This research concerns the design and analysis of different Side-Looking Sonar experiments in order to satisfy different operational requirements. The different designs and analysis have been done via computer simulation. Side-Looking Sonar (also known as side-scan sonar) is known for very high quality, high resolution, ocean bottom imaging. Hence, it is used for bathymetric surveys, commonly called seafloor mapping. It is able to rapidly survey large ocean areas for bottom and suspended sea-mines or other kinds of threats. Another operational aspect of these systems is that they allow autonomous underwater vehicles (AUVs) to conduct operations, mostly in shallow water and near land. Thus Side-Looking Sonar can be a very useful device in littoral warfare operations. This research has defined the basic parameters that rule the operation of a Side-Looking Sonar and, furthermore, analyzed various aspects that affect the performance of these parameters. Special focus was given to the various operational requirements and conditions that a designer or a user may encounter in realistic situations. Toward that end, many numerical examples are presented. Moreover, the research has tried to indicate the various problems that may arise when a Side-Looking Sonar operates in its near-field region and suggests certain solutions. The active sonar equation and its factors were explained and were evaluated for a realistic example of mine detection as well.

This research concerns the design and analysis of different Side-Looking Sonar experiments in order to satisfy different operational requirements. The different designs and analysis have been done via computer simulation. Side-Looking Sonar (also known as side-scan sonar) is known for very high quality, high resolution, ocean bottom imaging. Hence, it is used for bathymetric surveys, commonly called seafloor mapping. It is able to rapidly survey large ocean areas for bottom and suspended sea-mines or other kinds of threats. Another operational aspect of these systems is that they allow autonomous underwater vehicles (AUVs) to conduct operations, mostly in shallow water and near land. Thus Side-Looking Sonar can be a very useful device in littoral warfare operations. This research has defined the basic parameters that rule the operation of a Side-Looking Sonar and, furthermore, analyzed various aspects that affect the performance of these parameters. Special focus was given to the various operational requirements and conditions that a designer or a user may encounter in realistic situations. Toward that end, many numerical examples are presented. Moreover, the research has tried to indicate the various problems that may arise when a Side-Looking Sonar operates in its near-field region and suggests certain solutions. The active sonar equation and its factors were explained and were evaluated for a realistic example of mine detection as well.

A First Course in Design and Analysis of Experiments

Recently two entomological field experiments were conducted at Dugway Proving Ground. The purpose of the first experiment was to compare the biting propensity of two strains of a species of insect. In each trial, four 15-foot radius circles were scribed, and 10 hosts, randomly selected, were positioned equidistantly along each circumference. The Number 1 position in each circle was oriented to true north. (See Figure 1.) At function time, 100 individuals of the appropriate strain were released at the center of each circle. In two of the circles, the A strain was used; in the other two circles, the B strain. The men were seated on the ground and remained relatively motionless throughout the trial. Sampling consisted of each man recording those bites actually received and entering the total number, for 5-minute intervals, on a data card. Sampling was conducted for 30 minutes following the release unless biting activity continued. In that event, sampling in all circles was extended for additional 5-minute periods until the biting activity had essentially ceased.

Recently two entomological field experiments were conducted at Dugway Proving Ground. The purpose of the first experiment was to compare the biting propensity of two strains of a species of insect. In each trial, four 15-foot radius circles were scribed, and 10 hosts, randomly selected, were positioned equidistantly along each circumference. The Number 1 position in each circle was oriented to true north. (See Figure 1.) At function time, 100 individuals of the appropriate strain were released at the center of each circle. In two of the circles, the A strain was used; in the other two circles, the B strain. The men were seated on the ground and remained relatively motionless throughout the trial. Sampling consisted of each man recording those bites actually received and entering the total number, for 5-minute intervals, on a data card. Sampling was conducted for 30 minutes following the release unless biting activity continued. In that event, sampling in all circles was extended for additional 5-minute periods until the biting activity had essentially ceased.

Recently two entomological field experiments were conducted at Dugway Proving Ground. The purpose of the first experiment was to compare the biting propensity of two strains of a species of insect. In each trial, four 15-foot radius circles were scribed, and 10 hosts, randomly selected, were positioned equidistantly along each circumference. The Number 1 position in each circle was oriented to true north. (See Figure 1.) At function time, 100 individuals of the appropriate strain were released at the center of each circle. In two of the circles, the A strain was used; in the other two circles, the B strain. The men were seated on the ground and remained relatively motionless throughout the trial. Sampling consisted of each man recording those bites actually received and entering the total number, for 5-minute intervals, on a data card. Sampling was conducted for 30 minutes following the release unless biting activity continued. In that event, sampling in all circles was extended for additional 5-minute periods until the biting activity had essentially ceased.

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xix, 728 p. : 24 cm

xix, 728 p. : 24 cm

Statistical analysis and design for optimizing gamma-prime strengthened cobalt-nickel base alloy composition

This article is from G3: Genes|Genomes|Genetics , volume 4 . Abstract High-throughput quantitative DNA sequencing enables the parallel phenotyping of pools of thousands of mutants. However, the appropriate analytical methods and experimental design that maximize the efficiency of these methods while maintaining statistical power are currently unknown. Here, we have used Bar-seq analysis of the Saccharomyces cerevisiae yeast deletion library to systematically test the effect of experimental design parameters and sequence read depth on experimental results. We present computational methods that efficiently and accurately estimate effect sizes and their statistical significance by adapting existing methods for RNA-seq analysis. Using simulated variation of experimental designs, we found that biological replicates are critical for statistical analysis of Bar-seq data, whereas technical replicates are of less value. By subsampling sequence reads, we found that when using four-fold biological replication, 6 million reads per condition achieved 96% power to detect a two-fold change (or more) at a 5% false discovery rate. Our guidelines for experimental design and computational analysis enables the study of the yeast deletion collection in up to 30 different conditions in a single sequencing lane. These findings are relevant to a variety of pooled genetic screening methods that use high-throughput quantitative DNA sequencing, including Tn-seq.

This article is from G3: Genes|Genomes|Genetics , volume 4 . Abstract High-throughput quantitative DNA sequencing enables the parallel phenotyping of pools of thousands of mutants. However, the appropriate analytical methods and experimental design that maximize the efficiency of these methods while maintaining statistical power are currently unknown. Here, we have used Bar-seq analysis of the Saccharomyces cerevisiae yeast deletion library to systematically test the effect of experimental design parameters and sequence read depth on experimental results. We present computational methods that efficiently and accurately estimate effect sizes and their statistical significance by adapting existing methods for RNA-seq analysis. Using simulated variation of experimental designs, we found that biological replicates are critical for statistical analysis of Bar-seq data, whereas technical replicates are of less value. By subsampling sequence reads, we found that when using four-fold biological replication, 6 million reads per condition achieved 96% power to detect a two-fold change (or more) at a 5% false discovery rate. Our guidelines for experimental design and computational analysis enables the study of the yeast deletion collection in up to 30 different conditions in a single sequencing lane. These findings are relevant to a variety of pooled genetic screening methods that use high-throughput quantitative DNA sequencing, including Tn-seq.

This article is from G3: Genes|Genomes|Genetics , volume 4 . Abstract High-throughput quantitative DNA sequencing enables the parallel phenotyping of pools of thousands of mutants. However, the appropriate analytical methods and experimental design that maximize the efficiency of these methods while maintaining statistical power are currently unknown. Here, we have used Bar-seq analysis of the Saccharomyces cerevisiae yeast deletion library to systematically test the effect of experimental design parameters and sequence read depth on experimental results. We present computational methods that efficiently and accurately estimate effect sizes and their statistical significance by adapting existing methods for RNA-seq analysis. Using simulated variation of experimental designs, we found that biological replicates are critical for statistical analysis of Bar-seq data, whereas technical replicates are of less value. By subsampling sequence reads, we found that when using four-fold biological replication, 6 million reads per condition achieved 96% power to detect a two-fold change (or more) at a 5% false discovery rate. Our guidelines for experimental design and computational analysis enables the study of the yeast deletion collection in up to 30 different conditions in a single sequencing lane. These findings are relevant to a variety of pooled genetic screening methods that use high-throughput quantitative DNA sequencing, including Tn-seq.

This article is from G3: Genes|Genomes|Genetics , volume 4 . Abstract High-throughput quantitative DNA sequencing enables the parallel phenotyping of pools of thousands of mutants. However, the appropriate analytical methods and experimental design that maximize the efficiency of these methods while maintaining statistical power are currently unknown. Here, we have used Bar-seq analysis of the Saccharomyces cerevisiae yeast deletion library to systematically test the effect of experimental design parameters and sequence read depth on experimental results. We present computational methods that efficiently and accurately estimate effect sizes and their statistical significance by adapting existing methods for RNA-seq analysis. Using simulated variation of experimental designs, we found that biological replicates are critical for statistical analysis of Bar-seq data, whereas technical replicates are of less value. By subsampling sequence reads, we found that when using four-fold biological replication, 6 million reads per condition achieved 96% power to detect a two-fold change (or more) at a 5% false discovery rate. Our guidelines for experimental design and computational analysis enables the study of the yeast deletion collection in up to 30 different conditions in a single sequencing lane. These findings are relevant to a variety of pooled genetic screening methods that use high-throughput quantitative DNA sequencing, including Tn-seq.

https://gioumeh.com/product/design-and-analysis-of-experiments-10th-edition-by-douglas-c-montgomery-solutions-manual-pdf/ -------------------------------------------------   Authors:  Douglas C. Montgomery Published:  Wiley2019   Edition:  10th   Pages:  709   Type:  pdf   Size:   123MB   Content:   official resources for 10th editions and covers all chapters of the textbook (chapters 1 to 15). It includes “Solution Manual”, “lecturer’s PowerPoint slides”, “Reserved Problems”, “Image Gallery”, “Supplemental Materials”, “Data Sets”, “Image Gallery” and “Sample Student Project”. The Solution Manual is available in Word format (one Word file for each of chapters) and includes answers for reserved problems too. Solution Sample:   sample file Download After Payment

https://gioumeh.com/product/design-and-analysis-of-experiments-10th-edition-by-douglas-c-montgomery-solutions-manual-pdf/ -------------------------------------------------   Authors:  Douglas C. Montgomery Published:  Wiley2019   Edition:  10th   Pages:  709   Type:  pdf   Size:   123MB   Content:   official resources for 10th editions and covers all chapters of the textbook (chapters 1 to 15). It includes “Solution Manual”, “lecturer’s PowerPoint slides”, “Reserved Problems”, “Image Gallery”, “Supplemental Materials”, “Data Sets”, “Image Gallery” and “Sample Student Project”. The Solution Manual is available in Word format (one Word file for each of chapters) and includes answers for reserved problems too. Solution Sample:   sample file Download After Payment

https://gioumeh.com/product/design-and-analysis-of-experiments-10th-edition-by-douglas-c-montgomery-solutions-manual-pdf/ -------------------------------------------------   Authors:  Douglas C. Montgomery Published:  Wiley2019   Edition:  10th   Pages:  709   Type:  pdf   Size:   123MB   Content:   official resources for 10th editions and covers all chapters of the textbook (chapters 1 to 15). It includes “Solution Manual”, “lecturer’s PowerPoint slides”, “Reserved Problems”, “Image Gallery”, “Supplemental Materials”, “Data Sets”, “Image Gallery” and “Sample Student Project”. The Solution Manual is available in Word format (one Word file for each of chapters) and includes answers for reserved problems too. Solution Sample:   sample file Download After Payment

https://gioumeh.com/product/design-and-analysis-of-experiments-10th-edition-by-douglas-c-montgomery-solutions-manual-pdf/ -------------------------------------------------   Authors:  Douglas C. Montgomery Published:  Wiley2019   Edition:  10th   Pages:  709   Type:  pdf   Size:   123MB   Content:   official resources for 10th editions and covers all chapters of the textbook (chapters 1 to 15). It includes “Solution Manual”, “lecturer’s PowerPoint slides”, “Reserved Problems”, “Image Gallery”, “Supplemental Materials”, “Data Sets”, “Image Gallery” and “Sample Student Project”. The Solution Manual is available in Word format (one Word file for each of chapters) and includes answers for reserved problems too. Solution Sample:   sample file Download After Payment

https://gioumeh.com/product/design-and-analysis-of-experiments-10th-edition-by-douglas-c-montgomery-solutions-manual-pdf/ -------------------------------------------------   Authors:  Douglas C. Montgomery Published:  Wiley2019   Edition:  10th   Pages:  709   Type:  pdf   Size:   123MB   Content:   official resources for 10th editions and covers all chapters of the textbook (chapters 1 to 15). It includes “Solution Manual”, “lecturer’s PowerPoint slides”, “Reserved Problems”, “Image Gallery”, “Supplemental Materials”, “Data Sets”, “Image Gallery” and “Sample Student Project”. The Solution Manual is available in Word format (one Word file for each of chapters) and includes answers for reserved problems too. Solution Sample:   sample file Download After Payment

https://gioumeh.com/product/design-and-analysis-of-experiments-10th-edition-by-douglas-c-montgomery-solutions-manual-pdf/ -------------------------------------------------   Authors:  Douglas C. Montgomery Published:  Wiley2019   Edition:  10th   Pages:  709   Type:  pdf   Size:   123MB   Content:   official resources for 10th editions and covers all chapters of the textbook (chapters 1 to 15). It includes “Solution Manual”, “lecturer’s PowerPoint slides”, “Reserved Problems”, “Image Gallery”, “Supplemental Materials”, “Data Sets”, “Image Gallery” and “Sample Student Project”. The Solution Manual is available in Word format (one Word file for each of chapters) and includes answers for reserved problems too. Solution Sample:   sample file Download After Payment

The concept of a shorted operator on the cone C sub n of nonnegative definite matrices of order n x n introduced by Krein and studied recently by Anderson and Trapp is extended to a wider class of matrices. For matrices in C sub n, the shorting operation is permissible with reference to any subspace S of the n dimensional Euclidean space E to the nth power, provided restrictions are symmetrically placed on the row and column spans. For general matrices, the shorting operation is uniquely defined if and only if certain conditions are satisfied by the matrix itself and by subspaces providing restrictions on the row and column spans. The key point in this development is a theorem of Anderson and Trapp which exhibits the shorted n.n.d. matrix as the limit of a sequence of parallel sum operators. Some applications of the shorted operator in mathematical statistics with special reference to the design and analysis of experiments are provided.

The Handbook on Experimental Statistics has been prepared as an aid to scientists and engineers engaged in Army research and development programs, and especially as a guide and ready reference for military and civilian personnel who have responsibility for the planning and interpretation of experiments and tests relating to the performance of Army equipment in the design and developmental stages of production. The present document, section 3, has to do with the planning and analysis of comparative experiments.

The Handbook on Experimental Statistics has been prepared as an aid to scientists and engineers engaged in Army research and development programs, and especially as a guide and ready reference for military and civilian personnel who have responsibility for the planning and interpretation of experiments and tests relating to the performance of Army equipment in the design and developmental stages of production. The present document, section 3, has to do with the planning and analysis of comparative experiments.

The Handbook on Experimental Statistics has been prepared as an aid to scientists and engineers engaged in Army research and development programs, and especially as a guide and ready reference for military and civilian personnel who have responsibility for the planning and interpretation of experiments and tests relating to the performance of Army equipment in the design and developmental stages of production. The present document, section 3, has to do with the planning and analysis of comparative experiments.

The design of an experiment can be always be considered at least implicitly Bayesian, with prior knowledge used informally to aid decisions such as the variables to be studied and the choice of a plausible relationship between the explanatory variables and measured responses. Bayesian methods allow uncertainty in these decisions to be incorporated into design selection through prior distributions that encapsulate information available from scientific knowledge or previous experimentation. Further, a design may be explicitly tailored to the aim of the experiment through a decision-theoretic approach using an appropriate loss function. We review the area of decision-theoretic Bayesian design, with particular emphasis on recent advances in computational methods. For many problems arising in industry and science, experiments result in a discrete response that is well described by a member of the class of generalised linear models. We describe how Gaussian process emulation, commonly used in computer experiments, can play an important role in facilitating Bayesian design for realistic problems. A main focus is the combination of Gaussian process regression to approximate the expected loss with cyclic descent (coordinate exchange) optimisation algorithms to allow optimal designs to be found for previously infeasible problems. We also present the first optimal design results for statistical models formed from dimensional analysis, a methodology widely employed in the engineering and physical sciences to produce parsimonious and interpretable models. Using the famous paper helicopter experiment, we show the potential for the combination of Bayesian design, generalised linear models and dimensional analysis to produce small but informative experiments.

The design of an experiment can be always be considered at least implicitly Bayesian, with prior knowledge used informally to aid decisions such as the variables to be studied and the choice of a plausible relationship between the explanatory variables and measured responses. Bayesian methods allow uncertainty in these decisions to be incorporated into design selection through prior distributions that encapsulate information available from scientific knowledge or previous experimentation. Further, a design may be explicitly tailored to the aim of the experiment through a decision-theoretic approach using an appropriate loss function. We review the area of decision-theoretic Bayesian design, with particular emphasis on recent advances in computational methods. For many problems arising in industry and science, experiments result in a discrete response that is well described by a member of the class of generalised linear models. We describe how Gaussian process emulation, commonly used in computer experiments, can play an important role in facilitating Bayesian design for realistic problems. A main focus is the combination of Gaussian process regression to approximate the expected loss with cyclic descent (coordinate exchange) optimisation algorithms to allow optimal designs to be found for previously infeasible problems. We also present the first optimal design results for statistical models formed from dimensional analysis, a methodology widely employed in the engineering and physical sciences to produce parsimonious and interpretable models. Using the famous paper helicopter experiment, we show the potential for the combination of Bayesian design, generalised linear models and dimensional analysis to produce small but informative experiments.

The past decade has witnessed a dramatic increase in the size and scope of biological and behavioral experiments. These experiments are providing an unprecedented level of detail and depth of data. However, this increase in data presents substantial statistical and graphical hurdles to overcome, namely how to distinguish signal from noise and how to visualize multidimensional results. Here we present a series of tools designed to support a research project from inception to publication. We provide implementation of dimension reduction techniques and visualizations that function well with the types of data often seen in animal behavior studies. This package is designed to be used with experimental data but can also be used for experimental design and sample justification. The goal for this project is to create a package that will evolve over time, thereby remaining relevant and reflective of current methods and techniques.

Computer experiments are often performed to allow modeling of a response surface of a physical experiment that can be too costly or difficult to run except using a simulator. Running the experiment over a dense grid can be prohibitively expensive, yet running over a sparse design chosen in advance can result in obtaining insufficient information in parts of the space, particularly when the surface calls for a nonstationary model. We propose an approach that automatically explores the space while simultaneously fitting the response surface, using predictive uncertainty to guide subsequent experimental runs. The newly developed Bayesian treed Gaussian process is used as the surrogate model, and a fully Bayesian approach allows explicit measures of uncertainty. We develop an adaptive sequential design framework to cope with an asynchronous, random, agent--based supercomputing environment, by using a hybrid approach that melds optimal strategies from the statistics literature with flexible strategies from the active learning literature. The merits of this approach are borne out in several examples, including the motivating computational fluid dynamics simulation of a rocket booster.

Book Source: Digital Library of India Item 2015.198544 dc.contributor.author: S. Singh dc.date.accessioned: 2015-07-08T12:50:20Z dc.date.available: 2015-07-08T12:50:20Z dc.date.digitalpublicationdate: 0000-00-00 dc.identifier.barcode: 5990010100343 dc.identifier.origpath: /rawdataupload/upload/0100/345 dc.identifier.copyno: 1 dc.identifier.uri: http://www.new.dli.ernet.in/handle/2015/198544 dc.description.scanningcentre: IIIT, Allahabad dc.description.main: 1 dc.description.tagged: 0 dc.description.totalpages: 227 dc.format.mimetype: application/pdf dc.language.iso: English dc.publisher: Bishwaabhaarti Prakaashan Naagpur dc.rights: Out_of_copyright dc.source.library: I I T Kanpur dc.subject.classification: Technology dc.subject.classification: Engineering. Technology In General dc.subject.classification: Mechanical Engineering In General. Nuclear Technology. Electrical Engineering. Machinery dc.title: Design And Analysis Of Experiments For Model Discrimination In Uniresponse

Scan of book Design of experiments, statistical principles of research design and analysis 2nd edition by Robert O Kuehl [2000] {001.4'22--dc21} reader's annotations left for decoration

It is the purpose of this study to examine the impact of "highly efficient" Design-of-Experiments (DOE) methods for combining sets of CFD generated analysis data with smaller sets of Experimental test data in order to accurately predict performance results where experimental test data were not obtained. The study examines the impact of micro-ramp flow control on the shock wave boundary layer (SWBL) interaction where a complete paired set of data exist from both CFD analysis and Experimental measurements By combining the complete set of CFD analysis data composed of fifteen (15) cases with a smaller subset of experimental test data containing four/five (4/5) cases, compound data sets (CFD/EXP) were generated which allows the prediction of the complete set of Experimental results No statistical difference were found to exist between the combined (CFD/EXP) generated data sets and the complete Experimental data set composed of fifteen (15) cases. The same optimal micro-ramp configuration was obtained using the (CFD/EXP) generated data as obtained with the complete set of Experimental data, and the DOE response surfaces generated by the two data sets were also not statistically different.

It is the purpose of this study to examine the impact of "highly efficient" Design-of-Experiments (DOE) methods for combining sets of CFD generated analysis data with smaller sets of Experimental test data in order to accurately predict performance results where experimental test data were not obtained. The study examines the impact of micro-ramp flow control on the shock wave boundary layer (SWBL) interaction where a complete paired set of data exist from both CFD analysis and Experimental measurements By combining the complete set of CFD analysis data composed of fifteen (15) cases with a smaller subset of experimental test data containing four/five (4/5) cases, compound data sets (CFD/EXP) were generated which allows the prediction of the complete set of Experimental results No statistical difference were found to exist between the combined (CFD/EXP) generated data sets and the complete Experimental data set composed of fifteen (15) cases. The same optimal micro-ramp configuration was obtained using the (CFD/EXP) generated data as obtained with the complete set of Experimental data, and the DOE response surfaces generated by the two data sets were also not statistically different.

It is the purpose of this study to examine the impact of "highly efficient" Design-of-Experiments (DOE) methods for combining sets of CFD generated analysis data with smaller sets of Experimental test data in order to accurately predict performance results where experimental test data were not obtained. The study examines the impact of micro-ramp flow control on the shock wave boundary layer (SWBL) interaction where a complete paired set of data exist from both CFD analysis and Experimental measurements By combining the complete set of CFD analysis data composed of fifteen (15) cases with a smaller subset of experimental test data containing four/five (4/5) cases, compound data sets (CFD/EXP) were generated which allows the prediction of the complete set of Experimental results No statistical difference were found to exist between the combined (CFD/EXP) generated data sets and the complete Experimental data set composed of fifteen (15) cases. The same optimal micro-ramp configuration was obtained using the (CFD/EXP) generated data as obtained with the complete set of Experimental data, and the DOE response surfaces generated by the two data sets were also not statistically different.

It is the purpose of this study to examine the impact of "highly efficient" Design-of-Experiments (DOE) methods for combining sets of CFD generated analysis data with smaller sets of Experimental test data in order to accurately predict performance results where experimental test data were not obtained. The study examines the impact of micro-ramp flow control on the shock wave boundary layer (SWBL) interaction where a complete paired set of data exist from both CFD analysis and Experimental measurements By combining the complete set of CFD analysis data composed of fifteen (15) cases with a smaller subset of experimental test data containing four/five (4/5) cases, compound data sets (CFD/EXP) were generated which allows the prediction of the complete set of Experimental results No statistical difference were found to exist between the combined (CFD/EXP) generated data sets and the complete Experimental data set composed of fifteen (15) cases. The same optimal micro-ramp configuration was obtained using the (CFD/EXP) generated data as obtained with the complete set of Experimental data, and the DOE response surfaces generated by the two data sets were also not statistically different.

It is the purpose of this study to examine the impact of "highly efficient" Design-of-Experiments (DOE) methods for combining sets of CFD generated analysis data with smaller sets of Experimental test data in order to accurately predict performance results where experimental test data were not obtained. The study examines the impact of micro-ramp flow control on the shock wave boundary layer (SWBL) interaction where a complete paired set of data exist from both CFD analysis and Experimental measurements By combining the complete set of CFD analysis data composed of fifteen (15) cases with a smaller subset of experimental test data containing four/five (4/5) cases, compound data sets (CFD/EXP) were generated which allows the prediction of the complete set of Experimental results No statistical difference were found to exist between the combined (CFD/EXP) generated data sets and the complete Experimental data set composed of fifteen (15) cases. The same optimal micro-ramp configuration was obtained using the (CFD/EXP) generated data as obtained with the complete set of Experimental data, and the DOE response surfaces generated by the two data sets were also not statistically different.

It is the purpose of this study to examine the impact of "highly efficient" Design-of-Experiments (DOE) methods for combining sets of CFD generated analysis data with smaller sets of Experimental test data in order to accurately predict performance results where experimental test data were not obtained. The study examines the impact of micro-ramp flow control on the shock wave boundary layer (SWBL) interaction where a complete paired set of data exist from both CFD analysis and Experimental measurements By combining the complete set of CFD analysis data composed of fifteen (15) cases with a smaller subset of experimental test data containing four/five (4/5) cases, compound data sets (CFD/EXP) were generated which allows the prediction of the complete set of Experimental results No statistical difference were found to exist between the combined (CFD/EXP) generated data sets and the complete Experimental data set composed of fifteen (15) cases. The same optimal micro-ramp configuration was obtained using the (CFD/EXP) generated data as obtained with the complete set of Experimental data, and the DOE response surfaces generated by the two data sets were also not statistically different.

Estimating the effects of interventions in networks is complicated when the units are interacting, such that the outcomes for one unit may depend on the treatment assignment and behavior of many or all other units (i.e., there is interference). When most or all units are in a single connected component, it is impossible to directly experimentally compare outcomes under two or more global treatment assignments since the network can only be observed under a single assignment. Familiar formalism, experimental designs, and analysis methods assume the absence of these interactions, and result in biased estimators of causal effects of interest. While some assumptions can lead to unbiased estimators, these assumptions are generally unrealistic, and we focus this work on realistic assumptions. Thus, in this work, we evaluate methods for designing and analyzing randomized experiments that aim to reduce this bias and thereby reduce overall error. In design, we consider the ability to perform random assignment to treatments that is correlated in the network, such as through graph cluster randomization. In analysis, we consider incorporating information about the treatment assignment of network neighbors. We prove sufficient conditions for bias reduction through both design and analysis in the presence of potentially global interference. Through simulations of the entire process of experimentation in networks, we measure the performance of these methods under varied network structure and varied social behaviors, finding substantial bias and error reductions. These improvements are largest for networks with more clustering and data generating processes with both stronger direct effects of the treatment and stronger interactions between units.