THE DESIGN AND ANALYSIS OF EXPERIMENTS. By Oscar Kempthorne. New York, John Wiley and Sons, Inc., 1952. 631 pp. $8.50. This book by a teacher of statistics (as well as a consultant for \"experimenters\") is a comprehensive study of the philosophical background for the statistical design of experiment. It is necessary to have some facility with algebraic notation and manipulation to be able to use the volume intelligently. The problems are presented from the theoretical point of view, without such practical examples as would be helpful for those not acquainted with mathematics. The mathematical justification for the techniques is given. As a somewhat advanced treatment of the design and analysis of experiments, this volume will be interesting and helpful for many who approach statistics theoretically as well as practically. With emphasis on the \"why,\" and with description given broadly, the author relates the subject matter to the general theory of statistics and to the general problem of experimental inference. MARGARET J. ROBERTSON

The Design and Analysis of Experiments

Surrogate-assisted, or meta-model based evolutionary computation uses efficient computational models, often known as surrogates or meta-models, for approximating the fitness function in evolutionary algorithms. Research on surrogate-assisted evolutionary computation began over a decade ago and has received considerably increasing interest in recent years. Very interestingly, surrogate-assisted evolutionary computation has found successful applications not only in solving computationally expensive single- or multi-objective optimization problems, but also in addressing dynamic optimization problems, constrained optimization problems and multi-modal optimization problems. This paper provides a concise overview of the history and recent developments in surrogate-assisted evolutionary computation and suggests a few future trends in this research area.

http://www.soft-computing.de/SECPublished.pdf

Surrogate-assisted evolutionary computation: Recent advances and future challenges

Statistics For Spatial Data

Nanoemulsion: Concepts, development and applications in drug delivery

Using surrogate models in evolutionary search provides an efficient means of handling today's complex applications plagued with increasing high-computational needs. Recent surrogate-assisted evolutionary frameworks have relied on the use of a variety of different modeling approaches to approximate the complex problem landscape. From these recent studies, one main research issue is with the choice of modeling scheme used, which has been found to affect the performance of evolutionary search significantly. Given that theoretical knowledge available for making a decision on an approximation model a priori is very much limited, this paper describes a generalization of surrogate-assisted evolutionary frameworks for optimization of problems with objectives and constraints that are computationally expensive to evaluate. The generalized evolutionary framework unifies diverse surrogate models synergistically in the evolutionary search. In particular, it focuses on attaining reliable search performance in the surrogate-assisted evolutionary framework by working on two major issues: 1) to mitigate the 'curse of uncertainty' robustly, and 2) to benefit from the 'bless of uncertainty.' The backbone of the generalized framework is a surrogate-assisted memetic algorithm that conducts simultaneous local searches using ensemble and smoothing surrogate models, with the aims of generating reliable fitness prediction and search improvements simultaneously. Empirical study on commonly used optimization benchmark problems indicates that the generalized framework is capable of attaining reliable, high quality, and efficient performance under a limited computational budget.

/pdf/generalizing-surrogate-assisted-evolutionary-computation-3qbrlsdn60.pdf

Generalizing Surrogate-Assisted Evolutionary Computation

A major issue in surrogate model-based design optimization is the modeling fidelity. An effective approach is to employ multiple surrogates based on the same training data to offer approximations from alternative modeling viewpoints. This approach is employed in a compressor blade shape optimization using the NASA rotor 37 as the case study. The surrogate models considered include polynomial response surface approximation, Kriging, and radial basis neural network. In addition, a weighted average model based on global error measures is constructed. Sequential quadratic programming is used to search the optimal point based on these alternative surrogates. Three design variables characterizing the blade regarding sweep, lean, and skew are selected along with the three-level full factorial approach for design of experiment. The optimization is guided by three objectives aimed at maximizing the adiabatic efficiency, as well as the total pressure and total temperature ratios. The optimized compressor blades yield lower losses by moving the separation line toward the downstream direction. The optima for total pressure and total temperature ratios are similar, but the optimum for adiabatic efficiency is located far from them. It is found that the most accurate surrogate did not always lead to the best design. This demonstrated that using multiple surrogates can improve the robustness of the optimization at a minimal computational cost.

Multiple surrogate modeling for axial compressor blade shape optimization

Objective: In this study, attempt has been focused to prepare a nanoemulsion (NE) gel for topical delivery of amphotericin B (AmB) for enhanced as well as sustained skin permeation, in vitro antifungal activity and in vivo toxicity assessment.Materials and methods: A series of NE were prepared using sefsol-218 oil, Tween 80 and Transcutol-P by slow spontaneous titration method. Carbopol gel (0.5% w/w) was prepared containing 0.1% w/w AmB. Furthermore, NE gel (AmB-NE gel) was characterized for size, charge, pH, rheological behavior, drug release profile, skin permeability, hemolytic studies and ex vivo rat skin interaction with rat skin using differential scanning calorimeter. The drug permeability and skin irritation ability were examined with confocal laser scanning microscopy and Draize test, respectively. The in vitro antifungal activity was investigated against three fungal strains using the well agar diffusion method. Histopathological assessment was performed in rats to investigate their tox...

Nanoemulsion gel-based topical delivery of an antifungal drug: in vitro activity and in vivo evaluation

This work presents a numerical optimization procedure for a low-speed axial flow fan blade with polynomial response surface approximation model. Reynolds-averaged Navier-Stokes equations with SST turbulence model are discretized by finite volume approximations and solved on hexahedral grids for flow analyses. The blade profile as well as stacking line is modified to enhance blade total efficiency, i.e., the objective function. The design variables of blade lean, maximum thickness and location of maximum thickness are selected, and a design of experiments technique produces design points where flow analyses are performed to obtain values of the objective function. A gradient-based search algorithm is used to find the optimal design in the design space from the constructed response surface model for the objective function. As a main result, the efficiency is increased effectively by the present optimization procedure. And, it is also shown that the modification of blade lean is more effective to improve the efficiency rather than modifying blade profile.

/pdf/design-optimization-of-low-speed-axial-flow-fan-blade-with-2f3am79q8e.pdf

Design optimization of low-speed axial flow fan blade with three-dimensional RANS analysis

Multiple surrogate based optimization of a bidirectional impulse turbine for wave energy conversion

In this study, a multi-objective optimization of an axial compressor rotor blade has been performed through genetic algorithm with total pressure and adiabatic efficiency as objective functions. The non-dominated sorting of genetic algorithm-II has been implemented and confidence check has been performed at k-means clustered points among all the Pareto-optimal solutions. Reynolds-averaged Navier—Stokes equations are solved to obtain the objective function and flow field inside the compressor annulus. The objective functions are used to generate Pareto-optimal front. The design variables are selected from blade lean and thickness through the Bezier polynomial formulation. By this optimization, maximum efficiency and total pressure are increased by 1.76 and 0.41 per cent, respectively, when two extreme clustered points are considered as optimal designs.

Abdus Samad

Papers

Multiple surrogate modeling for axial compressor blade shape optimization

Nanoemulsion gel-based topical delivery of an antifungal drug: in vitro activity and in vivo evaluation

Design optimization of low-speed axial flow fan blade with three-dimensional RANS analysis

Multiple surrogate based optimization of a bidirectional impulse turbine for wave energy conversion

Shape optimization of an axial compressor blade by multi-objective genetic algorithm