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Discusses a new approach to solving optimal designing and control problems in aircraft gas‐turbine engine components. This approach is a combination of optimal designing problems with optimal control problems, allowing the formation of a single problem of optimal designing of controllable systems. The solving of this problem would involve simultaneous optimization of both design parameters and control laws. Allows the making of technically correct and substantiated decisions, taking into consideration several efficiency criteria for gas‐turbine engine components; a specific feature being the determination of a set of competitive optimal solutions in terms of different efficiency criteria values. Demonstrates the effectiveness of this approach by an example of multicriteria design optimization of a controllable axial flow compressor. Presents the results of a search of compressor blade rows geometrical parameters sets and of compressor stator blades control laws which are Edgeworth‐Pareto optimal for four operating modes. Shows a possibility of increasing compressor efficiency considerably by choosing the most preferable design parameters set and implementing in airborne digital control system a number of control laws optimal for different operating modes.

This paper aims to present the optimal design procedure of a symmetrical 2-DOF parallel planar robot with flexible joints by considering several performance criteria based on the workspace size, dynamic dexterity and energy of the control.

Consequently, the optimal design consists in determining the dimensional parameters to maximize the size of the workspace, maximize the dynamic dexterity and minimize the energy of the control action. The design criteria are derived from the kinematics, dynamics, elastodynamics and the position control law of the robot. The analysis of the design criteria is performed by means of the design space and atlases.

Finally, the multi-objective design optimization derived from the optimal design procedure is solved by using multi-objective genetic algorithms, and the results are analyzed to assess the validity of the proposed approach.

An alternative approach to the design of a planar parallel robot with flexible joints that permits determining the structural parameters by considering kinematic, dynamic and control operational performance.

Purpose — This study proposes an optimal survey design method for multi-day and multi-period panels that maximizes the statistical power of the parameter of interest under the conditions that non-linear changes in response to a policy intervention over time can be expected.

Design/methodology/approach — The proposed method addresses balances among sample size, survey duration for each wave and frequency of observation. Higher-order polynomial changes in the parameter are also addressed, allowing us to calculate optimal sampling designs for non-linear changes in response to a given policy intervention.

Findings — One of the most important findings is that variation structure in the behaviour of interest strongly influences how surveys are designed to maximize statistical power, while the type of policy to be evaluated does not influence it so much. Empirical results done by using German Mobility Panel data indicate that not only are more data collection waves needed, but longer multi-day periods of behavioural observations per wave are needed as well, with the increase in the non-linearity of the changes in response to a policy intervention.

Originality/value — This study extends previous studies on sampling designs for travel diary survey by dealing with statistical relations between sample size, survey duration for each wave, and frequency of observation, and provides the numerical and empirical results to show how the proposed method works.

Introduces papers from this area of expertise from the ISEF 1999 Proceedings. States the goal herein is one of identifying devices or systems able to provide prescribed performance. Notes that 18 papers from the Symposium are grouped in the area of automated optimal design. Describes the main challenges that condition computational electromagnetism’s future development. Concludes by itemizing the range of applications from small activators to optimization of induction heating systems in this third chapter.

Distributed photovoltaic (DPV) projects generally have output risks, and the production effort of the supplier is often private information, so the buyer needs to design the optimal procurement contract to maximise its procurement utility.

Based on the principal-agent theory, we design optimal procurement contracts for DPV projects with fixed payments and incentive factors under three situations, i.e. symmetry information, asymmetry information without monitoring and asymmetry information with monitoring. We obtain the optimal production effort and expected utility of the supplier, the expected output and expected utility of the buyer and analyse the value of the information and monitoring.

The results show that under asymmetric information without monitoring, risk-averse suppliers need to take some risk due to output risk, which reduces the optimal production effort of the supplier and the expected output and expected utility of the buyer. Therefore, when the monitoring cost is below a certain threshold value, the buyer can introduce a procurement contract with monitoring to address the asymmetry information. In addition, under asymmetric information without monitoring, the buyer should choose a supplier with a low-risk aversion.

Considering the output risk of DPV projects, we study the optimal procurement contract design for the buyer under asymmetric information. The results provide some theoretical basis and management insights for the buyer to design optimal procurement contracts in different situations.

This work seeks to present the theoretical study considerations and the characteristics of a general design methodology in optimal time for electronic systems using numerical methods and optimal control theory. Through this, the design problem of a system is formulated in terms of optimal control in minimal time.

This general design methodology includes the traditional design strategy (TDS), and the modified traditional design strategy (MTDS), where the model of the system is part of the optimization procedure but an objective function of the optimization process is constructed such as includes the traditional objective function and some penalty functions that feign the model of the system. Many special control functions are introduced artificially to generalize the methodology and produce several design trajectories for the same optimization process – the first and final trajectories correspond to TDS and MTDS, respectively. The combination of these trajectories produce an infinite number of design strategies, some of these are quasi‐optimal in time and only one is optimal in time.

Qualitative and numeric results of this iterative process are generated in a personal computer in a C++ language elaborated with a visual C++ graphic user interface. An algorithm is constructed to form an optimal in time design strategy switching from a MTDS subset to a TDS subset. Results of measured times are analyzed, showing that there is a control input U, such that the objective function is minimized in a minimum time.

These ideas are proposed using method of gradient optimization and special acceleration effect.

The purpose of this paper is to present a tri-optimization approach to optimize design solutions regarding the building shape and envelope properties considering their implications on thermal comfort, visual comfort and building energy consumption (EN). The optimization approach has been applied to obtain the optimal design solutions in five typical cities across all climatic regions of China.

The method comprises a tri-optimization process with nine main steps to optimize the three objectives (thermal comfort, visual comfort and building EN). The design variables considered are four types of building shape (pyramid, rectangular, cylindrical and dome shape) and different envelope properties (insulation thickness [INS] of external walls/roof, window type [WT] and window-to-envelop surface area ratio [WESR]). The optimization is performed by using the Taguchi and constraint limit method.

The results show that the optimal design solutions for all climatic regions favor cylindrical shape and triple-layer low-E glazing window. The highest insulation level of 150 mm is preferred in three climatic regions, and the INS of 90 mm is preferred in the other two climate regions. In total, 10% WESR is preferred in all climatic regions, except the mild region. When the constraint limit of lighting intensity requirement by Leadership in Energy and Environmental Design (LEED) is applied, the rectangular shape building is the optimal solution for those with 10% WESR.

The method proposed in the paper is innovative in that it optimizes three different objectives simultaneously in building design with better accuracy and calculation speed.

Building designers can easily follow the proposed design guide in their practice which effectively bridges the gap between theory and practice. The optimal design solutions can provide a more comfortable living environment and yet less EN, which can help achieve the sustainability requirement of green buildings.

The solutions presented in the paper can serve as a useful guide for practical building designers which creates economic and commercial impact. In addition, the theory and practical examples of the study can be used by building regulators to improve the energy-efficient building design standard in China.

The research is the first attempt that adopts tri-optimization approach to generate the optimal solutions for building shape and envelope design. The tri-optimization approach can be used by building designers to generate satisfactory design solutions from the architectural viewpoint and meanwhile to find combinations of the building shape and envelope properties that lead to design solutions with optimal building performance.

This paper aims to describe main ideas and demonstrates results of the research activities carried out by the authors in the field of optimal design concepts for induction heater for surface hardening. The main goal of the research studies is the application of different optimization methods and numerical finite element method (FEM) codes for field analysis to solve the optimal design problem that is mathematically formulated in terms of the one of the most important optimization criteria for surface hardening technology, e.g. maximum temperature uniformity within the hardening surface layer.

Evolutionary algorithm based on Adaptive Gaussian Process-Assisted Differential Evolution for MEMS Design Optimization (AGDEMO) and alternance method of parametric optimization based on optimal control theory are applied as effective tools for the practice-oriented problem for optimization of induction heater design based on non-linear coupled electromagnetic and temperature field analysis. Different approaches are used for combining FEM codes for interconnected field analysis and optimization algorithms into automated optimization procedure.

Optimization procedures are tested and investigated for optimal design problem solution on the examples of induction hardening of steel cylindrical billet.

Solved problems are based on the design of practical industrial applications. The developed optimization procedures are planned to be applied to the wide range of real-life problems of the optimal design of different electromagnetic devices and systems.

This paper describes main ideas and results of the research activities carried out by the authors in the field of optimal design of induction heaters for hardening based on numerical coupled electromagnetic and temperature field analysis. The implementation of the automated procedure that combines a numerical FEM code for coupled field analysis with an optimization algorithm and its subsequent application for designing induction heaters makes the proposed approach specific and original. This paper also demonstrates that different optimization strategies used (evolutionary algorithm based on AGDEMO and alternance method of optimal control theory) are effective for real-life industrial applications for optimization of induction heaters design.

The purpose of this paper is to present systematic optimal design procedures for the Gough-Stewart platforms used as engineering motion simulators.

Three systematic optimal design procedures are proposed to solve the engineering design problems for the Gough-Stewart platform used as motion simulators. In these systematic optimal design procedures, two contradicting design optimality criteria with good representations of performances of the Gough-Stewart platforms are chosen as the objective functions. In addition, the two objective function optimization problems are solved by using the multi-objective evolutionary algorithms.

In the systematic optimal design procedures, multiple compromised design solutions are found by using Elitist Non-Dominated Sorting Genetic Algorithm version II in the primary design stage, and many candidates can be used in the secondary design stage for higher decisions. Two higher decision methods have been presented to choose the final solutions.

This paper proposes three systematic optimal design procedures to solve the practical design problems of the Gough-Stewart platforms used as motion simulators, which are very important for the engineering designers.

The aim of this research paper is to generalize the previous works on the design of accelerated life tests (ALTs) for periodic inspection and Type I censoring and to promote the use of an exponentiated Weibull (EW) distribution in accelerated life testing.

Statistically optimal ALT plans are suggested for items whose lifetime follows the EW distribution under periodic inspection and Type I censoring. It is assumed that the mean lifetime (scale parameter) is a log‐linear function of stress and that the shape parameters are independent of stress. Given shape parameters, design stress and high test stress, the test plan is optimized with respect to the low test stress and the proportion of test units are also allocated to this test stress. The asymptotic variance (AsVar) of the maximum likelihood estimator of log mean life at the design stress is used as an optimality criterion with equally spaced inspection times. A FORTRAN program was written to calculate the optimal plans. Procedures for planning of an ALT, including selection of sample size, have also been discussed. An illustration of the optimal ALT plans has been done through a numerical example.

Computational findings for various values of the shape parameters indicate that the AsVar of log mean life at the design stress is insensitive to the number of inspection times and to misspecifications of imputed failure probabilities at design and high test stresses. Computational findings also show that optimal designs of ALT previously obtained for exponential, Rayleigh, and Weibull distributions become special cases of the EW distribution. Thus, the EW distribution is a useful and widely applicable reliability model for optimal ALT plans.

The present investigation features the EW distribution of lifetimes of test items and it generalizes the previous works on accelerated life testing. Furthermore, the propose test plans can be applied to estimate the lifetime of highly reliable product or material, if a researcher designs a test under the assumption of this model.