Static-Kinematic Optimality Criteria

1989 ◽  
pp. 21-80
Author(s):  
George I. N. Rozvany
Keyword(s):  
Optimality Criteria

An Optimality Criteria-Based Method for Discrete Design Optimization under Buildability Constraints

2014 ◽  
Author(s):  
Mattias Schevenels ◽  
Sean McGinn ◽  
Anke Rolvink ◽  
Jeroen Coenders
Keyword(s):  
Design Optimization ◽  
Optimality Criteria

Topology Optimization Using Node-Based Smoothed Finite Element Method

2015 ◽  
Vol 07 (06) ◽  
pp. 1550085 ◽  
Author(s):  
Z. C. He ◽  
G. Y. Zhang ◽  
L. Deng ◽  
Eric Li ◽  
G. R. Liu
Keyword(s):  
Finite Element Method ◽  
Finite Element ◽  
Topology Optimization ◽  
Optimization Design ◽  
Solid Mechanics ◽  
Optimality Criteria ◽  
Topology Optimization Problem ◽  
Design Variables ◽  
Smoothed Finite Element Method ◽  
Element Method

The node-based smoothed finite element method (NS-FEM) proposed recently has shown very good properties in solid mechanics, such as providing much better gradient solutions. In this paper, the topology optimization design of the continuum structures under static load is formulated on the basis of NS-FEM. As the node-based smoothing domain is the sub-unit of assembling stiffness matrix in the NS-FEM, the relative density of node-based smoothing domains serves as design variables. In this formulation, the compliance minimization is considered as an objective function, and the topology optimization model is developed using the solid isotropic material with penalization (SIMP) interpolation scheme. The topology optimization problem is then solved by the optimality criteria (OC) method. Finally, the feasibility and efficiency of the proposed method are illustrated with both 2D and 3D examples that are widely used in the topology optimization design.

Optimization Problem of a Linear Induction Generator with a Liquid Working Fluid

2021 ◽  
Vol 64 (6) ◽  
pp. 16-21
Author(s):  
Konstantin Kim ◽  
◽  
Konstantin Kim ◽  
Andrey Vataev ◽  
◽  
...  
Keyword(s):  
Computer Technology ◽  
Optimization Problem ◽  
Magnetic System ◽  
Optimality Criteria ◽  
Working Fluid ◽  
Conducting Liquid ◽  
Calculation Algorithm ◽  
Linear Induction ◽  
Induction Generators ◽  
Calculation Results

We present the problem of optimization of cylindrical linear induction generators. The stator winding is located on the inner and outer cores of the magnetic system. The working fluid is a well-conducting liquid moving at a constant speed along the length of the channel. Since the generator is one of the elements of a complex installation the problem of optimizing it should be coordinated with a similar problem for the installation as a whole. This is done with the help the parameters common to all installation elements. Such parameters are the speed and flow rate of the working fluid. We describe the calculation algorithm with the use of computer technology. The efficiency and specific gravity of the generator are accepted as optimality criteria. The calculation results for a generator with the power of 20 MW are presented.

Новый критерий оптимальности пространственно-временных матриц

2020 ◽  
Author(s):  
A.A. Reznev ◽  
V.B. Kreyndelin
Keyword(s):  
Computational Complexity ◽  
Large Scale ◽  
Noise Immunity ◽  
Optimality Criterion ◽  
Mimo Systems ◽  
Optimality Criteria ◽  
Space Time ◽  
Space Time Codes ◽  
Golden Code ◽  
New Criterion

The application of optimality criteria for the study of space-time codes is considered. Known rank and determinant criteria are described. The computational complexity of determinant criteria is presented taking into account some estimation of the real CPUs specifications. An algorithm for calculating a new optimality criterion is described. The computational complexity of the new optimality criterion is evaluated. The new criterion is applied to the study of the space-time Golden matrix. The obtained criterion value is used to modify the Golden code. The modeling for Golden code demonstrates that criterion works and gives us better levels for noise immunity. The proposed optimality criterion is acceptable in terms of computational complexity even for a large number of antennas, which is typical for large-scale MIMO systems. Рассматривается применение критериев оптимальности для исследования пространственно-временных кодов.Описаны известные ранговый и детерминантный критерии. Для детерминантного критерия оценена вычислительная сложность с учетом определения специальных высокопроизводительных процессоров. Описан алгоритм расчета нового критерия оптимальности. Проведена оценка вычислительной сложности нового критерия оптимальности. Новый критерий применен для исследования пространственно-временной матрицы Голден. Полученное значение критерия использовано для модификациикода Голден. Продемонстрированы кривые помехоустойчивости для кода Голден и кода Голден с модифицированным параметром, получен энергетический выигрыш. Предложенный критерий оптимальности приемлем с точки зрения вычислительнойсложности даже при большом числе антенн, характерном для систем широкомасштабного MIMO.

scholarly journals Optimal reinsurance: a reinsurer’s perspective

2017 ◽  
Vol 12 (1) ◽  
pp. 147-184 ◽  
Author(s):  
Fei Huang ◽  
Honglin Yu
Keyword(s):  
At Risk ◽  
Closed Form ◽  
Value At Risk ◽  
Optimality Criteria ◽  
Total Loss ◽  
Optimal Reinsurance ◽  
Numerical Examples ◽  
Dependency Structure ◽  
Closed Form Solutions

AbstractIn this paper, the optimal safety loading that the reinsurer should set in the reinsurance pricing is studied, which is novel in the literature. It is first assumed that the insurer will choose the form of the reinsurance contract by following the results derived in Cai et al. Different optimality criteria from the reinsurer’s perspective are then studied, such as maximising the expectation of the profit, maximising the utility of the profit and minimising the value-at-risk of the reinsurer’s total loss. By applying the concept of comonotonicity, the problem in which the reinsurer is facing two risks with unknown dependency structure is also solved. Closed-form solutions are obtained when the underlying losses are zero-modified exponentially distributed. Finally, numerical examples are provided to illustrate the results derived.

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