Optimization of Helicopter Subfloor Components Under Crashworthiness Requirements Using Neural Networks

Multidisciplinary design optimization (MDO) was performed on a helicopter rotor blade. The blade was modeled as a rigid flapping blade for dynamics; Blade Element Theory (BET) was the analysis approach to model the aerodynamic loading, and a simple linearly elastic hollowed rectangular section was the main structural component. MATLAB was used to solve the flapping differential equations and its Sequential Quadratic Programming (SQP) and Genetic Algorithm (GA) were used for the optimization. A Particle Swarm Optimization (PSO) routine was also tested. The optimization process consisted of three cases. The first case was a simple cantilever beam under centrifugal and an assumed bending loads. The optimization was performed using the SQP, GA, and PSO algorithms. The SQP resulted in the superior design with 75.45 compared to the GA's 87.1 and the PSO's 79.2, but a local minimum was present. The second case was an expansion of the first case by turning it into multidisciplinary problem. Aerodynamics was included in the design variables and objective function. Only the SQP algorithm was used and there was a reduction in hub vertical shear by 33.6%. The blade mass increased by 36.84%. The last case was an improvement to the second by creating a multiobjective problem by including the hub radial shear and the results were improved significantly by reducing the hub vertical shear by 34.06% and radial shear by 17.87% with a reduction of blade mass by 23.86%.

Comparison of SQP and AL algorithms for deterministic constrained production optimization of hydrocarbon reservoirs

The present work proposes a methodology for optimization of a liquefied biomethane (LBM) production plant. The LBM production plant comprises amine-based absorption upgrading followed by a single expander refrigeration cycle. The processes were modeled using Aspen HYSYS® and optimized through a Sequential Quadratic Programming algorithm. Any changes in the operating conditions of the upgrading process will affect the cooling demand in the liquefaction, while the opposite is not true. Based on this, a sequential optimization approach starting with the upgrading process is proposed. In order to accommodate the connection between the processes, different objective functions were formulated for the sequential optimization approach. The results from the sequential approach were compared with an overall optimization approach, where the entire LBM plant was optimized simultaneously. The results indicate that the same solution was obtained both for the sequential approach and the simultaneous approach. For the sequential approach, however, the best result was observed when the interaction between the upgrading and liquefaction processes was accounted for by considering the effect of the upgrading process on the exergy requirement in the liquefaction process.

This paper discusses a kind of optimization problem with linear complementarity constraints, and presents a sequential quadratic programming (SQP) algorithm for solving a stationary point of the problem. The algorithm is a modification of the SQP algorithm proposed by Fukushima et al. [Computational Optimization and Applications, 10 (1998), 5-34], and is based on a reformulation of complementarity condition as a system of linear equations. At each iteration, one quadratic programming and one system of equations needs to be solved, and a curve search is used to yield the step size. Under some appropriate assumptions, including the lower-level strict complementarity, but without the upper-level strict complementarity for the inequality constraints, the algorithm is proved to possess strong convergence and superlinear convergence. Some preliminary numerical results are reported.

In this article, the artificial intelligence techniques have been used for the solution of Falkner–Skan (FS) equations based on neural networks optimized with three methods including active set technique, sequential quadratic programming and genetic algorithms (GA) hybridization. Log-sigmoid activation function is used in artificial neural network architecture. The proposed techniques are applied to a number of cases for Falkner–Skan problems, and results were compared with GA hybrid results in all cases and were found accurate. The level of accuracy is examined through statistical analyses based on a sufficiently large number of independent runs.

The objective of this study is to propose the effective method of reducing the maximum contact pressure at the contact edge of press-fitted shaft by optimizing the location and the size of stress relief groove in hub face. Finite element analysis was performed in order to determine the contact pressure and stress state on a press-fitted assembly and optimization were applied to select optimal size and location of stress relief groove. In order to reduce the cost of optimization design, approximation optimization method is used and the optimum results obtained by Sequential Quadratic Programming and genetic algorithm are compared. Optimized results show that the maximum contact pressure at the contact edge of press-fitted shaft with optimal stress relief groove decreased about 30%. And the both optimization algorithms can effectively reduced the maximum contact pressure at the contact edge of press-fitted shaft.

Performance analysis of support vector machine, Gaussian Process Regression, sequential quadratic programming algorithms in modeling hydrogen-rich syngas production from catalyzed co-gasification of biomass wastes from oil palm

A dynamic system model for solving convex nonlinear optimization problems

The sequential quadratic programming and genetic algorithms are finely tuned on the optimal design of a brushless DC wheel motor. Combining both methods, a hybrid approach is tested. Multiobjective optimizations are performed using Nondominated Sorting Genetic Algorithm, Strength Pareto Evolutionary Algorithm, and a variable weighted sum of objectives with the sequential quadratic programming. The Pareto fronts are compared in term of accuracy, uniformity, and coverage criteria using some quality indicators.

Optimal Design of Heat Exchanger Networks by Using SQP Algorithm Based on GPU Acceleration

Recently, with the wide adoption of electric vehicles, transportation network has shown different characteristics and been further developed. In this paper, we present a new time-flow impedance function, which may be more realistic than the existing time-flow impedance functions. Based on this new impedance function, we present an optimization model for a mixed-mode traffic network in which battery electric vehicles (BEVs) and gasoline vehicles (GVs) are chosen. We suggest two approaches to handle the model: One is to use the interior point (IP) algorithm and the other is to employ the sequential quadratic programming (SQP) algorithm. Three numerical examples are presented to illustrate the efficiency of these approaches. In particular, our numerical results show that more travelers prefer to choosing BEVs when the distance limit of BEVs is long enough and the unit operating cost of GVs is higher than that of BEVs, and the SQP algorithm is faster than the IP algorithm.

본 논문은 사각 보강 패널에 사용되는 모자형 보강재의 크기 최적화에 대한 결과를 제시하였다. 보강 패널은 1500 ~ 2500 m/s 의 속도를 가지는 발사체와 충돌한다. 모자형 보강재의 크기를 결정하기 위해서 크기 최적화가 수행되었다. 크기 최적화를 수행하기 위해서는 3 개의 함수들로 구성되어 있다. 이 함수들은 목적함수, 제한함수 그리고 설계 함수로 이루어져 있다. 목적 함수는 보강 패널의 1 차 고유진동수의 최대화가 되도록 하는 것이다. 제함 함수는 보강재의 부피가 사각 패널 부피의 10 % 이내가 되도록 하는 것이며, 설계 변수로는 모자형 보강재의 치수가 된다. 최적화된 모자형 보강재를 이용하한 보강 패널을 사용하여 초고속 충돌에 대한 시뮬레이션을 수행하였으며, 최적화된 보강재에 대해서 속도와 운동 에너지 변화에 대한 결과를 얻었다. 보강 패널의 충돌 저감을 평가하기 위해서 발사체의 운동에너지와 속도를 무차원화 계산을 수행하여 비교 분석 하였다.

The main objective of the optimal reagent addition was to maximize the quantity of product with minimal quantity of feed. In the present study, the optimal addition of reagents during the chemical leaching of coal was computed. Chemical leaching of coal was carried out using aqueous solution of caustic to dissolve and remove the mineral matter. Simulation studies were carried out using the optimal reagent addition for chemical leaching of coal in batch reactors. This was experimentally validated, using the bench-scale reactor setup with hierarchical optimization architecture. Chemical leaching experiments were conducted using West Bokaro coal. Samples collected at various time intervals during the experiment were analyzed. Variations in silica (SiO 2 ) and alumina (Al 2 O 3 ) concentrations, which were main constituents present in coal ash, were evaluated with respect to time for different concentrations of caustic. The simulation studies for optimal addition were carried out at 6, 8 and 10 intervals. An objective function, required for maximum ash removal, was solved, using sequential quadratic programming (SQP) algorithm to find out the optimum sequence for reagent dosing. An improvement of about 1% (wt) ash reduction on an average was observed with implementation of optimal reagent addition.

The gradient magnetic field has been widely used in biomedicine and other fields. Electrical coils are often used in engineering to generate the required magnetic field. In practical application, the uniformity of the magnetic gradient coil has a great influence on the accuracy of measuring point positioning of the magnetic sensor. In this paper, the gradient uniformity is taken as the optimization goal, and the sequential quadratic programming (SQP) algorithm is creatively used to study the coil design and optimization method. Firstly, based on the design of the standard Helmholtz coil configuration, the parameters to be optimized are determined. The objective function of the magnetic field gradient uniformity is then established, and the objective function is iteratively optimized by the SQP optimization algorithm. Numerical simulation results show that the gradient uniformity of the magnetic field is greatly improved by optimizing the coil configuration.

This paper deals with the synthesis of planar and spherical adjustable fourlink mechanisms whose coupler point can approximately trace two or more paths prescribed by a large number of points. A single adjustment in one of the mechanism parameters is used to approximately obtain the prescribed paths. A two stage approach is used to first determine the parameters of the driving dyad and then the parameters of the driven dyad. The use of novel and appropriate objective functions with minimum number of variables and a sequential quadratic programming (SQP) algorithm to search for the optimal mechanism parameters leads to quick and accurate synthesis of the four-link mechanisms.