Multi‐Objective Optimization of Interior Ballistic Performance Using NSGA‐II

This paper investigates an interior ballistic design with equal and unequal web thicknesses of seven‐perforation propellant grains using optimization methods. In order to reveal the influence of the web thickness of the propellant grains on the overall interior ballistic performance, burning seven‐perforation propellant grains with both equal and unequal web thickness is modeled. A currently popular evolution algorithm (EA) is used to compare two charge shapes, and to seek which one could achieve the optimal ballistic performance. Complete optimization of the interior ballistic performance is a complex process in view of the conflicting objectives to be achieved and a solution to such problems is sought by converting them into a single composite objective and using many tedious measurements. In this paper, a true multi‐objective optimization of the interior ballistic charging design is carried out by considering three objectives simultaneously. The non‐dominated sorting genetic algorithm version II (NSGA‐II) is used to solve this multi‐objective optimization problem (MOP). In order to check its implementation, both the conventional optimization algorithm‐hill climbing method (HCM) and NSGA‐II are used to solve the same single objective problem. The NSGA‐II used to capture the full Pareto‐optimal front is capable of identifying the trade‐off among the conflicting objectives thereby providing alternative useful designs for a designer. Furthermore, for seven‐perforation propellant grains, the results of using equal web thickness are compared with those of unequal web thickness, and it is shown that the two charge shapes produce no distinct difference in the interior ballistic performance.     

混合装药内弹道性能优化及应用

为了研究混合装药结构对内弹道性能的影响,以某舰炮为工程背景,提出内弹道多目标优化设计方案,研究了目标函数、设计变量及约束条件.采用e-约束法对次要目标函数进行简化处理,利用当前流行的进化算法—遗传算法进行优化计算,并针对内弹道数学模型特性,对算法进行改进.最后,通过进化计算得到的优化方案为一组非劣解集,同时验证了该改进...     

Multi‐Objective Optimization of Pseudo‐Dynamic Operation of Naphtha Pyrolysis by a Surrogate Model

A simple pseudo‐dynamic surrogate model is developed in the framework of the state space model with the feed‐forward neural network to replace the complex free radical pyrolysis model. The surrogate model is then applied to investigate the multi‐objective optimization of two key performance objectives with distinct contradiction: the mean yields of key products and the day mean profits. The ?‐constraint method is employed to solve the multi‐objective optimization problem, which provides a broad range of operation conditions depicting tradeoffs of both key objectives. The Pareto‐optimal frontier is successfully obtained and five selected cases on the frontier are discussed, suggesting that flexible operations can be performed based on industrial demands.     

Multi‐Objective Optimization of a Cross‐Flow Plate Heat Exchanger Using Entropy Generation Minimization

Multi‐objective optimization of a cross‐flow plate fin heat exchanger (PFHE) by means of an entropy generation minimization technique is described. Entropy generation in the PFHE was separated into thermal and pressure entropy generation as two objective functions to be minimized simultaneously. The Pareto optimal frontier was obtained and a final optimal solution was selected. By implementing a decision‐making method, here the LINMAP method, the best trade‐off was achieved between thermal efficiency and pumping cost. This approach led to a configuration of the PFHE with lower magnitude of entropy generation, reduced pressure drop and pumping power, and lower operating and total cost in comparison to single‐objective optimization approaches.     

Study on Dynamic Burning Rate Equation of Propellant

Aiming at the difficulty that the actual burning law of propellant in the gun bore couldn't be described exactly by static burning rate equation in the closed‐bomb, propellant dynamic burning rate equation and its acquisition method were established in the paper, which are based on static burning rate equation and considering burning gas flow and loading density influence factors in bore. A numerical code for interior ballistic two‐phase flow was successfully developed. And corresponding firing tests were also carried out. The comparison of simulations with interior ballistic code and firing test results show that the propellant dynamic burning rate equation makes the maximum errors of muzzle velocity and pressure in breech decreased from 2.97 % to 0.75 % and from 6.68 % to 0.38 %, respectively. This method not only improves simulation precision of gun muzzle velocity and pressure significantly, but also provides a means to improve the design accuracy for interior ballistic performance.     

Interior Ballistics Two‐Phase Reactive Flow Model Applied to Small Caliber Projectile‐Gun System

Multi‐dimensional multi‐component two‐phase flow modeling of solid propellant combustion in weapons is the new trend of the interior ballistics codes. Most of these codes are designated to large caliber guns and rockets simulation. Only a small number of investigations on small‐caliber gun have been recently reported, where the need of high‐performance and reliable small‐caliber guns stimulated significant interest in developing techniques to understand the phenomenology of small‐caliber ballistics and predict the behavior and the performance of this type of weapons. In this paper, a numerical model describing the combustion of solid propellant in small‐caliber gun is presented. The governing equations with customize parameters were derived in the form of coupled, non‐linear axisymmetric partial differential equations. They were further implemented into the CFD code Fluent. A numerical test showed that Fluent is able to handle correctly the interaction between the moving projectile and the combustion gases in the chamber. The interior ballistics curves along with the performance of small‐caliber gun 5.56 mm were adequately predicted. The numerical results were in agreement with the experimental results.     

Numerical simulation and optimisation of unconventional three‐section simulated countercurrent moving bed chromatographic reactor for oxidative coupling of methane reaction

The simulated countercurrent moving‐bed chromatographic reactor (SCMCR) has been reported to significantly enhance methane conversion and C₂ product yield for oxidative coupling of methane (OCM) reaction, which is otherwise a low per pass conversion reaction. A mathematical model of an unconventional three‐section SCMCR for OCM was first developed and solved using numerically tuned kinetic and adsorption parameters. The model predictions showed good agreement with available experimental results of SCMCR for OCM. Effects of several process parameters on the performance of SCMCR were investigated. A multi‐objective optimisation problem was solved at the operating stage using state‐of‐the‐art AI‐based non‐dominated sorting genetic algorithm with jumping genes adaptations (NSGA‐II‐JG), which resulted in Pareto Optimal solutions. It was found that the performance of the SCMCR could be significantly improved under optimal operating conditions. © 2011 Canadian Society for Chemical Engineering     

Robust Optimal Operation of Two‐Chamber Microbial Fuel Cell System Under Uncertainty: A Stochastic Simulation Based Multi‐Objective Genetic Algorithm Approach

This investigation is performed to study the optimal operation decision of two‐chamber microbial fuel cell (MFC) system under uncertainty. To gain insight into the mechanism of uncertainty propagation, a Quasi‐Monte Carlo method‐based stochastic analysis is conducted not only to elucidate the effect of each uncertain parameter on the variability of power density output, but also to illustrate the interactive effects of the all uncertain parameters on the performance of MFC. Moreover, a systematic stochastic simulation‐based multi‐objective genetic algorithm framework is proposed to identify a set of Pareto‐optimal robust operation strategies, which is helpful to provide an imperative insight into the relationship between the mean and standard deviation of output power density. The results indicate that (1) the coefficient of variance (COV) value of output power density has a linear relationship with the COV value of each uncertainty parameter as well as all interactive parameters; and (2) a significant performance improvement with respect to both mean and standard deviation of power density is observed by implementing the multi‐objective robust optimization. These results thus validate that the proposed uncertainty analysis and robust optimization framework provide a promising tool for robust optimal design and operation of fuel cell systems under uncertainty.     

Investigation of the curing behavior of a novel epoxy photo‐dielectric dry film (ViaLux™ 81) for high density interconnect applications

The objective of this work was to determine the cure kinetics of ViaLux™ 81 photo‐dielectric dry film and to optimize its curing schedule for the fabrication of sequentially built up high density interconnect‐printed wiring boards. Photosensitive epoxy materials such as the photo‐dielectric dry film studied herein have complicated curing regimes. This is attributed to the long lifetime of the curing catalyst that is generated by ultraviolet exposure. Dynamic differential scanning calorimetry (DSC) experiments revealed a two‐peak curing mechanism, which could not be separated at lower heating rates. The activation energies for the two cure events, calculated using the Kissinger method, were found to be 129 and 124 kJ/mol, respectively. A cure‐dependent activation energy was also determined using the isoconversional method, and a “model‐free” approach was adopted to simulate the evolution of degree‐of‐cure under dynamic and isothermal conditions. The results suggest that cure cycles of approximately 15 min at temperatures above 165°C can result in a degree‐of‐cure of 90% and above. This implies that faster fabrication is possible with either rapid thermal curing equipment or continuous cure surface mount technology furnaces. © 2000 John Wiley & Sons, Inc. J Appl Polym Sci 78: 430–437, 2000     

Low‐order optimal regulation of parabolic PDEs with time‐dependent domain

Observer and optimal boundary control design for the objective of output tracking of a linear distributed parameter system given by a two‐dimensional (2‐D) parabolic partial differential equation with time‐varying domain is realized in this work. The transformation of boundary actuation to distributed control setting allows to represent the system's model in a standard evolutionary form. By exploring dynamical model evolution and generating data, a set of time‐varying empirical eigenfunctions that capture the dominant dynamics of the distributed system is found. This basis is used in Galerkin's method to accurately represent the distributed system as a finite‐dimensional plant in terms of a linear time‐varying system. This reduced‐order model enables synthesis of a linear optimal output tracking controller, as well as design of a state observer. Finally, numerical results are prepared for the optimal output tracking of a 2‐D model of the temperature distribution in Czochralski crystal growth process which has nontrivial geometry. © 2014 American Institute of Chemical Engineers AIChE J, 61: 494–502, 2015     

一种基于多指标的火药装药弹道设计方法

在传统火药装药弹道设计方法的基础上,将装药弹道模型的求解与评价相结合,给出一种基于多指标的弹道设计新方法.该方法可建立基于3种计算模型的火药装药弹道多指标决策模型.模型中可灵活设置设计变量的变化范围,并能根据火炮的特点协调评价指标间的权重关系,模型的求解可对各设计方案进行排序.该方法使得火药装药弹道设计与评价工作同步进行,使弹道方案的选择更具科学性,且能在实验之前对各方案进行比较,对靶场实弹射击具有指导意义.计算实例表明,该方法是有效的.     

Impact of Fuel Cell Power Plants on Multi‐objective Optimal Operation Management of Distribution Network

This paper presents an interactive fuzzy satisfying method based on hybrid modified honey bee mating optimization and differential evolution (MHBMO‐DE) to solve the multi‐objective optimal operation management (MOOM) problem, which can be affected by fuel cell power plants (FCPPs). The objective functions are to minimize total electrical energy losses, total electrical energy cost, total pollutant emission produced by sources, and deviation of bus voltages. A new interactive fuzzy satisfying method is presented to solve the multi‐objective problem by assuming that the decision‐maker (DM) has fuzzy goals for each of the objective functions. Through the interaction with the DM, the fuzzy goals of the DM are quantified by eliciting the corresponding membership functions. Then, by considering the current solution, the DM acts on this solution by updating the reference membership values until the satisfying solution for the DM can be obtained. The MOOM problem is modeled as a mixed integer nonlinear programming problem. Evolutionary methods are used to solve this problem because of their independence from type of the objective function and constraints. Recently researchers have presented a new evolutionary method called honey bee mating optimization (HBMO) algorithm. Original HBMO often converges to local optima, in order to overcome this shortcoming, we propose a new method that improves the mating process and also, combines the modified HBMO with DE algorithm. Numerical results for a distribution test system have been presented to illustrate the performance and applicability of the proposed method.     

A multi‐objective optimization approach to polygeneration energy systems design

Polygeneration, typically involving co‐production of methanol and electricity, is a promising energy conversion technology which provides opportunities for high energy utilization efficiency and low/zero emissions. The optimal design of such a complex, large‐scale and highly nonlinear process system poses significant challenges. In this article, we present a multiobjective optimization model for the optimal design of a methanol/electricity polygeneration plant. Economic and environmental criteria are simultaneously optimized over a superstructure capturing a number of possible combinations of technologies and types of equipment. Aggregated models are considered, including a detailed methanol synthesis step with chemical kinetics and phase equilibrium considerations. The resulting model is formulated as a non‐convex mixed‐integer nonlinear programming problem. Global optimization and parallel computation techniques are employed to generate an optimal Pareto frontier. © 2009 American Institute of Chemical Engineers AIChE J, 2010     

Emulation and Calculation of the Burning Surface of 3D Grains of Partially Cut Multi‐Perforated Stick Propellant using the Level Set Method

In order to calculate the burnt mass fraction of complex three‐dimensional (3D) propellant grains to meet the requirements of interior ballistic modelling; the level set method is introduced to emulate and calculate the burning surface area of partially cut 7‐perforated propellant. The surface evolution and simulation of a 3D grain of partially cut 7‐perforated propellant are divided into two parts: the grain configuration initialization and the level set calculation of the propellant regression process. Parallel layer burning is assumed so that the burning surface regresses layer by layer in a direction normal to the surface until the grain is burnt completely. As the burnt mass fraction increases, the remaining propellant volume decreases gradually. The level set method easily simulates the slivering process for complex grain geometries. In this way, the burnt mass fraction of partially cut 7‐perforated propellant grain can be calculated by the level set method for the entire combustion process. Results show that the level set method is suitable to capture the burning surface for each burning step and its related parameters, such as the burning area, the remaining propellant volume and burnt mass fraction. More importantly, the level set method gives a possible solution to the coupling of grain combustion with the internal fluid simulation by the pressure and velocity. It is impossible for geometry‐based methods to integrate the internal fluid parameters in an interior ballistic model. Also, the level set method will benefit substantially the grain design and lead to improved internal ballistic performance.     

Bang‐Bang solution of nonlinear time‐optimal control problems using a semi‐exhaustivesearch

At times, the objective is to seek a bang‐bang control policy for nonlinear time‐optimal control problems. The usefulness of iterative dynamic programming (IDP) has been shown in the literature for solving such problems. However, the convergence to the optimal solution has been obtained from about 50% of the guessed values near the optimum. In this paper, we present a semiexhaustive search method for seeking such solutions and a comparison is made with the IDP. The results show that the convergence can be obtained from a significantly higher number of guessed values chosen over a much wider region around the optimum.     

Solving Multi‐objective MILP Problems in Process Synthesis using the Multi‐Criteria Branch and Bound Algorithm

The paper briefly describes the problem of process synthesis in the area of chemical engineering, and suggests its formulation as a Multi‐Objective Programming problem. Process synthesis optimization is usually modeled as Mixed Integer Linear Programming (MILP) or Mixed Integer Non‐Linear Programming (MINLP) with an economic objective function. We claim that incorporating more criteria (e.g., environmental criteria) in this kind of combinatorial optimization problem offers the decision makers the opportunity to refine their final decision by examining more than one solution (a set of efficient or Pareto optimal solutions instead of one optimal solution). For solving the multi‐objective process synthesis problem, an improved version of the Multi‐Criteria Branch and Bound (MCBB) algorithm, which has been developed by the same authors, is used. MCBB is a vector maximization algorithm capable of deriving all efficient points (supported and unsupported), for small and medium sized Multi‐Objective MILP problems. The application of MCBB in two examples from process synthesis is also presented.     

一种基于密闭爆发器试验的发射药燃烧渐增性定量评价方法

通过对比分析发射药膛内动态燃烧和密闭爆发器静态燃烧过程,发现一种理想的发射药应具备特殊的燃烧猛度分布.通过分析理想发射药和实际发射药燃烧猛度分布的差异,提出了一种基于密闭爆发器试验的发射药静态燃烧渐增性定量评价方法,评价结果与动态试验结果具有良好的一致性.     

Non‐Isothermal Kinetic Study of the Thermal Decomposition of Melamine 3‐Nitro‐1,2,4‐triazol‐5‐one Salt

Study on thermal behavior of 3‐nitro‐1,2,4‐triazol‐5‐one (NTO) salts was required to obtain important data for application purposes. These compounds have been shown to be useful intermediates for gun propellant ingredients, high energetic ballistic modifiers for solid propellants and other potential applications. In this paper, thermal decomposition and non‐isothermal kinetics of melamine 3‐nitro‐1,2,4‐triazol‐5‐one salt (MNTO) were studied under non‐isothermal conditions by DSC and TG methods. The kinetic parameters were obtained from analysis of the DSC and TG curves by Kissinger and Ozawa methods. The critical temperature of thermal explosion (T_b) was 574 K. The results show that MNTO is thermally more stable than NTO when compared in terms of the critical temperature of thermal explosion. Finally, the values of ΔS^#, ΔH^#, and ΔG^# of its decomposition reaction were calculated.