Surrogate-based multi-objective optimization of a composite laminate with curvilinear fibers

A variable stiffness design can increase the structural performance of a composite plate and provides flexibility for trade-offs between structural properties. In this paper, we examine the simultaneous optimization of stiffness and buckling load of a composite laminate plate with curvilinear fiber paths. The problem, which falls in the area of multi-objective optimization, is formulated and solved through a surrogate-based optimization algorithm capable of finding the set of optimum Pareto solutions. We integrate surrogate modeling into an evolutionary algorithm to reduce the high computational cost required to solve the optimization process. The results show that a curvilinear fiber path can increase both buckling load and stiffness simultaneously over the quasi-isotropic laminate. Furthermore, the optimum direction for varying the fiber angle is dependent on the loading direction and boundary conditions. The results for a plate under uniform compression with free transverse edges shows that varying the fiber orientation perpendicular to the loading direction can increase the buckling load by 116% with respect to that of a quasi-isotropic laminate.     

CFD simulation and optimization of the flow field in horizontal turbo air classifiers

This paper mainly presents a numerical study of the gas flow in horizontal turbo air classifiers. The effect of the air-inlet direction on the performance of classifier was also investigated through powder classification experiments. The simulated results show that the vertical vortex is the dominant flow in conical part of the classifier and there exists the horizontal vortex in the classifying chamber. The tangential velocity profile resembles a Rankine vortex inside the rotor cage. The vertical vortex intensity increases with increasing the inlet air velocity, while the rotor cage speed has limited effect on the control of gas pathlines in the classifying chamber. Horizontal turbo air classifiers are divided into four quadrants according to the air-inlet direction. For classifiers in quadrants I and III, a double-layer flow with opposite directions generates around the rotor cage which causes a secondary vortex. The secondary vortex is eliminated and the airflow becomes uniform in the classifier that belongs to quadrant Π or IV. The experimental results with fluidized catalytic cracking catalysts and fly ash demonstrate that cut sizes of this classifier decrease averagely by 5 μm and 2.2 μm respectively, and the classification accuracy increases by 7.5–10.3%.     

On-line optimization of quality in a manufacturing system

One of the major obstacles contributing to the cost, time and efficiency of improving the quality output of manufacturing systems is the propagation of defectives or errors through the system. Design of Experiments (DoE), the response surface plot and a Neural Network Metamodel (NNM) can be used automatically to detect the interrelationship of the system without the need for complex analytical tools and costly intervention. A case study is conducted here to demonstrate the capability of DoE, the response surface plot and NNM in building a decisionsupport model for achieving six-sigma quality for a manufacturing system with a significant shift in the mean number of defectives produced. The case study is based on a discrete event simulation model of an actual manufacturing system. A response surface plot is used as an off-line decision support tool. Alternatively, a grid search method implemented on the NNM can be used as an on-line decision support tool in the manufacturing system with a real-time database system.     

Biased Monte Carlo optimization: the basic approach

It is well-known that the Monte Carlo method is very successful in tackling several kinds of system simulations.It often happens that one has to deal with rare events, and the use of a variance reduction technique is almost mandatory, in order to have Monte Carlo efficient applications. The main issue associated with variance reduction techniques is related to the choice of the value of the biasing parameter. Actually, this task is typically left to the experience of the Monte Carlo user, who has to make many attempts before achieving an advantageous biasing.A valuable result is provided: a methodology and a practical rule addressed to establish an a priori guidance for the choice of the optimal value of the biasing parameter. This result, which has been obtained for a single component system, has the notable property of being valid for any multicomponent system.In particular, in this paper, the exponential and the uniform biases of exponentially distributed phenomena are investigated thoroughly.     

Experimental investigation and robustness of Ardehali inequality in four-photon six-qubit Greenberger-Horne-Zeilinger state

By exploiting both the photons’ polarization and spatial degrees of freedom, we experimentally demonstrate a hyper-entangled four-photon six-qubit Greenberger-Horne-Zeilinger (GHZ) state. Based on the state, we experimentally test the multi-particle Ardehali inequality and prove the theoretical prediction of Ardehali. Furthermore, we experimentally first investigate the robustness of the Ardehali inequality for the four-photon six-qubit GHZ state in a bit-flip noise environment and show the good robustness against decoherence for this inequality. Our works provide an experimental benchmark to estimate the safety of quantum channel in the noisy environment.     

On the robustness and efficiency of the SAM algorithm for structural optimization

A parameter study involving a feasibility factor, adaptive tolerance refinement and active set and move limits is conducted on the interior feasible direction method with successive approximations (SAM). The study involves the sizing design of five structural problems of which one is a large power transmission tower to be designed in adherence with a standard design code. It is concluded that a move limit is dispensable for this application while adaptive tolerance refinement makes the algorithm more efficient. Optimal ranges for the feasibility factor and active set limits are suggested. This study reaffirms and quantifies the exceptional stability and robustness of the SAM algorithm.     

A normal modes technique to reduce the order of poroelastic models: application to 2D and coupled 3D models

A reduced‐order model for structures involving poroelastic materials is proposed in this paper. The approach is based on a separation of the solid and fluid phases of the porous material into separate substructures. For each individual substructure, a decoupled normal mode basis is considered, from which a set of vectors for the decomposition is selected. The preserved modes are completed by an additional family to correct for the influence of the static response of the non‐preserved. It is shown that the only neglected phenomenons in the model are the inertia of the non‐preserved modes and part of their intercoupling. The following three features render the proposed scheme computationally attractive: (i) real valued matrices are involved in the transformations; (ii) the assembly of complex, frequency dependent matrices is only performed at the stage of solving for a particular frequency; and (iii) the number of normal modes required are selected using a novel method. The computational efficacy is demonstrated, on a simple but realistic 3D case, through numerical results obtained using a reduced number of DOFs, showing a significant reduction of computational cost compared with traditional methods. Copyright © 2013 John Wiley & Sons, Ltd.     

Numerical investigation of influence of treatment of the coke component on hydrodynamic and catalytic cracking reactions in an industrial riser

A three dimensional gas-solid reactive flow model based on the Eulerian-Eulerian approach was used to simulate the hydrodynamic, heat transfer and catalytic cracking reaction within in a conventional Fluid Catalytic cracking (FCC) riser. A 12-lump kinetic model was used to represent the catalytic cracking reaction network. It was proposed a catalyst deactivation model as a function of the weight percentage of coke amount on the catalyst to replace the deactivation model dependent of the residence time. It was compared the effects of novel treatment for coke component (coke produced in the solid phase) with common treatment (coke produced in the gas phase) on the fluid dynamic and catalytic cracking. The results showed that the treatment for coke component affects radial distribution of coke mass flow. It also showed that the treatment for coke plays an important role in simulation with catalyst deactivation as a function of coke amount on catalyst.     

Energy and cement quality optimization of a cement grinding circuit

This study aimed at optimizing both the energy efficiency and the quality of the end product by modifying the existing flowsheet of the cement grinding circuit. As a general application, mill filter stream is sent to the air classifier owing to its coarser size distribution than the desired product. However, the study proved that some further evaluations i.e., quality tests and chemical assays, could make it possible to treat this stream as a final product. Consequently, directing this stream to the final product silo could be considered. Within the study, sampling survey was undertaken initially that was followed by the modelling and simulation works. The calculations implied that the production rate increased by 4.45% that corresponded to energy saving of 4.26%. As the plant decided to change the flow sheet, another sampling campaign was arranged to validate the outputs of the simulation studies. In that case, the real data showed that the increase in production rate was 3.68% and 28-Days strength of the cement improved by 2.9%. As a result, the simulation outputs were found to be in agreement with the real data hence the efficiency of the cement production, both quality and energy, for a given circuit was improved.     

暖通空调CFD技术应用现状及展望

本文介绍了暖通空调CFD技术及其应用情况，并指出了CFD技术在国内外HVAC中的研究现状及最新动态，同时对今后HVAC领域中CFD技术的应用进行了展望。     

A conceptual framework for combining artificial intelligence and optimization in engineering design

Research in artificial intelligence and optimization (OR) has had significant impact on the formulation and solution of computational methods in engineering design. This paper presents a conceptual framework for understanding a more powerful technology that is evolving from a combination of these approaches. The paper first proposes generalized representations of engineering design models that involve quantitative and qualitative aspects. Second, it presents a general classification of AI and OR models in terms of model attributes, in order to establish mappings with generic solution techniques. Third, the requirements of solution methods are discussed, as well as several schemes for the integration of AI and optimization to identify future research directions. Several specific approaches are included to illustrate various ways in which AI and optimization can be combined for tackling computational design models.     

Design optimization and fabrication of a hybrid composite flywheel rotor

This paper discusses three different rim design cases of a hybrid composite flywheel rotor using strength ratio optimization. The rotor is composed of four hybrid composite rims. These rims are made from carbon–glass/epoxy with varying volume fractions of hoop wound reinforcements. Optimization is performed to reduce the maximum strength ratio during two rotor states: stationary and the maximum allowable rotational speed. The input specifications for optimization are: maximum useable energy (35 kW h), rotational speed (15,000 rpm), height, and inner radius. In the first case, the rims are wound simultaneously by continuous winding. However, in the second case, the rims are wound separately, and interferences are incorporated for their assembly by press fit. In the third case, a hybrid version of the first two cases is used, whereby two pairs of rims are wound at the same time, and in a secondary operation, the first pair is press fitted to the second pair. Each case has different fabrication costs and different strength ratios. The third case rotor has been successfully manufactured by filament winding with in situ curing, followed by press fit assembly of machined rims.     

A response-modeling approach to global optimization and OUU

The general problem considered is an optimization problem involving selecting design parameters that yield an optimal response. We assume some initial response data are available and further experimentation (physical experiments and/or computer simulations) are to be used to obtain more information. We assume further that resources and system complexity together restrict the number of experiments or simulations that can be performed. Consequently, levels of the design parameters used in the simulations must be selected in a way that will efficiently approximate the optimal design ‘location’ and the optimal value. This paper describes an algorithmic ‘response-modeling’ approach for performing this selection. The algorithm is demonstrated using a simple analytical surface and is applied to two additional problems that have been addressed in the literature for comparison with other approaches.     

Global optimization of a feature-based process sequence using GA and ANN techniques

Operation sequencing has been a key area of research and development for computer-aided process planning (CAPP). An optimal process sequence could largely increase the efficiency and decrease the cost of production. Genetic algorithms (GAs) are a technique for seeking to ‘breed’ good solutions to complex problems by survival of the fittest. Some attempts using GAs have been made on operation sequencing optimization, but few systems have intended to provide a globally optimized fitness function definition. In addition, most of the systems have a lack of adaptability or have an inability to learn. This paper presents an optimization strategy for process sequencing based on multi-objective fitness: minimum manufacturing cost, shortest manufacturing time and best satisfaction of manufacturing sequence rules. A hybrid approach is proposed to incorporate a genetic algorithm, neural network and analytical hierarchical process (AHP) for process sequencing. After a brief study of the current research, relevant issues of process planning are described. A globally optimized fitness function is then defined including the evaluation of manufacturing rules using AHP, calculation of cost and time and determination of relative weights using neural network techniques. The proposed GA-based process sequencing, the implementation and test results are discussed. Finally, conclusions and future work are summarized.     

试论空气分离产品生产与供应的优化

简述了空气分离产品生产与供应优化的概念 ,侧重论述了优化的十项措施 ,提出了经各项优化措施形成的空分产品供应系统     

CFD simulation and performance optimization of a new horizontal turbo air classifier

A new horizontal turbo air classifier equipped with two inclined air inlets has been introduced. The flow field and classification performance of the classifier have been investigated using CFD method and response surface methodology (RSM). Simulation results show that the flow field is composed of the primary swirling flow and the secondary upward washing air, and the uniformly distributed swirling flow occupies the classifying chamber. The tangential gas velocity reaches the maximum value on the outer surface of the rotor cage, generating strong centrifugal force for the particle classification. The discrete phase model (DPM) can predict the cut sizes, but cannot present the fish-hook phenomenon. The desirable experimental condition by targeting the cut size of 20 μm and minimizing the classifying accuracy index is, rotor speed of 1373.6 rpm, primary air volume flow rate of 261.8 m³/h and secondary air volume flow rate of 42.4 m³/h. The corresponding fine and coarse fraction loss are less than 1.42% and 7.24%, respectively. This study provides a new strategy to design the horizontal turbo air classifier.     

并联涡旋式压缩机油气平衡系统模拟分析及结构优化

采用两相流Eulerian模型对并联涡旋式压缩机油气平衡系统进行CFD模拟分析,得出引起2台压缩机油位差的原因是吸气管到各台压缩机分管路压差的不平衡。为减小2台压缩机的油位差,在其中1台压缩机吸气口端设置限流环,并进行模拟分析及试验验证。结果表明,在压缩机吸气口端设置直径为23 mm的限流环可以使2台压缩机的油位差不大于2 mm,满足并联涡旋式压缩机安全运行要求。     

Effect of firm characteristics,supplier relationships and techniques used on Supply Chain Risk Management (SCRM): an empirical investigation on French industrial firms

Recent years have witnessed a focus on managing risks in supply chains. On the one hand, there are many cases where events like natural disasters, strikes and terrorism have significantly influenced the performance of organisational supply chains and in turn their competitiveness. And on the other hand, operational activities and strategic decisions of the firms (concerning, for e.g. supply, procurement, production, delivery, commercialisation, demand management, planning, etc.) can be different than expected and so create uncertainties. Uncertainties, whether they are external or internal, impact organisations leading to increased supply chain risk. Realising the potential implications of these situations on supply chain competitiveness, an attempt has been made to define risks and their sources and to identify the management that can help reduce the negative impact of risks on supply chains. In this paper, a framework for supply chain risk management (SCRM) is proposed and is applied using the data collected from 164 French companies, in manufacturing sector. The literature review, theoretical framework and empirical research undertaken in this work have led to identifying critical success management for SCRM. The focus of this paper is the inter-organisational management of supply chain risk: the collaborative relationship (with industrial and supply partners) can be considered as an efficient way to make SCRM. The paper finishes with a summary of the findings and conclusions, along with suggestions for future research projects.     

Efficiency and optimization of an annular fin with combined heat and mass transfer – An analytical solution

An analysis was carried out to study the efficiency of annular fin when subjected to simultaneous heat and mass transfer mechanisms. The temperature and humidity ratio differences are the driving forces for the heat and mass transfer, respectively. Analytical solutions are obtained for the temperature distribution over the fin surface when the fin is fully wet. The effect of the atmospheric pressure on the fin efficiency was also studied, in addition to fin optimum dimensions. It is demonstrated that the closed-form solutions for a dry-fin case presented in many text books are special cases for the solutions presented in this paper.     

D-optimal mixture design: optimization of ternary matrix blends for controlled zero-order drug release from oral dosage forms

The objective of the present study was to develop a tablet formulation with a zero-order drug release profile based on a balanced blend of three matrix ingredients. To accomplish this goal, a 17-run, three-factor, two-level D-Optimal mixture design was employed to evaluate the effect of Polyox^™ (X₁), Carbopol® (X₂), and lactose (X₃) concentrations on the release rate of theophylline from the matrices. Tablets were prepared by direct compression and were subjected to an in vitro dissolution study in phosphate buffer at pH 7.2. Polynomial models were generated for the responses Y₄ (percent released in 8 h) and Y₆ (similarity factor or f₂). Fitted models were used to predict the composition of a formulation that would have a similar dissolution profile to an ideal zero-order release at a rate of 8.33% per hour. When tested, dissolution profile of the optimized formulation was comparable to the reference profile (f₂ was 74.2, and n [release exponent] was 0.9). This study demonstrated that a balanced blend of matrix ingredients could be used to attain a zero-order release profile. Optimization was feasible by the application of response surface methodology, which proved efficient in designing controlled-release dosage forms.