Design Optimization Techniques Research Articles

DCEO Biotechnology: Tools To Design, Construct, Evaluate, and Optimize the Metabolic Pathway for Biosynthesis of Chemicals.

Chemical synthesis is a well established route for producing many chemicals on a large scale, but some drawbacks still exist in this process, such as unstable intermediates, multistep reactions, complex process control, etc. Biobased production provides an attractive alternative to these challenges, but how to make cells into efficient factories is challenging. As a key enabling technology to develop efficient cell factories, design-construction-evaluation-optimization (DCEO) biotechnology, which incorporates the concepts and techniques of pathway design, pathway construction, pathway evaluation, and pathway optimization at the systems level, offers a conceptual and technological framework to exploit potential pathways, modify existing pathways and create new pathways for the optimal production of desired chemicals. Here, we summarize recent progress of DCEO biotechnology and examples of its application, and provide insights as to when, what and how different strategies should be taken. In addition, we highlight future perspectives of DCEO biotechnology for the successful establishment of biorefineries.

Multiobjective Performance Optimization of a Coaxial Compound Rotorcraft Configuration

This paper presents the results of a rotorcraft preliminary design problem, solved as a multiobjective design optimization problem. A lift- and thrust-augmented coaxial compound configuration is used to demonstrate the approach. The basic optimization problem is converted into a sequence of approximate optimization problems, in which approximate Pareto frontiers are calculated based on response surfaces, obtained from radial basis function interpolation of all the designs analyzed at every step of the sequence. The Pareto frontiers are computed using a genetic algorithm. The designs are analyzed using a high-fidelity rotorcraft analysis that includes nonlinear finite element models of the rotor blade and a free vortex wake model of the rotor inflow. The results presented indicate that 1) the preliminary design problem can be effectively solved using formal numerical design optimization techniques, which therefore can complement classical design methodologies; 2) with appropriate physics-based constraints, the design optimization can be carried out by the computer completely unattended; 3) the optimization methodology is sufficiently robust to deal with multiple local optima and other numerical difficulties; and 4) the methodology is efficient enough to allow the use of high-fidelity analyses throughout the optimization, with the use of graphical processing unit computing (Compute Unified Device Architecture/Fortran) contributing to the computational efficiency.

Propulsion System Integration and Optimization at the Preliminary Design Phase

Abstract: The turbine design of an aero-engine is a multidisciplinary iterative process that involves not only the designing of several components (disc, coverplate, fixing, platform, airfoil, shroud segments, turbine support case, etc.) but also the synergy of multiple disciplines and the application of all their knowledge to create the ideal set of components for the design conditions. Traditionally, the design of these components has been separated into the pre-detailed and the detailed design phases. Unfortunately during the preliminary stage of the design, engineers are not given enough time to achieve a balance between the fidelity of the results and the time taken to achieve them. This may lead to costly redesign as it is extremely difficult to correct an unsatisfactory concept at a detailed design phase of an engine. The use of Multidisciplinary Design Optimization techniques at a preliminary design phase (Preliminary MDO or PMDO) allows correcting this.A design system was created that integrates the design of the turbine based on the thermal and mechanical stresses, aerodynamics and the cooling requirements. This was part of an eventual total Propulsion System Integration and Optimization (PSIO) system. Through leveraging of commercial software such as CAD and CAE, parametric models were created for each turbine component and analyzed. This system allows more knowledge to be injected into the early stages of the design process as it helps to rapidly synthesize a turbine and evaluate its attributes over a wide range of alternative designs at a much higher degree of fidelity than was previously possible at the pre-detailed stage. This system also decreases the risk of human errors as it requires less manual inputs than previous pre-detailed processes. This risk is further reduced by the automation of data transfer between the disciplines that would usually be coordinated manually. This automation does not negate the need of engineering judgment but rather more effectively uses an engineer’s time by letting him focus only on value added decisions. Finally, the simplifications of the procedures that accompany such a system enable one engineer to be able to design a whole turbine from its components geometric design to the analyses’ results post-processing.

초고층 메가 구조시스템의 최적설계에 관한 연구

According to these high-rise buildings under construction in Korea in recent years, core walls and mega structural system were introduced as a optimal structural system with a certain scale and plane form, what need to be done included the structural characteristics of this kind of system and the accurate results, and what's more, the study of simple optimization methods should also be investigated. In this paper, models of mega structural system were determined, Approximation concept of Stiffness-Based Optimal Design Technique based on optimal design technique of preceding research and displacement sensitivity analysis were formalized, lateral drift constraint equation was created, rate of change response of structures with the changing of design parameters was determined. The possibility of optimal selection and cross section calculation of mega structural members can be known through those processes.

Resource provisioning system for mobile operator network's datacenters

The problem of the mobile data traffic and the number of services growing becomes global, moreover, volume and frequency of control traffic transmitted through the network are increasing. Therefore there is a need for its effective management to ensure the quality of service required by users and optimal use of mobile network resources. In such circumstances the load on the server that is created in the process of the connection establishing and its serving has its considerations. Dynamic resource provisioning is a useful technique for handling the variations seen in communication systems workloads. Virtualization technology allows to implement this approach. An analytic model of a system would be attractive as it would be able to evaluate system characteristics under a wide range of conditions, and to be computed comparatively easily. It also can incorporate numerical optimization techniques for system design. In this paper the problem of provisioning system design for virtualized network functions is solved. This paper presents a novel approach to correctly allocate resources in data centers, such that SLA violations and energy consumption are minimized. Proposed approach first analyzes historical workload traces to identify long-term patterns that establish a base workload. Then it employs two techniques to dynamically allocate capacity: predictive and reactive provisioning. The combination of predictive and reactive provisioning achieves a significant improvement in meeting SLAs, conserving energy and reduces provisioning costs. The method for adapting the size of network function's resource allocation control interval is proposed. It provides dynamic configuration of the system, reducing excessive service data transmitted in the network and decreasing the load of network nodes. The workload service system model is built. It outlines a method of workload forecasting taking into account long-term accumulated statistics and recent trends observed in a network. It allows to get a rational level of management costs and final values of service quality. Experiments on the study of the proposed methods in the system of Mathcad are conducted.

Optimal current profile control for enhanced repeatability of L-mode and H-mode discharges in DIII-D

In this work, model-based control techniques are used to obtain target current profiles in low-confinement-mode (L-mode) as well as high-confinement-mode (H-mode) DIII-D discharges. The control problem is formulated as a trajectory optimization problem to search for a feasible path from the expected initial condition to the desired target. The result comprises a sequence of feedforward (open-loop) control requests and a corresponding state evolution from the initial condition to the desired target. On top of this optimal feedforward control sequence a feedback (closed-loop) controller based on a linearized model and optimal control design techniques is added to track the desired state evolution. The effectiveness of the control approach is demonstrated with experiments.

Response surface experiments: A meta-analysis

Response Surface Methodology is a set of experimental design techniques for system and process optimization that is commonly employed as a tool in chemometrics. In the last twenty years, thousands of studies involving response surface experiments have been published. The goal of the present work is to study regularities observed among factor effects in these experiments. Using the Web of Science Application Program Interface, we searched for journal articles associated with response surface studies and extracted over 20,000 records from all Science Citation Index and Social Science Citation Index disciplines between 1990 and the end of 2014. We took a random sample of these papers, stratified by the number of factors, and ended up with a total of 129 experiments and 183 response variables. Extracting the data from each publication, we reanalyzed the experiments and combined the results together in a meta-analysis to reveal information about effect sparsity, heredity, and hierarchy. We empirically quantify these principles to provide a better understanding of response surface experiments, to calibrate experimenter expectations, and to guide researchers toward more realistic simulation scenarios and improved design construction.

Model-based design of optimal experiments for nonlinear systems in the context of guaranteed parameter estimation

Abstract An approach to the design of experiments is presented in the framework of bounded-error (guaranteed) parameter estimation for nonlinear static and dynamic systems. The guaranteed parameter estimation determines non-asymptotic confidence limits on the unknown parameters of a mathematical model. An essential part of the solution procedure is the approximation of the joint-confidence region. In this contribution, we develop and analyze the procedure and different ways of achieving a tight over-approximation of the solution set of guaranteed parameter estimation based on the expected values of parameters. Finally we propose to solve the problem of the design of experiments as a bilevel program. We demonstrate our approach and analyze the nature of the problem in the static and dynamic case studies. The proposed approach is also compared to the experiment design in the context of least-squares estimation and to the linearization-based techniques for optimal experiment design proposed in the literature earlier.

Pareto Ranking Bisection Algorithm for EM-Driven Multi-Objective Design of Antennas in Highly-Dimensional Parameter Spaces

A deterministic technique for fast surrogate-assisted multi-objective design optimization of antennas in highly-dimensional parameters spaces has been discussed. In this two-stage approach, the initial approximation of the Pareto set representing the best compromise between conflicting objectives is obtained using a bisection algorithm which finds new Pareto-optimal designs by dividing the line segments interconnecting previously found optimal points, and executing poll-type search that involves Pareto ranking. The initial Pareto front is generated at the level of the coarsely-discretized EM model of the antenna. In the second stage of the algorithm, the high-fidelity Pareto designs are obtained through optimization of corrected local-approximation models. The considered optimization method is verified using a 17-variable uniplanar antenna operating in ultra-wideband frequency range. The method is compared to three state-of-the-art surrogate-assisted multi-objective optimization algorithms.

Dual-Donor Organ Exchange

Owing to the worldwide shortage of deceased-donor organs for transplantation, living donations have become a significant source of transplant organs. However, not all willing donors can donate to their intended recipients because of medical incompatibilities. These incompatibilities can be overcome by an exchange of donors between patients. For kidneys, such exchanges have become widespread in the last decade with the introduction of optimization and market design techniques to kidney exchange. A small but growing number of liver exchanges have also been conducted. Over the last two decades, a number of transplantation procedures emerged where organs from two living donors are transplanted to a single patient. Prominent examples include dual-graft liver transplantation, lobar lung transplantation, and simultaneous liver-kidney transplantation. Exchange, however, has been neither practiced nor introduced in this context. We introduce dual-donor organ exchange as a novel transplantation modality, and through simulations show that living-donor transplants can be significantly increased through such exchanges. We also provide a simple theoretical model for dual-donor organ exchange and introduce optimal exchange mechanisms under various logistical constraints.

A modified particle swarm optimisation algorithm and its application in vehicle lightweight design

Particle swarm optimisation (PSO) is a global optimisation algorithm, which imitates the cooperation behaviour reflected in flocks of birds, fishes, etc. Because of its simple implementation and strong optimisation capacity, the PSO algorithm is becoming very popular in diverse engineering design applications. However, PSO is also seriously affected by the premature convergence problem similar to other global optimisation algorithms. It is generally known that diversity loss is one of the crucial impact factors. To improve the diversity of particles and enhance the algorithm's optimisation ability, the standard PSO algorithm is improved by a mutation operator, the optimal Latin hypercube design (OLHD) technique and boundary reflection method. Optimisation ability of the modified PSO is superior to the standard version through experimental comparison of eight benchmark functions. Combined with kriging surrogate model technique, the modified PSO algorithm is applied to a vehicle lightweight design problem. The frontal structure achieves 5.06 kg (13.95%) weight saving without performances loss after being optimised.

Automated thermal and stress preliminary analyses applied to a turbine rotor

The use of Multidisciplinary Design Optimization (MDO) techniques at the preliminary design phase (PMDO) of a gas turbine engine allows investing more effort at the pre-detailed phase in order to prevent the selection of an unsatisfactory concept early in the design process. Considering the impact of the turbine tip clearance on an engine's efficiency, an accurate tool to predict the tip gap is a mandatory step towards the implementation of a full PMDO system for the turbine design. Tip clearance calculation is a good candidate for PMDO technique implementation considering that it implies various analyses conducted on both the rotor and stator. As a first step to the development of such tip clearance calculator satisfying PMDO principles, the present work explores the automation feasibility of the whole analysis phase of a turbine rotor preliminary design process and the potential increase in the accuracy of results and time gains. The proposed conceptual system integrates a thermal boundary conditions automated calculator and interacts with a simplified air system generator and with several design tools based on parameterized CAD models. Great improvements were found when comparing this work's analysis results with regular pre-detailed level tools, as they revealed to be close to the one generated by the detailed design tools used as target. Moreover, this design process revealed to be faster than a common preliminary design phase while leading to a reduction of time spent at the detailed design phase. By requiring fewer user inputs, this system decreases the risk of human errors while entirely leaving the important decisions to the designer.

English

English

Robust target cascading for improving firing accuracy of combat vehicle

Complex systems like combat vehicles contain numerous subsystems and components. Therefore, simultaneous consideration of hierarchy of a system, subsystems, and components is necessary for optimization. Multidisciplinary design optimization techniques have been researched to design the complex system. However most of these techniques premise integration process of total system which requires great time and cost. To reduce time and cost for the integration process, we introduce a target cascading technique that optimizes complex hierarchy system with several subproblems of each subsystem and component. Another challenge is to improve the firing accuracy of combat vehicle under various uncertainties. Robust design is therefore necessary to improve the firing accuracy of combat vehicles. To utilize these two concepts in optimization process, statistical information of firing angle is used as linking variables for problem formulation of robust target cascading. Furthermore, analysis of variance, surrogate modeling and statistical approach evaluating firing accuracy are employed to enhance efficiency of optimization. Finally, optimum design of a combat vehicle is achieved by using robust target cascading while improving firing accuracy.

Performance and design optimization of two model based wave energy permanent magnet linear generators

Linear generators are a quickly growing segment of renewable ocean wave energy converters. This paper presents the modeling, simulation and optimal design of two types of permanent magnet linear generators for generating 3-phase voltages based on finite element analysis and intelligent design optimization techniques. Each generator stator and rotor configurations are modeled by using Computer Aided Three Dimensional Interactive Application software and the magnetic field simulation studies are carried out by using finite element method software ANSYS. Two intelligent evolutionary methods, Scatter Search optimization and Particle Swarm Optimization techniques are employed on design analysis programs which are developed by using Visual C++ software to derive optimal design parameters of the linear generator models. Simulation results show the effective exploration of the design and analysis objectives.

Fabrication of flex sensors through direct ink write technique and its electrical characterization

The present work is intended to fabricate low-cost flex sensor from conductive carbon paste using direct ink write (DIW) technique. DIW method is one of the additive manufacturing processes, which is capable to deposit a variety of material on a variety of substrates by a different mechanism to feature resolution at a microns level. It is widely used in the electronic industry for fabrication of PCBS and electrodes for different electronic devices. The DIW system in present study extrudes material stored in the syringe barrel through nozzle using compressed air. This mechanism will assist in creating patterns on a variety of substrates. Pneumatic controller is employed to control deposition of material, while computer-controlled X–Y stage is employed to control pattern generation. For effective and control patterning, printing parameters were optimized using Taguchi design optimization technique. The conductive carbon paste is used as ink for pattern generation on flexible PET substrate. Samples of flex sensor having different dimensions are prepared through DIW. The fabricated sensors were used as flexion sensor, and its electrical characteristic was evaluated. The obtained sensors are stable and reliable in performance.

A new intelligent decision making system combining classical methods, evolutionary algorithms and statistical techniques for optimal digital FIR filter design and their performance evaluation

Filtering is one of the most important processes in electrical engineering. In digital systems, there are several methods that have been developed for filter designs. In this study, a new decision making system, which can be operated online or offline, based on statistical tests are developed for choosing the most appropriate FIR filter coefficients. For this purpose, this coefficients are optimized comparatively with nine evolutionary algorithms by using combination of some of fourteen windowing and four error functions (more than six hundred different combinations) as well as can be found via nine classical methods. As the evolutionary algorithms use random variables to achieve their results, they may not always make same design on each run. Therefore, this system is need to make a valid comparison between the algorithms employed. The key feature of proposed system is artificial intelligence phase which sorts algorithms from best to worst under certain criteria according to chosen error function after using Kruskal-Wallis and multiple comparison tests. The proposed new approach in this intelligent decision making system, which can be also used for special, practical and educational purposes, gives the best results in between the algorithms for FIR filter design according to user requirements.

Rapid design optimization of antennas using variable-fidelity EM models and adjoint sensitivities

Purpose Development of techniques for expedited design optimization of complex and numerically expensive electromagnetic (EM) simulation models of antenna structures validated both numerically and experimentally. The paper aims to discuss these issues. Design/methodology/approach The optimization task is performed using a technique that combines gradient search with adjoint sensitivities, trust region framework, as well as EM simulation models with various levels of fidelity (coarse, medium and fine). Adaptive procedure for switching between the models of increasing accuracy in the course of the optimization process is implemented. Numerical and experimental case studies are provided to validate correctness of the design approach. Findings Appropriate combination of suitable design optimization algorithm embedded in a trust region framework, as well as model selection techniques, allows for considerable reduction of the antenna optimization cost compared to conventional methods. Research limitations/implications The study demonstrates feasibility of EM-simulation-driven design optimization of antennas at low computational cost. The presented techniques reach beyond the common design approaches based on direct optimization of EM models using conventional gradient-based or derivative-free methods, particularly in terms of reliability and reduction of the computational costs of the design processes. Originality/value Simulation-driven design optimization of contemporary antenna structures is very challenging when high-fidelity EM simulations are utilized for performance utilization of structure at hand. The proposed variable-fidelity optimization technique with adjoint sensitivity and trust regions permits rapid optimization of numerically demanding antenna designs (here, dielectric resonator antenna and compact monopole), which cannot be achieved when conventional methods are of use. The design cost of proposed strategy is up to 60 percent lower than direct optimization exploiting adjoint sensitivities. Experimental validation of the results is also provided.

Multi-objective optimization of functionally graded thickness tubes under external inversion over circular dies

This paper optimizes the crushing response and energy absorption of functionally graded thickness (FGT) inversion tubes using the multi-objective optimization method. The numerical results, which are validated by the experimental tests, confirm that optimizing geometry of FGT inversion tube improves energy absorption capacity with respect to ordinary uniform tube UT with the same weight. Our study shows that the die radius r, coefficient of friction μ d between the die and tube, and thickness distribution function have great influence on the responses of FGT inversion tubes. The specific energy absorption (SEA), peak crushing force $$ {P}_{max} $$ , and dynamic amplification factor (DAF) are selected as the objectives of crashworthiness optimal design. Finally, the weighted average method, multi design optimization (MDO) technique, constrained single-objective optimization, and geometrical average method were employed to find the optimal configuration of the proposed inversion tube. The results give new design ideas to improve crashworthiness performance of inversion tubes.

Evaluation of Mechanical Properties of Synthetic Fiber Reinforced Polymer Composites by Mixture Design Analysis

The objective of this work is to investigate the mechanical properties of Glass fiber reinforced Epoxy composites with different weight proportions. Using hand lay-up technique followed by compression moulding, five composites were prepared by varying the Glass fiber from 15 wt.% to 35 wt.%. The necessary mechanical tests were conducted as per ASTM standards and surface morphology of the fractured surfaces was examined by Scanning Electron Microscope (SEM). As per the obtained results, it is concluded that the 25 wt.% fiber loaded composite has superior mechanical properties. The results are mathematically evaluated by adopting Mixture Design optimization technique. The predicted values establish that 25 wt.% fiber loading shows good mechanical properties compared to other combinations.