Multidisciplinary Design Optimization Problems Research Articles

A Memetic Approach to Multi-Disciplinary Design and Numerical Optimization Problems using Intensify Slime Mould Optimizer

A Memetic Approach to Multi-Disciplinary Design and Numerical Optimization Problems using Intensify Slime Mould Optimizer

Multidisciplinary design optimization of dynamic positioning system for semi-submersible platform

The dynamic positioning system (DPS) is a complex mechatronic system consisting of multiple sub-disciplines. For such highly coupled sub-disciplines and sub-systems within the DPS, the conventional sequential design strategy cannot manage this interaction adequately and explicitly. Achieving the global optimal solution becomes challenging due to the discoordination between the structure and dynamic performance parameters. To address this challenge, a co-design strategy is explored by combining the structure and dynamic performance parameters based on a simultaneous dynamic optimization approach. To minimize the operating cost of the DPS, the radial basis function (RBF) model is extended to the constraint condition. The alpine skiing optimization (ASO) algorithm is applied to solve the multidisciplinary design optimization problem. The results indicate that the co-design strategy not only reduce the operating cost of the DPS but also achieve a smaller rolling response and yaw response.

From Requirements to Product: an MBSE Approach for the Digitalization of the Aircraft Design Process

AbstractDuring the aircraft conceptual design phase, many different design options need to be explored and compared in a short time frame. To speed up this process, efforts have been made in the past decades to digitalize parts of the design process, with a focus on the automation of the repetitive and non‐creative tasks inherent to the iterative design process. Whilst many of the newly developed methodologies focus on specific parts of the design process, a holistic model‐based design framework, incorporating the latest design technology developments, is lacking. To fill this gap, this paper presents the latest version of the Design and Engineering Engine (DEE) framework, originally proposed in the early 2000s and progressively matured through the experience of several international research collaborations. The DEE enables the setup and execution of Multidisciplinary Design Analysis and Optimization (MDAO) problems for aircraft (sub)systems, leveraging the automated, rule‐based modeling capabilities offered by Knowledge‐Based Engineering (KBE) and recent developments in the automatic formulation and integration of MDAO workflows. While the traditional MDAO process focuses on a given product architecture, the DEE allows also architectural design studies and makes use of Model‐Based Systems Engineering (MBSE) principles to address the whole design process, from requirements modeling up to the automatic verification of the requirements. In practice, the DEE provides a single conceptual framework or template from which specific design framework instances can be formulated and executed, according to the user's needs. This paper describes the DEE architecture and its implementation concepts. Furthermore, it demonstrates the application of the DEE template to four different scenarios, ranging from a simple requirement verification study, up to the simultaneous synthesis and optimization of an aircraft system and its production process, including multiple system architecture options.

A new Lagrangian solution scheme for non-decomposable multidisciplinary design optimization problems

A new Lagrangian solution scheme for non-decomposable multidisciplinary design optimization problems

Multidisciplinary design optimization for rapid revisit missile early warning constellation system

In order to improve the comprehensive design performance of the missile early warning constellation system (MEWCS), the multidisciplinary design optimization (MDO) for rapid revisit MEWCS is studied. The MDO problem consists of orbit, coverage, attitude and orbit control, payload, power, and structure disciplines, which is formulated to minimize the revisit time subject to the constraints such as eclipse time, signal to noise ratio, and entire mass of satellite. To efficiently and effectively solve the studied MDO problem, a filter-based mode pursuing sampling intelligent exploring method using discriminative coordinate perturbation (FMPS-DCP) is used to obtain the optimized configuration of constellation and optimized parameters of satellite system. The engineering example shows that the revisit time of MEWCS for the target area is significantly reduced after optimization, which illustrates the effectiveness and practicality of the work in this paper.

Multidisciplinary Topology Optimization Using Generative Adversarial Networks for Physics-Based Design Enhancement

AbstractThe computational cost of traditional gradient-based topology optimization is amplified for multidisciplinary design optimization (MDO) problems, most notably when coupling between physics disciplines is accounted for. To alleviate this, we investigate new methods and applications of generative adversarial networks (GANs) as a surrogate for MDO. Accepting physical fields from each physics discipline as input, the trained network produces an optimal design that closely resembles that of the iterative gradient-based approach. With this model as a baseline, we introduce a novel architecture that performs physics-based design enhancement of optimal single-physics designs to produce multiphysics designs. By providing the network with boundary conditions from a secondary physics discipline, we obtain multiphysics structures while avoiding the need for costly coupled multiphysics analysis, thereby generating significant savings in computational effort. We demonstrate our approach by designing a series of structures optimized for both thermal and elastic performance. With the physics-based design enhancement GAN, we obtain thermoelastic structures that outperform those produced by the baseline multiphysics GAN architecture.

A self-adaptive local diversity-preserving NSGA-II for the multidisciplinary design optimization of a miniaturized supersonic rocket

The miniaturized supersonic rocket or missile is an innovative concept most notably characterized by its small caliber, short length, and high flight speed. Tightly coupled design parameters and constrained objectives bring high complexity to launch safety assessments. The commonly used heuristic algorithm NSGA-II cannot address this complexity with its original density estimation strategy. In this paper, a multidisciplinary design optimization (MDO) based on a multidisciplinary feasible (MDF) architecture is established and simplified according to specific design requirements. The design system includes one coupled numerical model, two drivers, and six disciplines. As an optimizer for an MDO problem, multi-objective evolutionary algorithms (MOEAs) with heuristic features are very conducive to exploring the performance of complex rocket systems. The coupling relationship between the design variables of a small dual-chamber rocket is complex, and its optimal design is challenging to obtain empirically by traditional methods. Optimizing its propulsion performance and launch safety is imperative. For engineering designs, the convergence process of algorithms is often passive with a high degree of randomness, and the diversity of the final results is difficult to guarantee. This paper improves the Pareto front's diversity in designing a miniaturized supersonic rocket by using a self-adaptive local diversity-preserving NSGA-II (SLDNSGA-II) in an MDO framework with an active control strategy.

Conceptual Aircraft Empennage Design Based on Multidisciplinary Design Optimization Approach

Within a conventional aircraft design process, the horizontal tail and vertical tail are generally sized via volume coefficient methods. In this manuscript, an improved method for conceptual aircraft tail design based on multidisciplinary design optimization (MDO) approach with stability and control constraints has been developed. To develop this method, first, the tail design requirements have been derived from the regulations and the fundamental functionalities of tail plans. Then, the empennage design is formulated as an MDO problem. Eventually the design optimization of horizontal and vertical tail is combined with the design optimization of the aircraft wing. A test case is presented for concurrent wing and tail plane design, which resulted in more than 9% reduction in aircraft block fuel weight and more than 3% reduction in aircraft maximal takeoff weight, which indicates a great potential for fuel burn and carbon reductions with empennage design optimization at conceptual aircraft design phase.

Multidisciplinary design optimization of engineering systems under uncertainty: a review

PurposeAs an advanced calculation methodology, reliability-based multidisciplinary design optimization (RBMDO) has been widely acknowledged for the design problems of modern complex engineering systems, not only because of the accurate evaluation of the impact of uncertain factors but also the relatively good balance between economy and safety of performance. However, with the increasing complexity of engineering technology, the proposed RBMDO method gradually cannot effectively solve the higher nonlinear coupled multidisciplinary uncertainty design optimization problems, which limits the engineering application of RBMDO. Many valuable works have been done in the RBMDO field in recent decades to tackle the above challenges. This study is to review these studies systematically, highlight the research opportunities and challenges, and attempt to guide future research efforts.Design/methodology/approachThis study presents a comprehensive review of the RBMDO theory, mainly including the reliability analysis methods of different uncertainties and the decoupling strategies of RBMDO.FindingsFirst, the multidisciplinary design optimization (MDO) preliminaries are given. The basic MDO concepts and the corresponding mathematical formulas are illustrated. Then, the procedures of three RBMDO methods with different reliability analysis strategies are introduced in detail. These RBMDO methods were proposed for the design optimization problems under different uncertainty types. Furtherly, an optimization problem for a certain operating condition of a turbine runner blade is introduced to illustrate the engineering application of the above method. Finally, three aspects of future challenges for RBMDO, namely, time-varying uncertainty analysis; high-precision surrogate models, and verification, validation and accreditation (VVA) for the model, are discussed followed by the conclusion.Originality/valueThe scope of this study is to introduce the RBMDO theory systematically. Three commonly used RBMDO-SORA methods are reviewed comprehensively, including the methods' general procedures and mathematical models.

Development of engineering cost models for integrated design optimization of onshore and offshore wind farms

The goal of any multidisciplinary design optimization problem for wind power plants is to reduce the overall levelized cost of the energy. When it comes to designing a wind farm, one has to find the best combination of multiple parameters, such as turbine types, turbine dimensions and farm layout, to ensure the minimum cost. Clearly, since any design parameter may affect several cost items and performance indices of a farm, the most cost-effective solution should handle the mutual coupling among all design variables. For example, increasing the turbine spacing surely has a positive impact on energy production due to the minimization of wake losses, but, at the same time, may have a detrimental impact on the cost of cabling. In order to assist multidisciplinary design activities for wind farms and wind turbines, the present work is aimed at developing a tool for preliminary estimation of the levelized cost of energy of land- and sea-based wind farms. Such a tool is based on a modular architecture, which will ease the integration of the tool in a multi-level design framework. Each module of the tool implements one or more engineering models to estimate all cost items along with the annual energy production of the farm, starting from a few pieces of information related to turbine types and dimensions, farm geometry and wind conditions.

Data-Driven Set Based Concurrent Engineering Method for Multidisciplinary Design Optimization

<div class="section abstract"><div class="htmlview paragraph">In the development of multi-disciplinary systems, many experts in different discipline fields need to collaborate with each other to identify a feasible design where all multidisciplinary constraints are satisfied. This paper proposes a novel data-driven set-based concurrent engineering method for multidisciplinary design optimization problems by using machine learning techniques. The proposed set-based concurrent engineering method has two advantages in the concurrent engineering process. The first advantage is the decoupling ability of multidisciplinary design optimization problems. By introducing the probabilistic representation of multidisciplinary constraint functions, feasible regions of each discipline sub-problem can be decoupled by the rule of product. The second advantage is an efficient concurrent study to explore feasible regions. A batch sampling strategy is introduced to find feasible regions based on Bayesian Active Learning (BAL). In the batch BAL, Gaussian Process models of each multi-disciplinary constraint are trained. Based on the posterior distributions of trained Gaussian Process models, an acquisition functions that combine Probability of Feasibility and Entropy Search are evaluated. In order to generate new sampling points in and around feasible regions, optimization problems to maximize the acquisition function are solved by assuming that the constraint function is Lipschitz continuous. To show the effectiveness of the proposed method, a practical numerical example of a multi-disciplinary vehicle design problem is demonstrated.</div></div>

An approach to optimize cooling channel parameters of Low pressure Die casting process for reducing shrinkage porosity in Aluminium alloy wheels

A majority of automobile wheel market is occupied by aluminium alloys because of numerous advantages, one of which includes light weight. Most of these Aluminium alloy wheels are cast using Low Pressure Die Casting process. However this process results in several defects in the wheel rims, majority of which are due to shrinkage porosity. The presence of such defects reduce the fatigue life of wheels as they act as high-stress zones. Hence, it is paramount to understand the possible defects with their location and intensity and minimize it. One possible way is to perform a multi-physics simulation of filling and solidification process and estimate the defect location and intensity. However minimizing these defects, calls for a highly non-linear space Multi-disciplinary Design Optimization (MDO) problem. Existing techniques such as full factorial analysis may not be computationally efficient when it comes to contribution from large number of parameters. Shrinkage porosity is affected by various process parameters like cooling channels flow rate, delay and duration of flow, mold pre-heat temperatures, pressure cycle of molten metal, alloy composition, etc. The current research focuses on development of an efficient approach to solve a MDO problem in LPDC process of AlSi7Mg0.3 alloy wheel rim. The work specifically deals with minimizing the shrinkage porosity by optimizing the cooling channel parameters like delay and duration. This can also be extended for other parameters like flow rates, pre-heat temperatures, pressure cycle, etc.

Correction to: An effective solution to numerical and multi-disciplinary design optimization problems using chaotic slime mold algorithm

Correction to: An effective solution to numerical and multi-disciplinary design optimization problems using chaotic slime mold algorithm

Multidisciplinary design optimization of a wide speed range vehicle with waveride airframe and RBCC engine

The wide speed range vehicle is believed to be ideal for space round trip and its design process incorporates a number of disciplines. To achieve an overall optimal design, we adopt the multidisciplinary design optimization (MDO) approach to design the wide speed range vehicle with waverider being the airframe and the rocket-based combine cycle (RBCC) engine being the propulsion system. The design process includes the modeling of each discipline, the MDO system integration and the operation of the MDO system. Disciplines integrated in the MDO architecture includes the geometry, the aerodynamics, the propulsion, the structural mass and the trajectory. The modeling processes of these disciplines are introduced in brief in this paper and some of them are novel in the relevant area. The Bi-level system integrated optimization (BLSIO) approach is proposed as the MDO strategy, which optimize the trajectory discipline using the state variables of the other disciplines in each iteration process. With an aircraft climb mission being the optimize background, the MDO process is applied successfully and the optimal design obtained by the MDO process reduced the minimum climb time by 10.02% in comparison with the reference model. The analysis shows that the reduce of the minimum climb time is mainly realized by optimizing the configuration of the vehicle and adapting the attack angle in the climb process. The optimal configuration has larger lift-to-drag ratio but fewer fuel remaining after the mission than the reference model. This concludes that although the optimized model is not superior to the reference model in all aspects, it has an overall performance within the design space. Also, the BLSIO approach is ideal in solving the MDO problem of the wide speed range mission.

An effective solution to numerical and multi-disciplinary design optimization problems using chaotic slime mold algorithm.

Slime mold algorithm (SMA) is a recently developed meta-heuristic algorithm that mimics the ability of a single-cell organism (slime mold) for finding the shortest paths between food centers to search or explore a better solution. It is noticed that entrapment in local minima is the most common problem of these meta-heuristic algorithms. Thus, to further enhance the exploitation phase of SMA, this paper introduces a novel chaotic algorithm in which sinusoidal chaotic function has been combined with the basic SMA. The resultant chaotic slime mold algorithm (CSMA) is applied to 23 extensively used standard test functions and 10 multidisciplinary design problems. To check the validity of the proposed algorithm, results of CSMA has been compared with other recently developed and well-known classical optimizers such as PSO, DE, SSA, MVO, GWO, DE, MFO, SCA, CS, TSA, PSO-DE, GA, HS, Ray and Sain, MBA, ACO, and MMA. Statistical results suggest that chaotic strategy facilitates SMA to provide better performance in terms of solution accuracy. The simulation result shows that the developed chaotic algorithm outperforms on almost all benchmark functions and multidisciplinary engineering design problems with superior convergence.

Parallel Adaptive Kriging Method with Constraint Aggregation for Expensive Black-Box Optimization Problems

Design optimization problems with black-box computation-intensive objective and constraints are extremely challenging in engineering practices. To address this issue, an efficient metamodel-based optimization strategy using parallel adaptive kriging method with constraint aggregation (PAKM-CA) is proposed. In PAKM-CA, the complex expensive constraints are aggregated using the Kreisselmeier and Steinhauser (KS) function. Besides, based on the notion of Pareto nondomination in terms of objective optimality and KS function feasibility, a novel parallel comprehensive feasible expected improvement (PCFEI) function considering the correlations of sample points is developed to effectively determine the sequential infill sample points. The infill sample points with the highest PCFEI function values are selected to dynamically refine the kriging metamodels, which simultaneously improves the optimality and feasibility of optimization. Moreover, the optimization time can be further reduced via the parallel sampling framework of PCFEI. Then the convergence and efficiency merits of PAKM-CA are demonstrated via comparing with competitive state-of-the-art metamodel-based constrained optimization methods on numerical benchmarks. Finally, PAKM-CA is applied to a practical long-range slender guided rocket multidisciplinary design optimization problem to illustrate its effectiveness and practicality for solving real-world engineering problems.

Multi-Objective Multidisciplinary Design Optimization of a Robotic Fish System

Biomimetic robotic fish systems have attracted huge attention due to the advantages of flexibility and adaptability. They are typically complex systems that involve many disciplines. The design of robotic fish is a multi-objective multidisciplinary design optimization problem. However, the research on the design optimization of robotic fish is rare. In this paper, by combining an efficient multidisciplinary design optimization approach and a novel multi-objective optimization algorithm, a multi-objective multidisciplinary design optimization (MMDO) strategy named IDF-DMOEOA is proposed for the conceptual design of a three-joint robotic fish system. In the proposed IDF-DMOEOA strategy, the individual discipline feasible (IDF) approach is adopted. A novel multi-objective optimization algorithm, disruption-based multi-objective equilibrium optimization algorithm (DMOEOA), is utilized as the optimizer. The proposed MMDO strategy is first applied to the design optimization of the robotic fish system, and the robotic fish system is decomposed into four disciplines: hydrodynamics, propulsion, weight and equilibrium, and energy. The computational fluid dynamics (CFD) method is employed to predict the robotic fish’s hydrodynamics characteristics, and the backpropagation neural network is adopted as the surrogate model to reduce the CFD method’s computational expense. The optimization results indicate that the optimized robotic fish shows better performance than the initial design, proving the proposed IDF-DMOEOA strategy’s effectiveness.

Multidisciplinary Lightweight Optimization for Front Impact Structure of Body Frame Based on Active and Passive Safety

The Analytic Target Cascading (ATC) is an effective method for solving hierarchical Multidisciplinary Design Optimization (MDO) problems. At the same time, this method suffers from poor convergence and low accuracy, which is caused by the inconsistency of system constraints. In this paper, a novel ATC method based on dynamic relaxation factor is proposed. The dynamic relaxation factor of consistency constraint is added in the system level and is adjusted by the deviation of the linking variables between the levels to ensure the feasible region of the design space. The effectiveness and accuracy of this method are verified by a mathematical example. This method is used to solve the lightweight problem of the trussed front part of the vehicle body frame based on active and passive safety to achieve the collaborative optimization of lightweight trussed frame, crash safety, and aerodynamic characteristics. The important value of the novel ATC method based on dynamic relaxation factor in engineering applications is proven.

A novel statistical approach to numerical and multidisciplinary design optimization problems using pattern search inspired Harris hawks optimizer

Classical Harris Hawks optimiser (HHO) algorithm has a notable approach for global optimization. However, for constrained engineering optimization problems it is easily to get stuck in local search space. To step up the global search process of the current Harris hawks optimiser and hang it out of the local search space, the research framework purpose is to identify the discovery process of the current optimiser; the Harris hawks optimiser novel version was implemented using the pattern search algorithm named as the hybrid Harris hawks pattern search algorithm (hHHO-PS). The efficiency of approached optimiser has also been evaluated for different problems of non-convex, nonlinear and highly constrained engineering optimal complications. To confirm performance of suggested algorithm, consideration was given to 23 standard CEC2005 benchmark issues and nine multidisciplinary engineering design optimization problems. After testing, the efficacy of approaching hHHO-PS optimization algorithm has been found to be much stronger than the traditional Harris hawks optimiser, gray wolf optimiser, ant lion optimiser and moth flame optimization and other currently documented heuristics, metaheuristics and hybrid form optimization approaches, and the suggested methodology endorses its efficacy in problems of multidisciplinary nature and engineering optimization.

Multidisciplinary Design Optimization of Low-Boom Supersonic Aircraft with Mission Constraints

Conceptual design of low-boom supersonic aircraft is heavily dictated by aircraft volume and lift distributions. These unique design characteristics make it a challenge to enforce mission requirements (such as static margins and trim requirements) during design optimization. This low-boom design challenge is resolved by using reversed equivalent area targets for low-fidelity low-boom inverse design and a block coordinate optimization (BCO) method for multidisciplinary design optimization (MDO). The corresponding low-boom MDO problem includes aircraft mission constraints on ranges, cruise speeds, trim for low-boom cruise, static margins for takeoff/cruise/landing, takeoff/landing field lengths, approach velocity, and tail rotation angles for trim at takeoff/landing, as well as fuselage volume constraints for passengers and main gear storage. The BCO method is developed to optimally resolve the conflicts between the low-boom inverse design objective and other design constraints. This method is successfully applied to design a low-boom supersonic configuration that carries 40 passengers, flies a low-boom mission with cruise Mach of 1.6 and range of 2500 nm, and cruises overwater at Mach 1.8 with range of 3600 nm. The generated configuration satisfies all specified mission constraints and has the potential to match a reversed equivalent area target with ground noise level below 70 PLdB.