Multidisciplinary Design Optimization Methodology Research Articles

Multidisciplinary design and optimization of intelligent Distributed Satellite Systems for EARTH observation

Recent advances in small, connected and intelligent satellite systems have created a wide range of opportunities for the adoption of intelligent Distributed Satellite Systems (iDSS) in communication, navigation and Earth Observation (EO) missions. iDSS are goal-oriented systems comprising of multiple satellites or modules that interact, communicate and/or cooperate with each other to accomplish the desired mission goals. The ability to mass-produce low-cost small satellites and contemporary developments in avionics/astrionics technology have spurred interest in iDSS, especially for Low Earth Orbit (LEO) satellite constellations and regional clusters. The SmartSat Cooperative Research Centre (CRC) and Australian space roadmap, as well as the landmark National Space Programme strategy and priorities, encompass EO. To date, insufficient progress and no conclusive outcome was made in terms of how contemporary Multidisciplinary Design Optimization (MDO) models and tools can be best tailored to the new capabilities and specificities of iDSS. The MDO of iDSS is challenging because it introduces new variables and highly non-linear interactions. In this context, we propose an MDO methodology to optimize an iDSS for persistent coverage over the entire Australian landmass. Several aspects of the iDSS are considered in this work, including the constellation model, subsystem models and the coupling interactions between different satellite subsystems and constellation design parameters. The constellation configuration, as well as the subsystems, are modelled using OpenMDAO, which is used to analyze and visualize the planned iDSS EO mission. The iDSS is then optimized using the Multidisciplinary Feasible (MDF) architecture approach and the iDSS interdependencies are numerically treated using the Nonlinear Block Gauss-Seidel (NLBGS) iterative solver. The resulting N2 diagrams are presented and the proposed solution is both spatially and temporally optimized, demonstrating that the proposed iDSS will enable near real-time persistent coverage over the entire Australian continent.

Reliability-based multidisciplinary design optimization of an aeroelastic unpowered guided aerial vehicle

Most Aeronautical and Astronautical Systems (AAS) are inherently complex, multidisciplinary, nonlinear, and computationally intensive for design and analysis. Utilizing the Reliability-Based Multidisciplinary Design Optimization framework can address the multidisciplinary nature of these systems while accounting for inherent uncertainties. In this paper, an efficient methodology for Reliability-Based Multidisciplinary Design optimization of an aerial vehicle is developed. The computational burden of reliability assessment could make its integration within a Multidisciplinary Design Optimization cycle a formidable task. In this respect, a multilevel Multidisciplinary Design Optimization architecture is proposed in which the computational cost is reduced by considering the reliability analysis, as needed only for critical subsystems. To this end, a single-level Reliability-Based Multidisciplinary Design Optimization is derived using the Performance Measure Analysis and the Karush-Kuhn-Tucker condition. The work demonstrates the integration of this formulation into the proposed multilevel Reliability-Based Multidisciplinary Design Optimization architecture. The proposed design architecture is implemented for an aeroelastic Unpowered Guided Aerial Vehicle whose outcomes are compared with previous results obtained via a mono-level Uncertainty-Based Multidisciplinary Design Optimization architecture.

A generalized methodology for multidisciplinary design optimization using surrogate modelling and multifidelity analysis

The advantages of multidisciplinary design are well understood, but not yet fully adopted by the industry where methods should be both fast and reliable. For such problems, minimum computational cost while providing global optimality and extensive design information at an early conceptual stage is desired. However, such a complex problem consisting of various objectives and interacting disciplines is associated with a challenging design space. This provides a large pool of possible designs, requiring an efficient exploration scheme with the ability to provide sufficient feedback early in the design process. This paper demonstrates a generalized optimization framework with rapid design space exploration capabilities in which a Multifidelity approach is directly adjusted to the emerging needs of the design. The methodology is developed to be easily applicable and efficient in computationally expensive multidisciplinary problems. To accelerate such a demanding process, Surrogate Based Optimization methods in the form of both Radial Basis Function and Kriging models are employed. In particular, a modification of the standard Kriging approach to account for Multifidelity data inputs is proposed, aiming to increasing its accuracy without increasing its training cost. The surrogate optimization problem is solved by a Particle Swarm Optimization algorithm and two constraint handling methods are implemented. The surrogate model modifications are visually demonstrated in a 1D and 2D test case, while the Rosenbrock and Sellar functions are used to examine the scalability and adaptability behaviour of the method. Our particular Multiobjective formulation is demonstrated in the common RAE2822 airfoil design problem. In this paper, the framework assessment focuses on our infill sampling approach in terms of design and objective space exploration for a given computational cost.

High-Fidelity Multidisciplinary Design Optimization Methodology with Application to Rotor Blades

A multidisciplinary design optimization procedure has been developed and applied to rotorcraft simulations involving tightly coupled, high-fidelity computational fluid dynamics and comprehensive analysis. A discretely consistent, adjoint-based sensitivity analysis available in the fluid dynamics solver provides sensitivities arising from unsteady turbulent flows on unstructured, dynamic, overset meshes, whereas a complex-variable approach is used to compute structural sensitivities with respect to aerodynamic loads. The multidisciplinary sensitivity analysis is conducted through integrating the sensitivity components from each discipline of the coupled system. Accuracy of the coupled system for high-fidelity rotorcraft analysis is verified; simulation results exhibit good agreement with established solutions. A constrained gradient-based design optimization for a HART-II rotorcraft configuration is demonstrated. The computational cost for individual components of the multidisciplinary sensitivity analysis is assessed and improved.

Efficient strategy for reliability-based optimization design of multidisciplinary coupled system with interval parameters

• The uncertainty propagation method in multidisciplinary system based on subinterval theory is presented. • The interval reliability displacement based multidisciplinary design optimization methodology is proposed. • The partial gradient region for interval reliability is converted to full gradient region for reliability displacement. • The convergence process can be accelerated by utilizing the gradient optimization algorithms. Non-probabilistic reliability based multidisciplinary design optimization has been widely acknowledged as an advanced methodology for complex system design when the data is insufficient. In this work, the uncertainty propagation analysis method in multidisciplinary system based on subinterval theory is firstly studied to obtain the uncertain responses. Then, based on the non-probabilistic set theory, the interval reliability based multidisciplinary design optimization model is established. Considering that the gradient information of interval reliability cannot be acquired in the whole design domain, which causes convergence difficulties and prohibitive computation, an interval reliability displacement based multidisciplinary design optimization method is proposed to address the issue. In the proposed method, the interval reliability displacement is introduced to measure the degree of interval reliability. By doing so, not only the connotation of the interval reliability is guaranteed, but more importantly, the partial gradient region for interval reliability is equivalently converted into full gradient region for reliability displacement. Consequently, the gradient information can be acquired under any circumstances and thus the convergence process is highly accelerated by utilizing the gradient optimization algorithms.

A novel aspect of composite sandwich fairing structure optimization of a two-stage launch vehicle (Safir) using multidisciplinary design optimization independent subspace approach

In this article, a novel composite sandwich structure analysis of launch vehicle fairing is considered by a new multidisciplinary design optimization methodology. Among the most important roles of this method, in addition to the convergence of optimization process, is tackling with complicated composite structure discipline. The bidirectional coupling existing between composite structure and trajectory disciplines is one of the complex problems of this multidisciplinary design optimization method. Accordingly, multidisciplinary design optimization based on independent subspaces is employed using the fixed point iteration method to achieve the best convergence at system level and segregate the disciplines. Therefore, the two proposed subspaces overcome the difficulties of common mentioned multidisciplinary design optimization of launch vehicles as the main novelty of this study. The first subspace is a multidisciplinary design optimization which includes propulsion, aerodynamics, weight and trajectory disciplines. The second one includes the novel composite fairing structure optimization as the other single discipline optimization which is analytically and numerically considered as a compact problem and is regarded as the other novelty of this work. In a case study, by applying the proposed architecture on Safir launch vehicle and considering propulsion, trajectory and also composite sandwich fairing structure design as the variables and then performing an optimization process, the Safir fairing mass is reduced from 100 kg to 57.8 kg. This causes the launch vehicle gross mass to decrease from 26 tons to 25.2 tons due to the payload nature of fairing. This 3% mass decrease of an operational launch vehicle, despite the preservation of mission performance, can be called an industrial novelty which leads to the cost reduction of space transportation. The proposed system engineering demonstrates the great importance of using multidisciplinary design optimization in complicated designs using independent subspaces to be employed for the design of future launch vehicles. It can also be a road map for future designers of space vehicles, especially those who want to consider structure optimization in the design loop of launch vehicles.

Efficient Aircraft Multidisciplinary Design Optimization and Sensitivity Analysis via Signomial Programming

This paper proposes a new methodology for physics-based aircraft multidisciplinary design optimization (MDO) and sensitivity analysis. The proposed architecture uses signomial programming (SP), a type of difference-of-convex optimization that is solved iteratively as a series of log-convex problems. A requirement of SP is that all constraints and objective functions must have explicit signomial formulas. The SP MDO architecture facilitates the low-cost computation of optimal sensitivities through Lagrange duality. The specific example of commercial aircraft MDO is considered. Using SP, a small-, medium-, and large-scale benchmark problem is solved 16, 39, and 26 times faster, respectively, than Transport Aircraft System Optimization (TASOPT), a comparable and widely used aircraft MDO tool. The SP solution times include computation of all optimal parameter and constraint sensitivities, a feature unique to the presented architecture. The reliability of SP is demonstrated by converging a commercial aircraft MDO problem for a number of different objective functions and evaluating both traditional and nontraditional aircraft configurations. While the presented example is commercial aircraft MDO, the SP MDO architecture is applicable to a range of engineering optimization problems.

A new multidisciplinary robust design optimization framework for an autonomous underwater vehicle in system and tactic design

The design process of an autonomous underwater vehicle requires mathematical model of subsystems or disciplines such as guidance and control, payload, hydrodynamic, propulsion, structure, trajectory and performance and their interactions. In early phases of design, an autonomous underwater vehicle is often encountered with a high degree of uncertainty in the design variables and parameters of system. These uncertainties present challenges to the design process and have a direct effect on the autonomous underwater vehicle performance. Multidisciplinary design optimization is an approach to find both optimum and feasible design, and robust design is an approach to make the system performance insensitive to variations of design variables and parameters. It is significant to integrate the robust design and the multidisciplinary design optimization for designing complex engineering systems in optimal, feasible and robust senses. In this article, we present an improved multidisciplinary design optimization methodology for conceptual design of an autonomous underwater vehicle in both engineering and tactic aspects under uncertainty. In this methodology, uncertain multidisciplinary feasible is introduced as uncertain multidisciplinary design optimization framework. The results of this research illustrate that the new proposed robust multidisciplinary design optimization framework can carefully set a robust design for an autonomous underwater vehicle with coupled uncertain disciplines.

Aircraft robust multidisciplinary design optimization methodology based on fuzzy preference function

This paper presents a Fuzzy Preference Function-based Robust Multidisciplinary Design Optimization (FPF-RMDO) methodology. This method is an effective approach to multidisciplinary systems, which can be used to designer experiences during the design optimization process by fuzzy preference functions. In this study, two optimizations are done for Predator MQ-1 Unmanned Aerial Vehicle (UAV): (A) deterministic optimization and (B) robust optimization. In both problems, minimization of takeoff weight and drag is considered as objective functions, which have been optimized using Non-dominated Sorting Genetic Algorithm (NSGA). In the robust design optimization, cruise altitude and velocity are considered as uncertainties that are modeled by the Monte Carlo Simulation (MCS) method. Aerodynamics, stability and control, mass properties, performance, and center of gravity are used for multidisciplinary analysis. Robust design optimization results show 46% and 42% robustness improvement for takeoff weight and cruise drag relative to optimal design respectively.

Preliminary study on launch vehicle design: Applications of multidisciplinary design optimization methodologies

The design of complex systems such as launch vehicles involves different fields of expertise that are interconnected. To perform multidisciplinary studies, concurrent engineering aims at providing a collaborative environment which often relies on data set exchange. In order to efficiently achieve system-level analyses (uncertainty propagation, sensitivity analysis, optimization, etc.), it is necessary to go beyond data set exchange which limits the capabilities of performance assessments. Multidisciplinary design optimization methodologies is a collection of engineering methodologies to optimize systems modelled as a set of coupled disciplinary analyses and is a key enabler to extend concurrent engineering capabilities. This article is focused on several examples of recent developments of multidisciplinary design optimization methodologies (e.g. multidisciplinary design optimization with transversal decomposition of the design process, multidisciplinary design optimization under uncertainty) with applications to launch vehicle design to illustrate the benefices of taking into account the coupling effects between the different physics all along the design process. These methods enable to manage the complexity of the involved physical phenomena and their interactions in order to generate innovative concepts such as reusable launch vehicles beyond existing solutions.

Mission-Based Multidisciplinary Aircraft Design Optimization Methodology Tailored for Adaptive Technologies

Aircraft design is an inherently multidisciplinary undertaking, which motivates the quest for increasingly optimized solutions using distributed design optimization architectures. In this study, a mission-based aircraft preliminary design optimization methodology tailored for the assessment of adaptive technologies is presented. Its most distinguishing feature relies on each subspace representing a different mission stage instead of the traditional aircraft design disciplines. It is tailored for the assessment of adaptive solutions where the optimum design is mission stage dependent. Low-fidelity models have been used for the aircraft design disciplines. The enhanced collaborative optimization architecture has been used, with the concepts of weighting coefficient and dynamic compatibility parameter being presented and duly assessed. Benchmarking case studies results are discussed, using wingspan, wing mean chord, propeller diameter, propeller pitch, flap relative chord, and flap deflection as design variables. A quantitative analysis of the profitability of variable span wing, variable camber flap, and variable pitch propeller is also featured.

Multidisciplinary design optimization of tunnel boring machine considering both structure and control parameters under complex geological conditions

A Tunnel Boring Machine (TBM) is an extremely large and complex engineering machine that usually works under a complicated geological environment to excavate tunnel underground. Considering the large number of sub-systems that usually belong to different disciplines, it is a challenging task to define, model, and optimize the whole TBM from the perspective of system engineering. Also, due to the complex mechanism and geological environment, the flexibility and efficiency of existing TBM excavation strategies are generally limited. To address these challenges, a multidisciplinary modeling is presented so that corresponding analytical or empirical models of each sub-system are formulated, and a multidisciplinary design optimization (MDO) method is applied to the TBM system optimization. Four excavation strategies are studied and compared, including: (i) two existing excavation strategies, and (ii) two new proposed excavation strategies by making control and/or structure parameters adaptive to geological conditions. Two case studies with these four excavation strategies are presented to illustrate the effectiveness and benefits of designing TBM using MDO methodologies. Wherein, Case I aims to minimize the construction period taking into account the restriction of sub-systems, and Case II simultaneously minimizes construction period, cost, and energy consumption. Since the resulting MDO formulation is straightforward to be solved as a single problem, the All-At-Once (AAO) method is utilized in this paper. The optimization results obtained by modeling the problem as MDO show that the excavation strategy with adaptive control and structure parameters can significantly reduce the total construction time, with lower cost and energy consumption.

Situated Design Optimization of Haptic Devices

It is a complex task to develop and optimize a high-performing haptic device. Design optimization scenarios with predefined and fixed sets of performance requirements are presented in literature. However, the early design optimization phases for haptic devices are characterized by requirement conflicting requirements with uncertainties. With a lack of knowledge, and/or an ill-defined design problem, the challenges are not only to find a high quality solution with reasonable computational effort. In this paper, a previously proposed model-based framework and methodology for multi-disciplinary design optimization of haptic devices is further developed to enable situated design scenarios, i.e. design cases that may be characterized by changing requirements, constraints, and/or performance objectives, driven by the knowledge gained in the design and optimization process itself. To provide both precision and computational efficiency, the proposed situated, i.e. flexible and adaptable, framework is based on an approach that combines design-of-experiments (DOE) with meta-modelling methods for multi-objective optimization problems. The proposed methodology is described and verified with a 6 degree-of-freedom (DOF) TAU haptic device optimization scenario, with changing ranges for the design variables and the constraints. Results from the case study strongly indicate that a thoroughly balanced and sequential DOE and metamodelling process is capable of being both effective and efficient in a situated design scenario. It is shown that the knowledge gained in the process, e.g. the number of sampling points and the most appropriate training method, may be used to efficiently balance the required computational effort with the required level of accuracy.

Multidisciplinary Design Optimization on Conceptual Design of Aero-engine

AbstractIn order to obtain better integrated performance of aero-engine during the conceptual design stage, multiple disciplines such as aerodynamics, structure, weight, and aircraft mission are required. Unfortunately, the couplings between these disciplines make it difficult to model or solve by conventional method. MDO (Multidisciplinary Design Optimization) methodology which can well deal with couplings of disciplines is considered to solve this coupled problem. Approximation method, optimization method, coordination method, and modeling method for MDO framework are deeply analyzed. For obtaining the more efficient MDO framework, an improved CSSO (Concurrent Subspace Optimization) strategy which is based on DOE (Design Of Experiment) and RSM (Response Surface Model) methods is proposed in this paper; and an improved DE (Differential Evolution) algorithm is recommended to solve the system-level and discipline-level optimization problems in MDO framework. The improved CSSO strategy and DE algorithm are evaluated by utilizing the numerical test problem. The result shows that the efficiency of improved methods proposed by this paper is significantly increased. The coupled problem of VCE (Variable Cycle Engine) conceptual design is solved by utilizing improved CSSO strategy, and the design parameter given by improved CSSO strategy is better than the original one. The integrated performance of VCE is significantly improved.

A multi-objective, multidisciplinary design optimization methodology for the conceptual design of a spacecraft bi-propellant propulsion system

Space propulsion systems play an increasingly important role in planning of space missions. The traditional method for design of space propulsion systems includes numerous design loops, which does not guarantee to reach the best optimal solution. Multidisciplinary Design Optimization (MDO) is an approach for the design of complex systems that considers a design environment with multiple disciplines. The aims of this study are to implement and compare Multidisciplinary Feasible and Collaborative Optimization architectures for the multi-Objective optimization of a bi-propellant space propulsion system design. Several disciplines such as thrust chamber, cooling, and structure were exploited in a proper combination. The main optimization objectives in the MDO frameworks were to minimize the total wet mass and maximize the total impulse by considering several constraints. Furthermore, Genetic Algorithm and Sequential Quadratic Programming are employed as the system-level and local-level optimizers. The presented design methodology provides an interesting decision making approach to select the best system parameters of space propulsion systems under conflicting goals.

Efficient aero-structural design optimization: Coupling based on reverse iteration of structural model

Traditional coupled multi-disciplinary design optimization based on computational fluid dynamics/computational structure dynamics (CFD/CSD) aims to optimize the jig shape of aircraft, and general multi-disciplinary design optimization methodology is adopted. No special consideration is given to the aircraft itself during the optimization. The main drawback of these methodologies is the huge expanse and the low efficiency. To solve this problem, we put forward to optimize the cruise shape directly based on the fact that the cruise shape can be transformed into jig shape, and a methodology named reverse iteration of structural model (RISM) is proposed to get the aero-structural performance of cruise shape. The main advantage of RISM is that the efficiency can be improved by at least four times compared with loosely-coupled aeroelastic analysis and it maintains almost the same fidelity of loosely-coupled aeroelastic analysis. An optimization framework based on RISM is proposed. The aerodynamic and structural performances can be optimized simultaneously in this framework, so it may lead to the true optimal solution. The aerodynamic performance was predicted by N-S solver in this paper. Test shows that RISM predicts the aerodynamic and structural performances very well. A wing-body configuration was optimized by the proposed optimization framework. The drag and weight of the aircraft are decreased after optimization, which shows the effectiveness of the proposed framework.

Evolutionary energy performance feedback for design: Multidisciplinary design optimization and performance boundaries for design decision support

In pursuit of including energy performance as feedback for architects’ early stage design decision making, this research presents the theoretical foundation of a designer oriented multidisciplinary design optimization (MDO) framework titled evolutionary energy performance feedback for design (EEPFD). Through a comprehensive literature review and gap analysis EEPFD is developed into an MDO methodology that provides energy performance as feedback for influencing architects’ decision making more fluidly and earlier than other approaches to date. Secondly, in response to the lack of an MDO best practice EEPFD is investigated and evaluated through two experiments. The first experiment demonstrates the ability to utilize EEPFD provided energy performance as feedback to pursue multiple architectural designs with competing objectives and tradeoffs. The second experiment identifies performance boundaries as a best practice for MDO applications to the early stage architectural design processes. The research synthesizes the results into the basis for measuring these performance boundaries as a best practice in the context where architects must gauge multiple design concepts with varying complexity coupled with performance objectives through EEPFD, thereby enhancing the influence of energy performance feedback on the early stage design process. Finally, future research into the use of performance boundaries for conceptual energy performance design exploration is discussed.

Multi-level design of an isolation transformer using collaborative optimization

Purpose – The purpose of this paper is to present an application of a multidisciplinary multi-level design optimization methodology for the optimal design of a complex device from the field of electrical engineering throughout discipline-based decomposition. The considered benchmark is a single-phase low voltage safety isolation transformer. Design/methodology/approach – The multidisciplinary optimization of a safety isolation transformer is addressed within this paper. The bi-level collaborative optimization (CO) strategy is employed to coordinate the optimization of the different disciplinary analytical models of the transformer (no-load and full-load electromagnetic models and thermal model). The results represent the joint decision of the three distinct disciplinary optimizers involved in the design process, under the coordination of the CO's master optimizer. In order to validate the proposed approach, the results are compared to those obtained using a classical single-level optimization method – sequential quadratic programming – carried out using a multidisciplinary feasible formulation for handling the evaluation of the coupling model of the transformer. Findings – Results show a good convergence of the CO process with the analytical modeling of the transformer, with a reduced number of coordination iterations. However, a relatively important number of disciplinary models evaluations were required by the local optimizers. Originality/value – The CO multi-level methodology represents a new approach in the field of electrical engineering. The advantage of this approach consists in that it integrates decisions from different teams of specialists within the optimal design process of complex systems and all exchanges are managed within a unique coordination process.

A combustor liner cooling system design methodology based on a fluid/structure approach

The paper presents a multi-disciplinary multi-objective design optimization methodology of a combustion chamber effusion cooling system. The optimizer drives an Artificial Neural Network and a Manufacturing Time Model in a repeated analysis scheme in order to increase the combustor liner LCF life and to reduce the liner cooling system manufacturing time, simultaneously. The ANN is trained with a set of fluid/structure/lifing simulations arranged in a three-levels dataset based on a properly designed DOE approach. The CFD simulations are carried out with a reliable and robust in-house developed three-dimensional high resolution reactive viscous flow solver, accounting for conjugate heat transfer approach; the liner structural analysis is performed with a standard FEM code while the liner life assessments are obtained through an in-house developed software operating on the temperature/stress fields. Results demonstrate the validity of the overall approach in a five-dimensional state space with truly moderate computational costs.

An uncertain multidisciplinary design optimization method using interval convex models

This article proposes an uncertain multi-objective multidisciplinary design optimization methodology, which employs the interval model to represent the uncertainties of uncertain-but-bounded parameters. The interval number programming method is applied to transform each uncertain objective function into two deterministic objective functions, and a satisfaction degree of intervals is used to convert both the uncertain inequality and equality constraints to deterministic inequality constraints. In doing so, an unconstrained deterministic optimization problem will be constructed in association with the penalty function method. The design will be finally formulated as a nested three-loop optimization, a class of highly challenging problems in the area of engineering design optimization. An advanced hierarchical optimization scheme is developed to solve the proposed optimization problem based on the multidisciplinary feasible strategy, which is a well-studied method able to reduce the dimensions of multidisciplinary design optimization problems by using the design variables as independent optimization variables. In the hierarchical optimization system, the non-dominated sorting genetic algorithm II, sequential quadratic programming method and Gauss–Seidel iterative approach are applied to the outer, middle and inner loops of the optimization problem, respectively. Typical numerical examples are used to demonstrate the effectiveness of the proposed methodology.