Multidisciplinary Design Optimization Techniques Research Articles

A scalable problem to benchmark robust multidisciplinary design optimization techniques

A scalable problem to benchmark robust multidisciplinary design optimization techniques

Multidisciplinary design and optimization of winged architectures for reusable launch vehicles

Partially reusable launch vehicle design has raised a great interest in order to minimize the costs of space transportation by recovering and refurbishing the first stage. Several configurations such as toss-back architectures are now operational. These concepts require additional rocket propellant in order to carry the boost-back and landing maneuvers, that induce several losses in terms of payload mass performance for the ascent mission. In order to limit these losses, this paper focuses on the design of two types of architectures to carry out the return-to-launch-site mission. The first, named glide-back, uses the main propulsion system of the first stage to perform a boost-back burn and returns to the landing site by a gliding mode using additional lifting surfaces. The second, named, fly-back, performs the return-to-launch-site mission using several air-breathing engines located in the nose of the first stage combined with lifting surfaces. In this paper, the design of a reusability kit, allowing to provide the first stage with both expendable and reusable capabilities, is investigated. This kit is composed of the lifting surfaces, the nose including the air-breathing propulsive system (for fly-back configuration), landing gears and additional avionics. This paper addresses the design of such reusability kits and presents the mission specifications, the design process relying on Multidisciplinary Design Optimization techniques, the vehicle performance and the optimal trajectories. Different analyses (aero-propulsive, optimal control, operational considerations, etc.) are detailed and trade-offs between the studied configurations are assessed.

A Safety-Focused System Architecting Framework for the Conceptual Design of Aircraft Systems

To reduce the environmental impact of aviation, aircraft manufacturers develop novel aircraft configurations and investigate advanced systems technologies. These new technologies are complex and characterized by electrical or hybrid-electric propulsion systems. Ensuring that these complex architectures are safe is paramount to enabling the certification and entry into service of new aircraft concepts. Emerging techniques in systems architecting, such as using model-based systems engineering (MBSE), help deal with such complexity. However, MBSE techniques are currently not integrated with the overall aircraft conceptual design, using automated multidisciplinary design analysis and optimization (MDAO) techniques. Current MDAO frameworks do not incorporate the various aspects of system safety assessment. The industry is increasingly interested in Model-Based Safety Assessment (MBSA) to improve the safety assessment process and give the safety engineer detailed insight into the failure characteristics of system components. This paper presents a comprehensive framework to introduce various aspects of safety assessment in conceptual design and MDAO, also considering downstream compatibility of the system architecting and safety assessment process. The presented methodology includes specific elements of the SAE ARP4761 safety assessment process and adapts them to the systems architecting process in conceptual design. The proposed framework also introduces a novel safety-based filtering approach for large system architecture design spaces. The framework’s effectiveness is illustrated with examples from applications in recent collaborative research projects with industry and academia. The work presented in this paper contributes to increasing maturity in conceptual design studies and enables more innovation by opening the design space while considering safety upfront.

From System Architecting to System Design and Optimization: A Link Between MBSE and MDAO

AbstractOptimization of system architectures can help deal with finding better system architectures in a large design space plagued by combinatorial explosion of alternatives. To enable architecture optimization, the design space should therefore be formalized into a numerical optimization problem, and it should be possible to quantitatively evaluate architecture alternatives. This paper presents a methodology for generating and modeling architecture design spaces using the Architecture Design Space Graph (ADSG), and using collaborative Multidisciplinary Design Analysis and Optimization (MDAO) techniques to evaluate architectures. Collaborative MDAO leverages disciplinary expertise while ensuring that analysis tools exchange data consistently and correctly using a central data schema. The problem solved in this paper is the missing link between architecture optimization and collaborative MDAO: the reflection of generated architectures in the central data schema. It is solved by the authors by mapping architecture components and Quantities of Interest (QOIs) to the central data schema using Data Schema Operations (DSOs). Such a mapping also assists the user in identifying missing or unnecessary disciplinary analysis tools. Three web‐based software tools implementing the methodology are presented. Finally, the methodology and tools are demonstrated using the design of a supersonic business jet as an example.

From FAST to FAST-OAD: An open source framework for rapid Overall Aircraft Design

To face the increasing environmental footprint of commercial aviation, industrial and research efforts have been focusing on exploring unconventional configurations and new propulsion paradigms, mostly based on electric technology. Such explorations require Overall Aircraft Design that has to be performed in an integrated multidisciplinary design environment. Such design environments are often limited to multidisciplinary analysis, adapted for a single aircraft configuration or require an important effort to be mastered. FAST-OAD is a software program developed by ONERA and ISAE-SUPAERO for aircraft sizing analysis and optimization that aims at user friendliness and modularity. It is an aircraft sizing code based on multidisciplinary design optimization techniques and the point mass approach to estimate the required fuel and energy consumption for a given set of TLARs. This paper presents the motivations for moving from the original software program, called FAST, to the open source code FAST-OAD based on OpenMDAO.

Multidisciplinary Design Optimization Framework with Coupled Derivative Computation for Hybrid Aircraft

Hybrid-electric aircraft are a potential way to reduce the environmental footprint of aviation. Research aimed at this subject has been pursued over the last decade; nevertheless, at this stage, a full overall aircraft design procedure is still an open issue. This work proposes to enrich the procedure for the conceptual design of hybrid aircraft found in literature through the definition of a multidisciplinary design optimization (MDO) framework aimed at handling design problems for such kinds of aircraft. The MDO technique has been chosen because the hybrid aircraft design problem shows more interaction between disciplines than a conventional configuration, and the classical approach based on multidisciplinary design analysis may neglect relevant features. The procedure has been tested on the case study of a single-aisle aircraft featuring hybrid propulsion with distributed electric ducted fans. The analysis considers three configurations (with 16, 32, and 48 electric motors) compared with a conventional baseline at the same 2035 technological horizon. To demonstrate the framework’s capability, these configurations are optimized with respect to fuel and energy consumption. It is shown that the hybrid-electric concept consumes less fuel/energy when it flies on short range due to the partial mission electrification. When one increases the design range, penalties in weight introduced by hybrid propulsion overcome the advantages of electrified mission segment: the range for which hybrid aircraft have the same performance of the reference conventional aircraft is named the “breakdown range.” Starting from this range, the concept is no longer advantageous compared to conventional aircraft. Furthermore, a tradeoff between aerodynamic and propulsive efficiency is detected, and the optimal configuration is the one that balances these two effects. Finally, multiobjective optimization is performed to establish a tradeoff between airframe weight and energy consumption.

Benchmarking of monolithic MDO formulations and derivative computation techniques using OpenMDAO

The design optimization of coupled systems requires the implementation of multidisciplinary design optimization techniques in order to obtain consistent and optimal solutions. The associated research topics include the development of optimization algorithms, computational frameworks, and multidisciplinary design optimization formulations. This paper presents a benchmarking of the combination of monolithic formulations and derivative computation techniques. The monolithic formulations include typical literature formulations as well as new normalized variable hybrid formulation. A novel test problem is proposed which consists in the sizing of a space launcher thrust vector control electro-mechanical actuator. Solving the single multidisciplinary coupling present in this problem is complex due to the possibility to face one, two, or no solutions depending on the external load and reducer gear ratio configuration. A larger scale version of this test problem is also proposed and tested by adding a high degree of freedom point-to-point trajectory. The tests are carried out in order to obtain typical performance criteria but also some proposed additional robustness criteria such as variation of the initial conditions or the external load scale. These additional criteria are particularly relevant in an industrial engineering design context where knowledge capitalization and reuse are sought. The most significant findings are the interesting performances of the new formulation in terms of computational cost and the robustness. Furthermore, the effect of the choice of derivative computation strategy on different performance criteria is assessed for the original and larger scale problem, and thus underlines the benefits of full analytic gradient-based optimization.

A Novel Framework for Simultaneous Topology and Sizing Optimization of Complex, Multi-Domain Systems-of-Systems

Abstract This article presents a novel design framework for topology and component sizing optimization of multi-domain dynamic systems described by conservation laws. Multidisciplinary design optimization (MDO) is a powerful tool for minimizing metrics such as inefficiency and cost for these systems-of-systems (SoS). However, quality of the designs identified from the optimization procedure depends on model accuracy and ability to capture inter-system interactions. This work utilizes a conservation-based, graphical modeling approach to capture physical system dynamics and interactions, and expands it to be used in MDO techniques. This yields three contributions to the literature. First, an augmented graph-based model is provided, expressing continuous and discrete design variable values as changes to vertex size, edge size, and edge connections of the dynamic system graph. Second, a sizing and topology optimization framework is developed using the augmented graph-based model as a basis. Third, analytical and numerical sensitivity functions are derived for a cooling system design problem, stemming from application of the design framework. The design framework is applied to two case studies for cooling subsystem design and electric vehicle (EV) powertrain design, with the goal of optimizing thermal and electrical component sizes, as well as discrete choices in the topology of the system being designed. These case studies provide examples for how the design framework enables analysis of alternatives (AoA) during early design stages.

Optimum target stiffness allocation for design of a reinforcing member on an existing structure

The reinforcing members are often added on an existing structure to improve stiffness of the structure up to required level. In general, the design targets for the reinforcing members need to be allocated for their designs. However, since the members are additively designed, it is difficult to predict behavior of the reinforcing members and their influence on the existing structure. Therefore, allocating the design targets is challenging task, and the targets based on engineering experience and intuition can lead to the repetitive design cycles. This paper proposes a method for determining target stiffness of a reinforcing member which makes an existing structure achieve the required performances. To utilize individual models of an existing structure and the reinforcing members in a design, the system of equations of the assembled structure is decomposed by using a substructuring technique. Additional boundary conditions are imposed on the interfaces between the structure and members to ensure consistency between models, and the target stiffness of the member is defined by using the boundary conditions. The optimal target stiffness and design of the members are determined through the use of a multidisciplinary design optimization technique, analytical target cascading. This method is applied to a simple portal frame and a body-in-white with reinforcing member of a vehicle manufactured by Hyundai Motor Company. By using the optimal target stiffness, reinforcing member of any shape can be designed independently and at little cost, without access of the existing structure model.

Investigation of Horizontal Micro Kaplan Hydro Turbine Performance Using Multi-Disciplinary Design Optimization

AbstractThis study investigates performance-based design optimization for a Kaplan hydro turbine at a maximum water head of 2.6 m (8.5 ft), a micro-sized horizontal Kaplan turbine with 7.6 cm (3.0 in) diameter that has fixed blades featured to attain the optimum performance for such type and size of hydro turbines. The optimization process includes solving design problems and enhance design development by applying a multi-disciplinary design optimization (MDO) technique. Varying the geometrical parameters of the turbine, i.e., dimensions, number of blades, blade wrap angles, and different rotational speeds (500–3000 rpm), are the relevant proposed disciplines of this study. Two multi-simulation matrices were solved by using the high-performance computing (HPC) cluster of the University of Wisconsin-Milwaukee. The first matrix includes different number of the blades (3, 4, 5, 6, and 7 blades) over six different rotational speeds (500, 1000, 1500, 2000, 2500, and 3000 rpm), while the second matrix includes 121 possible combinations of blade wrap angles starting at 60–60 deg (hub-shroud) angle to 110–110 deg angle with 5 deg increment alternated at both sides, the hub and the shroud.

Design optimization of aircraft landing gear assembly under dynamic loading

Aircraft landing gear assemblies comprise of various subsystems working in unison to enable functionalities such as taxiing, take-off and landing. As development cycles and prototyping iterations begin to shorten, it is important to develop and improve practical methodologies to meet certain design metrics. This paper presents an efficient methodology that applies high-fidelity multi-disciplinary design optimization techniques to commercial landing gear assemblies, for weight, cost, and structural performance by considering both structural and dynamic behaviours. First, a simplified landing gear assembly model was created to complement with an accurate slave link subassembly, generated based of drawings supplied from the industrial partner, Safran Landing Systems. Second, a Multi-Body Dynamic (MBD) analysis was performed using realistic input motion signals to replicate the dynamic behaviour of the physical system. The third stage involved performing topology optimization with results from the MBD analysis; this can be achieved through the utilization of the Equivalent Static Load Method (ESLM). Lastly, topology results were generated and design interpretation was performed to generate two designs of different approaches. The first design involved trying to closely match the topology results and resulted in a design with an overall weight savings of 67%, peak stress increase of 74%, and no apparent cost savings due to complex features. The second design focused on manufacturability and achieved overall weight saving of 36%, peak stress increase of 6%, and an estimated 60% in cost savings.

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.

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.

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.

Unmanned aerial vehicle derivative design optimization based on light sport aircraft

Manufacturers often develop new products by modifying and extending existing products in order to achieve new market demands while minimizing development time and manufacturing costs. In this research, an efficient derivative design process was developed to efficiently adapt existing aircraft designs according to new requirements. The proposed design process was evaluated using a case study that derives an unmanned aerial vehicle design from a baseline manned 2-seatlight sport aircraft. Multiple unmanned aerial vehicle operational scenarios were analysed to define the requirements of the derivative aircraft. These included patrol, environmental monitoring, and communications relay missions. Each mission has different requirements and therefore each resulting derivative unmanned aerial vehicle design has different geometry, devices, and performance. The derivative design process involved redefining the design requirements and identifying the minimum design variable set that needed to be considered in order to efficiently adapt the baseline design. Uncertainty was considered as well to enhance the reliability of the optimized result when it considered different conditions for each mission. An optimization method based on the possibility based design optimization was proposed to handle uncertainty that arises in the design requirements for the multi-role nature of unmanned aerial vehicles. In this paper, the possibility based design optimization method was implemented with multidisciplinary design optimization technique to derive the derivative unmanned designs based on originally manned aircraft. This approach prevented constraint violation via uncertainty variations in the operating altitude and payload weight for each. The unmanned aerial vehicle derivative designs satisfying the requirements of three different missions were derived from the proposed design process.

Computational Design for Sport Buildings

The design of sport buildings has great impact on top-sport as well as on recreational sport-activities. It implies challenging tasks in meeting the performance-requirements. This includes the control of factors like daylight/lighting, air flow, thermal conditions, just to name a few. Such factors impact the performance of athletes and are hard to control in large sport halls; their control is even harder when the public/audience is located within the halls and require different climate conditions. While mechanical installations are often needed during competitions in order to guarantee constant conditions, relaying on mechanical installations during the daily and recreational use of the venues challenges their medium/long term sustainability. Computational form finding approaches can favour the achievement of high-performing and sustainable sport buildings. In this light, the paper tackles the use of Multi-objective and Multidisciplinary design optimization. The paper presents the concept of Multi-objective Multidisciplinary design optimization techniques to support trade-off decisions between multiple conflicting design objectives and interdisciplinary design methodology, during the conceptual design of sport buildings. The proposed method is based on parametric modelling, performance simulation tools and algorithms for computational optimization, for which the paper tackles three specific aspects. First of all, due to the complexity of large sport buildings, the formulation of the optimization and the screening of the related design variables is crucial in order to obtain a meaningful design space, which helps reducing unnecessary computational burden. Secondly, assessing performance based on measurements and analyses is crucial and can be supported by performance simulations tools; however effectively integrating performance simulations tools in the early phase of the design requires new tools. In this light, a customized computational process for the rapid assessment of temperature and airflow patterns is presented. Thirdly, the process requires the combination of design optimization and design exploration, while searching for well-performing solutions. The importance of design exploration is emphasized also for sub-optimal solutions. In order to facilitate the design exploration, the combination of optimization algorithms, multi-variate analysis algorithms and options for exploring design solutions via an interactive dashboard connected to a database are presented. To exemplify the method, specific case studies are developed as collaboration between Delft university of Technology and South China university of Technology.

Multi-modular design optimization and multidisciplinary design optimization

Purpose – For complex engineering problems, multidisciplinary design optimization (MDO) techniques use some disciplines that need to be run several times in different modules. In addition, mathematical modeling of a discipline can be improved for each module. The purpose of this paper is to show that multi-modular design optimization (MMO) improves the design performances in comparison with MDO technique for complex systems. Design/methodology/approach – MDO framework and MMO framework are developed to optimum design of a complex system. The nonlinear equality and inequality constrains are considered. The system optimizers included Genetic Algorithm and Sequential Quadratic Programming. Findings – As shown, fewer design variables (optimization variables) are needed at the system level for MMO. Unshared variables are optimized in the related module when shared variables are optimized at the system level. The results of this research show that MMO has lower elapsed times (14 percent) with lower F-count (16 percent). Practical implications – The monopropellant propulsion upper-stage is selected as a case study. In this paper, the efficient model of the monopropellant propulsion system is proposed. According to the results, the proposed model has acceptable accuracy in mass model (error < 2 percent), performance estimation (error < 6 percent) and geometry estimation (error < 10 percent). Originality/value – The monopropellant propulsion system is broken down into the three important modules including propellant tank (tank and propellant), pressurized feeding (tank and gas) and thruster (catalyst, nozzle and catalysts bed) when chemical decomposition, aerothermodynamics, mass and configuration, catalyst and structure have been considered as the disciplines. The both MMO and MDO frameworks are developed for the monopropellant propulsion system.

Multidisciplinary Design Optimization of a Deorbit Maneuver Considering Propulsion, TPS, and Trajectory

Unguided reentry capsules are usually involved in ballistic entry. The final states (such as altitude and velocity of parachute activity) depend on initials parameters. Reentry trajectory parameters differently affect the thermal protection system (TPS), required deorbit propellant, and structural load. The purpose of this paper is to optimal design of deorbit parameters to minimize the thermal protection system mass and deorbit propellant mass using multidisciplinary design optimization technique. Genetic Algorithm (GA) is used to simultaneously optimize TPS discipline, propulsion discipline and trajectory in present of the all trajectory and configuration constraints. To do this, every discipline is mathematically modeled. A suitable framework of Multidisciplinary design optimization (MDO) is developed. Then, the reentry mission is optimized according to the each discipline. The results show that simultaneous optimization is more efficient in comparison with single discipline optimization such as TPS optimization or deorbit propulsion system (propulsion and propellant) optimization.

Multidisciplinary Design Optimization of Hydrogen Peroxide Monopropellant Propulsion System using GA and SQP

intense simplification in production, storage and handling of hydrogen peroxide develop a renewed interest in hydrogen peroxide thrusters especially for low cost attitude control or orbit correction (orbit maintenance). Chemical decomposition, aerothermodynamics flow and structure demand different optimum conditions such as tank pressure, catalyst bed pressure, concentration of H2O2 and geometry. These parameters play important role in propulsion system's mass and performance. Discipline conflicts are solved by Multidisciplinary Design Optimization (MDO) techniques with synchronized optimization for all subsystems respect to any criteria and limitations. In this paper, monopropellant propulsion system design optimization algorithm is presented and result of the design algorithm is validated. Results of the design algorithm have been compared with data of two different operational thrusters. According to the results, the proposed model can suitably predict total mass and performance with errors below than 10%. Then, MDO framework is proposed for the monopropellant propulsion system. Optimum propellant mass, thrust level, mass flow rate, nozzle geometry, catalyst bed length and diameter, propellant tank mass, feeding subsystem mass and total mass are derived using hybrid optimization (GA+SQP) for two space missions.

Multidisciplinary Design Optimization and Analysis of Hydrazine Monopropellant Propulsion System

Monopropellant propulsion systems are widely used especially for low cost attitude control or orbit correction (orbit maintenance). To optimize the total propulsion system, subsystems should be optimized. Chemical decomposition, aerothermodynamics, and structure disciplines demand different optimum condition such as tank pressure, catalyst bed length and diameter, catalyst bed pressure, and nozzle geometry. Subsystem conflicts can be solved by multidisciplinary design optimization (MDO) technique with simultaneous optimization of all subsystems with respect to any criteria and limitations. In this paper, monopropellant propulsion system design algorithm is presented and the results of the proposed algorithm are validated. Then, multidisciplinary design optimization of hydrazine propulsion system is proposed. The goal of optimization can be selected as minimizing the total mass (including propellant), minimizing the propellant mass (maximizing the Isp), or minimizing the dry mass. Minimum total mass, minimum propellant mass, and minimum dry mass are derived using MDO technique. It is shown that minimum total mass, minimum dry mass, and minimum propellant mass take place in different conditions. The optimum parameters include bed-loading, inlet pressure, mass flow, nozzle geometry, catalyst bed length and diameter, propellant tank mass, specific impulse (Isp), and feeding mass which are derived using genetic algorithm (GA).