Multidisciplinary Design Optimization Process Research Articles

Aerostructural Wing Optimization Using a Structural Surrogate in a Coupled Adjoint Formulation

As the number of disciplines included in the multidisciplinary design-optimization process continues to increase, it is envisioned that some of the disciplinary tools will take the form of surrogate models, whereas others remain physics-based, depending on the requirements and stage of the design process. To simulate this in the context of an aerostructural optimization of an aircraft wing, the work presented herein features a high-fidelity aerodynamic flow solver, while a surrogate is employed to model the wing structure. This approach includes the evaluation of the sensitivities of both the aerodynamic and structural disciplines, using a coupled-adjoint formulation to enable gradient-based optimization. An important aspect of the method is that the surrogate is trained only once, prior to the optimization, and held fixed throughout. The surrogate in effect parameterizes the structural design process, and outputs the weight and stiffness of an optimized structure, given inputs of geometry parameters and sizing loads. To minimize the number of surrogate inputs and enable the representation of the entire structural design space, parameterized loads are used to build the surrogate. The method is applied to the optimization of the NASA Common Research Model, illustrating the effectiveness of the new approach.

A Fast Aerodynamic Model for Aircraft Multidisciplinary Design and Optimization Process

A multidisciplinary design analysis and optimization process is developed at ONERA for the design of tube and wing and blended wing–body aircraft configurations. This process is composed of different disciplinary modules (geometry, propulsion, aerodynamics, structure, handling qualities and flight mission), and the overall process considers different fidelity levels for these modules at each step of the design process. This article describes the low-fidelity aerodynamic module used during the preliminary design optimization process. Analytical formulations retained for lift and drag components are presented in the first part. Then, the performances estimated by the aerodynamic module on some reference configurations are compared with both numerical and experimental data, showing a quite good agreement for both tube and wing and blended wing–body configurations not only for global performance but also for individual drag components.

Integrated Optimal Design for Hybrid Electric Powertrain of Future Aircrafts

This paper presents the integrated optimal design of the powertrain of a hybrid regional aircraft using multidisciplinary design optimization (MDO). The sizing of the main components of the propulsion chain is performed over the flight mission under various scenarios regarding energy management strategies and technological assessments. For that purpose, a complete set of multidisciplinary surrogate models are integrated into the MDO process, taking account not only of the main electrical, thermal and mechanical aspects but also of environmental constraints such as partial discharges in electric motors regarding flight conditions. Several MDO formulations are investigated comparing local (i.e., motor mass minimization) and global optimizations (i.e., powertrain mass then fuel burn minimization at aircraft level). Results emphasize main systemic couplings showing that despite future technological progress, the series hybrid architecture is heavier than a conventional thermal aircraft. Nevertheless, thanks to the whole aircraft optimization, potential gains related to kerosene consumption can be reached, reducing the environmental footprint. The “energy gains” focused on in this paper may be added with aerodynamic gains potentially involved in more electric powertrain. This work has been carried out in the frame of the HASTECS project under the Clean Sky II program which aims at reducing CO2 emissions and environmental impacts of the aviation sector.

Conceptual design and optimization of a general aviation aircraft with fuel cells and hydrogen

In this paper11This paper has been modified from the conference paper "Conceptual Design and Optimization of a General Aviation Aircraft with Fuel Cells and Hydrogen", presented at the Aerospace Europe Conference in Bordeaux, France, February 2020., an aircraft design from scratch and optimization is carried out to investigate the potentials of using liquid hydrogen and fuel cells in general aviation. The focus is set on finding an efficient aircraft configuration that considers all drive train-related components such as the hydrogen tank, fuel cells, and electric motors as well as passenger seats and cargo hold. The paper starts with the definition of the design requirements and specifications. Following, the baseline design of the aircraft in the Suave tool is presented. This includes making extensions to the Suave tool regarding the compatibility of fuel cells and hydrogen energy networks. Eventually, a multidisciplinary design optimization process of selected design variables together with the design constraints is carried out in Suave. Finally, the design is evaluated in terms of performance and emissions by drawing a comparison with conventional general aviation aircraft.

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.

Multidisciplinary Design and Architecture Optimization of a Reusable Lunar Lander

With renewed interest in lunar exploration and the upcoming deployment of the lunar space station, the Lunar Orbital Platform-Gateway (LOP-G), a scientific community, is focusing on the design of a lander to bring people back to the lunar surface. This work focuses on optimizing two aspects of the lunar lander concurrently: the mission architecture and the vehicle design, often treated independently in the literature. A methodology is introduced to enumerate and preliminarily rank all possible mission architectures. The best mission architectures are then coupled with a multidisciplinary design optimization process by modeling the various components of the spacecraft and optimizing over a set of design parameters. The need for fast computational models, particularly in trajectory optimization, resulted in an analytical approximation of gravitational losses. This work resulted in a hierarchy of mission architectures that are ranked according to the average mass necessary to perform the mission. This work is intended to help a system engineer designing a lunar lander in choosing the best number of vehicles, the number of reuses, and the mission profile for his/her mission requirements.

Multidisciplinary design optimization of an electric propulsion system of a hybrid UAV considering wind disturbance rejection capability in the quadrotor mode

The electric propulsion system (EPS) of a quadrotor fixed-wing hybrid unmanned aerial vehicle (QFHUAV) has important effects on its time of endurance and wind disturbance rejection capability in the quadrotor mode. Currently, the design methods of most QFHUAV EPSs are to design the quadrotor propulsion system and fixed-wing propulsion system independently. The wind disturbance rejection capability in the quadrotor mode is not considered in these methods. The multidisciplinary design optimization (MDO) method can reduce design time and cost by integrating all the disciplines involved in the design process. The MDO solutions are generally more optimal with respect to standard optimal sequential solutions. This paper proposes an MDO for a QFHUAV EPS considering the wind disturbance rejection capability in the quadrotor mode. The analysis modules of the propeller/rotor discipline, brushless direct current motor discipline, electronic speed control discipline, lithium polymer battery discipline, time of endurance discipline, mass discipline, and wind disturbance rejection capability in the quadrotor mode discipline are modeled. The MDO process of a QFHUAV EPS is constructed according to the transfer process and coupling relations among the discipline parameters. Based on the mission profile, the flight performance and mission requirements of the QFHUAV are created and a multi-objective optimization design model of the QFHUAV EPS is modeled. The Multidisciplinary feasible approach is implemented to decompose the mass coupling variables between the EPS component analysis modules and the mass analysis module effectively. A multi-objective evolutionary algorithm named NSGA-II is used to discover the full Pareto front for the multi-objective problem. The proposed MDO method is used to design the EPS of a self-developed QFHUAV. The optimal combination of QFHUAV EPS components is determined based on the optimization results. A comparison between the optimization results and the actual flight performance of the QFHUAV shows that the flight performance is in good agreement with the optimization results, which indicates that the MDO method proposed in this paper is feasible and reasonable.

Multiphysics Modeling of Gas Turbine Based on CADSS Technology

Product modeling has been applied in product engineering with success for geometric representation. With the application of multidisciplinary analysis, application-driven models need specific knowledge and time-consuming adjustment work based on the geometric model. This paper proposes a novel modeling technology named computer-aided design-supporting-simulation (CADSS) to generate multiphysics domain models to support multidisciplinary design optimization processes. Multiphysics model representation was analyzed to verify gaps among different domain models’ parameters. Therefore, multiphysics domain model architecture was integrated by optimization model, design model, and simulation model in consideration of domain model’s parameters. Besides, CADSS uses requirement space, domain knowledge, and software technology to describe the multidisciplinary model’s parameters and its transition. Depending on the domain requirements, the CADSS system extracts the required knowledge by decomposing product functions and then embeds the domain knowledge into functional features using software technology. This research aims to effectively complete the design cycle and improve the design quality by providing a consistent and concurrent modeling environment to generate an adaptable model for multiphysics simulation. This system is demonstrated by modeling turbine blade design with multiphysics simulations including computational fluid dynamics (CFD), conjugate heat transfer (CHT), and finite element analysis (FEA). Moreover, the blade multiphysics simulation model is validated by the optimization design of the film hole. The results show that the high-fidelity multiphysics simulation model generated through CADSS can be adapted to subsequent simulations.

Evaluation of Three Design approach of a bipropellant propulsion system including multidisciplinary design optimization, Robust and Optimum-Robust

Considering the importance of the presence of uncertainties in the design of complex engineering systems, in this research multidisciplinary design optimization process for a bipropellant propulsion system in the presence of uncertainties, which in addition to minimizing the system mass, has a high robust. Based on this, the multidisciplinary design view of the bipropellant propulsion system is expressed in both optimum design and optimum robust design. The continued with the application of uncertainties, the mass, operational and geometric results of the propulsion system are expressed in terms of optimum design, robust design and optimum robust design. According to the results, it is shown that the lowest mass occurs in optimum design mode. But with uncertainties, it is observed at this point that it has the least robust and reliability. It also attempts to explain the difference between the concepts of robust design and optimum design with the help of results

Multidisciplinary Design Optimization for a Centrifugal Compressor Based on Proper Orthogonal Decomposition and an Adaptive Sampling Method

A centrifugal compressor is required to increase aerodynamic efficiency, ensure structural integrity, and reduce processing costs. This paper presents a dimension reduction technique based on proper orthogonal decomposition (POD) in combination with an adaptive sampling method to reduce computational costs. Design of experiment (DOE) is first used to choose initial sampling points. Then, parts of the sampling points are selected to format the snapshot matrix. Subsequently, the number of principal components to be retained is determined after POD analysis. An adaptive sampling point adding approach is used to increase new sampling points. The approach places more points around the regions of initial optimum designs by learning the information from previous data through POD analysis. Finally, the POD coefficients are selected to act as new design variables in the following multidisciplinary design optimization process. The method is first tested by three mathematical benchmark functions. The proposed method is then used to optimize a centrifugal compressor, of which the results are verified by tests. A normalized isentropic efficiency improvement of 3.7% and 3.0% in the maximum speed state and cruise state has been obtained after optimization. Additionally, the processing costs are reduced by about 30% owing to the number of blades reduced.

Multidisciplinary Optimization of Unmanned Aircraft Considering Radar Signature, Sensors, and Trajectory Constraints

This paper presents a multidisciplinary design optimization framework applied to the development of unmanned aerial vehicles with a focus on radar signature and sensor performance requirements while simultaneously considering the flight trajectory. The primary emphasis herein is on the integration and development of analysis models for the calculation of the radar cross section and sensor detection probability, whereas traditional aeronautical disciplines such as aerodynamics and mission simulation are also taken into account in order to ensure a flyable concept. Furthermore, this work explores the effect of implementing trajectory constraints as a supplementary input to the multidisciplinary design optimization process and presents a method that enables the optimization of the aircraft under a three-dimensional flight scenario. To cope with the additional computational cost of the high-fidelity radar cross section and sensor calculations, the use of metamodels is also investigated and an efficient development methodology that can provide high-accuracy approximations for this particular problem is proposed. Overall, the operation and performance of the framework are evaluated against five surveillance scenarios, and the obtained results show that the implementation of trajectory constraints in the optimization has the potential to yield better designs by 12–25% when compared to the more “traditional” problem formulations.

Multidisciplinary design optimization of hard rock tunnel boring machine using collaborative optimization

A multidisciplinary design optimization model is developed in this article to optimize the performance of the hard rock tunnel boring machine using the collaborative optimization architecture. Tunnel boring machine is a complex engineering equipment with many subsystems coupled. In the established multidisciplinary design optimization process of this article, four subsystems are taken into account, which belong to different sub-disciplines/subsytems: the cutterhead system, the thrust system, the cutterhead driving system, and the economic model. The technology models of tunnel boring machine’s subsystems are build and the optimization objective of the multidisciplinary design optimization is to minimize the construction period from the system level of the hard rock tunnel boring machine. To further analyze the established multidisciplinary design optimization, the correlation between the design variables and the tunnel boring machine’s performance is also explored. Results indicate that the multidisciplinary design optimization process has significantly improved the performance of the tunnel boring machine. Based on the optimization results, another two excavating processes under different geological conditions are also optimized complementally using the collaborative optimization architecture, and the corresponding optimum performance of the hard rock tunnel boring machine, such as the cost and energy consumption, is compared and analysed. Results demonstrate that the proposed multidisciplinary design optimization method for tunnel boring machine is reliable and flexible while dealing with different geological conditions in practical engineering.

The Effect of Sub-systems Design Parameters on Preliminary Aircraft Design in a Multidisciplinary Design Environment

Abstract The remarkable complexity of the aircraft design is due to several reasons and one of these is certainly the high number of completely different design disciplines involved in the process. Many efforts are spent to harmonize and optimize the aircraft design trying to consider all disciplines together with the same level of detail. Within the ongoing H2020 AGILE research, an aircraft MDO (Multidisciplinary Design Optimization) process is setting up linking several design tools and, above all, competences together. This paper focuses on the evaluation of the effects of the main on-board systems design parameters on the other disciplines. Starting from a baseline aircraft (AGILE DC1 regional turbofan), the effect of each parameters have been quantified in terms of variation of aircraft weight, fuel consumption and engine performance. This analysis represents a useful starting point to better understand the importance and the influence of novel On-Board Systems configurations, such as More and All Electric, to the overall aircraft design.

Preliminary Sub-Systems Design Integrated in a Multidisciplinary Design Optimization Framework

Abstract The aircraft design is a complex subject since several and completely different design disciplines are involved in the project. Many efforts are made to harmonize and optimize the design trying to combine all disciplines together at the same level of detail. Within the ongoing AGILE (Horizon 2020) research, an aircraft MDO (Multidisciplinary Design Optimization) process is setting up connecting several design tools and competences together. Each tool covers a different design discipline such as aerodynamics, structure, propulsion and systems. This paper focuses on the integration of the sub-system design discipline with the others in order to obtain a complete and optimized aircraft preliminary design. All design parameters used to integrate the sub-system branch with the others are discussed as for their redefinition within the different detail level of the design.

Modeling of CPACS-based fuselage structures using Python

PurposeThe purpose of this paper is to present some of the key achievements. At DLR, a sophisticated interdisciplinary aircraft design process is being developed, using the CPACS data format (Nagel et al., 2012; Scherer and Kohlgrüber, 2016) as a means of exchanging results. Within this process, TRAFUMO (Scherer et al., 2013) (transport aircraft fuselage model), built on ANSYS and the Python programming language, is the current tool for automatic generation and subsequent sizing of global finite element fuselage models. Recently, much effort has gone into improving the tool performance and opening up the modeling chain to further finite element solvers.Design/methodology/approachMuch functionality has been shifted from specific routines in ANSYS to Python, including the automatic creation of global finite element models based on geometric and structural data from CPACS and the conversion of models between different finite element codes. Furthermore, a new method for modeling and interrogating geometries from CPACS using B-spline surfaces has been introduced.FindingsSeveral new modules have been implemented independently with a well-defined central data format in place for storing and exchanging information, resulting in a highly extensible framework for working with finite element data. The new geometry description proves to be highly efficient while also improving the geometric accuracy.Practical implicationsThe newly implemented modules provide the groundwork for a new all-Python model generation chain, which is more flexible at significantly improved runtimes. With the analysis being part of a larger multidisciplinary design optimization process, this enables exploration of much larger design spaces within a given timeframe.Originality/valueIn the presented paper, key features of the newly developed model generation chain are introduced. They enable the quick generation of global finite element models from CPACS for arbitrary solvers for the first time.

Aeroelastic multidisciplinary design optimization of a swept wind turbine blade

AbstractMitigating loads on a wind turbine rotor can reduce the cost of energy. Sweeping blades produces a structural coupling between flapwise bending and torsion, which can be used for load alleviation purposes. A multidisciplinary design optimization (MDO) problem is formulated including the blade sweep as a design variable. A multifidelity approach is used to confront the crucial effects of structural coupling on the estimation of the loads. During the MDO, ultimate and damage equivalent loads are estimated using steady‐state and frequency‐domain–based models, respectively. The final designs are verified against time‐domain full design load basis aeroelastic simulations to ensure that they comply with the constraints. A 10‐MW wind turbine blade is optimized by minimizing a cost function that includes mass and blade root flapwise fatigue loading. The design space is subjected to constraints that represent all the necessary requirements for standard design of wind turbines. Simultaneous aerodynamic and structural optimization is performed with and without sweep as a design variable. When sweep is included in the MDO process, further minimization of the cost function can be obtained. To show this achievement, a set of optimized straight blade designs is compared to a set of optimized swept blade designs. Relative to the respective optimized straight designs, the blade mass of the swept blades is reduced of an extra 2% to 3% and the blade root flapwise fatigue damage equivalent load by a further 8%.

Multi-Disciplinary Design Optimization of Mixed-Use Structure and Energy Performance

Multi-Disciplinary Design Optimization (MDO) is a research methodology that has been applied in engineering fields to design and evaluate decisions from multiple professions using objective function. However, the application of the MDO model to the field of Urban Design is rare. This paper addresses problems of energy performance of varying Mixed Land Use structures in the context of aspatial distribution. Through the identification of objective factors, which allow for quantification of the multi-layered problem of land use distribution, MDO can be used to derive a Pareto front that can be used to limit the design decision space or choices of future designs. The data needs for the MDO process are intensive and require a high level of contextualization for the best outcomes. The MDO models test options of design decisions through constructing the relationship between the mixed-use ratio and building energy performance at the urban scale. Preliminary findings of mixed-use optimization using aspatial parameters are discussed to better understand this relationship.

A comprehensive computational multidisciplinary design optimization approach for a tidal power plant turbine

Multidisciplinary design optimization has become a powerful technique to facilitate continuous improvement of complex and multidisciplinary products. Parametric modeling is an essential part with tremendous impact on the flexibility and robustness of multidisciplinary design optimization. This article investigates the effect of relational and non-relational parameterization techniques on the robustness and flexibility of the conceptual design of a multidisciplinary product. Bench marking between relational and non-relational parameterization and their effect on flexibility and robustness indicate that the relational parameterization is an efficient method in the multidisciplinary design optimization process. The inherent properties of the method contribute to an efficient parametric modeling with improved communication between different disciplines. This enhances the performance of the multidisciplinary design optimization process and allows a more flexible and robust design. The considered disciplines are computer-aided design, computational fluid dynamics, finite element analysis, and dynamic simulation. A high-fidelity geometry created in a computer-aided design environment is computer-aided design centric approach and later used in computational fluid dynamics, finite element analysis for a better understanding of the product as it leads to precise outcomes. The proposed approach is implemented for the conceptual design of a novel product, a tidal power plant developed by Minesto AB using a multidisciplinary design optimization process.

Aerodynamic wing shape optimization based on the computational design framework CEASIOM

PurposeA collaborative design environment is needed for multidisciplinary design optimization (MDO) process, based on all the modules those for different design/analysis disciplines, and a systematic coupling should be made to carry out aerodynamic shape optimization (ASO), which is an important part of MDO.Design/methodology/approachComputerized environment for aircraft synthesis and integrated optimization methods (CEASIOM)-ASO is developed based on loosely coupling all the existing modules of CEASIOM by MATLAB scripts. The optimization problem is broken down into small sub-problems, which is called “sequential design approach”, allowing the engineer in the loop.FindingsCEASIOM-ASO shows excellent design abilities on the test case of designing a blended wing body flying in transonic speed, with around 45 per cent drag reduction and all the constraints fulfilled.Practical implicationsAuthors built a complete and systematic technique for aerodynamic wing shape optimization based on the existing computational design framework CEASIOM, from geometry parametrization, meshing to optimization.Originality/valueCEASIOM-ASO provides an optimization technique with loosely coupled modules in CEASIOM design framework, allowing engineer in the loop to follow the “sequential approach” of the design, which is less “myopic” than sticking to gradient-based optimization for the whole process. Meanwhile, it is easily to be parallelized.

Reduced Design Load Basis for Ultimate Blade Loads Estimation in Multidisciplinary Design Optimization Frameworks

The aim is to provide a fast and reliable approach to estimate ultimate blade loads for a multidisciplinary design optimization (MDO) framework. For blade design purposes, the standards require a large amount of computationally expensive simulations, which cannot be efficiently run each cost function evaluation of an MDO process. This work describes a method that allows integrating the calculation of the blade load envelopes inside an MDO loop. Ultimate blade load envelopes are calculated for a baseline design and a design obtained after an iteration of an MDO. These envelopes are computed for a full standard design load basis (DLB) and a deterministic reduced DLB. Ultimate loads extracted from the two DLBs with the two blade designs each are compared and analyzed. Although the reduced DLB supplies ultimate loads of different magnitude, the shape of the estimated envelopes are similar to the one computed using the full DLB. This observation is used to propose a scheme that is computationally cheap, and that can be integrated inside an MDO framework, providing a sufficiently reliable estimation of the blade ultimate loading. The latter aspect is of key importance when design variables implementing passive control methodologies are included in the formulation of the optimization problem. An MDO of a 10 MW wind turbine blade is presented as an applied case study to show the efficacy of the reduced DLB concept.