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Topology optimization is an engineering design methodology that optimizes designs by manipulating material distribution. Level-set topology optimization (LSTO), a boundary-based method, has gained popularity for its crisp description of the design and natural handling of the topological changes during the optimization. However, its availability in commercial or open-source software is limited, and the entry barrier to LSTO could be considered high. This paper presents a workflow that seamlessly integrates LSTO with COMSOL Multiphysics, enabling broader accessibility. The workflow utilizes COMSOL’s versatile finite element solver to create models via their graphical user interface, which are then converted into MATLAB code. The LSTO modules, written in C++ for efficiency, are imported within MATLAB, adding topology optimization to the automatically generated COMSOL finite element code. The workflow leverages COMSOL’s adjoint sensitivity capabilities for efficient sensitivity computations during optimization. Three open-source examples showcasing the workflow’s effectiveness are provided, demonstrating heat conduction, fluid flow, and conjugate heat transfer optimization problems, and highlighting the power and versatility of the proposed approach.

Developing modern products involves numerous domains (controlling, production, engineering, etc.) and disciplines (mechanics, electronics, software, etc.). The products have become increasingly complex while their time to market has decreased. These challenges can be overcome by Model-Based Systems Engineering (MBSE), where all development data (requirements, architecture, etc.) is stored and linked in a system model. In an MBSE system model, product requirements at the system level can lead to numerous technical variants with conflicting objectives at the parameter level. To determine the best technical variants or tradeoffs, Multidisciplinary Analysis and Optimization (MDAO) is already being used today. Linking MBSE and MDAO allows for mutually beneficial synergies to be expected that have not yet been fully exploited. In this paper, a new approach to link MBSE and MDAO is proposed. The novelty compared to existing approaches is the reuse of existing MBSE system model data. Models developed during upstream design and test activities already linked to the MBSE system model were integrated into an MDAO problem. Benefits are reduced initial and reconfiguration efforts and the resolution of the MDAO black-box behavior. For the first time, the MDAO problem was modeled as a workflow using activity diagrams in the MBSE system model. For a given system architecture, this workflow finds the design variable values that allow for the best tradeoff of objectives. The structure and behavior of the workflow were formally described in the MBSE system model with SysML. The presented approach for linking MBSE and MDAO is demonstrated using an example of an electric coolant pump.

The vertical-takeoff–horizontal-landing (VTHL) two-stage-to-orbit (TSTO) system is a kind of novel launch vehicle in which a reusable first stage can take off vertically like a rocket and land horizontally like an airplane. The advantage of the VTHL TSTO vehicle is that the launch costs can be reduced significantly due to its reusable first stage. This paper presents an application of multidisciplinary analysis optimization on preliminary sizing in conceptual design of the VTHL TSTO vehicle. The VTHL TSTO concept is evaluated by multidisciplinary analysis, including geometry, propulsion, aerodynamics, mass, trajectory, and static stability. The preliminary sizing of the VTHL TSTO vehicle is formulated as a multidisciplinary optimization problem. The focus of this paper is to investigate the impacts of the first-stage reusability and propellant selection on the staging altitude and velocity, size, and mass of the VTHL TSTO vehicles. The observations from the results show that the velocity and altitude of the optimal staging point are determined mainly by the reusability of the first stage, which in turn affects the size and mass of the upper stage and the first stage. The first stage powered by hydrocarbon fuel has a lower dry mass compared with that powered by liquid hydrogen.

In wind turbine optimization, the standard power regulation strategy follows a constrained trajectory based on the maximum power coefficient. It can be updated automatically during the optimization process by solving a nested maximization problem at each iteration. We argue that this model does not take advantage of the load alleviation potential of the regulation strategy and additionally requires significant computational effort. An alternative approach is proposed, where the rotational speed and pitch angle control points for the entire operation range are set as design variables, changing the problem formulation from nested to one‐level. The nested and one‐level formulations are theoretically and numerically compared on different aerodynamic blade design optimization problems for AEP maximization. The aerodynamics are calculated with a steady‐state blade element momentum method. The one‐level approach increases the design freedom of the problem and allows introducing a secondary objective in the design of the regulation strategy. Numerical results indicate that a standard regulation strategy can still emerge from a one‐level optimization. Second, we illustrate that novel optimal regulation strategies can emerge from the one‐level optimization approach. This is demonstrated by adding a thrust penalty term and a constraint on the maximum thrust. A region of minimal thrust tracking and a peak‐shaving strategy appear automatically in the optimal design.

The current study aims to develop a methodology for obtaining topology-optimal structures made of short fiber-reinforced polymers. Each iteration of topology optimization involves two consecutive steps: the first is a simulation of the injection molding process for obtaining the fiber orientation tensor, and the second is a structural analysis with anisotropic material properties. Accounting for the molding process during the internal iterations of topology optimization makes it possible to enhance the weight efficiency of structures—a crucial aspect, especially in aerospace. Anisotropy is considered through the fiber orientation tensor, which is modeled by solving the plastic molding equations for non-Newtonian fluids and then introduced as a variable in the stiffness matrix during the structural analysis. Structural analysis using a linear anisotropic material model was employed within the topology optimization. For verification, a non-linear elasto-plastic material model was used based on an exponential-and-linear hardening law. The evaluation of weight efficiency in structures composed of short-reinforced composite materials using a dimensionless criterion is addressed. Experimental verification was performed to confirm the validity of the developed methodology. The evidence illustrates that considering anisotropy leads to stiffer structures, and structural elements should be oriented in the direction of maximal stiffness. The load-carrying factor is expressed in terms of failure criteria. The presented multidisciplinary methodology can be used to improve the quality of the design of structures made of short fiber-reinforced composites (SFRC), where high stiffness, high strength, and minimum mass are the primary required structural characteristics.

Electric vertical takeoff and landing (eVTOL) aircraft attract attention due to their unique characteristics of reduced noise, moderate pollutant emission, and lowered operating cost. However, the benefits of electric vehicles, including eVTOL aircraft, are critically challenged by the energy density of batteries, which prohibit long-distance tasks and broader applications. Since the takeoff process of eVTOL aircraft demands excessive energy and couples multiple subsystems (such as aerodynamics and propulsion), multidisciplinary analysis and optimization (MDAO) become essential. Conventional MDAO, however, iteratively evaluates high-fidelity simulation models, making the whole process computationally intensive. Surrogates, in lieu of simulation models, empower efficient MDAO with the premise of sufficient accuracy, but naive surrogate modeling could result in an enormous training cost. Thus, this work develops a twin-generator generative adversarial network (twinGAN) model to intelligently parameterize takeoff power and wing angle profiles of an eVTOL aircraft. The twinGAN-enabled surrogate-based takeoff trajectory design framework was demonstrated on the Airbus A3 Vahana aircraft. The twinGAN provisioned two-fold dimensionality reductions. First, twinGAN generated only realistic trajectory profiles of power and wing angle, which implicitly reduced the design space. Second, twinGAN with three variables represented the takeoff trajectory profiles originally parameterized using 40 B-spline control points, which explicitly reduced the design space while maintaining sufficient variability, as verified by fitting optimization. Moreover, surrogate modeling with respect to the three twinGAN variables, total takeoff time, mass, and power efficiency, reached around 99% accuracy for all the quantities of interest (such as vertical displacement). Surrogate-based, derivative-free optimizations obtained over 95% accuracy and reduced the required computational time by around 26 times compared with simulation-based, gradient-based optimization. Thus, the novelty of this work lies in the fact that the twinGAN model intelligently parameterized trajectory designs, which achieved implicit and explicit dimensionality reductions. Additionally, twinGAN-enabled surrogate modeling enabled the efficient takeoff trajectory design with high accuracy and computational cost reduction.

Current unmanned aerial vehicles have been designed by applying the traditional approach to aircraft conceptual design which has drawbacks in terms of the individual analysis of each discipline involved in the conception of new aircraft, the reliance on the designer’s experience and intuition, and the inability of evaluating all possible design solutions. Multidisciplinary analysis and optimization focus on solving these problems, by synthesizing all the disciplines involved and accounting for their mutual interaction. This study presents a multidisciplinary analysis and optimization method for conceptually designing electrical flying-wing micro-unmanned aerial vehicles. The conceptual design task was formulated as a non-linear mathematical programming problem. The method considers the trimming of the UAV during each mission profile phase, consisting of the climb, cruise, and descent. We used two algorithms, one for design space exploration and another for optimization. Typical examples of solving conceptual design problems were considered in the work: the modernization of an existing UAV; the effect of the change of the payload and endurance change on the takeoff weight; and the influence of different static margins on aerodynamic characteristics. The advantages of using this design method are the remotion of additional internal cycles to solve the sizing equation at each optimization step, and the possibility of not only obtaining a unique optimal solution but also a vector of optimal solutions.

The aviation industry aims to reduce environmental impact by adopting alternative propulsion systems, including hydrogen-based, hybrid-electric, and all-electric architectures, requiring a new Thermal Management System (TMS). In addition, new design methods are needed for the TMS, at the system and component levels, to handle various fluids and varying fluid properties. Within the TMS, heat exchangers are critical components that may require significant space and must be considered early in the design process. This paper presents a parametric sizing methodology for heat exchangers suitable for early design phases within a Multidisciplinary Design Analysis and Optimization (MDAO) framework, specifically for cryogenic heat transfer. The method combines physical equations with validated empirical relationships, using iterative solver algorithms for sizing. To address multi-variable design challenges, the methodology integrates discretization schemes for fluid properties, temperature, and energy calculations, and constraint-based optimization with a weighted-sum approach for solution selection. The methodology is validated with a commercial heat exchanger, and cross-validated with a cryogenic Heat Exchanger (HX). A case study for an all-electric hydrogen fuel cell aircraft architecture with a 7.6 MW propulsion system is presented to demonstrate the effectiveness of the methodology. The presented heat exchanger performance can be predicted across multiple conditions quickly enough to enable large design space exploration. Overall, the presented model is a crucial element for the design of a TMS for future aircraft with hydrogen-based propulsion 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.

The increasing demands for travelling comfort and reduction of carbon dioxide emissions have been considered substantially in the stage of conceptual aircraft design. However, the design of a modern aircraft is a multidisciplinary process, which requires the coordination of information from several specific disciplines, such as structures, aerodynamics, control, etc. To address this problem with adequate accuracy, the multidisciplinary analysis and optimization (MAO) method is usually applied as a systematic and robust approach to solve such complex design issues arising from industries. Since MAO method is tedious and computationally expensive, genetic programming (GP)-based metamodeling techniques incorporating MAO are proposed as an effective approach to minimize the wing stiffness of a large aircraft subject to aerodynamic, aeroelastic and stability constraints in the conceptual design phase. Based on the linear small-disturbance theory, the state-space equation is employed for stability analysis. In the process of multidisciplinary analysis, aeroelastic response simulations are performed using Nastran. To construct metamodels representing the responses of the interests with high accuracy as well as less computational burden, optimal Latin hypercube design of experiments (DoE) is applied to determine the optimized distribution of sampling points. Following that, parametric optimization is carried out on metamodels to obtain the optimal wing geometry shape, elastic axis positions and stiffness distribution, and then the solution is verified by finite element simulations. Finally, the superiority of the GP-based metamodel technique over genetic algorithm is demonstrated by multidisciplinary design optimization of a representative beam-frame wing structure in terms of accuracy and efficiency. The results also show that GP metamodel-based strategy for solving MAO problems can provide valuable insights to tailoring parameters for the effective design of a large aircraft in the conceptual phase.