Search Results (35)

Lightweight composite aircraft wing design increasingly depends on combining multidisciplinary design optimization (MDO), aeroelastic tailoring, and stacking sequence optimization. However, an overview of these interconnected fields is lacking. This study applies a PRISMA-ScR-based scoping review of 54 selected articles to map current approaches, identify emerging trends, and highlight remaining gaps. Key findings indicate six MDO architectures—with hybrid methods being increasingly preferred—and demonstrate that aeroelastic tailoring (e.g., ply angle manipulation) enhances performance while reducing weight. Manufacturing constraints (ply continuity, blending, symmetry) are addressed in a subset of the reviewed literature, with opportunities for broader integration. Critical future priorities include integrating manufacturing process models into MDO and incorporating durability considerations (fatigue, impact). This work synthesizes current approaches, identifies emerging trends, and provides a roadmap for the development of next-generation lightweight, high-performance composite wings. Full article

The flying-wing configuration offers inherent advantages in aerodynamic efficiency and stealth; however, conventional fixed-wing designs face fundamental performance trade-offs when tasked with multi-role missions. This paper introduces a multidisciplinary design optimization (MDO) framework for a morphing wing unmanned aerial vehicle (UAV) to overcome this limitation. The proposed UAV integrates four complementary morphing strategies—shear-type variable sweep, variable span, morphing wingtip, and a continuously variable camber trailing edge—to adapt its geometry for different flight phases. An automated parametric modeling platform is developed, enabling the dynamic generation of 3D CAD models driven by design variables. This geometry is coupled with a suite of analysis modules for aerodynamics, propulsion, weight estimation, flight performance, and radar cross-section. The multi-mission profile, including takeoff, climb, cruise, turning, and landing, is decomposed into several phase-specific single-objective optimization subproblems, which are solved using an elitist real-coded genetic algorithm. The results quantify the optimal morphing configurations for each phase, demonstrating significant performance gains over the baseline, such as a 17% increase in range. Critically, the study analyzes the trade-off between aerodynamic benefits and the weight penalty of morphing mechanisms, revealing that both range and maneuverability are the most sensitive to the added weight. The proposed framework uses mission-phase-specific optimum geometries to define the required morphing envelope, actuation ranges, and net performance benefit of a candidate morphing flying-wing UAV after considering mechanism-induced mass penalties. This framework provides a quantitative basis for mission-driven morphing decisions and establishes a viable approach for designing highly adaptive next-generation UAVs. Full article

Distributed optimal design brings significant solutions for experiment optimization in complex engineering design problems. A Kriging-based augmented Lagrangian Method is proposed with the help of the Multi-fidelity Hamiltonian Kriging (MHK) surrogate model. The Multi-fidelity Hamiltonian Kriging-based Augmented Lagrangian Method (MHK-ALM) uses subsystem surrogate models constructed from multi-fidelity data to speed up the inner loop solution of ALM, while also reducing the iterations of the outer loop of ALM. The MHK-ALM is illustrated with one numerical simulation of a multi-fidelity constrained NASA speed reducer problem, demonstrated with a multidisciplinary design optimization of a solid-propellant ballistic missile. The engineering application of the multidisciplinary design optimization (MDO) problem shows that the proposed method can perform precisely over certain advanced surrogate-based optimization frameworks. The MHK-ALM can be applied for any other distributed optimal design problems where one need complex subsystem decomposition. Full article

Modern engineering systems may require multidisciplinary design optimization (MDO) to account for the interactions between coupled physical phenomena. When uncertainties affect model parameters or design variables, these analyses must be extended to uncertainty-based MDO (UMDO), in which objectives and/or constraints are expressed as statistical quantities. However, solving UMDO problems is computationally demanding, especially when costly simulators are involved and the budget must be allocated among uncertainty quantification, multidisciplinary coupling resolution, and optimization. This article introduces a generic multi-fidelity strategy, implemented in the open-source GEMSEO framework, to efficiently address UMDO problems. A fidelity level is defined by a number of samples to estimate the statistics; the higher the fidelity, the higher the number. The strategy solves the UMDO problem for each level by using the solution of the previous level as an initial guess. Numerical experiments are deployed on a simplified overall aircraft design (OAD) problem and a hybrid electric regional aircraft (HERA) case. The results show that, with two fidelity levels, restricting samples and iterations at the low-fidelity stage improves overall performance. This allows the multi-fidelity framework to significantly reduce computational cost compared with single-fidelity approaches (up to 45% for OAD and 40% for HERA) while maintaining or improving solution accuracy. Full article

This study presents an aerostructural optimization framework for the preliminary design of a low-Reynolds-number composite UAV wing, aiming to simultaneously enhance aerodynamic efficiency and structural performance. While previous work has primarily addressed aerodynamic optimization in isolation, the present approach integrates Computational Fluid Dynamics (CFD) and Finite Element Method (FEM) analyses within a surrogate-based optimization (SBO) framework. The design space includes both aerodynamic parameters—aspect ratio, taper ratio, sweep angle, and twist—and structural variables related to the internal wing layout and component thicknesses. To reduce the computational cost associated with high-fidelity simulations, Kriging surrogate models are employed in conjunction with an Expected Improvement (EI) infill strategy, enabling efficient exploration of the coupled design space. The framework is evaluated through multiple independent optimization runs using different initial sampling strategies, demonstrating consistent convergence toward feasible high-performance designs. The surrogate models exhibit strong predictive capability, as confirmed by Root Mean Square Error (RMSE) and Leave-One-Out (LOO) cross-validation metrics. The results indicate that aerodynamic variables, particularly aspect ratio and twist, are the primary drivers of range performance. However, structural variables—most notably skin thickness—strongly influence constraint satisfaction, especially with respect to buckling and strength requirements, and therefore play a key role in defining the feasible design space. The optimal configuration achieves a maximum range of approximately 203 km while satisfying all strength, stiffness, and aerodynamic constraints. Overall, the proposed methodology provides an efficient and robust tool for the early-stage aerostructural design of low-Reynolds-number UAV wings. Full article

This work presents the numerical assessment of a civil aircraft horizontal tailplane (HTP) using a fully parametric structural model developed through the Ansys Parametric Design Language (APDL). The objective is to evaluate the structural integrity, efficiency, and dynamic behavior of the HTP under realistic operational conditions within the HERFUSE Clean Aviation framework. The study includes linear static analyses for load distribution and critical stress regions, modal analysis for dynamic response characterization, and linear buckling analyses to determine stability assessment. Safety margins are computed for representative load cases across spars, skins, and ribs. The workflow will be integrated and connected to Multidisciplinary Optimization (MDO) loops for higher-level design trade-offs. Full article

Forward-swept wings are more suitable for natural laminar flow than backward-swept wings. However, in order to reduce the difficulty of optimization, most aero-structural optimization studies of forward-swept wings do not consider the automatic laminar–turbulent transition, discrete variables, or large-scale constraints, which may result in undesirable optimization results. In this article, an efficient aero-structural optimization method for the composite forward-swept natural laminar flow (FSNLF) wing is proposed, which can solve MDO problems with those issues. Reynolds-averaged Navier–Stokes (RANS) equations coupled with the dual e^N transition method are used to simulate subsonic viscous flows. A surrogate-based optimization (SBO) algorithm combining a discrete variable handling method is developed to solve the multidisciplinary design optimization (MDO) problem involving many discrete ply thickness variables of predefined angles (0°/±45°/90°). The Kreisselmeier–Steinhauser (KS) method is employed to handle large-scale geometric constraints, ply fraction constraints and material failure constraints. To verify the effectiveness of the proposed method, we perform the aero-structural optimization of an A320-class composite FSNLF wing. Results show that the proposed method offers great potential in the aero-structural optimization of the composite FSNLF wing. It can handle 32 discrete variables and 11,089 constraints, the drag coefficient and mass of the wing are reduced significantly, and the area of the laminar flow region on the wing upper surface is increased by 24.3% compared with the baseline. Full article

With support from the Romanian Nucleu Program, INCAS has taken the initiative to develop a multidisciplinary optimization (MDO) environment capable of generating reusable microlauncher concepts that could be used as the first building blocks in the development and production of a locally based launch vehicle. This paper will present the main work carried out towards the accomplishment of this task, with several mathematical models being proposed to fill in the gaps associated with microlauncher reusability. Towards the end of the paper, a partially reusable microlauncher concept is proposed, which is capable of successfully accomplishing both the main mission, that of inserting a 100 kg satellite into a 400 km altitude, circular polar orbit, and the recovery mission, where the first-stage assembly (including the interstage) is recovered from a secondary location. Preliminary cost estimates are also given for the entire lifespan of the microlauncher, focusing on the economic feasibility of a reusable concept for small launch vehicles. Full article

To meet the stringent reliability requirements of rotor blades in turbomachines, greater effort should be devoted to improving both aerodynamic and structural performance in blade design. This paper introduces an aero-structural multi-disciplinary design optimization (MDO) method for compressor rotor blades using a discrete adjoint method and an equivalent stress model (ESM). The principles of the ESM are firstly introduced, and its accuracy in calculating equivalent stress is validated through comparison with a commercial program. Both the aerodynamic performance and the maximum equivalent stress (MES) are selected as optimization objectives. To modify the blade profile, the steepest descent optimization method is utilized, in which the necessary sensitivities of the cost function to the design parameters are calculated by solving the adjoint equations. Finally, the aero-structural MDO of a transonic compressor rotor, NASA Rotor 67, is conducted, and the Pareto solutions are obtained. The optimization results demonstrate that the adiabatic efficiency and the MES are competitive in improving multi-disciplinary performance. For most of the Pareto solutions, the MES can be considerably reduced with increased adiabatic efficiency. Full article

In multidisciplinary design optimization (MDO) of high-end equipment, parameter uncertainty significantly undermines performance robustness. Existing methods are limited in convergence efficiency and in controlling uncertainty propagation. To address this gap, we propose a multidisciplinary robust collaborative optimization method under parameter uncertainty (MRCO-PU). The approach augments traditional Collaborative Optimization (CO) with a collaborative optimization method based on weight distribution difference information (CO-WDDI) to accelerate cross-disciplinary convergence. It also integrates a double-layer EI–Kriging robust optimization model to enhance robustness under complex coupling and small-sample conditions. The MRCO-PU method targets single-objective, strongly coupled, multi-constraint MDO problems with high per-evaluation cost. The method was validated on a mathematical case and on a cantilever roadheader cutting-head case. In the mathematical case, the robust feasibility of the constraints increased from 0.49 to 1.00. In the engineering case, the specific energy consumption decreased by 6.3% under the premise of fully satisfying the robust feasibility of the constraints, leading to operational cost minimization under uncertainty. This work provides an effective approach to multidisciplinary robust optimization for high-end equipment. Full article

This paper addresses inherent limitations in unmanned aerial vehicle (UAV) undercarriages hindering vertical takeoff and landing (VTOL) capabilities on uneven slopes and obstacles. Robotic landing gear (RLG) designs have been proposed to address these limitations; however, existing designs are typically limited to ground slopes of 6–15°, beyond which rollover would happen. Moreover, articulated RLG concepts come with added complexity and weight penalties due to multiple drivetrain components. Previous research has highlighted that even a minor 3-degree slope change can increase the dynamic rollover risks by 40%. Therefore, the design optimization of robotic landing gear for enhanced VTOL capabilities requires a multidisciplinary framework that integrates static analysis, dynamic simulation, and control strategies for operations on complex terrain. This paper presents a novel, hybrid, compliant, belt-driven, three-legged RLG system, supported by a multidisciplinary design optimization (MDO) methodology, aimed at achieving enhanced VTOL capabilities on uneven surfaces and moving platforms like ship decks. The proposed system design utilizes compliant mechanisms featuring a series of three-flexure hinges (3SFH), to reduce the number of articulated drivetrain components and actuators. This results in a lower system weight, improved energy efficiency, and enhanced durability, compared to earlier fully actuated, articulated, four-legged, two-jointed designs. Additionally, the compliant belt-driven actuation mitigates issues such as backlash, wear, and high maintenance, while enabling smoother torque transfer and improved vibration damping relative to earlier three-legged cable-driven four-bar link RLG systems. The use of lightweight yet strong materials—aluminum and titanium—enables the legs to bend 19 and 26.57°, respectively, without failure. An animated simulation of full-contact landing tests, performed using a proportional-derivative (PD) controller and ship deck motion input, validate the performance of the design. Simulations are performed for a VTOL UAV, with two flexible legs made of aluminum, incorporating circular flexure hinges, and a passive third one positioned at the tail. The simulation results confirm stable landings with a 2 s settling time and only 2.29° of overshoot, well within the FAA-recommended maximum roll angle of 2.9°. Compared to the single-revolute (1R) model, the implementation of the optimal 3R Pseudo-Rigid-Body Model (PRBM) further improves accuracy by achieving a maximum tip deflection error of only 1.2%. It is anticipated that the proposed hybrid design would also offer improved durability and ease of maintenance, thereby enhancing functionality and safety in comparison with existing robotic landing gear systems. Full article

This study explores the multidisciplinary design optimization (MDO) of the NASA Common Research Model (CRM) wingbox, utilizing both metallic and composite materials while addressing various constraints, including static strength, stiffness, aeroelasticity, and manufacturing considerations. The primary load-bearing wing structure is designed with high structural fidelity, resulting in a higher number of structural elements representing the wingbox model. This increased complexity expands the design space due to a greater number of design variables, thereby enhancing the potential for identifying optimal design alternatives and improving mass estimation accuracy. Finite element analysis (FEA) combined with gradient-based design optimization techniques was employed to assess the mass of the metallic and composite wingbox configurations. The results demonstrate that the incorporation of composite materials into the CRM wingbox design achieves a structural mass reduction of approximately 17.4% compared to the metallic wingbox when flutter constraints are considered and a 23.4% reduction when flutter constraints are excluded. When considering flutter constraints, the composite wingbox exhibits a 5.6% reduction in structural mass and a 5.3% decrease in critical flutter speed. Despite the reduction in flutter speed, the design remains free from flutter instabilities within the operational flight envelope. Flutter analysis, conducted using the p-k method, confirmed that both the optimized metallic and composite wingboxes are free from flutter instabilities, with flutter speeds exceeding the critical threshold of 256 m/s. Additionally, free vibration and aeroelastic stability analyses reveal that the composite wingbox demonstrates higher natural frequencies compared to the metallic version, indicating that composite materials enhance dynamic response and reduce susceptibility to aeroelastic phenomena. Fuel mass was also found to significantly influence both natural frequencies and flutter characteristics, with the presence of fuel leading to a reduction in structural frequencies associated with wing bending. Full article

The transformation of the aeronautical industry towards sustainable and cost-effective manufacturing is essential for enhancing aircraft performance while reducing environmental impacts and production costs. This study integrates Life Cycle Assessment (LCA), Life Cycle Costing (LCC), and machine learning to enhance sustainable design in aeronautics. A Multi-disciplinary Optimization (MDO) approach was applied to a composite airframe panel, revealing that increased panel mass elevates the impacts of Climate Change (CC) and Resource Use (fossils), largely due to carbon fiber and energy-intensive manufacturing. A Random Forest model predicted LCA/LCC outcomes, facilitating real-time, sustainability-driven decisions. Optimization reduced environmental impacts by 15%. Recommendations include bio-based composites and renewable energy use to further lower environmental costs. Full article

The aviation industry, responsible for a significant portion of global CO₂ emissions, faces the need to transition to more sustainable aircraft. Electric aircraft driven by battery-powered propulsion and further structural weight reductions have emerged as potential solutions. This research presents structural topology optimization methods developed at the Netherlands Aerospace Centre using (1) a hybrid approach with different scales for aircraft design, from component to full-scale aircraft. Furthermore, (2) multi-material designs are being explored in combination with additive manufacturing technology. Method 1: At the full aircraft level, the study employed a preliminary design methodology that combines shell and solid elements in a 3D model utilizing Abaqus software 2023. A topology optimization was carried out with strain energy and weight as the design responses, subject to specified volume and symmetry constraints. Different aircraft configurations were investigated, including blended wing designs, with each impacting the load paths and structural performance. A start was made in translating the optimized design to actual aerospace features such as frames and Door-Surround Structures (DSS). Method 2: The ability to manufacture multi-material metal parts via additive manufacturing presents opportunities for the design of aircraft components and shows weight-saving potential. The multi-material topology optimization method is explored for a relevant aerospace wing component. The results revealed widespread possibilities for general topology optimization methods to be applied in aircraft structural design at different scales. Load paths can be identified and their integration into multi-disciplinary design optimization (MDO) is promising. Novel structural designs for blended wing aircraft can be obtained for multiple load-cases. This research addresses questions concerning the aircraft-level and component-level feasibility of optimized designs, optimization features, inertia relief, and mesh size influence. The findings show the potential to optimize battery-powered aircraft through innovative structural design, contributing to a potentially lower weight and further reductions in environmental impact. This study serves as a first step towards lightweight future electric aircraft design and underscores the importance of integrating innovative solutions to reduce the climate impact of the aviation industry. Full article

Considering the influence of high-speed obstacle avoidance trajectory in the optimization design stage of intelligent bus aerodynamic shape. A collaborative optimization method aiming at aerodynamic structure and trajectory control system for intelligent bus rollover stability is proposed to reduce the interference of lateral aerodynamic load caused by large bus side area on driving stability and improve the rollover safety of intelligent bus in high-speed obstacle avoidance process. At the conceptual design stage, a multidisciplinary co-design optimization frame of aerodynamics/dynamics/control is built, and an adaptive Gaussian Process Regression approximate modeling method is proposed to establish an approximate model of high-precision and high-efficiency rollover evaluation index with rollover stability as the optimization objective and obstacle avoidance safety and resistance to crosswind interference as constraints. Taking rollover stability and obstacle avoidance safety as the optimization objectives, the integrated design of static structural parameters and dynamic control parameters of intelligent buses is carried out. The results show that the proposed MDO method can obtain the aerodynamic shape of the vehicle body with low crosswind sensitivity and a safe and stable obstacle avoidance trajectory. Compared with the initial trajectory, the peak lateral load transfer rate during the obstacle avoidance process decreases by 33.91%, which significantly reduces the risk of rollover. Compared with the traditional serial optimization method, the proposed co-design optimization method has obvious advantages and can further improve the driving safety performance of intelligent buses. Full article