Browse Topic: Design processes
Engineers designing pivot points for outdoor and heavy-duty equipment have long accepted the familiar routine: grease it, run it, regrease it and repeat. This approach may work well enough. But as equipment pushes into harsher environments and tighter service windows, “well enough” is no longer the standard. Maintenance-free pivots - pivots that run dry, resist corrosion and hold their tolerances through seasons of hard use - offer an increasingly compelling alternative. Designing one requires rethinking what a pivot point actually is, with most engineers focusing only on the bearing itself. While this instinct is understandable, it's also incomplete. A pivot consists of a bearing, shaft and housing working together, requiring a systems-level approach. Optimize only one element and you may still end up with a failure.
This paper presents the design, implementation, and validation of an aerial-launch FPV (First-Person View) drone system that was developed to provide a complex environment with flexible deployment and precise delivery capabilities. The integrated system is composed of a hybrid VTOL carrier aircraft, a number of FPV drones, and an aerial mounting / release equipment. Using the AYK-250 platform, the carrier has a vertical take-off and landing function and long-time endurance. In terms of the FPV drones, it is built upon the high performance MARK4 5-inch frame that has high agility and high payload. The release module uses a single-hook point structure with a limit stop. The FPV drones are released stably, and the separation is reliable in flight. Comprehensive flight tests proved all workflows completely, involving carrier take-off, cruise with drones mounted, sequential aerial launch, and subsequent autonomous attitude recovery and route tracking by the FPV drones. The test results confirm the system’s capability for reliable launch from an aerial platform coupled with precise guidance, establishing a credible technical solution for expanding the practical applications of FPV drones in distributed tasks. Results show that our system can be launched via an aerial platform with an accurate guide and is a viable technological solution to spread FPF Drones for operational strategies in a more distributed way.
Efficient optimization of aerodynamic shapes is a critical challenge in aircraft design. Traditional CFD-based optimization workflows suffer from high computational costs and low efficiency, which severely restricts their practical engineering application. In this paper, a novel aerodynamic optimization method based on a hierarchical neural network with adaptive activation functions is proposed. The network adopts learnable B-spline activation functions and is hierarchically constructed in accordance with the sharing status of B-spline control points. After being trained to achieve fast and accurate prediction of aerodynamic performance, the network can effectively replace the traditional CFD module in the optimization loop. The primary advantage of the proposed method is that it significantly reduces the computational cost during the optimization process while ensuring that the prediction accuracy is not compromised. This work thereby presents a novel strategy and technical framework for streamlining the design process of hypersonic vehicles.
Civil aircraft, as typical complex product systems, exhibit characteristics such as a high concentration of high-tech technologies, strong interdisciplinarity, a high level of system integration, long development cycles, substantial project investments, and complex management. During the R&D process of civil aircraft projects, there are often high risks in performance, cost, and schedule. Delays in the schedule can lead to losses in project manpower and material resources, as well as project failure. A mature objective criteria system for maturity assessment provides a reference basis for determining whether the project has reached its optimal state at a specific stage, thereby reducing project management risks and increasing the probability of project success. This research will adopt a research approach combining theoretical studies with practical case analysis. First, it will conduct extensive and in-depth investigations into various maturity models and their applications across the entire product lifecycle within relevant fields. A requirement maturity model and requirement maturity KPI (Key Performance Indicator) indicators will be established to clarify the maturity status of requirements at different development stages, enabling judgment of whether the project is ready to proceed to the next development phase. Concurrently, by developing a KPI statistical system platform integrating application servers and data processing tools, a scientific and quantitative inspection mechanism will be implemented to visualize project development progress, status, and risk data. This will provide actionable insights for project decision-making and achieve effective project management and control.
As acoustic requirements for NVH trim components become increasingly constrained by mass, cost, and sustainability targets, traditional approaches to inner dash design based on spatially averaged Transmission Loss (TL) metrics are reaching their practical limits. In fully built vehicles, the acoustic performance of the inner dash is governed by its global insulation capability but also by strong spatial heterogeneity and its interaction with spatially distributed noise sources such as the power unit, gearbox, and tyre-road excitation. This paper presents a test-based methodology for the spatial optimisation of inner dash acoustic performance using reciprocal holography. By applying a calibrated sound power source within the vehicle cabin and measuring the reciprocal response in the engine bay and wheel-arch regions, a high-resolution spatial Transmission Loss “hologram” of the inner dash is obtained under in-situ conditions. The resulting spatial data enables the identification of localised acoustic weak points that are not observable using conventional testing methods. To bridge the gap between passive component characterisation and real-world vehicle operation, the spatial TL hologram is subsequently evaluated using representative operational source sound power data to prioritise acoustically relevant regions. This enables the transmitted acoustic energy to be evaluated under realistic driving conditions. The holographic data is then coupled with a parametric acoustic model of the inner dash system, allowing localised mass redistribution to be optimised using a genetic algorithm while respecting packaging and manufacturing constraints.
In the automotive industry, controlling noise transmission through vehicle components is essential for passenger comfort and regulatory compliance. Traditionally, Transmission Loss (TL) is estimated using simplified CAD-based metrics, which lack accuracy at high frequencies and for complex assemblies. Modeling complex vehicle components introduces challenges, such as representing fluid-structure and trim interactions, with spatially varying trim thicknesses. This study presents an industrial application implementing the Virtual SEA (Statistical Energy Analysis) method to evaluate TL for a firewall. The study discusses strategies for subsystem adaptation and analytical trim modeling, highlighting the importance of managing spatial averaging effects. The proposed workflow integrates laboratory measurements of trim materials, advanced subsystem definition, diffuse sound field (DSF) excitation and radiation in free-field condition. Virtual SEA results are systematically validated against Finite Element Method (FEM) simulations (where the frequency range allows) and experimental data. Virtual SEA demonstrates strong agreement with FEM, especially at mid and high frequencies where FE starts to be cumbersome, confirming its suitability for industrial Noise, Vibration, and Harshness (NVH) applications. While some limitations remain—such as the inability to fully model mixed-component subsystems—ongoing research and practical workarounds are proposed. In conclusion, the Virtual SEA approach enables accurate and efficient TL prediction for vehicle components up to higher frequencies that FEM can achieve, supporting NVH targets and facilitating knowledge transfer to engineering teams. This work advances simulation-based acoustic transparency analysis for modern automotive design.
Numerical simulations are essential in the aircraft structures design process to assess safety margins and ensure structural integrity. Safe water landings ("ditching") impose extreme transient fluid-structure interaction (FSI) loads on aircraft. Traditionally, these interactions have been managed using simplified added-mass techniques, which often fail to capture nonlinear effects and free-surface topology changes. This paper showcases the modeling strategy of applying the mesh-free Finite Pointset Method (FPM) coupled two-way with the Virtual Performance Solution (VPS) explicit Finite Element Method structural solver to holistically model external ditching phases (impact, landing, and flotation). Guided high-speed panel tests at flight-representative velocities and legacy model-scale datasets are used to evaluate pressure timing, magnitude, and structural response. We examine gauge-pressure cut-off treatments for robustness during cavitation/ventilation regimes and explore rough-water effects using validated numerical wave generation. Results demonstrate strong correlation on rigid panels, credible multi-phase pressure histories, and highly stable aircraft-scale kinematics. Crucially, FPM's implicit time integration enables practical engineering runtimes where explicit Smoothed Particle Hydrodynamics (SPH) or meshed Arbitrary Lagrangian-Eulerian (ALE) frameworks would be computationally prohibitive.
A challenge in establishing rotor performance map for sizing tool during design cycle is the rotor performance uncertainty for full vehicle. Sometimes, simplified tests at different setup/scale are conducted to guide performance map, but this introduces another uncertainty due to configuration difference from full vehicle. To aid insights, validated computational fluid dynamics simulations (using CREATE-AV™ Helios) were carried out to examine hovering rotor performance prediction variations at different design stages, or different modeling/testing setup with identical blade design. Quantitative rotor figure of merit differences has been demonstrated along with descriptions of underlying physical reasons. The examined model setup includes isolated rigid blades with and without flapping, elastic blades, model-scale blades, whirl-tower conditions, blades installed on fuselage, and full-vehicle including tail rotor. Both fully turbulent flow and laminar-turbulence transition flow assumptions were simulated. Rigid blades showed a negative impact on performance due to the lack of nose-down elastic twist. Model scale suffered from lower Reynolds number effects but took advantage of delayed laminar-turbulence transition. Whirl-tower blockage and altered vortex trajectories reduced peak figure of merit but delayed stall. Fuselage installation increased performance with partial in-ground effect. However, the tail rotor disrupted the main rotor vortex system and caused a substantial figure of merit drop. The figure of merit variation summary from current study can provide qualitative trends and rough estimate of the rotor performance change along different analysis or test condition during new design process. Well validated computational fluid dynamics simulations can be used as a risk-reduction approach by comparing with simplified-model results (used during fast design cycle) with the expected full-vehicle model results.
We present a nonlinear topology optimization framework for designing crash--tolerant rotorcraft substructures by maximizing plastic work under prescribed crush displacement and volume constraints. The quasi-static response is modeled using a rate-independent elastoplastic formulation to capture path-dependent inelastic deformation of metallic components. A path-dependent adjoint method is developed to efficiently compute sensitivities of accumulated plastic work, revealing a mechanistic decomposition into elastic stiffness, deviatoric response, and yield surface contributions. Optimized 2D and 3D subfloor structures develop emergent plastic hinge networks and distributed deformation paths, significantly enhancing energy absorption compared to uniform designs. The results demonstrate that topology optimization can directly embed energy-dissipating mechanisms into primary rotorcraft structures, providing a practical framework for crashworthy rotorcraft and eVTOL airframe designs.
Building a trusted digital twin and decision-centric simulation ecosystem The automotive industry has been experiencing significant change and transformation. Electrification, software-defined vehicles, advanced driver assistance systems, and increasing electrical system integration are fundamentally reshaping how vehicles are designed and validated. As integration complexity continues to increase, the expectations for design cycle times are being compressed. Programs that once relied on extended validation timelines are now expected to deliver the same level of confidence in a fraction of the time. Traditional engineering workflows were built around sequential design phases, iterative simulations, and heavy reliance on physical validation. Design concepts were documented, prototypes were constructed, tests were performed, and results were compiled in reports and specifications that informed the next iteration. That approach worked well when systems were less complex and product life cycles were longer. In recent years, the volume of data, the speed of development, and the interconnected nature of modern vehicle architectures demand a different approach.
Funding from Google and the U.S. Department of Energy helped a team of researchers develop an assortment of agentic AI-enabled tools to help optimize traditional aerospace design processes. Rensselaer Polytechnic Institute, Troy, NY A Rensselaer Polytechnic Institute (RPI) engineering professor, Shaowu Pan, Ph.D. and his team of students have integrated agentic AI into computational fluid dynamics (CFD) to optimize the aerospace design process and alleviate bottlenecks. Pan's advances address priorities outlined in Winning the Race: America's AI Action Plan, which emphasizes that “high-quality data has become a national strategic asset” and calls for “the world's largest and highest quality AI-ready scientific datasets.”
A Rensselaer Polytechnic Institute (RPI) engineering professor, Shaowu Pan, Ph.D. and his team of students have integrated agentic AI into computational fluid dynamics (CFD) to optimize the aerospace design process and alleviate bottlenecks.
Army researchers recently developed a 3D-printable, easy-to-assemble drone designed to enhance intelligence, surveillance and reconnaissance capabilities. Army Research Laboratory, Adelphi, MD Researchers at the U.S. Army Combat Capabilities Development Command, or DEVCOM, Army Research Laboratory (ARL) harnessed bottom-up Soldier innovation to develop an experimental 3D-printed small unmanned aerial system, or drone, that was demonstrated at the inaugural U.S. Army Best Drone Warfighter Competition in Huntsville, Alabama. Known as the Soldier Portable Autonomous Reconnaissance Transitioning Aircraft, or SPARTA, the drone was developed at DEVCOM ARL in collaboration with Soldiers. By incorporating Soldier feedback early in the design process and leveraging ARL's world-class research facilities, researchers developed a 3D-printable, easy-to-assemble drone designed to enhance intelligence, surveillance and reconnaissance capabilities. ARL is actively working to partner the technology with industry to get into the hands of the warfighter.
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