Browse Topic: Design processes
Space membrane structures, prized for their lightness, affordability, and ability to fold and unfold, hold immense promise for missions like deep space exploration, high-definition Earth observation, and advanced satellite communications. However, these structures are typically large-size, ultra-lightweight, highly flexible, and have very low damping. This leads to complex dynamic behavior featuring significant geometric nonlinearity, strong coupling between rigid and flexible motions, and uncertain parameters. Consequently, creating accurate yet simple mathematical models for designing controllers is exceptionally difficult, and traditional control methods that rely on such models often fall short. To tackle this problem, this study focuses on a membrane antenna spacecraft equipped with cable actuators, aiming to suppress its vibrations during attitude maneuvers. Using the Deep Deterministic Policy Gradient (DDPG) algorithm, an intelligent vibration controller based on the Actor-Critic architecture is designed. This approach is data-driven, meaning it learns effective control strategies directly through interaction with the system’s environment, bypassing the need for a precise pre-defined mathematical model. Simulation results show that the controller demonstrated superior vibration suppression and strong adaptability across several key scenarios.
The case for advanced powder coatings. Corrosion in automotive components is often framed as a cost problem, but for design and manufacturing engineers it is more accurately understood as a system-level failure mechanism. Nowhere is this more visible than in wheel assemblies, where environmental exposure, mechanical damage, and coating performance intersect. For wheel manufacturers, corrosion is not simply a maintenance concern; it is a function of material selection, geometry, surface preparation, and coating system design, all interacting under real-world conditions. The global cost of corrosion is frequently cited in trillions of dollars annually. Estimates from the Association for Materials Protection and Performance (AMPP) place this figure at over $3 trillion, with up to 35% considered preventable through improved design, material selection, and protective strategies. For engineers, the more relevant point is that a significant proportion of corrosion-related failure can be addressed through earlier-stage decisions. In practice, this means designing components and processes that limit the initiation and propagation of corrosion, rather than relying on coatings as a final barrier alone.
MIT students designed, built, and tested a jet engine with AI copilots, assessing AI's usefulness in developing high-performance aerospace systems. Massachusetts Institute of Technology, Cambridge, MA Artificial intelligence has rapidly transformed software engineering. But when the task is to conceive, design, and make a complex physical system such as a jet engine, are those AI tools equally transformative? The Jet-engine AI Research and Validation Intensive Sprint (JARVIS) Challenge set out to explore that question by asking MIT undergraduates to discover whether AI can help them build faster and better. Rather than treating AI as a writing assistant or coding accelerator, the challenge put it into a safety-critical hardware environment where every calculation, material choice, machining delay, and assembly tolerance mattered.
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.
I wanted to return to the topic from last month's column, my visit to the annual Summit of the International Transport Forum, the world's largest gathering of transport ministers and a global transport policy event, in Leipzig, Germany. Since there wasn't a lot of news directly related to automotive engineering news, I wanted to share a bit from this high-level event through editorial privilege. It was interesting to hear from some of the people in charge around the world as they shared their views on our current transportation situation and what they are doing to affect the future. At a panel on EV expansion, for example, the people in the room seemed to agree that the commercial EV industry has moved beyond proving the viability of battery-electric trucks and is shifting focus to overcoming infrastructure, regulatory, and systems integration challenges that continue to limit large-scale deployment. Even better, for those of us who've been listening to the arguments for and against electric vehicles for multiple decades now, representatives from across the industry - including logistics providers, vehicle manufacturers, charging companies, and policy groups - said that zero-emission freight transport has become a business strategy today, more so than any sort of sustainability effort as it might have been in years past. In the same vein, what might have been a battery-electric transport pilot program in 2016 can today be integrated into an organization's core freight operations.
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.
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.
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.
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.
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.
Items per page:
50
1 – 50 of 4881