Browse Topic: Digital twin
Quenching is the most critical step in the sequence of heat-treating operations, aiming to preserve the solid solution formed at the solution heat-treating temperature by rapidly cooling the material to near room temperature. Currently, there is no reliable, performance-informed quenching process that can consistently reduce the high scrap rate of airframe aluminum forging parts, which often suffer from significant residual stress and distortion. This limitation stems from the complex interactions between temperature, phase transformations, and stress/strain behavior—each influenced by the evolving temperature distribution and microstructural state of the workpiece. Conventional modeling techniques for quenching processes typically lump these multiscale, multi-physics phenomena into a simplified heat transfer coefficient (HTC). However, determining the spatial and temporal variations of HTC through experiments is both prohibitively time-consuming and costly. To address this challenge
Traditional safe-life methodologies for rotorcraft structural components often result in overly conservative life estimates, increasing maintenance costs and reducing aircraft availability. This study explores the integration of digital twin concepts with probabilistic modeling and machine learning to enhance structural life assessment, demonstrated through a practical case involving the Royal Canadian Air Force CH-146 Griffon helicopter. A probabilistic fatigue model determines a fatigue life distribution by incorporating material variability and uncertain operational loads inferred directly from flight data. Unlike conventional approaches, this method dynamically estimates load spectra, including uncertainty instead of relying on conservative assumptions. Monte Carlo simulations are used to quantify structural risk and assess the impact of load and material uncertainties. Sensitivity analyses highlight these uncertainties’ contributions to failure probability. The proposed approach
This paper discusses the development of a flight dynamics model (or digital twin) of a compact and re-configurable coaxial-propeller-based micro air vehicle (MAV) in hover, edgewise, and maneuvering flight using a hybrid physics-based plus data-driven approach. The MAV has a mass of 366 grams (0.81 lb), and features a 52 mm (2.05 in) diameter cylindrical fuselage, foldable propellers, and a two-axis gimbal thrust vectoring mechanism for pitch and roll control. The aircraft has been successfully launched from a pneumatic cannon and has achieved stable and controlled flight. A physics-based flight dynamics model of this novel MAV has been developed using Rotorcraft Comprehensive Analysis System (RCAS). RCAS is able to predict the translational dynamics near hover reasonably well; however, the accuracy decreases for rotational dynamics in edgewise flight resulting in significant differences between predicted dynamics and flight test data, known as residual dynamics. The current hybrid
As per certification requirements, for a large rotorcraft that does not meet the Category A requirements, the Height-Velocity (HV) avoid region must be determined in total power failure condition. The development of a digital twin representative of the real rotorcraft behaviour allows to reduce flight testing hours and to increase flight tests safety, especially in such critical conditions, thus decreasing risks and costs. In this work, an extensive simulation activity has been carried out to generate HV charts for a medium twin-engine helicopter in case of loss of both engines. An in-house software that emulates pilot logics has been exploited, coupled with a Flightlab model representative of the rotorcraft and validated against flight data. Manoeuvres performed after a dual engine failure were simulated starting from an all engine operative hover out of ground effect (HOGE) and in ground effect (HIGE) or level flight condition until landing, in a grid of heights and velocities and
Developing and operating an advanced air mobility service is challenging in many ways. The technical complexity of the task, the lack of available supporting infrastructure, the regulatory environment, and the necessity of collaboration among all stakeholders are barriers to a wide-spread implementation. Digital tools are now available to the whole industry to tackle those challenges, one of them being the virtual twin experience technology. The virtual twin experience is generated by the digital twin technology applied to a certain domain of application. It is a system of systems designed to provide users with a unique and immersive experience of reality, without physically being present in the real-world environment. It offers capabilities that go beyond traditional means, enabling organizations and users to achieve tasks and insights that were previously not possible. This paper describes how the virtual twin experience is adopted within the advanced air mobility ecosystem, that is
The airframe digital twin analysis framework developed at the National Research of Canada is being transposed to safe life applications for rotorcraft components. A probabilistic safe life prediction approach, consisting of uncertain material property data and uncertain load spectra is used to calculate risk assessment metrics, such as the cumulative probability of failure, the hazard rate, and the average hazard rate as a function of time. A demonstration of this approach is presented for a CH-146 Griffon component, for which the uncertain loads are estimated from a model developed through machine learning. This preliminary assessment shows the feasibility of using digital twin concepts as a viable alternative to traditional deterministic life predictions, with the potential to reduce maintenance costs and increase aircraft availability.
Quenching is a heat treatment process for the rapid cooling of a metallic workpiece in water, oil, or air to obtain certain desired material properties. It is the most critical step in the sequence of heat-treating operations to preserve the solid solution formed at the solution heat-treating temperature by rapidly cooling to near room temperature. Because of the complex interaction between temperature, phase-transformation, and stress/strain relation that depends on the temperature distribution and the microstructure of the workpiece, there is no performance-informed quenching process that can be applied reliably to reduce the high scrap rate of airframe aluminum forging parts with a significant amount of residual stress and distortion. Since large aluminum forging parts are increasingly used in aerospace structures to enable structural unitization, it is important to construct a digital twin modeling approach to mirror the physical quenching process for minimizing scrap rate
The Autoclave processing is commonly used in manufacturing high-performance fibre-reinforced thermoset composite components in the aerospace industry. Variations in the cure cycle, sometimes even apparently minor deviations from the prescribed cure cycle, can harm the laminate properties. Given the costly and time-consuming autoclave manufacturing process, there is a strong need to cure the maximum number of parts in the shortest possible time without compromising quality. In order to achieve high-rate automated manufacturing with the optimized autoclave process, it is important to construct a digital twin modelling approach to mirror the physical composite curing process in the virtual domain based on the integration of high-fidelity multi-physics models. The resulting digital twin includes a thermal CFD model, a thermo-chemo-mechanical module, and an efficient and accurate block coupling between these two modules. The customized Abaqus driven by local and spatial variation of the
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