Browse Topic: Digital twin
Traditional safe-life methodologies for rotorcraft structural components rely on deterministic safety factors to account for uncertainty in loads, material properties, and operational usage. While effective for ensuring safety, these approaches lead to early retirement lives and reduced aircraft availability. This paper presents an updated digital twin-based probabilistic framework for rotorcraft component fatigue life assessment that integrates a probabilistic stress–life (S-N) material model, machine learning-based load estimation from flight data, and Monte Carlo uncertainty propagation. The approach is demonstrated for a critical location on the CH-146 Griffon main rotor yoke. Compared with earlier work, the present study advances the framework through independent validation of the load-estimation model and application to available in-service flight data from multiple mission categories. A probabilistic sensitivity analysis is used to examine the separate and combined effects of material variability and load-estimation uncertainty on fatigue life, cumulative probability of failure, and hazard rate. For the CH-146 demonstration case, the results indicate that the material fatigue strength uncertainty has a major impact on the lower tail of the life distribution and the corresponding reliability-based life, whereas load-estimation accuracy uncertainty has a secondary influence on risk metrics. The application of the digital twin framework to operational, search and rescue, and training mission data further shows that mission-specific usage variability plays an important role in the evolution of fatigue damage accumulation and structural risk. Overall, the proposed framework provides a more informative basis for risk-based rotorcraft life assessment by explicitly quantifying uncertainty and incorporating aircraft-specific operational data. The study is intended as a step toward validation of the framework rather than a completed operational deployment.
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 and enable rapid process tailoring for reduced distortion, we have developed and validated a digital twin-based Quenching Laboratory Software (QLAB) tool. QLAB integrates a thermal multi-phase computational fluid dynamics (CFD) model, sequentially coupled with a Mechanical Threshold Stress (MTS) model and a precipitation model. The thermal CFD component captures turbulent flow, multi-phase transformations, and the complex heat transfer stages of quenching including vapor blanket formation, nucleate boiling, and convection—to accurately predict temperature evolution. The MTS-precipitation model quantifies the effects of microstructural precipitates on the material's mechanical response under thermal loading. QLAB has been thoroughly validated using representative aluminum airframe components, including aluminum bars with pockets and Lcorner parts. We demonstrate the tool's predictive accuracy by comparing its output against experimentally measured temperature and distortion fields. Finally, we apply the validated QLAB to conduct a virtual quenching test on a simplified aluminum airframe structure, showcasing its potential for performance-informed process optimization.
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 model utilizes the residual dynamics via a data-driven approach to correct the physics-based model. Using the measured vehicle states and control inputs, a deep neural network (DNNs) was trained to learn the residual forces and torques. The resulting hybrid model reduced prediction errors by 55% on average compared to the RCAS model based on pure physics.
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 provides probabilistic life predictions, supporting risk-based maintenance strategies to potentially optimize operational efficiency. The long-term goal is to develop an adaptive digital twin model that continuously updates with new operational flight data, enhancing predictive accuracy for helicopter fleet management.
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 comprised of the aircraft manufacturer, the vertiport provider and the operator. It reviews the different business processes on electric vertical take-off and landing (eVTOL) vehicle design and manufacturing, vertiport design, and operations, and highlights the associated benefits of such approach. Through these processes, we will see how the virtual twin experiences shape the future of product design, manufacturing, and operations. It changes the way in which stakeholders collaborate in a complex system of systems context. The paper concludes on the ways that the virtual twin experience provides organizations enhanced visualization and understanding, expanded trade-space exploration, and improved decision-making, and how it enables innovators and enterprises alike with greater visibility, efficiency, flexibility, and compliance.
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 for different weights and altitudes combinations. Sensitivity analyses on the parameters that affect the manoeuvres the most have been performed and the effects of the assumptions and requirements at touchdown on the HV avoid region investigated.
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.
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 turbulence-induced heat transfer coefficient (HTC) imposed through one-way coupling determines the thermo-mechanical response in composite parts. Using the developed digital twin tool (SMARTCLAVE), HTC's spatial and temporal variation can be generated digitally without invoking an expensive and time-consuming experimental approach. The predicted local boundary conditions are used in SMARTCLAVE to determine the cure kinetics, temperature distribution, and thermal-mechanical response that drives the residual stress and distortion of composite parts after curing. The accuracy of the digital twin for autoclaving is demonstrated first using a benchmark problem followed by the capability demonstration with a single-part L-beam assembly. The benefits of using the digital twin tool are illustrated via the optimal placement of multiple parts in an autoclave to balance the throughput and quality.
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, increasing production efficiency, and engineers and machine operators' handling of variances in forging operations. A high-fidelity modeling of the coupling of thermal, metallurgical, and mechanical interactions is a key component to creating a digital twin of the physical quenching process. A high-fidelity thermal multi-phase computational fluid dynamics (CFD) model is applied to simulate fluid dynamics and temperature fields in the quenchant tank. The developed immersogeometric modeling approach is used next for an efficient model generation of a 3D workpiece with various dipping orientations. Given the temperature and pressure profiles predicted from the CFD-based heat transfer module, residual stress and distortion prediction modules are developed by including temperature and pressure fields mapping and temperature and strain rate dependent property evolution via Abaqus' user-defined subroutines. Verification and demonstration studies are performed using aluminum coupons dipped into a quenching tank with different orientations. Time histories of the temperature and residual stress fields were predicted to explore the relationship between the process and performance.
ABSTRACT
ABSTRACT
The digital twin (DT) refers to a digital replica or virtual model of actual physical product or process that can be applicable for various purposes. In this study, a digital reproduction of the next generation active twist blade, meeting superior durability characteristics and high strength requirements under severe operating environments of a helicopter rotor, is attempted using the up-to-date computed tomography (CT) scheme combined with modern digital image processing technique. The CT scan covers much portion of the blade root, transition, and tip regions where substantial variations in external geometries and/or interior structural layouts are present while limited zones in the airfoil blade region being considered as nonuniform. A three-dimensional (3D) finite element-based DT simulation model is constructed using the high-resolution CT-scan images. The detailed lamination geometries and sequences of layered composites in the blade skin and spar are implemented in the DT model which can be exploited further for durability study or strength analysis. The reconstructed 3D analysis model is used to determine the structural properties of the blade. In parallel, either mechanical or optical measurement methods along with two-dimensional (2D) blade sectional analysis are carried out to cross validate their predictions. Overall, fair to good correlation is obtained between the different set of results. The agreement is good for mass, elastic axis, and flap bending while less satisfactory results are obtained with the torsion rigidity. A sensitivity analysis is also conducted to clarify the impact of modeling cables, nose weight, and manufacturing imperfections on the structural property evaluation of the blade.
The objective of the joint National Research Council of Canada (NRC) and The Boeing Company Technology Development Program (TDP) entitled 'Canadian Vertical Lift Autonomy Demonstration' (CVLAD) is to evaluate automated and supervised autonomous flight systems on NRC Bell 412 Advanced Systems Research Aircraft (ASRA) and Royal Canadian Air Force Boeing CH-147F Chinook demonstrators. Boeing technologies such as Degraded Visual Environment Pilotage System and Advanced Vehicle Management System form the foundation of an autonomy solution that aims to satisfy Royal Canadian Air Force, US Army, and other Armed Service branch end-use objectives for force multiplication, tactical advantage, pilot assistance, reduced crew operations, and enhanced fleet productivity. The Boeing Company engaged NRC under a Cooperative Research Agreement since 2016 as part of a number of strategies to upgrade Medium-Heavy Lift H-47 Chinook capabilities prior to long-term aircraft replacement in the 2030 to 2060 timeframe. A recent achievement of the CVLAD TDP by its Boeing Phantom Works, Boeing Chinook Program, Aurora Flight Sciences, and NRC Flight Research Laboratory team was the development of Automated Flight Guidance methods addressing system safety and performance. Design and evaluation activities occurred in Boeing Software-/Hardware in-loop facilities as well as on the NRC Bell 412 ASRA. The CVLAD team is using a blend of traditional Systems Engineering 'V-Shaped' Life Cycle Model, System of Systems, and Model-Based processes to develop a cyber-physical system that aims to meet end-user concept of operations and requirements. Significant benefits of virtual development tools such as component-vehicle digital twins and surrogate inflight simulation facilities are achieved as they promote effective collaboration, efficient design, and relevant verification/validation methodologies. Business models can be made more robust by phasing the introduction of technology where effective automation provides users with near-term benefits, while providing a foundation for safe, reliable, and trusted autonomous capabilities for long-term production.
A key component for implementing the digital twin approach is to apply a validated high fidelity simulation tool to generate a mapping between the virtual test and structural performance. Due to the high computational intensity of physical simulation tools, their application for a complex system along with its error estimation can be time consuming. In addition, given the limited data gathered from sensors, onsite inspection, and tests at different configurations, it is imperative to create a high fidelity and efficient model based on the previously gathered information and enhance the model when more data points are gathered. Motivated by this, we develop a machine learning based digital twin simulation framework to predict fatigue life of a structural component from available information gathered. Different from a conventional physical simulation approach, the prediction error from the physical simulation and machine learning are explicitly obtained, in addition to the improvement of the computational efficiency at the prediction stage via the trained machine learning model. To illustrate the idea of this modeling strategy, we applied our developed 3D extended finite element toolkit for Abaqus (XFA3D) as a virtual testing tool for fatigue crack path and life prediction of a welded metallic component in conjunction with the observed testing data. Using machine learning techniques, we first estimate prediction error from the physical model based on previous cross validation results, and then predict the fatigue life in the presence of uncertainties associated with fabrication induced imperfection, welding induced residual stress, and the machine learning errors. It is found that the inclusion of as-manufactured characteristics and uncertainties are essential for the application of a digital twin approach for the total life management of aging structures.
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