Browse Topic: Thermodynamics
Vertical Take-Off and Landing (VTOL) aircraft introduce complex monitoring challenges due to distributed propulsion, lightweight structures, and variable operating conditions. This paper presents advanced Frequency and Orders domain techniques that repurpose existing flight control, propulsion, and structural sensor data to enhance observability without additional instrumentation. By transforming vibration, acoustic, and electrical signals into frequency and order domains, the approach enables detection of harmonics, resonance, and fault signatures tied to rotor dynamics, supporting adaptive control and predictive maintenance. Beyond rotor systems, these techniques are equally effective for monitoring electric motor health, gearbox wear, bearing degradation, and structural coupling effects in composite airframes. They also provide insight into power electronics and thermal management systems by identifying spectral anomalies linked to electrical imbalance or cooling inefficiencies. Aggregated fleet data strengthens prognostic capabilities, enabling early detection of systemic issues and trend analysis. Applications include mitigating ground resonance and modal instabilities, as well as improving reliability of propulsion and structural subsystems. Integration into avionics emphasizes computational efficiency, scalability, and compliance with standards such as DO-160 [1], DO-178 [2], ARP4761 [3] and ARP4764 [4]. Simulation and bench testing confirm feasibility, demonstrating potential to enhance safety, reliability, and lifecycle cost for next-generation urban air mobility platforms.
There is a continued and growing need for better analysis and simulation of complex transmission systems with the rise of hybrid electric powerplants coming to future aviation vehicles. In this paper we discuss how reduced order modeling can help to efficiently predict the thermal behavior of gearboxes during operations smartly reusing data from SPH based oil flow simulations. To solve the thermal problem, a dynamic non-linear Reduced Order Model (ROM) is generated to estimate the Gear-Oil heat transfer coefficient (HTC) based on variable gearbox RPM and Oil fill level.
Hybrid additive manufacturing (AM) and subtractive manufacturing (SM) processes utilize the combination of AM (e.g., LPBF and DED) and SM (e.g., milling and turning operations) to produce the final part. Due to the poor surface roughness resulting from the uneven melting of powders in AM, the subtractive process is a necessary finishing operation to improve the surface roughness of the AM part. The hybrid AM/SM technology combines the benefits of AM and SM processes to create complex geometry while introducing good surface finish and compressive stress to prevent crack initiation. However, the relationship between large process parameter space and the residual stress/distortion in the part is not well understood, which impedes the adoption of hybrid AM/SM to minimize the residual stress in the final product. To expedite the process optimization, we establish a pipeline for the sequential modeling of additive manufacturing (AM) and subtractive manufacturing (SM) processes. Key accomplishments achieved under this study include (1) development of thermal abstraction technique for the AM process to speed up the macroscale level heat transfer analysis based on the manufacturing factors including scanning vector, laser power, dwelling time, etc.; (2) development of the sequentially coupled thermal-mechanical model to predict the residual stress and distortion after AM process by passing the temperature history obtained from heat transfer analysis to the mechanical analysis at each time point; (3) validation of the thermal-mechanical model for AM using thin-wall structure from literature and cantilever beam structure from UNT’s experiments data; (4) conduction of the parametric study on the chamber temperature and part design in the AM process to demonstrate how the temperature gradient and supporting structure affect the residual stress and distortion; (5) exploration of macro and micro scale models to predict the bulk and surface residual stress after cutting; (6) applying the developed modeling framework to tailoring the hybrid AM/SM process. To support model verification and demonstration, we print cantilever beam structure with different supporting structure designs and cutting strategies to study how these factors affect the final part residual stress and distortion. The data collected in the printing and cutting process is used to examine the applicability of the developed simulation tool.
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 experimentally investigates direct effects of lightning strikes on flax fiber-reinforced polymers. Highcurrent artificial lightning strikes are conducted on coupon level to evaluate thermo-mechanical damage and to quantify the sufficiency of copper wire mesh as lightning strike protection (LSP). The dataset shall also serve for verification of prospected numerical simulation. The natural fiber flax, as a sustainable source of composite reinforcement, has been demonstrated to be suitable for semi-structural parts of rotorcraft. However, its low electrical and thermal conductivity requires a functional LSP layer for aviation applications. The test panels are investigated regarding their material combination, stacking sequence and level of LSP. Results show that two as well as three layers of 72 g/m2 copper mesh are not sufficient to withstand the standardized lightning current component A waveform of 200 kA. The high induced currents and low capability of energy dissipation leads to electro-explosion of metal and transient mechanical forces from shock waves causing mechanical damage on the test panels. Back surface-velocities increase with higher peak currents and higher level of protection results in lower damage. It is shown that a stacking of copper wire mesh results in less arc root dispersion.
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
The current US DoD has recognized that their asymmetric advantage is eroding1. Adversaries have had over 25 years to counter the US playbook and weapon systems (Ref. [1]). The US Army Future Vertical Lift (FVL) programs have identified several key tenets that their airborne weapon systems need to ensure they maintain asymmetric advantage. (1) New and upgraded mission capabilities of their airborne platforms need to get to the field faster (Ref. [2]). One of the current roadblocks to achieving this is the extensive full-system regression testing that ends up being required when there are mission system changes (Ref. [3]). (2) More competition is needed to help generate "quicker, better, faster" capabilities (Ref. [4]). "Vendor lock" inherent in current system designs hinders the speed at which technology advances (Ref. [4, 5]). (3) Improved portability of mission capability across the FVL and enduring fleet (Ref. [6, 7]). The ability to more easily reuse technology will help maintain advantage by eliminating the time needed to develop platform specific solutions (Ref. [4, 6]). The request for Modular Open System Architecture (MOSA) solutions has been a practice to try to address the items above (Ref. [8]). Most air vehicle and mission system providers are today providing MOSA solutions but the required benefits have not yet been fully realized. MOSA standards as they exist today do a very good job of identifying electronics hardware and software architectures. However, they fall short on physical aircraft integration and consistency in architecture among aircraft systems. Minimizing aircraft wiring and structural modifications, increasing speed to fielding, and portability among multiple systems types are all part of integrating highly MOSA compliant solutions. The US Army FVL programs have required a "digital backbone" (Ref. [7, 9, 10]) to address these integration issues and ensure that they can maintain asymmetric advantage. Unique requirements affecting the digital backbone include: - Power and power distribution (Ref. [9]) - Thermal management (Ref. [9, 11]) - Packaging and installation (Ref. [9]) - Air Vehicle data distribution (Ref. [9]) - Mission System data distribution (Ref. [9]) - Isolation of air vehicle and mission system (Ref. [9]) This paper will provide an introduction to the envisioned digital backbone for US Army, Future Vertical Lift aircraft. The paper will also offer discussion of digital backbone impacts on aircraft and avionics size, weight, power and cost, as well as technology considerations to address interoperability, safety, security, qualification, and accommodations for new, as well as, legacy avionics technology.
Battery power and energy density are important parameters for emerging concepts for more / all-electric vehicles. Electric propulsion and power system performance is also important. To better understand how electric propulsion and power systems component performance influences overall vehicle design, a sensitivity assessment was performed noting changes in vehicle gross weight and energy usage. Updated versions of the Revolutionary Vertical lift Technology (RVLT) Project vertical takeoff and landing (VTOL) urban air mobility (UAM) reference vehicles and missions were used. NASA electric vehicle studies are discussed which were used to help select the range of electric propulsion and power system performance parameters used in this assessment. Thermal management systems (TMS) considerations are also important; new and innovative power management and distribution (PMAD) systems can reduce electric system weight and losses, reducing thermal management constraints often imposed by electric systems modest maximum use temperatures. Vehicles with higher disk loadings require higher power levels per unit weight for VTOL operations, which make them more sensitive to electric system weights and efficiencies. Battery, all-electric vehicles show different sensitivities to component performance than turboelectric or hybrids systems. Battery, all-electric propulsion systems may increase vehicle weight and size, but still results in lower mission energy usage than their hydrocarbon-fueled versions. Significant vehicle weight growth to electric propulsion and power system power-to-weight reductions also occurs at different levels among the various concepts. From these results, one can more readily identify required component performance levels, potential component choices, or research and development paths.
A phenomenological simulation for a variable-voltage hybrid-electric powertrain was developed and compared with test data acquired on a 4 hp powertrain to understand the fundamental characteristics of such a system. The powertrain was modeled component by component, and compared with over 500 experimental data points, from the engine alone to the engine generator, to the engine-generator with four distributed propulsors. The principal conclusion of the predictive simulation and the experimental data was that generator voltage is a key parameter that needs careful control relative to rotor speed. For any operating state -- defined by rotor torque and RPM -- the generator voltage should be minimized to minimize engine specific fuel consumption. In general the system is influenced more by the engine generator than electric motors. Hence greater rotor torque and lower rotor RPM is desired. It was found that steady state performance can be confidently predicted with the engine model, if the thermal efficiency is calibrated with engine data. The overall understanding gained from this work is that the optimal operation of hybrid-electric powertrains in VTOL is closely coupled with controls and rotor aeromechanics as well as engine gas dynamics and thermodynamics, but can be captured with relatively simple phenomenological models.
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