Search Results (43)

The recovery of low- and medium-temperature waste heat using Organic Rankine Cycles (ORCs) is increasingly important for improving the efficiency and sustainability of industrial and energy systems. In compact ORC turboexpanders, high specific power output and large pressure ratios often require single- or two-stage turbines operating in transonic or supersonic regimes. Under these conditions, stator blade design is complicated by strong compressible-flow effects and, for organic working fluids, by real-gas thermodynamic behavior. Conventional supersonic stator design methods, such as the method of characteristics, are mainly applicable to the diverging supersonic portion of the blade passage, while the converging region is typically defined using empirical or heuristic prescriptions. This paper presents a physics-informed neural-network-based design method for supersonic turbine stator blades. The proposed framework generates the complete inter-blade passage, including both the converging and diverging regions, starting from a prescribed mean-line geometry and Mach number distribution. The velocity field is obtained by solving the governing equations of steady, inviscid, adiabatic, irrotational compressible flow within a PINN formulation. A hard boundary-condition strategy is used to impose the specified mean-line velocity distribution exactly, while real-fluid thermodynamic effects are incorporated through lookup tables for the speed of sound and density. The blade contours are then reconstructed from stream-function isolines predicted from the computed velocity field. The method is demonstrated for two working fluids: air, treated as a perfect gas, and toluene undergoing transcritical expansion. The resulting blade passages are first validated using inviscid CFD simulations, which show close agreement between the prescribed and computed mean-line Mach number distributions. Turbulent CFD simulations of the final blade cascades confirm smooth acceleration through the inter-blade passage, with no strong internal shocks and only weak fishtail shocks downstream of the trailing edge. For both fluids, the post-expansion ratio is approximately unity and the exit flow angle remains close to the prescribed blade metal angle, indicating well-matched supersonic stator designs. The results demonstrate that the proposed PINN-based design method provides a physically consistent approach for generating supersonic stator blade profiles for both ideal-gas and real-gas turbine applications. Full article

Shock buffet is one of the critical issues affecting the aerodynamic performance, flight quality, and flight safety of large aircraft. To overcome the limitations of traditional experimental measurement methods, such as insufficient capability in capturing flow features and high cost, an integrated experimental system tailored for extreme cryogenic and high-Reynolds-number conditions is developed based on the conventional tuft technique. This system comprises “preparation of low-flow-disturbance fluorescent mini-tufts, high-efficiency large-area tuft taping, automatic generation of digital streamline, and flow topology analysis”. Furthermore, a technique for assessing the transonic shock buffet onset using dynamic flow visualization with fluorescent mini-tufts is proposed. This paper takes a typical supercritical airfoil as the research object. First, through high-precision numerical simulations, it reveals that low-energy, unstable boundary-layer separation is the core driving force for the development and maintenance of shock buffet, and that flow separation characteristics serve as an important basis for determining the shock buffet onset. Subsequently, experimental validation is conducted in a 0.3 m high-Reynolds-number transonic wind tunnel. Using a dual-excitation-band composite light source, simultaneous measurements of pressure-sensitive paint (PSP) and fluorescent mini-tuft patterns are realized. The experimental results show that under extreme conditions, characterized by a wide total temperature range of 110 K to 280 K and strong scouring at Mach numbers from 0.6 to 0.9, the fluorescent mini-tufts (approximately 0.05 mm in diameter) exhibit excellent flow-following capability without any detachment. The digitized flow patterns of the fluorescent mini-tufts, obtained via computer image recognition algorithms, clearly reveal the location and area of boundary-layer separation. The trends show good agreement with the cryogenic PSP results, providing an important reference for determining the shock buffet onset. Full article

High-speed, low-pressure turbines in geared turbofans operate at transonic exit Mach numbers and low Reynolds numbers. Engine-relevant data remain scarce. The SPLEEN C1 linear cascade was investigated at $M_{\mathrm{out}}=0.70–0.95$ and $R{e}_{\mathrm{out}}=\mathrm{65,000}–\mathrm{120,000}$ under steady inlet flow. Experiments were combined with 2D RANS and MISES, including transition modeling and inlet-turbulence decay calibrated to measurements. Results are consistent with conventional LPT behavior: loss decreased with increasing Mach and Reynolds numbers, except when shocks interacted with the blade boundary layer ($M\approx 0.95$). Profile loss dropped by $23\%$ from $M=0.70$ to $0.95$ at $Re=\mathrm{70,000}$, as well as by $19\%$ at $M=0.80$ when open separation is suppressed. Secondary loss decreased by up to $25\%$ at $Re=\mathrm{70,000}$ and showed weak sensitivity to the Reynolds number. A coupled loss model predicted profile loss with a root-mean square error of 4.7%. Secondary-loss modeling reproduced global trends: separating endwall dissipation from mixing kept errors within $\pm 10\%$ for most cases, but accuracy degraded near the shock–boundary layer interaction case and at the highest Reynolds number. Mixing dominated endwall loss (∼75%), with the passage vortex contributing ∼50% (±10%) of the mixing component. Full article

Mesh-agnostic models have advantages in processing flow field data with various topologies and densities, and they can easily incorporate partial differential equations. Beyond physics-informed neural networks, mesh-agnostic models have been studied for data-driven predictions of simple flows. In this study, a data-driven mesh-agnostic model is proposed to predict the transonic flow field of various supercritical airfoils. The model consists of two subnetworks, i.e., ShapeNet and HyperNet. ShapeNet is an implicit neural representation used to predict spatial bases of the flow field. HyperNet is a simple neural network that determines the weights of these bases. The input of ShapeNet is extended to ensure accurate prediction for different airfoil geometries. To reduce overfitting while capturing shock waves and boundary layers, a multi-resolution ShapeNet combining two activation functions is proposed. Additionally, a physics-guided loss function is proposed to enhance accuracy. The proposed model is trained and tested on various supercritical airfoils under different free-stream conditions. Results show that the model can effectively utilize airfoil samples with different grid sizes and distributions, and it can accurately predict the shock wave and boundary layer velocity profile. The proposed mesh-agnostic model can be used as a decoder in any conventional models, contributing to their application in complex and three-dimensional geometries. Full article

This paper presents a unified aerodynamic design and optimization framework for morphing Blended-Wing-Body (BWB) Unmanned Aerial Vehicles (UAVs) operating in subsonic and near-transonic regimes. The proposed framework integrates parametric CAD modeling, Computational Fluid Dynamics (CFD), and surrogate-based optimization using Response Surface Methodology (RSM) to establish a generalized approach for geometry-driven aerodynamic design under multi-Mach conditions. The study integrates classical aerodynamic principles with modern surrogate-based optimization to show that adaptive morphing geometries can maintain efficiency across varied flight conditions, establishing a scalable and physically grounded framework that advances real-time, high-performance aerodynamic adaptation for next-generation BWB UAVs. The methodology formulates the optimization problem as drag minimization under constant lift and wetted-area constraints, enabling systematic sensitivity analysis of key geometric parameters, including sweep, taper, and twist across varying flow regimes. Theoretical trends are established, showing that geometric twist and taper dominate lift variations at low Mach numbers, whereas sweep angle becomes increasingly significant as compressibility effects intensify. To validate the framework, a representative BWB UAV was optimized at Mach 0.2, 0.4, and 0.8 using a parametric ANSYS Workbench environment. Results demonstrated up to a 56% improvement in lift-to-drag ratio relative to an equivalent conventional UAV and confirmed the theoretical predictions regarding the Mach-dependent aerodynamic sensitivities. The framework provides a reusable foundation for conceptual design and optimization of morphing aircraft, offering practical guidelines for multi-regime performance enhancement and early-stage design integration. Full article

This paper presents an experimental and computational investigation of novel, ejector-based, Coandă-surface-assisted tip leakage mitigation schemes. The predicted changes in the key performance metrics are presented after explaining the aerodynamic concept development for the novel tip geometries. The performance metrics are the stage total-to-total isentropic efficiency, tip-gap mass flow rate, and a figure of merit based on rotor exit total pressure. The schemes are based on direct geometric modifications to the turbine blade tip, effectively promoting an effective redirection of tip leakage fluid via specific channels. The proposed ejector channels operate in conjunction with strategically located Coandă surfaces to alter the path of the leakage fluid, promoting an effective leakage fluid delivery into the blade’s wake. Multiple schemes are assessed, including single-ejector, single-ejector with “hybrid” squealer, double-channeled, and triple-channeled designs. The designs are evaluated computationally for the HP stage of the Axial Flow Turbine Research Facility AFTRF at Penn State University. Extensive experimental validation of the baseline flow computations for the HP stage is also presented. Upper-bound efficiency gains of 0.49% and mass flow reductions of 14.80% compared to an untreated flat tip for the large-scale turbine test rig AFTRF are reported. Evaluation of the current tip designs in a high-speed turbine cascade environment with a transonic exit flow has also been completed. The detailed results from the high-speed investigation and heat transfer impact are in the process of being published. Implementation in the high-speed environment of the same design concepts also returned non-negligible performance gains. Full article

The trend towards higher bypass ratios and downsized cores in modern compressors leads to locally reduced Reynolds numbers, intensifying flow separation and unsteadiness, which limits the reliability of RANS models and motivates the use of LES as a feasible and attractive high-fidelity approach for these conditions. In this paper, we assess the capabilities of low- and high-fidelity numerical tools for predicting the effects of varying incidence angles for a linear compressor cascade at a Reynolds number of 150,000 and a Mach number of 0.6 based on the inflow conditions. The comparison is supported by experiments carried out at the Transonic Cascade Wind Tunnel at the DLR in Cologne, which feature an incidence angle variation of plus/minus 5 degrees. Particular emphasis is put on the numerical setup to reproduce the cascade experiment, discussing the effects of spanwise domain size, axial-velocity density ratio and inflow turbulence. The effects of the incidence angle variation are studied on the basis of instantaneous and mean flow quantities with a focus on separation, transition and loss mechanisms. Full article

This paper addresses the long-standing question of understanding the origin and evolution of low-frequency unsteadiness interactions associated with shock waves impinging on a turbulent boundary layer in transonic flow (Mach: $1.1$ to $1.3$). To that end, high-speed experiments in a blowdown open-channel wind tunnel have been performed across a convergent–divergent nozzle for different expansion ratios (PR = 1.44, 1.6, and 1.81). Quantitative evaluation of the underlying spectral energy content has been obtained by processing time-resolved pressure transducer data and Schlieren images using the following spectral analysis methods: Fast Fourier Transform (FFT), Continuous Wavelet Transform (CWT), as well as coherence and time-lag evaluations. The images demonstrated the presence of increased normal shock-wave impact for PR = 1.44, whereas the latter were linked with increased oblique $\lambda$-foot impact. Hence, significant disparities associated with the overall stability, location, and amplitude of the shock waves, as well as quantitative assertions related to spectral energy segregation, have been inferred. A subsequent detailed spectral analysis revealed the presence of multiple discrete frequency peaks (magnitude and frequency of the peaks increasing with PR), with the lower peaks linked with large-scale shock-wave interactions and higher peaks associated with shear-layer instabilities and turbulence. Wavelet transform using the Morlet function illustrates the presence of varying intermittency, modulation in the temporal and frequency scales for different spectral events, and a pseudo-periodic spectral energy pulsation alternating between two frequency-specific events. Spectral analysis of the pixel densities related to different regions, called spatial FFT, highlights the increased influence of the feedback mechanism and coupled turbulence interactions for higher PR. Collation of the subsequent coherence analysis with the previous results underscores that lower PR is linked with shock-separation dynamics being tightly coupled, whereas at higher PR values, global instabilities, vortex shedding, and high-frequency shear-layer effects govern the overall interactions, redistributing the spectral energy across a wider spectral range. Complementing these experiments, time-resolved numerical simulations based on a transient 3D RANS framework were performed. The simulations successfully reproduced the main features of the shock motion, including the downstream migration of the mean position, the reduction in oscillation amplitude with increasing PR, and the division of the spectra into distinct frequency regions. This confirms that the adopted 3D RANS approach provides a suitable predictive framework for capturing the essential unsteady dynamics of shock–boundary layer interactions across both temporal and spatial scales. This novel combination of synchronized Schlieren imaging with pressure transducer data, followed by application of advanced spectral analysis techniques, FFT, CWT, spatial FFT, coherence analysis, and numerical evaluations, linked image-derived propagation and coherence results directly to wall pressure dynamics, providing critical insights into how PR variation governs the spectral energy content and shock-wave oscillation behavior for nozzles. Thus, for low PR flows dominated by normal shock structure, global instability of the separation zone governs the overall oscillations, whereas higher PR, linked with dominant $\lambda$-foot structure, demonstrates increased feedback from the shear-layer oscillations, separation region breathing, as well as global instabilities. It is envisaged that epistemic understanding related to the spectral dynamics of low-frequency oscillations at different PR values derived from this study could be useful for future nozzle design modifications aimed at achieving optimal nozzle performance. The study could further assist the implementation of appropriate flow control strategies to alleviate these instabilities and improve thrust performance. Full article

Rotating detonation engines (RDEs) are expected to have higher specific work and efficiency, but the high-temperature transonic flow delivered by the combustor poses relevant design and technological difficulties. This work proposes a 1D model for turbine internal cooling design which can be used to explore multiple design options during the preliminary design of the cooling system. Being based on an energy balance applied to an infinitesimal control volume, the model is general and can be adapted to other applications. The model is applied to design a cooling system for a pre-existing stator blade geometry. Both the inputs and the outputs of the 1D simulation are in good agreement with the values found in the literature. Subsequently, 1D results are compared to a full conjugate heat transfer (CHT) simulation. The agreement on the internal heat transfer coefficient is excellent and is entirely within the uncertainty of the correlation. Despite some criticality in finding agreement with the thermal power distribution, the Mach number, the total pressure drop, and the coolant temperature increase in the cooling channels are accurately predicted by the 1D code, thus confirming its value as a preliminary design tool. To guarantee the integrity of the blade at the extremities, a cooling solution with coolant injection at the leading and trailing edge is studied. A finite element analysis of the cooled blade ensures the structural feasibility of the cooling system. The computational economy of the 1D code is then exploited to perform a global sensitivity analysis using a polynomial chaos expansion (PCE) surrogate model to compute Sobol’ indices. Full article

Kolmogorov–Arnold Networks (KANs) are a recent development in machine learning, offering strong functional representation capabilities, enhanced interpretability, and reduced parameter complexity. Leveraging these advantages, this paper proposes a KAN-based reduced-order model (ROM) for unsteady aerodynamics and aeroelasticity. To effectively capture temporal dependencies inherent in nonlinear unsteady flow phenomena, an architecture termed Kolmogorov–Arnold Gated Recurrent Network (KAGRN) is introduced. By incorporating a recurrent structure and a gating mechanism, the proposed model effectively captures time-delay effects and enables the selective control and preservation of long-term temporal dependencies. This architecture provides high predictive accuracy, good generalization capability, and fast prediction speed. The performance of the model is evaluated using simulations of the NACA (National Advisory Committee for Aeronautics) 64A010 airfoil undergoing harmonic motion and limit cycle oscillations in transonic flow conditions. Results demonstrate that the proposed model can not only accurately and efficiently predict unsteady aerodynamic coefficients, but also effectively capture nonlinear aeroelastic responses. Full article

A low Reynolds number (Re) environment leads to severe deterioration in compressor performance, and it is necessary and challenging to accurately predict performance at a low Re during the design phase of a compressor. This study first reveals the mechanism of typical flow characteristics in transonic compressor at a low Re via simulations. When comparing the cases with different Re, the equivalent blade profile variation due to the growth of the boundary-layer thickness is found to be the main reason for changing the flow field. On the basis of boundary-layer theory, a prediction model of the equivalent profile is developed for the viscous effect on the boundary layer, and a multiline strategy is applied to calculate the blade-load radial redistribution. The equivalent blade prediction error at different Re is up to 7.8% compared to the CFD results. Ultimately, this strategy improves the radial spatial resolution compared to the original method and is able to predict the compressor performance at a low Re with pressure ratio and efficiency errors of 0.23% and 1.8%, respectively. Full article

Dynamic stability derivatives are critical parameters in the design of trajectories and attitude control systems for flight vehicles, as they directly affect the divergence behavior of vibrations in an aircraft’s open-loop system when subjected to disturbances. This study focuses on the estimation of dynamic stability derivatives using a computational fluid dynamics (CFD)-based force oscillation method. A transient Reynolds-averaged Navier–Stokes solver is utilized to compute the time history of aerodynamic moments for an aircraft model oscillating about its center of gravity. The NASA Common Research Model serves as the reference geometry for this investigation, which explores the impact of pitching, rolling, and yawing oscillations on aerodynamic performance. Periodic oscillatory motions are imposed while using a dynamic mesh technique for CFD analysis. Preliminary steady-state simulations are conducted to validate the computational approach, ensuring the reliability and accuracy of the applied CFD model for transonic flow. The primary goal of this research is to confirm the efficacy of CFD in accurately predicting stability derivative values, underscoring its advantages over traditional wind tunnel experiments at high angles of attack. The study highlights the accuracy of CFD predictions and provides detailed insights into how different oscillations affect aerodynamic performance. This approach showcases the potential for significant cost and time savings in the estimation of dynamic stability derivatives. Full article

Multi-hole pressure probes are crucial for turbomachinery flow measurements, yet conventional data processing methods often lack generalization for complex flows. This study introduces an innovative approach by integrating machine learning techniques with the structural risk minimization (SRM) principle, significantly enhancing the generalization capability of regression models. A comprehensive framework has been developed, combining SRM-based machine learning regression methods, such as ridge regression and kernel ridge regression, with hyperparameter optimization and S-fold cross-validation, to ensure robust model selection and accuracy. Validated using the McCormick function and applied to VKI-RG transonic turbine cascade measurements, the SRM-based methods demonstrated superior performance over traditional empirical risk minimization approaches, with lower error ratios and higher $R^2$ values. Novel insights from SHAP analysis revealed subtle but significant differences in aerodynamic parameters, including a $0.63122°$ discrepancy in exit flow angle predictions, guiding the probe design and calibration strategies. This study presents a holistic workflow for improving multi-hole pressure probe measurements under high-subsonic conditions, representing a meaningful enhancement over traditional empirical methods and providing valuable references for practical applications. Full article

Turbomachinery shock wave patterns occur as a natural result of operating at off-design points and are accountable for some of the loss in performance. In some cases, shock wave–boundary layer (SW-BLIs) interactions may even lead to map restrictions. The current paper refers to experimental findings on a transonic linear cascade specifically designed to mitigate shock waves using porous walls on the blades. Schlieren visualization reveals two phenomena: Firstly, the shock waves were dissipated in all bladed passages, as predicted by the CFD studies. Secondly, a lower-pressure wave pattern was observed upstream of the blades. It is this phenomenon that the paper reports and attempts to describe. Attempts to replicate this pattern using Reynolds-averaged Navier–Stokes (RANS) calculations indicate that the numerical method may be too dissipative to accurately capture it. The experimental campaign demonstrated a 4% increase in flow rate, accompanied by minimal variations in pressure and temperature, highlighting the potential of this approach for enhancing turbomachinery performance. Full article

A transonic hump with a Mach number (M∞) of 0.875 was utilized to compare the prediction capabilities of RANS, URANS, IDDES, and IDDES-SLA for shock-induced separation. The results matched the experimental data concerning pressure, friction coefficients, velocity distribution, and other parameters. The improved delayed detached eddy simulation with a modified sub-grid scale (IDDES-SLA) method performed the best amongst tested numerical methods, demonstrating differences of 1.3% and 4.1% from the experimental results for separation and reattachment locations, respectively. The primary difference between IDDES and IDDES-SLA is reflected in detailed data, such as velocity and high-frequency characteristics, although both methods exhibit similar simulation capabilities for the main structural features of the flow field. The POD and PSD analyses of the flow field results demonstrated that the IDDES-SLA method was more capable of resolving higher modes of flow field. Full article