Search Results (114)

There is a significant demand for flexibility in steam turbines, including rapid cold starts and load changes, as well as operation at low partial loads. Both industrial plants and systems for electricity and heat generation are impacted. These new operating modes result in complex, asymmetric temperature fields and additional thermally induced stresses. These lead to casing deformations, which affect blade tip gap and casing flange sealing integrity. The exact progression of heat flux and heat transfer coefficients within the cavities of steam turbines remains unclear. The current methods used in the calculation departments rely on simplified, averaged estimates, despite the presence of complex flow phenomena. These include swirling inflows, temperature gradients, impinging jets, unsteady turbulence, and vortex formation. This paper presents a novel sensor and its thermal measurements taken on a full-scale steam turbine test rig. Numerical calculations were performed concurrently. The results were validated by measurements. Additionally, the distribution of the heat transfer coefficient along the cavity was analysed. The rule of L’Hôpital was applied at specific locations. A method for handling axial variation in the heat transfer coefficient is also proposed. Measurements were taken under real-life conditions with a full-scale test rig at MAN Energy Solutions SE, Oberhausen, with steam parameters of 400 °C and 30 bar. The results at various operating points are presented. Full article

Understanding the transient flow phenomena accompanying projectile discharge is essential for improving the safety, efficiency, and predictability of small-scale ballistic systems. Despite extensive numerical studies on muzzle flows and shock formation, experimental visualization and quantitative data on the coupling between pressure waves, jet structures, and projectile motion remain limited. This work addresses this gap by employing high-speed schlieren imaging and schlieren image velocimetry (SIV) to investigate the near-field aerodynamics of an airsoft-type projectile propelled by a CO₂ jet. Three optical configurations were analyzed—a Toepler single-mirror system, a Z-type without knife edge, and a Z-type with knife edge—to assess their sensitivity and suitability for resolving acoustic and turbulent features. The measured velocity of concentric pressure waves (≈355 m/s) agrees with the theoretical local speed of sound, validating the optical calibration. Projectile tracking yielded a mean speed of 71 ± 1.6 m/s, with drag and kinetic energy analyses confirming significant near-muzzle deceleration due to jet–projectile interaction. The SIV analysis provided additional insight into the convection velocity of coherent jet structures (≈75 m/s), tangent velocity fluctuations (±0.8 m/s), and vorticity distribution along the jet boundary. The results demonstrate that even compact schlieren setups, when coupled with quantitative image analysis, can capture the essential dynamics of unsteady compressible flows, providing a foundation for future diagnostic development and modeling of projectile–jet interactions. Full article

Low-permeability reservoirs play a crucial role in global energy supply, yet their efficient development is hindered by complex seepage mechanisms and strong nonlinear flow behavior. This study systematically investigates the characteristics of oil–water relative permeability curves and the associated non-Darcy flow phenomena in low-permeability sandstone reservoirs. Through unsteady-state water flooding experiments on native cores with permeabilities ranging from 2.99 to 34.40 mD, we analyzed the influence of permeability on relative permeability curves and categorized the water-phase curves into concave-downward and linear types. A dynamic quasi-threshold pressure gradient model was established, incorporating the corrected permeability and water saturation. Furthermore, a novel relative permeability calculation model was derived by integrating the threshold pressure gradient into the non-Darcy flow framework. Validation against the traditional Johnson–Bossler–Naumann (JBN) method demonstrated that the proposed model more accurately captures the flow behavior in low-permeability media, showing lower oil-phase permeability and higher water-phase permeability. The findings provide a reliable theoretical basis for optimizing water flooding strategies and enhancing recovery in low-permeability reservoirs. Full article

This study focuses on the numerical analysis of a centrifugal pump’s suction capability, aiming to reliably predict its suction performance characteristics. The main emphasis of the research was placed on the influence of different turbulence models, the quality of the computational mesh, and the comparison between steady-state and unsteady numerical approaches. The results indicate that steady-state simulations provide an unreliable description of cavitation development, especially at lower flow rates where strong local pressure fluctuations are present. The unsteady k–ω SST model provides the best overall agreement with experimental NPSH₃ characteristics, as confirmed by the lowest mean deviation (within the ISO 9906 tolerance band, corresponding to an overall uncertainty of ±5.5%) and by multiple operating points falling entirely within this range. This represents one of the first detailed unsteady CFD verifications of NPSH prediction in centrifugal pumps operating at high rotational speeds (above 2900 rpm), achieving a mean deviation below ±5.5% and demonstrating improved predictive capability compared to conventional steady-state approaches. The analysis also includes an evaluation of the cavitation volume fraction and a depiction of pressure conditions on the impeller as functions of flow rate and inlet pressure. In conclusion, this study highlights the potential of advanced hybrid turbulence models (such as SAS or DES) as a promising direction for future research, which could further improve the prediction of complex cavitation phenomena in centrifugal pumps. Full article

Efficient prediction of aerodynamic loads on wind turbine blades under yawed inflow remains challenging due to the complexity of three-dimensional unsteady flow phenomena. In this work, a modified blade element momentum (BEM) method, incorporating multiple correction models, is systematically compared with high-fidelity computational fluid dynamics (CFD) simulations for the NREL Phase VI wind turbine across a range of inflow velocities (7–15 m/s) and yaw angles ($0^{°}$–${60}^{°}$). A normalized absolute error metric, referenced to experimental measurements, is employed to quantify prediction discrepancies at different yaw conditions, wind speeds, and spanwise blade locations. Results indicate that the corrected BEM method maintains good agreement with measurements under non-yawed attached flow, with errors within 2%, but its accuracy declines substantially in separated and yawed flow regimes, where errors can exceed 20% at high yaw angles (e.g., ${60}^{°}$) and low tip-speed ratios. CFD flow-field visualizations, including vorticity and Q-criterion iso-surfaces, reveal that yawed inflow strengthens vortex interactions on the leeward side and generates Coriolis-driven spanwise vortex structures, promoting stall progression from tip to root. These unsteady phenomena induce load fluctuations that are not captured by steady-state BEM formulations. Based on these insights, future studies could incorporate vortex structure and spanwise flow features extracted from CFD into unsteady correction models for BEM, enhancing prediction robustness under complex operating conditions. Full article

Nitrogen compression requires centrifugal compressors to operate under relatively high ambient pressure. However, the internal instability characteristics of compressors handling high-density working fluids remain unclear. Therefore, this study employs Dynamic Mode Decomposition (DMD) to investigate unsteady flow fluctuations within an isolated centrifugal impeller under both best efficiency and near-stall conditions at high ambient pressure. Results show that as the throttling process progresses, distinct unsteady phenomena emerge within the impeller. Under near-stall conditions, the frequency of the instability is 0.44 times the blade passage frequency (BPF), manifesting as periodic pressure fluctuations throughout the entire blade passage. This instability originates from periodic passage blockages caused by fluctuations in tip leakage flow. Additionally, the pressure fluctuations at the impeller inlet exhibit a noticeable lag compared to those in the latter half of the passage. Through DMD analysis, it is found that after the tip leakage vortex exits the blade, it interacts with the pressure surface of the adjacent blade, affecting the tip loading of the neighboring blade and forming a dynamic cycle. However, this vortex is not the primary flow structure responsible for the instability. These insights into the nature of unsteady disturbances provide valuable implications for future stall warning and instability prediction technologies. Full article

This study investigated the aerodynamic structures generated by transverse jet injection in supersonic flows around high-speed vehicles. The unsteady evolution of these structures was analyzed using an improved delayed detached Eddy simulation (IDDES) approach based on the Reynolds stress model (RSM). The simulations successfully reproduced experimentally observed shock systems and vortical structures. The time-averaged flow characteristics were compared with the experimental results, and good agreement was observed. The flow characteristics were analyzed, with particular emphasis on the formation of counter-rotating vortex pairs in the downstream region, as well as complex near-field phenomena, such as flow separation and shock wave/boundary layer interactions. Time-resolved spectral analysis at multiple monitoring locations revealed the presence of a global oscillation within the flow dynamics. Within these regions, pressure fluctuations in the recirculation zone lead to periodic oscillations of the upstream bow shock. This dynamic interaction modulates the instability of the windward shear layer and generates large-scale vortex structures. As these shed vortices convect downstream, they interact with the barrel shock, triggering significant oscillatory motion. To further characterize this behavior, dynamic mode decomposition (DMD) was applied to the pressure fluctuations. The analysis confirmed the presence of a coherent global oscillation mode, which was found to simultaneously govern the periodic motions of both the upstream bow shock and the barrel shock. Full article

Tidal turbines represent a promising renewable energy source, generating power from ocean currents. However, due to tidal range variations, they sometimes become partially exposed to the free surface. When this occurs, the turbine experiences reduced power generation and unsteady torque caused by the asymmetric flow. Such conditions can lead to long-term degradation of turbine performance and reliability. From this perspective, a key question arises regarding how significantly power generation differs when turbines are exposed to the free surface. This study was conducted with the objective of quantitatively evaluating the differences in power generation and torque acting on the turbine due to free-surface exposure, in order to address this question. Numerical simulations considering free-surface exposure effects were developed to quantitatively assess these phenomena through Computational Fluid Dynamics (CFD). Additionally, this numerical model was validated by comparison against experimental data and verified by convergence tests. The results revealed that the tidal turbine exhibited power generation differences ranging from a maximum of 45% to a minimum of 0.44%, depending on the degree of free-surface exposure. These findings are expected to serve as valuable indicators for power generation when operating tidal turbines. 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

The unsteady interactions in rotating detonation turbine engines (RDTE) remain poorly understood. To address this, a 2D numerical model integrating a rotating detonation combustor (RDC) with a first-stage turbine is established to analyze flow structures and aerodynamics under various detonation modes. Proper orthogonal decomposition (POD) reveals intrinsic links between flow features and performance metrics. Results show that while the RDC generates total pressure gain, it induces significant unsteady flow. Guide vanes partially suppress pressure fluctuations but cannot eliminate total pressure losses or circumferential non-uniformity, reducing rotor efficiency. Increasing detonation wave numbers decreases total pressure gain at rotor inlet but improves flow uniformity: the counterclockwise double-wave mode exhibits optimal performance (27.9% work gain, 5.0% instability, 86.4% efficiency), whereas the clockwise single-wave mode shows the poorest (20.9% work gain, 11.8% instability, 84.0% efficiency). POD analysis indicates first-order modes represent time-averaged flow characteristics, while low-order modes capture non-uniform pressure distributions and pairing phenomena, reconstructing wave propagation. The study highlights discrepancies between turbine inlet’s actual unsteady flow and conventional quasi-steady design assumptions, proposing enhancing mean flow characteristics and increasing first-mode energy proportion to improve work extraction. These findings clarify the detonation wave mode–turbine performance correlation, offering insights for RDTE engineering applications. Full article

Accurate prediction of cavitating flows is essential for improving the performance and durability of marine and hydrodynamic systems. This study investigates the influence of different cavitation models—Kunz, Merkle, and Schnerr–Sauer—on the numerical prediction of cavitation around a hemispherical head-form body using computational fluid dynamics (CFD). Additionally, the effects of turbulence modeling approaches, including Reynolds-averaged Navier–Stokes (RANS) and partially averaged Navier–Stokes (PANS), are examined to assess their capability in capturing transient cavitation structures and turbulence interactions. The results indicate that the Schnerr–Sauer model, which incorporates bubble dynamics based on the Rayleigh–Plesset equation, provides the most accurate prediction of cavitation structures, closely aligning with experimental data. The Merkle model shows intermediate accuracy, while the Kunz model tends to overpredict cavity closure, limiting its ability to capture unsteady cavitation dynamics. Furthermore, the PANS turbulence model demonstrates superior performance over RANS by resolving more transient cavitation phenomena, such as cavity shedding and re-entrant jets, leading to improved accuracy in pressure distribution and vapor volume fraction predictions. The combination of the PANS turbulence model with the Schnerr–Sauer cavitation model yields the most consistent results with experimental observations, highlighting its effectiveness in modeling highly dynamic cavitating flows. Full article

In this study, a numerical investigation of unsteady natural convection heat transfer (HT) and entropy generation (EG) is performed within a trapezoid-shaped cavity containing thermally stratified water. The cavity’s bottom wall is heated, the sloped walls are thermally stratified, and the top wall is cooled. The finite volume (FV) method is employed to solve the governing equations. This study uses a Prandtl number (Pr) of 7.01 for water, an aspect ratio (AR) of 0.5, and Rayleigh numbers (Ra) varying between 10 and 10⁶. To examine the flow behavior within the cavity, various relevant parameters are determined for different Ra values. These parameters include streamline and isotherm contours, temperature time series, limit point and limit cycle analysis, average Nusselt number (Nu) at the heated walls, average entropy generation (E_avg), and average Bejan number (Be_avg). It is found that the flow transitions from a steady symmetrical state to a chaotic state as the Ra value increases. During this transition, three bifurcations occur. The first is a pitchfork bifurcation between Rayleigh numbers of 9 × 10⁴ and 10⁵, followed by a Hopf bifurcation between Rayleigh numbers of 10⁵ and 2 × 10⁵. Finally, another bifurcation occurs, shifting the flow from periodic to chaotic between Rayleigh numbers of 4 × 10⁵ and 5 × 10⁵. The present study shows an increase in E_avg of 94.97% between Rayleigh numbers of 10³ and 10⁶, while the rate of increase in Nu is 81.13%. The findings from this study will enhance understanding of the fluid flow phenomena in a trapezoid-shaped cavity filled with stratified water. The current numerical results are compared and validated against previously published numerical and experimental data. Full article

Vehicle acceleration typically occurs at traffic lights, intersections, or congested sections within urban streets, where high densities of pedestrians and vehicles pose a direct threat to respiratory health due to PM_2.5 dispersion. Computational Fluid Dynamics (CFD) simulations, combined with the Dynamic Mode Decomposition (DMD) method, are used to analyze the dynamic characteristics of PM_2.5 dispersion during vehicle acceleration. The DMD method can effectively analyze the dynamic change in pollutant concentration in an unsteady flow field and clarify the influence mechanism of vehicle acceleration on pollutant dispersion. The results indicate that PM_2.5 dispersion during the initial stage of acceleration is primarily influenced by low-frequency and large-scale flows, such as exhaust emissions, natural wind, and trailing vortices. In the middle stage, PM_2.5 dispersion tends to stabilize, while in the final stage, high-frequency modes dominate, and intense flow field fluctuations significantly enhance PM_2.5 dispersion. Furthermore, the analysis reveals the critical role of upward and downward airflow phenomena around the vehicle in driving PM_2.5 dispersion. This study offers a new perspective on the dispersion characteristics of PM_2.5 under unsteady flow conditions in urban streets and provides a scientific basis for developing speed management strategies to mitigate the impact of pollutant dispersion. Full article

Fractional calculus plays a pivotal role in modern scientific and engineering disciplines, providing more accurate solutions for complex fluid dynamics phenomena due to its non-locality and inherent memory characteristics. In this study, Caputo’s time fractional derivative operator approach is employed for heat and mass transfer modeling in unsteady Maxwell fluid within a cylinder. Governing equations within a cylinder involve a system of coupled, nonlinear fractional partial differential equations (PDEs). A machine learning technique based on the Levenberg–Marquardt scheme with a backpropagation neural network (LMS-BPNN) is employed to evaluate the predicted solution of governing flow equations up to the required level of accuracy. The numerical data sheet is obtained using series solution approach Homotopy perturbation methods. The data sheet is divided into three portions i.e., $80\%$ is used for training, $10\%$ for validation, and $10\%$ for testing. The mean-squared error (MSE), error histograms, correlation coefficient (R), and function fitting are computed to examine the effectiveness and consistency of the proposed machine learning technique i.e., LMS-BPNN. Moreover, additional error metrics, such as R-squared, residual plots, and confidence intervals, are incorporated to provide a more comprehensive evaluation of model accuracy. The comparison of predicted solutions with LMS-BPNN and an approximate series solution are compared and the goodness of fit is found. The momentum boundary layer became higher and higher as there was an enhancement in the value of Caputo, fractional order α = 0.5 to α = 0.9. Higher thermal boundary layer (TBL) profiles were observed with the rising value of the heat source. Full article

The growing demand for increasing the engine power of a liquid rocket is driving the development of high-power De-Laval nozzles, which is primarily achieved by increasing the expansion ratio. A high-expansion-ratio for De-Laval nozzles can cause flow separation, resulting in unsteady, asymmetric forces that can limit nozzle life. To enhance nozzle performance, various separation control methods have been proposed, but no methods have been fully implemented thus far due to the uncertainties associated with simulating flow phenomena. A numerical study of a high-area-ratio rocket engine is performed to analyze the aeroelastic performance of its structure under flow separation conditions. Based on numerical methodology, the flow inside a rocket nozzle (the VOLVO S1) is analyzed, and different separation patterns are comprehensively discussed, including both free shock separation (FSS) and restricted shock separation (RSS). Since the location of the flow separation point strongly depends on the turbulence model, both the single transport equation and two-transport-equation turbulence models are simulated, and the findings are compared with the experimental results. Therefore, the Spalart–Allmaras (SA) turbulence model is the ideal choice for this rocket nozzle geometry. A wavelet is used to analyze the amplitude frequencies from 0 to 100 Hz under various pressure fluctuation conditions. Based on a clear understanding of the flow field, an aeroelastic coupling method is carried out with loosely coupled computational fluid dynamics (CFD)/computational structural dynamics (CSD). Some insights into the aeroelasticity of the nozzle under separated flow conditions are obtained. The simulation results show the significant impact of the structural response on the inherent pressure pulsation characteristics resulting from flow separation. Full article