Supersonic Flow Field Research Articles

Flowfield reconstruction in a supersonic isolator based on proper orthogonal decomposition and sensor compression coupling under variable Mach numbers

The supersonic inflow passes through the shock train in the isolator of the scramjet to complete deceleration and pressurization, followed by combustion and energy release, providing strong thrust. When the back pressure generated by combustion is disturbed forward, the location of shock train leading edge (STLE) will also change accordingly. Once it moves to the entrance of the isolator, it will cause unstart. Accurately detecting STLE in the isolator of a scramjet is crucial for controlling the shock train and preventing the inlet from unstart. Therefore, based on the sparse reconstruction of compressive sensing and sensor compression coupling, a supersonic flowfield reconstruction model (POD-STLE) based on proper orthogonal decomposition (POD) was constructed to reconstruct the supersonic flowfield and detect the location of STLE in the supersonic isolator. The experiments were conducted on the shock oscillation under variable Mach numbers and back pressures, to construct the experimental dataset. Combining supersonic flowfield reconstruction and matrix decomposition, different sensor layouts were constructed, which can ensure accuracy and stability while saving sensor resources. The POD-STLE was applied to the flowfield reconstruction of the supersonic isolator, and the location of STLE was detected under variable and constant conditions, ultimately achieving the expected reconstruction effect and detection accuracy. This study provides a new research method for detecting the location of STLE in the supersonic isolator of a scramjet and provides technical for exploring supersonic flowfield.

On the Challenges of Integrating a Rotating Detonation Combustor with an Industrial Gas Turbine and Important Design Considerations for Row-1 Blades

Abstract In an effort to increase the efficiency and performance of gas turbine power cycles, pressure gain combustion (PGC) has gained significant interest. Since Rotating Detonation Combustors (RDC) can provide a quasi-steady mode of operation, research has been triggered to integrate RDC with power-generating gas turbines. However, the presence of subsonic and supersonic flow fields which are generated due to the shock waves that stem from the detonation wave front and the highly non-uniform temperature and velocity profiles may cause a depreciation in the turbine performance. The current study seeks to investigate the challenges of integrating the RDC with nozzle guide vanes (NGV) of an industrial, can-annular gas turbine and attempts to understand the major contributors that impact efficiency and identify the key areas of optimization that need to be considered for maximizing performance. The RDC was integrated with the NGVs through a non-optimized straight duct-type geometry with a diffuser cone. 3-Dimensional Numerical analyses were performed to investigate sources of total pressure loss and to understand the unsteady effects of RDC which contribute towards the deterioration of performance. The entropy generation at different regions of interest was calculated to identify the major irreversibilities in the system. Also, total pressure and temperature distribution along the radial direction at the exit of the transitional duct is presented.

Application of High-Speed Self-Aligned Focusing Schlieren System for Supersonic Flow Velocimetry

A self-aligned focusing schlieren (SAFS) system combines the field of view of a conventional schlieren system with the defocus blur of a focusing schlieren system away from the object plane. It can be assembled in a compact form, measuring 1.2 m (4 ft) in length in the described case. The depth of field is sufficiently shallow to distinguish specific spanwise features in a supersonic flow field within a 76.2 mm (3 in) wide test section. As a result, the boundary-layer perturbations on windows and window-material defects and surface imperfections are blurred. Analytical forms are derived for depth of field and vignetting of the SAFS system. A laser spark velocity measurement in Mach 2 flow is performed by tracking the blast wave of a laser spark using 500 kHz SAFS imaging with a 200 ns optical pulse width. The flow Mach number and stagnation temperature are measured by comparing the blast-wave dynamics to an analytical solution. Additionally, schlieren image velocimetry is performed by analyzing natural flow perturbations in 500 kHz SAFS images using a self-correlation method. Comparing the spectra of gas density perturbations from the core flow and a near-wall region reveals a significant difference, with high-frequency prevalence at the boundary-layer location.

Advances in Femtosecond Coherent Anti-Stokes Raman Scattering for Thermometry

The combustion process is complex and harsh, and the supersonic combustion flow field is also characterized by short duration and supersonic speed, which makes the real-time diagnostic technology for the transient environment extremely demanding. It is of great significance to realize high time-resolved accurate measurement of temperature, component concentration, and other parametric information of the combustion field to study the transient chemical reaction dynamics of the combustion field. Femtosecond CARS spectroscopy can effectively avoid the collision effect between particles in the measurement process and reduce the influence of the non-resonant background to improve the measurement accuracy and realize the time-resolved measurement on a millisecond scale. This paper introduces the development history of femtosecond CARS spectroscopy, points out its advantages and disadvantages, and looks forward to the future development trend to carry out high time-resolved measurements, establish a database of temperature changes in various complex combustion fields, and provide support for the study of engine mechanisms.

Supersonic combustion field evolution prediction in scramjet engine using a deblurring multi-scale attention network

Traditional deep learning technologies often overlook crucial spatiotemporal information when reconstructing supersonic combustion flow fields. These models can only reconstruct the current flow field, resulting in poor visual quality due to the atomization effect. This research introduces experimental studies on scramjet combustion flows with variable injection pressures at the inlet of a scramjet Mach 2.5, creating image datasets of flame evolution processes over different predicted time spans (5, 10, and 15 ms). It develops a multi-scale attention algorithm with deblurring (DB-MSAN) capabilities to predict the flame image after a certain period using the wall pressure signal from the previous moment. The algorithm employs two multi-head attention channels at different scales to extract timing information, encouraging the model to focus on the anisotropy of the input data and to identify more significant features. The DB module utilizes the adapted atmospheric scattering model to jointly estimate the transmission map and atmospheric light, achieving an exceptional defogging effect with a minimal computational expense. The model’s overall performance is assessed regarding efficiency and accuracy, comparing DB-MSAN’s performance differences with those of Chen’s and the ResNet16 models across multi-span, large-span, various test sets, and sparse wall measurement points. Experimental results indicated that, compared to other methods, DB-MSAN achieves a maximum peak signal-to-noise index improvement of 76.10 % and an SSIM index improvement of 155.26 % when predicting average-quality images. The prediction accuracy remains stable, even with sparse pressure input. In addition, DB-MSAN strikes an optimal balance between the number of parameters and model accuracy; it represents only 16.97 % of the volume compared to Chen’s model, which is 585 MB.

Spectral feature extraction of rocket exhaust plume using spectral proper orthogonal decomposition

The spectral characterization of flow-field parameters provides a new perspective for understanding the spatiotemporal evolution of unsteady supersonic exhaust plumes and for extracting typical structures. In this study, a large-eddy simulation is performed to calculate the three-dimensional unsteady supersonic plume flow field of rocket engines, and a spectral proper orthogonal decomposition (SPOD) method with a spatiotemporal separation is established. This approach is used to extract the coherent structural features of the unsteady exhaust plume flow field and analyze the mode space structure at different frequencies. The three-dimensional reconstruction and denoising of the exhaust plume flow-field parameters can be achieved via the frequency- and time-domain reconstructions of the SPOD algorithm and oblique projection method, respectively. The ground rocket exhaust plume of ballistic evaluation motor-II is analyzed. The results indicate that the SPOD method can effectively extract the single-frequency mode structure of the reactive supersonic flow field, and that low-order behavior appears in the m = 0 and m = 1 azimuth modes. The potential core exhibits a high-frequency wave-packet structure that is affected by shock waves and shear layers. Time-domain reconstruction based on the oblique projection method facilitates the capture of the dynamic characteristics of the flow field. For the first-order SPOD mode, the frequency- and time-domain reconstruction errors are 3.3% and 1.5%, respectively. The frequency-domain reconstruction method exhibits a 4% improvement in denoising ability compared to low-pass filters. This study provides a novel method for the spectral characterization and spatiotemporal feature extraction of supersonic exhaust plume flow fields.

Stream-Surface Iteration-Based Flowfield Calculation Method for Pressure-Controllable Waverider Design

The performance of waveriders is greatly influenced by the wall pressure distribution. To achieve the goal of designing a waverider based on the complex two-dimensional pressure distribution, this study proposes a methodology called the stream-surface iteration-based flowfield calculation (SIFC) method. This method discretizes the three-dimensional flowfield with curved stream surfaces and approximates the flow around each point with a unique axisymmetric flow. Moreover, this method iteratively solves the shape of each stream surface and the corresponding flowfield to deal with ill-posed problems. To verify its effectiveness, the SIFC method was first applied to inversely solve the two external annular supersonic flowfields. Then, it was used for the waverider design. The computational fluid dynamics results showed that the relative error of the wall pressure was less than 2.6% for the annular supersonic flowfields. The wall pressure determines the geometric shapes and mechanical properties of waveriders. The pressure center of a waverider can be shifted by 12% of the length in the flow direction and 7.4% of the width in the crossflow direction by altering the input wall pressure. This shift will cause the waverider to produce more than 30% extra trim drag when cruising at the design Mach number.

Improving the quality of the supersonic flow field of free-jet wind tunnels

The fixed-total-pressure-based air-blowing method in free-jet wind tunnels only produces uniformity in a limited area for supersonic jets. To address the issue, we investigated the effect of total pressure on static pressures at the nozzle exit and test chamber. We proposed an air-blowing method for pressure matching and established corresponding equations while suggesting ways to determine its parameters. The pressure-matching method was experimentally verified to analyze and compare the effects of different air-blowing methods and pressure-matching parameters on jet uniformity. The results of the study are as follows: (1) The pressure-matching-based air-blowing method improved the uniformity of the flow field at Mach numbers (Ma) of 1.5, 2.0, and 3.0 by 445.5%, 275%, and 215.8%, respectively, when compared to the air-blowing method that maintains the total pressure above the designed value. (2) The pressure-matching-based air-blowing method reduced the root-mean-square deviation in the Ma of the flow field at a Ma of 2.0 by 47.1% compared to the air-blowing method that maintains the total pressure at the designed value. The findings indicate that the proposed air-blowing technique based on pressure matching can enhance the uniformity of the supersonic jet and the area of the uniform region in the flow field. This method is highly significant in improving the capabilities of free-jet wind tunnel facilities.

Reconstruction of Supersonic Flowfield Using Physical Neural Network Based on Channel Interaction

The evolution of shock waves in a combustor is a complex process comprising multiple time series, which results in a long computational cycle for unsteady numerical simulations. Current data-driven methods integrating physical information provide novel opportunities to rapidly and accurately predict the flowfield in the combustor. In this study, we construct a dataset under Mach numbers from 2 to 5 based on a self-designed scramjet combustor model and propose a physical convolutional neural network flowfield reconstruction method based on channel interaction (PICNNCI). This method embeds Euler equations that characterize the flow characteristics of inviscid fluids as physical constraints to solve the low-accuracy problem of traditional convolutional neural networks arising due to insufficient prior information. The model first performs dimensional transformations on temporal and spatial information input and converts a one-dimensional column vector into a two-dimensional vector to meet the convolution operation rules. Then, convolution is used for feature extraction to output the predicted flowfield, including velocity, pressure, and density fields. Finally, the predicted flowfield is optimized using the Euler equation and compared with the results obtained by numerical calculation and traditional neural network methods. The average peak signal-to-noise ratio of PICNNCI reaches 34.01 dB, and the linear correlation reaches 0.994.

Research Progress on Active Secondary Jet Technology in Supersonic Flow Field of Aerospace Propulsion Systems

As humans continue to explore the aerospace field, higher demands have been placed on new types of propulsion systems. Meanwhile, active secondary flow has been applied to various aspects of engines over the past seventy years, significantly enhancing engine performance. For the new generation of propulsion systems, active secondary flow remains a highly promising technology. This article provides an overview of the application of active secondary flow in engines, including a review of the past research on the secondary jet flow field, and an introduction of the more prominent applications of the jet in engines and its research progress. Finally, the problems existing in the current application of the secondary jet are summarized, and the future direction of the research is anticipated.

GAN-based generation of realistic compressible-flow samples from incomplete data

Predictive sampling of compressible flows is an important aspect of aerodynamic design, analysis, and optimization. The process is usually done by generating flow fields from computational fluid dynamics (CFD) simulations and solving governing evolution equations, from which quantities of interest (QoI) are obtained by post-processing. With this study, we propose a data-driven approach for the predictive sampling of compressible flows around airfoils. This paper demonstrates the potential of generative adverserial networks (GANs), particularly the Pix2Pix model, to predict supersonic flow fields around airfoils. Building upon the similarities between our generator architecture and FlowGAN, which also employs the Pix2Pix model, our work extends the capabilities of FlowGAN to encompass supersonic flows and higher-resolution predictions. Our methodology introduces a multi-variable prediction framework, enabling the simultaneous prediction of various flow variables, including pressure, velocity, and density. We achieve the generation of flow fields with a relative error of less than 1.5%. We utilize the same framework to train a model capable of generating synthetic schlieren images, exhibiting a similarity structure index greater than 0.97, underscoring the high-fidelity generation capacity of our model. Addressing the challenges of generalization to unseen Mach numbers and angles of attack, we adopt transfer learning. By fine-tuning a pre-trained model on a limited number of examples from the unseen parameters, we obtain an improved prediction of shock wave positions. Furthermore, we present a practical application of our methodology by reconstructing flow fields from schlieren images. This application could prove valuable in real-world scenarios, providing a useful tool for studying and comprehending complex flow phenomena.

Analysis of influencing factors on numerical simulation of transverse jet in supersonic flow

Numerical simulation is becoming an important research method in the field of supersonic flow. Its accuracy and reliability have always been the key to its further application and the focus of current researches. In this study, a series of numerical simulations of transverse supersonic jet in supersonic free flow are carried out based on Reynolds averaged (RANS) solver. Firstly, the flow field structure obtained by different calculation cases is studied, and the mechanism of the difference of flow field structure is described. Furthermore, by changing the accuracy of difference scheme for the convection term and the turbulence model, the different simulation results are compared with the experimental data. The deviation from numerical simulation compared with the experiment is further analyzed to clarify the critical factors affecting the calculation reliability. The results show that improving the accuracy of the scheme cannot effectively improve the calculation results, while choosing an appropriate turbulence model can be helpful to improve the calculation accuracy. When the numerical simulation of jet flow mixing in supersonic flow field is carried out, the conclusions of this study can provide a support for determining the numerical model method and error analysis.

Intelligent flow field reconstruction based on proper orthogonal decomposition dimensionality reduction and improved multi-branch convolution fusion

The rapid and accurate reconstruction of the supersonic combustor flow field is of great significance for sensing and predicting the combustion state. Existing deep learning methods pay less attention to the convergence speed of flow field reconstruction, which results in longer training and prediction times for the models. This study proposes a method for reconstructing the flow field in supersonic combustor by combining a reduced-order model based on proper orthogonal decomposition (POD) with a multi-branch convolutional neural network. This method first analyzes the effectiveness of POD reconstruction. Then, based on the wall pressure data of the supersonic engine combustor, it performs flow field image reconstruction. Finally, through error calculation and gradient updating with low-resolution principal component flow field shadow images obtained from the POD algorithm, the high-precision and efficient prediction of flow field images is achieved. Different equivalence ratio hydrogen fuel combustion experiments were conducted in a pulsed combustion wind tunnel with an incoming flow Mach number of 2.5. The learning model was trained and tested using the dataset obtained from these experiments. Numerous experiments demonstrated that the model can effectively reconstruct the wave structures of complex flow fields. Multiple evaluation indicators indicated that the reconstructed flow field of the combustor shows good agreement with that obtained from ground wind tunnel testing. Furthermore, after introducing the POD dimensionality reduction model, the training time was reduced by 32.03%, effectively improving the training time complexity of the model.

Research on time sequence prediction of supersonic cascade flow field based on compressed sensing artificial neural network

The aviation engine that achieves efficient energy conversion is the core power equipment for low-carbon navigation, and the thermal components represented by the combustion chamber greatly affect the stable state of the compressor cascade flow field. Accurate, comprehensive and promptly monitoring of the compressor cascade flow state is related to the safe operation of the engine. A flow field time sequence prediction framework named compressed convolutional gate recurrent unit (CC-GRU) was proposed in this study to predict future supersonic cascade flow parameters based on the flow field at previous moments. CC-GRU embedded convolution into gate recurrent unit (GRU) to deal with the complex spatial-temporal behavior of supersonic cascade flow field. Firstly, unsteady numerical simulations driven by linear changes in back pressure were carried out, and the dataset for model training and validation was obtained to verify the feasibility of time sequence prediction of the cascade flow field. The verification results indicate that the framework can comprehensively predict the flow field with high back pressure in the future according to the flow field with low back pressure in the past period, and further validated the effectiveness of the proposed time sequence prediction model in ground wind tunnel experiments. The CC-GRU model can accurately capture the fine shock wave structure and flow separation zone, with a relative error of less than 10% in predicting the pressure field, and is mainly concentrated within the shock wave structure. Therefore, this research provides new research perspective and technical support for comprehensive and promptly condition monitoring of supersonic cascade flow field.

Effects of pulsed hydrogen injection on mixing and combustion performance in a supersonic flow field

An unsteady numerical method based on the Reynolds-averaged Navier–Stokes equations was developed to study the effects of a sine-wave pulsed-injection strategy on the hydrogen/airflow operating performance and flow structure (mixing and combustion process) in a supersonic flow field. In the numerical simulations, hydrogen was injected transversely into a supersonic flow field at different sine-wave pulse frequencies, after which it underwent mixing with the free stream and combustion. Compared with steady injection, it was found that pulsed injection can improve the mixing performance with its characteristic alternating high and low pressures, and different pulse frequencies were found to produce diverse effects. Additionally, the mixing length, which is related to the uniformity in the distribution of the hydrogen mass fraction, was found to be proportional to the penetration depth in the flow field. Both the mixing length and penetration depth of the fuel were found to be shortest at a pulse frequency of 5 kHz. Within a certain frequency range, a pulsed-injection strategy can modify the heat-release law, decrease the length of the pre-combustion shock train, and improve combustion performance. The penetration depth was found to be the greatest at a pulse frequency of 10 kHz, and this increased the thrust augmentation by 0.14%.

Loosely coupled analysis strategy for melt-ablation modeling of thermal protection material in supersonic gas-particle two-phase impingement flow

In this study, a two-way fluid-thermal-ablation loosely coupled analysis strategy is used to investigate the melt-ablation process of the copper specimen plate in the scaled ducted launcher. By coupling a discrete-phase model for simulate the supersonic gas-particle two-phase exhaust plume impingement flow field of a small scaled solid propellant rocket and a solidification/melting model for simulate the melt-ablation process of a copper specimen plate, the ablation depth profile of the copper specimen plate at a burn time of 3.6 s is successfully estimated. The numerical model is validated by experimental-scale test which shows that the simulation results using the two-way fluid-thermal-ablation loosely coupled analysis strategy can accurately predict the ablation depth profile of the copper specimen plate.

Aero Elastic Analysis of Aero Spiked Body in a Supersonic Flow Field Using Experiment and Simulations

The focus of this work is to study the structural response of an aero spiked body in a supersonic flow field of M 2.5 and at zero angle of attack. Fluid-Structure interaction simulations and wind tunnel testing have been carried out on the model for the above condition. 2-way coupled Fluid-Structure Interaction simulations have been carried out to study the aero elastic effect of the aero spike material. The wind tunnel testing for the select condition has been carried out at zero angle of attack for steel spike. The pressure measurement on the flat face cylinder from testing has been compared with that of the simulation data and presented here. Detailed analysis of aero spike deflections, stresses and induced forces due to the shock wave field have been discussed here in detail.

Numerical study on coupled flow characteristics of a gas-solid two-phase jet in a supersonic crossflow

In this study, the Eulerian-Lagrangian scheme coupled with the gas-solid two-phase Reynolds-average Navier-Stokes (RANS) method was used to study the airflow and particles flow characteristics of particles of different diameters injected into a supersonic flow field by a sonic jet. The results show that introducing small particles has little effect on the flow field. When the particle diameter is 1e-6 m, most particles are mainly distributed in the shear layer, and some parts of the particles are sucked into the surface trailing counter-rotating vortex pairs (TCVP) and the wall boundary layers by the airflow. The introduction of large diameter particles hinders the jet's development, reduces the Mach disk's height, and makes the counter-rotating vortex pair (CVP) closer to the wall. This leads to a decrease in the streamwise velocity and an increase in the wall-normal velocity near the wall downstream of the jet. The streamwise velocity increases and the wall-normal velocity decreases at the far wall downstream of the jet. At the same time, it exacerbates the Kelvin-Helmholtz (K-H) instability in the shear layer on the windward side of the jet, forming a large-scale coherent structure between the shear layer and crossflow. The penetration depth and diffusion range of particles increase with particle diameter and density, and the velocity decreases with particle diameter increase by comparing the trajectories of particles with different diameters and densities.

An improved flamelet/progress variable modeling in a hydrogen-fueled scramjet

The compressible nature of the supersonic combustion flow field results in significant temperature and pressure fluctuations. In the standard Flamelet/Progress Variable (FPV) model, not only is it difficult to specify the tabulation boundary conditions for the flamelet database, but using a single fixed flamelet database to describe the entire complex flow field will likely introduce significant errors. To account for this, an improved FPV model is proposed in this study. Dynamic adjustment of the tabulation pressure in the flamelet database is achieved by scaling the benchmark flamelet database to match the local pressure. Real-time calibration of the tabulation oxidant temperature and fuel temperature is also achieved by real-time statistics and interpolation between four flamelet databases. This allows the improved FPV model to adaptively match the target supersonic combustion flow field without the difficulty of specifying tabulation pressures and temperatures as in the standard FPV model, and it avoids the limitations imposed by the single flamelet database. An additional species transport equation is also incorporated into this model to improve species prediction. Based on the simulations of a hydrogen-fueled scramjet with low-and-high total temperature, the improved model is shown to better restore wall-pressure profiles, combustion mode predictions and OH (hydroxyl radical) distributions, compared to the standard FPV model and comparable studies. This indicates that the effects of compressibility on the chemical reaction rates have been considered.

A methodology for estimating hypersonic engine performance by coupling supersonic reactive flow simulations with machine learning techniques

We propose a methodology used to estimate the performance of hypersonic engines by coupling some machine learning methods with a generated CFD database and one-dimensional expansion equations. The present work also investigates the effects of fuel struts geometrical parameters on the supersonic combustion which takes place in a dual-mode ramjet/scramjet engine operating at stratospheric flight conditions with a cruise speed of Mach 8. We solved 2D compressible RANS flow equations coupled with the β-pdf method using Ansys Fluent commercial code, and built a CFD database with varying three design parameters which are strut V-settlement angle, strut location, and wedge angle. The numerical code was verified/validated using the data obtained in the DLR scramjet combustor. Three main objective functions in the combustor (combustion efficiency along 14 stations having 10 cm interval between each other, averaged flow variables at exit of combustor, and total pressure recovery factor in the combustor) were selected to evaluate the struts configuration parameters effects on supersonic combustion flow physics and engine performance. These objective functions, i.e. dependent variables were regressed with independent design parameters by three machine learning techniques which are Kernel regression, Gaussian process regression, and Artificial neural network. The machine learning models of the flow variables at the combustor throat provided initial conditions for a given fuel strut configuration to the expansion solutions for the nozzle component in order to calculate the engine performance. The Artificial neural network was found the most successful technique in overall regression of the objective functions. We also discovered that the most significant design variable is the strut wedge angle for all investigated phenomena in the supersonic reactive flow field.