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"Unsteady flow"
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Intelligent aerodynamic modelling method for steady/unsteady flow fields of airfoils driven by flow field images based on modified U-Net neural network
An intelligent modelling method driven by flow field images for predicting steady and unsteady flow filed around aerofoils has been developed. Signed Distance Field (SDF) images achieve dimensionality enhancement of aerofoil geometric information, and ‘synthesised images’ achieve dimensionality enhancement of the angle of attack of the aerofoil and Mach number. An intelligent aerodynamic model for steady flow field of aerofoils is constructed based on the U-Net neural network architecture, and further incorporating a long short-term memory (LSTM) module to construct a U-Net-LSTM neural network architecture to extract the temporal features. Typical NACA aerofoils results show that, the prediction error for steady flow is less than 1.98%, while the prediction error for unsteady flow is less than 2.56%. Additionally, the model demonstrates good generalization capability, with a generalization error for steady flow less than 2.45% and a generalization error for unsteady flow less than 3.34%. This research provides a new method for intelligent aerodynamic modelling based on physical representations. Compared to existing methods, this method avoids the need for extracting aerofoil geometry information and eliminates the necessity of predicting the flow field point by point, making it more concise and efficient. Highlights 1. An aerodynamic model was constructed using U-Net to rapidly predict the steady flow field around airfoils. 2. A Long Short-Term Memory (LSTM) module was incorporated to capture temporal information, enabling the rapid prediction of the unsteady flow field around airfoils. To address the problem of ‘dimension loss’ in the modelling datasets, effective data dimensionality enhancement was achieved using SDF images and ‘synthesized images’.
Journal Article
Characterization and Improvement of Heat Resistance of a Polymer-Ceramic Pressure-Sensitive Paint at High Temperatures
Degradation of fast response pressure-sensitive paints (PSP) above room temperature is a serious problem for PSP measurements in high-temperature environments. A standard polymer-ceramic PSP (PC-PSP) composed of platinum(II)-5,10,15,20-tetrakis-(2,3,4,5,6-pentafluorphenyl)-porphyrin (PtTFPP), titania particles and poly(isobutyl methacrylate) (polyIBM) was characterized to elucidate the degradation mechanism. Applying a two-gate lifetime-based method, the PC-PSP has sufficient pressure and temperature sensitivities even at 100 °C, while the luminescence intensity significantly decreases during the test. Subsequent measurements on thermal and photostability as well as luminescence spectra reveal that the main cause of the degradation is the photodegradation of PtTFPP due to direct exposure of the dye molecules to the atmosphere. In order to suppress such degradation, a small amount of urethane resin is added to the dye solution as a simple additional step in the preparation of PC-PSP. The addition of the urethane resin significantly reduces the degradation of the PSP, although its time response is slightly slower than that of the standard PC-PSP.
Journal Article
A holistic methodology for evaluating flood vulnerability, generating flood risk map and conducting detailed flood inundation assessment
Flood risk assessment (FRA) is a process of evaluating potential flood damage by considering vulnerability of exposed elements and consequences of flood events through risk analysis which recommends the mitigation measures to reduce the impact of floods. This flood risk analysis is a technique used to identify and rank the level of flood risk through modeling and spatial analysis. In the present study, Musi River in the Osmansagar basin is taken in to consideration to evaluate the flood risk, which is located at Hyderabad. The input data collected for the study encompasses Hydrological and Meteorological datasets from Gandipet Guage station in Hyderabad, raster grid data for Osmansagar basin along with several indicators data influencing flood vulnerability. The primary research objective is to conduct a quantitative assessment of the Flood vulnerability index (FVI), to develop a comprehensive flood risk map and to evaluate the magnitude of damaging flood parameters, inundated volume and to analyze the regions inundated in the study area. In risk analysis, FVI determines the degree of which an area is susceptible to the negative impact of flood through various influencing indicators, Flood hazard map segregate the regions based on flood risk level through spatial analysis in Arc-GIS. A part of this study includes an integrated methodology for assessing flood inundation using Quantum Geographic Information Systems (QGIS) data modelling for spatial analysis, Hydraulic Engineering Center’s River Analysis System (HEC-RAS) hydraulic modelling for unsteady flow analysis and a machine learning technique i.e. XGBoost, to enhance the accuracy and efficiency of flood risk assessment. Subsequently, inundation map produced using HEC-RAS is superimposed with building footprints to identify vulnerable structures. The results obtained by risk analysis using hydraulic modeling, GIS analysis, and machine learning technique illustrates the flood vulnerability, areas having high flood risk and inundated volume along with predicted flood levels for next 10 years. These findings demonstrate the efficiency of the holistic approach in identifying vulnerability, flood-prone areas and evaluating potential impacts on infrastructure and communities. The outcomes of the study assist the decision-makers to gain valuable insights into flood risk management strategies.
Journal Article
Efficient neural network training method for unsteady flow field prediction based on data pool
In recent years, neural network technology has made significant progress in the field of unsteady flow field prediction, leading to the development of many innovative methods. However, deep learning-based unsteady flow field prediction techniques typically rely on autoregressive models, which inevitably face the issue of error accumulation. Existing solutions often suffer from challenges such as complex hyperparameter configurations and reduced training efficiency. To address these issues, this study makes the following contributions: (1) A novel training method is proposed to enhance model convergence by refining the training strategies employed. This approach achieves improved performance without necessitating complex hyperparameter configurations. (2) An innovative curriculum learning-based timestep reset strategy is introduced. This strategy further improves the convergence of neural networks and enhances prediction accuracy. A detailed comparative study was conducted on different training methods within the architecture of convolutional neural networks. Experimental results show that the proposed training strategy significantly improves prediction accuracy, with an improvement of up to an order of magnitude. Moreover, even when the training set spans only 600 timesteps, the model remains stable when predicting up to 9000 timesteps. Finally, our method also demonstrates high efficiency in terms of training time.
Journal Article
Assessment of Water Management Rehabilitation in the Palingkau Swamp Irrigation Area SP1, SP2, SP3
The irrigation network of the Palingkau Swamp Irrigation Area (DIR) consists of channels built from river channels as the main source of irrigation water, namely Anjir and primary, secondary, and collector canals. This irrigation network has experienced damage and a decline in functionality owing to sedimentation and lack of maintenance, necessitating rehabilitation. Canal normalization combined with the construction of water gates is a strategic measure to improve water management. This study aims to estimate the water management performance in the Palingkau DIR. For this purpose, an unsteady flow hydraulic simulation was conducted using HEC-HMS software under existing conditions and after rehabilitation. The analysis focused on the water level in the primary and secondary channels related to the performance of water supply and drainage to facilitate leaching in the planting area. The simultaneous tidal measurement data from two Anjir locations were used as the upstream boundary. The results show that after rehabilitation there is an increase in the water level in the primary channel and a decrease in the water level in the secondary channel. The results are taken into consideration in determining the operating rules of the water gates to optimize the supply and drainage in the planting area.
Journal Article
A novel spatial-temporal prediction method for the effects of fish movement on flow field based on hybrid deep neural network
In the fish passage facility design, understanding the coupled effects of hydrodynamics on fish behaviour is particularly important. The flow field caused by fish movement however are usually obtained via time-consuming transient numerical simulation. Hence, a hybrid deep neural network (HDNN) approach is designed to predict the unsteady flow field around fish. The basic architecture of HDNN includes the UNet convolution (UConv) module and the bidirectional convolutional long-short term memory (BiConvLSTM) module. Specifically, the UConv module extracts crucial features from the flow field graph, while the BiConvLSTM module learns the evolution of low-dimensional spatio-temporal features for prediction. The numerical results showcase that the HDNN achieves accurate multi-step rolling predictions of the effect of fish movement on flow fields under different tail-beat frequency conditions. Specifically, the average and standard deviation of PSNR and SSIM for the proposed HDNN model for 60 time-step rolling predictions on the entire sequences of four test sets being respectively larger than 34 dB and 0.9. The HDNN delivers a speedup of over 130 times compared to the numerical simulator. Moreover, the HDNN demonstrates commendable generalisation capabilities, enabling the prediction of spatial-temporal evolution within unsteady flow fields even at unknown tail-beat frequencies.
Journal Article
Unsteady Flow Field Analysis of Axial Compressor Cascade Based on Proper Orthogonal Decomposition and Dynamic Mode Decomposition Methods
The analysis of the unsteady flow field in axial compressor cascade is conducted using methods such as proper orthogonal decomposition (POD) and dynamic mode decomposition (DMD). Data on the unsteady flow field of the Stage-35 compressor cascade are acquired via computational fluid dynamics (CFD) simulations and subsequently processed using POD and DMD for dimensionality reduction. Using singular value decomposition, the POD technique identifies the dominant modes, showing that the first nine modes account for 99% of the energy in the flow field, thus highlighting the primary flow structures. On the other hand, the DMD approach isolates the periodic and high-frequency dynamics within the flow field by decomposing the dynamic modes, effectively identifying fine variations in the unsteady flow. The study examines the flow field at three distinct moments within an unsteady cycle, specifically at 1/4T, 1/2T, and 3/4T, reconstructing the flow field at each instance and performing root mean square error analysis. Reconstruction results and error analysis demonstrate that the POD method excels at reconstructing low-frequency features, whereas the DMD method accurately identifies the unsteady dynamic aspects of the flow field, excelling in resolving high-frequency details. Both methods demonstrate high feasibility regarding the accuracy and efficiency of flow field reconstruction.
Journal Article