Base pressure behaviour in a suddenly expanded duct at supersonic mach number regimes using Taguchi design of experiments

Experimental investigations are carried out to study the control of base pressure without and with the use of micro-jets through suddenly expanded axi-symmetric passage in the supersonic regime. Four micro jets having an orifice diameter of 1mm were located at 90◦ intervals. In the base area, active controls jets have been placed on a pitch of a circle diameter that is 1.3 times the exit diameter of the nozzle. The jets were dispensed abruptly into the axi-symmetric tube maintained at a cross-sectional area of 4.84 times the exit nozzle area. The variation of base pressure as a function of flow control parameters namely Mach number, nozzle pressure ratio (NPR) and length to diameter) ratio (L/D) are evaluated experimentally. This study also assesses the impact of flow control variables on base pressure for two cases viz. with control and without control respectively. An L9 orthogonal array of Taguchi and the analysis of variance were employed to investigate the percentage of contribution of these parameters and their interactions affecting the base pressure. The correlations between the various factors affecting the base pressure were obtained by using multiple linear regression equations. Confirmation tests were conducted in order to test the developed linear regression equations for their practical significance. Both the regression models were found to be significant and reliable with a percentage deviation lying in the range of −6.12% to 10.26% for base pressure without control and −13.92% to 6.58% for base pressure with control. Analysis of variance was also performed in order to determine the statistical significance of each parameter on the total variability of base pressure. The study concluded that Mach number is the most influential parameter affecting base pressure followed by NPR and L/D

Study of effect of flow parameters on base pressure in a suddenly expanded duct at supersonic mach number regimes using CFD and design of experiments

Effectiveness of active control of micro jets has been examined by conducting experiments through an abruptly expanded axi-symmetric duct in a view to control base pressure. For this purpose, 1mm orifice diameter micro jets have been deployed at an interval of 900 along the exit diameter of the nozzle. The experiments have been conducted by considering three flow parameters at three levels. Mach number (M), length to diameter (L/D) ratio and area ratio (AR) are the three parameters used to conduct and analyze the flow experiments. Base pressure is considered to be the response variable. The experimentation has been carried out for two cases, i) without active control; ii) with active control. An L9 orthogonal array has been implemented to plan the experiments. It is observed that the control becomes effective for lower area ratios when compared to the higher ones. In addition to this, at high area ratios suction at the base decreases and hence base pressure continuous to diminish with increasing L/D until it reaches a value of L/D=6. The obtained experimental results are subjected to multiple linear regression analysis and Analysis of variance (ANOVA). The performances of the two linear regression models were tested for their prediction accuracy with the help of 15 random test cases. It is observed that, both linear regression models for base pressure without and with control are statistically adequate and capable of making accurate predictions. Furthermore, this work also concludes that, Mach number is the most significant factor affecting base pressure followed by area ratio and L/D ratio for both cases of experimentation. The obtained experimental results are further validated by CFD analysis and are found to be in good concurrence with each other

Modelling of suddenly expanded flow process in supersonic Mach regime using design of experiments and response surface methodology

The present work is an attempt to model, analyze, and control the flow at the base of an abruptly expanded circular duct by using design of experiments (DOE) and response surface methodology (RSM). Tiny-jets in the form of orifice were positioned at an interval of 900, 6.5 mm from the primary axis of the main jet of the nozzle. Experiments were conducted to measure two responses namely, base pressure without the use of micro jets or active control (WoC) and base pressure with the use of micro jets or active control (WC). Mach number (M), nozzle pressure ratio (NPR), area ratio (AR) and length to diameter ratio (L/D) were considered as the input variables (parameters), which control the outputs (i.e. base pressure). Non-linear regression models based on central composite design (CCD) and Box-Behnken design (BBD) have been developed in order to facilitate the input-output relationships. Moreover, the significance of main, square and interaction terms of the developed models have been tested by performing analysis of variance (ANOVA). The ANOVA and significance test results and their respective correlation coefficient values indicate that both the CCD and BBD regression models are statistically adequate for both the base pressure responses of without control and with control respectively. The performances of the nonlinear models have been validated for accuracy prediction by use of 15 test cases. The performance of BBD model is found to be better in forecasting base pressure for both cases of without control and with control when compared to the CCD model

Heat transfer characteristics of fullerene and titania nanotube nanofluids under agitated quench conditions

Distilled water and aqueous fullerene nanofluids having concentrations of 0.02, 0.2, and 0.4 vol % and titania (titanium dioxide, TiO2) nanofluids of 0.0002, 0.002, and 0.02 vol % were analyzed for heat transfer characteristics. Quenching mediums were stirred at impeller speeds of 0, 500, 1,000, and 1,500 RPMs in a typical Tensi agitation system. During the quenching process, a metal probe made of ISO 9950 Inconel was used to record the temperature history. The inverse heat conduction method was used to calculate the spatial and temporal heat flux. The nanofluid rewetting properties were measured and matched to those of distilled water. The maximum mean heat flux was 3.26 MW/m2, and the quickest heat extraction was 0.2 vol % fullerene nanofluid, according to the results of the heat transfer investigation

Prediction of base pressure in a suddenly expanded flow the processes at supersonic Mach Number Regimes using ANN and CFD

The sudden expansion of flow in a supersonic flow regime has gained relevance in the recent pasts for a wide run of applications. A number of kinematic as well as geometric parameters have been significantly found to impact the base pressure created within the suddenly expanded stream. The current research intends to create a predictive model for base pressure that is established in the abruptly extended stream. The artificial neural network (ANN) approach is being utilized for this purpose. The database utilized for training the network was assembled utilizing computational fluid dynamics (CFD). This was done by the design of experiments based L27 Orthogonal array. The three input parameters were Mach number (M), nozzle pressure ratio (NPR) and area ratio (AR) and base pressure was the output parameter. The CFD numerical demonstrate was approved by an experimental test rig that developed results for base pressure and used a nozzle and sudden extended axisymmetric duct to do so. The ANN architecture comprised of three layers with eight neurons in the hidden layer. The algorithm for optimization was Levenberg-Marquardt. The ANN was able to successfully predict the base pressure with a regression coefficient R2 of less than 0.99 and RMSE=0.0032. The importance of input parameters influencing base pressure was estimated by using the ANN weight coefficients. Mach number obtained relative importance of 47.16% claiming to be the most dominating factor

Experimental and numerical investigation of suddenly expanded flow field for supersonic Mach numbers with and without annular cavities

The influence of cavities on a suddenly expanded flow field was analyzed. Air flow was passed through a convergent divergent axisymmetric nozzle, and expanded suddenly into a circular parallel shroud with annular cavities. Base pressure and wall pressures were measured for combinations of process variables, such as Mach number (M), nozzle pressure ratio (NPR), area ratio (AR), the length to diameter ratio (L/D) of the enlargement section, and the cavity aspect ratio. The experimental results showed that the base pressure fin the suddenly expanded flow field was significantly influenced by annular cavities for low area ratios and high nozzle pressure ratios. The cavities also yielded a weaker vortex street in the near wake in the vicinity of the nozzle exit, causing a slight increase in the base pressure for low Mach numbers. The wall pressure studies showed that the introduction of cavities generated secondary vortices which reduced the oscillatory nature of the flow along the duct length. The generation of secondary vortices was confirmed by a numerical analysis of the suddenly expanded flow field without and with annular cavities. The two-dimensional coupled implicit Reynolds Averaged Navier-Stokes equations and the two equation standard k-ε turbulence model simulated the process numerically. The governing equations (continuity, momentum, and energy), along with the boundary conditions, were solved by the finite element method. They were flow patterns for various combinations of the process variables demonstrated that there is formation of secondary vortices for flows with annular cavities. Due to the formation of near wake and free shear instability, the vortices of these flows caused the boundary layer to roll up, forming secondary vortices in the suddenly expanded flow field. Immediately following their formation, the vortices underwent a strong three-dimensional distortion

CFD-based optimization of base pressure behavior on suddenly expanded flows at supersonic Mach numbers

: The base pressure developed in a suddenly expanded flow process majorly depends on Mach number (M), nozzle pressure ratio (NPR), area ratio (AR), and length to diameter ratio (L/D). Numerical analysis of the flow process was carried out using the computational fluid dynamics (CFD) technique and was validated by experiments. The input-output test cases for CFD analyses were developed by two statistical methods, namely central composite design (CCD) and Box-Behnken design (BBD). The BBD model yielded better prediction accuracy and was used for generating data that trained the recurrent and backpropagation neural networks. The recurrent neural network outperformed both the backpropagation neural network and Box-Behnken design. Furthermore, to assess the right range of conditions for maximizing base pressure, the genetic algorithm (GA), desirability function approach (DFA), and particle swarm optimization (PSO) techniques were implemented. The PSO and GA techniques were found to be better, as they carried out search operations in many directions in multi-dimensional space simultaneously

Experimental and numerical investigation of expansion corner effects on isolator performance

In the present study, the effect of the expansion corner on the suddenly expanded flow process has been studied. Experimental investigations have been carried out on a convergent-divergent (C-D) nozzle and isolator duct, where the expansion of the channel is formed through the presence of a 1, 2, and 3 expansion corners (EC) respectively. Flow from nozzle exit of the nozzle of Mach, M = 2.0 was suddenly expanded into the axi-symmetric duct having a cross-sectional area of 4.84 times the nozzle exit area. The wall static pressure along the length of the duct and the Pitot pressure at the exit plane of the duct was measured for all the configurations. Computational fluid dynamics (CFD) the technique was employed for visualizing the shock train in the expanded duct. The isolator with one expansion a corner was found to be more efficient in achieving a high static pressure rise. The experimental and numerical wall static pressure distribution values were compared for isolators with EC = 2 and found to be in good agreement with each other with a maximum absolute percentage deviation of 11%

Base drag estimation in suddenly expanded supersonic flows using backpropagation genetic and recurrent neural networks

In recent years, base pressure management has gained a lot of industrial importance due to its applications in missiles and projectiles. For certain aerodynamic vehicles, the base pressure becomes a critical factor in regulating the base drag. That prompted the current work to develop input-output relationships for a suddenly expanded flow process using experiments and neural network-based forward and reverse mapping. The objective of forwarding mapping (FM) is to predict the responses, namely base pressure (β), base pressure with the cavity (βcav), and base pressure with rib (βrib), for a known combination of flow and geometric parameters, namely Mach number (M), nozzle pressure ratio (η), area ratio (α), and length to diameter ratio (ψ). On the other hand, an effort is made to decide the optimal set of flow and geometric parameters for achieving the desired base pressure by reverse mapping (RM). Neural network-controlled backpropagation and recurrent and genetic algorithms have been employed to carry out the forward and reverse mapping trials. A batch mode of training was employed to conduct a parametric study for adjusting and optimizing the neural network parameters. Due to the requirement of massive data for batch mode training, the data required for training was achieved using the response equations developed through response surface methodology. Further, the forecasting performances of the neural network algorithms are compared with the regression models (FM) and among themselves (RM) through random test cases. The findings indicate that all evolved neural network (NN) models could make accurate predictions in both forward and reverse mappings. The results obtained would help aerodynamic engineers control various parameters and their values that affect base drag

Analysis of parallel flow type internally cooled membrane-based liquid desiccant dehumidifier using a neural networks approach

In this paper, we report an intelligent model based on ANN to optimize the performance of an internally cooled membrane-based liquid desiccant dehumidifier (IMLDD). IMLDD can effectively mitigate dehumidification deterioration caused by changes in the temperature of the desiccant solution. The mediums of desiccant solution and air are isolated by means of a semi-permeable membrane on both sides in the IMLDD. The temperature of the desiccant solution is reduced by the cooling media that flows through the tubes placed within the solution channels. Generally, many fluid flow parameters like air, cooling water, desiccant solution, etc., play a critical role in controlling the performance of an IMLDD. For our study, inlet air temperature (Tai), inlet concentration of the desiccant solution (Cdsi), flow rate of the desiccant solution at the inlet m_ dsi ð Þ, and inlet cooling temperature of water (Tcwi) have been considered as the operating parameters/conditions. The outputs or responses namely dehumidification efficiency (gdh), Exergy efficiency (gex), and unmatched coefficient (num) analyze the performance of the IMLDD. The data comprising of massive input output was achieved using the response surface methodology (RSM) based central composite design (CCD). Backpropagation algorithm (BP), artificial bee colony (ABC), and genetic algorithm (GA) models were used to train the neural network (NN) parameters using the data collected from the CCD-based response equation. Forward and reverse mapping models were developed using the trained ANNs. Forward modeling predicts the performance parameters of the IMLDD (i.e., gdh, gex, and nuc) for known combinations of operating parameters (i.e., Tai, Cdsi, m_ dsi, Tcwi). Similarly, reverse modeling aims at predicting the operating conditions for a known set of performance parameters. The performances of the employed NN models were tested using fifteen arbitrarily generated test cases. The experimental and neural network predicted results were found to be in line with each other for both forward and reverse models. The forward modeling results could assist engineers with off-line tracking, by predicting the response without executing experiments. The reverse modeling prediction will aid in dynamically adjusting the operating parameters to achieve the optimal thermodynamic output characteristics