Showing papers by "Ujjwal K. Saha published in 2023"

Fluidic Thrust Vector Control of Aerospace Vehicles: State-of-the-Art Review and Future Prospects

Abstract: An efficient propulsion system holds the key for the smooth operation of any aerospace vehicle over different flight regimes. Apart from generating the necessary thrust, emphasis has also been laid on vectoring the direction of thrust. The primitive modes of thrust vectoring chiefly focussed on mechanical means such as the use of gimbals or hinges. The current state of the art technologies demand more efficient methods for thrust vectoring, which minimize the use of mechanical components. These methods, termed as fluidic thrust vector control methods, employ secondary jets for achieving the required attitude and trajectory of the aerospace vehicle. Such methods have greatly helped in reducing aircraft weight, reduction of aircraft maintenance requirements, and enhancement of stealth characteristics of aerospace vehicles. This work presents a review of the various fluidic thrust vectoring systems, starting with a brief overview of traditional thrust vectoring systems, followed by a discussion on the various aspects of fluidic thrust vectoring systems. It also highlights the effect of the various geometrical and operating conditions on the performance parameters of the thrust vectoring system such as the thrust vector angle, system thrust ratio and thrust vectoring efficiency among others. For ensuring the comprehensive character of this work, synthetic jet vectoring techniques have also been included due to its non-mechanical nature and similarities with purely fluidic thrust vectoring techniques.

A New Method to Develop Homogeneous and Heterogeneous Porous Micromodels Applicable to Enhanced Oil Recovery and Flow Visualization Experiments

Abstract: In this paper, a new method to fabricate micromodels having homogeneous and heterogeneous porous structure is reported to gain the fundamental insight into the flow through porous media. The technique of micro particle image velocimetry (PIV) is used to map the pore scale velocity field inside the micromodels. A thin perforated metal sheet composed of uniformly distributed circular holes is used as the master pattern, and the replica of the negative of this perforated sheet is transferred to a Polydimethylsiloxane (PDMS) substrate using a method similar to the soft lithography. This method allows an efficient fabrication of micromodels having different porosity by adjusting and selecting the perforated sheets of different hole sizes. The prepared micromodels were tested for its applicability and reliability by carrying out the measurements of pore scale velocity distribution using the micro-PIV technique. The experiments with micromodels with high porosity but different grain arrangements showed qualitative as well as quantitative difference in the velocity field. The pressure drop across the two ends of micromodel is also measured. The varation of pressure difference with the flow rate is found to be non linear with significant effect of the patterns of micropillars. However, at low porosity the variation of pressure difference with the flow rate is found linear and there is almost no influence of the micropillar patterns. The flow visualization measurements are also conducted with the dual porosity micromodels and the flow patterns were examined by analyzing the velocity vector maps.

Aerodynamic analysis of a 2-stage elliptical-bladed Savonius wind rotor: Numerical simulation and experimental validation

Abstract: The Savonius rotor is a type of vertical-axis wind turbine that utilizes drag to generate power. Its design simplicity and ability to self-start at low wind speeds make it an attractive option for small-scale wind energy generation. Previous research has focused on optimizing the geometric configuration of a 1-stage, 2-bladed rotor with semicircular blade profiles to improve its performance and mitigate negative torque. However, limited research has been conducted on 2-stage, 2-bladed Savonius rotors with elliptical blade profiles. The present investigation aims to fill this research gap by evaluating the aerodynamic characteristics of a 2-stage, 2-bladed Savonius rotor with elliptical profiles. 3D unsteady simulations using the SST k-ω turbulence model in ANSYS Fluent are conducted to evaluate the performance coefficients over a range of tip speed ratios. The numerical results are validated through wind tunnel experiments. The results show that the CPmax (maximum power coefficient) for the 1-stage rotor is 0.19 and 0.12 for numerical and experimental analyses, respectively. For the 2-stage rotor, the CPmax is 0.21 and 0.17 for numerical and experimental analyses, respectively. These findings suggest that the 2-stage rotor with elliptical profiles has the potential to efficiently harness wind energy.

A Neural Network Based Design of a Planar Double Divergent Nozzle

Abstract: Rocket nozzles are the crucial components for an outer space vehicle as they generate the necessary thrust for propulsion. Nozzles handling such supersonic flows encounter various shock phenomena, making it crucial for design consideration. A major challenge for vehicles is to reduce the number of stages and operate with a single nozzle throughout the journey without major performance loss. Altitude adaptive nozzles pose various solutions for such endeavour. Dual bell nozzles are introduced for flow adaptation at sea level and also at higher altitude without major performance loss. Previous studies have hinted at these nozzles as being able to generate higher coefficient of thrust and thrust-to-weight ratio than single-divergent nozzles. In our study we intend to explore the optimum design of a Planar Double Divergent Nozzle. We intent to arrive at an optimal design solution of the nozzle by training our Neural Network based model. Neural net would be trained based on the nozzle geometry and flow parameters input for an optimum design. The model will be further trained to predict the performance parameters of nozzle – Coefficient of thrust and Normalized moment.

Role of optimization and soft-computing techniques in the design and development of futuristic Savonius wind turbine blades: A review

Abstract: The use of metropolitan wind power by small-scale wind turbines has become an emerging technique to reduce the battle among growing energy needs. However, the available technical designs are not yet adequate to develop a reliable and distributed wind energy converter for low wind speed conditions. The Savonius wind turbine rotor, or simply Savonius rotor, seems to be particularly promising for such conditions, however, it suffers from low power coefficient. The blade profile/shape is an important aspect of designing the Savonius rotor. In this context, the use of optimization techniques (OTs) along with soft-computing techniques (SCTs) can significantly help to arrive at the intended design parameters. The selection of rotor blades developed through OTs and SCTs can significantly improve the rotor performance. This review study aims to summarize the OTs and SCTs used till date in the blade design of Savonius rotors.

Evaluating the Operating Parameters of a Compression Ignition Engine Fueled with Waste Cooking Oil - Diesel Blends Using Artificial Neural Network and Ensemble Methods

Abstract: Fossil fuels being the primary source of energy to industrial and power sectors are being consumed at an alarming rate. There is a dire need to search for alternative fuels and optimise the Internal Combustion (IC) engine performance parameters. Traditional methods of testing and optimising the performances of IC engine are complex, time consuming and expensive. This has led the researchers to shift their focus to faster and computationally feasible techniques like soft computing (SC) and machine learning (ML) algorithms, which predict the optimum performance with a substantial accuracy. The present study focuses on the implementation of ANN and ensembling methods (Random Forest Regression and Extreme Gradient Boosting Algorithm) modelling of a CI diesel engine run on waste cooking oil (WCO). A single-cylinder, four-stroke, variable compression ratio diesel engine's performance, combustion, and emission parameters have been predicted using ANN and ML approaches. These models have been developed to predict the brake power, brake thermal efficiency, brake specific fuel consumption, ignition delay, combustion duration, carbon monoxide, carbon dioxide and oxides of nitrogen. All the models have been trained by tuning and optimising different number of hyper-parameters and training algorithms (Levenberg-Marquardt, Scaled Conjugate Gradient and Broyden-Fletcher-Goldfarb-Shanno). Further the most optimum parameters have been selected using hyper-parameter optimization. The mathematical models were assessed for their generalization capability by subjecting them to a set of new testing data.