極超音速揚力飛行体形状に対する非能動的熱空気力学的流れ制御に関する研究

学位の種別:課程博士University of Tokyo(東京大学

Mach Number Dependence of Flow Instability around a Spiked Body

A forward-facing aerospike have been identified as a passive flow control device for enhancing the aerodynamic efficiency and reducing the heat transfer in high-speed flows. In addition, it has been reported that the presence of a spike brings in unsteadiness in the form of oscillation and pulsation to the structure. Previous researchers have investigated the aerothermodynamic coefficients, together with offering a detailed explanation of the flow physics and associated unsteadiness, and their dependence on the spike's geometric characteristics (spike nose, and length-to-fore-body diameter ratio, L/D). This work focuses on ascertaining the role of flow speeds (free-stream Mach number), and their energy content, in governing the physics around a spiked body, which is yet to be established. Numerical investigation has been carried out using axisymmetric Navier-Stokes laminar flow solver for Mach number range of 2.0 to 7.0. A round-tip spike with flat-face cylindrical after-body have been simulated for spike length ratio of L/D = 2.0, with spike diameter to fore-body diameter of 0.1. The flow unsteadiness has been analyzed with drag and pressure coefficients variation at different Mach numbers. It was found that the flow field around the spiked blunt nose behaves in pulsation mode at lower Mach numbers 2, 3 and transition to oscillatory mode at higher Mach numbers 5, 6 and 7, while remain almost stable at Mach 4. The limit of Strouhal Number for characterizing the pulsation and oscillation modes at various Mach numbers for spike length of L/D = 2 with flat after-body is observed as 0.2, however it may very well depend on other geometric parameters of spike and after-body.Comment: 8 pages, 8 figures presented at 2nd International Conference on Recent Advances in Fluid and Thermal Sciences (iCraft 2020 Virtual), March 19-21, 2021, submitted to AIP Conference Serie

SELF LEARNING HAND RECOGNITION SYSTEM USING SOFT COMPUTING

Abstract — As the day to day security is a major concern the authentication problem is very crucial. The hand recognition system provides efficient way to produce the authentication using image processing. Hand recognition geometry as the name suggest uses the shape of the hand to identify the person. Unlike iris, face or fingerprints, the human hand is not unique. The existing systems use finger length, thickness and curvature for the purpose of verification but not for identification. Hand recognition geometry data is relatively easier to collect to other technologies e.g. for fingerprint collection good frictional skin is required by image systems. The main objective of this study is to develop a system which can increase the accuracy of the hand recognition using soft computing, the system should be capable to self-learn, about the correct and incorrect palm prints and add them to its database. A robust palm print recognition approach using neural network is proposed in this study. Neural networks offer a number of advantages, including requiring less formal statistical training, ability to implicitly detect complex nonlinear relationships between dependent and independent variables, ability to detect all possible interactions between predictor variables, and the availability of multiple training algorithms. Palm print recognition, Preprocessing, Feature extraction, Matching and Results and Feedback to the database are the six steps that are followed in the proposed approach. Keywords-Palm print recognition, self-learning, neural networks. I