Daido University

About: Daido University is a education organization based out in Nagoya, Japan. It is known for research contribution in the topics: Ultimate tensile strength & Proton exchange membrane fuel cell. The organization has 209 authors who have published 423 publications receiving 3223 citations.

Local strain and its influence on mechanical–electromagnetic properties of twisted and untwisted ITER Nb3Sn strands

Abstract: It is important to evaluate the local strain exerted on superconducting filaments in Nb3Sn strands, because it influences both superconducting and mechanical properties, in particular for the ITER (International Thermonuclear Experimental Reactor) project. The local strain in the twisted and untwisted Nb3Sn strands was directly measured at room temperature as well as at low temperatures by means of quantum beam techniques. The local strain consists of thermal strain and lattice strain. The latter changes as a function of external strain. The interrelation between the force-free strain and the intrinsic strain showing a maximum critical current was considered on the basis of the present experimental data as well as the recent theory. The thermal strains along both directions parallel and transverse to the strand axis were numerically evaluated. Their evaluated results could explain well the observed values, when To is the recovery temperature of pure Cu. The force-free strain along the axial direction is deduced to be distributed among grains with different crystal orientation with respect to the axial direction. It is suggested that this fact affects the definition of intrinsic strain.

Density-Functional Tight-Binding Study on the Effects of Interfacial Water in the Adhesion Force between Epoxy Resin and Alumina Surface

Abstract: Adhesion is one of the most interesting subjects in interface phenomena from the viewpoint of wide-range applications as well as basic science. Interfacial water has significant effects on coatings...

Detection of Road Surface States from Tire Noise Using Neural Network Analysis

Abstract: This report proposes a new processing method for automatically detecting the states of road surfaces from tire noises of passing vehicles. In addition to multiple indicators of the signal features in the frequency domain, we propose a few feature indicators in the time domain to successfully classify the road states into four categories: snowy, slushy, wet, and dry states. The method is based on artificial neural networks. The proposed classification is carried out in multiple neural networks using learning vector quantization. The outcomes of the networks are then integrated by the voting decision-making scheme. Experimental results obtained from recorded signals for ten days in the snowy season demonstrated that an accuracy of approximately 90% can be attained for predicting road surface states using only tire noise data.