scholarly journals First-principles calculations of thermal properties of the mechanically unstable phases of the PtTi and NiTi shape memory alloys

2018 ◽  
Vol 147 ◽  
pp. 296-303 ◽  
Author(s):  
Sara Kadkhodaei ◽  
Axel van de Walle
2017 ◽  
Vol 28 (15) ◽  
pp. 2082-2094 ◽  
Author(s):  
George N Frantziskonis ◽  
Sourav Gur

In this study, NiTi shape memory alloys coupled in series with Al are considered as building blocks for thermal diodes. It is shown that the strong nonlinearity in the temperature-dependent thermal properties of NiTi in conjunction with the very different thermal properties of Al can result into a thermal diode of high thermal rectification ratio. As a first level of study, Ni50Ti50 is considered and the effects of various NiTi-Al geometrical configurations, initial temperature, and temperature difference at two ends on the thermal rectification ratio are studied numerically. Within the adopted temperature range (300–400 K, where phase transformation in NiTi occurs), it is shown that NiTi-Al thermal diodes are feasible with rectification ratio up to 4.8, which is quite higher than the ratios in currently known solid-state thermal diodes. This fundamental computational study could provide an important basis and motivation for the development of the next generation of high-temperature solid-state thermal diodes based on smart material such as NiTi shape memory alloys or others.


ChemInform ◽  
2010 ◽  
Vol 42 (2) ◽  
pp. no-no
Author(s):  
Peter Entel ◽  
Mario Siewert ◽  
Antje Dannenberg ◽  
Markus E. Gruner ◽  
Manfred Wuttig

2019 ◽  
Vol 33 (08) ◽  
pp. 1950055 ◽  
Author(s):  
Daichi Minami ◽  
Tokuteru Uesugi ◽  
Yorinobu Takigawa ◽  
Kenji Higashi

A key property for the design of new shape memory alloys is their working temperature range that depends on their transformation temperature T0. In previous works, T0 was predicted using a simple linear regression with respect to the energy difference between the parent and the martensitic phases, [Formula: see text]E[Formula: see text]. In this paper, we developed an accurate method to predict T0 based on machine learning assisted by the first-principles calculations. First-principles calculations were performed on 15 shape memory alloys; then, we proposed an artificial neural network method that used not only computed [Formula: see text]E[Formula: see text] but also bulk moduli as input variables to predict T0. The prediction error of T0 was improved to 49 K for the proposed artificial neural network compared with 188 K for simple linear regression.


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