Active research of SMAs has shown that its Seebeck coefficient is sensitive to its martensitic phase transformation and has the potential to determine the SMAs state of transformation. The combination of Shape Memory Alloys, which have a positive Seebeck coefficient, and Constantan which has a negative Seebeck coefficient (-35 mV/K) results in a thermocouple capable of measuring temperature. The work presented in this thesis is based on the development and design of this sensor.
This sensor is used to study the hysteretic behavior of SMAs. Although Shape Memory Alloys (SMAs) exhibit a myriad of nonlinearities, SMAs show two major types of nonlinear hysteresis. During cyclic loading of the SMAs, it is observed that one type of hysteretic behavior depends on the rate of heating the SMAs, whilst the variation of maximum temperature of an SMA in each cycle results in the other hysteretic behavior. This later hysteretic behavior gives rise to major and minor nonlinear loops of SMAs. The present work analyzes the nonlinearities of hysteretic envelopes which gives the different maximum temperatures reached for each hysteretic cycle with respect to stress and strain of the SMA. This work then models this behavior using Adaptive Neuro Fuzzy Inference System (ANFIS) and compares it to experimental results. The nonlinear learning and adaptation of ANFIS architecture makes it suitable to model the temperature path hysteresis of SMAs.
For those interested his thesis should be available at http://etd.vt.edu in a couple of weeks. We are fortunate to have Mr. Malladi continue to his PhD with us.
Way to go Sriram!