<p>Raman spectroscopy is a molecular spectroscopy technique that uses monochromatic light to provide a fingerprint to identify structural components and chemical composition. Depending on the changes in the unit-cell parameters and volume under the application of stress and temperature, the Raman spectrum undergoes changes in the wavenumber of Raman-active modes that allow identification of sample characteristics. Due to the various advantage of mechanical Raman spectroscopy (MRS), the use of this technique in the characterization and modeling of chemical changes under stress and temperature have gained popularity. </p> <p> Quantitative information regarding the local behavior of interfaces in an inhomogeneous material during shock loading is limited due to challenges associated with time and spatial resolution. Recently, we have extended the use of MRS to high-strain rate experiments to capture the local thermomechanical response of mock energetic material and obtain material properties during shock wave propagation. This was achieved by developing a novel method for <i>in‑situ</i> measurement of the thermo‑mechanical response from mock energetic materials in a time‑resolved manner with 5 ns resolution providing an estimation on local pressure, temperature, strain rate, and local shock viscosity. The results show the solid to liquid phase transition of sucrose under shock compression. The viscous behavior of the binder was also characterized through measurement of shock viscosity at strain rates higher than 10⁶/s using microsphere impact experiments.</p> <p> This technique was further extended to perform Raman spectral imaging over a microscale domain of the sample with a nano-second resolution. This was achieved by developing a laser-array Raman spectral imaging technique where simultaneous deconvolution of Raman spectra over the sample domain was achieved and Raman spectral image was reconstructed on post-processing. We developed a Raman spectral imaging system using a laser array and analysis was performed over the interface of sucrose crystals bonded using an epoxy binder. This study provides the Raman spectra over the microstructure domain which enabled the detection of localized melting under shock compression. The distribution of shock pressure and temperature over the microstructure was obtained using mechanical Raman analysis. The study shows the effects of an actual interface on the propagation of shock waves where a higher dissipation of shock energy was observed compared to an ideal interface. This increase in shock dissipation is accompanied by a decrease in both the maximum temperature, as well as the maximum pressure within the microstructure during shock wave propagation.</p>

<p>Ni-based superalloys continue to exert themselves as the industry standards in high stress and highly corrosive/oxidizing environments, such as are present in a gas turbine engine, due to their excellent high temperature strengths, thermal and microstructural stabilities, and oxidation and creep resistances. Gas turbine engines are essential components for energy generation and propulsion in the modern age. However, Ni-based superalloys are reaching their limits in the operating conditions of these engines due to their melting onset temperatures, which is approximately 1300 °C. Therefore, a new class of materials must be formulated to surpass the capabilities Ni-based superalloys, as increasing the operating temperature leads to increased efficiency and reductions in fuel consumption and greenhouse gas emissions. One of the proposed classes of materials is termed refractory complex concentrated alloys, or RCCAs, which consist of 4 or more refractory elements (in this study, selected from: Ti, Zr, Hf, V, Nb, Ta, Cr, Mo, and W) in equimolar or near-equimolar proportions. So far, there have been highly promising results with these alloys, including far higher melting points than Ni-based superalloys and outstanding high-temperature strengths in non-oxidizing environments. However, improvements in room temperature ductility and high-temperature oxidation resistance are still needed for RCCAs. Also, given the millions of possible alloy compositions spanning various combinations and concentrations of refractory elements, more efficient methods than just serial experimental trials are needed for identifying RCCAs with desired properties. A coupled computational and experimental approach for exploring a wide range of alloy systems and compositions is crucial for accelerating the discovery of RCCAs that may be capable of replacing Ni-based superalloys. </p> <p>In this thesis, the CALPHAD method was utilized to generate basic thermodynamic properties of approximately 67,000 Al-bearing RCCAs. The alloys were then down-selected on the basis of certain criteria, including solidus temperature, volume percent BCC phase, and aluminum activity. Machine learning models with physics-based descriptors were used to select several BCC-based alloys for fabrication and characterization, and an active learning loop was employed to aid in rapid alloy discovery for high hardness and strength. This method resulted in rapid identification of 15 BCC-based, four component, Al-bearing RCCAs exhibiting room-temperature Vickers hardness from 1% to 35% above previously reported alloys. This work exemplifies the advantages of utilizing Integrated Computational Materials Engineering- and Materials Genome Initiative-driven approaches for the discovery and design of new materials with attractive properties.</p> <p> </p> <p><br></p>

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<div>In contrast to traditional steel and aluminum, short fiber reinforced polymer composites (SFRCs) provide promising alternatives in material selection for automotive and aerospace applications due to their potential to decrease weight while maintaining excellent mechanical properties. However, uncertainties about the influence of complex microstructures and defects on mechanical response have prevented widespread adoption of material models for</div><div>SFRCs. In order to build confidence in models’ predictions requires deepened insight into the heterogenous damage mechanisms. Therefore, this research takes a micro-mechanics standpoint of assessing the damage behavior of SFRCs, particularly micro-void nucleation at the fiber tips, by passing information of microstructural attributes within neighborhoods of incipient damage and non-damage sites, into a framework that establishes correlations between the microstructural information and damage. To achieve this, in-situ x-ray tomography of the gauge sections of two cylindrical injection molded dog-bone specimens, composed of E-glass fibers in a polypropylene matrix, was conducted while the specimens were monotonically loaded until failure. This was followed by (i) the development of microstructural characterization frameworks for segmenting fiber and porosity features in 3D images, (ii) the development of a digital volume correlation informed damage detection framework that confines search spaces of potential damage sites, and (iii) the use of a Gaussian process classification framework to explore the dependency of micro-void nucleation on neighboring microstructural defects by ranking each of their contributions. Specifically, the analysis considered microstructural metrics related to the closest fiber, the closest pore, and the local stiffness, and the results demonstrated that less stiff resin rich areas were more relevant for micro-void nucleation than clustered fiber tips, T-intersections of fibers, or varying porosity volumes. This analysis provides a ranking of microstructural metrics that induce microvoid nucleation, which can be helpful for modelers to validate their predictions on proclivity of damage initiation in the presence of wide distributions of microstructural features and</div><div>manufacturing defects. </div>