Engineering topics
Gupta, Varun
Publications and source records attributed to Gupta, Varun.
CO 2 -Responsive Fracturing Fluids for Enhanced Geothermal Systems (Final Report)
Our group has recently developed StimuFrac, a non-toxic stimuli-responsive fracturing fluid consisting of a CO 2 -reactive polymer which has shown at the lab-scale to consistently fracture rock cores at significantly lower net pressures in a range of representative geothermal pressure/temperature conditions. However, until now the mechanism/s responsible for more effective fracturing, of critical importance to optimize fracturing performance as well as strategize injection methodologies for field deployment, was not understood. In this document, we report (1) on the two main mechanisms responsible for fracturing rock at lower net pressures with StimuFrac; (2) the phase behavior of StimuFrac/CO 2 under geothermal wellbore conditions; and (3) based on high-temperature true triaxial stimulations, detailed evidence that StimuFrac/CO 2 is the best performing stimulation fluid under EGS T/P conditions as compared to water, waterless CO 2 , and CO 2 /water fracturing fluids. This is because i) it requires significantly lower volumes of CO 2 due to its reduced leak off into the formation by the crosslinked polymer solution; ii) large fractures can be generated reproducibly at both low and high CO 2 injection flow rates, and iii) the reversible (previously reported) viscosity increase of StimuFrac could be beneficial to transport proppants when they become available for EGS. These results were particularly evident for hot nearly dry rock as well as partially and nearly fully water-saturated granitic rock. GTO requires StimuFrac to be evaluated in fully water-saturated rock to determine whether the above-described performance applies under these conditions. Since (1) GTO considers there is no enough evidence for Sa different StimuFrac formulation to work below 90C (where full water-saturation in an open system is possible) and (2) no polyaxial loading frame larger than a few centimeters that can do hydraulic fracturing tests while maintaining the rock sample fully saturated with water at 200 C exists; PNNL concludes that the only way to determine if StimuFrac represents an advanced fracturing fluid alternative for EGS, is to perform a stimulation in an actual EGS reservoir.
Scalable Direct Recycling of Cathode Black Mass from Spent Lithium‐Ion Batteries
Abstract End of life (EoL) lithium‐ion batteries (LIBs) are piling up at an intimidating rate, which is alarming for environmental health. With further expected rapid growth of LIB use, the magnitude of spent battery accumulation is also expected to grow. LiNi x Co y Mn z O 2 (NCM) cathode materials are a dominant chemistry in high energy LIBs, and make up a huge portion of this waste accumulation. Direct recycling is one of the most promising ways to turn this waste to wealth, but has been limited to lab‐scale, due to lack of robustness, namely the tedious pretreatment required that involves toxic organic solvents. Herein, a process that integrates the pretreatment and relithiation of the cathode black mass is demonstrated. Cathode material from EoL electric vehicle (EV) batteries is treated in a 100 g per batch operation and the regenerated cathode active material demonstrates 100% electrochemical performance recovery, with 91% yield rate, and shows promise for further scale up. This process has the advantages of integration, scalability, and universality, which clears the barricade for direct recycling to move from lab to industry scale with considerable profitability.
Uncertainty Quantification Framework for Predicting Material Response with Large Number of Parameters: Application to Creep Prediction in Ferritic-Martensitic Steels Using Combined Crystal Plasticity and Grain Boundary Models
This paper presents an uncertainty quantification (UQ) framework for the physics-based model prediction of material response with a large number of parameters. The application problem presented in this work is that of predicting creep in Grade 91 steel at 600°C. The material response is defined with a physically based microstructural model with constitutive equations emulating several observed phenomena in Grade 91 and embodied into an explicit geometry mesoscale finite element model for prior austenite grains and grain boundaries. Creep within the grains and in grain boundaries are represented by crystal plasticity for dislocation motion and a physics-based model for cavity growth and nucleation, respectively. The creep behavior of this material is influenced by several parameters, some of which have a wide range of variation based on experimental data. UQ combined with microstructural modeling can discover the core microstructural causes of experimental variability, leading to improved materials with lower variability in critical long-term material properties. In this study, we investigate the model's uncertainty to identify material properties that may be modified during production to increase creep life and analyze different components of the crystal plasticity model for improvements. For this purpose, a quantity of interest is defined as time to minimum creep rate, which correlates well to the creep failure of the material. A deep neural network model was trained and validated to be used as a surrogate for the finite element model. Then, a variance-based sensitivity analysis is performed on the surrogate model to find the Sobol indices of the input parameters in respect to the output quantity of interest. The Sobol indices are used to reduce the dimensionality of the model. Generalized polynomial chaos expansion is used on the reduced basis models to propagate the uncertainty from the input parameters to the quantity of interest using the deep neural network surrogate model. These results are benchmarked against uncertainty propagation using Monte Carlo simulations. In conclusion, the UQ performed through the reduced basis model captures almost all the uncertainty in the model with significantly fewer simulations, making it possible to perform the UQ directly via simulations with the finite element model rather than surrogate machine-learned models.
Current Challenges in Efficient Lithium‐Ion Batteries’ Recycling: A Perspective
Abstract Li‐ion battery (LIB) recycling has become an urgent need with rapid prospering of the electric vehicle (EV) industry, which has caused a shortage of material resources and led to an increasing amount of retired batteries. However, the global LIB recycling effort is hampered by various factors such as insufficient logistics, regulation, and technology readiness. Here, the challenges associated with LIB recycling and their possible solutions are summarized. Different aspects such as recycling/upcycling techniques, worldwide government policies, and the economic and environmental impacts are discussed, along with some practical suggestions to overcome these challenges for a promising circular economy for LIB materials. Some potential strategies are proposed to convert such challenges into opportunities to maintain the global expansion of the EV and other LIB‐dependent industries.