Fluidics for Energy

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The global energy challenge is a fluids problem. The world's smallest fluids technologies have already had large scale impact in conventional energy applications. Fluidics (microfluidics and nanofluidics generally) are now employed commercially for chemical effectiveness testing that improves the efficiency of oil and gas operations worldwide - an outcome of our last Discovery grant. This program sets out a new direction: Fluidics for renewable energy. Deep geothermal energy is an abundant renewable resource that is uniquely stable and reliable. The newest geothermal methods contain the working fluid in a closed circuit and lever powerful thermosiphon pumping. The key to realizing the potential of this technology is finding a working fluid that is stable and maximizes energy recovery under harsh operating conditions. Achieving this goal will require stability testing (Aim 1), thermal property measurements (Aim 2), and their automation as a thermal fluid development system (Aim 3). Aim 1 will provide the first fluidic system for energy working fluid stability testing under operational conditions. The stability of the test fluid, here a phase change slurry, will be assessed within a fluidic chip that provides hot-cold temperature cycling, pressures, and shear rates matching geothermal operations. Aim 2 will provide the thermal property measurements. While the fluidics community have excelled at measuring physical properties of fluids, the measurement of thermal properties (heat capacity, thermal conductivity) requires a fresh approach. Here we will abandon conventional approaches, and employ an all-silicon chip that is visually-opaque but infrared-transparent. Thermal properties of the fluid are determined from the temperature of fluids within thermally-isolated silicon islands. This approach will provide thermal property measurements that surpass existing methods in accuracy and throughput, as required for Aim 3. Aim 3 automates the application of Aim-1 and Aim-2 methods to develop optimized formulations. Previous fluidic testing answered well-defined questions (e.g. chemical A with oil B). However, the challenge of developing an optimized working fluid is open-ended (e.g. many potential concentrations of many potential ingredients). Such challenges require both rapid iterative testing and intelligent control. Here we combine the above high-throughput fluidic testing methods with machine-guided experiment planning in a closed, automated loop that optimizes for both thermal performance and stability. Beyond the geothermal application that focuses this work, we see broad applicability. An emerging application is the development of high-efficiency, low-impact refrigerants. Also the merger of fluidic testing with machine learning represents an essential maturation of this field. This project provides an outstanding opportunity for HQP at the intersection of thermofluids, fluidics and automation - all in the service of renewable energy.