Fluidics for Energy

能源流体学

• 批准号: RGPIN-2020-06117
• 负责人: Sinton, David
• 金额: $ 4.66万
• 依托单位: University of Toronto
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2022
• 资助国家: 加拿大
• 起止时间: 2022-01-01 至 2023-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=759697
• 关键词: Fluidics; Energy

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.

全球能源挑战是一个流体问题。世界上最小的流体技术已经在传统能源应用中产生了巨大的影响。射流技术（通常是微流体和纳米流体）现在被商业化用于化学有效性测试，提高了全球石油和天然气运营的效率-这是我们最后一次发现资助的成果。该计划提出了一个新的方向：可再生能源的射流。 深层地热能是一种丰富的可再生资源，具有独特的稳定性和可靠性。最新的地热方法包括封闭回路中的工作流体和杠杆强大的热虹吸泵送。实现这项技术潜力的关键是找到一种稳定的工作流体，并在恶劣的操作条件下最大限度地回收能量。实现这一目标将需要稳定性测试（目标1），热性能测量（目标2），以及它们作为热流体开发系统的自动化（目标3）。目标1将提供第一个流体系统的能源工作流体的稳定性测试在操作条件下。测试流体的稳定性，这里是相变浆料，将在流体芯片内进行评估，该流体芯片提供与地热操作相匹配的热-冷温度循环、压力和剪切速率。 目标2将提供热性能测量。虽然流体界在测量流体的物理性质方面表现出色，但热性质（热容，热导率）的测量需要一种新的方法。在这里，我们将放弃传统的方法，并采用全硅芯片，这是视觉上不透明，但红外透明。流体的热特性由隔热硅岛内流体的温度确定。这种方法将提供在准确性和吞吐量方面超过现有方法的热性能测量，如目标3所要求的。 Aim 3自动应用Aim-1和Aim-2方法开发优化配方。以前的流体测试回答了明确定义的问题（例如，化学品A与油B）。然而，开发优化的工作流体的挑战是开放式的（例如，许多潜在成分的许多潜在浓度）。这些挑战需要快速迭代测试和智能控制。在这里，我们将上述高通量流体测试方法与机器引导的实验规划结合在一个封闭的自动循环中，优化了热性能和稳定性。 除了专注于这项工作的地热应用之外，我们还看到了广泛的适用性。一个新兴的应用是开发高效、低影响的制冷剂。此外，流体测试与机器学习的合并代表了该领域的基本成熟。该项目为HQP在热流体，流体和自动化的交叉点提供了一个绝佳的机会-所有这些都是为可再生能源服务的。