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: 2021
• 资助国家: 加拿大
• 起止时间: 2021-01-01 至 2022-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=741030
• 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动的问题。世界上最小的流体技术已经在传统能源应用中产生了大规模的影响。流体技术（一般是微流体和纳米流体）现在被商业应用于化学有效性测试，提高了全球石油和天然气作业的效率——这是我们上一次获得Discovery资助的结果。这个项目提出了一个新的方向：可再生能源的流体学。深层地热能是一种储量丰富、稳定可靠的可再生能源。最新的地热方法包含工作流体在一个封闭的回路和杠杆强大的热虹吸泵。实现该技术潜力的关键是找到一种稳定的工作液，并在恶劣的工作条件下最大限度地提高能量回收率。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将提供第一个在运行条件下进行能量工作流体稳定性测试的流体系统。测试流体（此处为相变浆液）的稳定性将在流体芯片中进行评估，该芯片可提供与地热作业相匹配的冷热温度循环、压力和剪切速率。目标2将提供热性能测量。虽然流体学界擅长测量流体的物理性质，但热性质（热容、导热系数）的测量需要一种新的方法。在这里，我们将放弃传统的方法，采用一种全硅芯片，它在视觉上是不透明的，但在红外下是透明的。流体的热性质由热隔离硅岛内流体的温度确定。根据Aim 3的要求，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。Aim 3自动应用Aim-1和Aim-2方法来开发优化配方。以前的流体测试回答了明确的问题（例如，化学品A与油B）。然而，开发优化工作液的挑战是开放式的（例如，许多潜在成分的许多潜在浓度）。这样的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计划结合在一个封闭的自动化循环中，以优化热性能和稳定性。除了这项工作的重点地热应用之外，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。流体测试与机器学习的结合也代表了这一领域的成熟。该项目为HQP在热流体、流体学和自动化的交叉领域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。