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: 2020
• 资助国家: 加拿大
• 起止时间: 2020-01-01 至 2021-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=717842
• 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性问题。世界上最小的流体技术已经在常规能源应用中产生了大规模的影响。流体(通常是微流体和纳米流体)现在被用于化学有效性测试，以提高全球石油和天然气作业的效率-这是我们上一次发现拨款的结果。这项计划提出了一个新的方向：可再生能源的流体技术。 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闭路循环的工质和杠杆强大的热虹吸泵。实现这项技术潜力的关键是找到一种在恶劣操作条件下稳定并最大限度地实现能量回收的工质。实现这一目标将需要稳定性测试(目标1)、热物性测量(目标2)以及作为热流体开发系统的自动化(目标3)。 AIM 1将提供第一个用于在运行条件下进行能量工作液稳定性测试的射流系统。测试流体(这里是相变泥浆)的稳定性将在流体芯片中进行评估，该芯片提供与地热操作相匹配的冷热温度循环、压力和剪切速率。 AIM 2将提供热物性测量。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、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，而采用视觉不透明但红外透明的全硅芯片。流体的热性质由热隔离硅岛内流体的温度决定。这种方法将提供在精度和吞吐量方面超过现有方法的热物性测量，这是Aim 3所要求的。 AIM 3自动应用AIM-1和AIM-2方法来开发优化配方。以前的射流测试回答了明确定义的问题(例如，化学A与油B)。然而，开发优化工作液的挑战是无限制的(例如，许多潜在成分的许多潜在浓度)。这样的挑战既需要快速迭代测试，也需要智能控制。在这里，我们将上述高通量射流测试方法与机器引导的实验计划相结合，在一个封闭的自动化环路中优化热性能和稳定性。 除了关注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，射流测试与机器学习的结合代表着该领域的本质成熟。这个项目为HQP提供了一个绝佳的机会，将热流体、流体和自动化结合在一起，所有这些都是为可再生能源服务的。