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; 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对传统能源应用产生了很大的影响。流体力学（通常通常在商业上用于化学有效性测试，可以提高全球石油和天然气运营的效率，这是我们上次发现赠款的结果。该程序阐​​明了一个新的方向：可再生能源的流体。 深地热能是一种丰富的可再生资源，具有独特的稳定和可靠。最新的地热方法包含闭路中的工作流体，并且杠杆强大的热节泵泵。意识到这项技术的潜力的关键是找到一种工作流体，该液体稳定，并在严格的操作条件下最大化能量回收率。实现此目标将需要稳定性测试（AIM 1），热属性测量（AIM 2）及其自动化为热流体开发系统（AIM 3）。 AIM 1将在操作条件下提供第一个用于能源工作流体稳定性测试的流体系统。在这里，将评估测试流体的稳定性，此处的变化浆液将在流体芯片中进行评估，该芯片可提供热冷的温度循环，压力和剪切速率与地热操作相匹配。 AIM 2将提供热属性测量。尽管流体界在测量流体的物理特性方面表现出色，但测量热性能（热容量，导热率）需要一种新的方法。在这里，我们将放弃常规方法，并采用视觉上但红外透明剂的全硅芯片。流体的热性能取决于热分离的硅岛中的流体温度。这种方法将提供热能测量值，以超过AIM 3所需的准确性和吞吐量。AIM3自动使用AIM-1和AIM-2方法的应用以开发优化的配方。以前的流体测试回答了定义明确的问题（例如，用油B化学a）。但是，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计划结合在一起，以封闭的自动环中的循环进行优化，以优化热性能和稳定性。 除了重点的地热应用外，我们还看到了广泛的适用性。新兴应用是高效，低影响制冷剂的发展。流体测试与机器学习的合并也代表了该领域的重要成熟。该项目为HQP提供了热门流动性，流体和自动化的交点的出色机会 - 所有这些都为可再生能源提供服务。