Optimization of An HPT Blade and Sector-Based Annular Rig Design for Supercritical Co2 Power Cycle Representative Testing

IF 1.4 4区 工程技术 Q3 ENGINEERING, MECHANICAL
Logan Tuite, James Braun, Guillermo Paniagua
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Abstract

Abstract As part of the ongoing research into the design of hardware for zero emission cycles, a first stage high-pressure turbine (HPT) blade is optimized for a 300 MWe supercritical CO2 (sCO2) power cycle using the surrogate-assisted genetic algorithm optimizer in Numeca FINE/Design 3D with objectives of increasing efficiency and decreasing heat load to the blade. Supercritical CO2 property tables are constructed from NIST REFPROP data for the condensable gas simulation in FINE/Turbo. A detailed mesh sensitivity study is performed for a baseline design to identify the proper grid refinement and efficiently allocate resources for the optimization. Seventy design variables are selected for the initial population generation. Self-organizing maps are then used to focus the design variables on the most important ones affecting the objective functions. The optimization results in approximately 3000 three-dimensional Reynolds Averaged Navier Stokes simulations of different blade shapes with increases in efficiency of up to 0.85 percentage points and decreases in heat load of 14%. Families of blade shapes are identified for experimental testing in an annular rig at the Purdue Experimental Turbine Aerothermal Laboratory. A design to adapt the annular cascade for testing optimized geometries is introduced, which features eccentric radius sectors allowing for scaled-up geometries of sCO2 optimized blade profiles to be tested at design cycle representative conditions at high Reynolds numbers in dry air. Analysis into the effects of Reynolds number, working fluid, and geometric relations are presented to prove the efficacy of the test method.
超临界Co2动力循环代表性试验高压高压叶片优化及扇形环空钻机设计
作为正在进行的零排放循环硬件设计研究的一部分,使用Numeca FINE/ design 3D中的代理辅助遗传算法优化器对第一级高压涡轮(HPT)叶片进行了300 MWe超临界CO2 (sCO2)功率循环优化,目标是提高效率并降低叶片的热负荷。超临界CO2属性表是根据NIST REFPROP数据构建的,用于FINE/Turbo中的可冷凝气体模拟。对基线设计进行了详细的网格灵敏度研究,以确定适当的网格细化和有效地分配资源进行优化。为初始人口生成选择了70个设计变量。然后使用自组织映射将设计变量集中在影响目标函数的最重要的变量上。优化结果表明,在大约3000个不同叶片形状的三维Reynolds平均Navier Stokes模拟中,效率提高了0.85个百分点,热负荷降低了14%。在普渡大学实验涡轮空气热实验室的环空钻机上,确定了叶片形状的家族进行实验测试。介绍了一种用于测试优化几何形状的环形叶栅设计,其特点是偏心半径扇区,允许sCO2优化叶片轮廓的几何形状放大,以便在干燥空气中高雷诺数的设计周期代表性条件下进行测试。分析了雷诺数、工质和几何关系对试验方法的影响,证明了试验方法的有效性。
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来源期刊
CiteScore
3.80
自引率
20.00%
发文量
292
审稿时长
2.0 months
期刊介绍: The ASME Journal of Engineering for Gas Turbines and Power publishes archival-quality papers in the areas of gas and steam turbine technology, nuclear engineering, internal combustion engines, and fossil power generation. It covers a broad spectrum of practical topics of interest to industry. Subject areas covered include: thermodynamics; fluid mechanics; heat transfer; and modeling; propulsion and power generation components and systems; combustion, fuels, and emissions; nuclear reactor systems and components; thermal hydraulics; heat exchangers; nuclear fuel technology and waste management; I. C. engines for marine, rail, and power generation; steam and hydro power generation; advanced cycles for fossil energy generation; pollution control and environmental effects.
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