深度学习使得跳滴凝结和结的综合评估成为可能
Li Chen1,2, Diwei Shi1,2, Xinyue Kang3
1Department of Engineering Mechanics, Tsinghua University, Beijing 100084, China.
ACS applied materials & interfaces
|May 1, 2024
概括
深度学习可以监测超疏水表面的跳滴凝结和结. 该框架量化热传递和防性能,适应各种条件,使用最小的数据.
科学领域:
- 表面科学与工程 表面科学与工程
- 材料科学是一种材料科学.
- 科学研究中的人工智能
背景情况:
- 超疏水表面可实现高效的跳滴凝结和结,对于传热和防雾/防应用至关重要.
- 由于高频率和多种形态,在各种超表面上监测动态滴滴行为 (凝结,跳跃,结) 是具有挑战性的.
研究的目的:
- 开发一个半监督的深度学习框架,用于监测超疏水表面的凝结和结过程.
- 为了能够量化物理性质,如热量流,跳跃特征和结率,用于性能评估.
- 创建一个可适应的系统,以优化表面设计,以提高传热和防能力.
主要方法:
- 开发一个半监督的深度学习框架来分析冷凝和结的光学观测.
- 识别短暂的滴滴分布和动态活动 (凝结,跳跃,结).
- 基于观察到的滴滴动态的物理性质的量化.
主要成果:
- 该框架成功地监测了各种超疏水表面的凝结和化动态.
- 它准确地识别了滴滴的行为,如凝聚,跳跃和结.
- 物理性质 (热量流,跳跃,结率) 被量化,提供了全面的性能洞察力.
结论:
- 深度学习框架提供了一种有效的方法来监测和量化超表面的凝结/结.
- 它的适应性不同表面形态和照明条件,与最小的注释数据,促进性能优化.
- 这种方法有助于设计先进的表面,以改善传热和防应用.
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