论文标题
使用超网和PINN对热交换器进行实时健康监测
Real-time Health Monitoring of Heat Exchangers using Hypernetworks and PINNs
论文作者
论文摘要
我们展示了基于物理信息的神经网络（PINN）模型，用于对热交换器的实时健康监测，该模型在提高热电厂的能源效率方面起着至关重要的作用。基于高网络的方法用于使域被构成的PINn了解热交换器对动态边界条件的热交换行为，从而消除了重新训练的需求。结果，与现有的PINN相比，我们达到了推理时间的降低，同时与基于物理的模拟保持准确性。这使得该方法对于预测维护数字双胞胎环境中的热交换器非常有吸引力。
We demonstrate a Physics-informed Neural Network (PINN) based model for real-time health monitoring of a heat exchanger, that plays a critical role in improving energy efficiency of thermal power plants. A hypernetwork based approach is used to enable the domain-decomposed PINN learn the thermal behavior of the heat exchanger in response to dynamic boundary conditions, eliminating the need to re-train. As a result, we achieve orders of magnitude reduction in inference time in comparison to existing PINNs, while maintaining the accuracy on par with the physics-based simulations. This makes the approach very attractive for predictive maintenance of the heat exchanger in digital twin environments.
