Application of machine learning algorithm in temperature field reconstruction

• URL: http://arxiv.org/abs/2502.13190v1
• Date: Tue, 18 Feb 2025 14:07:47 GMT
• Title: Application of machine learning algorithm in temperature field reconstruction
• Authors: Qianyu He, Huaiwei Sun, Yubo Li, Zhiwen You, Qiming Zheng, Yinghan Huang, Sipeng Zhu, Fengyu Wang,
• Abstract summary: This study focuses on the stratification patterns and dynamic evolution of reservoir water temperatures. It aims to estimate and reconstruct the temperature field using limited and noisy local measurement data.
• Score: 10.34360541161895
• License:
• Abstract: This study focuses on the stratification patterns and dynamic evolution of reservoir water temperatures, aiming to estimate and reconstruct the temperature field using limited and noisy local measurement data. Due to complex measurement environments and technical limitations, obtaining complete temperature information for reservoirs is highly challenging. Therefore, accurately reconstructing the temperature field from a small number of local data points has become a critical scientific issue. To address this, the study employs Proper Orthogonal Decomposition (POD) and sparse representation methods to reconstruct the temperature field based on temperature data from a limited number of local measurement points. The results indicate that satisfactory reconstruction can be achieved when the number of POD basis functions is set to 2 and the number of measurement points is 10. Under different water intake depths, the reconstruction errors of both POD and sparse representation methods remain stable at around 0.15, fully validating the effectiveness of these methods in reconstructing the temperature field based on limited local temperature data. Additionally, the study further explores the distribution characteristics of reconstruction errors for POD and sparse representation methods under different water level intervals, analyzing the optimal measurement point layout scheme and potential limitations of the reconstruction methods in this case. This research not only effectively reduces measurement costs and computational resource consumption but also provides a new technical approach for reservoir temperature analysis, holding significant theoretical and practical importance.

Related papers

• An Expeditious Spatial Mean Radiant Temperature Mapping Framework using Visual SLAM and Semantic Segmentation [7.1869288530375135]
  Mean radiant temperature (MRT) is very challenging to measure. Most common measurement methodologies are time-consuming and not user-friendly. This paper proposes a novel MRT measurement framework that uses visual simultaneous localization and mapping (SLAM) and semantic segmentation techniques.
  arXiv  Detail & Related papers  (2024-10-12T08:50:35Z)
• Observation-Guided Meteorological Field Downscaling at Station Scale: A Benchmark and a New Method [66.80344502790231]
  We extend meteorological downscaling to arbitrary scattered station scales and establish a new benchmark and dataset. Inspired by data assimilation techniques, we integrate observational data into the downscaling process, providing multi-scale observational priors. Our proposed method outperforms other specially designed baseline models on multiple surface variables.
  arXiv  Detail & Related papers  (2024-01-22T14:02:56Z)
• Continuous Field Reconstruction from Sparse Observations with Implicit Neural Networks [11.139052252214917]
  This work presents a novel approach that learns a continuous representation of a physical field using implicit neural representations. In experimental evaluations, the proposed model outperforms recent INR methods.
  arXiv  Detail & Related papers  (2024-01-21T22:18:29Z)
• Multi-Modal Learning-based Reconstruction of High-Resolution Spatial Wind Speed Fields [46.72819846541652]
  We propose a framework based on Vari Data Assimilation and Deep Learning concepts. This framework is applied to recover rich-in-time, high-resolution information on sea surface wind speed.
  arXiv  Detail & Related papers  (2023-12-14T13:40:39Z)
• Bayesian inference of composition-dependent phase diagrams [47.79947989845143]
  We develop a method in which Bayesian inference is employed to combine thermodynamic data from molecular dynamics (MD), melting point simulations, and phonon calculations, process these data, and yield a temperature-concentration phase diagram. The developed algorithm was successfully tested on two binary systems, Ge-Si and K-Na, in the full range of concentrations and temperatures.
  arXiv  Detail & Related papers  (2023-09-03T20:57:10Z)
• Information Entropy Initialized Concrete Autoencoder for Optimal Sensor Placement and Reconstruction of Geophysical Fields [58.720142291102135]
  We propose a new approach to the optimal placement of sensors for reconstructing geophysical fields from sparse measurements. We demonstrate our method on the two examples: (a) temperature and (b) salinity fields around the Barents Sea and the Svalbard group of islands. We find out that the obtained optimal sensor locations have clear physical interpretation and correspond to the boundaries between sea currents.
  arXiv  Detail & Related papers  (2022-06-28T12:43:38Z)
• A deep learning method based on patchwise training for reconstructing temperature field [10.422905687540172]
  This work proposes a novel deep learning method based on patchwise training to reconstruct the temperature field of electronic equipment accurately from limited observation. The maximum absolute errors of the reconstructed temperature field are less than 1K under the patchwise training approach.
  arXiv  Detail & Related papers  (2022-01-26T10:42:23Z)
• Physics-Informed Deep Reversible Regression Model for Temperature Field Reconstruction of Heat-Source Systems [10.151316399254718]
  This work develops a novel physics-informed deep reversible regression models for temperature field reconstruction of heat-source systems. First, we define the temperature field reconstruction task of heat-source systems mathematically, numerically model the problem, and further transform the problem as an image-to-image regression problem. Based on the law of forward and backward propagation of deep models, this work develops the deep reversible regression model which can better learn the physical information near the boundary.
  arXiv  Detail & Related papers  (2021-06-22T17:01:53Z)
• Role of topology in determining the precision of a finite thermometer [58.720142291102135]
  We find that low connectivity is a resource to build precise thermometers working at low temperatures. We compare the precision achievable by position measurement to the optimal one, which itself corresponds to energy measurement.
  arXiv  Detail & Related papers  (2021-04-21T17:19:42Z)
• Real-time estimation of the optically detected magnetic resonance shift in diamond quantum thermometry [47.50219326456544]
  We investigate the real-time estimation protocols for the frequency shift of optically detected magnetic resonance (ODMR) of nitrogen-vacancy centers in nanodiamonds (NDs) Efficiently integrating multipoint ODMR measurements and ND particle tracking into fluorescence microscopy has recently demonstrated stable monitoring of the temperature inside living animals.
  arXiv  Detail & Related papers  (2020-06-12T01:44:35Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.