What question did this study set out to answer?

Develop and validate a physics-informed universal kriging framework to reconstruct high-fidelity 3D temperature fields in sparse-sensor ultra-low-temperature biobank freezers.

October 3, 2025ProcessesOpen Access

Adaptive Lag Binning and Physics-Weighted Variograms: A LOOCV-Optimised Universal Kriging Framework with Trend Decomposition for High-Fidelity 3D Cryogenic Temperature Field Reconstruction

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Authors

JTJ. TangShanghai Power Equipment Research Institute YCY. ChenUniversity of Shanghai for Science and Technology BLBaolin LiuUniversity of Shanghai for Science and Technology

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Implication

Validation study demonstrates sub-degree accuracy in 3D cryogenic freezer temperature reconstruction, indicating improved biobank monitoring.

Key Points

Develop and validate a physics-informed universal kriging framework to reconstruct high-fidelity 3D temperature fields in sparse-sensor ultra-low-temperature biobank freezers.
Adapted universal kriging for 3D cryogenic thermal monitoring incorporating eight novel lag-binning methods tailored to sparse, anisotropic sensor layouts.
Implemented a leave-one-out cross-validation pipeline to automatically optimize trend models, binning strategies, logistic weighting, and variogram fitting.
Validated the algorithm using empirical thermal monitoring data collected from an operating 3000 L cryogenic chest freezer.
Achieved sub-degree temperature reconstruction accuracy across the operating cryogenic storage space.
Identified quadratic detrending as dominant for capturing physics-guided vertical thermal stratification.
Stabilized variogram estimation despite sparse sensor placement, successfully establishing centimeter-scale virtual sensing.

Cite This Study

Tang et al. (2025) studied this question.

synapsesocial.com/papers/6ab1e0dc7f73075692b376ea https://doi.org/10.3390/pr13103160

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Implication

Validation study demonstrates sub-degree accuracy in 3D cryogenic freezer temperature reconstruction, indicating improved biobank monitoring.

Adaptive Lag Binning and Physics-Weighted Variograms: A LOOCV-Optimised Universal Kriging Framework with Trend Decomposition for High-Fidelity 3D Cryogenic Temperature Field Reconstruction

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Cite This Study

Adaptive Lag Binning and Physics-Weighted Variograms: A LOOCV-Optimised Universal Kriging Framework with Trend Decomposition for High-Fidelity 3D Cryogenic Temperature Field Reconstruction

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