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In recent years, electronic packaging has evolved significantly to meet demands for higher performance, lower costs, and smaller designs. This shift has led to heterogeneous packaging, which integrates chips of varying stack heights and results in non-uniform heat flux and temperature distributions. These conditions pose substantial thermal management challenges, as they can create large temperature gradients, which increase thermal stress and potentially compromise chip reliability. This study explores single-phase liquid cooling for multi-chip modules (MCMs) through a comprehensive experimental and machine learning approach. It investigates the impact of chip spacing, height, fluid flow rate, fluid inlet location, and heat flux uniformity on chip temperature and the thermohydraulic performance of a commercial cold plate. Results show that increasing coolant flow from 1 LPM to 2 LPM decreased thermal resistance by 26 %, with heat losses remaining below 5 %. The left inlet configuration improved temperature uniformity compared to the right, though both yielded comparable thermal performance. Adjusting heater spacing impacted temperature distribution based on inlet position, and lowering one heater by 1 mm raised its temperatures by 15 ◦C due to increased thermal resistance from thermal interface material. A transient test demonstrated the cold plate’s quick response to power surges, in which there is only a 1 ◦C spike above steady state. Complementing these findings, an Artificial Neural Network (ANN) model was developed with optimized architecture specifically for the unique challenges of this study. The ANN model was rigorously validated using an independent dataset, achieving highly accurate temperature predictions (R2 = 0.99) within 2.5 % of experimental
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This content will become publicly available on April 1, 2026
Machine learning assisted inverse heat transfer problem to find heat flux in ablative materials
Thermal ablation of materials is a complex phenomenon that involves physical and chemical processes for the thermal protection of systems. However, due to the extreme thermal conditions and moving boundaries, predicting temperature and heat flux at the ablative material is quite challenging. A physics-informed neural network is a promising technique for many such inverse problems, including the prediction of unsteady heat flux. However, traditional physics-informed machine learning algorithms struggle with heat flux predictions in thermal ablation problems due to moving boundary conditions and lack of temperature data in the inaccessible domain. This study presents a hybrid approach, where an artificial neural network (ANN) is used for the accessible domain of the material and a physics-based numerical solution (PNS) technique is used in the inaccessible domain of the material, to find heat flux at the ablative surface. Temperature data at the accessible sensor points are used to train the ANN model. The heat flux at the ablative boundary was iteratively obtained from the numerical solution of the energy equation in the inaccessible domain by matching the ANN-predicted temperature at the last accessible sensor point. Our results indicate that this hybrid methodology significantly outperforms traditional physics-informed machine learning techniques, achieving excellent accuracy in predicting the temperature profiles and heat fluxes under complex conditions for both constant and variable heat flux and properties. By addressing the limitations of conventional physics-informed machine learning methods, our approach provides a robust and reliable solution for modeling the intricate dynamics of ablative processes.
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- Award ID(s):
- 2244082
- PAR ID:
- 10601154
- Publisher / Repository:
- Elsevier
- Date Published:
- Journal Name:
- Materials Today Communications
- Volume:
- 45
- Issue:
- C
- ISSN:
- 2352-4928
- Page Range / eLocation ID:
- 112337
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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