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1. Experimental and Modeling Uncertainty Considerations for Determining the First Item Ignited in a Compartment Using a Bayesian Method

   https://doi.org/10.1115/1.4052796  

   Cabrera, J. M.; Moser, R. D.; Ezekoye, O. A. (March 2022, Journal of Verification, Validation and Uncertainty Quantification)

   Abstract Fire scene reconstruction and determining the fire evolution (i.e., item-to-item ignition events) using the postfire compartment is an extremely difficult task because of the time-integrated nature of the observed damages. Bayesian methods are ideal for making inferences amongst hypotheses given observations and are able to naturally incorporate uncertainties. A Bayesian methodology for determining probabilities to items that may have initiated the fire in a compartment from damage signatures is developed. Exercise of this methodology requires uncertainty quantification of these damage signatures. A simple compartment configuration was used to quantify the uncertainty in damage predictions by firedynamicssimulator (fds) and, a compartment evolution program, jt-risk as compared to experimentally derived damage signatures. Surrogate sensors spaced within the compartment use heat flux data collected over the course of the simulations to inform damage models. Experimental repeatability showed up to 4% uncertainty in damage signatures between replicates. Uncertainties for fds and jt-risk ranged from 12% up to 32% when compared to experimental damages. Separately, the evolution physics of a simple three-fuel-package problem with surrogate damage sensors were characterized in a compartment using experimental data, fds, and jt-risk predictions. A simple ignition model was used for each of the fuel packages. The Bayesian methodology was exercised using the damage signatures collected, cycling through each of the three fuel packages, and combined with the previously quantified uncertainties. Only reconstruction using experimental data was able to confidently predict the true hypothesis from the three scenarios. 

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2. A Modified Directional Flame Thermometer: Development, Calibration, and Uncertainty Quantification

   https://doi.org/10.1115/1.4046657  

   Cabrera, J. M.; Moser, R. D.; Ezekoye, O. A. (March 2020, Journal of Verification, Validation and Uncertainty Quantification)

   Abstract The directional flame thermometer (DFT) is a robust device used to measure heat fluxes in harsh environments such as fire scenarios but is large when compared to other standard heat flux measurement devices. To better understand the uncertainties associated with heat flux measurements in these environments, a Bayesian framework is utilized to propagate uncertainties of both known and unknown parameters describing the thermal model of a modified, smaller DFT. Construction of the modified DFT is described along with a derivation of the thermal model used to predict the incident heat flux to its sensing surface. Parameters of the model are calibrated to data collected using a Schmidt–Boelter (SB) gauge with an accuracy of ±3% at incident heat fluxes of 5 kW/m2, 10 kW/m2, and 15 kW/m2. Markov Chain Monte Carlo simulations were used to obtain posterior distributions for the free parameters of the thermal model as well as the modeling uncertainty. The parameter calibration process produced values for the free parameters that were similar to those presented in the literature with relative uncertainties at 5 kW/m2, 10 kW/m2, and 15 kW/m2 of 17%, 9%, and 7%, respectively. The derived model produced root-mean-squared errors between the prediction and SB gauge output of 0.37, 0.77, and 1.13 kW/m2 for the 5, 10, and 15 kW/m2 cases, respectively, compared to 0.53, 1.12, and 1.66 kW/m2 for the energy storage method (ESM) described in ASTM E3057. 

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3. Novel Subcooled Boiling Chamber With Submerged Condensation for High Heat Flux Removal for Data Center Application

   https://doi.org/10.1109/ITherm55375.2024.10709501  

   Shukla, Maharshi Y; Kandlikar, Satish G (May 2024, IEEE)

   Electronic components, especially the CPUs/GPUs used in data centers are concentrated heat-generating sources. Their large Thermal Design Power (TDP), small die area and confined packaging make their thermal management a unique challenge. While conventional single-phase cooling methods fail to dissipate such large amounts of heat efficiently, recently developed two-phase cooling systems also lack the holistic approach of combining efficient boiling and condensation mechanisms. It is hypothesized that subcooled boiling with submerged condensation and reduced saturation pressure will result in high-heat flux dissipation while maintaining low surface temperatures. The novel boiling chamber presented in this work is demonstrated in a compact configuration that fits in a 1U/2U server rack by combining submerged condensation with subcooled pool boiling. The boiling chamber is filled with 13% and 40% fill ratio of water and Novec-7000 and experimentally investigated on a thermal test vehicle. Results show that the boiling chamber dissipates about 400 W of heat with a surface temperature of less than 80 °C using Novec-7000 working fluid. When tested with water, the device dissipated more than 750 W of heat (heat flux ≈ 67 W/cm2) with a surface temperature of less than 90 °C. Though the surface temperature rose to 120°C, further testing shows the device to dissipate more than 1 kW from a 34.5 × 32 mm2 plain copper chip. High-speed images identify submerged condensation and small diameters of vapor bubbles. Further enhancements can be achieved by implementing enhanced boiling and condensation surfaces. Lastly, a guide to design considerations and future work is provided to unlock the greater performance potential of the novel boiling chamber. 

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4. Machine learning assisted inverse heat transfer problem to find heat flux in ablative materials

   https://doi.org/10.1016/j.mtcomm.2025.112337  

   Islam, Md Shariful; Dutta, Prashanta (April 2025, Materials Today Communications)

   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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5. The Role of Fuel Characteristics and Heat Release Formulations in Coupled Fire-Atmosphere Simulation

   https://doi.org/10.3390/fire6070264  

   Shamsaei, Kasra; Juliano, Timothy W.; Roberts, Matthew; Ebrahimian, Hamed; Lareau, Neil P.; Rowell, Eric; Kosovic, Branko (July 2023, Fire)

   In this study, we focus on the effects of fuel bed representation and fire heat and smoke distribution in a coupled fire-atmosphere simulation platform for two landscape-scale fires: the 2018 Camp Fire and the 2021 Caldor Fire. The fuel bed representation in the coupled fire-atmosphere simulation platform WRF-Fire currently includes only surface fuels. Thus, we enhance the model by adding canopy fuel characteristics and heat release, for which a method to calculate the heat generated from canopy fuel consumption is developed and implemented in WRF-Fire. Furthermore, the current WRF-Fire heat and smoke distribution in the atmosphere is replaced with a heat-conserving Truncated Gaussian (TG) function and its effects are evaluated. The simulated fire perimeters of case studies are validated against semi-continuous, high-resolution fire perimeters derived from NEXRAD radar observations. Furthermore, simulated plumes of the two fire cases are compared to NEXRAD radar reflectivity observations, followed by buoyancy analysis using simulated temperature and vertical velocity fields. The results show that while the improved fuel bed and the TG heat release scheme have small effects on the simulated fire perimeters of the wind-driven Camp Fire, they affect the propagation direction of the plume-driven Caldor Fire, leading to better-matching fire perimeters with the observations. However, the improved fuel bed representation, together with the TG heat smoke release scheme, leads to a more realistic plume structure in comparison to the observations in both fires. The buoyancy analysis also depicts more realistic fire-induced temperature anomalies and atmospheric circulation when the fuel bed is improved. 

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