Nonlinear inverse heat conduction: Digitally filtered space marching with phase-plane and cross-correlation analyses

Chen, Hongchu; Frankel, J. I.; Keyhani, M.

doi:10.1080/10407790.2017.1347004

Cited by 13 publications

(4 citation statements)

References 30 publications

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“…[ 30,36,42 ] It was observed that the PTC heating unit can provide a constant heat source for which the calculated thermal conductivities of SS304 and polytetrafluoroethylene (PTFE) at 100 °C were 16.2 ± 0.04 W m −1 K −1 and 0.25 ± 0.005 W m −1 K −1 respectively. The observed thermal conductivity values were in good agreement with the values stated by the suppliers and with those cited in previous works [ 43–45 ] as well. The effect of modifications on the morphology was determined from field emission scanning electron microscopy (FE‐SEM) images.…”

Section: Methodssupporting

confidence: 91%

Thermal Synergistic Effect on CFRP Laminates with Modified Fiber/Matrix Systems for Heat Transfer Applications

Aravind

Manu

Roy

et al. 2023

Macro Chemistry & Physics

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Carbon fiber reinforced polymer (CFRP) laminates are modified to enhance their suitability for various thermal applications. A synergistic approach utilizing the effect of various conductive and insulative modifiers with diglycidyl ethers of bisphenol A (DGEBA) epoxy resin and/carbon fiber (CF) is explored. In CFRP laminates developed after modifications made in epoxy resin using a thermoplastic material, such as polycarbonate (PC) and/or acrylonitrile butadiene styrene (ABS), exhibit high thermal resistance (TR) of 77.1% compared to unmodified CFRP. In contrast, modifications made using conductive mediums like phosphonium (P), imidazolium (I), or silanized‐graphene oxide (SGO) have lower TR of 25.7%, 30.5%, and 32.4%, respectively. A temperature gradient (TG) enhancement of 75% is reported for the 1.5 wt% PC/ABS modified CFRP laminates. On the contrary, modifications using 0.5 parts per hundred (phr)P, 0.5 phr I, and 1 g L−1 SGO in epoxy reduce the TG by 25%, 30%, and 32%, respectively. Thermogravimetric analysis (TGA) and differential scanning calorimetry (DSC) analyses are done to explore the thermal characteristics of each case of modification. Finally, scanning electron microscopy images confirm the distribution profile of the modifiers used. Based on the types of modifications performed, the current study can offer insightful information on the thermal performances of modified CFRP laminates.

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Section: Methodssupporting

confidence: 91%

Thermal Synergistic Effect on CFRP Laminates with Modified Fiber/Matrix Systems for Heat Transfer Applications

Aravind

Manu

Roy

et al. 2023

Macro Chemistry & Physics

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“…The RPV was assumed to be made of stainless steel 304, with a specific heat of 500 J/kg/K and a density of 8,000 kg/m 3 . The thermal conductivity was chosen to virtually linearly increase from 14.9 to 25.4 W/K/m over the 300-1000 K range [10]. During the steady-state calculation, the density was reduced to 800 kg/m 3 to accelerate convergence without changing the final solution.…”

Section: Materials Propertiesmentioning

confidence: 99%

“…uses Griffin to solve an eigenvalue problem and provides the power density to the thermal model, which is composed of three sub-models: the full-core heat transfer model, the pin-level heat transfer model, and the thermal-hydraulics model, described in Section 3.2.3, 3.2.4, and 3.2.2, respectively. They are solved by MOOSE modules10 , BISON and RELAP-7, respectively, in a pseudo-transient mode. This means that a transient calculation is performed until the solution stops changing significantly, currently at around 5 × 10 6 s of simulation.…”

mentioning

confidence: 99%

FY22 Status Report on the ART-GCR CMVB and CNWG International Collaborations

Labouré

Lindell

Ortensi

et al. 2022

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This work presents the numerical model for the high temperature test reactor (HTTR) loss of forced cooling (LOFC) experiment with the INL codes Griffin, BISON, and RELAP-7, based on the Multiphysics Object-Oriented Simulation Environment (MOOSE) framework. Promising results were obtained, with the overall behavior of the reactor successfully captured. Changes in the heat transfer coupling, as compared to the fiscal year (FY)-21 model, enabled drastic improvement of the steady-state solution, both in terms of computational time and global energy discrepancies. The former was reduced by a factor of roughly 60, whereas the latter decreased from 11% to less than 2%. Furthermore, the discrepancy in the steady-state multiplication factor was improved from +2,300 and +2,900 pcm (for the 30 and 9 MW cases) to -700 and +1,200 pcm, respectively, and now falls well within the large measurement uncertainties stemming from graphite impurities. Validation of the Monte Carlo model used to generate cross sections was also performed against available measurements. Though significant, the discrepancies remain acceptable overall in light of the large uncertainty.Numerous improvements are still needed to better compare with the experiment involving the 9 MW case and to instill greater confidence in the model's ability to accurately predict the 30 MW behavior. Specifically, the power levels predicted by the 9 MW transient simulation following re-criticality remain low, pointing to an underestimation of the passive cooling of the core. A key aspect of future work will be to better understand the flow pattern during the LOFC event, particularly to determine if natural or forced convection is occurring inside the reactor pressure vessel (RPV). More generally, additional validation data would be immensely useful for further enhancing the numerical model and better matching the experiments. In addition, a more sophisticated thermal-hydraulics model that simulates all the channels as a single system model should be considered to take into account the rest of the primary loop. Finally, even if the results are iv in better agreement with the experiments, sensitivity analysis and uncertainty quantification will be necessary to evaluate the model uncertainty. v

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“…Therefore, many valuable techniques have been proposed to transform it into a relatively well-posed problem. Traditional algorithms used in the field of IHCPs include sequential function specification method (SFSM) [3], iterative techniques [4,5,6,7,8], Tikhonov regularization [9,10], singular value decomposition with model-reduction [11], digital filter method [1,12,13], genetic algorithm [14,15], Bayesian framework [16], neural networks [17,18,19,20,21,22,23] and so on.…”

Section: Introductionmentioning

confidence: 99%

Predicting Surface Heat Flux on Complex Systems via Conv-LSTM

Wang¹,

Wang²,

Ren³

2021

Preprint

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Existing algorithms with iterations as the principle for 3D inverse heat conduction problems (IHCPs) are usually time-consuming. With the recent advancements in deep learning techniques, it is possible to apply the neural network to compute IHCPs. In this paper, a new framework based on Convolutional-LSTM is introduced to predict the transient heat flux via measured temperature. The inverse heat conduction models concerned in this work have 3D complex structures with non-linear boundary conditions and thermophysical parameters. In order to reach high precision, a forward solver based on the finite element method is utilized to generate sufficient data for training. The fully trained framework can provide accurate predictions efficiently once the measured temperature and models are acquired. It is believed that the proposed framework offers a new pattern for real-time heat flux inversion.

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Nonlinear inverse heat conduction: Digitally filtered space marching with phase-plane and cross-correlation analyses

Cited by 13 publications

References 30 publications

Thermal Synergistic Effect on CFRP Laminates with Modified Fiber/Matrix Systems for Heat Transfer Applications

Thermal Synergistic Effect on CFRP Laminates with Modified Fiber/Matrix Systems for Heat Transfer Applications

FY22 Status Report on the ART-GCR CMVB and CNWG International Collaborations

Predicting Surface Heat Flux on Complex Systems via Conv-LSTM

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