Volcanic Soils: Inverse Modeling of Thermal Conductivity Data

Tarnawski, V. R.; Tsuchiya, Fujio; Coppa, P.; Bovesecchi, G.

doi:10.1007/s10765-018-2480-2

Cited by 35 publications

(7 citation statements)

References 21 publications

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“…Our data show that the thermal conductivity of dry unlithified volcanic material varies from ~0.2-0.3 1). These values are in broad agreement with previously published studies, who report thermal conductivities for dry unlithified volcanic material of 0.15−0.20 W•m −1 •K −1 (McCombie et al, 2017;Kuznetsova, 2017;Tarnawski et al, 2019;Heap et al, 2022a).…”

Section: Influence Of Alteration On the Thermal Properties Of Dry Vol...supporting

confidence: 93%

“…The thermal conductivity, thermal diffusivity, and specific heat capacity of dry unlithified volcanic deposits from the lava dome at La Soufrière de Guadeloupe (Eastern Caribbean) were measured to be 0.15−0.20 W•m −1 •K −1 , 0.17−0.24 mm 2 •s −1 , and 0.68−1.04 J•kg −1 •K −1 , respectively (Heap et al, 2022a). The average dry and watersaturated thermal conductivity of volcanic soils from Hokkaido (Japan) was measured to be 0.14 and 0.52 (Tarnawski et al, 2019). Finally, dry rhyolitic and basaltic ash from Kamchatka (Russia) was found to have a thermal conductivity of ~0.…”

Section: Introductionmentioning

confidence: 99%

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The thermal conductivity of unlithified granular volcanic materials: The influence of hydrothermal alteration and degree of water saturation

Heap

Wadsworth

Jessop

2023

Journal of Volcanology and Geothermal Research

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Section: Influence Of Alteration On the Thermal Properties Of Dry Vol...supporting

confidence: 93%

Section: Introductionmentioning

confidence: 99%

The thermal conductivity of unlithified granular volcanic materials: The influence of hydrothermal alteration and degree of water saturation

Heap

Wadsworth

Jessop

2023

Journal of Volcanology and Geothermal Research

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“…For training and validation of the ANN, a total of 384 data points are collected from literature to establish a generic dataset [17,6,18,19]. Fig.…”

Section: Methodsmentioning

confidence: 99%

Effective thermal conductivity of unsaturated soils based on deep learning algorithm

et al. 2020

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Soil thermal conductivity plays a critical role in the design of geo-structures and energy transportation systems. Effective thermal conductivity (ETC) of soil depends primarily on the degree of saturation, porosity and mineralogical composition. These controlling parameters have nonlinear dependencies, thus making prediction a nontrivial task. In this study, an artificial neural network (ANN) model is developed based on the deep learning (DL) algorithm to predict the effective thermal conductivity of unsaturated soil. A large dataset is constructed including porosity, degree of saturation and quartz content from literature to train and validate the developed model. The model is constructed with a different number of hidden layers and neurons in each hidden layer. The standard errors for training and testing are calculated for each variation of hidden layers and neurons. The network with the least error is adopted for prediction. Two sand types independent of training and validation data reported in the literature are considered for prediction of the ETC. Five simulation runs are performed for each sand, and the computed results are plotted against the reported experimental results. The results conclude that the developed ANN model provides an efficient, easy and straightforward way to predict soil thermal conductivity with reasonable accuracy.

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“…For this reason, estimating soil thermal conductivity from general soil properties has attracted increasing attention. Several thermal conductivity models have been developed in the past [5][6][7][8][9][10][11][12][13][14][15][16][17][18][19][20]. Of these, six are based on semi-physical principles [6,9,10,14,16,20], one is based on a statistical-physical principle [11] and the remaining eight are based on empirical principles [5,7,8,12,13,15,17,19].…”

Section: Introductionmentioning

confidence: 99%

Estimating Soil Thermal Conductivity by Weighted Average Models with Soil Solids as a Continuous Medium

et al. 2022

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In an attempt to further simplify and to refine the modeling of soil thermal conductivity (λ), two novel weighted average models (WAMs) were developed in which soil solids represent the continuous phase. In the first model, WAMs-1, the continuous phase consists of two distinctive minerals groups (quartz and compounded remaining soil minerals), while air and water are treated as dispersed components. In the second model, WAMs-2, all soil minerals are compounded and considered the continuous phase, while air and water are dispersed components. In contrast to de Vries’ original WAM with two continuous phases (soil air or soil water), the proposed models are very simple due to the following assumptions: using soil solids as a single continuous medium lead to eliminating the discontinuity of thermal conductivity when switching between soil air and soil water as continuous medium, and using the thermal conductivity of dry air simplifies a complex expression for an apparent thermal conductivity of humid soil air. Both models were successfully calibrated and validated using 39 Canadian Field Soil database and 3 Standard Sands and were successfully applied to 10 Chinese soils.

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Volcanic Soils: Inverse Modeling of Thermal Conductivity Data

Cited by 35 publications

References 21 publications

The thermal conductivity of unlithified granular volcanic materials: The influence of hydrothermal alteration and degree of water saturation

The thermal conductivity of unlithified granular volcanic materials: The influence of hydrothermal alteration and degree of water saturation

Effective thermal conductivity of unsaturated soils based on deep learning algorithm

Estimating Soil Thermal Conductivity by Weighted Average Models with Soil Solids as a Continuous Medium

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