An estimate of ground heat flux for a seasonal snow cover

An estimation of ground heat flux for two locations has been done using temperature gradient method. Effective media approach has been adopted for predicting the effective thermal conductivity of ground. For comparison, in situ measurement of effective thermal conductivity of ground has also been done by thermal probe method. The measured values of thermal conductivity are in agreement with the calculated values. The estimated values of ground heat flux have been used to evaluate the melt rate at ground-snow interface.

Soft Computing – A Way Ahead to Recover Heat Flux for Short Duration Experiments

The present investigations provide a pathway for implementation of soft computing based Adaptive Neuro-Fuzzy Inference System (ANFIS) technique for prediction of surface heat flux from short duration temperature measurement in shock tubes or shock tunnels. Computational modeling of a co-axial thermal probe is carried out to get the necessary temperature-time histories for different temporal variations of applied heat loads. Different possible inputs are assessed while defining the most suitable ANFIS structure for the recovery of step or ramp heat loads. This proposition is then tested for recovery of heat flux in a given range or of given time history. In each case, the uncertainty band is found to be in the acceptable range. The final assessment of this novel methodology is performed for recovery of heat flux signal from temperature measurement in a shock tube-based experiment. An in-house fabricated fast response coaxial thermal probe (CTP), prepared from chromel (3.25 mm diameter and 10 mm length) and constantan (0.91mm diameter and 15 mm length) is employed for these experiments. The surface heat flux recovered from the experimental signal using ANFIS is seen to have excellent agreement with the conventional analytical method in terms of both trend and magnitude, within an uncertainty band of ± 2%. Therefore present investigations advocate the use of soft computing technique for heat flux recovery in a short duration temperature measurement due to its accuracy of prediction, lesser complexities in mathematical modeling, and being less computationally intensive.

Soil thermal conductivity dataset

<p>Soil thermal conductivity (STC) is required parameter for coupled water and heat transport for land surface models. However, unlike soil hydraulic properties, no global dataset is available for STC. The objective of this study was to collate literature data and to take new measurements in order to establish a big STC dataset that would facilitate the evaluation and development of STC models. We collected over 8000 STC measurements made on over 400 soil types around the world following rigid filtering criteria and processes. All the STC data in the dataset were based on transient-heat-flow methods (e.g., non-steady-state method, line-heat source, needle probe, thermal probe, dual and single probe heat pulse method, thermo-time domain reflectometry). Each soil contains at least five water contents in addition to known soil physical properties such as texture and bulk density. This presentation will give a brief introduction about the STC dataset as well as call for contributions to it.</p>