Refine
Year of publication
Document Type
- Article (23)
- Conference Proceeding (2)
- Part of a Book (1)
Keywords
- Borehole heat exchanger (1)
- Finite differences (1)
- Heat transport (1)
We present an effective finite difference formulation for implementing and modeling multiple borehole heat exchangers (BHE) in the general 3-D coupled heat and flow transport code SHEMAT. The BHE with arbitrary length can be either coaxial or double U-shaped. It is particularly suitable for modeling deep BHEs which contain varying pipe diameters and materials.
Usually, in numerical simulations, a fine discretization of the BHE assemblage is required, due to the large geometric aspect ratios involved. This yields large models and long simulation times. The approach avoids this problem by considering heat transport between fluid and the soil through pipes and grout via thermal resistances. Therefore, the simulation time can be significantly reduced.
The coupling with SHEMAT is realized by introducing an effective heat generation. Due to this connection, it is possible to consider heterogeneous geological models, as well as the influence of groundwater flow. This is particularly interesting when studying the long term behavior of a single BHE or a BHE field. Heating and cooling loads can enter the model with an arbitrary interval, e.g. from hourly to monthly values. When dealing with large BHE fields, computing times can be further significantly reduced by focusing on the temperature field around the BHEs, without explicitly modeling inlet and outlet temperatures. This allows to determine the possible migration of cold and warm plumes due to groundwater flow, which is of particular importance in urban areas with a high BHE installation density.
The model is validated against the existing BHE modeling codes EWS and EED. A comparison with monitoring data from a deep BHE in Switzerland shows a good agreement. Synthetic examples demonstrate the field of application of this model.
Modeling contribution to risk assessment of thermal production power for geothermal reservoirs
(2013)
Numerische Simulation des Gefrierprozesses bei der Baugrundvereisung im durchströmten Untergrund
(2008)
We present first results from a newly developed monitoring station for a closed loop geothermal heat pump test installation at our campus, consisting of helix coils and plate heat exchangers, as well as an ice-store system. There are more than 40 temperature sensors and several soil moisture content sensors distributed around the system, allowing a detailed monitoring under different operating conditions.In the view of the modern development of renewable energies along with the newly concepts known as Internet of Things and Industry 4.0 (high-tech strategy from the German government), we created a user-friendly web application, which will connect the things (sensors) with the open network (www). Besides other advantages, this allows a continuous remote monitoring of the data from the numerous sensors at an arbitrary sampling rate.Based on the recorded data, we will also present first results from numerical simulations, taking into account all relevant heat transport processes.The aim is to improve the understanding of these processes and their influence on the thermal behavior of shallow geothermal systems in the unsaturated zone. This will in turn facilitate the prediction of the performance of these systems and therefore yield an improvement in their dimensioning when designing a specific shallow geothermal installation.
Quantifying and minimizing uncertainty is vital for simulating technically and economically successful geothermal reservoirs. To this end, we apply a stochastic modelling sequence, a Monte Carlo study, based on (i) creating an ensemble of possible realizations of a reservoir model, (ii) forward simulation of fluid flow and heat transport, and (iii) constraining post-processing using observed state variables. To generate the ensemble, we use the stochastic algorithm of Sequential Gaussian Simulation and test its potential fitting rock properties, such as thermal conductivity and permeability, of a synthetic reference model and—performing a corresponding forward simulation—state variables such as temperature. The ensemble yields probability distributions of rock properties and state variables at any location inside the reservoir. In addition, we perform a constraining post-processing in order to minimize the uncertainty of the obtained distributions by conditioning the ensemble to observed state variables, in this case temperature. This constraining post-processing works particularly well on systems dominated by fluid flow. The stochastic modelling sequence is applied to a large, steady-state 3-D heat flow model of a reservoir in The Hague, Netherlands. The spatial thermal conductivity distribution is simulated stochastically based on available logging data. Errors of bottom-hole temperatures provide thresholds for the constraining technique performed afterwards. This reduce the temperature uncertainty for the proposed target location significantly from 25 to 12 K (full distribution width) in a depth of 2300 m. Assuming a Gaussian shape of the temperature distribution, the standard deviation is 1.8 K. To allow a more comprehensive approach to quantify uncertainty, we also implement the stochastic simulation of boundary conditions and demonstrate this for the basal specific heat flow in the reservoir of The Hague. As expected, this results in a larger distribution width and hence, a larger, but more realistic uncertainty estimate. However, applying the constraining post-processing the uncertainty is again reduced to the level of the post-processing without stochastic boundary simulation. Thus, constraining post-processing is a suitable tool for reducing uncertainty estimates by observed state variables.