An application of computational fluid dynamics to predict shock loss factors at raise junctions in underground mine ventilation systems

Shock loss occurs when there is a change in the area of a drift or airflow direction. These shock losses accumulate throughout the ventilation system and will increase the mine resistance as underground development deepens or becomes more complex. Hence, shock losses should be carefully considered when establishing the resistance through a series of branches as they can be a significant contributor to the estimated mine resistance. Using computer software to simulate a ventilation system is common but applying reasonable shock loss factors in the software is not a simple or direct process. Determining shock loss values includes the consideration of historical measurements, application of factors and formulas from literature based on the geometry and complexity of junctions, and the geometry variation at different locations. For example, the shock loss at the bottom of a ventilation raise can consist of a 90° bend and a sudden area change. This paper presents an approach for estimating shock loss factors near raise junctions using computational fluid dynamics (CFD). Current and future work will also be compared with field measurements. Results from this approach are compared with software defaults as well as classical techniques in published literature and standards. Introduction The ventilation system is one of the most critical systems in underground mines. It provides fresh air to personnel and clears contaminants out of mines. Ventilation network models are popular in operating mines to show airflow distributions and calculate pressure losses in mine ventilation systems. Without access to additional information, a ventilation network model would rely on shock factors preset in ventilation network software or hand-calculated shock factors based on textbook equations. However, the presets in the software and the hand-calculated shock factors are predominantly based on theoretical calculations and/or empirical data derived from much smaller-scale (such as pipes) and much simpler (such as a small round pipe connected to another small round pipe) geometries compared to that of an underground mine (such as a small round raise connected to a large arched drift where the shock loss is a combined effect of a bend and an area change). Hence, applying these presets and hand-calculated shock factors to ventilation network models may not generate accurate results. This paper presents a proposed method for isolating the shock loss from overall pressure loss data in a CFD analysis. This paper also presents two experiments conducted in two operating underground hard-rock mines in Canada. Three-dimensional (3D) CFD models and network models were separately created to represent the geometry of the experiment locations, and results from the CFD models and the network models are compared with field measurements. Methods, results and discussion It is necessary to isolate shock losses from the overall (between inlet and outlet) total pressure loss. This can be demonstrated as shown in Fig. 1 to estimate a sudden expansion shock factor. If the geometry is not separated into different models, the upstream and downstream friction losses must be subtracted from the overall pressure loss. The shock loss associated with the expansion is determined by dPshock = dP3 – dP1 – dP2, where dPshock is the pressure drop caused by shock loss, dP3 is the total pressure difference between the inlet and outlet in model 3 in Fig. 1, and dP1 and dP2 are the total pressure difference between the inlet and outlet in models 1 and 2, respectively. Four model geometries were developed based on typical interface dimensions between drop raises and transfer drifts. Figure 2 shows the four model geometries with dimensions. Both 3D CFD models and ventilation network models were created for the four geometries. Velocity magnitude contours from the four CFD models were plotted (see full paper). The velocity magnitude contour difference is due to the increased mass flow/momentum resulting from 20 m/s inlet velocity in a 1.524-m diameter raise versus a 3.048-m diameter raise. Four network models (with different shock factor presets) were created for each geometry using a ventilation network solver, Ventsim. The results were compared, and it is apparent that the shock factor and loss results from the CFD models and hand calculations are much greater than those from the network models, especially for models 1 and 2. The accuracy of the results is not verified until compared with experimental data. Hence, two experiments were conducted at two operating hard-rock mines in Canada. Data collected from the experiments were used to verify the accuracy of the CFD model and network model. Experiment 1 was conducted in a fresh air transfer drift. Unfortunately, the hose hung up within the first approximately 60.96 m (200 ft), rendering the efforts unsuccessful. Experiment 2 was conducted on two levels, as shown in Fig. 3. One CFD model was created for experiment 2 to investigate the overall shock loss that occurred in the ventilation system. The results are compared and shown in Fig. 4. It is apparent that the shock factor and loss results from the CFD model and hand calculations are closer to those from experiment 2, raise 2 and much greater than those from the network models. Conclusions and future work In this paper, an application of CFD methods to predict shock loss factors at raise junctions in underground mine ventilation systems is presented. The CFD modeling approach makes it possible to determine junction shock losses for situations that are not readily available in existing literature or reference material. Four different geometries were created to demonstrate the CFD modeling technique. Two field experiments were conducted at two different mine sites to verify the accuracy of the CFD results. Results from the CFD model of experiment 1 do not agree well with the field data. Efforts to characterize internal raise losses using trailing hose measurements were unsuccessful in experiment 1 due to incomplete data resulting from the hose hanging up in the raise. However, experiment 2 was successful. There is a good agreement on the total pressure loss between the results from the CFD model and the field data for experiment 2 despite more complexity in the geometry of experiment 2. The ultimate intention of this work is to develop a shock factor database using the proposed CFD modeling technique that is applicable to underground mines worldwide. This will help ventilation engineers and specialists design accurate and reliable ventilation network models, increasing the efficiency of ventilation systems and reducing their carbon footprint. The network model shock factor presets should be used with great caution and should not be applied in all the junctions in a ventilation network model to avoid underestimating the shock losses, potentially leading to incorrect decisions (for example, in fan selection or raise sizing). The following factors were found to contribute to the total pressure losses: the overbreak of the raises during construction; the connection type (for example, sharp 90° turn, fillet) between the raise and horizontal drift; accuracy of the instruments (for example, vane anemometers, manometers); methods of air velocity measurement (for example, fixed points, traverse); and wall roughness height. Another finding was that the Ansys CFX and Fluent solvers could generate different results based on a preliminary study on the total pressure losses obtained with the same mesh and near-identical solver settings (understanding that some settings are fundamentally different). References A full list of references is in the full paper.

An application of computational fluid dynamics to predict shock loss factors at raise junctions in underground mine ventilation systems

Authored by: H. Zhang, L. Falk and C. Allen

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