Session F14: Convection and Buoyancy-Driven Flows: Heat Transfer and Forced Convection (3:55pm - 4:40pm CST)

Surface Cooling Effects of a Counter Rotating Vortex Pair Induced by Vortex Generators

An experimental investigation was carried out to study the effects of trapezoidal vortex generators (VGs) on air flow and the corresponding surface heat transfer at a Reynolds number of 4800. The flow induced by VGs was characterized using particle image velocimetry (PIV), while infrared (IR) thermography was used simultaneously to determine surface temperature. Measurements were performed in the near-wake region, where an induced counter-rotating vortex pair (CVP) was dominant. Results revealed that VGs led to enhanced local heat transfer due to the interactions of the induced flow structures with the heated surface. The enhanced heat transfer process occurred in the vicinity of the CVP up to a downwind distance of 1.5 VG height. This phenomenon was associated to the accelerated flow in the streamwise direction induced by the CVP. Furthermore, the role of geometric factors such as inclination angle, taper angle, and spanwise spacing of VGs had a direct effect on the induced flow structures and the resulting surface heat transfer enhancement.   

Effects of height configuration on heat shedding and flow characteristics in a model solar PV farm

Large scale solar farms supply an increasing amount of the worlds electricity supply. However, high operation temperatures can strongly reduce efficiency and panel lifetime, negatively affecting the levelized cost of energy. The convective heat transfer coefficient for a utility-scale solar farm with varied PV panel height configurations is studied with combined thermal and particle-image-velocimetry measurements in a scaled wind tunnel experiment. The measurements show that height configuration plays a major role in both mid-array flow behavior and array heat shedding. Subsequent flow analysis shows the complex relationship between the array and the passing wind contributes to the heat transfer coefficient.   

Parameterization of Turbulence, Heat Transfer and Spatial Characterization for Utility-Scale Solar Farms

Solar panels suffer efficiency loss from high panel temperatures with extended sun exposure and internal heating. Studies show convective mitigation is achieved with induced turbulence and altered array configuration. However, current understanding oa array flow behavior lacks quantification to fully inform solar farm design. This work explores parametrization of flow dynamics and thermal effects in solar arrays toward predictive model development related to spatial variation and experimental data. Wind tunnel experiments were performed on 4x10 panel solar arrays, varying unit height and angle, subjected to imposed panel heating ($\Phi_q=450$-1050 W/m$^2$) and inflow conditions ($Re_L=2.7*10^3$-$12*10^3$; $TI=$11\%-18\%). Thermal data were obtained via panel-mounted thermocouples, and flow behavior was captured with particle image velocimetry (PIV). Inspired by forest canopy research, variations of $K$-$\varepsilon$ modeling are applied to flow parameters, finding scale-dependent closure quantities tailored to solar farms. Results are related to spatial heterogeneity via two-dimensional (2D), lacunarity-based analysis projected in 3D space. Continued work compares spatial distribution to configuration-dependent convective behavior, determining relative effects on panel cooling.   

The influence of streamwise row spacing on convective heat transfer in solar photovoltaic arrays

When the temperature of solar photovoltaic (PV) modules exceeds 25$^{\mathrm{o}}$C, efficiency drops and module degradation accelerates. Thus, the PV community aims to reduce module operating temperatures. However, existing cooling systems require additional power or assembly, which precludes their adoption in utility-scale PV plants. Previous studies of solar farms have illustrated that incoming flow characteristics and geometric parameters can substantially impact the convective heat transfer coefficient, $h$. However, the impact of row spacing within realistic, three-dimensional solar farms on convective cooling has not yet been studied. Here, six solar farm arrangements are developed with varying streamwise row spacing. The spatial heterogeneity of these cases is characterized by a novel application of lacunarity. To represent the fluid-structure interaction and heat transfer, high-resolution large-eddy simulations are performed with the Uintah platform. A control volume analysis is used to compute $h$ and study the influence of row spacing on the convective heat transfer. Row-specific $h$ values are calculated to explore the spatial variation of cooling and the relationship with the velocity and thermal boundary layers generated by the solar farm.   

Conjugate heat transfer of rotating axisymmetric bodies

The flow physics and conjugate heat transfer of axisymmetric rotating bodies are studied using high fidelity CFD simulations coupled to a kernel-based model of the heat equation for the structure's internal temperature. The structure is heated from one of its bases and exposed to a free-flowing fluid. This model is representative of heat fins used in many practical engineering systems. The geometry, rate of rotation, and structure's material properties have a significant effect on the temperature distribution and heat transfer coefficient. We explore how these parameters at different Reynolds numbers affect the system's thermal performance and discuss the connection between flow and thermal instabilities and the rotational speed of the body.   

Machine learning modeling of convective wall heat transfer in turbulent wall fire simulations

Accurate modeling of convective wall heat flux is vital for the predictive modeling of turbulent wall fire problems. High grid-resolution near the wall is required to produce accurate modeling of the heat flux on the wall. This requirement is not feasible in engineering modeling studies of wall fires. Wall models are thus needed to reconstruct wall heat flux so that the grid requirement is not so restrictive. In this work, we examine the feasibility and potential of using machine learning to reconstruct the convective wall heat flux in wall fire modeling. High-fidelity large-eddy simulations of a turbulent fire propagating along a vertical wall are conducted to produce the training data for machine learning. A temperature gradient correction factor is introduced to compensate for the loss of accuracy of temperature gradient when discretized on a coarse grid. The random forest machine learning model is used to train the model for the correction factor. The performance of the trained model is assessed in a priori analysis for the wall fire modeling. Good performance is observed. The potential of using this modeling approach in engineering wall fire modeling studies is discussed.   

Numerical investigation of turbulent heat transfer enhancement via modified internal tube profiles

\section*{Abstract} The convective heat transfer of a highly-turbulent single phase gas flow is numerically studied inside three pipes equipped with patterned surface textures. The Reynolds and Prantl numbers of the flow are 90,000 and 0.836, respectively. The selected enhancement methods are ellipsoidal inward-facing dimples, inserted coil and spiral corrugations. Wall adapting local eddy viscosity SGS turbulence (WALE) is used, which is a subgrid scale model based on the square of the velocity gradient tensor that accounts for behaviour near the wall. The incompressible mass and momentum equations are solved on three-dimensional grids with the finite volume method using second-order temporal and spatial schemes. The averaged friction factor and Nusselt number of the flow are calculated for the three enhancement techniques, showing that the heat transfer enhancement of the dimpled tube is considerably higher than the other two. The reasons for this observation are discussed along with the effects of the surface texture on velocity and temperature fields, turbulence kinetic energy and Reynolds stresses, and the transient temperature-velocity interactions near the wall.