Heat Transfer Computational Fluid Dynamics Research Articles

Computational Fluid Dynamics Heat Transfer Analysis of Double Pipe Heat Exchanger and Flow Characteristics Using Nanofluid TiO2 with Water

A device called a heat exchanger is used to exchange heat transfer between two fluids with different temperatures. Because of its durability and ability to handle high-pressure application, the concentric double pipe heat exchangers are widely utilized for numerous industrial applications. To conserve pumping power energy, many researchers were involved in study of the nanoparticles to be embedded in the fluid, which will enrich the fluid thermal conductivity and surface area. This article demonstrates the flow characteristics and convective heat transfer of nanofluids containing 0.2, 0.4 and 0.6 of vol% TiO2 nanoparticles dispersed in water under turbulent conditions, which mainly can be used for cooling nuclear reactors applications. Reynolds numbers varying from 4000 to 18,000 are examined numerically. The convective heat transfer coefficient results of the nanofluid agree well against experimental data, which are slightly more than that of base water at 1.94%. The results of the numerical model showed that the convective heat transfer coefficient of nanofluids will increase when the Reynolds and volume fraction increases. By increasing the temperature of the annular hot water, the heat transfer rate will increase, showing no major impact to the convective heat transfer coefficient of nanofluids. A generalised solution predicting the convective heat transfer coefficient for extensive nanoparticle materials is proposed. The conclusion of the empirical equation is tested among published data and the results are highly congruent, confirming the strength of the gamma equation.

Thermal full-field prediction of an air-cooled data center using a novel multi-scale approach based on POD and CFD coupling

A full-knowledge of the temperature distribution in a data center is crucial for the design and operation to avoid thermal failure. However, it is a great challenge to conduct full-field simulations using the computational fluid dynamics/heat transfer (CFD/HT) method due to the unacceptable computational cost caused by the multi-scale structure of data centers. In this paper, a novel multi-scale model based on POD and CFD coupling is proposed to predict the thermal and fluid characteristics in data centers from room scale to chip scale. The temperature and flow fields in an air-cooled data center are predicted and analyzed under the scales of room, server, IGBT and chip. Based on the CFD results of Room-Server level, 16 sets of design cases’ temperature fields for Server-IGBT level are compared using the POD and CFD methods, which shows that the maximum average absolute deviation and average relative deviation are 0.004 °C and 0.59%, respectively. Subsequently, three sets of off-design cases for Server-IGBT level and IGBT-Chip level are predicted using the aforementioned methods, yielding the maximum relative deviation 7.21% and 0.98%, respectively. The salient outcome is the remarkable 175-fold reduction in CPU processing time achieved with the POD model compared to traditional CFD simulations.

Optimization of rifled tube by parametric changes using CFD for goodness factor enhancement

AbstractIn this study, extensive research was conducted using computational fluid dynamics (CFD) ANSYS®–FLUENT, with the k–ε turbulence heat transfer module to optimize the rifled‐tube heat exchanger through parametric modifications for heat transfer and Goodness factor enhancement. This is based on predicting the performance of various heat transfer surfaces such as rectangular and two opposite right‐angled triangular ribs. The objective of this research was to enhance the heat transfer efficiency of a rifled tube with rectangular ribs, a height of 0.775 mm, and helix angles of 30° and 58°. The research findings demonstrated that the heat transfer rate improved owing to the establishment of a secondary helical or swirl flow near the wall region, leading to an enhanced heat transfer. The modification of the rib geometry from rectangular to two opposite right‐angled triangles increased the heat transfer by 17%, whereas the pressure drop remained relatively constant at 0.62 mm of rib height. This benefit is directly translated into heat transfer without any change in pressure drop. The values of the friction factor (f), Nusselt number (Nu), and goodness factor are appropriate for the new geometry in relation to the reference geometry. Rifled‐tube designers can use CFD heat transfer analysis to optimize the design, reduce the need for physical prototypes, and test similar applications and rib configurations.

Data driven modeling advancements for air temperature predictions in data centers

While data center cooling energy usage optimization studies have been performed through computational fluid dynamics/heat transfer (CFD/HT), and heuristic methods, data driven modeling techniques are now also being used for these applications. This paper investigates the air temperature prediction capabilities of static artificial neural network (ANN), Gaussian progress regression (GPR), support vector regression (SVR), relevance vector machine (RVM), linear regression, and regression trees; and transient long-short term memory (LSTM), and nonlinear autoregressive neural network with external input (NARX)) data driven modeling frameworks. The static study compared various models and found that GPR provided the best results (average error of 0.56 °C), closely followed by the ANN and SVR (average error of 0.60 °C and 0.68 °C respectively) methods. The transient study compared models based on an experimental data set and found that NARX outperforms LSTM for normal operations (0.83 °C and 1.07 °C average error respectively), and that data driven models are able to provide relatively good predictions, even if the input variables are slightly outside the training domain.

Machine learning approach to predict heat transfer and fluid flow characteristics of integrated pin fin-metal foam heat sink

Integrated pin fin-metal foam (PF-MF) heat sink offers enhanced thermal performance owing to the higher surface area and flow mixing. By realizing the strong influence of geometrical parameters of PFs, morphological properties of MF, and coolant hydrodynamics on the thermal-hydraulic performance of the PF-MF heat sink, this article presents a machine learning (ML) surrogated multiparameter and multi-objective optimization framework to determine optimal PF-MF heat sink layouts within the laminar flow regime to enhance the thermal performance of the heat sink with a minimal pumping power penalty. Four supervised regression ML models, namely random forest, extreme gradient boosting, support vector regressor, and artificial neural networks (ANN), were considered while searching for the best surrogate. Four geometrical parameters of PFs, such as PFs diameter (D), height (h), longitudinal pitch, transverse pitch, two morphological properties of MF, i.e., pore density porosity and coolant flow condition, i.e., Reynolds number have been considered as design parameters. The heat transfer and fluid flow performance of the heat sink are evaluated using nondimensional Nusselt number and friction factor A high-fidelity 3-D computational fluid dynamics/heat transfer (CFD/HT) model has been developed to evaluate the thermal-hydraulic performance of all PF-MF heat sink layouts. Furthermore, utilizing the numerical data, ML models have been trained and fine-tuned to accurately predict the overall performance of the PF-MF heat sink. Upon testing the prediction and interpolation capability of all considered ML models, ANN outperforms other models, hence, ANN model has been coupled with genetic algorithm (GA) optimizer to identify three different optimized PF-MF heat sink layouts at three different Finally, the result shows that the ANN surrogated GA-optimized PF-MF heat sink layout can improve the overall thermal-hydraulic performance by approximately 19–35% compared to the best PF-MF heat sink layout among the used input feature space for ML model.

Local hotspot thermal management using metal foam integrated heat sink

Single phase liquid-cooled parallel micro/mini-channel heat sinks are commonly used for electronic cooling because of their superior thermal–hydraulic performance. However, heterogeneous integration of electronic chips, especially in 2.5D and 3D integrated circuits (ICs), results in highly non-uniform heat flux distribution and consequently generates localized hotspots. Conventional micro/mini-channel heat sinks often fail to suffice concentrated local hot spots due to the insufficient heat transfer area and local heat transfer coefficient. Therefore, in this paper, three novel water-cooled aluminum (Al) metal foam (MF) integrated compound heat sink layouts, namely solid fin-MF, rectangular fin-MF, and MF heat sink, have been proposed to mitigate local hotspot resulting from non-uniform heat generation. The main concept of the proposed compound heat sinks is to deploy MF in the hotspot region to take advantage of the superior thermal performance of metal foam with minimal pumping power. 3D computational fluid dynamics/heat transfer (CFD/HT) simulations have been performed to quantify and compare the effectiveness of the proposed compound heat sinks with conventional plate-fin heat sink over a wide coolant flow rates/pressure difference ΔP range within the laminar flow regime. Numerical results showed that the solid fin-MF layout provides the best thermal–hydraulic performance compared to all other layouts and offers the highest temperature uniformity, especially at a higher coolant flow rate. Moreover, for the solid fin-MF layout, an optimal porosity and pore density of MF has been identified as 0.9 and 5 PPI (pores per inch), respectively. Finally, at ΔP of 250 Pa and 400 Pa, the optimized solid fin-MF heat sink layout successfully dissipated ∼22.6% and ∼33.4% higher hotspot heat flux compared to the conventional solid fin layout with ∼21% and ∼18.3% lower pumping power penalty while maintaining the peak junction temperature below 85°C.

Investigation of thermal-hydraulic performance of metal-foam heat sink using machine learning approach

Metal foam heat sinks (MFHSs) are often used for electronic cooling because of their high specific heat transfer surface area, fluid mixing capability, and lightweight. Thermal-hydraulic performance of MFHS is known to be related to the morphological parameters of the metal foam (MF), physical dimensions of the heat sink, and coolant flow condition. Moreover, the highly random and complex geometrical nature of MFHS makes full-scale numerical simulation and experimental analysis expensive. In this paper, we have utilized machine learning (ML) methods to predict the thermal-hydraulic performance of MFHS, considering MF morphology, heat sink dimension, and coolant flow condition as input features. Considering local thermal non-equilibrium conditions for MFHS, a database of 1000 data points is generated utilizing a validated high-fidelity computational fluid dynamics/heat transfer (CFD/HT) model. The data sets consist of two non-dimensional descriptors, i.e., Nusselt number (Nu) and friction factor (f), along with the four input features such as MF morphological parameters, i.e., porosity and pore density, heat sink geometrical dimensions, i.e., hydraulic diameter (Dh) and flow condition, i.e., Reynolds number (Re). Five different ML-based regression models, namely k-nearest neighbor (KNN), random forest (RF), extreme gradient boosting (XGBoost), support vector regressor (SVR), and artificial neural networks (ANN), have been developed. Hyper-parameters of each ML model have been systematically optimized, and optimized model's performance have been compared using statistical score metrics and considering the initialization effect. Additionally, in order to demonstrate the robustness of the developed ML models, individual model performance has been evaluated by independent datasets. Results indicate that for testing datasets, all ML algorithms, except KNN, can predict the thermal-hydraulic performance of MFHS reasonably well, with a mean absolute percent error (MAPE) of about 4.59% and a 1.24 standard deviation (SD). However, SVR and ANN outperform other models for independent datasets with a MAPE of less than 3.1%.

A three-dimensional conjugate heat transfer model for methanol synthesis in a modular millireactor

In this work, a modular millireactor (MMR) is designed and modeled using the computational fluid dynamics (CFD) tool OpenFOAM. First, the method is validated against a conventional packed bed reactor (PBR) model (1D) with Aspen Plus. Next, the method is applied to study the effects of pressure (2–6 MPa) and temperature (483–533 K) on the performance of the MMR. Conjugate heat transfer (CHT) CFD results for the MMR are compared against a corresponding PBR at isothermal conditions. For the MMR, the methanol yield is shown to vary between 9–23 % within the studied parameter range. Overall, the MMR outperforms the PBR at conditions studied in this work. The maximum difference in methanol yield between MMR and the PBR is noted to be a factor of 1.71 at 533 K and 5 MPa. Such a large discrepancy advocates the usage of 3D CHT/CFD.

Computational fluid dynamics assessed changes of nasal airflow after inferior turbinate surgery

ObjectiveTo demonstrate how Computational Fluid Dynamics (CFD) simulations can reveal important airflow changes in the nasal cavities due to surgical interventions. Material and methodsThe steady inspiratory airflow of eight patients was studied pre- and postoperatively with heat transfer from the mucous membrane by performing CFD calculations to patient specific cone beam computed tomography (CBCT) images. Eight patients with the largest distance from pre- and postoperative mean changes in inferior turbinate volumetry and Visual Analogue Scale (VAS) results were selected. ResultsCalculated CFD heat transfer results from the anterior parts of the inferior turbinates, where surgical interventions were performed, decreased significantly. The heat transfer results were in line with VAS changes. ConclusionSurgical interventions reduced heat transfer in the operated parts of the inferior turbinates and were in line with VAS changes. CFD is an option in assessing patient well-being as a function of airflow parameters from mucous membrane with larger data sets. The limitations of the study were the small sample size and the preliminary nature of the study.

Flow-Assisted Evaporative Cooling for Electric Motor

This article examines a novel concept of flow-assisted latent heat-driven two-phase evaporative cooling (EC) confined in-between slot liner and active-winding of electric motor. Wicking microstructure-enhanced PDMS liner axially sucks coolant in the form of thin film between the PDMS liner and active-winding and eventually enables thin-film evaporation on the outer surface of the active-winding. Therefore, EC-based thermal management eliminates a major thermal resistance between the winding and the coolant and enhances the heat extraction from the winding without compromising the electromagnetic (EM) performance. Two-way coupled EM—computational fluid dynamics/heat transfer (CFD/HT) and EM—lumped parameter thermal network (LPTN) models have been developed to assess the electrothermal performance of the EC for steady and transient conditions. Taking a case study of a 125-kW jacket-cooled BMW i3 motor and dielectric coolant FC-84, EC is shown to be capable of handling a maximum steady-state rms current density of 26 A/mm <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> at an evaporative HT coefficient of 5000 W/ <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\text{m}^{2}\cdot \text {K}$ </tex-math></inline-formula> , which is about 78.7% higher compared to the traditional jacket cooling (JC). In the case of EC, a maximum steady-state and transient rms current density of 30 A/mm <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> (106.2% higher compared to the JC) and 40.8 A/mm <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> , respectively, have been realized by using high thermal conductivity epoxy (1.9 W/m.K) impregnation material. The thermal performance of the EC is also assessed and compared with JC over a dynamic drive cycle. Finally, a motorette testing has been performed to demonstrate the applicability of the proposed EC method and validate the developed modeling framework.

Experimental and CFD study on influence of viscosity on layer melt crystallization

• Layer melt crystallization of aqueous sucrose solutions with different viscosities. • Viscosity influence on freezing based on ice crystal yield and purity results. • CFD study on heat transfer inside melt crystallizer prior to ice crystallization. • Temperature distributions and thermal boundary layers of undercooled solutions. In the present work, the influence of solution viscosity on growth kinetics and purification efficiency in layer melt crystallization was investigated. Melt crystallization experiments were conducted for three different types of aqueous sucrose solution as they are ideal solutions and a relatively wide viscosity range can be investigated with a moderate change of freezing points. The aqueous 10 wt%, 23 wt%, and 30 wt% sucrose solutions have a dynamic viscosity value of 2.01 mPas, 4.74 mPas, and 7.21 mPas at their respective freezing points of −0.63 °C, −1.78 °C, and −2.64 °C. The solution temperature distribution was predicted by computational fluid dynamics (CFD) simulations run in COMSOL Multiphysics 5.6 software. Experimental results showed that a higher solution viscosity caused a higher crystal layer impurity and lower crystal yields in static layer melt crystallization. The cooling process of different solutions predicted by a CFD heat transfer study showed that the supersaturation region is wider for less concentrated solutions as cooling proceeds more rapidly. Hence, the temperature gradients obtained follow the boundary layer theory, i.e., the thinner the boundary layer, the faster the heat transfer.

LiDAR-based Computational Fluid Dynamics heat transfer models for bushfire conditions

Post-bushfire damage assessments show no significant reduction in property damage when compared with past bushfire events. Other than the severity of the attack and the behaviour of the bushfires, the architectural design of the houses, construction materials and, many other factors affect bushfire house ignition. These are generally ignored in the simulations using simplified models. An improved bushfire risk assessment can be conducted if the exact surrounding and the architectural design of the existing houses are considered. However, this is not widely practised due to associated high cost and time, which can be minimised by the adaptation of modern technologies in an efficient way. This study proposes a novel approach to assess the effect of bushfires on individual building structures (houses) in bushfire-prone areas, through heat transfer modelling. It utilises ground and airborne Light Detection and Ranging (LiDAR) point cloud to model individual building structures with all their complex features and the surroundings with good dimensional accuracy. These point cloud maps were used to develop heat transfer models by using Fire Dynamics Simulator (FDS) to investigate wind velocities, temperature profiles and pressure distributions during bushfires. A case study was conducted for a selected house (elevated building structure) using the proposed approach and the results were compared against a simplified cuboid model of the same house. The simulation results show that the proposed approach is capable of identifying the most vulnerable locations of the building structure compared to the simplified model, which emphasises its applicability in bushfire planning and damage mitigation.

Experimental and Computational Analysis of Additive Manufactured Augmented Backside Liner Cooling Surfaces for Use in Micro-Gas Turbines

Abstract Augmented backside cooling refers to the enhancement of the backside convection of a combustor liner using extended heat transfer surfaces to fully utilize the cooling air by maximizing the heat transfer to pumping ratio characteristic. Although film cooling has and still is widely used in the gas turbine industry, augmented backside cooling has been in development for decades now. The reason for this is to reduce the amount of air used for liner cooling and to also reduce the emissions caused by using film cooling in the primary zones. In the case of micro-gas turbines, emissions are of even greater importance, since the regulations for such engines will most likely become stricter in the following years due to a global effort to reduce emissions. Furthermore, the liners investigated in this paper are for a 10 kWe micro-turbine, destine for various potential markets, such as combined heat and power for houses, electric vehicle hybrids and even small unmanned aerial vehicles. The majority of these markets require long service intervals, which in turn requires the combustor liners to be under the least amount of thermal stress possible. The desire to also increase combustor inlet temperatures with the use of recuperated exhaust gases, which in turn increase the overall system efficiency, limits the cooling effectiveness of the inlet air. Due to all these reasons, an advanced form of augmented backside cooling would be of substantial significance in such a system. Currently, some very simple designs are used in the form of straight plain fins, transverse strips, or other similar geometries, but the creation of high heat transfer efficiency surfaces in such small sizes becomes very difficult with traditional subtractive manufacturing methods. When using additive manufacturing though these types of surfaces are not an issue. This paper covers the comparison of experimental results with conjugate heat transfer computational fluid dynamics (CFD) models and empirical heat balance models for two different additively manufactured (AM) liner cooling geometries and an AM blank liner. The two cooling fin geometries include a rotating plain fin and an offset strip fin. The liners were tested in an AM-built reverse flow radial swirl stabilized combustion chamber at a variety of operating conditions. During the experiments, the surfaces were compared using a thermal camera to record the outer liner temperature, which was viewed through a quartz outer casing. The experimental results showed that the cooling surfaces were effective at reducing the liner temperatures with minimal pressure losses for multiple operating points. Those results were then compared against the conjugate heat transfer CFD models and the empirical calculations used to design the surfaces initially. From this comparison, it was noticed both the CFD and empirical calculations under-predicted the wall temperatures. This is thought to be due to inaccuracies in the predicted flame temperatures and the assumed emissivity values used to calibrate the thermal imaging camera. Further uncertainties arise from the assumption of a constant air and hot gas temperature and mass flow along the cooling surfaces and the lack of data for the surface roughness of the parts.

Conjugate Heat Transfer Simulations to Evaluate the Effect of Anisotropic Thermal Conductivity on Overall Cooling Effectiveness

Abstract Ceramic matrix composites (CMCs) show promise as higher temperature capable alternatives to traditional metallic components in gas turbine engine hot gas paths. However, CMC components will still require both internal and external cooling, such as film cooling. The overall cooling effectiveness is determined not only by the design of coolant flow, but also by the conduction through the materiel itself. CMCs have anisotropic thermal conductivity, giving rise to heat flow that differs somewhat relative to what we have come to expect from experience with traditional metallic components. Conjugate heat transfer computational fluid dynamics (CFD) simulations were performed in order to isolate the effect anisotropic thermal conductivity has on a cooling architecture consisting of both internal and external cooling. Results show the specific locations and unique effects of anisotropic thermal conduction on overall effectiveness. Thermal conductivity anisotropy is shown to have a significant effect on the resulting overall effectiveness. As CMCs begin to make their way into gas turbine engines, care must be taken to ensure that anisotropy is characterized properly and considered in the thermal analysis.

Developing a CFD heat transfer model for applying high expansion foam in an LNG spill

Liquefied natural gas (LNG) is widely used to cost-effectively store and transport natural gas. However, a spill of LNG can create a vapor cloud, which can potentially cause fire and explosion. High expansion (HEX) foam is recommended by the NFPA 11 to mitigate the vapor hazard and control LNG pool fire. In this study, the parameters that affect HEX foam performance were examined using lab-scale testing of foam temperature profile and computational fluid dynamics (CFD) modeling of heat transfer in vapor channels. A heat transfer model using ANSYS Fluent® was developed to estimate the minimum HEX foam height that allows the vapors from LNG spillage to disperse rapidly. We also performed a sensitivity analysis on the effect of the vaporization rate, the diameter of the vapor channel, and the heat transfer coefficient on the required minimum height of the HEX foam. It can be observed that at least 1.2 m of HEX foam in height are needed to achieve risk mitigation in a typical situation. The simulation results can be used not only for understanding the heat transfer mechanisms when applying HEX foam but also for suggesting to the LNG facility operator how much HEX foam they need for effective risk mitigation under different conditions.

Compact Transient Thermal Model of Microfluidically Cooled Three-Dimensional Stacked Chips With Pin-Fin Enhanced Microgap

Abstract Three-dimensional (3D) stacked integrated circuit (SIC) chips are one of the most promising technologies to achieve compact, high-performance, and energy-efficient architectures. However, they face a heat dissipation bottleneck due to the increased volumetric heat generation and reduced surface area. Previous work demonstrated that pin-fin enhanced microgap cooling, which provides fluidic cooling between layers could potentially address the heat dissipation challenge. In this paper, a compact multitier pin-fin single-phase liquid cooling model has been established for both steady-state and transient conditions. The model considers heat transfer between layers via pin-fins, as well as the convective heat removal in each tier. Spatially and temporally varying heat flux distribution, or power map, in each tier can be modeled. The cooling fluid can have different pumping power and directions for each tier. The model predictions are compared with detailed simulations using computational fluid dynamics/heat transfer (CFD/HT). The compact model is found to run 120–600 times faster than the CFD/HT model, while providing acceptable accuracy. Actual leakage power estimation is performed in this codesign model, which is an important contribution for codesign of 3D-SICs. For the simulated cases, temperatures could decrease 3% when leakage power estimation is adopted. This model could be used as electrical-thermal codesign tool to optimize thermal management and reduce leakage power.

Computational fluid dynamics heat transfer analysis of double pipe heat exchanger using nanofluid MnFe2O4 with ethylene glycol/water

The heat exchanger is an essential instrument in heat transfer. Various efforts have been carried out to improve heat transfer features. One of them is by adjusting the conventional cooling fluid using nanofluid. This study used MnFe2O4 nanoparticle along with fluid obtained from a mixture of ethylene glycol (EG) and water. This research aims to identify heat transfer characteristics of a double pipe heat exchanger with MnFe2O4-EG/Water nanofluid using the CFD simulation method. The EG/Water mixture variation was 20, 40, 60, and 80% with MnFe2O4 concentration of 0.05%. Besides, the nanofluid flow was also varied by 0.2; 0.4; and 0.6 l min−1. The research results show that MnFe2O4 nanofluid effects on heat transfer characteristics encounter an increase following the rise of MnFe2O4 nanoparticle addition and higher water flow. In contrast, the addition of ethylene glycol carries a dreadful effect on this research. The improvement of heat transfer characteristic is hugely influenced by the increased thermal conductivity along with the addition of MnFe2O4 nanoparticle.

Fluid dynamic simulations of EG-W (ethylene glycol–water)​ mixtures to predict nanofluid heat transfer coefficients

In previous scholarly studies, the water and ethylene glycol mixtures as base liquids have been considered in the ratio of 40:60 (60EGW), with specific investigations focusing on a nanofluid in the form of Silicon dioxide (SiO 2 ) in relation to its thermo-physical properties, besides its nature of the heat transfer coefficients (HTC). In this study, the main aim lies in SiO 2 heat transfer coefficient prediction. With the approach of computational fluid dynamics (CFD) considered, the experimental conditions constitute 40EGW. For SiO 2 nanoparticles, their viscosity and thermal conductivity are experimentally assessed, having been dispersed in 60EGW and 40EGW. Also, the existing literature and experimental values are used to conduct regression analysis. With thermal features on the focus, there is also the formulation of correlations. Furthermore, there is the invocation of correlations in heat transfer coefficients and CFD simulations that target Al 2 O 3 and SiO 2 nanoparticles, with predictions entailing their dispersion in 40EGW and 60EGW base fluids. The inlet temperature is set at 60 to 80 °C, and it is at this stage that there is CFD simulation implementation. For the volume concentration, the ranges include 0.0 percent to 1.5 percent. The existing literature is relied upon relative to the validation of the coefficients of heat transfer, besides previously predicted nanofluids. From the results, compared to the case of 40EGW base fluid, there is 51 percent and 53 percent enhancement when the experimental conditions are set at 1.0 percent volume concentration, with temperatures being 80 °C. From the computational outcomes, Al 2 O 3 /40EGW nanofluids tend to be associated with significant heat transfer coefficients. Specifically, the enhancement of heat transfer for these materials stands at 52.9%. On the other hand, the enhancement of heat transfer when the base fluid of SiO 2 /40EGW is considered stands at 51.0%. These values are obtained when the experimental conditions are considered at 80 °C, as well as a volume concentration of 1.0%. Therefore, when aspects of heat transfer coefficients and thermal conductivity are considered, the superiority of 40EGW is confirmed. As such, the study confirms the impact of the base nanofluids set at 40EGW relative to the coefficients of heat transfer and also the thermal properties of materials.

Predicting Rotor Heat Transfer Using the Viscous Blade Element Momentum Theory and Unsteady Vortex Lattice Method

Calculating the unsteady convective heat transfer on helicopter blades is the first step in the prediction of ice accretion and the design of ice-protection systems. Simulations using Computational Fluid Dynamics (CFD) successfully model the complex aerodynamics of rotors as well as the heat transfer on blade surfaces, but for a conceptual design, faster calculation methods may be favorable. In the recent literature, classical methods such as the blade element momentum theory (BEMT) and the unsteady vortex lattice method (UVLM) were used to produce higher fidelity aerodynamic results by coupling them to viscous CFD databases. The novelty of this research originates from the introduction of an added layer of the coupling technique to predict rotor blade heat transfer using the BEMT and UVLM. The new approach implements the viscous coupling of the two methods from one hand and introduces a link to a new airfoil CFD-determined heat transfer correlation. This way, the convective heat transfer on ice-clean rotor blades is estimated while benefiting from the viscous extension of the BEMT and UVLM. The CFD heat transfer prediction is verified using existing correlations for a flat plate test case. Thrust predictions by the implemented UVLM and BEMT agree within 2% and 80% compared to experimental data. Tip vortex locations by the UVLM are predicted within 90% but fail in extreme ground effect. The end results present as an estimate of the heat transfer for a typical lightweight helicopter tail rotor for four test cases in hover, ground effect, axial, and forward flight.

Thermal and Electromagnetic Stator Vent Design Optimisation for Synchronous Generators

This article deals with the stator cooling of an air-cooled, synchronous generator with a power rating of 400 kVA, which has been improved by adding two radial vents to the stator. To ensure an optimal vent design, a novel combined thermal and electromagnetic modelling approach is developed. A parametric 3D Conjugate Heat Transfer Computational Fluid Dynamics (CFD) model is used for the thermal modelling. An electromagnetic 2D Finite Element Analysis determined the impact that venting the stator has on the loss distribution. The models are coupled by deriving analytical correlations between the combined vent width and rotor copper, rotor iron and stator iron losses. These correlations are implemented into the optimisation procedure of the parametric CFD model. Five design parameters are optimised simultaneously with the aim of minimising the peak stator winding temperature. The modeling approach was validated experimentally by thermal, torque and mass flow measurements on the benchmark machine, as well as the newly designed prototype.