Performance Evaluation Criteria Research Articles

Numerical study on heat transfer and flow characteristics of symmetric Tesla-type microchannel heat sinks

This study numerically investigates the thermal performance of multi-stage Tesla valve microchannels (TVM) and a symmetric variant (SYMTVM) using single-phase deionized water, with mass flow rates ranging from 0.5459 to 1.6377 g/s. Both designs, particularly under reverse flow, exhibit lower peak temperatures and reduced temperature gradients compared to forward flow. The SYMTVM, characterized by its increased vortices and bifurcations, periodically disrupts and redevelops the thermal boundary layer, enhancing heat transfer efficiency. The TVM exhibits a significantly higher pressure drop than the SYMTVM across all flow conditions, with the reverse flow in TVM reaching up to 2.48 times that of forward flow and exceeding SYMTVM, especially at a peak flow rate of 1.6377 g/s. The performance evaluation criteria (PEC) and thermal resistance criteria (PECTR) demonstrate the superior performance of SYMTVM, with a reduced connection angle between the trunk and helix regions enhancing thermal efficiency. Furthermore, the SYMTVM-Reverse structure with a 30° interconnection angle demonstrates superior performance compared to the conventional rectangular microchannel (RM), achieving a Nusselt number (Nu) that is five times higher than that of the RM and an overall performance up to 2.59 times greater. These results indicate the SYMTVM-Reverse’s high heat transfer efficiency and its capacity to effectively balance thermo-hydraulic performance, even with increased power dissipation.

Optimization and mechanistic analysis of the configurational parameters of a serrated trench for improving film cooling performance

Combinations of trench and holes in film cooling design for turbine blades have been suggested recently. In this work, structural optimization is performed for one of our previously proposed serrated trenches. Geometric parameters including serrated angle, width, and height of the trench, which are the key factors affecting the flow and cooling characteristics, are optimized. The relative area-averaged cooling effectiveness, the relative pressure drop coefficient, and the performance evaluation criterion (PEC) are the optimizing objectives. The multi-objective genetic algorithm is employed as the search strategy to achieve PEC maximization at blowing ratios in the range of 0.5–2.0. The individual variations of each parameter are studied by controlling the variables using the response surface method. It shows that the trench height is the most influential factor on flow and heat transfer; while the trench serrated angle mainly affects the heat transfer; and the trench width has a weak effect on both, depending on the blowing ratio. To achieve maximum PEC, the trench height needs to enlarge with the increase in blowing ratio, while contrary to this, the trench width needs to increase under low blowing ratios and decrease under high blowing ratios, and the optimum trench serrated angle is within the range of 80°–85° at all blowing ratios. The optimum geometry reduces the pressure loss while improves the cooling effectiveness by 8.43 %–17.97 % compared to the baseline trench. This work is instructive for the design and application of practical structures for blade cooling.

A ridge estimation method for the Waring regression model: simulation and application

This study focuses on parameter estimation in the presence of multicollinearity for the count response that follows the Waring distribution. The Waring regression model deals with over-dispersion. So, this study proposed the Waring ridge regression (WRR) model as a solution for multicollinearity with over-dispersion. We conducted a theoretical comparison between the ridge estimator and the maximum likelihood estimators using matrix and scalar mean squared error as a performance evaluation criterion. Several ridge parameters are considered for the WRR estimator. The performance of these parameters is numerically evaluated using a Monte Carlo simulation study and a real application. The results of the simulation and application demonstrate the superiority of the WRR model with different ridge parameters over the maximum likelihood estimator.

Experimental analysis and optimization of a concentrated thermo-photovoltaic collector with bi-facial receiver

In this study, a concentrated thermo-photovoltaic collector is examined experimentally and numerically, while it was geometrically optimized. The main objective is the collector's thermal performance evaluation and optical optimization for operation in Greece. Experiments were conducted in Athens, Greece, from July 10th to 17th, 2023, investigating the thermal operation of the collector in a temperature range of approximately 40–80 °C. The collector's slope was set to 12.3° for maximum solar irradiance utilization during the specified dates, considering south orientation. A numerical model was created using SolidWorks Flow Simulation and COMSOL Multiphysics, The model was validated with the experimental data, achieving mean deviation less than 6.5 % and a maximum deviation of 9.6 %. Various geometries were optically examined using Tonatiuh software. By applying a performance evaluation criterion, a new design is proposed and compared to the initial design across four different months, considering constant tilt angle. A maximum thermal efficiency of 49.19 % at the lower inlet temperature was found experimentally. The temperature of the PV cells was found to be highest where the solar rays are concentrated. Shape optimization revealed significant enhancements in optical efficiency, particularly at negative incident angles. The new geometry showed substantial improvement, with enhancements exceeding 20 % considering daily operation.

Traffic Light Control in Vehicular Network Systems using Fuzzy Logic

Vehicular concentration is getting worse in big and developed cities as we approach the year 2020, creating a challenge. Accurate traffic signal management is a key to efficient free traffic movement and increasing transport security levels. It is thus important to have traffic lights that change with the traffic intensity information that is collected in real-time. Thus, the objective of this study is to design a viable traffic signal control algorithm that would cause minimal delay to drivers. Building on the basis of fuzzy logic, the approach sets input attributes like the volume of the street and the queue of vehicles to identify the right traffic light settings. Performance criteria for evaluation include the average waiting time of the vehicles, and the findings of simulations in this regard show 21.7 seconds, which is faster than the benchmark formula of 25 seconds. This improvement is attributed to the fact that the fuzzy control system is adjustable.

An Empirical Investigation of Organizational Readiness towards Hospital Autonomy.

We aimed to investigate Tehran's University of Medical Sciences (TUMS) affiliated hospitals organizational readiness toward implementing the 'Autonomous Hospitals' program as a change initiative from a managerial perspective in 2020. A census covering all eligible managers working in TUMS affiliated hospitals, Tehran, Iran (350 individuals) was carried out. Overall, 281 questionnaires were returned (a 30% non-responsiveness rate). A standard construct was adopted for data collection which was validated through a process of translation- back translation, face validity, and content validity (CVI=0.86, CVR=0.76). The reliability was acquired using Cronbach's alpha coefficient (0.87 and over 0.7). Both descriptive and inferential statistics were employed to draw conclusions .SPSS 26 was used for data analysis. Total organizational readiness for change (TORC) in hospitals was 60.75%±10.11 showing a state of medium to upper-medium readiness status. Also, the 'Clear mandate and centralized leadership' theme scored the lowest mean (53.02%±15.78) for ORC. 'Hospital accreditation level' (r=-0.14, P≤0.05), 'bed occupancy rate' (r=-0.19, P ≤0.05), and 'leadership status' (r=0.26, P≤0.001), also showed significant association with TORC. In addition, 'standardized bed occuPancy rate' (P≤0.05, B=-2.41), a 'male' leader (P ≤0.05, B=3.42), and 'academic affiliation' (P≤0. 1, B=-9.52), were good Predictors of TORC based on 'Backward Multiple Linear Regression' analysis. Full support from hospital and headquarters executives, delegation of sufficient decision-making authority to hospital managers, and implementation of comprehensive performance evaluation criteria were prerequisites for robust hospital autonomy in TUMS-affiliated hospitals.

Genetic parameters and genotype-by-environment interaction estimates for growth and feed efficiency related traits in Chinook salmon, Oncorhynchus tshawytscha, reared under low and moderate flow regimes

BackgroundA genotype-by-environment (G × E) interaction is defined as genotypes responding differently to different environments. In salmonids, G × E interactions can occur in different rearing conditions, including changes in salinity or temperature. However, water flow, an important variable that can influence metabolism, has yet to be considered for potential G × E interactions, although water flows differ across production stages. The salmonid industry is now manipulating flow in tanks to improve welfare and production performance, and expanding sea pen farming offshore, where flow dynamics are substantially greater. Therefore, there is a need to test whether G × E interactions occur under low and higher flow regimes to determine if industry should consider modifying their performance evaluation and selection criteria to account for different flow environments. Here, we used genotype-by-sequencing to create a genomic-relationship matrix of 37 Chinook salmon, Oncorhynchus tshawytscha, families to assess possible G × E interactions for production performance under two flow environments: a low flow regime (0.3 body lengths per second; bl s−1) and a moderate flow regime (0.8 bl s−1).ResultsGenetic correlations for the same production performance trait between flow regimes suggest there is minimal evidence of a G × E interaction between the low and moderate flow regimes tested in this study, for Chinook salmon reared from 82.9 ± 16.8 g (x¯\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${\\overline{\ ext{x}}}$$\\end{document} ± s.d.) to 583.2 ± 117.1 g (x¯\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${\\overline{\ ext{x}}}$$\\end{document} ± s.d.). Estimates of genetic and phenotypic correlations between traits did not reveal any unfavorable trait correlations for size- (weight and condition factor) and growth-related traits, regardless of the flow regime, but did suggest measuring feed intake would be the preferred approach to improve feed efficiency because of the strong correlations between feed intake and feed efficiency, consistent with previous studies.ConclusionThis new information suggests that Chinook salmon families do not need to be selected separately for performance across different flow regimes. However, further studies are needed to confirm this across a wider range of fish sizes and flows. This information is key for breeding programs to determine if separate evaluation groups are required for different flow regimes that are used for production (e.g., hatchery, post smolt recirculating aquaculture system, or offshore).

Comparison and development of cross-study normalization methods for inter-species transcriptional analysis.

Performing joint analysis of gene expression datasets from different experiments can present challenges brought on by multiple factors-differences in equipment, protocols, climate etc. "Cross-study normalization" is a general term for transformations aimed at eliminating such effects, thus making datasets more comparable. However, joint analysis of datasets from different species is rarely done, and there are no dedicated normalization methods for such inter-species analysis. In order to test the usefulness of cross-studies normalization methods for inter-species analysis, we first applied three cross-study normalization methods, EB, DWD and XPN, to RNA sequencing datasets from different species. We then developed a new approach to evaluate the performance of cross-study normalization in eliminating experimental effects, while also maintaining the biologically significant differences between species and conditions. Our results indicate that all normalization methods performed relatively well in the cross-species setting. We found XPN to be better at reducing experimental differences, and found EB to be better at preserving biological differences. Still, according to our in-silico experiments, in all methods it is not possible to enforce the preservation of the biological differences in the normalization process. In addition to the study above, in this work we propose a new dedicated cross-studies and cross-species normalization method. Our aim is to address the shortcoming mentioned above: in the normalization process, we wish to reduce the experimental differences while preserving the biological differences. We term our method as CSN, and base it on the performance evaluation criteria mentioned above. Repeating the same experiments, the CSN method obtained a better and more balanced conservation of biological differences within the datasets compared to existing methods. To summarize, we demonstrate the usefulness of cross-study normalization methods in the inter-species settings, and suggest a dedicated cross-study cross-species normalization method that will hopefully open the way to the development of improved normalization methods for the inter-species settings.

Prediction of groundwater level using artificial neural network as an alternative approach: a comparison assessment with numerical groundwater flow model

ABSTRACT Over-exploitation of groundwater in arid and semi-arid regions requires an understanding of dynamics for effective management strategies. This study uses an artificial neural network (ANN) to predict groundwater levels using initial water level, pumping, and hydrological and meteorological input variables. A sensitivity analysis assessed predictors’ impact on accurate groundwater level prediction. The accuracy of predictions by the ANN model was examined through different performance evaluation criteria and the result achieved is satisfactory as compared to reasonable statistical indicator values. A comparison of results simulated by the ANN model and numerical groundwater flow model (MODFLOW) was carried out and it was found that the prediction of the ANN model was consistently superior to that of the numerical model. Therefore, data-driven models such as ANN can provide better predictions of groundwater level that will aid in groundwater resource management.

Experimental and numerical investigation of the influence of varying micro-channel width on flow and heat transfer uniformity

This study integrates experimental and numerical methods to investigate the heat transfer and flow characteristics of microchannel heat sinks with varying width distributions. Water is used as the working fluid and laminar flow model is applied for simulation. Specifically, ten different width distribution structures were designed, including uniform-width configurations and nine non-uniform-width structures (I to IX) with progressively refined intermediate channel widths. Numerical simulations were employed to analyze the flow and heat transfer behaviors, and experimental validations were conducted on both the uniform-width structure and structures II, IV, V, VI, and IX. Results indicate that in uniform-width channel structures, the pressure drop and mass flow rate in intermediate channels exceed those in side channels, resulting in uneven temperature distribution at the base. Conversely, refining the intermediate channel widths under constant total width promotes more uniform flow and wall temperature distributions. A Performance Evaluation Criterion for Non-uniform Channels (PECNC) was introduced to identify the optimal structure. Ultimately, structure VI, characterized by continuously refined intermediate widths, emerged as the optimal design for achieving uniform temperature distribution. A mathematical expression for calculating the optimal microchannel width distribution conducive to uniform temperature distribution was also established.

Beyond Traditional Metrics: Exploring the Potential of Hybrid Algorithms for Drought Characterization and Prediction in the Tromso Region, Norway

Meteorological drought, defined as a decrease in the average amount of precipitation, is among the most insidious natural disasters. Not knowing when a drought will occur (its onset) makes it difficult to predict and monitor it. Scientists face significant challenges in accurately predicting and monitoring global droughts, despite using various machine learning techniques and drought indices developed in recent years. Optimization methods and hybrid models are being developed to overcome these challenges and create effective drought policies. In this study, drought analysis was conducted using The Standard Precipitation Index (SPI) with monthly precipitation data from 1920 to 2022 in the Tromsø region. Models with different input structures were created using the obtained SPI values. These models were then analyzed with The Adaptive Neuro-Fuzzy Inference System (ANFIS) by means of different optimization methods: The Particle Swarm Optimization (PSO), The Genetic Algorithm (GA), The Grey Wolf Optimization (GWO), and The Artificial Bee Colony (ABC), and PSO optimization of Support Vector Machine (SVM-PSO). Correlation coefficient (r), Root Mean Square Error (RMSE), Nash–Sutcliffe efficiency (NSE), and RMSE-Standard Deviation Ratio (RSR) served as performance evaluation criteria. The results of this study demonstrated that, while successful results were obtained in all commonly used algorithms except for ANFIS-GWO, the best performance values obtained using SPI12 input data were achieved with ANFIS-ABC-M04, exhibiting r: 0.9516, NSE: 0.9054, and RMSE: 0.3108.

Thermal analysis of metal foam and nanofluid integration in an asymmetrical heated channel

The flow and heat transport presentation of the nanofluid flowing through the metal foam (MF) positioned inside the asymmetrical heated horizontal channel is evaluated computationally in the current study. The MF of 20 pores per inch (PPI) with porosity 0.918 is inserted into horizontal channel test section. The test segment is heated from the top wall with the help of plate (aluminum) heater assemblage positioned at the top of the channel. The novelty of the current examination is to extract the consequences of nanofluids such as Al2O3-H2O and CuO-H2O flowing through the MF filled test segment at various velocities ranging from 0.02 to 0.15 m/s. The conjugate heat transport examination is executed through MF region using integrated DEF (Darcy Extended Forchheimer) with LTE (local thermal equilibrium models). The improvement in thermal presentation achieved for nanofluids are likened with H2O and H2O-C2H6O2 (water with 50 % ethylene glycol) solution and also with clear channel. The procedure adopted in the current analysis is initially confirmed with the help of comparison of present work result with literature upshots. The outcomes are presented in terms of friction factor (FF), Nusselt number (Nu), Colburn j factor, performance evaluation criteria (PEC) and pumping power (PP). The CuO-H2O nanofluid showed the best performance (average PEC of 1.4) among all the fluids considered in the present examination.

Experimental Forced Convection Study Using a Triply Periodic Minimal Surface Porous Structure with a Nanofluid: Comparison with Numerical Modeling

Triply periodic minimal surfaces (TPMSs) show potential as porous materials in different engineering applications. Amongst them, heat sink is the subject of this paper. The advantage of such a structure is the ability to design it based on the intended applications. In the present paper, an attempt is made to experiment with a better understanding of the performance of TPMSs in heat sink applications. The experiment was conducted for different flow rates, and two heat sink materials, aluminum and silver, were used. In addition, two fluids were used experimentally: The first was water, and the second was a mixture of water containing 0.6% aluminum nanoparticles and identified as a nanofluid. The applied heat flux was maintained constant at 30,800 W/m2. The results reveal experimentally and confirm numerically that the TPMS structure secures a uniform heat extraction in the system. The development of the boundary layer in the porous structure is reduced due to the current structure design. A higher Nusselt number is obtained when the nanofluid is used as the circulating fluid. The performance evaluation criteria in the presence of the nanofluid exceed 100.

Active and hybrid battery thermal management system using microchannels, and phase change materials for efficient energy storage

Efficient battery thermal management (BTM) is key to the safety and performance of Lithium-ion batteries. This study focuses on cooling a module of 15 prismatic Lithium-titanate cells at an 8C discharge rate using finite volume numerical modeling. Initially, the impact of Reynolds numbers and microchannel count on temperature is examined. Then, Phase change materials (PCM) are introduced to increase thermal management efficiency. Three cases for optimizing the hybrid-BTM system are compared: nanoparticles (1%–5% volume fraction), copper foam (93 % porosity), and micro-encapsulated PCM slurry (5%–15 % volume fraction). The results indicate that an increase in microchannels improves the performance evaluation criterion (PEC) by 93.1 %. Additionally, using copper foam increases the PEC by 18.43 %, 17.44 %, 16.53 %, and 16.31 % at Reynolds numbers 800, 1200, 1600, and 2000, respectively. Furthermore, the BTM system using 5 % nanoparticles demonstrates the best performance, reducing the module temperature by 2.41 % in the presence of copper foam and by 1.78 % in the pure state. Moreover, while the MPCM slurry effectively reduces the module temperature to a desirable level, the high pumping costs significantly decrease the PEC. Additionally, the total entropy of the BTM system follows frictional entropy, although thermal entropy effects dominate the PCM.

Two-Phase Lattice Boltzmann Study on Heat Transfer and Flow Characteristics of Nanofluids in Solar Cell Cooling

During solar cell operation, most light energy converts to heat, raising the battery temperature and reducing photoelectric conversion efficiency. Thus, lowering the temperature of solar cells is essential. Nanofluids, with their superior heat transfer capabilities, present a potential solution to this issue. This study investigates the mechanism of enhanced heat transfer by nanofluids in two-dimensional rectangular microchannels using the two-phase lattice Boltzmann method. The results indicate a 3.53% to 22.40% increase in nanofluid heat transfer, with 0.67% to 6.24% attributed to nanoparticle–fluid interactions. As volume fraction (φ) increases and particle radius (R) decreases, the heat transfer capability of the nanofluid improves, while the frictional resistance is almost unaffected. Therefore, the performance evaluation criterion (PEC) of the nanofluid increases, reaching a maximum value of 1.225 at φ = 3% and R = 10 nm. This paper quantitatively analyzes the interaction forces and thermal physical parameters of nanofluids, providing insights into their heat transfer mechanisms. Additionally, the economic feasibility of nanofluids is examined, facilitating their practical application, particularly in solar cell cooling.

Heat transfer enhancement and pressure drop performance of Al2O3 nanofluid in a laminar flow tube with deep dimples under constant heat flux: An experimental approach

This study examines the heat transfer enhancement and pressure drop of Al2O3 nanofluid in deep dimpled tubes in both longitudinal and circumferential directions. It explores mechanisms that improve the thermal performance of this novel tube geometry. Experiments were conducted using plain and deep dimpled tubes under laminar flow with Reynolds numbers from 500 to 2250, a constant heat flux of 10,000 W/m2, and nanofluid concentrations from 0.1 wt% to 1 wt%. The findings indicate that local velocity enhancement, vortex generation, and flow rotation and mixing are the three main mechanisms that improve the thermal performance of deep dimpled tubes. The results demonstrate that a deep dimpled tube with 1 wt% nanofluid can increase the convective heat transfer coefficient by up to 3.42 times compared to a smooth tube at Re = 2250. At this Reynolds number, the Nusselt number reaches a maximum of 41.80, and the friction factor ratio increases by only 1.82. Additionally, circumferential analysis reveals how dimple-induced vortices enhance heat transfer. The results also indicate that the tube geometry modification changes the flow regime zones, allowing turbulent flow at lower Reynolds numbers near Re = 2000, as identified by Nusselt number and friction factor plots. The deep dimpled tube has a low improvement penalty, with the highest friction factor of 0.38 at Re = 500 and high thermal enhancement, resulting in a performance evaluation criterion (PEC) of up to 2.80 in the studied region. However, the deep dimpled tube is unsuitable for Reynolds numbers below 1000. For higher velocities, replacing simple tubes with deep dimpled tubes in traditional heat exchangers is highly recommended.

The Role of the New Media in Spreading the Culture of Volunteering in the Iraqi Society: Facebook as a Model

Volunteering is considered and continues to be the cornerstone of building society, spreading love, and fostering social cohesion among its members. This practice varies from time to time and from one community to another. Sometimes it decreases, and other times it increases. It can take the form of monetary donations or other charitable deeds. Volunteering is a humanitarian act closely tied to all the meanings of goodness and pure, righteous deeds for the sake of Allah. It is connected to the religious aspect, as Allah says: 'So whoever volunteers good, it is better for him. So volunteering has become a necessity among life's necessities due to its social message aimed at participation in building, developing, and strengthening the foundations of society. Consequently, the culture of volunteering has become an integral part of the cultures of developed societies, representing a system of principles, ethics, standards, and practices that encourage initiative and positive actions that benefit others (1). At the global level, volunteering has received increasing attention from the United Nations through its programs and institutions that provide assistance to the poor, marginalized, afflicted, and refugees. December 5th has been designated as International Volunteer Day as a tribute by the international organization to the significant role that volunteering can play in all areas of humanitarian and developmental work (2). In this research, the study indicates a significant increase in the number of donors among both young and adult individuals. The study seeks to shed light on the role of new media in promoting a culture of volunteering within Iraqi society, especially among the audience in Thi Qar province. The aim of promoting the culture of volunteering through new media platforms is to instill positive values of volunteering, encourage active participation in volunteer programs at the individual, institutional, and community levels, and extend this to regional and international levels. It emphasizes that volunteering is not merely about good intentions; rather, it has evolved into a professionally performed activity that employs new skills in management, finance, service delivery, and communication with others. The study emphasizes the importance of setting performance standards and evaluation criteria to develop, renew, and innovate new mechanisms for volunteering (3).

The physical mechanism of heat transfer enhancement for Al2O3-water nanofluid forced flow in a microchannel with two-phase lattice Boltzmann method

PurposeThe purpose of this paper is to analyze the mechanism of nanofluid enhanced heat transfer in microchannels and promote the application of nanofluids in industrial processes such as solar collectors, electronic cooling and automotive batteries.Design/methodology/approachThe two-phase lattice Boltzmann method was used to calculate the flow and heat transfer characteristics of Al2O3 nanofluids in a microchannel at Re = 50. By comparing the simulation results of pure water, nanofluids without calculated nanoparticle-fluid interaction forces and nanofluids with calculated nanoparticle-fluid interaction forces, the effects of physical properties improvement and interaction forces on flow and heat transfer are quantified.FindingsThe findings show that the nanofluid (φ = 3%, R = 10 nm) increases the average Nusselt number by 22.40% at Re = 50. In particular, 16.16% of the improvement relates to nanoparticles optimizing the thermophysical parameters of the base fluid. The remaining 6.24% relates to the disturbance of the thermal boundary layer caused by the interaction between nanoparticles and the base fluid. Moreover, the nanoparticle has a negligible effect on the average Fanning friction factor. Ultimately, we conclude that the nanofluid is an excellent heat transfer working medium based on its performance evaluation criterion, PEC = 1.225.Originality/valueTo the best of the authors' knowledge, this research quantifies for the first time the contribution of nanoparticle-liquid interactions and nanofluids physical properties to enhanced heat transfer, advancing the knowledge of the nanoparticle's behavior in liquid systems.

دراسة عددية لتعزيز انتقال الحرارة للتدفق المضطرب في ال نابيب المدملة

The importance of heat transfer enhancement has gained greater significance in many engineering applications, and a great amount of effort has been devoted to the use different techniques to improve the thermal performance of flowing fluids in pipes. Dimpled tubes were used for this purpose in this study. In this investigation, the heat transfer-flow characteristics of turbulent flow in dimpled tubes subjected to uniform heat flux are numerically investigated. The rate of heat transfer, friction factor, and performance evaluation criterion were determined for various designs of dimpled tubes and compared with smooth tubes. The considered cases are conducted in the Reynolds number range of 3000 to 12,000. The ANSYS Fluent R19 is used for this purpose. The Reynolds-averaged Navier-Stokes equations(RANS) are used to model the governing flow equations. The realizable k-ε turbulence model is used with enhanced wall conditionsto simulate turbulent flow adjacent to the inner wall surface. The results revealed that the rate of heat exchange in dimpled tubes higher than that of smooth ones. but with additional pressure loss. Moreover, the heat transfer performance of the staggered arrangement is higher than the inline arrangement by 17 %. Also, the performance evaluation criteria (PEC) increases with the increasing in dimples diameter, and decreases with increasing in relative pitch spacing, the maximum enhancement reaches 31% for d = 5 mm, and relative pitch spacing S/D = 2 at Re = 4000.

Enhancing thermal efficiency in twisted tri-lobe double pipe heat exchangers via integrated CFD and AI approaches

Industrial energy efficiency relies on precise heat exchanger design. Engineers aim to enhance heat transfer while minimizing pressure drop. Twisted tube Double Pipe Heat Exchangers (DPHEs) excel, inducing secondary flows that disrupt boundary layers and enhance fluid mixing. This investigation aims to provide insights into the relationships among specific factors and the thermal performance of a DPHE with twisted tri-lobe tubes, utilizing water as the working fluid. This is accomplished by employing an approach that integrates computational fluid dynamics with an artificial neural network, coupled with a single-objective genetic algorithm. Data inquiry was facilitated using response surface methodology, considering five design variables including the Reynolds numbers of the outer and inner twisted tubes, outer and inner twist ratios, and twisting direction. The simulation employed the turbulent k-ω shear-stress transport model, and the governing equations were solved using the finite volume method. The results underscored the efficacy of the developed multi-approach algorithm in maximizing the Performance Evaluation Criterion (PEC) both within the specified domain (10000 ≤ Re ≤ 20000 & 5≤twist ratio≤20) and beyond the domain (5000 ≤ Re ≤ 50000 & 2.5≤twist ratio≤25) of design variables. In the specified domain, a PEC of 1.16 was achieved, demonstrating the algorithm's effectiveness. Furthermore, the proposed method, extrapolated to the data space, yielded a noteworthy improvement in PEC of 16.35 %. In both domains, the optimized set of design variables for maximizing the PEC comprised a minimum outer Reynolds number, maximum inner Reynolds number, maximum outer twist ratio, and minimum inner twist ratio.