Thermal Design Optimization Research Articles

Innovative simplified approach and numerical investigation for flow and heat transfer in internally threaded tubes

Internally threaded tubes exhibit exceptional heat transfer capabilities, holding paramount significance in enhancing energy efficiency, reducing energy consumption, and tackling thermal challenges across aerospace, automotive, and environmental domains. Nonetheless, their intricate structural features, software modelling intricacies, extensive grid requirements, and challenges in maintaining grid quality render numerical simulation investigations a demanding endeavor. This study elucidates the influence mechanisms of thread spacing on the distribution of single-phase and two-phase flows. It delves into the condensation heat transfer of the R32 refrigerant and the single-phase heat transfer of water within internally threaded tubes. At the same Reynolds number, the heat transfer rate can be improved by approximately 19.61% with the optimized internally threaded parameters. When the Reynolds number varies from 18,626 to 51,222, the heat transfer rate per unit length for Tube-5 remains around 2,478.9 W/m, with a variation of less than 5.19%. Furthermore, it presents a simplified numerical simulation approach tailored for internally threaded pipes. This method manipulates wall roughness and regulates wall rotation speed to mimic fluid dynamics phenomena akin to conventional numerical simulation techniques. The findings reveal that the proposed simplified numerical simulation approach yields heat transfer and pressure drop predictions with less than 3% discrepancies compared to traditional numerical simulation predictions. Simultaneously, compared to directly simulating threaded pipes through conventional means, this approach significantly mitigates grid generation complexities, slashing computational time costs by at least 85% and facilitating efficient thermal design and structural optimization processes.

A coupled plasma-thermal model for hollow cathode power decomposition and parametric analysis

Abstract A 2D axisymmetric transient coupled plasma-thermal model is developed to simulate the plasma behavior during the self-sustained discharge of hollow cathodes, which presents a complete hollow cathode structure and energy transfer processes in multiphysics fields. The model has been validated by quantitative agreement between the simulation results and experimental data on the plasma and emitter temperature at the NSTAR cathode. The effects of thermal protection design, operating conditions, and geometric design on the cathode performance are analysed through electric and thermal power decomposition. The parametric analysis shows that the optimal thermal protection design is to use a 1/3 thickness cathode tube with 4 layers of radiation shielding close to the tube, which reduces 43.7% conductive and 61.1% radiative heat dissipation, respectively. Increasing the inlet flow rate counter-intuitively reduces the emitter temperature due to the potential reversal in the diffusion electric field dominated region, revealing that the flow rate can be traded for the dual optimisation of lifetime and power consumption. Under high current conditions, the IAT effect dominates the plume resistance to increase the discharge voltage after an inflexion point, which is the main factor limiting the cathode performance. A large internal radius gives a uniform low emission and helps to prolong life, while the orifice length should be avoided to be longer than 4 times the orifice radius due to the significantly enhanced Joule heating in the narrow orifice. The orifice radius determines the power deposition due to electron and ion bombardment through potential penetration. For high-current discharger cathodes dominated by electron bombardment, large or through-orifice designs are preferred, while for low-current neutralizer cathodes dominated by electron bombardment, small-orifice designs are recommended.

Optimization of building thermal environment and VR industrial heritage landscape design enhanced by computer vision algorithms

With the acceleration of urbanization, the optimization of thermal environment of buildings has become an important topic to improve energy efficiency and environmental sustainability. This paper aims to enhance the optimization of building thermal environment through computer vision algorithm, and explore its application in VR industrial heritage landscape design, so as to achieve the dual goals of building energy conservation and cultural inheritance. In this study, advanced computer vision technology is used to monitor and analyze the thermal performance of buildings in real time. Through the construction of thermal environment model, combined with deep learning algorithm to extract building thermal energy data, and use VR technology for visual display, so as to provide scientific basis for design decisions. The research objects include several representative industrial heritage buildings, and analyze their thermal energy consumption characteristics and optimization potential. The results show that the application of the computer vision algorithm reduces the heat loss of the building and significantly improves the energy efficiency. At the same time, the VR visual model intuitively shows the design effect after thermal optimization, enhancing the communication and understanding between designers and stakeholders. Therefore, the combination of computer vision and VR technology can effectively improve the optimization ability of building thermal environment and promote the sustainable design and operation of traditional industrial heritage.

Optimization design of low-carbon building thermal energy based on optical sensing and virtual reality image scene reconstruction

With the intensification of global climate change and energy crisis, the design of low-carbon buildings has gradually become a research hotspot in the field of architecture. Effective thermal optimization design not only reduces energy consumption, but also improves the comfort and sustainability of the building. This study aims to explore the optimization design of low-carbon building heat energy based on light sensing technology, combined with virtual reality image scene reconstruction, to provide an innovative design method to achieve intelligent management and optimization of building heat energy. Through the introduction of light sensing technology, the light intensity and heat distribution inside and outside the building are monitored, and the combination of data analysis and model simulation is used to evaluate the heat use of the building. On this basis, virtual reality scenes are constructed to reconstruct the heat flow and distribution of buildings, providing designers with intuitive visualization tools. The research shows that the design method based on light sensing can effectively identify the areas of heat waste in the building, and reduce the energy consumption of the building by optimizing the window layout, adding greening and improving the thermal management system. Virtual reality image scene reconstruction makes the design process more intuitive, helps the design team to quickly adjust and optimize, and improves the efficiency and accuracy of the design.

Multi-objective optimization and artificial neural network models for enhancing the overall performance of a microchannel heat sink with fins inspired Tesla valve profile

Microchannel heat sinks (MCHSs) are advanced cooling systems used in various applications such as electronics cooling, thermal management of high-power microprocessors, and microelectronic devices. These heat sinks are designed to efficiently dissipate heat generated by electronic components to maintain optimal operating temperatures and ensure device reliability. Employing appropriate fins within MCHSs is a crucial strategy to enhance heat removal efficiency. Since few investigations have been performed on the simultaneous thermo-hydraulic features in microchannels with Tesla valves, this research addresses this gap by suggesting that the microchannels of a heat sink be reinforced with novel Tesla valve-shaped fins. Tesla valves are designed to offer different levels of resistance depending on the flow direction. This characteristic allows for selective control of pressure drop and heat transfer efficiency by simply switching the flow direction. Utilizing Tesla valve-shaped fins is an almost new approach that differentiates the current research from conventional designs. The advantage of this structure is that under different circumstances, depending on whether more heat transfer is required or less pressure drop (ΔP), the flow direction can be changed, and the maximum efficiency of the MCHS can be accessed. The other innovative aspect of this study lies in the integration of artificial neural network (ANN) models to optimize the efficiency of MCHSs equipped with Tesla valve-shaped fins. The simulations were conducted to characterize the flow and thermal features of the MCHS. Then, these data were used as the benchmark for training the ANN models. Four ANN models were introduced to anticipate the Nusselt number (Nu) and ΔP in each forward and reverse direction. The divider's acute angle (α), the divider's obtuse angle (β), and the divider's radius (R) were selected as the input data. Multi-objective optimization and genetic algorithms were used to achieve the optimal conditions. In the optimal thermal design (Setting 2: MCHS with fin variables of α = 60 β = 180, and R = 80 μm in reverse flow direction), the overall performance had a significant enhancement of 57.1 % compared to the device lacking fins. Furthermore, in Setting 2, the overall performance of the MCHS and its diodicity experienced notable improvements of approximately 12.1 % and 38.5 %, respectively. Implementing the parameters of Setting 5 (MCHS with fin variables of α = 60, β = 152.32, and R = 73.10 μm in forward direction) during fin production resulted in a notable enhancement of about 41.1 % in the device's overall performance.

Inverse problem method application in building physics numerical optimization: new insight into ideal envelope properties determination

ABSTRACT Building thermal design optimization is pivotal for energy efficiency and built environment improvement. The optimal thermophysical properties for building walls remain ambiguous. This ambiguity stems from the constraints of traditional building physics, which: (1) disjoins the enhancement of wall thermal performance from the building service system management; and (2) relies on the forward method that fails to identify the ideal variable thermophysical property, the solution to which is inherently a function. This study elucidates the optimal temperature-dependent volumetric specific heat ( ρ c p ( t ) ) and thermal conductivity ( k ( t ) ) through the inverse problem and variation method. Illustrative example indicates that the optimal ρ c p ( t ) closely resembles a combination of two ‘ δ ’ functions, while the optimal k ( t ) mirrors a square wave function. Utilizing these ideal thermophysical properties, the building energy consumption can be significantly reduced by 77.1% with the optimized ρ c p ( t ) and 79.9% with the optimized k ( t ) . The ideal thermophysical properties for active buildings differ significantly from those recommended for passive buildings, underscoring that minimal energy consumption does not necessarily imply minimal discomfort. Therefore, the thermophysical properties of active and passive buildings should be optimized distinctly. This research offers valuable insights for defining ideal thermophysical properties of active building walls and construction materials.

Thermal optimization design of high-power charging station charging module

Abstract When facing significant heat generation from a 30 kW high-power charging station, traditional air-cooling forced convection methods are insufficient to meet the cooling requirements. In this study, based on an in-depth analysis of power modules, we designed a key component of the liquid cooling system, the cold plate, through theoretical calculations. Using simulation techniques, we analyzed the temperature distribution within the cold plate and performed design optimization under conditions of a 4C charging rate and a thermal load of 1598 W. Considering the actual operating environment of the charging equipment, we selected a 50% volume fraction of ethylene glycol-water solution as the coolant and simulated its performance under extreme outdoor conditions of 50°C. Simulation results showed that the optimized liquid-cooled cold plate module achieved a junction temperature of 69.73°C, significantly lower compared to the initial temperature of 109.9°C, resulting in an approximately 36% improvement in heat dissipation efficiency. This significant enhancement not only highlights the advantages of the liquid cooling system in managing the thermal load of high-power charging stations but also addresses the issue of excessive heat load faced by traditional cooling methods. The outcomes of this research provide important theoretical and practical foundations for the thermal design of charging equipment, which is expected to play a critical role in improving the performance and reliability of charging stations.

Lumped Parameter Thermal Network Modeling and Thermal Optimization Design of an Aerial Camera.

The quality of aerial remote sensing imaging is heavily impacted by the thermal distortions in optical cameras caused by temperature fluctuations. This paper introduces a lumped parameter thermal network (LPTN) model for the optical system of aerial cameras, aiming to serve as a guideline for their thermal design. By optimizing the thermal resistances associated with convection and radiation while considering the camera's unique internal architecture, this model endeavors to improve the accuracy of temperature predictions. Additionally, the proposed LPTN framework enables the establishment of a heat leakage network, which offers a detailed examination of heat leakage paths and rates. This analysis offers valuable insights into the thermal performance of the camera, thereby guiding the refinement of heating zones and the development of effective active control strategies. Operating at a total power consumption of 26 W, the thermal system adheres to the low-power limit. Experimental data from thermal tests indicate that the temperatures within the optical system are maintained consistently between 19 °C and 22 °C throughout the flight, with temperature gradients remaining below 3 °C, satisfying the temperature requirements. The proposed LPTN model exhibits swiftness and efficacy in determining thermal characteristics, significantly facilitating the thermal design process and ensuring optimal power allocation for aerial cameras.

Optimization of Geothermal Heat Pump Systems for Sustainable Urban Development in Southeast Asia

This study examines the optimization of ground source heat pump (GSHP) systems and energy piles for sustainable urban development, focusing on Southeast Asia. GSHPs, which utilize geothermal energy for indoor HVAC needs, offer a sustainable alternative to traditional systems by utilizing consistent subsurface temperatures for heating and cooling. The study highlights the importance of understanding thermal movement within the soil, especially in soft marine clays prevalent in Southeast Asia, to improve GSHP system efficiency. Using a one-dimensional finite difference model, the study examines the effects of soil thermal conductivity and density on system performance. The results show that GSHP systems, especially when integrated with energy piles, significantly reduce electricity consumption and greenhouse gas emissions, underscoring their potential to mitigate the urban heat island effect in densely populated areas. Despite challenges posed by the region’s hot and humid climate, which could affect long-term effectiveness, the study highlights the need for further study, including field experiments and advanced modeling techniques, to optimize GSHP configurations and fully exploit geothermal energy in urban environments. The study’s insights into soil thermal dynamics and system design optimization contribute to advancing sustainable urban infrastructure development.

Hierarchical thermal modeling and surrogate-model-based design optimization framework for cold plates used in battery thermal management systems

The continued advancement of battery-powered electric vehicles (EVs) towards higher energy and power densities poses significant thermal challenges for batteries. This work proposes a generalized design optimization framework for battery thermal management systems (BTMS), responding to the need for EVs with enhanced battery thermal performance, safety, and lifetime. This framework combines hierarchical thermal modeling and surrogate-model-based design optimization, explicitly tailored for liquid-cooled cold plates commonly used in EV BTMS. The hierarchical thermal modeling component decouples the battery cell, battery module, and cold plate models to reduce computational modeling costs while preserving the relative thermal performance of different cold plate designs. The design optimization component leverages our pioneer deep encoder–decoder hierarchical (DeepEDH) convolutional neural network surrogate modeling methodology to predict the cold plate’s full pressure, velocity, and temperature fields. Computationally efficient DeepEDH neural networks replace costly transient thermal simulations of cold plates and compute the objectives for evolutionary optimization using the Non-dominated Sorting Genetic Algorithm II (NSGA-II). This work’s novel modeling and optimization framework produces optimal cold plate designs that consider battery-module-specific design features, including distributed battery cell heat generation, packaging materials, and heat-spreading characteristics. The proposed framework is evaluated through a cold plate design for modular BTMS, thoroughly assessing the impact of optimization objectives, flow path constraints, modeling assumptions, and design variable choices. The results demonstrate the effectiveness of the combined hierarchical thermal modeling and design optimization framework, with the optimized cold plates achieving reductions of 6.26K, 6.29K (22.7%), and 16.16Pa (18.7%) for maximum temperature, maximum temperature difference, and pressure loss, respectively. Moreover, compared to other methodologies that apply direct optimization without hierarchical and surrogate models, our approach significantly reduces the computational cost — from approximately 4800h to 13.5h. Our generalized multi-objective optimization framework is an effective and efficient design tool that can be leveraged to advance thermal management innovations for next-generation battery systems.

In-plane thermal conductivity of hexagonal boron nitride from 2D to 3D

The in-plane thermal conductivity of hexagonal boron nitride (h-BN) with varying thicknesses is a key property that affects the performance of various applications from electronics to optoelectronics. However, the transition of the thermal conductivity from two-dimensional (2D) to three-dimensional (3D) h-BN remains elusive. To answer this question, we have developed a machine learning interatomic potential within the neuroevolution potential (NEP) framework for h-BN, achieving a high accuracy akin to ab initio calculations in predicting its thermal conductivity and phonon transport from monolayer to multilayers and bulk. Utilizing molecular dynamics simulations based on the NEP, we predict the thermal conductivity of h-BN with a thickness up to ∼100 nm, demonstrating that its thermal conductivity quickly decreases from the monolayer and saturates to the bulk value above four layers. The saturation of its thermal conductivity is attributed to the little change in phonon group velocity and lifetime as the thickness increases beyond four layers. In particular, the weak thickness dependence of phonon lifetime in h-BN with a nanoscale thickness results from its extremely high phonon focusing along the in-plane direction. This research bridges the knowledge gap of phonon transport between 2D and 3D h-BN and will benefit the thermal design and performance optimization of relevant applications.

An improved methodology for thermal calculation and design optimization of a thermosiphon waste heat boiler for CCPP

The study is dedicated to enhancing design and computational methodologies for thermosiphon waste heat boilers (WHB) within combined cycle power plants (CCPP). The optimized WHB design and an improved thermal calculation approach are shown. The efficiency of the proposed WHB has been improved by optimizing the area and arrangement of the surface. The novel method incorporates considerations for the internal thermal resistance within thermosyphons and the graded velocity of natural circulation within the evaporation circuit. To refine the calculation method, empirical and experimental data concerning internal temperature gradient in thermosiphon functioning within a heat load spectrum of up to 17 kW/m2, were used. Important findings include three categories. Firstly, increasing the number of sections will slightly increase capital costs and decrease WHB gas temperature, which could be neglected by the unification of thermosiphon sections. Secondly, by applying this modified methodology to a power plant facility featuring a 6700 kW gas turbine engine (GTE), notable adjustments in thermal power were realized, amounting to 157 kW (approximately 6 %), along with corresponding electrical power adjustments of 149 kW (approximately 2 %). Third, the new method is limited to a GTE power of up to 10 MW due to the experimental data used for validation.

Data-driven dimensional analysis of critical heat flux in subcooled vertical flow: A two-stage machine learning approach

This study presents a novel two-stage machine learning algorithm that identifies dominant dimensionless numbers for critical heat flux (CHF) prediction, circumventing the limitations of traditional dimensional analysis by integrating the Buckingham Pi theorem with advanced computational methods, including the active subspace method and machine learning. When applied to extensive datasets, including 1438 data points from subcooled departure from nucleate boiling (DNB) conditions and validated against a non-overlapping 24,284-point lookup table dataset, our algorithm effectively discerns and validates the dimensionless numbers critical to the CHF phenomena. Notably, it identifies a significant negative correlation between a newly discovered dominant dimensionless number and the boiling number Bochf = q″w,chf / (G hfg), and introduces a new simplified scaling law for water under earth's gravitational conditions, outperforming existing models with substantial predictive accuracy. This scaling law and the identified dimensionless numbers, derived from an initial dataset of 1438 data points, exhibit exceptional generalization capabilities when validated against a significantly larger dataset of 24,284 points. These findings also emphasize the nonlinear effects of inlet quality on the boiling number Bochf. The contribution of this research lies in its potential to deepen the understanding of CHF based on data-driven fashion, with the potential to enhance thermal management and design optimization across various applications.

Performance simulation of metal hydride based flat coupled beds with differential sizes for cooling/heating

Hydrogen being the most abundant element in the universe has great potential towards decarbonisation at the point of usage. Metal hydride-based heat pumps/ refrigerators are getting increasing acceptance due to its environment friendliness and safety. These systems operate over a wide range of temperatures and pressures. The thermal performance of these systems depends mainly on the reaction kinetics of the alloy pairs. Difference in the reaction rates of the alloy pairs, causes a certain amount of hydride to go unutilized during cycling. While a variety of reactor designs and heat transfer enhancements are suggested, optimal thermal design of coupled beds with minimum weight and cycle time remains a major challenge. In the present study, numerical simulation of differently sized coupled beds based on the alloy pair of LaNi4.7Al0.3 - La0.8Ce0.2Ni5 is carried out using COMSOL Multiphysics® commercial code. The effect of mass ratio on the hydrogen pressure and hydride concentration in the coupled bed is highly significant. It is found that 12 % increase in COP (0.28) is obtained for a cycle time of 260 s and mass ratio of 1.5. This corresponds to minimum alloy weight ensuring effective transfer of hydrogen across HT-LT reactors.

Novel process development and mechanism analysis of purification of polymer monomers by combining pressure swing distillation and extraction distillation

Methyl methacrylate is a new polymer material monomer, which is often used in the manufacture of plexiglass, and has very high industrial application value. Producing high purity methyl methacrylate requires a clean and effective separation process. In this work, an efficient separation method of methyl methacrylate/water/methanol azeotrope based on pressure swing distillation coupled extractive distillation is established. The best entrainer in terms of both environmental impact and effectiveness is chosen by examining the impact of relative volatility. The feasibility of pressure swing distillation and the best separation order are determined by ternary phase diagram analysis. A multi-objective optimization method is used to optimize the three designed processes, targeting gas emissions and total annual costs, thereby obtaining the best process plans and parameters. On this basis, the optimal thermal integration design of the three processes is carried out. The analysis of the four processes is conducted from the viewpoints of the energy, economy, environment and exergy. The findings demonstrate that the pressure swing extractive distillation process when combined can significantly increase economy and lessen mixture separation difficulty. Compared with the traditional process, the total annual cost, gas emission and energy loss are reduced by 8.20%, 10.46%, 11.69%, and the effective energy utilization rate is 50.31%. The development of this process is of great significance for the recovery of wastewater containing methyl methacrylate and methanol and the separation of high-purity methyl methacrylate.

Thermal field simulation and material parameter optimization for spaceborne annular truss antennas

The operational efficacy of large spaceborne annular truss antennas in orbit is significantly influenced by solar irradiation and alternating Earth shadow. This antenna system periodically encounters diverse extreme environments that impact the precision of the antenna surface performance. Consequently, this study presents an optimized thermal control design and conducts temperature field simulation calculations for such antennas. Initial efforts are directed toward analyzing the variables influencing the antenna structure’s temperature, with specific attention paid to the distinctive compositional characteristics of high-precision antennas. As a subsequent step, orthogonal tests are implemented, facilitating the development of an antenna thermal analysis model. This model assists in the identification of principal variables influencing the antenna’s temperature field. Finally, the antenna’s optimal thermal design is drawn upon the biogeography-based optimization (BBO) algorithm, enabling the derivation of ideal material parameters for the thermal design of the antenna. This methodology offers theoretical guidance for future thermal control design of large spaceborne annular truss antennas.

Thermal Optimization Design of a Intelligent Programmable Controller Based on CFD Software

With the increasing functionality of intelligent programmable controllers and the continuous improvement in power and component density, the problem of rapidly rising temperatures during operation has become increasingly prominent, directly affecting the controller's lifespan and stability[1]. Therefore, improving the heat dissipation efficiency of intelligent programmable controller modules has become a key focus of research. This paper introduces a method of installing aluminum alloy heat sinks on the core chip of the controller and carefully designing the fin shape and size of the heat sink to enhance heat conduction and heat convection effects, improving heat dissipation performance. This paper also constructs a thermal equivalent model of the controller and simulates the impact of different fin parameters on heat dissipation performance. Experimental results show that when the fin height is 5 millimeters, thickness is 2 millimeters, and spacing is 4 millimeters, the heat sink exhibits the highest thermal conductivity and the lowest thermal resistance, achieving optimal heat dissipation effectiveness.

Development of a multi-stage model for freezing of a suspended binary solution droplet

The importance of developing spray freeze-drying technology for extending the shelf life of biological and pharmaceutical materials has never been greater, given the increasing food shortage and the strong demand for pharma processes. In particular, the optimal thermal design and application of spray freeze-drying technologies now depend on the estimation of nucleation behavior for droplet solidification (especially the droplets of binary mixtures). Although earlier nucleation models could estimate the nucleation rate and temperature of solidifying droplets, few considered extreme environmental factors, such as extremely low ambient temperatures below −60C∘. To ensure the preservation and storage of biological and pharmaceutical products, such as vaccines and protein drugs, these conditions are essential. Hence, developing an accurate and trustworthy mathematical framework for simulating nucleation is paramount. In this study, a multi-stage, hybrid analytical-numerical model for droplet solidification is developed while coupled with a gradient-based optimization algorithm. Specifically, the five-stage solidification of binary mixtures is simulated (including supercooling of liquid, nucleation, recalescence, equilibrium freezing, and subcooling of solid), which captures the dynamic behaviors of temperature and composition or solute concentration during phase change. The heterogeneous nucleation of a binary-mixture droplet in a frigid environment is predicted and validated against a series of experiments on single suspended droplets at a wide range of ambient temperatures between −20 and −160C∘. The freezing curves of different solute concentrations are also validated against experimental data. It is found that significant variations in interfacial tension lead to abrupt changes in nucleation temperature for extremely cold environments. Further, the effects of the concentration, contact angle, droplet size, and heat transfer coefficient are investigated.

Comparing global sensitivity analysis methods for the joint thermal design parameters of a space manipulator based on the Sobol’ and PAWN

The Global Sensitivity Analysis method works well for screening and ranking impacting factors. It can help with thermal design optimization and thermal model calibration. This study compares the use of Sobol's variance-based method with PAWN's density-based method for 12 thermal design parameters (such as the properties of thermal control coating and the contact heat transfer coefficient between components) of a space manipulator joint thermal model. The two techniques have high consistent parameter sensitivity. The analysis's findings indicate that the junction electric box and shell's temperature variations are significantly influenced by the cooling surface's coating properties. The cooling surface's absorption rate has a bigger impact than emissivity does. Only the characteristics of the multilayer mask and the state of parcel coverage have a significant impact on the temperature of the joint shell. The outcomes offer technical backing for cooperative optimization in thermal design. The identical no-effect parameters are discovered to be recognized by Sobol' and PAWN at the same time, but they are ordered in different ways. The limitation of Sobol's technique, which uses variance as a sensitivity index, may be the cause of this outcome. Finally, the sensitivity response results are re-substituted into the finite element thermal model. The changing rule of temperature curve is used to further confirm the accuracy of the sensitivity results.

Optimum thermal design for heat and mass transfer of non-Newtonian liquid within converging conduit with thermal jump and zero-mass flux

The performance of heat transfer materials is strongly dependent on physical characteristics such as viscosity, thermal conductivity, and density. Among these, evolving thermal conductivity is a critical element in fluid heat transmission. In general, the poor thermal conductivity of industrial fluids is a significant barrier for heat transfer properties. The problem, however, has been revealed with the emergence of nanoparticles generated by various materials. This effort aims to demonstrate the mass and heat transfer attributes of the Carreau nanofluid through non-parallel channel with zero mass flux and variable thermal conductivity. The Buongiorno nanofluid model introduces the dual diffusion phenomena of nanomaterial. The analysis are carried out after reduction of governing ODE's into first order. The reduced equations are tickled numerically with Rung-Kutta method. The findings demonstrate that the thermal source, Brownian motion and thermal conductivity parameters exhibits a linear ascent on temperature profile, whereas effect of Prandtl number Pr is conflicting. The mass transfer rate is impacted negatively by the mass diffusivity value. The existence of the variables thermal conductivity factor ∈1 and mass diffusivity ∈2 leads to an elevated rate of heat and mass transmission. In the current investigation, a comparison is made for heat and mass transfer performance.