Location Of Heat Source Research Articles

Effect of thermal radiation and inclined magnetic field on thermosolutal mixed convection in a partially active wavy trapezoidal enclosure filled with hybrid nanofluid

This work focuses on the examination of the magneto-thermosolutal convection in a lid-driven wavy trapezoidal enclosure in the presence of thermal radiation. The wavy cavity is filled with radiative Fe3O4-Cu-H2O hybrid nanoliquid. In this work, two cases are considered depending on the location of heat and concentration sources. A portion of the vertical walls are kept hot and in high concentration while the remaining parts are in adiabatic condition. The adiabatic flat upper lid is moving towards the right with equal speed. In addition, the lower wavy border is cold and low concentration. The governing Navier–Stokes equations are modeled to describe thermosolutal phenomena within the wavy enclosure. These equations are solved by reconstructing a recently developed compact scheme. Computed outcomes are presented in terms of streamlines, isotherms, iso-concentrations, average Nusselt and Sherwood numbers to evoke the thermosolutal phenomena for various physical parameters. Also, computing in-house code is validated with the published numerical and experimental and literatures. This investigation surveys the roles of several well-defined parameters such as Richardson number (Ri), Lewis number (Le), Buoyancy ratio number (N), Radiation parameter (Rd), Hartmann number (Ha), inclination of angle (γ), undulation of the wavy surface (d) and solid volume fraction (ϕhnp) of the hybrid nanofluid. An increase in the Lewis number (Le) enhances species diffusion within the system but diminishes thermal transport. This work reveal that a change in ϕhnp from 0% to 4%, heat transfer is upgraded up to 4.28% in Case I and 3.93% in Case II while mass transfer is declined by 2.24% in Case I and 1.60% in Case II. Results indicate that Case I performed better than Case II in the case of energy and solutal transfer. This work has many practical applications such as heat exchangers, electronic device cooling, food processing and porous industrial processes.

Advancements in Li-Ion Battery Safety: A Multiphysics and Deep Learning Approach for Thermal Runaway Prediction

The increasing adoption of Lithium-ion batteries (LIBs) in various applications necessitates an enhanced focus on safety, particularly concerning the thermal runaway (TR) phenomenon. This study introduces a novel framework combining multiphysics modeling and deep learning (DL) to predict TR in cylindrical Li-ion batteries. The primary objective is to prevent catastrophic battery failures by identifying critical thermal conditions leading to TR.Our methodology integrates state-of-the-art convolutional neural networks (CNNs) and the "You Only Look Once" (YOLO) object detection model to classify and detect the evolution of TR stages in LIBs. The CNNs, trained on simulated thermal images generated through comprehensive multiphysics modeling, classify TR into three distinct stages: safe operation, critical condition, and actual TR occurrence. The YOLO model further pinpoints the location of TR heat sources, enhancing the predictive accuracy.The multiphysics model employed captures the coupled thermal and electrochemical behavior of LIBs, focusing on the solid electrolyte interface (SEI) formation/decomposition on the anode as the primary degradation mechanism. The simulated thermal imagery, reflecting real-time battery conditions, serves as a dataset for training our DL models. This combination of physics-based and data-driven approaches allows for a detailed understanding of TR dynamics.In our study, we also explored the efficiency of other neural network architectures, notably ResNet, EfficientNet, and MobileNet for their potential in TR prediction. ResNet, known for its deep residual learning framework, demonstrated proficiency in handling the complex, layered features of thermal images. EfficientNet, optimized for efficiency and accuracy, showed promise in its scalable architecture and balanced depth, width, and resolution, making it a viable alternative for real-time TR detection. The MobileNet known for its lightweight architecture and efficiency on mobile devices, was evaluated for its capability to process thermal images in a resource-constrained environment. It demonstrated a remarkable balance between computational efficiency and predictive accuracy, making it a viable option for real-time TR monitoring in portable LIB applications.While MobileNet, ResNet, and EfficientNet each showed potential in their respective capacities, our primary CNN-YOLO framework emerged as the most effective for TR prediction in this study. Its proficiency in extracting spatial features and detecting anomalies in real-time was critical for early TR identification in LIBs.Results demonstrate high accuracy in predicting TR stages and heat source locations, emphasizing the ability of the integrated approach. The study highlights the importance of thermal management and real-time monitoring in LIBs, offering insights into the thermal behavior under various load conditions. The predictive framework not only advances the fundamental understanding of TR in LIBs but also provides practical implications for enhancing battery safety in applications like electric vehicles and consumer electronics.In conclusion, this work presents a significant stride in battery research, paving the way for safer and more reliable energy storage solutions. The combined multiphysics modeling and DL framework offer a robust tool for predicting and preventing TR in LIBs, contributing to the broader goal of advancing battery technology in the modern energy landscape.

The Influence of the Fire Point on the Thermal Dynamic Disaster in the Goaf

A thermal dynamic disaster in the goaf is one of the most serious coal mine disasters formed by coal spontaneous combustion and gas interweaving. However, the influence of the high-temperature hidden fire source formed in the goaf on the evolution law of thermal dynamic disasters is not clear, and effective prevention and control measures cannot be taken. Therefore, this paper uses the experimental platform of thermal dynamic disaster in the goaf to study the influence of different fire point positions on the development of thermal dynamic disaster in the goaf through a similar simulation experiment of thermal dynamic disaster evolution in the goaf and analyzes the corresponding relationship between temperature and CO concentration in the upper corner. The results show that under different locations of heat source, the high-temperature heat source of coal spontaneous combustion migrates to the air leakage side with sufficient oxygen supply, and an oxygen-poor circle is formed near the ignition point. Under the action of air leakage flow, CH4 accumulates in the deep part of the goaf on the return air side. Due to the increase in coal, part of CH4 is produced, which leads to the increase in concentration of CH4 at the ignition point. Under the action of different heat sources, the changing trend of concentration of CO and temperature in the return air corner is the same, but the temperature change in the return air corner shows a lag compared with the change in the concentration of CO, so concentration monitoring of CO can reflect the evolution process of the fire field in the goaf more quickly than temperature monitoring.

Simulation and Experimental Investigation of the Effect of Pore Shape on Heat Transfer Behavior of Phase Change Materials in Porous Metal Structures.

With the gradual increase in energy demand in global industrialization, the energy crisis has become an urgent problem. Due to high heat storage density, small volume change, and nearly constant transition temperature, phase change materials (PCMs) provide a promising method to store thermal energy. In this work, we designed and fabricated three kinds of porous metal structures with hexagonal, rectangular, and circular pores and explored the phase change process of PCMs within them. A two-dimensional numerical model was established to investigate the heat transfer process of PCMs within different shapes of porous metal structures and analyze the influence of heat source location on the thermal performance of the thermal storage units. Visualization experiments were also carried out to reveal the melting process of PCMs within different porous metal structures by a digital camera. The results show that paraffin in a porous metal structure with hexagonal pores has the fastest melting rate, while that in a porous metal structure with circular pores has the slowest melting rate. Under the bottom heating mode, the melting time of the paraffin in porous metal structures with hexagonal pores is shortened by 18.6% compared to that in porous metal structures with circular pores. Under the left heating mode, the corresponding melting time is shortened by 16.7%. These findings in this work will offer an effective method to design and optimize the structure of porous metal and improve the thermal properties of PCMs.

Rapid heat source layout optimization in three‐dimensional integrated circuits using artificial neural network reduced‐order model in combination with Bayesian optimization

AbstractIn this study, an efficient optimization framework was developed to determine the parameters of through‐silicon vias and the layout of heat sources in three‐dimensional integrated circuits (3D ICs), employing an artificial neural network (ANN) reduced‐order model in conjunction with a Bayesian optimization (BO) algorithm. The proposed method effectively predicts the temperature distribution in 3D ICs and refines their thermal parameters, offering solutions to thermal management challenges. Latin hypercube sampling was utilized for data sampling, enhancing the previously established rapid thermal analysis method through parameterization of heat source locations. The temperature distribution data for varying hotspot locations in 3D ICs were fitted using an appropriately defined objective function, leading to the development of a reduced‐order ANN model that accelerates temperature prediction. The computational results demonstrate that the neural network model exhibits a deviation in predicted values of less than 2%, and the coefficient of determination R2 approximately 0.93, underscoring high predictive accuracy. Additionally, the optimization outcomes and the efficiency of the selected BO algorithm were thoroughly evaluated. Notably, the BO algorithm achieved the global optimum in just 4.07 s across 250 iterations, demonstrating an effective power distribution strategy for the 3D ICs model.

Geothermal heat source estimations through ice flow modelling at Mýrdalsjökull, Iceland

Abstract. Geothermal heat sources beneath glaciers and ice caps influence local ice-dynamics and mass balance but also control ice surface depression evolution as well as subglacial water reservoir dynamics. Resulting jökulhlaups (i.e., glacier lake outburst floods) impose danger to people and infrastructure, especially in Iceland, where they are closely monitored. Due to hundreds of meters of ice, direct measurements of heat source strength and extent are not possible. We present an indirect measurement method which utilizes ice flow simulations and glacier surface data, such as surface mass balance and surface depression evolution. Heat source locations can be inferred accurately to simulation grid scales; heat source strength and spatial distributions are also well quantified. Our methods are applied to the Mýrdalsjökull ice cap in Iceland, where we are able to refine previous heat source estimates.

Three-dimensional finite element-based data-driven thermal model of in-situ automated fiber placement

This study proposes a data-driven model for thermal history prediction during in-situ consolidation of thermoplastic composites using Hot Gas Torch (HGT)-assisted Automated Fiber Placement (AFP). Temperature data was generated using a three-dimensional finite element (FE) model that was fitted to and validated with experimentally measured temperatures. Temperature curves for various combinations of heat source velocity and locations through the thickness and width were extracted from the fitted FE model. Three feedforward neural networks were trained to predict the temperature distribution during processing. The work demonstrates the effectiveness and potential of using neural networks for online prediction and optimization.

Fluid‐structure interaction study of an oscillating heat source effect on the natural convection flow

AbstractThe objective of this paper is to investigate fluid‐structure interaction (FSI) within conjugate natural convection. An oscillating fin, featuring a heat source placed at different locations, is examined using the Arbitrary Lagrangian–Eulerian formulation. The Galerkin finite element method is utilized to solve nonlinear dimensionless equations. Verification of grid independence is conducted and the model undergoes validation. Simulation outcomes for three fin positions (left, center, and right) and three heat source locations (bottom, middle, and top) are presented, illustrating streamlines, isotherms, and the average Nusselt number. The governing equations and boundary conditions are addressed using the finite element method. Temperature profiles in four scenarios are analyzed, along with horizontal velocities at different levels (0, D/2, D from the bottom wall). Dimensionless time (10−5 ≤ t ≤ 3), Ra = 106, Kr = 10, E = 1011 are used as parameters. The impact of the heat source position on vibratory motion is evaluated through Nusselt number variation, affecting heat exchange in different cases. The results show that heat transfer is minimal for a source location at the center of the fin (c). These findings also offer insights into FSI applications in economics and industry, guiding practical design considerations. Additionally, coupling the vibratory motion of the heat source with the flexible oscillating fin at the same frequency enhances understanding of the system.

Geothermal target detection integrating multi-source and multi-temporal thermal infrared data

Thermal infrared remote sensing (TIRS) technology is intriguing for geothermal anomaly detection due to its time efficiency and cost-effectiveness. Land surface temperature (LST) derived from TIRS indicates potential geothermal anomalies. However, LST on different natural features, exhibiting relatively cold/hot anomalies in daytime and nighttime, affected the identification of abnormal areas. The aim of this study was to highlight geothermal anomalous areas by integrating multi-view daytime and nighttime LST data. Specifically, the winter LST time series of drillings on 2013–2023 was processed based on Landsat-8 TIRS in diurnal scenarios. An information aggregation classification method about Dempster-Shafer evidence theory was presented to target recognition at nighttime by the ASTER LST product. This day-night information fusion analysis effectively highlighted potential areas with geothermal anomalies. The results demonstrated that the geothermal anomalies were mainly distributed along the Ruili-Longling Fault and Wanding Fault in a Northeast (NE) – Southwest (SW) direction, and the NE side was more significant than the SW side. These findings suggested that the east side was closer to the magma heat source, and the presumed magma heat source location aligned closely with the detected geothermal anomalies. Geological data were employed for geological interpretation of the LST anomaly area, eliminating non-geothermal influences. Along with drilling data verification, this method was successfully used to identify geothermal anomaly areas in Ruili City, Yunnan Province. Overall, this study provided valuable insights into the detection of geothermal anomalies through TIRS, contributing to the successful development of geothermal resources.

Characterization of the acoustic pressure waveforms in Rijke tubes with spatially varying heat sources and temperature distributions

In this work, we implement an asymptotic expansion technique that leverages a small perturbation parameter that arises in the context of one-dimensional tubes with open-open end point configurations and spatially varying heat sources. This approach, when paired with a spectral collocation eigensolver, enables us to produce accurate predictions of the pressure mode shapes and frequencies over a wide range of parameters. These include the temperature gain across the heat source, the heat source length and location, and the overall thermal profile. The latter is intended to reproduce different flow heating configurations that emulate Rijke tube characteristics. Specifically, this investigation begins by considering three piecewise representations of the heat source by juxtaposing constant–constant temperatures before and after a heating element whose temperature is prescribed locally using three analytical functions: linear, exponential, and power-law profiles. This is followed by a logistic distribution that can be globally applied to provide a uniformly valid, continuous, and differentiable thermal profile spanning the entire tube, including the heat source element. Our fundamental formulation relies on Green's functions and an integral formulation that enables us to extract all acoustic frequencies analytically. These are found to increase monotonically with successive elevations in the temperature gain across the heat source, retractions of the heat source, length reductions in the heat source, and smoothing of the temperature gain. Along similar lines, the pressure mode shapes are found to exhibit blunter and often linear variations for higher temperature gains, longer heat sources, and upstream displacements of the heat source toward the inlet.

Non-contact heating methods of carbon fiber thermoplastic composites based on Joule thermal properties: Optimization and validation

Carbon fiber matrix composites have garnered significant attention due to their exceptional properties, finding wide-ranging applications in industries such as aerospace, transportation, and military. In this study, a novel approach involving the addition of nickel was explored to fabricate carbon fiber-reinforced thermoplastic polymer composites and composite materials filled with nylon 12, utilizing nickel foam as matrix. Subsequently, electromagnetic-thermal multiphysics coupling models were established to analyze the induction heating process. The study investigated the effects of nickel content, spatial location of the main heat source, coil current, and coil frequency on the temperature rise and heat generation. Additionally, a linear fit was applied to determine the material surface temperature and related factors during induction heating. Furthermore, a theoretical formula was derived for induction heating under ideal conditions, elucidating the heat transfer mechanism and the influence of various factors on heat generation. The findings of this research serve as a reference for optimizing the utilization of Joule heat in composites, thereby expanding the application of carbon fiber-reinforced polymer composites in temperature regulation and operational control.

A combined multiphysics modeling and deep learning framework to predict thermal runaway in cylindrical Li-ion batteries

In this paper, machine learning (ML)/deep learning (DL) has been employed to predict the thermal runaway (TR) occurrence in Li-ion batteries (LIBs). State-of-the-art convolutional neural networks (CNNs) are adopted to forecast the evolution of the TR phenomenon with a classification approach of three distinct stages, namely, safe operation, critical condition of triggered the TR, and the actual TR occurrence. In addition, the “you look only once” (YOLO) object detection ML model is used to identify the location of the TR heat sources within the battery. The neural networks are trained on simulated thermal images, computed with the multiphysics modeling of LIBs. The multiphysics modeling approach comprises a coupled thermal, electrochemical (P2D model), and degradation sub-models. The degradation phenomenon leading to the TR considered in this paper is the solid electrolyte interface (SEI) formation/decomposition on the negative electrode. The proposed ML model exhibits high accuracy in predicting the TR evolution stages and heat source locations. The combined multiphysics and ML modeling approach developed in this work provides qualitative insights for ‘on-the-fly’ prediction of the TR, as well as a framework for extending data-driven methodologies to broad applications in electrochemistry.

Weighted Centroids in Adaptive Nelder–Mead Simplex: With heat source locator and multiple myeloma predictor applications

This paper introduces a novel approach for enhancing the Nelder–Mead Simplex method by utilizing the weighted mean of simplex vertices to efficiently determine the search direction towards optima. The Nelder–Mead algorithm is commonly used for solving unconstrained optimization problems through iterative direct search. While the Nelder–Mead algorithm excels in low-dimensional scenarios, its challenges in higher dimensions are addressed in our study.We reveal that the Nelder–Mead Simplex algorithm’s operations rely not only on the problem dimension but also on adaptive descending parameters for calculating the weighted centroid of vertices. The adaptive weights are determined based on the concept that generating a descending sequence depends on the fitness values of vertices. Consequently, the search direction tends to prioritize vertices with superior fitness values over others.Through extensive testing on renowned high-dimensional benchmark functions, the proposed algorithm outperforms both the standard and adaptive Nelder–Mead simplex algorithms in terms of performance demonstrating superior consistency and reliability. Furthermore, it is applied to an inverse heat source location problem formulated as a linear elliptic partial differential equation, serving as a large-scale real-world application. Additionally, the algorithm successfully addresses a neural network model solving a classification problem concerning the prediction of multiple myeloma, a type of bone marrow cancer, in patients, achieving an average accuracy of 92.3% with a minimal standard deviation of 0.13%. When the results obtained from eight different methods used in the study are compared in terms of performance in general, the proposed method is among the top three optimization algorithms. These real-world applications showcase the method’s versatility.

Evaluation of photovoltaic-based solar chimney–assisted stack ventilation within a large space hall: A case study

Photovoltaic (PV)-based solar chimneys could assist stack ventilation within a large space hall. This study considered a comprehensive office building with a PV-based solar chimney as a case study to explore measures for enhancing stack ventilation. A computational fluid dynamics (CFD) model was established, and the influences of the inner heat source, chimney inlet position, solar heat gain and photovoltaic (PV) arrangement on the ventilation rate were investigated. The results revealed that PV-based solar chimneys could assist stack ventilation and would effectively provide a suitable indoor environment for occupied areas during the transitional season. Raising the chimney inlet position is conducive to improving the ventilation rate and cooling performance. The increase in the inlet position from a height of 0.2 m to 3.8 m would increase the ventilation rate up to 5%. The heat gain from the PV backside is an important method of enhancing the ventilation rate. A 200 W/m2 heat gain can lead to a 16.4% increase in ventilation. Moreover, the lower the heat source location, the higher the ventilation efficiency. This study provides basic strategies for optimising design schemes to improve natural ventilation efficiency.

Detection of surface moving heat source using experimental temperature measurements on the opposite surface and inverse techniques

The identification of mobile sources is a widely researched area in multiple scientific and technical domains. Detecting mobile heat sources is an essential task in different fields, including fire safety, environmental monitoring, material science, and energy systems. In this investigation, we present an innovative solution to solve the inverse problem of detecting mobile heat sources, utilizing a three-dimensional transient inverse heat conduction problem (IHCP) formulation. The goal is to identify a moving heat source from thermographic infrared measurements of the rear face. The proposed method includes a finite volume approach with model reduction techniques to improve computational efficiency. Additionally, we use the conjugate gradient method to solve the inverse problem. We verified the proposed approach by conducting experiments on a metal plate with a moving heat source from a laser and comparing recalculated temperature of the front face with thermocouple measurements. Our results indicate that our method can detect the location and trajectory of the mobile heat source with high accuracy and efficiency. This study also highlighted the limits of source speed that can be detected by this method. This technique could be considered as an excellent option for real-time monitoring of mobile heat sources in practical applications.

Performance analysis and multi-objective optimization of a combined system of Brayton cycle and compression energy storage based on supercritical carbon dioxide

The energy storage system plays a pivotal role in optimizing the power grid’s peak mobilization. In this study, we propose a combined cycle of supercritical carbon dioxide (sCO2) recompression cycle (sCO2-RC) coupled with compressed sCO2 energy storage (S-CCES) system. Two distinct layouts are thoroughly investigated, each corresponding to different auxiliary heat source locations: utilizing waste heat to heat hot water of S-CCES (SCW-CCES) and heat CO2 of S-CCES (SCC-CCES). Through comprehensive thermodynamic modeling and analysis, we evaluate the performance of both layouts and conduct multi-objective optimization using a genetic algorithm. The results indicate that, under identical design conditions, the heat input leads to a respective increase of 4.25 MW and 7.02 MW in the output power of S-CCES for the SCW-CCES and SCC-CCES layouts. Furthermore, parametric analysis reveals that the performance of SCC-CCES surpasses that of SCW-CCES when considering performance indicators other than round-trip efficiency (RTE). The results obtained from multi-objective optimization demonstrate that the optimal solution for SCW-CCES achieves a higher RTE of 25.94 %, while the optimal solution for SCC-CCES exhibits a superior levelized cost of electricity and exergy efficiency, amounting to 68.94 $/MWh and 58.76 %, respectively.

Opposing Mixed Convection in an Open Parallelogram Cavity with the Horizontal Channel: Effects of the Heat Source Length and Location

In this work, a numerical simulation was used to investigate the impact that the length and positioning of the heat source had on the mixed convection opposing flow that occurred within the horizontal channel that included an open parallelogram cavity. When the wall being heated is facing away from the direction of the incoming force. Different placements of the heat source along the cavity's sidewalls were explored, with the length of the heat source set at (ε) (0.25<ε<1). The cool, steady-speed air came in through the sides of the canal. All other walls are adiabatic, while the vertical walls on the inflow and outflow sides are isothermal. The governing equations were solved using the finite element technique. For several different values of the Richardson number (Ri=0.1-100), we estimated the flow and heat fields. While the Prandtl number is held at 0.71 and the Reynolds number is maintained at 100. The average Nusselt values, as well as the findings of the flow and temperature fields, were reported. The findings demonstrate that both the Richardson number (Ri) and the distance from the heat source (ε) positively affect the heat transfer rate. It was also determined that for all Richardson numbers, the highest average Nusselt number is attained at the higher portion of the right wall of the hollow.

Predicting thermoacoustic stability characteristics of longitudinal combustors using different endpoint conditions with a low Mach number flow

Combustion instability frequently occurs in propulsion and power generation systems. It is characterized by large-amplitude acoustic oscillations leading to undesirable consequences. Designing a stable combustor by predicting its stability characteristics is therefore essential. This study centers upon modeling a straight one-dimensional combustor with an acoustically compact heat source, low Mach numbers, and different end point conditions. To predict the stability characteristics, we examine six combustor configurations (open–closed, closed–closed, open–choked, closed–choked, open–open, and closed–open). A Galerkin expansion technique is implemented to capture the acoustic disturbances. The unsteady heat release is modeled using an N−τ formulation. The results show that steepening of the mean temperature gradient causes the eigenfrequency associated with an open outlet to increase more rapidly than that of a choked nozzle. Compared to a choked boundary, an open outlet generates higher eigenfrequencies and lower sound energy when coupled with an open inlet. Conversely, it triggers lower eigenfrequencies and higher sound energy using a closed inlet. The maximum possible growth of sound energy, Gmax, remains positively correlated with the inlet temperature, interaction index N, and inlet Mach number, but inversely proportional to the temperature gradient. The heat source extrema leading to the most and least amplified system energy seem to shift upstream, when the mean temperature gradient is successively increased. Their coordinates are similar in half-open tubes and exhibit a converse relation between the open–open and closed–choked tubes. At sufficiently low Mach numbers, the choked and closed outlets show equivalence in acoustic frequencies, transient energy evolution, and optimal heat source locations.

Mapping high-resolution energy consumption CO2 emissions in China by integrating nighttime lights and point source locations

High-resolution CO2 emission inventories are essential to accurately assess spatiotemporal patterns of carbon emissions, analyze factors affecting carbon emissions, and develop sound emission reduction policies. The top-down approach is often used to map CO2 emissions from energy consumption due to its simplicity. However, the spatial proxy variables commonly used in this method, such as nighttime light (NL), land use, and population, are difficult to reflect the spatial distribution of CO2 emissions from large point sources. Therefore, this study uses the active fire product provided by Visible Infrared Imaging Radiometer Suite (VIIRS) sensors on Suomi National Polar-Orbiting Partnership (Suomi-NPP) satellite to extract the location of industrial heat sources in China, and then develops an improved CO2 emission estimation model by integrating industrial heat sources, Global Energy Monitor (GEM) power plant location and nighttime lights. The model is used to map CO2 emissions from energy consumption at a resolution of 1 km*1 km from 2012 to 2019 in China. It is found that the overall accuracy of the model is greatly improved at the provincial level, the R2 value is >0.75, and RMSE is distributed in 40–110 Mt. At the grid level, the improved model allocates more carbon emissions to the grid where the point source is located, which makes the spatial distribution of CO2 emissions more reasonable.

Thermosolutal mixed convection in a partially heated and concentrated enclosure with various wall speed ratios

In this work, the effects of various wall speed ratios and different heating and concentrating area of the side walls on thermosolutal mixed convection are analyzed. Nine cases are considered depending on the location of heat and concentration sources. Both the horizontal walls are in motion in the same or opposite direction with different speed ratios. A portion of length B of the vertical walls is heated and concentrated uniformly while the remaining parts of these walls are in adiabatic condition. The horizontal walls are considered with cold temperature and low concentration. The governing equations are modeled to describe thermosolutal phenomena. These equations are solved by reconstructing a recently developed compact scheme. To strengthen the analysis performed in this study, we have validated our in-house code with the numerical and experimental data. Computed results are presented in terms of streamlines, isotherms, iso-concentrations, and average Nusselt and Sherwood numbers to evoke the thermosolutal phenomena for various physical parameters. The computed outcomes are scrutinized for different values of the well-defined parameters namely thermal Grashof number (), Prandtl number (), Richardson number (), buoyancy ratio (), wall speed ratios () and Lewis number (). This numerical study has been carried out for different combinations of partially active walls to reveal their effects on heat and mass transfer. It is observed that the thermal and solutal transfer performance becomes effective with maximum wall speed ratios and lower Richardson numbers.