Convection Parameter Research Articles

Discriminant Analysis for Severe Storm Environments in South-Central Brazil

Abstract Severe storms produce hazardous weather phenomena, such as large hail, damaging winds, and tornadoes. However, relationships between convective parameters and confirmed severe weather occurrences are poorly quantified in south-central Brazil. This study explores severe weather reports and measurements from newly available datasets. Hail, damaging wind, and tornado reports are sourced from the PREVOTS project from June 2018 to December 2021, while measurements of convectively induced wind gusts from 1996 to 2019 are obtained from METAR reports and from Brazil’s operational network of automated weather stations. Proximal convective parameters were computed from ERA5 reanalysis for these reports and used to perform a discriminant analysis using mixed-layer CAPE and deep-layer shear (DLS). Compared to other regions, thermodynamic parameters associated with severe weather episodes exhibit lower magnitudes in south-central Brazil. DLS displays better performance in distinguishing different types of hazardous weather, but does not discriminate well between distinct severity levels. To address the sensitivity of the discriminant analysis to distinct environmental regimes and hazard types, five different discriminants are assessed. These include discriminants for any severe storm, severe hail only, severe wind gust only, and all environments but broken into “high” and “low” CAPE regimes. The best performance of the discriminant analysis is found for the “high” CAPE regime, followed by the severe wind regime. All discriminants demonstrate that DLS plays a more important role in conditioning Brazilian severe storm environments than other regions, confirming the need to ensure that parameters and discriminants are tuned to local severe weather conditions.

An Ensemble of Neural Networks for Moist Physics Processes, Its Generalizability and Stable Integration

AbstractWith the recent advances in data science, machine learning has been increasingly applied to convection and cloud parameterizations in global climate models (GCMs). This study extends the work of Han et al. (2020, https://doi.org/10.1029/2020MS002076) and uses an ensemble of 32‐layer deep convolutional residual neural networks, referred to as ResCu‐en, to emulate convection and cloud processes simulated by a superparameterized GCM, SPCAM. ResCu‐en predicts GCM grid‐scale temperature and moisture tendencies, and cloud liquid and ice water contents from moist physics processes. The surface rainfall is derived from the column‐integrated moisture tendency. The prediction uncertainty inherent in deep learning algorithms in emulating the moist physics is reduced by ensemble averaging. Results in 1‐year independent offline validation show that ResCu‐en has high prediction accuracy for all output variables, both in the current climate and in a warmer climate with +4K sea surface temperature. The analysis of different neural net configurations shows that the success to generalize in a warmer climate is attributed to convective memory and the 1‐dimensional convolution layers incorporated into ResCu‐en. We further implement a member of ResCu‐en into CAM5 with real world geography and run the neural‐network‐enabled CAM5 (NCAM) for 5 years without encountering any numerical integration instability. The simulation generally captures the global distribution of the mean precipitation, with a better simulation of precipitation intensity and diurnal cycle. However, there are large biases in temperature and moisture in high latitudes. These results highlight the importance of convective memory and demonstrate the potential for machine learning to enhance climate modeling.

Deriving Severe Hail Likelihood from Satellite Observations and Model Reanalysis Parameters Using a Deep Neural Network

Abstract Geostationary satellite imagers provide historical and near-real-time observations of cloud-top patterns that are commonly associated with severe convection. Environmental conditions favorable for severe weather are thought to be represented well by reanalyses. Predicting exactly where convection and costly storm hazards like hail will occur using models or satellite imagery alone, however, is extremely challenging. The multivariate combination of satellite-observed cloud patterns with reanalysis environmental parameters, linked to Next Generation Weather Radar (NEXRAD) estimated maximum expected size of hail (MESH) using a deep neural network (DNN), enables estimation of potentially severe hail likelihood for any observed storm cell. These estimates are made where satellites observe cold clouds, indicative of convection, located in favorable storm environments. We seek an approach that can be used to estimate climatological hailstorm frequency and risk throughout the historical satellite data record. Statistical distributions of convective parameters from satellite and reanalysis show separation between nonsevere and severe hailstorm classes for predictors that include overshooting cloud-top temperature and area characteristics, vertical wind shear, and convective inhibition. These complex, multivariate predictor relationships are exploited within a DNN to produce a likelihood estimate with a critical success index of 0.511 and Heidke skill score of 0.407, which is exceptional among analogous hail studies. Furthermore, applications of the DNN to case studies demonstrate good qualitative agreement between hail likelihood and MESH. These hail classifications are aggregated across an 11-yr Geostationary Operational Environmental Satellite (GOES) image database from GOES-12/13 to derive a hail frequency and severity climatology, which denotes the central Great Plains, the Midwest, and northwestern Mexico as being the most hail-prone regions within the domain studied.

Investigating Spatial Variations of Compound Heat–Precipitation Events in Guangdong, China through a Convection-Permitting Model

Compound heat–precipitation events exert significant impacts on severe weather occurrences. Intense vertical air movement, driving vigorous convection, primarily contributes to the formation of extreme precipitation. Nevertheless, such compound events’ temporal and spatial variation patterns at convection-permitting resolutions remain inadequately explored. This study assesses the performance of the Convection-Permitting Model (CPM) against a model of convection parameterization while investigating the spatial dynamics of compound heat–precipitation events in Guangdong, China. Our findings indicate that the CPM exhibits heightened reliability and precision in simulating temperature and precipitation patterns, especially in extreme precipitation simulation, which would be highly underestimated without a convection-permitting process. Projections from the CPM reveal that, across historical and future periods, the occurrence frequency and fraction of T-P events (instances of extreme heat followed by extreme precipitation) surpass those of P-T events (occurrences of extreme precipitation followed by extreme heat). For T-P events, the CPM exhibits better capability in capturing high-frequency occurrence areas, whereas the results of the relatively low-resolution model show less distinct spatial variations. Both types of events exhibit noticeable upward trends yearly within each period. By the close of this century, the provincial average frequency of P-T events is anticipated to decrease from 20.32 times to 14.55 times. In contrast, the frequency of T-P events is projected to increase from 87.7 times to 101.38 times. These projected changes underscore the shifting dynamics of compound heat–precipitation events in the study region.

Wave propagation through a stationary field of clouds: A homogenisation approach

AbstractThe effect of a subgrid‐scale cloud field on the propagation of long atmospheric waves is investigated using a new scale‐consistent formulation based on the asymptotic theory of homogenisation. A key aim is to quantify potential model errors in wave propagation speeds, introduced by using averaged fields in place of the fully resolved circulation, in the setting of a simple stratified Boussinesq midlatitude ‐channel model. The effect of the cloud field, represented here by a random array of strongly nonlinear axisymmetric circulations, is found to appear in the large‐scale governing equations through new terms which redistribute the large‐scale buoyancy and horizontal momentum fields in the vertical. These new terms, which have the form of nonlocal integral operators, are linear in the cloud number density and are fully determined by the solution of a linear elliptic equation known as a cell problem. The cell problem in turn depends on the details of the nonlinear cloud circulations. The integral operators are calculated explicitly for example cloud fields and then dispersion relations are compared for different waves in the presence of clouds at realistic densities. The main finding is that baroclinic Rossby waves are significantly slowed and damped by the clouds, whilst inertia–gravity waves are affected almost exclusively by damping, most strongly at the lowest frequencies. In contrast, all waves with a barotropic structure are found to be almost unaffected by the presence of clouds, even at the highest realistic cloud densities. An important consequence of this study is a new approach to the closure of subgrid‐scale cloud fields in the parameterisation of convection in large‐scale atmospheric models.

Keller box computation for entropy generation analysis in the coating flow of magneto viscoelastic polymer nanofluid over a circular cylinder

PurposeThe need for precise synthesis of customized designs has resulted in the development of advanced coating processes for modern nanomaterials. Achieving accuracy in these processes requires a deep understanding of thermophysical behavior, rheology and complex chemical reactions. The manufacturing flow processes for these coatings are intricate and involve heat and mass transfer phenomena. Magnetic nanoparticles are being used to create intelligent coatings that can be externally manipulated, making them highly desirable. In this study, a Keller box calculation is used to investigate the flow of a coating nanofluid containing a viscoelastic polymer over a circular cylinder.Design/methodology/approachThe rheology of the coating polymer nanofluid is described using the viscoelastic model, while the effects of nanoscale are accounted for by using Buongiorno’s two-component model. The nonlinear PDEs are transformed into dimensionless PDEs via a nonsimilar transformation. The dimensionless PDEs are then solved using the Keller box method.FindingsThe transport phenomena are analyzed through a comprehensive parametric study that investigates the effects of various emerging parameters, including thermal radiation, Biot number, Eckert number, Brownian motion, magnetic field and thermophoresis. The results of the numerical analysis, such as the physical variables and flow field, are presented graphically. The momentum boundary layer thickness of the viscoelastic polymer nanofluid decreases as fluid parameter increases. An increase in mixed convection parameter leads to a rise in the Nusselt number. The enhancement of the Brinkman number and Biot number results in an increase in the total entropy generation of the viscoelastic polymer nanofluid.Practical implicationsIntelligent materials rely heavily on the critical characteristic of viscoelasticity, which displays both viscous and elastic effects. Viscoelastic models provide a comprehensive framework for capturing a range of polymeric characteristics, such as stress relaxation, retardation, stretching and molecular reorientation. Consequently, they are a valuable tool in smart coating technologies, as well as in various applications like supercapacitor electrodes, solar collector receivers and power generation. This study has practical applications in the field of coating engineering components that use smart magnetic nanofluids. The results of this research can be used to analyze the dimensions of velocity profiles, heat and mass transfer, which are important factors in coating engineering. The study is a valuable contribution to the literature because it takes into account Joule heating, nonlinear convection and viscous dissipation effects, which have a significant impact on the thermofluid transport characteristics of the coating.Originality/valueThe momentum boundary layer thickness of the viscoelastic polymer nanofluid decreases as the fluid parameter increases. An increase in the mixed convection parameter leads to a rise in the Nusselt number. The enhancement of the Brinkman number and Biot number results in an increase in the total entropy generation of the viscoelastic polymer nanofluid. Increasing the strength of the magnetic field promotes an increase in the density of the streamlines. An increase in the mixed convection parameter results in a decrease in the isotherms and isoconcentration.

Regional Variability in the Environmental Controls of Precipitation Regimes in the Tropics

AbstractThe environmental factors that control precipitation regimes in Boreal winter rainfall in the tropics and their regional variabilities are examined using a simple statistical analysis and a machine learning (ML) model. Radar‐derived precipitation from field campaigns at Darwin Australia, Manaus Brazil, and the Equatorial Indian Ocean, along with the corresponding large‐scale environmental variables from ERA5 reanalysis, are used. The dependence of marginal distributions of frequencies of these regimes on five environmental variables are calculated. The variables are hourly column integrated precipitable water (PW), convective available potential energy, convective inhibition (CIN), and lower and upper tropospheric wind shear. The simple ML model that predicts the probability of transition from suppressed to an active regime as a function of the environmental variables is designed and optimized for a potential application as a trigger function for convection parameterizations. To the first order the analysis shows an abrupt increase in the probability of an active regime near PW > 60 mm and CIN of <100 J kg−1. The key differences in the frequencies of active regimes among the regions are found to be related to the fact that over Darwin there is strong variability in PW while CIN is generally low. Over Amazon, on the other hand, both PW and CIN are quite variable. Over the Dynamics of the Madden‐Julian Oscillation domain the comparatively frequent low PW is compensated for by consistently low CIN resulting in a moderate frequency of an active regime.

Numerical approach for the modified heat transport process under improved Darcian and nonlinear mixed convective effects

Analysis regarding heat transport subjected to non-Darcian media has increased in science and technological utilization. Typical applications comprise grain storage, nuclear waste discarding, production of crude oil, pollution in groundwater, porous insulation, and several others. This motivates us to demonstrate the heat transport phenomenon in the viscous fluid's flow deformed by a linearly stretchable surface submerged in Darcy-Forchheimer porous medium. Properties of heat transfer are elaborated with nonlinear mixed convection and Cattaneo-Christov heat model. Slip phenomenon is implemented at the wall surface. The governing equations are achieved in dimensionless form through appropriate transformations. Numerical solutions are constructed through shooting scheme along with 4–5 order Runge-Kutta Fehlberg technique. Graphical behaviors of velocity field and liquid temperature are constructed through various physical parameters. Results witnessed that decrement occurs in velocity field for dominant behavior of velocity slip parameter while it enhances for dominant nonlinear mixed convection parameter.

Convective flow of hybrid nano particles in combination of TiO2+CuO/engine oil MoS2+ZnO/engine oil and Al2O3+Cu/engine oil with viscous dissipation over vertically moving surface: Numerical and Galerkin approach

Engine oil has a vast application sector as it is the most essential working lubricant for industrial machineries, engines of vehicles, and aircraft combustion processing. In order to find out more effective engine oil-based hybrid nanofluid in terms of heat transfer capabilities and to minimize the production cost, the novelty of our present study focused on three different combinations of nanoparticles (TiO2+CuO, MOS2+ZnO, and Al2O3+Cu) that are dispersed in Engine oil (EO). The fundamental aim is to examine the thermal and flow fluctuation of a convective dissipative engine oil-based hybrid nano liquid to understand the physical quantities and thermal properties over a vertically moving plate by employing the Galerkin Method (GM). Similarity variables are initiated to permute the set of governing equations into the nonlinear coupled ordinary differential equations along with Bc’s, which are solved using both numerical (Maple) and analytical method (GM). This MAPLE software uses the fourth-fifth-order Runge-Kutta-Fehlberg (RFK45) technique as a default function to solve numerically boundary value problems. Validation for this study has been conducted by comparison with previously published results. The results of the present study are given for both opposing and assisting flow with the distribution of fluid motion and temperature profiles, wall shear stress, and rate of heat transfer. The assisting flow has great significance on the flow velocity and temperature with increasing mixed convection parameters. The Brinkmann number and Prandtl number have a larger flow near the wall in temperature distribution and then increase and decrease respectively satisfying the asymptotic boundary conditions. Our results also reveal that the TiO2+CuO/Engine oil has a significant augment in terms of heat transfer than that of MoS2+ZnO/Engine oil and Al2O3+Cu/Engine oil.

MHD dissipative Powell-Eyring fluid flow due to a stretching sheet with convective boundary conditions and slip velocity

The novelty and motivation of this research can be emphasized by examining how the heat transfer mechanism of a non-Newtonian Powell-Eyring fluid, which flows because of a stretched sheet, is affected by factors like viscous dissipation, the slip velocity phenomenon, and Joule heating. In addition, the investigation delves into the heat transfer behavior of the fluid flow when it comes into contact with a convectively heated stretched surface that is influenced by varying fluid properties. This analysis also takes into account the influence of changing fluid characteristics and the presence of magnetic field. The numerical solutions of modelled equations that governing the problem are detected using the shooting technique. Also, in order to confirm the validity of the present investigation, a proper comparison with certain published works as a particular case of the present model is presented, and a perfect agreement is noted. With the use of diagrams and tables, the flow problem’s effective parameters are thoroughly discussed. Likewise, through a tabular representation, the values of the local Nusselt number and the skin-friction coefficient are computed and analyzed. Many significant conclusions can be drawn from numerical results. Most importantly, the local Nusselt number rises monotonically with both the surface convection parameter and the slip velocity parameter, but the local skin-friction coefficient has the opposite trend. The results indicate that the nanofluid temperature is enhanced by factors such as the surface convection parameter, magnetic field, and viscous dissipation. On the other hand, the slip velocity phenomenon leads to the opposite effect.

Significance of heat transfer rate in water-based nanoparticles with magnetic and shape factors effects: Tiwari and Das model

Nanofluids are implementable in a variety of applications, such as heat exchangers, the healthcare sector, the cooling of various devices, hybrid-powered machines, microelectronics, power plants, chemical processes, astronomical technology, cancer treatment, etc. Nanofluids also have enhanced heat transmission and thermal efficiency. The heat radiation of nanoparticles and the natural-convective flow of electrically conducting nanofluids over the rotating disk using Darcy Forchheimer’s porous media, thermal radiation is investigated in this paper. The nanoparticles titanium dioxide and single-walled carbon nanotubes are taken into account with base fluid water. The main goal of this investigation is to enhance heat transfer in nanofluids. The mathematical solution for the model has been obtained through the utilization of cylindrical coordinates. The flow model, which forms the basis of the investigation, is constructed around partial differential equations (PDEs). To address the inherent nonlinearity of these PDEs, physical similarities are employed to transform them into ordinary differential equations (ODEs). Subsequently, the fourth-order Runge–Kutta technique is employed via Matlab to solve these ODEs. The graphical examination of the velocities and temperature with various parameters is an exquisite display of scientific artistry. The magnetic field component is anticipated to exhibit an inverse correlation with velocities, while the temperature profile is expected to surge with the rise of the nonlinear mixed convection parameter. Additionally, the skin friction and Nusselt number are meticulously computed and presented in a tabular format, adding a touch of elegance to the already breathtaking analysis. By boosting the radiation parameter, the Nusselt value declined. Moreover, it is observed that the nanofluids having a laminar nanoparticle shape have a greater heat transfer rate.

Analysing entropy generation of MHD (50:50) slip flow over an inclined needle

The primary objective of this study is to quantify the rate of entropy generation within the magnetohydrodynamic (MHD) slip flow system over the inclined needle. Entropy generation is a measure of the irreversibility and inefficiency in the flow process. The slip flow condition at the fluid interface can significantly impact the flow characteristics and heat transfer rates. In the hybrid nanofluid flow, which consists of non-magnetic and magnetic (Al2O3 and Fe3O4) nanoparticles, H2O+C2H6O2 (50:50) are considered as the base fluid. Furthermore, the effects of inclined magnetic fields are taken into interpretation. The PDE’s governing equations are converted into ODE’s using similarity transformations and solved by a numerical technique based on BVP4C. The results illustrate that crucial parameters such as the magnetic parameter, mixed convection parameter, nanoparticles of solid volume fractions, and Prandtl numbers are pointedly impacted by momentum and thermal profiles. The entropy and Bejan number also consider being various relationship-combined parameters. These analyses protest that raising the magnetic parameter estates an increase in the hybrid nanofluid thermal profile under slip circumstances. Examined magnetic field impact on flow and entropy generation in MHD flows, revealing significant changes in entropy generation due to interaction between magnetic field and nanoparticles. This analysis understands the impact of MHD and slip effects on entropy generation, particularly in the context of the newly emerging 50:50 fluid mixture. Hybrid nanofluids have been shown to have improved thermal conductivity compared to traditional fluids, which can enhance the cooling or heating capabilities of the inclined needle.

Temperature-dependent convective parameters for RRc 1D models

ABSTRACT Non-linear pulsation modelling of classical variable stars is among the first topics that were developed at the beginning of the computational era. Various developments were made, and many questions were answered in the past 60 yr, and the models became more complex, describing the genuinely 3D convection in a single dimension. Despite its successes, the recent public availability of the mesa radial stellar pulsations (mesa rsp) module and the emerging results from multidimensional codes made clear that the eight free convective parameters, unique to these models, together with the underlying physical models need calibration. This could be done by comparing them against multidimensional codes, but before that, it is important to scrutinize the free parameters of the 1D codes using observations. This is a follow-up work of our previous calibration on the convective parameters of the budapest-florida and mesa rsp pulsation codes for RRab stars. In this letter, we extend the previous calibration to the RRc stars and the RR Lyrae stars in general. We found that correlations of some of the parameters are present in RRc stars as well but have a different nature, while high-temperature RRc stars’ pulsation properties are very sensitive to the chosen parameter sets.

Impact of AWiFS derived land use/land cover over the intensely urbanised domain of National Capital Region (NCR) - Delhi in simulating monsoon weather

Land-Use/Land-Cover (LULC) plays a crucial role in meteorological models because they determine the crustal properties that interfere with the exchange of energy, momentum, and moisture between the land surface and the atmosphere. The current study integrates a legitimate ground-truth LULC based on the Advanced Wide Field Sensor (AWiFS) in the Weather Research and Forecasting (WRF) model framework for simulating short-term monsoon weather over the National Capital Region (NCR Delhi), the biggest urban settlement in the Monsoon Zone. AWiFS provides LULC data at 56 m spatial resolution. The newly implemented AWiFS LULC precisely distinguishes the WRF model default Moderate Resolution Imaging Spectroradiometer (MODIS) classification and urban representation. Both simulations most well capture the urban meteorological influence over the urban core. However, the AWiFS more accurately depicts NCR urban Land Use (LU), highlighted in the spatial and Probability Density Function (PDF) distributions of various meteorological variables, and captures numerous small-scale suburban settlements outside the central core zone. The impact of the omission of the Noida suburban in MODIS LU is also notable in the planetary boundary layer (PBL). Convective parameters and precipitation correlate strongly, and the AWiFS simulation considerably enhanced Convective Available Potential Energy (CAPE), Convective Inhibition (CIN) and other associated variables. Further, it indicates that a higher magnitude (>200 J kg−1 in difference) of CAPE is simulated over Faridabad, likely an urban-induced downwind effect due to the accurate representation of the Noida suburban in AWiFS. The study highlights the importance of updated LULC in enhancing model realisation, and AWiFS-based simulation showed improved performance.

Generalized thermal properties of hybrid NANOLIQUID composed of aluminum oxide (Al2O3) and silver (Ag) nanoparticles with water (H2O) as base liquid

AbstractHeat transfer rate is numerically analyzed in convective flow of Al2O3‐Ag/ H2O hybrid nanoliquid through a stretching sheet by incorporating induced magnetic field. Results of entropy generation in system are evaluated as well. Considered physical factors associated with heat transfer are heat generation parameter and viscous dissipation. The system of nonlinear partial differential equations is modeled and dimensionally simplified by implementing boundary layer approximation assumption and proper similarity transformations. Adam's Bashforth method is applied to get highly accurate and stable numerical solutions. Numerical results of flow variables, entropy generation number and physical quantities are interpreted by way of graphs and bar charts to perceive the extensive significance of the problem. It is visualized that rise in numeric values of mixed convection parameter λ1 leads to enhance velocity; entropy generation number and Nusselt number while suppress temperature. High magnitude of heat generation parameter δ augments velocity and temperature but reverse behavior is observed for Nusselt number and entropy generation number. Moreover, the factor of viscous dissipation significantly modifies rate of flow and heat transfer under the effect of no‐slip condition on sheet. The present study is useful in different fields of industries, technological processes, mechanical processes, and electrical processes due to the applications of magnetic hybrid nanofluid with improved heat and mass transfer.

Nonlinear Approximation for Mixed Convection Flow From a Vertical Plate With Exponentially Decaying Internal Heat Generation and Nonlinear Thermal Radiation

Abstract This study considered dynamic features of mixed convection flow over a vertical plate influenced by nonlinear thermal radiation and exponentially decaying internal heat generation. The importance of the nonlinear density variation with temperature (NDT) and convective heating is also analyzed. The governing equations are transformed into ordinary differential equations using the similarity variables and solved in MAPLE 2022 by a Runge–Kutta Ferhlberg fourth-fifth order scheme. The results obtained show that, with an increase in the convection process, the internal heat generation convects more fluid away and consequently reduced the rate of heat flowing back into the plate. For a weak internal heat generation λx=0.5, the plate temperature is less than one (1) and the heat is observed to flow from the plate into the fluid on the surface of the right plate. Furthermore, for weak convection and nonlinear thermal radiation effects, the rate at which the heat flows into the plate increases with the convective heat transfer parameter increase. However, for a strong nonlinear thermal radiation effect, the rate at which the heat flows out of the plate increases. The flow feature is not only governed by the rate of internal heat generation but the generation as well reverses the heat flow from the plate since the temperature of the near the plate surface T is much higher than the environmental temperature Tf.

Mitigation of thermal runaway in air cooled Li-ion batteries using a novel cell arrangement coupled with air flow improviser: A numerical investigation and optimization

The concept of e-mobility is gaining the utmost importance owing to stringent emission norms. However, the safety issues associated with propulsion batteries are posing a major threat to their success. The occurrences of accidents caused due to thermal runaway in electric two-wheelers grabbed global attention. In this research work, an effort is being taken to address this concern, by arranging the battery cells in a pentagonal arrangement and employing forced air convection. The influence of air velocity (0.15, 0.30, and 0.45 m/s) and air inlet position (60, 75, and 90 mm) and intermediate cell distance (4, 6, and 8 mm) on the thermal performance of the battery is investigated using a numerical approach. Maximum cell temperature, pressure drop across the channel, maximum air velocity, maximum turbulent kinetic energy, and maximum turbulence intensity are considered output responses. A grey relational analysis (GRA) is used to identify the optimum level for all input parameters considering all output responses. The optimal forced convection parameters for the proposed pentagonal cell arrangement are the intermediate cell distance of 6 mm, air velocity of 0.45 m/s, and air inlet position of 90 mm from the center of the cell arrangement. With this arrangement, the peak cell temperature is reduced from 48.32 to 36.83 °C (8%). The study is extended with an airflow improviser to examine the influence of its geometrical parameters (improviser angle, fillet angle, and its distance from the center of the battery module) on the battery cell temperature. At the optimal conditions, the presence of an airflow improviser increases the air velocity from 1.76 to 2.302 m/s (19%) and turbulence intensity from 196 to 0.350 m2/s2 (33%). Thus, the cell temperature could be reduced to the extent of 8.6% by adopting the pentagonal cell arrangement with airflow improviser and optimal operating parameters.

Sensitivities of Subseasonal Unified Forecast System Simulations to Changes in Parameterizations of Convection, Cloud Microphysics, and Planetary Boundary Layer

Abstract NOAA has been developing a fully coupled Earth system model under the Unified Forecast System framework that will be responsible for global (ensemble) predictions at lead times of 0–35 days. The development has involved several prototype runs consisting of bimonthly initializations over a 7-yr period for a total of 168 cases. This study leverages these existing (baseline) prototypes to isolate the impact of substituting (one-at-a-time) parameterizations for convection, microphysics, and planetary boundary layer on 35-day forecasts. Through these physics sensitivity experiments, it is found that no particular configuration of the subseasonal-length coupled model is uniformly better or worse, based on several metrics including mean-state biases and skill scores for the Madden–Julian oscillation, precipitation, and 2-m temperature. Importantly, the spatial patterns of many “first-order” biases (e.g., impact of convection on precipitation) are remarkably similar between the end of the first week and weeks 3–4, indicating that some subseasonal biases may be mitigated through tuning at shorter time scales. This result, while shown for the first time in the context of subseasonal prediction with different physics schemes, is consistent with findings in climate models that some mean-state biases evident in multiyear averages can manifest in only a few days. An additional convective parameterization test using a different baseline shows that attempting to generalize results between or within modeling systems may be misguided. The limitations of generalizing results when testing physics schemes are most acute in modeling systems that undergo rapid, intense development from myriad contributors—as is the case in (quasi) operational environments.

A coupled analysis on human thermal comfort and the indoor non-uniform thermal environment through human exergy and CFD model

The importance of human thermal comfort evaluation lies of the development of healthy buildings as well as low-carbon buildings. In recent years, the applications of the second law of thermodynamics to thermal comfort offered a new perspective and this study, for the first time, an integrated human exergy model and the indoor CFD model for a coupled analysis is put forward, which supplements a critical link between human and building environment. In this paper, a predefined office model is investigated to improve the efficiency and thermal comfort of occupants. Based on the human exergy model, the parameters of metabolism, respiration, evaporation, convection, radiation, skin, and core heat storage terms are optimized. Additionally, the effects of gender, age, and clothing thermal resistance on the exergy model values are also separately explored. Finally, the optimized human exergy model is combined with computational fluid dynamics (CFD) to calculate, under different air supply conditions, the distribution of exergy consumption values in the predefined room model. The results show that the factors of gender, age, and thermal resistance of clothing have some influences on human exergy consumption, while the above factors do not have a significant enough influence on the model in the thermal comfort interval. Moreover, the combination of the human exergy model and CFD can offer some suitable air supply conditions for the development of indoor thermal comfort and work efficiency. This study presented a new tool that bridges human thermal sensation and indoor thermal environment.

Analytical simulation of magneto-marangoni convective flow of Walter-B fluid with activation energy and Soret–Dufour effects

Marangoni convection is discovered by varying gradients of surface tension. Marangoni induced flow plays a vital role in melting of coating flow technology, drying wafers, crystals, soap film stabilization, wielding and microfluidics, in which the flow creates unwanted impacts under gravity on micro-level in the same manner as buoyancy-induced natural convection. The Magneto-Marangoni convective flow of Walter-B fluid over a vertical permeable surface is addressed in the current research. The Dufour–Soret effects are taken into account along with activation energy and radiation. Flow through a porous media is modeled via Darcy and Forchheimer theory. The surface tension gradient becomes stronger by increasing the Marangoni convection parameter, which results in stronger induced flows and more efficient heat and mass movement inside the liquid. The result is a more uniform distribution of these qualities throughout the liquid as the temperature and concentration profiles drop. With higher viscoelastic parameter levels, the fluid accelerates and the velocity profile increases due to decreased viscosity. Due to an augmentation in the Dufour and Soret number, the thermal and concentration of the Walter-B fluid boost up respectively.