Ambient Temperature Data Research Articles

Yttrium speciation in sulfate-rich hydrothermal ore-forming fluids

Rare Earth elements (REE) are gaining importance due to their increasing industrial applications and usefulness as petrogenetic indicators. REE-sulfate complexes are some of the most stable REE aqueous species in hydrothermal fluids, and may be responsible for REE transport and deposition in a wide variety of geological environments, ranging from sedimentary basins to magmatic hydrothermal settings. However, the thermodynamic properties of most REE-sulfate complexes are derived from extrapolation of ambient temperature data, since direct information on REE-sulfate complexing under hydrothermal conditions is only available for Nd, Sm and Er to 250 °C (Migdisov and William-Jones, 2008, 2016).We employed ab initio molecular dynamics (MD) simulations to calculate the speciation and thermodynamic properties of yttrium(III) in sulfate and sulfate-chloride solutions at temperatures and pressures up to 500 °C and 800 bar. The MD results were complemented by in situ X-Ray Absorption Spectroscopy (XAS) measurements. Both MD and XAS show that yttrium(III) sulfate complexes form and become increasingly stable with increasing temperature (≥200 °C). The MD results also suggest that mixed yttrium-sulfate-chloride complexes (that cannot be distinguished from mixtures of chloride and sulfate complexes in XAS experiments) form at ≥ 350 °C. Two structures with two different Y(III)-S distances (monodentate and bidentate) are observed for Y(III)-sulfate bonding. The formation constants, derived via thermodynamic integration, for the Y(III) mono- and di-sulfate complexes parallel the trends for those of Nd, Sm and Er determined experimentally to 250 °C.The derived formation constants were used to fit revised Helgeson-Kirkham-Flowers equation-of-state parameters that enabled calculation of formation constants for Y(SO4)+ and Y(SO4)2− over a wide range of temperatures and pressures. The presence of sulfate increases the solubility of Y(III) under specific conditions. Since the stability of sulfate is redox sensitive, Y(III) solubility becomes highly redox-sensitive, with rapid precipitation of Y minerals upon destabilisation of aqueous sulfate.

Seasonal Photoinhibition of Photosystem II in a Cold-Climate Canadian Cactus (Opuntia cespitosa)

Few members of the Cactaceae family occur in cold climates resulting in our limited knowledge about their response to such conditions. We used chlorophyll fluorescence to assess the impact of seasonal changes in the extremes of a continental climate on the photosynthetic performance of Opuntia cespitosa in Canada. Quantum yield (QY, FV/FM) was sampled on opposing faces of four cladodes (pads) from April to December, and data for ambient air temperature, morning sky conditions and soil moisture were collected. Soil moisture was negatively related to FV/FM, but only on sun-facing pad faces. We found that FV/FM was variable at the start and ending periods while the summer months had consistently high values. FV/FM decline began more than a month earlier on sun-facing pad faces than shaded faces, with some shaded faces with FV/FM > 0.8 even in December. Indeed, when sun- and shade-facing faces were inverted on one pad in late November, FV/FM increased on the newly shaded face, suggesting remarkable resilience to recover from photoinhibition. Overall, we suggest photoinhibition of photosystem II (PSII) is related to low temperature and secondarily to light exposure. Although Opuntia only suffers from photoinhibition during the coldest months, photoinhibition appears to be dynamic and reversible. The seasonal response of Opuntia cespitosa appears to mimic that observed for the cold tolerant evergreen conifers.

Effect of calving season on the parameters and components of the lactation curve in Holstein dairy cows managed in a semi-desert climate.

The lactation curve in dairy cows is influenced by the calving season, which is highly dependent on the warm climate in semi-arid regions. Objective herein was to evaluate effects of calving season on the parameters and components of the lactation curve in Holstein cows. The study included 278,317 milk records collected from 1086 cows from the 1st to 5th calving and good body condition score. The cows were grouped according to the season in which they calved: winter-calved (CS1), summer-calved (CS2), and autumn-calved cows (CS3). Ambient temperature and humidity data were used to calculate the temperature-humidity index (THI). The NLIN procedure was used to estimate the parameters of the lactation curve that served to calculate the components. The mixed procedure was executed to analyze the fixed effect of calving season. Associations between lactation curve traits were tested using correlation and regression analyses. A univariate model was utilized to calculate heritability. Average THI values during the lactation period were 73.5, 68.5, and 69.5 units for CS1, CS2, and CS3 groups, respectively. Initial milk production and increasing rate to the maximum milk yield in CS1 and CS3 groups were higher (P < 0.05) than CS2 cows. However, persistency and total milk yield during the entire lactation period were superior (P < 0.05) for CS2 and CS3 cows compared to CS1 cows, probably due to the moderate heat stress during the lactation period in the CS1 group. In cows from CS2 and CS3 groups, total milk production at 305days was moderately correlated with initial milk production (r = 0.47; P < 0.05), and highly correlated with milk yield at peak day (r = 0.91; P < 0.05) which resulted as reliable predictor for total milk yield during the entire lactation (R2 = 0.83). In conclusion, the THI prevailing during the differentcalving seasons appeared to be an important factor influencing the performance of the lactation curve.

Improved exponential model for thermal error modeling of machine-tool spindle based on fruit fly optimization algorithm

Establishing a robust thermal error model is crucial for thermal error compensation of machine tools. In this paper, an improved exponential model for thermal error modeling was proposed. First, the improved exponential model is established by combining the exponential model and the temperature-dependent model. Second, the improved fruit fly optimization algorithm is used to determine the parameters. Finally, two strategies are used to select the optimal sensor location from six potential ambient temperature sensors. The ambient temperature data is proved to be effective in improving the robustness of the model. The average root mean square error of the proposed model, the exponential model without ambient temperature, and the linear model are 1.18 μm, 1.79 μm, and 2.70 μm, respectively. The modeling results show that the proposed model has high accuracy and strong robustness, which is suitable for workshop environment and variable conditions in thermal error modeling of machine tools.

Asian dust storms result in a higher risk of the silicosis hospital admissions.

Previous studies found that silicosis was majorly associated with occupation-related risks. However, little evidence was available to clarify the relation between Asian dust storm (ADS) and silicosis hospital admissions. This present paper aims to investigate the association between ADS events and hospital admissions for silicosis. We applied a Poisson time-series regression on the 2000-2012 National Health Insurance Research Database in Taiwan, linking air quality data and ambient temperature data to estimate the impact of ADS on silicosis hospital admissions in the age-specific groups. A total of 2154 hospital admissions were recorded for silicosis in Taiwan, for a daily average number of 0.45. The number rises from 0.43 on a day without ADS to 0.70 on the outbreak day and continues increasing to 0.83 one day after outbreak. Among patients under 45, the effect of ADS appears on the event day as well as several post-event days (lag2-6) at the significant level of p < 0.1. There is also a significant lag effect on post-event day 2 (p < 0.05) for those aged above 74. Asian dust storms do result in a rise of silicosis hospital admissions, particularly for those above 74, those under 45, and for females.

An improved particle swarm optimization for optimal configuration of standalone photovoltaic scheme components

AbstractThe hybrid energy system design problem needs efficacy tools to reach optimal results in remote areas. The metaheuristic optimization methods are the best choice to address complex problems. This article presents an improved approach based on an energy management strategy for optimal sizing and configuration of standalone photovoltaic scheme components. Improved particle swarm optimization for optimization and configuration of photovoltaic panel and battery system is applied using MATLAB and hourly solar radiation, ambient temperature data, and load demand. The objective of the optimization problem is to define the optimal sizing of the system components to meet the load demand to compose a cost‐effective and reliable scheme. The optimized system results from the improved approach are compared by original particle swarm optimization and simulated annealing algorithms. So, the optimum design of the hybrid scheme is analyzed and compared based on different reliability values. The results show that the improved approach finds the optimal results easily with lower cost, faster convergence, and better reliability indexes in different reliability indexes in comparison with the other approaches. In this regard, for different reliability values (1%, 3%, and 5%), the improved particle swarm optimization shows approximately 22.9% cost saving in comparison with the simulated annealing, and improved particle swarm optimization shows approximately 0.35% cost saving in comparison with the particle swarm optimization. Also, show the reliability of the standalone hybrid photovoltaic system.

Applied Mathematical Model for Solar Energy Collection and Thermal Storage

Solar thermal storage systems (STSS) are renewable energy systems that provide a continuous, controllable source of heat for many applications. The inherent variability of solar insolation poses a challenge to the use of direct solar power for continuous operations. A dynamic non-linear model of the STSS was initialised via a steady state analysis and verified by drying 1000 kg of cocoa beans per batch from 60% to 8% moisture content. Ambient temperature and insolation data for Trinidad in 2017 were used as inputs. This was achieved by modifying flow rates, varying aspect ratio of the storage tank, and observing their effect on storage tank temperature profiles and drying air temperatures. The steady state analysis determined the tank volume as 12.57 m3 and a required mass airflow rate of 1020 kg/h. A dynamic model of the STSS revealed an optimal tank aspect ratio of 2:4 m (D: H) and that three AE-40 solar collectors were sufficient. The effect of the circulation rate between the storage tank and solar collectors on energy storage was found to be negligible. The need for temperature control was demonstrated and a control strategy developed. A pilot plant was built using recommended specifications, albeit without temperature control. As predicted, drying was more than sufficient, but poor control led to burnt cocoa beans. The application of this work extends well beyond the cocoa bean test case, and the open-source models built can be applied to optimise the design of any solar heating application.

An attempt of using public ambient temperature data in swine genetic evaluation for litter-size traits at birth in Japan

Context Large-scale genetic evaluation is promising for efficiently improving sow lifetime productivity, and therefore, a reasonable operational model should be pursued to analyse phenotypic data collected from around Japan, where unique seasonality exists. However, the information on establishing the model is currently insufficient. Aims To obtain the fundamental information on using ambient temperature information in developing the model for routine swine genetic evaluation in Japan, by analysing total number born, number born alive, and number stillborn collected at a Japanese farm, together with off-farm ambient temperature measured at the nearest Automated Meteorological Data Acquisition System station. Methods Five repeatability animal models were exploited, considering the effects of farrowing season (Model 1), farrowing month (Model 2), quadratic regressions of daily maximum ambient temperature of farrowing day (Model 3), season and temperature (Model 4), or month and temperature (Model 5). Key results Patterns of the effects of daily maximum temperature of farrowing day estimated using Model 3 were similar to those of farrowing season by Model 1 and those of farrowing month by Model 2. Adding the effect of daily maximum temperature of farrowing day (Models 4 and 5) could explain phenotypic variability better than only considering either of farrowing season or month (Models 1 and 2). Estimated heritability was stable among the models and the rank correlation of predicted breeding values among models was &gt;0.98 for all traits. Conclusions The results indicated the possibility that using public ambient temperature can capture a large part of the phenotypic variability in litter-size traits at birth caused by seasonality in Japan. Implications This study could support the availability of public meteorological data in the development of flexible operational models for future swine genetic evaluation in Japan.

A novel methodology to obtain ambient temperatures using multi-rotor UAV-mounted sensors

Unmanned Aerial Vehicle (UAV)-mounted temperature sensors can collect a large amount of valuable 3-dimensional environmental data, especially at locations unsuitable for the installation of traditional weather stations. This paper developed a novel methodology to obtain accurate ambient temperature data using a multi-rotor UAV. Firstly, air temperature sensors and a multi-rotor UAV model were selected based on the objectives of this study. Secondly, field experiments were conducted to determine the influence of in-flight multi-rotor UAV on ambient temperature, to determine the most appropriate locations for the temperature sensor, namely underneath the multi-rotor UAV center and between the landing gears. The data collection, processing, and analysis methods were thus proposed. Thirdly, the accuracy of the proposed methodology was evaluated at a target height by comparing the measured result with that measured by a nearby weather station, and comparing the vertical profile of measured data with the data released by National Environment Agency (NEA) in Singapore. A satisfactory agreement between the two results was observed with an error of between 0 and 0.3 °C. In addition, a case study was conducted to compare ambient temperatures at various heights above different ground materials utilizing the proposed methodology in a park. Besides, the correlation analysis results provided the directions to improve the measurement accuracy.

Thermodynamic and mathematical analysis of geothermal power plants operating in different climatic conditions

This study presents the thermodynamic and mathematical analysis of the different cooling systems for geothermal power plants at various climatic conditions. The existing binary geothermal power plant data, located in Turkey, were processed in the commercial software Ebsilon Professional to model the power plant and cooling systems. The dry-bulb temperature and relative humidity values from data obtained from local meteorological stations in the analyzed region are used as variables in the model. The reference air-cooled cooling system, the wet-tower cooling system, the additional dry cooling system and three different hybrid cooling systems are modelled and analyzed separately in the reference geothermal power plant model. Accordingly, mathematical equations are developed to evaluate the power production and water consumption characteristics of cooling systems on the thermodynamic performance of geothermal power plants depending on weather conditions. The power production and water consumption of geothermal power plants can be calculated using hourly ambient temperature and relative humidity data at different climatic conditions with these equations. Considering water reserves in the area, operation periods of the hybrid cooling systems can be determined. Moreover, power plant operators can compare the performance and water consumption characteristics of hybrid cooling systems for different conditions with these equations.

Association between ambient temperature and dementia‐related hospital admissions in England

AbstractBackgroundThe impacts of a changing climate on dementia burdens have not been widely explored, despite the increasing pressures associated with dementia on the public health and social care systems. This study explores the association between ambient temperature and the dementia‐related emergency hospital admission, using the national hospital inpatient dataset for England. We also examine important modifiers of the heat‐risk, including separate results for each Government region of the country.MethodsDaily counts of emergency admissions with a primary diagnosis of dementia (ICD codes F00‐F03) and daily ambient temperature data from Met Office land surface stations were obtained for the period 2000‐2009 in England. Poisson regression was used to detect short‐term associations between daily mean temperature and daily counts of dementia admissions, adjusting for overdispersion and day‐of‐week effects, and including spline functions to model any underlying trends and seasonal patterns in the series. Distributed lag non‐linear models employing cross‐basis functions were used to characterise the exposure‐response relationship, at both national and regional levels at different lags of temperature; a 7‐day lag structure was used to explore potential delayed effects of temperature on the week leading up to admission. The modifying effects of age‐group, urban/rural residence, and quintiles of area‐level deprivation were also examined.ResultThere was some suggestion of seasonality in dementia admissions, with numbers peaking in the summer and winter months each year. After seasonal adjustment, there was an increase in the risk of dementia‐related hospital admission associated with high temperature, with adverse effects becoming apparent once daily mean temperatures rose above 15.0°C. At the national level, we estimated an approximately 10% increase in the risk of dementia admission for every 5°C increase in temperature above 15.0°C. When disaggregated by regions, the risk was highest in London. Separate results by age‐group, deprivation and urban/rural residence will be presented.ConclusionThe findings are likely to have important public health implications for priority settings, such as for hospital emergency department planning during hot seasons. The dangers of heat‐induced symptoms among dementia sufferers can be anticipated by caregivers whenever hot weather is forecast.

Mozaiklerin Tekrar Gömülmesi: Perge’de Kurulan İzleme İstasyonu Sonuçlarına Göre Altı Farklı Gömme Sisteminin Karşılaştırılması

The effectiveness of six different reburial systems was investigated by means of a monitoring station installed on a part of approximately 110 square meters of mosaic, defined as basilica thermarum in the Southern Baths of the ancient city of Perge. The scope of the study covers the monitoring of six different reburial systems consisting of only soil filling on the tessellatum layer; only sand filling on the tessellatum layer; sand filling on geotextile laid on the tessellatum surface; sand and gravel, respectively on geotextile laid on the tessellatum surface; geotextile and pozzolana on the tessellatum surface, and finally, approximately 5 cm sand, geotextile, and 15 cm sand on the tessellatum surface. Key parameters, which lead to deterioration such as moisture content in these systems, system response to the precipitation, temperature change, acidity, salt content, and plant formation were evaluated comparatively.&#x0D; In these systems, Decagon 10HS soil moisture sensor for moisture measurements and Apogee ST100 soil temperature sensor for temperature measurements were employed. Atmospheric humidity, atmospheric temperature and precipitation data were collected with a Davis® Vantage Vue™ Wireless Weather Station set. Data of salt content, acidity and elemental composition were obtained through conductivity analysis, pH tests, X-ray fluorescence (PED-XRF) analysis. Having reached under the reburial system, species analysis of plants that cause mosaic deterioration was realized, and the coverage degree of those plants was determined according to the Braun-Blanquet cover-abundance scale. This research, in which data for the years 2017-2018 were evaluated, showed that each monitored reburial system had certain advantages and disadvantages according to the above-mentioned parameters.

Self-Consumption and Self-Sufficiency Improvement for Photovoltaic System Integrated with Ultra-Supercapacitor

This research study uses a computer simulation based on real input data to examine the impact of a supercapacitor module working as a fast response energy storage unit in renewable energy systems to increase energy self-consumption and self-sufficiency. The evaluated system includes a photovoltaic system with a capacity of 3.0 kWp and between 0 and 5 supercapacitor units with a capacity of 500 F per module. The study was carried out using experimental data for electrical load, solar irradiance, and ambient temperature for the year 2020, with a 1 min temporal resolution. The daily average ambient temperature was 10.7 °C, and the daily average solar irradiance was 3.1 kWh/m2/day. It is assumed that the supercapacitor could only be charged from a photovoltaic system using renewable energy and not from the grid. The simulation results showed that using the supercapacitors to feed the short and large peaks of the electrical load significantly increases energy self-consumption and self-sufficiency. With only five supercapacitor modules, yearly energy self-sufficiency increases from 28.09% to 40.77%.

Association between ambient air pollutants and meteorological factors with SARS-CoV-2 transmission and mortality in India: an exploratory study

BackgroundThe Coronavirus disease 2019 (COVID-19) pandemic poses a serious public health concern worldwide. Certain regions of the globe were severely affected in terms of prevalence and mortality than other. Although the cause for this pattern is not clearly understood, lessons learned from previous epidemics and emerging evidences suggest the major role of ecological factors like ambient air pollutants (AAP) and meteorological parameters in increased COVID-19 incidence. The present study aimed to understand the impact of these factors on SARS-CoV-2 transmission and their associated mortality in major cities of India.MethodsThis study used secondary AAP, meteorological and COVID-19 data from official websites for the period January-November 2020, which were divided into Pre-lockdown (January-March 2020), Phase I (April to June 2020) and Phase II (July to November 2020) in India. After comprehensive screening, five major cities that includes 48 CPCB monitoring stations collecting daily data of ambient temperature, particulate matter PM2.5 and 10 were analysed. Spearman and Kendall’s rank correlation test was performed to understand the association between SARS-CoV-2 transmission and AAP and, meteorological variables. Similarly, case fatality rate (CFR) was determined to compute the correlation between AAP and COVID-19 related morality.ResultsThe level of air pollutants in major cities were significantly reduced during Phase I compared to Pre-lock down and increased upon Phase II in all the cities. During the Phase II in Delhi, the strong significant positive correlation was observed between the AAP and SARS-CoV-2 transmission. However, in Bengaluru, Hyderabad, Kolkata and Mumbai AAP levels were moderate and no correlation was noticed. The relation between AT and SARS-CoV-2 transmission was inconclusive as both positive and negative correlation observed. In addition, Delhi and Kolkata showed a positive association between long-term exposure to the AAP and COVID-19 CFR.ConclusionOur findings support the hypothesis that the particulate matter upon exceeding the satisfactory level serves as an important cofactor in increasing the risk of SARS-CoV-2 transmission and related mortality. These findings would help public health experts to understand the SARS-CoV-2 transmission against ecological variables in India and provides supporting evidence to healthcare policymakers and government agencies for formulating strategies to combat the COVID-19.

Design and Simulation of an Off-Grid Photovoltaic System with Duty Cycle Prediction Using Neural Network Controller

Global concerns over the inappropriate utilization of abundant renewable energy sources, the damages due to instability of fuel prices, and fossil fuels' effect on the environment have led to an increased interest in green energy (natural power generation) from renewable sources. In renewable energy, photovoltaic is relatively the dominant technique and exhibits non-linearities, leading to inefficiencies. Maximum Power Point is required to be tracked rapidly and improve the power output levels. The target is to use a Neural network controller by training historical data of ambient irradiance and temperature levels as inputs and voltage levels as output for the photovoltaic module to predict duty cycles across the DC-DC converter. The DC-DC converter is the electrical power conditioner at the Botswana International University of Science and Technology, Palapye Off-Grid photovoltaic system. Perturb and Observe algorithm on PSIM environment is only implemented to acquire the historical data for the training and Matlab for the modeling of the network. Relatively long period ambient irradiance and temperature data of Palapye were acquired from the Southern African Science Service Centre for Climate Change and Adaptive Land Management (SASSCAL) WeatherNet in Botswana. Matlab environment was used for the simulation of the backpropagation algorithm for training. The Neural network's feedforward to optimize the non-linear nature of the PV module input and output relationship with relatively fewer processes is required. The results show promising, and the Mean Errors appear to be typically about 0.1 V, and the best performance is 193.5812 at Epoch 13, while the regression delivered a relatively low measured error. The maximum power delivered by the duty cycles from the model with 90 % prediction accuracy. The article demonstrates Neural Network controller is more efficient than the conventional Perturb and Observe Maximum Power Point algorithm.

Use of machine-learning for monitoring solar thermal plants

A machine-learning algorithm (MLA) was developed to assess the operational state of solar thermal plants, based on the data of only one temperature sensor, and the irradiance and ambient temperature data from the nearest weather station. A detailed requirements analysis of the situation results in the classification of a multivariate time series problem. Neural networks used in the field of data science are ideally suited for problems of this type. Data from the operational monitoring system, which runs a rule-based algorithm, were used to train the neural network using the software framework TensorFlow. It was shown that the chosen MLA can detect malfunctions such as heat loss due to gravity-driven circulation during night. However, further development towards a practical tool requires not only far more data for training and validation. It became clear that corresponding pressure data are needed to classify temperature transients and to attribute these classes to certain malfunctions.

Seasonal variation in the total alkalinity and δ13CDIC values of cave drip water in a ventilated cave in response to the regional climatology, southwest China

The simulation and monitoring of environmental proxies in cave systems is of great significance for understanding the controlling factors of stalagmite geochemistry and for further analysing the signal transmission process and mechanism in cave systems. In this study, we collected samples from three drip points in Liangfeng Cave from 2015 to 2020, Guilin City, Southwest China. The δ13CDIC values, total alkalinity and modern calcite deposition rates at the drip points in the cave were measured during the monitoring period. Comparison of these data with atmospheric temperature and rainfall data from the study area revealed that (1) the δ13CDIC and total alkalinity values of cave drip water show a seasonal pattern involving low values that increase in summer and autumn and high values that decrease in winter and spring, which is synchronized with the changes in the regional rainfall and temperature. (2) The modern calcite deposition rate also shows characteristics of significant seasonal variation. In addition, modern calcite deposition rate of drip water (site LF-4) decreased year by year. On an interannual time scale, the recharge regime of this drip water is likely to be the main control on the modern calcite deposition rate. (3) The δ13CDIC values of the drip water in Liangfeng Cave (well ventilated) are generally higher than those of another cave (weakly ventilated) in the study area for winter and spring. The cave ventilation effect leads to a higher δ13CDIC value in the drip water during times of low cave pCO2. (4) Because Liangfeng Cave is a well-ventilated cave, the drip water could record the seasonal regional climatology variation. Observations from the well-ventilated Liangfeng Cave provide a reference point for the interpretation of regional climatology signals from speleothem proxy records.

Measuring Aerosol Chlorides for Atmospheric Corrosion Studies in Cold Alaskan Climate

Atmospheric corrosion is a complex process, which involves chemical, electrochemical, and physical changes to the metal exposed. Atmospheric corrosion occurs when a metal surface is under a thin layer of moisture, but not completely immersed, and the metal surface corrodes while exposed to environmental factors. Atmospheric corrosion of metal alloys in cold environments is assumed to be negligible and limited corrosion data are available in cold climates. However, studies in the Arctic and Antarctic regions have shown significant corrosion damages when exposed to cold conditions. The rate of atmospheric corrosion on various alloys is affected by environmental parameters that include temperature, rainfall, humidity, chloride-ion deposition rate, and time of wetness (TOW). Two important factors that affect atmospheric corrosion rates are aerosol chlorides and TOW. The main goal of this research is to monitor and measure the aerosols chlorides in the atmosphere, measure TOW and correlate the degradation of carbon steel alloys that are widely used in land, sea, and aerospace transportation, the oil and gas, fisheries, and mining applications. The main objectives achieved are design of Combined Chloride and Corrosion Portable Racks (C3PR) and deployment of C3PR racks in four different Alaskan environments. Chloride deposition rate measured using ASTM standards for three different exposure periods along with TOW, ambient temperature and RH data are correlated to the atmospheric corrosion rate of carbon steel to understand the underlying atmospheric corrosion mechanisms. The results from this research work will give a better understanding on the atmospheric corrosion studies in cold arctic/sub-arctic environment and lead to more externally funded projects.

Operational features of battery-powered electric vehicles in Russia and methods of assessing a state of health of traction batteries

Electric vehicle manufacturers pay particular attention to climatic conditions in the Russian Federation, seeing that chemical reaction rates usually decrease when the environment temperature drops. The analysis of ambient temperature data in the region where electric vehicles will most likely be used aims to select extreme temperatures for calculating powers of a thermostat control system for traction batteries. An electric vehicle simulation model to calculate the power of the thermostat control system for traction batteries is described. An empirical model to assess a state of health of traction batteries is selected. This simulation model adequately reflects thermal processes in a traction battery and can be used for further research.

Two-phase spray cooling for high ambient temperature data centers: Evaluation of system performance

A lab-scale spray cooled rack system with a server and simulated heaters, operating with near-ambient temperature coolant of approximately 30 °C, was developed for data center cooling application. This system has significant energy saving potential as it eliminates the need for a chiller unit, which is usually necessary in conventional air-cooling schemes. The system was designed to dissipate a maximum heat load of 5.6 kW. Despite its higher operating temperature, our investigation shows that the spray cooled system leads to better cooling performance of the microprocessors as compared to conventional air-cooling scheme. A durability study for more than 40 h of cumulative intermittent operation showed no degradation in system performance when operated at high chamber temperature conditions (>35 ℃). The thermal performance of the spray cooled system and the effects of parameters such as spray nozzle flow rate, condenser fan power, condenser flow rate and heat loads were systematically characterized. It is found that the thermal performance of the simulated heaters is mainly affected by the spray nozzle flow rate, while the condenser fan power and condenser cooling water flow rate strongly affect the condensation rate and are, therefore, critical parameters for spray chamber pressure control. In summary, this work demonstrates the possibility of employing the newly developed spray cooled system for the thermal management of high-performance servers of data centers in tropical climates and identified key operating parameters that are useful for the design, operation and implementation of this system.