Air Temperature Research Articles

Understanding freshwater to marine transitional scale growth in Atlantic salmon (Salmo salar)

Abstract The first marine circulus is the scale feature associated with marine entry and has been used to support research evaluating the relationships between size, growth, and survival for Atlantic salmon. Ambiguity in growth rates and circulus deposition during the transition from freshwater to the marine environment leads to difficulty in the correct identification of the first marine circulus. In this study, scale growth occurring after the last freshwater annulus (plus-growth) was characterized for Atlantic salmon smolts migrating out of the Narraguagus River in Maine. Plus-growth was present in over 95% of scales from smolts leaving the river and represented on average 12.2% of total freshwater growth. These findings suggest plus-growth presents a significant source of error in growth analyses, if ignored, and the characterization of this scale feature helps to inform accurate identification of marine entry for future studies. Run day was a driver of plus-growth, suggesting extended freshwater residency provides opportunity for growth prior to smolt emigration. The negative association between fork length and plus-growth suggests smaller smolts that delay migration are demonstrating compensatory growth. Cumulative air temperature was positively associated with plus-growth, suggesting warming spring temperatures driven by climate change may influence size at marine entry for smolts.

Green Management for Disasters Using an Inflatable Tent Prototype with a Solar Power Plant

Given the ever-increasing number of natural disasters, disaster management must be understood and implemented by all parties in a manner that includes green management and technology. This research begins with the problem of disaster preparedness management which currently uses less environmentally friendly facilities and infrastructure, including using tents with conventional structures and technological systems that are less flexible and require electricity with a gasoline-fueled generator. The aim of this research is to create a green technology-based disaster response facility in the form of an inflatable tent prototype for disaster preparedness using solar electricity. The method used in this study is experimental, designing and manufacturing prototype inflatable tents that use solar power plants. Tests are carried out on several variables: strength of tarpaulins, speed in construction and disassembly, comfort of air temperature, and performance of solar power generation. The results are of the experimental method are: the design and application of two prototype inflatable tents dimensions 6x9 square meters with a capacity of 20 patients, a 3,600 WP power plant using solar power, and a VRLA battery with a voltage capacity of 48 volts and a current of 200 Ah that is capable storing 4,800 watts of electricity. Thus, these devices meet electricity needs for air conditioning, lighting, and medical equipment in inflatable tents without using electricity from the state electricity company or gasoline generators. Keywords: disaster, inflatable tent, solar power generation

Intensive leaf cooling promotes tree survival during a record heatwave

Increasing heatwaves are threatening forest ecosystems globally. Leaf thermal regulation and tolerance are important for plant survival during heatwaves, though the interaction between these processes and water availability is unclear. Genotypes of the widely distributed foundation tree species Populus fremontii were studied in a controlled common garden during a record summer heatwave-where air temperature exceeded 48 °C. When water was not limiting, all genotypes cooled leaves 2 to 5 °C below air temperatures. Homeothermic cooling was disrupted for weeks following a 72-h reduction in soil water, resulting in leaf temperatures rising 3 °C above air temperature and 1.3 °C above leaf thresholds for physiological damage, despite the water stress having little effect on leaf water potentials. Tradeoffs between leaf thermal safety and hydraulic safety emerged but, regardless of water use strategy, all genotypes experienced significant leaf mortality following water stress. Genotypes from warmer climates showed greater leaf cooling and less leaf mortality after water stress in comparison with genotypes from cooler climates. These results illustrate how brief soil water limitation disrupts leaf thermal regulation and potentially compromises plant survival during extreme heatwaves, thus providing insight into future scenarios in which ecosystems will be challenged with extreme heat and unreliable soil water access.

ENHANCING WEIGHTED FUZZY TIME SERIES FORECASTING THROUGH PARTICLE SWARM OPTIMIZATION

Climate change is a complex process that has far-reaching consequences for daily living. Temperature is one of the climatic features. Knowing its future value through a forecasting model is critical, as it aids in earlier strategic decision-making. Without considering spatial factors, this study investigates an Air Temperature variable forecasting. Weighted Fuzzy Time Series (WFTS) is one of the forecasting techniques. Furthermore, the length of the interval and the extent to which previous values (Order length) are utilized in predicting the subsequent value are pivotal factors in WFTS modelization and its forecasting accuracy. Therefore, this research investigates the interval length and the Order length of the WFTS through the Particle Swarm Optimization (PSO) approach. The variable used is the air temperature in Malang, Indonesia. The dataset is taken from BMKG-Indonesia. The forecasting performance of classical WFTS is enhanced by setting an appropriate order level and employing Particle Swarm Optimization (PSO) to determine the optimal interval fuzzy length. As indicated by the Evaluation matrices in the result section, the proposed optimization overtaken the classical WFTS in term of accuracy. The evaluation indicates a Mean Absolute Percentage Error (MAPE) value of 1.25 and a Root Mean Square Error (RMSE) of 0.32 for the Proposed model. In contrast, the classical WFTS demonstrates a MAPE of 2.26 and RMSE of 0.58. The implementation of the PSO provides solid insights for Air temperature forecasting accuracy.

Machine Learning and Wavelet Transform: A Hybrid Approach to Predicting Ammonia Levels in Poultry Farms.

Ammonia (NH3) is a major pollutant in poultry farms, negatively impacting bird health and welfare. High NH3 levels can cause poor weight gain, inefficient feed conversion, reduced viability, and financial losses in the poultry industry. Therefore, accurate estimation of NH3 concentration is crucial for environmental protection and human and animal health. Three widely used machine learning (ML) algorithms-extreme learning machine (ELM), k-nearest neighbor (KNN), and random forest (RF)-were initially used as base algorithms. The wavelet transform (WT) with ten levels of decomposition was then applied as a preprocessing method. Three statistical metrics, including the mean absolute error (MAE) and the correlation coefficient (R), were used to evaluate the predictive accuracies of algorithms. The results indicate that the RF algorithms perform robustly individually and in combination with the WT. The RF-WT algorithm performed best using the air temperature, relative humidity, and air velocity inputs with a MAE of 0.548 ppm and an R of 0.976 for the testing dataset. In summary, applying WT to the inputs significantly improved the predictive power of the ML algorithms, especially for inputs that initially had a low correlation with the NH3 values.

Unraveling the Link: How Air Pollution and Temperature Shape Ischemic Stroke Risk: A Prospective Study

Unraveling the Link: How Air Pollution and Temperature Shape Ischemic Stroke Risk: A Prospective Study

Airflow Characteristics in Conveyor Type Damper for Hot Air Temperature Changes

The aim of this study was to present the characteristics of a hot airflow in a conveyor type dampers under the hot air temperature changes. The amount of space occupied by the damper was reduced by 50 %, and by efficiently managing the damper's airflow rate even in the face of extreme external conditions and constant air volume proportional control, a conveyor-type damper with a significant energy-saving effect has been explored. It was configured to control the degree to which it was opened by operating the folding conveyor drive mechanism, enabling the damper to modify airflow. In this study, the mass flow rate of heated air through the conveyor-type damper increased in proportion to the damper port's expansion. The mass flow rate of air decreased as the temperature of air intensified. In addition, the thermal energy of the air flowing in the conveyor-type damper improved in ratio to the increase in the damper opening. The findings indicate a 50 % reduction in the damper's occupied space, indicating a significant energy-saving impact of the conveyor-type damper. This is because the damper's air volume is effectively controlled even in the face of severe external conditions, as it is proportionately controlled throughout. The understanding of the properties of airflow for the hot temperature change is strengthened by this research. Furthermore, this study may provide useful direction for future research and industry optimization.

Container buildings used for residential and business purposes in Johannesburg, South Africa and potential heat-related health risks

Background Outdoor and indoor air temperature affects human health and wellbeing. Climate change projections suggest that global temperatures will continue to increase, and this poses a threat to health. Buildings (for housing and business purposes) that can protect humans from the adverse effects of temperature are essential, especially in the context of climate change. Method In this cross-sectional study, we measured the indoor temperature inside shipping containers comprising a seven-storey block of apartments and businesses in Johannesburg, South Africa for 14 days. We assessed indoor temperature and relative humidity; evaluated measured temperatures in relation to thresholds known to be associated with adverse health risks; and sought to understand heat-health perceptions and symptoms of people living and working in shipping container units. Results Median indoor apparent temperature (AT) (a combination of temperature and relative humidity) was 16°C with values ranging from 6°C (observed at 8:00) to 42°C (observed at 17:00). Insulated units had temperatures between 2°C and 9°C cooler than the uninsulated unit. Heat-health risks from AT exposure were likely in all units, although there was variation in the number of occurrences that AT measurements exceeded the four symptom bands of caution, extreme caution, danger and extreme danger. Indoor AT was found to be 7°C higher on average when compared to outdoor AT. Some participants believed that their units were hot during hot weather and most people opened windows or did nothing during hot weather. Few participants reported experiencing adverse heat-health impacts, except for experiencing headaches (58%) and feeling tired or weak (40%). Conclusion Residents, tenants, or business owners using shipping containers should consider insulation installation and adequate windows/air conditioning for ventilation, especially in hot climates. Further research and awareness regarding heat-health risks of living or working in these spaces is needed.

Resonant Forcing by Solar Declination of Rossby Waves at the Tropopause and Implications in Extreme Precipitation Events and Heat Waves—Part 2: Case Studies, Projections in the Context of Climate Change

Based on the properties of Rossby waves at the tropopause resonantly forced by solar declination in harmonic modes, which was the subject of a first article, case studies of heatwaves and extreme precipitation events are presented. They clearly demonstrate that extreme events only form under specific patterns of the amplitude of the speed of modulated airflows of Rossby waves at the tropopause, in particular period ranges. This remains true even if extreme events appear as compound events where chaos and timing are crucial. Extreme events are favored when modulated cold and warm airflows result in a dual cyclone-anticyclone system, i.e., the association of two joint vortices of opposite signs. They reverse over a period of the dominant harmonic mode in spatial and temporal coherence with the modulated airflow speed pattern. This key role could result from a transfer of humid/dry air between the two vortices during the inversion of the dual system. Finally, focusing on the two period ranges 17.1–34.2 and 8.56–17.1 days corresponding to 1/16- and 1/32-year period harmonic modes, projections of the amplitude of wind speed at 250 mb, geopotential height at 500 mb, ground air temperature, and precipitation rate are performed by extrapolating their amplitude observed from January 1979 to March 2024. Projected amplitudes are regionalized on a global scale for warmest and coldest half-years, referring to extratropical latitudes. Causal relationships are established between the projected amplitudes of modulated airflow speed and those of ground air temperature and precipitation rate, whether they increase or decrease. The increase in the amplitude of modulated airflow speed of polar vortices induces their latitudinal extension. This produces a tightening of Rossby waves embedded in the polar and subtropical jet streams. In the context of climate change, this has the effect of increasing the efficiency of the resonant forcing of Rossby waves from the solar declination, the optimum of which is located at mid-latitudes. Hence the increased or decreased vulnerability to heatwaves or extreme precipitation events of some regions. Europe and western Asia are particularly affected, which is due to increased activity of the Arctic polar vortex between longitudes 20° W and 40° E. This is likely a consequence of melting ice and changing albedo, which appears to amplify the amplitude of variation in the period range 17.1–34.2 days of poleward circulation at the tropopause of the Arctic polar cell.

Seasonal environmental fluctuations alter the transcriptome dynamics of oocytes and granulosa cells in beef cows

BackgroundExamining the mechanistic cellular responses to heat stress could aid in addressing the increasing prevalence of decreased fertility due to elevated ambient temperatures. Here, we aimed to study the differential responses of oocytes and granulosa cells to thermal fluctuations due to seasonal differences. Dry beef cows (n = 10) were housed together, synchronized and subjected to a stimulation protocol to induce follicular growth before ovum pick-up (OPU). Two OPU’s were conducted (summer and winter) to collect cumulus-oocyte-complexes (COCs) and granulosa cells. In addition, rectal temperatures and circulating blood samples were collected during OPU. Oocytes were separated from the adherent cumulus cells, and granulosa cells were isolated from the collected OPU fluid. RNA was extracted from pools of oocytes and granulosa cells, followed by library preparation and RNA-sequencing. Blood samples were further processed for the isolation of plasma and leukocytes. The transcript abundance of HSP70 and HSP90 in leukocytes was evaluated using RT-qPCR, and plasma cortisol levels were evaluated by immunoassay. Environmental data were collected daily for three weeks before each OPU session. Data were analyzed using MIXED, Glimmix or GENMOD procedures of SAS, according to each variable distribution.ResultsAir temperatures (27.5 °C vs. 11.5 °C), average max air temperatures (33.7 °C vs. 16.9 °C), and temperature-humidity indexes, THI (79.16 vs. 53.39) were shown to contrast significantly comparing both the summer and winter seasons, respectively. Rectal temperatures (Summer: 39.2 ± 0.2 °C; Winter: 38.8 ± 0.2 °C) and leukocyte HSP70 transcript abundance (Summer: 4.18 ± 0.47 arbitrary units; Winter: 2.69 ± 0.66 arbitrary units) were shown to increase in the summer compared to the winter. No visual differences persisted in HSP90 transcript abundance in leukocytes and plasma cortisol concentrations during seasonal changes. Additionally, during the summer, 446 and 940 transcripts were up and downregulated in oocytes, while 1083 and 1126 transcripts were up and downregulated in the corresponding granulosa cells, respectively (Fold Change ≤ -2 or ≥ 2 and FDR ≤ 0.05). Downregulated transcripts in the oocytes were found to be involved in ECM-receptor interaction and focal adhesion pathways, while the upregulated transcripts were involved in protein digestion and absorption, ABC transporters, and oocyte meiosis pathways. Downregulated transcripts in the granulosa cells were shown to be involved in cell adhesion molecules, chemokine signaling, and cytokine-cytokine receptor interaction pathways, while those upregulated transcripts were involved in protein processing and metabolic pathways.ConclusionIn conclusion, seasonal changes dramatically alter the gene expression profiles of oocytes and granulosa cells in beef cows, which may in part explain the seasonal discrepancies in pregnancy success rates during diverging climatic weather conditions.

Carbon dioxide fertilization enhanced carbon sink offset by climate change and land use in Amazonia on a centennial scale

The Amazon, the Earth's largest tropical forest, plays a critical role in the global carbon cycle, acting as a significant carbon sink. Recent studies, however, indicate a decline in its carbon sequestration capacity due to climate variability, intensive deforestation, and fires. This study aims to examine the impacts of these factors on the carbon dynamics of the Amazon over a centennial scale based on dynamic global vegetation models (DGVMs) of Trendy-v11. It was found that the Amazon region exhibited significant spatiotemporal variations in net land carbon (C) fluxes, and was a net C sink (40.02 ± 242.64 Tg C yr−1) during 1901–2021. The Amazonian net biome productivity (NBP) showed a 6-decades-scale shift from a decreasing trend (−3.78 Tg C yr−2) during 1901–1959 to an increasing trend (2.39 Tg C yr−2) during 1960–2021. The Amazonian NBP was negatively related to air temperature while positively related to dry-season precipitation during 1901–2021. Furthermore, the increase of atmospheric CO2 concentration during 1901–2020 enhanced Amazonian NBP by 36.40 ± 8.39 Pg C, which was largely offset by land use change (−18.84 ± 12.02 Pg C) and climate change (−10.03 ± 5.00 Pg C). Our findings underscore the critical need for sustainable management practices in the Amazon to enhance its C sink and preserve its function in the global climate system.

Microplastic levels in the indoor air of buildings based on plastic waste recycling in Indonesia

Introduction: Microplastics are a new type of contamination in the environment caused by plastic fragmentation and degradation. The generation of plastic waste increases every year, therefore efforts are made to recycle it into building materials. It is unclear how building use resulting from recycling plastic waste affects the development of microplastics in the atmosphere. The purpose of this study is to determine the airborne concentrations of microplastics in buildings constructed from recycled plastic waste. Materials and methods: The study measured microplastic levels in the air for 30 days in a miniature building made from plastic waste. Samples taken during the dry season in Indonesia in 2023. Air sampling is carried out by passive method. Visual observation of the shape, and amount of microplastics using a microscope. Results: Microplastics are found in the air of building spaces made from recycled plastic waste with varying levels every day. The average level of microplasty in the air for 30 days was 30,8 particles/m2/day. Maximum microplastic rate of 63 particles/m2/day, and minimum rate of 18 particles/m2/ day. The average air temperature when sampling is 25,48 ºC, air humidity is 68,37 % and ultraviolet intensity is 860 mwatt/cm2. Conclusions: Microplastic levels in the air of building spaces made from recycled plastic waste that can not be identified can be identified whether they are still within safe limits or not. This is because there is no regulation in Indonesia even in the world that regulates the safe limit of microplastic levels in the environment.

Leveraging explainable machine learning for enhanced management of lake water quality

Freshwater lakes worldwide suffer from eutrophication caused by excessive nutrient loads, particularly nitrogen (N) and phosphorus (P) from wastewater and runoff, affecting aquatic life and public health. Using a large (1800 km2) subtropical lake as an example (Lake Okeechobee, Florida, USA), this study aims to (1) predict key water quality parameters using machine learning (ML) algorithms based on easily measurable variables, (2) identify spatial patterns of these parameters, and (3) determine environmental drivers influencing turbidity levels. The study employs four ML algorithms—Extreme Gradient Boosting (XGB), Light Gradient-Boosting Machine (LGBM), Support Vector Regression (SVR), and Random Forests (RFs)—to predict total phosphorus (TP), total nitrogen (TN), nitrate + nitrite (NOx-N), and turbidity, via station-specific and lake-wide modeling approaches. The station-specific models uncover spatial patterns, while the lake-wide models support operational decision-making. Results indicated that lake stage (water level), water temperature, and, most notably, turbidity were the main nutrient predictors, with XGB demonstrating superior prediction performance. Spatial analysis using K-means clustering identified three distinct lake regions based on nutrient levels and turbidity. Due to its importance, SHapley Additive exPlanations (SHAP) were employed to identify and quantify environmental factors affecting turbidity. Inflows and lake stage were found as primary drivers of turbidity near lake inlets, while wind speed and air temperature affected turbidity in the middle of the lake. This research advances the understanding of lake water quality dynamics, emphasizing the importance of frequent monitoring of turbidity and its environmental drivers for enhanced management and future mitigation efforts.

An energy-environment coupled simulation framework for multi-scale and multi-facet evaluation of data center

This paper focuses on analyzing the thermal environment and optimizing energy consumption in data centers, which has largely omitted from previous studies. The thermal environmental of data centers is simulated and analyzed by utilizing the established “server-rack-room” multi-scale heat transfer numerical model. Based on this, the coupling simulation model of thermal environment and energy consumption in data centers is proposed to explore the relationship between them, and the corresponding optimization strategy is put forward. The energy consumption simulated by energy-environment coupled model and non-coupled model can reach a discrepancy of over 30 %, which indicates that the thermal environment impacts the power consumption of the data center significantly. Besides, the effect of several operational parameters of air conditioning system on the thermal environment and energy consumption of data center is analyzed. Through the particle swarm optimization algorithm, the optimal system parameters, which meet the requirements of thermal environment and lead to the lowest energy consumption, are found for typical cities in different climatic regions. The recommended settings include the supply air temperature of about 24 ∼ 26 ℃, the air supply volume of 8 m3/s, and the temperature difference of about 3.5 ℃ between the supply air temperature and the supply chilled water temperature. Under the optimized parameter setting, the highest temperature of server decreases to below 71 ℃, and the energy saving rate is more than 6 %.

A fast quadrupole-based analytical model for solving transient heat transfer in ventilated double-skin walls and assessing their energy saving potential

This research proposes a fast and accurate method for modeling transient heat transfer in ventilated double-skin façades (VDFs) with forced ventilation to predict their energy-saving potential. Using the thermal quadrupole formalism, the method solves the VDF problem in the Laplace domain while considering the full transient nature of the heat transfer; the only approximation is in space. The solution involves obtaining a general transfer function that predicts heat transfer rates or air temperatures at ventilated cavity's exit. The quadrupole method is validated against computational fluid dynamic numerical simulations conducted under similar meteorological, design and boundary conditions. A very good agreement was found (less than 3 % average deviation) with a significant reduction in computation time (less than 3s against 6h for CFD calculations). The model is computationally efficient and can consider important factors such as the opacity of the walls, construction materials, and air cavity design parameters. Finally, the model allows the assessment of the energy-saving potential of VDFs under various scenarios compared to conventional systems, which helps contributing to more sustainable building design practices. The energy efficiency of a VDF configuration was compared against a conventional wall system. Energy savings of 8.4 and 5.2 % were obtained, in cold and hot climates respectively.

Analysis of Diurnal Air Temperature Trends and Pattern Similarities in Highland and Lowland Stations of Italy and UK

ABSTRACTIn this study, an analysis of hourly air temperatures in four groups of 32 stations from the UK highland (5 stations), UK lowland (4 stations), Italian highland (11 stations), and Italian lowland (12 stations) at different altitudes was carried out over the period from 2002 to 2021. The study aimed to examine the trends of each hour of the day during this period, over different averaging time windows (10‐day, 30‐day, and 60‐day). The trends were computed using the Mann–Kendall trend test and Sen's slope estimator. The similarity of trends within and across the groups of stations was assessed using the hierarchical clustering with dynamic time warping technique. An additional analysis was conducted to show the correlation of trends among the group of stations using the correlation distance matrix. Hierarchical clustering and distance correlation analysis show trend similarities and correlations, also indicating dissimilarities among different groups. Using 30‐day averages, significant warming trends in specific months at the Italian stations are evident, especially in February, July, August, and December. The UK highland stations did not show statistically significant trends, but clear pattern similarities were found within the groups, especially in certain months. The ultimate goal of this article is to provide insights into temperature dynamics and climate change characteristics on regional and diurnal scales.

Multiple-Win Effects and Beneficial Implications from Analyzing Long-Term Variations of Carbon Exchange in a Subtropical Coniferous Plantation in China

To improve our understanding of the carbon balance, it is significant to study long-term variations of all components of carbon exchange and their driving factors. Gross primary production (GPP), respiration (Re), and net ecosystem productivity (NEP) from the hourly to the annual sums in a subtropical coniferous forest in China during 2003–2017 were calculated using empirical models developed previously in terms of PAR (photosynthetically active radiation), and meteorological parameters, GPP, Re, and NEP were calculated. The calculated GPP, Re, and NEP were in reasonable agreement with the observations, and their seasonal and interannual variations were well reproduced. The model-estimated annual sums of GPP and Re over 2003–2017 were larger than the observations of 11.38% and 5.52%, respectively, and the model-simulated NEP was lower by 34.99%. The GPP, Re, and NEP showed clear interannual variations, and both the calculated and the observed annual sums of GPPs increased on average by 1.04% and 0.93%, respectively, while the Re values increased by 4.57% and 1.06% between 2003 and 2017. The calculated and the observed annual sums of NEPs/NEEs (net ecosystem exchange) decreased/increased by 1.04%/0.93%, respectively, which exhibited an increase of the carbon sink at the experimental site. During the period 2003–2017, the annual averages of PAR and the air temperature decreased by 0.28% and 0.02%, respectively, while the annual average water vapor pressure increased by 0.87%. The increase in water vapor contributed to the increases of GPP, Re, and NEE in 2003–2017. Good linear and non-linear relationships were found between the monthly calculated GPP and the satellite solar-induced fluorescence (SIF) and then applied to compute GPP with relative biases of annual sums of GPP of 5.20% and 4.88%, respectively. Large amounts of CO2 were produced in a clean atmosphere, indicating a clean atmospheric environment will enhance CO2 storage in plants, i.e., clean atmosphere is beneficial to human health and carbon sink, as well as slowing down climate warming.

Phenological response of plants of different biomorphs to climate change in Western Siberia

The results of a phenology study of 78 species of perennial plants from biomorphological groups of chamaephytes, hemicryptophytes and geophytes over a 20-year period (1996—2015) are discussed. Against the background of air temperature and precipitation changes of the warm season in Novosibirsk, the timing shift in phenological events have been analyzed using calculated linear trends. It is found that the trends for species groups are multidirectional and vary significantly in magnitude. At the same time, most of the shifts in phenology are due not to trends, but to the interannual variability of climatic indicators.

Assessment of Wind Energy Potential and Optimal Site Selection for Wind Energy Plant Installations in Igdir/Turkey

Wind energy is an eco-friendly, renewable, domestic, and infinite resource. These factors render the construction of wind turbines appealing to nations, prompting numerous governments to implement incentives to augment their installed capacity of wind turbines. Alongside augmenting the installed capacity of wind turbines, identifying suitable locations for their installation is crucial for optimizing turbine performance. This study aims to evaluate potential sites for wind power plant installation via a GIS, a mapping technique. The Analytic Hierarchy Process (AHP) was employed to assess the locations, including both quantitative and qualitative aspects that significantly impact the wind farm suitability map. Utilizing the GIS methodology, all datasets were examined through height and raster transformations of land surface temperature, plant density index, air pressure, humidity, wind speed, air temperature, land cover, solar radiation, aspect, slope, and topographical characteristics, resulting in the creation of a wind farm map. The correlation between the five-year meteorological data and environmental parameters (wind direction, daily wind speed, daily maximum and minimum air temperatures, daily relative humidity, daily average air temperature, solar radiation duration, daily cloud cover, air humidity, and air pressure) influencing the wind power plant in Iğdır province, including Iğdır Airport, Karakoyunlu, Aralık, and Tuzluca districts, was analyzed. If wind energy towers are installed at 1 km intervals across an area of roughly 858,180 hectares in Igdir province, an estimated 858,180 GWh of wind energy can be generated. The GIS-derived wind power plant map indicates that the installation sites for wind power plants are located in regions susceptible to wind erosion.

Loss and Thermal Analysis of a High-Power-Density Permanent Magnet Starter/Generator

Reducing heat and improving the overall operation stability of the motor play a key role in the design of a starting engine. This paper focuses on the loss and thermal analysis of a permanent magnet (PM) brushless machine used in starter generators. The loss of the starter generator was calculated through a combination of theoretical analysis and the finite element method. A thermal analysis model was established based on the division of the fluid domain, boundary grid, heat source setting, and so on. The temperature fields of the whole motor and the main components were calculated and analyzed. The main factors affecting the air cooling effect were analyzed, including air flow rate, air temperature, and motor speed. A prototype experimental platform of the SG motor was built. The efficiency and temperature rise in the motor were tested. The temperature values were compared with the calculated values. The experimental results show that the performance of the motor is excellent, and the error between the temperature and the design calculation is less than 10% under each load torque. The accuracy of the thermal analysis method is verified. The correctness of the motor transient model was also confirmed through a temperature rise experiment under rated conditions, providing a research basis for improving operation efficiency.