Ultrasonic wind measurement instruments are widely used in meteorological monitoring, wind energy assessment, and environmental safety due to their simple structure, fast response, and strong adaptability. However, under complex wind field conditions, the shadow effect can introduce significant measurement errors. To reduce these errors, this article presents a novel ultrasonic wind measurement instrument. First, computational fluid dynamics (CFDs) was employed to optimize the probe geometry, transducer arrangement, and relative positioning, thereby minimizing instrument-induced disturbances to the ambient flow. Second, an error-correction model was developed by integrating CFD simulation data with a neural network algorithm. This model captures local disturbance characteristics and establishes a nonlinear mapping between wind speed and direction errors and the undisturbed flow field, enabling dynamic correction of instrument outputs. Finally, a wind-tunnel experimental platform was constructed to evaluate the proposed instrument. Experimental results show that the wind speed error was reduced to 0.061 m/s, and the wind direction error decreased to 0.221 degrees, confirming the effectiveness of the proposed design and correction approach.

Ground-based air temperature measurements are highly susceptible to solar and environmental long-wave radiation, often leading to temperature deviations of up to approximately 1 degrees C. This study proposes a low-uncertainty air temperature measurement instrument specifically designed to minimize such radiation-induced temperature deviations. Computational fluid dynamics (CFD) simulations were first performed to optimize the structural design of the instrument, thereby enhancing internal airflow and improving both radiation shielding efficiency and convective heat dissipation. Subsequently, the radiation-induced temperature deviations of the optimized structure were quantitatively analyzed under various environmental conditions. A multi-layer perceptron (MLP) neural network was then employed to develop a temperature deviation correction model using the CFD-generated dataset. Finally, field comparative experiments were conducted using a 076B aspirated temperature measurement instrument as a reference. Experimental results show that, before correction, the proposed instrument exhibited a maximum radiation-induced temperature deviation of 0.43 degrees C and a mean temperature deviation of 0.31 degrees C. The root mean square error (RMSE), mean absolute error (MAE), and correlation coefficient (r) between the experimental and predicted radiation-induced temperature deviations were 0.08 degrees C, 0.07 degrees C, and 0.99, respectively. After applying the correction model, the maximum radiation-induced temperature deviation decreased to 0.12 degrees C, and the mean deviation decreased to 0.02 degrees C, demonstrating excellent consistency. In conclusion, the proposed instrument achieves efficient ventilation and low-uncertainty temperature measurement without relying on mechanical components.

Solar radiation frequently causes traditional temperature sensors to deviate from the true ambient temperature, with errors reaching approximately 1 degrees C, thereby considerably affecting the reliability of meteorological data. To address this issue, this study presents a novel temperature sensor designed for natural ventilation, utilizing an optimized thermal design structure and computational fluid dynamics (CFD) simulation techniques to analyze its thermal performance under complex environmental conditions. Additionally, a radiative error correction model was developed using neural network algorithms to enhance measurement accuracy further. Field experimental results indicate that the sensor effectively reduces radiative error to below 0.1 degrees C, with root mean square error (RMSE) and mean absolute error (MAE) values of 0.047 degrees C and 0.038 degrees C, respectively. This research demonstrates the significant engineering applicability of this design in the field of atmospheric temperature measurement.

Air temperature measurements in atmospheric environmental monitoring are susceptible to radiation-induced bias under natural ventilation. This study develops a low-power naturally ventilated air temperature sensor and a correction method combining computational fluid dynamics (CFD) with machine learning. The sensor integrates a Pt100 thin-film platinum resistance probe (Heraeus Holding GmbH, Hanau, Germany), symmetric guide plates, and a dual aluminum-plate radiation shield to reduce radiative heating while improving airflow around the probe. A three-dimensional fluid–solid coupled heat-transfer model was established in ANSYS FLUENT 15.0 to optimize guide-plate spacing and inclination angle and quantify the effects of solar radiation, long-wave radiation, scattered radiation, air density, wind speed, solar elevation angle, and surface albedo on radiation error. CFD results identified a guide-plate spacing of 24 mm and an inclination angle of 45° as the preferred parameters. A multilayer perceptron (MLP) model trained with CFD-derived data was validated in field experiments using a Model 076B aspirated radiation shield (Met One Instruments, Inc., Grants Pass, OR, USA) as the reference. The model predicted radiation error with a root mean square error (RMSE) of 0.052 °C, a mean absolute error (MAE) of 0.042 °C, and a correlation coefficient of 0.92. The proposed sensor and correction method provide a low-power and easy-to-maintain approach for reducing radiation-induced bias in naturally ventilated air-temperature measurements, with potential applications in meteorological observation, air-quality monitoring, and agricultural microclimate assessment.

Global temperatures are rising by approximately 0.1 degrees C per decade. Existing air temperature measurement systems often report temperatures higher than actual air temperature due to the effects of solar radiation, leading to errors of up to 1 degrees C. As a result, there is an urgent need for a new temperature measurement system with improved radiation protection and ventilation capabilities. Furthermore, a specialized temperature error correction model is essential for the new system. Computational fluid dynamics (CFD) software was employed to simulate the radiation shielding and ventilation performance of the new system. Temperature differences between the new system and actual air temperature under various environmental conditions were quantified using CFD software. Subsequently, a specialized temperature difference correction model, incorporating multiple environmental variables, was developed using a neural network algorithm. Finally, the measurement accuracy of the new system was evaluated through field comparison experiments. During the experiments, a 076B fan aspirated temperature measurement system with an error of less than 0.03 degrees C served as the reference system. Before correction, the new system exhibited a maximum temperature difference of 0.69 degrees C and an average temperature difference of 0.35 degrees C compared to the reference system. The mean absolute error, root mean square error, and correlation coefficient between the temperature differences from the correction model and the experimental data were 0.07 degrees C, 0.08 degrees C, and 0.9 degrees C, respectively. After correction, the average temperature difference decreased to 0.06 degrees C. These results indicate that the new system has significant potential for highaccuracy temperature measurement.

Air temperature sensors often exhibit measurement inaccuracies due to solar radiation effects, leading to overestimations of actual air temperature by up to 1 degrees C. To address this issue, a novel temperature sensor with enhanced radiation shielding and ventilation was developed. The proposed design incorporates an S-shaped flow diversion device, two silver-plated mirrored aluminum plates, two hemispherical flow diversion structures, and a sensor probe. Computational fluid dynamics (CFDs) simulations were conducted to evaluate radiation shielding efficiency and airflow performance. Radiation-induced errors were assessed under varying environmental conditions, and a neural network algorithm was implemented to develop a correction model. Experimental results showed that, prior to correction, the average radiation-induced errors for the new sensor, the 41 003 model, and the 43 502 model were 0.443 degrees C, 0.533 degrees C, and 0.317 degrees C, respectively, with maximum errors of 0.996 degrees C, 1.42 degrees C, and 0.905 degrees C. After correction, the average radiation-induced error of the new sensor decreased to 0.147 degrees C, with a maximum of 0.485 degrees C. The 41 003 and 43 502 models achieved reductions to 0.171 degrees C (maximum: 0.598 degrees C) and 0.118 degrees C (maximum: 0.37 degrees C), respectively. The new sensor demonstrated a mean absolute error (MAE) of 0.434 degrees C, root mean square error (RMSE) of 0.351 degrees C, and correlation coefficient ( ${r}$ ) of 0.855 in corrected radiation-induced errors compared to experimental data. In contrast, the 41 003 model exhibited an MAE of 0.604 degrees C, RMSE of 0.471 degrees C, and r of 0.832, while the 43 502 model achieved an MAE of 0.268 degrees C, RMSE of 0.218 degrees C, and r of 0.901. These findings highlight the effectiveness of the new sensor design and correction model in mitigating radiation-induced measurement errors, providing improved accuracy for atmospheric temperature sensing applications.

This study presents a novel radiation measurement system capable of simultaneously measuring solar and upward longwave radiation, with the goal of achieving measurement accuracy within +/- 5% under the tested experimental conditions. A multiphysics heat transfer analysis based on computational fluid dynamics (CFDs) was first conducted to quantify the influence of key environmental factors on the thermal response of the sensing elements. Subsequently, an environmental correction model was developed using a multilayer perceptron (MLP) neural network to compensate for the nonlinear effects of meteorological variables. Finally, a field comparison platform was constructed to assess the system's performance. During the experiments, solar radiation data from a Kipp and Zonen CMP10 pyranometer and longwave radiation values derived from the Stefan-Boltzmann law were used as reference standards. The results showed that the relative errors for solar and longwave radiation measurements ranged from -3.66% to 3.69% and -3.86% to 3.81%, respectively. The root mean square errors (RMSEs) between the estimated and measured values were 15.4 W/m2 for solar radiation and 16.7 W/m2 for longwave radiation, with corresponding mean absolute errors (MAEs) of 9.8 and 11.4 W/m2. The correlation coefficients were 0.98 and 0.96, respectively, indicating a strong agreement with the reference data. These results demonstrate the high accuracy and robustness of the proposed system, highlighting its potential for applications in energy balance analysis, climate monitoring, and agroecological research.

Global temperatures are rising at approximately 0.1 degrees C per decade. Existing air temperature observation sensors often measure temperatures higher than the actual values due to solar radiation effects, leading to errors of up to 1 degrees C, which significantly affects the accuracy of meteorological observations. Traditional naturally ventilated temperature sensors exhibit significant limitations in reducing radiation errors, making it challenging to meet the precision requirements of 0.05 degrees C or even higher in atmospheric science research. To address this challenge, this paper proposes and designs a novel naturally ventilated temperature sensor. The core sensing element of the sensor employs a Pt100 thin-film platinum resistor, and its performance is optimized through computational fluid dynamics (CFD) methods and a multi-layer perceptron (MLP) network to reduce radiation errors. External field comparative experiments with the 076B artificially ventilated temperature monitoring device, using its measurements as temperature references, have validated the effectiveness of the new sensor in reducing radiation errors. Experimental results indicate that the new sensor has a root mean square error (RMSE) of 0.047 degrees C, a mean absolute error (MAE) of 0.039 degrees C, and a correlation coefficient (r) of 0.999. The average radiation error of the calibrated sensor is 0.03 degrees C. These findings fully demonstrate the significant advantages of this sensor in improving the accuracy of temperature measurements.

Temperature variations directly affect the electrical performance and reliability of high-power semiconductor devices. Therefore, to prevent semiconductor devices from overheating and to ensure stable operation, the design of efficient heat sinks to enhance the thermal management of microchips is particularly critical. This work focuses on high-power insulated-gate bipolar transistor (IGBT) modules as a representative application and addresses the challenges of high thermal-failure risk and stringent temperature uniformity under high power-density operation by proposing a novel liquid-cooled plate (LCP) heat sink integrating serpentine channels, cooling fins, and staggered turbulence promoters. The serpentine channel increases the contact time between the coolant and the IGBT, enhancing heat exchange, while the additional fins enlarge the heat transfer area, further improving dissipation. Additionally, various turbulence promoters are introduced into the flow channels to disrupt laminar flow and induce turbulence, thereby increasing the heat transfer coefficient and thermal exchange efficiency. Computational fluid dynamics (CFDs) simulations are employed to analyze the thermal performance of the LCP heat sink, and experimental comparisons are conducted to evaluate the effects of different turbulence promoter structures and distributions on temperature uniformity. Simulation results indicate that the inclusion of turbulence promoters reduces the IGBT maximum temperature from 369.48 to 343.94 K, while the alternating arrangement of promoters significantly improves temperature uniformity, maximizing thermal performance. Experimental results indicate that increasing the coolant flow rate can lower the maximum IGBT temperature by up to 22 K, with performance stabilizing at 8.5 L/min. Moreover, when the spacing of the heat dissipation fins is 3.5 mm, the temperature of the LCP reaches its minimum, approximately 3 K lower than that observed with other spacing configurations.

To address the critical role of atmospheric temperature in climate change and disaster monitoring, enhancing measurement accuracy to 0.1 degrees C is essential. Current instruments are susceptible to radiation interference, resulting in errors of approximately 1 degrees C. This study introduces a novel temperature sensor that improves accuracy by combining natural ventilation with forced ventilation. Silver-coated aluminum plates (95 % reflectivity) and white-coated deflectors (87 % reflectivity) minimize solar radiation errors. A neural network algorithm, along with CFD simulations, further corrects radiation errors under varying weather conditions. Field tests based on the 076B ventilation device demonstrate that this new sensor reduces the average radiation error to 0.02 degrees C, achieving a RMSE of 0.034 degrees C and a MAE of 0.028 degrees C. The correlation coefficient (r) with the reference temperature reached 0.999, demonstrating the sensor's high precision and providing an effective solution for reducing temperature measurement errors to below 0.1 degrees C. Um die entscheidende Rolle der atmosph & auml;rischen Temperatur im Klimawandel und bei der Katastrophen & uuml;berwachung zu adressieren, ist eine Verbesserung der Messgen & auml;uigkeit auf 0,1 degrees C unerl & auml;sslich. Aktuelle Instrumente sind anf & auml;llig f & uuml;r Strahlungsst & ouml;rungen, was zu Fehlern von etwa 1 degrees C f & uuml;hrt. Diese Studie stellt einen neuartigen Temperatursensor vor, der die Genauigkeit durch die Kombination von nat & uuml;rlicher Bel & uuml;ftung und erzwungener Bel & uuml;ftung verbessert. Silberbeschichtete Aluminiumplatten (95 % Reflektivit & auml;t) und wei ss beschichtete Abweiser (87 % Reflektivit & auml;t) minimieren Strahlungsfehler. Ein neuronales Netzwerk-Algorithmus sowie CFD-Simulationen korrigieren zus & auml;tzlich Strahlungsfehler unter variierenden Wetterbedingungen. Feldtests basierend auf dem 076B Bel & uuml;ftungsger & auml;t zeigen, dass dieser neue Sensor den durchschnittlichen Strahlungsfehler auf 0,02 degrees C reduziert und einen RMSE von 0,034 degrees C sowie einen MAE von 0,028 degrees C erreicht. Der Korrelationskoeffizient (r) mit der Referenztemperatur betrug 0,999, was die hohe Pr & auml;zision des Sensors demonstriert und eine effektive L & ouml;sung zur Reduzierung der Temperaturmessfehler auf unter 0,1 degrees C bietet.

Atmospheric temperature is fundamental information for various industries, such as production, life, and scientific research. The temperature error induced by the solar rays can reach 1 °C or even higher. A hemispherical shell-shaped atmospheric temperature measuring instrument that can reduce heat pollution and increase air velocity was designed. First, the instrument was optimized using computational fluid dynamics (CFD) software packages. Then, the CFD software packages were employed to quantify the temperature errors of the instrument with varying situations. A neural network model was employed to develop a temperature error correction model that can be targeted for multi-variable changes. This model provides accurate correction data when the influencing factors change continuously. Finally, field experiments were performed. The experimental data analysis indicates that the mean temperature error and the maximum error of the instrument before correction are 0.08 and 0.25 °C, respectively. The root mean square error, the mean absolute error, and the correlation coefficient between measured temperature errors from experiments and corrected temperature errors from the correction model are 0.099, 0.016, and 0.952 °C, respectively. By utilizing a temperature error correction model, the measuring error of the instrument can be minimized to a range between −0.05 and 0.04 °C. Consequently, the instrument is anticipated to enhance temperature measurement accuracy to ∼0.1 °C.

To reduce the influence of radiation on temperature measurement, this research introduces a novel radiation shield. To reduce solar radiation effects, aluminum foil was used to cover the external surfaces of the plates. In addition, to reduce indirect radiation effects, a black coating was applied to cover the internal surfaces of the plates. The middle plates facilitate airflow to the sensors, thus accelerating the diffusion of radiant heat and reducing radiation interference. Temperature errors of the sensors equipped with the new radiation shield induced by various radiations (direct solar radiation, reflected radiation, diffused radiation, long-wave radiation) were quantified by employing computational fluid dynamics (CFD) and neural network methods. The experimental results showed that the mean absolute error, root mean square error, and correlation coefficient between the experimental and predicted temperature errors were 0.036 degrees C, 0.046 degrees C, and 0.99, respectively.

In order to meet the better performance requirements of pressure detection, a microelectromechanical system (MEMS) piezoresistive pressure sensor utilizing an array-type aluminum–silicon hybrid structure with high sensitivity and low temperature drift is designed, fabricated, and characterized. Each element of the 3 × 3 sensor array has one stress-sensitive aluminum–silicon hybrid structure on the strain membrane for measuring pressure and another temperature-dependent structure outside the strain membrane for measuring temperature and temperature drift compensation. Finite-element numerical simulation has been adopted to verify that the array-type pressure sensor has an enhanced piezoresistive effect and high sensitivity, and then this sensor is fabricated based on the standard MEMS process. In order to further reduce the temperature drift, a thermodynamic control system whose heating feedback temperature is measured by the temperature-dependent structure is adopted to keep the working temperature of the sensor constant by using the PID algorithm. The experiment test results show that the average sensitivity of the proposed sensor after temperature compensation reaches 0.25 mV/ (V kPa) in the range of 0–370 kPa, the average nonlinear error is about 1.7%, and the thermal sensitivity drift coefficient (TCS) is reduced to 0.0152%FS/°C when the ambient temperature ranges from −20 °C to 50 °C. The research results may provide a useful reference for the development of a high-performance MEMS array-type pressure sensor.

To enhance meteorological detection methods, an atmospheric boundary layer detection system based on a rotary-wing unmanned aerial vehicle (UAV) was proposed. Computational fluid dynamics (CFD) was employed to model the surrounding airflow distribution during UAV hovering, thereby determining the optimal positions for sensor installation. A novel radiation shield was designed for the temperature sensor, offering both excellent radiation shielding and superior ventilation. To further improve temperature measurement accuracy, an error correction model based on CFD and neural network algorithms was designed. CFD was used to quantify the temperature measurement errors of the sensor under different environmental conditions. Subsequently, random forest and multilayer perceptron algorithms were employed to train and learn from the simulated temperature errors, resulting in the development of the error correction model. To validate the accuracy of the detection system, comparative experiments were conducted using the measurement values from the 076B temperature observation instrument as a reference. The experimental results indicate that the mean absolute error, root mean square error, and correlation coefficient between the experimental temperature errors and the algorithm-predicted errors are 0.055, 0.066, and 0.971 degrees C, respectively. The average error of the corrected temperature data is 0.05 degrees C, which shows substantial agreement with the reference temperature data. During UAV hovering, the average discrepancies between the temperature, humidity, and air pressure data of the detection system and the ground-based reference data are 0.6 degrees C, 1.6% RH, and 0.77 hPa, respectively.

Temperature sensors may produce a measurement error of up to 1 °C because of the influence of solar radiation. In order to obtain a relatively minimal temperature error, a new temperature observation system was proposed in this paper for measuring surface air temperatures. Firstly, a radiation shield was designed with two aluminum plates, eight vents, and a multi-layer structure which is able to resist direct solar radiation, reflected radiation, and upwelling long-ware radiation, as well as ensuring the temperature sensor probe could work effectively. Then, the effect of different solar radiation intensities, wind speeds, scattered radiation intensities, long-wave radiation intensities, and underlying surface reflectivity levels on radiation error was calculated through a computational fluid dynamics (CFD) method. The mapping relationship was established between the various influencing factors and the solar radiation error. A back-propagation (BP) network algorithm was used to fit the discrete data obtained from the simulation to obtain the solar radiation error correction equation. Finally, the solar radiation error correction equation was verified. Outdoor experiments were conducted to confirm this system’s measurement accuracy. According to the experimental findings, the root-mean-square error was only 0.095 °C, which is a relatively high degree by which to reduce the temperature error. In addition, the average difference between the corrected value of the temperature observation system and the reference value was barely 0.084 °C.

Because of the effects of radiation, existing air temperature instruments used in the meteorological detection field can produce radiation errors of approximately 1 °C. We developed an atmospheric temperature measuring instrument to reduce radiation error. We used the computational fluid dynamics (CFDs) approach to optimize the ability of the instrument to block radiation and guide airflow to the sensor. We mounted two aluminum plates and two airflow deflectors above and below the sensor. The outer surfaces of the plates were covered with high-reflectivity silver film, which could effectively block direct and reflected solar radiation. The deflectors had a streamlined shape, which could effectively guide airflow to the sensor. To further improve the accuracy of the instrument, we used the CFD approach to quantify its radiation errors under different meteorological conditions. Then, we utilized a neural network algorithm to develop a high-precision and universal radiation error correction algorithm. Subsequently, we conducted experiments to evaluate the accuracy of the new instrument. The experimental results indicated that the new instrument had a root means square error (RMSE), mean absolute error (MAE), and correlation coefficient of 0.0027 °C, 0.023 °C, and 0.99, respectively. The mean value, the upper and lower 95% confidence interval of the measured radiation errors of the new instrument were 0.088 °C, 0.096 °C, and 0.079 °C, respectively. These findings suggest that the new instrument has the potential to reduce the measurement error to less than 0.1 °C.

To minimize the impact of various radiations on atmospheric temperature observation, a new natural ventilation temperature observation instrument is designed in this paper. First of all, the temperature measuring instrument model is constructed using the means of computational fluid dynamics. Then, the radiation error of the device is quantified in different environmental conditions. Next, a back propagation neural network algorithm is adopted to fit a radiation error modified equation with multivariable changes. Finally, the measured values of a 076B forced ventilation temperature monitoring device are adopted as the temperature reference, and field tests are conducted. The average error of this new device is 0.12 °C. The root mean square error, mean square error, and correlation coefficient between the measured values of the new instrument and the reference temperature are 0.047 °C, 0.036 °C, and 0.999 °C, respectively.

大气科学研究对地表气温观测精度有高达0.1℃甚至0.05℃的需求.然而,现有的地表气温观测仪器受到太阳直接辐射、下垫面反射辐射、长波辐射和散射辐射等影响,辐射误差可达1℃.本文设计了 一种基于导流装置的地表气温观测仪器.首先,利用计算流体动力学(Computational Fluid Dynamics,CFD)方法量化该仪器在各种环境条件下的辐射误差;然后,在此基础上,利用极限学习机(Extreme Learning Machine,ELM)方法拟合可针对多变量变化的辐射误差订正方程;最后,为验证该仪器的观测精度,进行了外场比对实验.在实验过程中,以076B型强制通风气温观测仪器的测量值作为温度基准.实验结果表明,该仪器的平均辐射误差和最大辐射误差分别为0.07℃和0.15℃.该仪器辐射误差的实验测量值与订正方程提供的辐射误差订正值之间的平均偏移量、均方根误差和相关系数分别为0.033℃、0.028℃和0.703.

Due to the influence of solar radiation, the observed values of existing meteorological temperature sensors may differ from the free air temperatures up to the order of 1 °C. This article proposed a temperature sensor consisting of a sensor probe, an airflow deflector, and two aluminum plates. The airflow deflector can effectively guide the airflow to the sensor probe and reduce radiation error. The two silver-plated mirror aluminum plates have up to 98% reflectivity. They can effectively block direct solar radiation, reflected radiation, long-wave radiation, and so on. The radiation protection and airflow guiding ability of the sensor are analyzed by the computational fluid dynamics (CFD) method. Then, the CFD approach obtains the radiation errors of the sensor under different environmental conditions. Next, the neural network algorithm fits the simulation data to form a high-accuracy radiation error correction approach. Finally, the 076B artificially ventilated temperature sensor is used as the temperature reference during experiments. The experimental results show that the mean absolute error (MAE) and the root-mean-square error (RMSE) between the radiation errors provided by the experiments and the radiation errors given by the neural network are 0.031 °C and 0.026 °C, respectively. After correction, the maximum, minimum, and average radiation errors of the new sensor are 0.095 °C, $-0.074\,\,^{\circ }\text{C}$ , and 0.01 °C, respectively. The correlation coefficient between the temperature results of the new sensor after correction and the reference temperature results is 0.999. These results show that this new sensor might reduce the measurement error to within 0.1 °C.