Measured Temperature Data Research Articles

Application of computationally inexpensive CFD model in steady-state and transient simulations of pulverized sewage sludge combustion

The combustion process of pulverized fuels with high ash contents, such as sewage sludge, is significantly impacted by the amount of slag deposition in a furnace. A CFD model based on the Steady Diffusion Flamelet (SFM) approach was applied to numerically simulate the combustion of pulverized sewage sludge in a drop tube furnace. Steady-state solutions of the Reynolds-Averaged Navier–Stokes (RANS) equations already displayed good agreement with species and temperature measurements. Still, they failed to accurately predict the significant amount of ash trapped on the internal furnace surfaces (minimum deposition of 28% of the generated ash versus 5% predicted by the RANS simulations). The SFM-based CFD model’s computational efficiency enabled the conduction of large eddy simulations (LES), significantly improving the model’s predictive capabilities (27% of the generated ash deposited). Additionally, the transient simulations further improved the agreement with temperature measurement data. A novel initialization procedure was developed, which allowed the transient LES simulations to be conducted in a computationally efficient manner. The SFM-based model can effectively support research and development efforts, even for large-scale systems which require high cell counts. It provides valuable insights during the early design phases of industrial furnaces for pulverized sewage sludge combustion.

The Evaluation of the Temperature Reduction Effects of Cool Roofs and Cool Pavements as Urban Heatwave Mitigation Strategies

Various types of damage, including health risks, caused by abnormal climate conditions have gradually increased in recent years, particularly in cities with a high degree of complexity. In particular, it is important to address heatwave damage risk from both technical and policy perspectives because it poses risks to older adults and/or disadvantaged individuals. This study was performed to assess the quantitative effects of two heatwave mitigation measures, cool roofs and cool pavements, in the Jangyumugye district of Gimhae, Republic of Korea. Thermal imaging cameras were used to measure surface temperatures after the installation of these measures; after the installation of cool roofs, indoor temperatures were also measured using temperature measurement data loggers. Then, a survey was conducted to obtain feedback from users of the associated facilities. From August to October 2019, surface temperatures were significantly reduced following the installation of cool roofs and cool pavements. Cool roofs constructed using slabs and panels provided average temperature reductions of 15.5 °C and 11.6 °C, respectively. Cool pavements showed a temperature reduction of ≥4 °C in both parking lot and alley sites. More than 30% of users noticed a reduction in temperature as a result of heatwave measure installation. These results may be used by the city to make informed policy decisions regarding the implementation of heatwave mitigation measures.

Spatially resolved indoor overheating evaluation using microscale meteorological simulation as input for building simulation – opportunities and limitations

To assess the spatial heat resilience of buildings in urban development we test the suitability of a toolchain approach from microscale meteorological simulations, resolving the spatial influences on local urban climate, to building performance simulations, evaluating the indoor overheating risk in buildings. This approach makes it possible to investigate how much microscale effects (e.g. buildings, trees e.g. roads) in open space influence the overheating intensity in a building depending on its location within a district. In this context, the question arises how realistic the microscale meteorological simulation is to be used as input for indoor overheating evaluation. In this context, we applied a 3D urban climate model (ENVI-met) and a 1D boundary layer model (HIRVAC) for two urban districts in Germany as meteorological input for an indoor thermal comfort evaluation of two representative buildings. The results demonstrate that ENVI-met simulations without using measured temperature data create unrealistically low diurnal variations in outdoor air temperature despite an overestimated solar irradiance. The implementation to building simulation leads to a significant underestimation of the heat resilience for both buildings and to wrong conclusions about the efficacy of passive heat adaptation measures. In contrast, the HIRVACsimulations show a more realistic representation of the meteorological variables (when measured data is used for calibration) but are not able to resolve urban 3D structures. Our findings point out that an adjusted boundary layer representation in microscale meteorological simulations is crucial to provide meteorological input suitable for realistic spatially resolved indoor overheating analysis.

Domination and effect of multi-parameters in direct chill casting based on establishment of thermo model by numerical simulation and experiment

In this paper, a mathematical model validated by in-situ temperature measurement data of the direct chill (DC) casting process has been established in order to study the domination and effect of multi-parameters including secondary cooling water flow rate (Q), casting speed (V), condition of primary cooling, and spraying water temperature (Tw) on DC casting index of sump depth (dsump), maximum and minimum temperature gradient (Tg,max and Tg,min) at mushy zone and shape of solidus line. Further, the height of impingement zone (Hip) is systematic quantitative analysis by a novel designed test (Hip=1.08865Q`+0.06272V−4.75597). The calculated temperature using the numerical simulation exhibits a good match with the actual temperature measurement data. The results of multi-factors on index by the designed orthogonal test indicate that V dominates the dsump (dsump=0.033+27.74V) and Tg,min (Tg,min=2569.15−766342.20V) at mushy zone, Q dominates the Tg,max (Tg,max=9195.12+2344770Q−27.83Tw) by analysing the range (R), variance (S) and significance (F). Likewise, by discussing the combination of R, S, F and microstructure observation, the priority influencing the shape of solidus line at center part was V. The Q dominates the shape of solidus line at the position between the center and edge part.

Full-scale numerical simulation and experimental validation of the thermal environment under the heating mode in an air-conditioned room

The numerical simulation of the air flow of the room equipped with the crossflow fan air conditioners has been conducted a lot. However, the simulation of the air flow distribution under the heating mode has always been difficult to match the experimental data well, due to the fact that both the air temperature and the flow are affected by the buoyancy. In addition, very few full-scale simulations were conducted. To precisely predict the air flow and temperature distribution of the room with the crossflow fan air conditioner, a full-scale steady numerical simulation was conducted with the Computational Fluid Dynamics (CFD) package. The full-scale physical model was constructed strictly according to the real settings, including the detailed components of the air-conditioner, such as the crossflow fan and the heat exchangers. The Shear-Stress Transport (SST) k-ω turbulent model was selected to simulate the low Reynold flow near the inner surface of the room, and to simulate the high Reynold and transitional flow near the fan blades of the air-conditioner. The porous medium model was used to simulate the heat exchange fins in the air-conditioner. Furthermore, an experiment under the same condition was also conducted to verify the accuracy of the simulation. Results showed that the simulated supply air flow rate was 537.48 m3/h, producing a 4.97% deviation from the experiment result. Meanwhile, the upward movement of the supply air from the outlet of the air-conditioner was observed, leading to higher air temperatures in the upper region of the room. Furthermore, among all 210 measured temperature data points in the room, 149 data points had the deviation within ±5%, and 194 data points had the deviation within ±10%. 9 abnormal data points with greater deviation near the door were detected, which might be resulted from the outdoor cold air leakage through the crack between the door and the floor in the experiment. Conclusively, the full-scale numerical simulation in this study produced an accurate and reliable solution to predict the air temperature distribution in buildings, thus providing a potential method for the design or optimization of the air-conditioners.

Implementation of Building a Thermal Model to Improve Energy Efficiency of the Central Heating System—A Case Study

This paper presents the concept of an innovative control of a central heating system in a multifamily building based on the original thermodynamic model, the resulting architecture of the control system, and the originally designed and manufactured wireless temperature sensors for thermal zones. The novelty of this solution is the developed layers of the control system: distributed measurement and correction analysis, which is based on the existing infrastructure and the local HVAC controller. This approach allows for the effective use of the measured temperature data from thermal zones and finally sending the value of the calculated correction of settings to the controller. Moreover, in the analytical layer, a model was also implemented that calculates the necessary amount of energy based on data from the subsystem of temperature sensors located in the thermal zones of the building. The use of the algorithmic strategy presented in this paper extends the functionality and significantly improves the energy efficiency of the existing, classic, reference heating control algorithm by implementing additional control loops. Additionally, it enables integration with demand-side response systems. The presented concept was successfully tested, achieving real energy savings for heating by 12%. These results are described in a case-study format. The authors believe that this concept can be used in other buildings and thus will have a positive impact on the energy savings used to maintain thermal comfort in buildings and significantly reduce CO2 emissions.

Deep learning model for displacement monitoring of super high arch dams based on measured temperature data

Dam displacement, an important indicator for the health monitoring of dam structures, can effectively reflect its operational status. Displacement prediction models based on measured data are currently an important tool for dam safety monitoring. However, a significant portion of current models rely on statistical or shallow machine-based approaches, posing challenges in extracting comprehensive and impactful features from environmental factors. To address the above problems, this paper proposes a novel deep learning model that combines Inception architectures with residual connections (Inception-ResNet) and Gate Recurrent Units (GRU). This model employs improved Inception-ResNet blocks with channel attention and spatial attention modules to extract features from dam deformation-related environmental factors sequences at multiple scales. Subsequently, GRU is utilized to learn from long-term dependencies. The proposed model fully combines the remarkable feature extraction capability of the Inception-ResNet block with the learning capability of GRU for long-term dependencies. The availability of the proposed model is tested with measured data of a super high arch dam. The experimental results show that the proposed model outperforms two typical shallow machine learning methods and two typical deep learning models in the four typical monitoring points selected, which demonstrates convincingly that the proposed model is able to predict dam deformation with high accuracy and robustness for dam structure safety monitoring.

Design and Implementation of Raspberry Pi Face Recognition + Thermal Imaging Temperature Measurement and Body Temperature Data Cloud Processing System

For temperature measurement in public places, this paper proposes and realizes a Raspberry Pi face recognition + thermal imaging temperature measurement and temperature data cloud processing system. This system uses a Raspberry Pi device as the temperature measurement terminal, connects the MLX90640 thermal imaging sensor and the BME280 environmental sensor for data collection, and calculates the core temperature of the human body from the collected data. The temperature measurement terminal uses Python language with OpenCV library for image processing, Alibaba Cloud API for face recognition, and the server side uses Python language with Django library for development. The system can quickly measure temperature and count the temperature measurement list, and view it in the cloud in real time to improve management efficiency.

A new Einstein coefficient method for mesopause-lower thermosphere atmosphere temperature retrieval under a non-local thermal equilibrium situation.

The mesopause-lower thermosphere (MLT) region is an important spatial region in the Earth's atmosphere, making it a valuable area to investigate the temperature variations. Kirchhoff's law fails with the altitude increase due to the non-local thermal equilibrium effect, resulting in an increase in the error of the method to retrieve the atmospheric temperature in the MLT region using the A-band spectral line intensity. In the non-LTE state, the temperature retrieval method based on the Einstein coefficients is proposed to retrieve atmospheric temperature in the 92-140 km height range using the airglow radiation intensity images obtained from the Michelson Interferometer for global high-resolution thermospheric imaging (MIGHTI) measurements. Results show that the temperature deviation of the two-channel combinations does not exceed 15 K in the altitude range of 92-120 km. This deviation increases up to 45 K when the altitude is in the range of 120-140 km due to the influence of the N2 airglow spectrum. The two-channel combinations self-consistency is increased by 85 K compared with the temperature obtained using the spectral line intensity retrieval. Additionally, the comparison of the retrieval results with the spectral line intensity method and the comparison with the atmospheric chemistry experiment Fourier transform spectrometer (ACE-FTS) temperature measurement data shows that the Einstein coefficient method is significantly more rational and accurate than the spectral line intensity method.

CF/PEKK advanced pultrusion thermoforming process temperature field simulation

Advanced pultrusion molding technology is one of the composite molding processes. It has unique advantages in the automated manufacturing of long trusses and long beam components. Thermoplastic composites are becoming popular for research in many fields such as aerospace because of their room-temperature storage and secondary use. Since the temperature history is critical to the molding quality of thermoplastic composites, the temperature field variation of CF/PEKK composites in the advanced pultrusion thermoforming process has been explored in the context of studying the advanced pultrusion molding process of thermoplastic composites in this paper. By constructing a 3D model of the temperature field and setting up a complete solution scheme in ANSYS simulation software, the simulated temperature field data were obtained, and a real-time temperature monitoring platform was set up to obtain the measured temperature data, and the comparison showed that the two temperature profiles fit well, which proved the applicability of the model and solution scheme to this study.

STORAGE ROOM TEMPERATURE AND HUMIDITY MONITORING IOT- BASED MEDICINE

This research is important as it portrays a notable advancement in current internet technology known as the Internet of Things (IoT), which facilitates advanced services through the interconnection of physical and virtual objects. One crucial aspect of everyday life is air quality, especially indoors, as indoor air pollution differs from outdoor air pollution. An important indicator to determine the cleanliness of a room is its humidity level. In this context, this research holds significant relevance as it can help ensure the effectiveness and safety of stored medications under appropriate temperature and humidity conditions. Therefore, this study designed a drug storage room that requires a tool to measure the humidity value of the room in real time and the humidity conditions in the room. The solution to this problem can be overcome using Internet of Things technology. In this project, a hygrometer, or humidity meter, based on Internet of Things technology is made. Humidity is measured by the DHT-11 sensor, which is integrated on the NodeMCU ESP8266 board. Humidity measurement data can be observed via the Blynk IoT platform available on smartphones. With this tool, humidity can be observed in real time, even from a distance. The results of this study are that the tool can measure the temperature and humidity of the medicine room, and the tool can send humidity and temperature measurement data to the Blynk IoT and MIT APP Inventor servers. On the Blynk IoT website, the dashboard can display temperature, humidity, and drug safety measurement data in real time.

Generating a virtual physical model through measurement data and reverse engineering: Applying a performance prediction model for an industrial gas turbine during start-up

The gas turbine virtual physical model can be applied to various fields such as performance optimization, diagnostics, and prognostics. For this purpose, it is important to increase the accuracy of the physical model. However, in order to generate such a model, the design information and off-design performance characteristics of its components are required, yet it is difficult to obtain this information because manufacturers do not generally release it. This study proposes a method for the generation of a virtual physical model to predict gas turbine system performance through reverse engineering, based on operational data but without information on component performance or design information; its effectiveness is discussed. There have been many studies to correct an initial gas turbine’s physical model using operational data. In the existing studies, detailed component performance and design information was required, to generate an initial physical model. It is normally difficult to obtain component performances and design information. Therefore, a method is proposed to generate a physical model at a reference point, using operating data and assumed information, and to create a final physical model through component performance map correction. An initial physical model is constructed based on the information and performance characteristics used for general gas turbine components, not design information of the target engine. The component blocks consisting of the gas turbine are simplified by combining them at each region. In addition, a method of estimating the moment of inertia of a gas turbine’s rotating parts, using operating data, was proposed. The target engine is a single-shaft industrial gas turbine consisting of a compressor, combustor, and turbine. A virtual physical model was created using one of the three datasets measured in the start-up operation; the accuracy of the generated model was verified using the other two datasets. Shaft speed, fuel mass flow rate, compressor exit temperature, compressor total pressure ratio, and exhaust gas temperature measurement data were used. As a result, it was confirmed that, for both the data used to generate the model and the data that were not, the model predicted the measured variables well. It is expected that the proposed method can be used to create a virtual physical model for use in operation optimization and diagnostics from the operator's point of view, where it is difficult to obtain component performance and design information.

Experimental and Numerical Study of the Influence of Solar Radiation on the Surface Temperature Field of Low-Heat Concrete in a Pouring Block

With the influence of intense solar radiation heat and the greater temperature difference between day and night, surface concrete with a drastic temperature change can easily experience a great nonlinear temperature difference, which increases the risk of early-age concrete cracking. In this study, a distributed optical fiber temperature sensing (DTS) system is used to monitor the surface temperature gradient of concrete in real time, and a solar radiation heat monitoring test is also carried out based on the Baihetan project. Based on this, a solar radiation loading model and a finite element model of a typical pouring block considering solar radiation are established. Combined with the measured temperature data and different calculation conditions, the surface temperature changes of medium-heat and low-heat concrete experiencing solar radiation are analyzed, and the temperature control effect of surface concrete with different surface insulation measures is further analyzed. The results show that the temperature variation of medium-heat concrete at the same depth is more obvious than that of low-heat concrete. Additionally, the temperature variation of low-heat concrete is noticeable within 20 cm of the top surface. In addition, in an intense solar radiation environment, covering the concrete with a 4- or 5-centimeter-thick polyethylene coil can effectively control the surface temperature gradient and maximum daily amplitude of low-heat concrete, and surface concrete cured by running water has a significant temperature control effect. Therefore, it is suggested that 22–24 °C water temperatures be used for water curing during periods of intense solar radiation during the day and a 4-centimeter-thick polyethylene coil be used for coverage at night. These study results have been employed in the Baihetan project to optimize the temperature control scheme of the pouring blocks.

Method and Application of Spatial Positioning for Valid Temperature-measuring Optical Fibers in Concrete Dams

Spatial positioning of Valid Temperature-measuring Optical Fiber (VTOF) is of great significance for accurately grasping the real temperature distribution of a concrete dam. However, there is barely an effective way to accurately locate the measuring points of optical fiber in concrete dams. A comprehensive data symmetry analysis technique is presented for determining the optimal spatial positioning of valid temperature-measuring optical fibers (VTOFs) in concrete dams. This approach includes a mathematical model to confirm the center of symmetry in monitoring data, which is required to reconstruct the real spatial temperature distribution. This process can identify VTOF locations and verify the reliability of temperature predictions. Experimental validation was conducted using findings from research on a new mathematical model, site measurements from a concrete dam, and corresponding data from distributed optical fibers. These results were successfully applied to the construction of the Baihetan dam; the concrete temperature from 405 pouring blocks in 5 typical dam monoliths was accurately monitored, and the temperature-measuring points of VTOFs were more than 24711. Meanwhile, the optical fiber temperature measurement and temperature field analysis system of Baihetan dam located in southwest China was developed based on accurately acquired temperature measurement data, which provides data basis and engineering application platform support for temperature evolution and temperature field analysis and precise temperature control management of pouring blocks.

Digital Integration of Temperature Field of Cable-Stayed Bridge Based on Finite Element Model Updating and Health Monitoring

A health monitoring system typically collects and processes data to observe the health status of a bridge. The cost limitations imply that only the measurement point data of a few key points can be obtained; however, the entire bridge monitoring information cannot be established, which significantly interferes with the data integrity of the structural monitoring system. In this study, a solution is proposed for reconstructing the monitoring data of the entire bridge. By updating the finite element (FE) model based on structural thermal analysis, numerical simulation technology, and other methods, the temperature field integration model of a cable-stayed bridge is realized. The temperature spatial expansion method of deep learning is established by using the complete simulated temperature field of the entire bridge based on limited measured temperature data; this data is verified and applied to the main beam and bridge tower, thereby establishing the complete measured temperature field of the whole bridge. This is of great significance in supplementing health monitoring information, allowing for the accurate monitoring and evaluation of the structural safety and service performance of long bridges.

Tracing the Soil Water Flux in Variably Saturated Zones Using Temperature Data

AbstractThe coexistence of water, vapor, and solid phases and the spatiotemporal changes in thermal conductivities in response to variations in water content pose a challenge to the heat‐tracing method used to calculate water flux in variably saturated zones. In this study, a new approach, which adopted finite difference numerical schemes along with the limited‐memory Broyden–Fletcher–Goldfarb–Shanno bounded (L‐BFGS‐B) parameter optimization algorithm, was developed to inversely trace vertical soil water fluxes in variably saturated zones using temperature‐depth time‐series data. The newly developed method did not utilize hydraulic parameters, and only temperature time‐series data were used at the adjacent depths as boundary conditions. The soil water, vapor flux, and spatiotemporal variations in thermal conductivity at a given location were optimized by fitting the temperature time‐series at the target depth using the L‐BFGS‐B algorithm. Further, the method was verified through laboratory experiments under different saturated conditions that were conducted in two columns with saturated and free drainage bottom boundaries, and five sets of different flow rates with a relatively stable flow condition. The soil water flux calculated using the proposed method was consistent with the observed data, thus, verifying the reliability of the approach in applications with relatively stable flow rates. Furthermore, the new method was applied to a field site to estimate the time‐varying soil water flux using the measured temperature data. The trends of the calculated soil water flux at different depths were consistent with the field hydrologic conditions and observations. Thus, this study provides a novel computationally efficient and easy‐to‐implement tool to determine soil water flux using temperature measurements.

Thermal design and analysis of JZJ-5 CubeSat

JZJ-5 is a 3U commercial CubeSat contracted by Nanjing University of Science and Technology. This paper first introduces the thermal control design of the CubeSat, then calculates the temperature field in the high and low temperature conditions by using the finite element method, and finally compares the measured temperature data in orbit with the thermal simulation data. The data results show that the temperature state of the CubeSat in orbit meets the requirements of thermal design, and the thermal control design scheme and simulation model are reasonable.

Elemental cycles in the coralline alga Neogoniolithon hauckii as a recorder of temperature variability in the Mediterranean Sea

Coralline algae play important ecological roles throughout the photic zone of the world’s oceans. Recent studies have shown that attached-living coralline algae can contain records of past climate variability. So far, algal-based paleo-reconstructions are mainly available from mid- to high-latitudes, while in low latitude and temperate regions only few examples exist. Here, we investigate samples from the attached-living encrusting coralline algal species Neogoniolithon hauckii (Rothpletz) R.A Townsend & Huisman [= Neogoniolithon mamillosum (Hauck) Setchell & L.R. Mason, nom illeg.] from a temperate site in the Mediterranean Sea to assess its potential as an environmental recorder. The specimens were collected at different water depths (20 and 40 m) in the Columbretes Islands (Spain). Sclerochronological analysis of sectioned samples revealed seasonal growth patterns. Mg/Ca, Li/Ca, and Ba/Ca ratios were measured in the algal skeletons using laser ablation inductively coupled mass spectrometry (LA-ICP-MS) in ultra-high resolution. We report a mean vertical extension rate of 1.1 to 1.2 mm/year (based on analysis of Mg/Ca cycles in 40 m and 20 m samples), representing the first growth rate measurement for this species. In addition, subannual banding patterns were mapped, measured, and could be linked to high frequency variability in laser-analyzed Mg/Ca ratios. Elemental ratios analyzed in Neogoniolithon hauckii were compared to in situ water temperatures measured at the water depth of sample collection. Our results show significant positive relationships between algal Mg/Ca (R=0.55) as well as Li/Ca ratios (R=0.46) and in situ measured temperature data (40 m specimen). Ba/Ca ratios show no significant correlation to temperature and may be influenced by other factors. These data suggest potential of this species for climate reconstructions in warm-temperate regions as Neogoniolithon hauckii is not only widely distributed in the Mediterranean, but also one of the few species that may be used as a temperature archive for mesophotic coralligenous assemblages that are strongly affected by the recent anthropogenic temperature rise. Further calibration studies are needed to test the element-temperature relationships on samples with longer growth records and in different settings and water depths.

Strength and tightness evaluation method for pipe flange connections considering thermal effects

The pipe flange connection is widely applied in chemical, power plants, petrochemical, offshore oil and gas industries, and its strength and tightness are significant for the safe operation. However, there are still no practical and applicable strength and tightness evaluation methods for pipe flange connections working in dramatically varying temperatures. This paper proposed an approach to evaluate the pipeline's strength and sealing performance considering thermal effects in actual operating conditions by combining the experimental measurement and thermostructural analysis. The critical thermodynamic parameters are identified through measured temperature data in operating conditions, and then these parameters are used in the thermostructural analysis to obtain the actual temperature and stress fields. Then, the strength and tightness in complex temperature cases can be evaluated accurately. The pipe flange connection of a liquefied natural gas (LNG) fueling station is analyzed to verify the presented method's effectiveness. This method applies to evaluating the pipeline's strength and tightness and can predict the pipeline's performances under extreme temperatures using the tested data within the measurement range and the corresponding thermostructural analysis. Furthermore, the work in this paper also provides a reference for the design and analysis of pipe flange connections working in complex temperature conditions.

Development and validation of a pavement temperature profile prediction model in a mechanistic-empirical design framework

An accurate temperature prediction tool is an important part of any mechanistic-empirical (M-E) pavement design and performance prediction method. In this paper, a one-dimensional finite control volume (FCV) model is introduced that predicts the temperature within a pavement structure as a function of time and depth. The main input data required for the model are continuous time series of air temperature for conductive heat transfer, solar radiation for radiative heat transfer, and wind speed for convective heat transfer. The heat balance equation for each control volume of the FCV model is solved using an implicit scheme. To validate the numerical model, comparisons were made to measured temperature data from four test sections in Sweden located in regions with different climatic conditions. A good agreement was obtained between the calculated and measured temperature values within the asphalt layer, and temperature in the granular layers with the values of the coefficient of determination R2 ranging from 0.866 to 0.979. The model is therefore suitable to be implemented as a pavement temperature prediction tool in M-E design.