Estimation Of Temperature Profiles Research Articles

Temperature Profile Estimation Downstream to T-Junction for Thermal Mixing of Fluids

The thermal mixing of hot and cold streams at T-junctions causes temperature fluctuations which may result in thermal fatigue. Hence for a T-junction, the knowledge of the temperature profile developed by the mixing of two fluids at different temperatures is essential. In this paper, the temperature profile downstream to the T- junction for steady laminar mixing of fluids at different temperatures is estimated by the enhanced conjugate gradient method (ECGM) algorithm. The ECGM is the combination of the recently developed Jaya algorithm and the traditional conjugate gradient method (CGM). Different test cases like the number of sensors, location of sensors and errors in the measurements have been considered. The performance of algorithms is determined by the root mean square (RMS) error. The estimation of the temperature profile by the ECGM algorithm is found to be better (RMS = 6.56 K) than CGM (RMS = 14.12 K).

Effect of welding parameters on weld surface roughness, hardness, and microstructure in laser-welded 340BH-steel and pure vanadium

This study delves into the influence of laser welding parameters on the microstructural evolution, surface roughness, and hardness of dissimilar lap joints formed by welding 340BH steel (upper) to cold-rolled pure vanadium (bottom) in an attempt to successfully develop metal membranes by adopting a novel technological route. Specifically, we examined power levels of 0.3 kW and 0.4 kW combined with varying welding speeds (40 mm/s, 50 mm/s, and 60 mm/s). The results indicate that defect-free joints were achievable at the higher welding speed of 60 mm/s. This conclusion is supported by the observed surface roughness profile. Elemental interdiffusion and temperature play pivotal roles in the formation of these dissimilar joints. These factors determine the dimensions of the heat-affected zone (HAZ) and the fusion zone (FZ) under all welding conditions. Notably, the high cooling rates during laser welding hindered the formation of intermetallic phases both in the FZ and at the interface between the metals. Microstructural examination of the HAZ/FZ on the Fe side showed a coarser microstructure when using a power of 0.4 kW, in contrast to the lath martensite observed at 0.3 kW. These observations align with the cooling curve derived from finite element analysis (FEA). FEA was also used to estimate temperature profiles throughout the welded area, and the results were consistent with experimental data. Further microstructural analysis using electron backscattered diffraction (EBSD) revealed a significant phenomenon: the formation of recrystallized grains of vanadium in the HAZ near the FZ on the vanadium side. Microhardness tests, conducted to gauge joint quality, displayed varying hardness levels across the base metal (BM), HAZ, and FZ interfaces. At a power of 0.3 kW, the HAZ displayed greater hardness than the FZ and BM due to the formation of lath martensite. Conversely, at a power setting of 0.4 kW, a reduction in hardness was observed within the HAZ, attributable to coarser ferrite formation.

The value of soil temperature data versus soil moisture data for state, parameter, and flux estimation in unsaturated flow model

AbstractThis synthetic study explores the value of near‐surface soil moisture and soil temperature measurements for the estimation of soil moisture and soil temperature profiles, soil hydraulic and thermal parameters, and latent heat and sensible heat fluxes using data assimilation (ensemble Kalman filter) in combination with unsaturated zone flow modeling (HYDRUS‐1D), for 12 United States Department of Agriculture soil textures in a homogeneous and bare soil scenario. The soil moisture profile is estimated with a root mean square error (RMSE) of 0.04 cm3/cm3 for univariate soil temperature assimilation and 0.01 cm3/cm3 for univariate soil moisture assimilation. Soil temperature assimilation performs better for soils with higher clay content compared to soils with higher sand content. The latent and sensible heat fluxes are estimated with smaller RMSE for univariate soil temperature assimilation compared to univariate soil moisture assimilation for 8 out of 12 soil types. As the climate condition changes from hot semi‐arid to sub‐humid climate, the soil moisture assimilation performs better for high permeable soil but worse for low permeable soil. In summary, the findings suggest that for most soil texture classes, assimilating soil temperature in vadose zone models is skillful to improve latent heat flux, soil moisture profile, and soil hydraulic parameters. Joint assimilation with soil moisture can further enhance the accuracy of the model outputs for all range of soil texture and climate conditions.

A 1-D model to retrieve the vertical profiles of minor atmospheric constituents for cloud microphysical modelling: III. Disturbed weather situations

A hybrid Monte-Carlo Gear solver developed earlier has been improvised to retrieve the vertical profiles of CH4 and N2O during disturbed weather situations such as western disturbances, tropical cyclones, and heavy rainfall events over the megacities: Delhi, Kolkata, Chennai, and Mumbai. Due to rapid changes in the temperature during the passage of these systems over megacities, the percentage differences of CH4 and N2O number concentrations were more compared to the Community Long-term Infrared Microwave Coupled Atmospheric Product System (CLIMCAPS). Therefore, the hybrid solver has been modified by improving maximum likelihood estimates of vertical temperature profiles. The number concentrations of CH4 and N2O during these weather events since 2012 are obtained from the CLIMCAPS dataset for bias correction. It is found that the modified methodology has improved the retrieval of CH4 and N2O vertical profiles by reducing the error percentages during daytime and nighttime over these megacities. The percentage error in estimated number concentrations of CH4 and N2O significantly reduced during (i) the passage of the western disturbance and rainy days of August 2020 over Delhi; (ii) the rainy days of June 2020 over Kolkata; (iii) the influence of supercyclonic storm Amphan (24 and 25 Nov 2020) over Chennai and (iv) rainy days of July 2020 over Mumbai. Implementing this solver in the global model may retrieve the number concentrations more accurately.

Tangential extreme ultraviolet and soft x-ray diagnostic system for time-resolved temperature measurement on the High Beta Tokamak-Extended Pulse.

The High Beta Tokamak-Extended Pulse has recently incorporated a tangential multi-energy extreme ultraviolet and soft x-ray diagnostic system. This system enables measurements of the electron temperature and the examination of mode dynamics within the tokamak. While other systems have been built for poloidal views over similar temperature ranges, this is the first multi-energy tangential-view system designed to work in a temperature range below 200eV in a tokamak. To facilitate these measurements, a filter wheel comprising five distinct groups of dual-filters has been developed and implemented. By employing a combination of 0.1μm aluminum and 0.2μm titanium filters, the system allows estimation of electron temperature profiles through reconstruction of the emission profile using the standard "double-foil" technique. The influence of impurities and filter oxide layers on measurement outcomes is examined. Results reveal that, while the absolute electron temperature values may exhibit some deviations, key characteristics like the electron temperature profile shape and inversion radius during sawtooth events remain consistent. This consistency confirms the system's suitability for core plasma studies. This system has proven effective in detecting and analyzing internal magnetohydrodynamic phenomena, such as sawteeth.

Data driven three-dimensional temperature and salinity anomaly reconstruction of the northwest Pacific Ocean

By virtue of the rapid development of ocean observation technologies, tens of petabytes of data archives have been recorded, among which, the largest portion are those derived from the orbital satellites, embodying the character of ocean surface. Nevertheless, the insufficiency of information below the subsurface restricts the utilization of these data and the understanding of ocean dynamics. To circumvent these difficulties, we present the spatially three-dimensional reconstruction of ocean hydrographic profiles at depth based on the satellites and in-situ measurement data. In this manuscript, long short-term memory network (LSTM) and Gaussian process regression (GPR) methods are invoked to predict the temperature and salinity profiles in the northwest Pacific Ocean, and to improve computational and storage efficiency, the proper orthogonal decomposition (POD) method is subtly incorporated into these two models. LSTM and GPR show satisfactory results, with the root mean square error (RMSE) of temperature is less than 1.45, and the RMSE of salinity is less than 0.19. The incorporation of the POD method substantially accelerates efficiency, particularly in the LSTM model, which improves 7.5-fold without significant accuracy loss. The sensitivity of different sea surface parameters on the reconstructed profiles reveals that sea surface height anomaly and latitude significantly influence the reconstruction of temperature anomaly (TA) and salinity anomaly (SA) profiles. Besides, sea surface salinity and sea surface temperature anomalies can improve the model's estimation ability for the upper TAs and SAs, respectively. The contribution of monthly climatology to temperature and salinity profile estimation is also explored in this paper. It is shown that adding monthly mean climatology to the input of the model can achieve more accurate estimates.

A Bayesian neural network approach for tropospheric temperature retrievals from a lidar instrument

ABSTRACT We have constructed a Bayesian neural network able of retrieving tropospheric temperature profiles from rotational Raman-scatter measurements of nitrogen and oxygen and applied it to measurements taken by the RAman Lidar for Meteorological Observations (RALMO) in Payerne, Switzerland. We give a detailed description of using a Bayesian method to retrieve temperature profiles including estimates of the uncertainty due to the network weights and the statistical uncertainty of the measurements. We trained our model using lidar measurements under different atmospheric conditions, and we tested our model using measurements not used for training the network. The computed temperature profiles extend over the altitude range of 0.7 km to 6 km. The mean bias estimate of our temperatures relative to the MeteoSwiss standard processing algorithm does not exceed 0.05 K at altitudes below 4.5 km, and does not exceed 0.08 K in an altitude range of 4.5 km to 6 km. This agreement shows that the neural network estimated temperature profiles are in excellent agreement with the standard algorithm. The method is robust and is able to estimate the temperature profiles with high accuracy for both clear and cloudy conditions. Moreover, the trained model can provide the statistical and model uncertainties of the estimated temperature profiles. Thus, the present study is a proof of concept that the trained NNs are able to generate temperature profiles along with a full-budget uncertainty. We present case studies showcasing the Bayesian neural network estimations for day and night measurements, as well as in clear and cloudy conditions. We have concluded that the proposed Bayesian neural network is an appropriate method for the statistical retrieval of temperature profiles.

Real‐time estimation of the electron temperature profile in DIII‐D by leveraging neural‐network surrogate models

AbstractControl of both the magnitude and the shape of tokamak profiles will be necessary to achieve stable, high‐performance plasmas. In order to reject disturbances in real time, feedback‐control algorithms rely on accurate real‐time knowledge of the plasma state. When diagnostics alone are insufficient, because they are limited in number or their measurements are too noisy, observers can be used to combine diagnostic data with a response model to provide a better estimation of different plasma properties. An observer has been developed to estimate the electron temperature profile in real time using both diagnostic data from the Thomson scattering system and a model based on the electron heat transport equation describing the evolution of the electron temperature profile. Neural network surrogate models are leveraged to help improve the overall model prediction while staying within computation time constraints for real‐time use. The observer algorithm is shown in offline tests to produce smooth profiles that are consistent with both the diagnostic data and the electron heat transport equation. When implemented into the real‐time plasma control system, this observer will provide valuable information on the electron temperature profile to many potential feedback‐control applications.

Асимптотика прогрева горных пород добычной скважиной

An asymptotic solution has been obtained by the method of integral relations of the problem of rock heating for the case of a production well operated in a steady state. In underground hydraulics and oil production, such asymptotics are widely used for practical estimates of temperature profiles along the wellbore and temperature at the wellhead. A unified formula for the outflow (inflow) of heat into the rock is proposed, which is used to estimate heat losses from the wellbore and the temperature profile along the wellbore after the initial period, after reaching the steady state.

Reactor performance estimation in microscale flow calorimeter for rapid characterization of exothermic reactions

Continuous flow calorimeters are a promising tool in process development and safety engineering, especially for flow chemistry applications to characterize the heat release and kinetic parameters of rapid chemical reactions. In this study, the digital accompaniment of an isoperibolic flow calorimeter for characterization of exothermic reactions is presented. To support experimental planning and evaluation, computational fluid dynamic simulations are carried out for single-phase flow in the microreactor. The residence time distribution is obtained and used for estimation of conversion and temperature profiles along the microreactor channel. This leads to an integration of CFD simulations into the calorimeter’s software-guided workflow reducing the experimental effort regarding the determination of thermokinetic data. The approach is tested for a highly exothermic test reaction, which provides further hints for future investigations.Article highlights• Estimation of conversion and temperature profiles within a microscale calorimeter• Combination of CFD simulations and reactor performance estimation• Approach was tested for highly oxidation of sodium thiosulfate• Estimated conversion and temperature profiles are in good agreement with experimental dataGraphical abstractComputational Fluid Dynamics simulations are combined with reactor performance estimation to support experimental design for the determination of thermokinetic data. Insights of the dynamic behavior are gained to assist the operator in the investigation of exothermic reactions.

Modular Method for Estimation of Velocity and Temperature Profiles in High-Speed Boundary Layers

The fluid property variation caused by viscous heating affects the mean velocity and Reynolds stresses in compressible turbulent boundary layers, and as a result also affects the resulting skin friction and wall heat transfer. We present a method based on the work of Huang et al. (AIAA Journal, Vol. 31, No. 9, 1993, pp. 1600–1604) that estimates the mean velocity and temperature profiles, and therefore also the friction and heat transfer coefficients, for a given Mach number, Reynolds number, and wall thermal condition. The method is modular in the sense that it works with multiple variable-property scaling formulas (or “transformations”), velocity–temperature relationships, viscosity–temperature relationships, and equations-of-state. The method is validated against multiple Direct Numerical Simulation (DNS) cases and was found to predict the skin friction and wall heat flux to within 8 and 11% compared to the current state-of-the-art (the Van Driest II method), which is within 16 and 13% for the same data.

Temperature Profile Estimation in the Core of a Sodium-Cooled Fast Reactor using CFD Modeling

The latest proposal for nuclear systems for application in space are 4th generation reactors that use liquid metals as coolant. The operating conditions, as well as its safety in these matters, is linked to its thermal and fluid dynamic behavior. This paper presents the estimation of the temperature profile of a sodium-cooled fast reactor from the conduction and convection heat transfer mechanisms. The temperature distribution is analyzed in the area of the fuel and the temperature profile of the coolant in the area of the high conductivity pipes. Is established the analysis of nuclear fuel in a one-dimensional, stationary and with power generation. In the GAP area, the analysis is carried out by natural convection, and finally, the cladding in contact with sodium by forced conduction-convection. As results, it is presented the simulation in Computational Fluid Dynamics to determine the temperature profile due to the behavior of the coolant in the pipe when subjected to a constant flow of heat supplied by the nuclear fuel pellets. The temperature profile will allow us to determine the parameters to establish the operating conditions of secondary power conversion systems.

Verification of vertical temperature profiles in the COSMO and ICON models with available observational data

This paper presents results of numerical estimates of vertical temperature profiles in the COSMO and ICON NWP models. Data from two vertical profilers, an MTP-5 microwave temperature profiler and a classic AVK-type radiosonde, are used for the estimates. Mean errors, correlation coefficients, and root-mean-square errors are calculated for different seasons. The simulation results are analysed depending on configurations of the horizontal resolution of the models and between the models. The estimates are plotted, and comparisons are made between the models using each of the measurement types: between different measurements for each model. The response to spatial variability in the measurements and model grid cells is assessed. Conclusions are formulated by comparing the quality of reproduction of the atmospheric boundary layer between the models and the model configurations. The findings provide useful information for the interpretation of modeling results by forecasters.

Enhanced Neural Network Implementation for Temperature Profile Extraction in Distributed Brillouin Scattering-Based Sensors

In this work two Neural Network (NN) based solutions are proposed to recover the distributed temperature profile of a sensing fiber, measured using a commercial Brillouin Optical Time-Domain Analysis (BOTDA) interrogator. A detailed analysis in terms of temperature accuracy and processing speed is carried out for both the proposed methods, comparing the results with the ones obtained from the application of classical fitting techniques, namely cross-correlation (CORR), Lorentzian fitting (LF) and Pseudo-Voigt fitting (PV), through both simulations and real measurements carried out in laboratory environment. The results show that the first NN implementation, which aims to maximize the accuracy of the temperature profile and the processing speed, can handle different width of frequency acquisition window but needs to be optimized for a specific frequency acquisition scanning step. The second NN implementation, however, can also handle different values of the acquisition scanning step with a minor performance drop. Simulations and experimental data show a massive advantage of NN implementations in terms of processing speed with respect to classical fitting techniques, with a slightly better accuracy of the estimated temperature profiles.

Numerical modeling of oxide particle evolution during additive manufacturing

In this study, oxide particle evolution during additive manufacturing was modelled to elucidate the evolution and suggest effective ways to increase oxide-dispersion strengthening. The model coupled the Kampmann and Wagner numerical model with an estimated temperature profile, solidification calculation, and thermodynamic databases. The calculated results from the model matched experimental data obtained from both directed-energy deposition and powder-bed fusion processes that use an austenitic stainless-steel powder. According to the calculation, most of the oxide particles nucleated during solidification. The oxygen concentration in the melt pool briefly reached 0.6–0.7 wt%. The interfacial energy between Si-Mn-Cr-O oxide and the melt pool was estimated to be 0.4–0.5 N/m. Finally, ways to maximize the dispersion strengthening effect in the AM material were identified and evaluated using the model.

Novel Method for the Estimation of Vertical Temperature Profiles Using a Coastal Acoustic Tomography System

Systems based on remote sensing technology, which use reciprocal acoustic signals to continuously monitor changes in the coastal oceanic environment, are referred to as coastal acoustic tomography (CAT) systems. These systems have been applied in regions in which heavy ship traffics, fishing and marine aquaculture activities make it difficult to establish in situ oceanic sensor moorings. Conventionally, CAT measurements were used to successfully produce horizontal maps of the depth-averaged current velocity and temperature in these coastal regions without attempting to produce a vertical temperature profile. This prompted us to propose a new method for vertical temperature profile estimation (VTPE) from CAT data using the available sea surface temperature (SST), near-bottom temperature (NBT), and water depth. The VTPE method was validated using data-assimilated and tide-included high-resolution ocean model outputs, including tide data, by comparing the estimated and simulated temperatures. Measurements were performed in the southern coastal region of Korea, where two CAT stations were moored to establish a continuous coastal ocean monitoring system. The validation results revealed that the algorithm performed well across all seasons. Sensitivity tests of the VTPE method with reasonable realistic random errors in the SST, NBT, and acoustic travel time measurements demonstrate that the method is applicable to CAT observation data because the monthly mean root-mean-squared difference (RMSD) for the vertical profiles for February, May, August, and November were 0.23, 0.30, 0.50, and 0.24°C, respectively. The VTPE method was applied to the CAT observation datasets acquired in February and August. The transceivers at the CAT stations were at depths 11 and 6 m on average. The RMSD between the estimated and observed temperatures in the middle layer (∼3 m depth) between two stations in February and August were 0.08 and 0.60°C, respectively, the accuracy of which is sufficient in largely time-varying coastal environments. We provide a novel method for continuous coastal subsurface environmental monitoring without interrupting maritime traffic, fishing, and marine aquaculture activities.

Minimum Number of Experimental Data for the Thermal Characterization of a Hot Water Storage Tank

This paper demonstrates that it is possible to characterize the water temperature profile and its temporal trend in a hot water storage tank during the thermal charge process, using a minimum number of thermocouples (TC), with minor differences compared to experimental data. Four experimental tests (two types of inlet and two water flow rates) were conducted in a 950 L capacity tank. For each experimental test (with 12 TC), four models were developed using a decreasing number of TC (7, 4, 3 and 2, respectively). The results of the estimation of water temperature obtained with each of the four models were compared with those of a fifth model performed with 12 TC. All models were tested for constant inlet temperature. Very acceptable results were achieved (RMSE between 0.2065 °C and 0.8706 °C in models with 3 TC). The models were also useful to estimate the water temperature profile and the evolution of thermocline thickness even with only 3 TC (RMSE between 0.00247 °C and 0.00292 °C). A comparison with a CFD model was carried out to complete the study with very small differences between both approaches when applied to the estimation of the instantaneous temperature profile. The proposed methodology has proven to be very effective in estimating several of the temperature-based indices commonly employed to evaluate thermal stratification in water storage tanks, with only two or three experimental temperature data measurements. It can also be used as a complementary tool to other techniques such as the validation of numerical simulations or in cases where only a few experimental temperature values are available.

An analytic model for computing the countercurrent flow of heat in tubing and annulus system and its application: Jet pump

Shale fields are currently over-reliant on electric submersible pumps (ESPs) in the early stage of artificial lift despite its weakness to sandy flow and often failing within the first three months. Jet pumps are reliable and cost-effective alternatives to the ESPs. Current studies on jet pumps have focused on designing parameters for maximizing performance; however, there have been very few investigations on predicting temperature profiles in the wellbore with a jet pump in use. Fluid temperature influences viscosity; hence, the production of heavy oil. Typically, the heavier the hydrocarbon, the greater is the variation in viscosity with temperature. Therefore accurate temperature prediction is essential for the efficient design of heavy oil production facilities. This paper offers an analytic model to compute temperature profiles in the tubing and annulus. Results show a good match with a field data set.Data set from a well in Tah field in China is used to validate the model. In addition, sensitivity analyses were performed that showed that injection temperature affects the annulus temperature profile, leading to significant changes in frictional pressure loss at shallow depths. In contrast, injection rate has less influence on temperature profiles, although, of course, higher fluid velocity causes greater frictional losses. Results show that a hotter and relatively small flow rate leads to lesser pumping cost by reducing frictional pressure drop during the operation.The proposed model ideally requires the bottom-hole temperature as a boundary condition (BC). When subsurface temperature data are unavailable, as often the case due to cost, a computing method is offered in the paper for reliable temperature profile estimation from only two data points at the wellhead.

Estimation and Uncertainties of Profiles and Equilibria for Fusion Modeling Codes

The results of transport modeling codes, e.g., GENE for the plasma core or SOLPS-ITER for the plasma edge, depend critically on reliable profile and equilibrium estimates. The propagation of uncertainties (UP) of input quantities to the results of modeling codes, e.g., power and particle exhaust and plasma stability, is frequently neglected because of the costs of running the codes as well as because of the missing uncertainty quantification of input quantities. The situation becomes even more cumbersome if profile gradients and their uncertainties are of major concern for transport analyses. Two different techniques are presented to estimate profiles, profile gradients, their uncertainties, and candidate profiles for UP in modeling codes. Markov Chain Monte Carlo sampling of the posterior probability density of an integrated data analysis approach is applied to estimate electron density and temperature profiles. Nonstationary Gaussian process regression is applied to estimate ion temperature and angular velocity profiles. Both methods provide in a natural way profile gradients, profile logarithmic gradients, and their uncertainties. Modeling codes benefit also from reliable equilibrium reconstructions and quantification of the uncertainty of various equilibrium parameters. For the analysis of diagnostics data, the position and uncertainty of flux surfaces as well as of the magnetic axis are important. For plasma transport and stability codes, the estimation of uncertainties of current and q-profiles is presented. For plasma edge codes the position of the separatrix contour and its uncertainty at various poloidal positions is of primary interest especially if steep profile gradients are present. Examples of uncertainties and their sources in magnetic scalar quantities, profiles, and separatrix contours are shown.

Estimating modern equilibrium temperatures in the Bakken Formation of North Dakota, USA: Application of an analytical solution to depth dependent changes in thermal conductivity

The mid-Paleozoic through late Cretaceous section in the Williston Basin records a transition from carbonate dominated marine sedimentation to that dominated by terrestrial and marine shales. Even though the intervening stratigraphy is complex the overall thermal profile is not. Equilibrium temperature profiles for 20 wells distributed across the North Dakota portion of the Williston may be closely approximated by a two or three-layer thermal model. This model consists of one or two upper layers with constant thermal conductivities that extend through the Tertiary and upper Cretaceous. A third layer from the top of the Cretaceous Greenhorn Formation to the top of the Mississippian-Devonian Bakken Formation exhibits a thermal gradient that is consistent with a gradual and constant increase in thermal conductivity with depth. This gradient is consistent with a nonlinear analytical solution that relates temperature and depth to a progressive increase in thermal conductivity with depth. These results provide an alternative approach to estimating temperature profiles from the Greenhorn Formation to the top of the Bakken Formation than those using more complex models consisting of many discrete layers. The method also provides a means of extrapolating temperatures recorded above the Madison Group to the top of the Bakken Formation. • Equilibrium temperature logs are nonlinear with depth between the Cretaceous and Miss-Dev. Bakken Fm. • Explained by an analytical expression for a temperature profile caused by gradual change in thermal conductivity with depth. • Nonlinear regression fits observed temperatures for more than a kilometer. • .Provides a means of extrapolating temperature profiles from the Madison to Bakken Fm. in the Williston Basin.