Predicted Temperature Profiles Research Articles

Analysis and numerical simulation of the sterilization of low-calorie grape jam

The objective of this work was to analyze and defined an appropriate heat treatment for the sterilization of a low-calorie grape jam (39.26 ± 0.63 °Brix; pH 3.63 ± 0.04) obtained using fruits reduced in carbohydrates by optimized pretreatment, and natural Stevia sweetener to replace sucrose. For this purpose, different types of information and tools were used. The physicochemical characteristics and proximal composition of the jam were determined. Information as to what microorganisms as common saprophyte fungi in grapes can develop in these products was gathered from the available literature. The thermophysical properties of the product were determined. Different combinations of recommended time and temperature combinations for heat treatments that inhibit microorganisms were analyzed. FEM numerical were developed to predict temperature profiles during heat treatment. Also, microbiological analysis of yeast and yeast screening and counting was performed. As a result of this study, two heat treatments were proposed to inhibit the fungus B. fulva and B. nivea (common in grapes). Two autoclave sterilization processes (121.1°C; 20-32 lb/in2) were defined by the time-temperature combinations, T1: 100°C-5 minutes; T2: 100°C-10 minutes, to apply and maintain the desired temperature of 100°C throughout the product for 1.3-27 seconds. Both treatments were considered suitable for sterilizing a 190 g jam container and were validated by microbiological analysis.

Numerical investigation on lean methane combustion with modified effective thermal conductivity of the porous media

In this work, to further optimise the porous media combustion model and explore the combustion characteristics of low-concentration methane in porous media, a thermal non-equilibrium model of porous media combustion is established with modified effective thermal conductivity of the porous media based on the volume average method. The effects of foam ceramics pore density, inlet velocity and wall heat loss coefficient, as well as combinations of different pore density foam ceramics on flame stability were studied. Results show that the predicted temperature profile matched well compared with the experimental data. The temperature inside the burner can be divided into the following four zones: preheating zone, combustion zone, cooling zone dominated by wall heat loss and cooling zone dominated by heat radiation at the burner outlet. Moreover, it was found that the porous media combustion model with modified effective thermal conductivity of the porous media could simulate the flame front shape in different pore density foam ceramics. On the contrary, the flame front shape in the simulation with constant thermal conductivity does not change and remains a uniform radial disc. In addition, the flame is easier to stabilise at the interface of the foam ceramic with different pore densities.

A Hybrid Seasonal Autoregressive Integrated Moving Average and Denoising Autoencoder Model for Atmospheric Temperature Profile Prediction.

Atmospheric parameter profile plays a vital role in the studies on meteorology and quantitative remote sensing. Besides measurement of the radiosonde and satellite remote sensing, the atmospheric temperature profile can be predicted by the time series model. However, the performance of the time series model is limited by the weak correlation of temperature data in the stratosphere. In this study, to predict monthly mean atmospheric temperature profile, a hybrid model named seasonal autoregressive integrated moving average and denoising autoencoder (SARIMA-DAE), which combines the traditional time series model with an artificial neural network (ANN), is proposed. The SARIMA is first used to predict the temperature at each altitude level independently; then, the DAE is employed to denoise the predicted temperature profile by SARIMA. The data set used is the L3 monthly mean gridded product of atmospheric infrared sounder, an instrument aboard NASA's Aqua satellite. Taking Maoming, a city in China, as an example, the absolute errors are within 1 K. The mean squared error and mean absolute percentage error on the test set are 0.12 and 0.0012, respectively. Compared with SARIMA, support vector machine regression, and ANN models, experimental results show that the SARIMA-DAE model improves the prediction accuracy at almost all altitude levels. Especially in the stratosphere, the advantage of the hybrid model is more pronounced. The model we proposed should, therefore, be of value to estimate missing data and predict atmospheric parameter profiles.

CFD PREDICTION OF THE TRAJECTORY OF HOT EXHAUST FROM THE FUNNEL OF A NAVAL SHIP IN THE PRESENCE OF SHIP SUPERSTRUCTURE

The superstructure of a modern naval ship is fitted with multitude of sensors for electronic surveillance, weapon discharge, navigation, communication and varieties of deck handling equipment. Locating these electronic equipment/sensors and its integration on board is of paramount importance to achieve optimal operational performance of the naval vessel. Among the many problems in locating these sensors (like stability, EMC EMI etc.,), the presence of entrapped hot gases from the ship exhaust affects the functioning of these electronics. Hence the prediction of temperature profile and trajectories of the ship exhaust plume from the funnel around the superstructure during the design stage is a mandatory requirement for positioning the sensors on superstructure. This trajectory prediction is not amenable to theoretical analysis or empirical calculation procedures in the modern warship superstructure. Experimental and CFD studies conducted on ship superstructure are the only reliable tools that are available to estimate temperature field as well as to study the exhaust smoke superstructure interaction on ships. This paper presents the CFD simulation of the published results for two cases, namely hot jet in a cross flow and hot exhaust with a cross flow on a generic frigate. Simulations have been made using k-ɛ turbulence model with different values of turbulent Schmidt number. It has been observed that temperature field is predicted with reasonable accuracy with turbulent Schmidt number of 0.2.

Investigations on the mitigation of ventilation air methane and energy recovery in site trial thermal flow-reversal reactor

In this paper, the key influencing factors on the ventilation air methane (VAM) utilization site trials were discussed. A new model of pressure loss and heat transfer between gas and solid phase in regenerative bed was innovatively developed. The factors of inlet gas flow rate, methane concentration fluctuations, the channel size in the monolith blocks and the energy recovery on the behaviors of site trial thermal flow-reversal reactor (TFRR) were studied systematically. Results show that the predicted temperature profile matched well compared with the site trial results. The shape of temperature profile in the reactor gradually evolves from M-shaped to ∩-shaped with the increase of the channel size of the honeycomb monolith bed. The residual methane concentration in the exhaust gas can be lower than that of 0.00079 vol% when the TFRR is filled with optimal monolith blocks with channel size of 3 ×3 mm. Using the heat exchanger in the reactor, about 40% to 44% thermal energy can be recovered from the TFRR site pilot plant.

Numerical simulation of heat and mass transfer during heating and cooling parts of canned‐green‐olive pasteurization

AbstractSimultaneous modeling of heat and mass transfer was applied in a cylindrical geometry through computational fluid dynamic (CFD). Two‐dimensional equations of mass, momentum and energy conservation were solved using finite elements method. The meshes were 7,882 triangular elements where 1,056 of them were in boundaries. The simulation was solved for a metal can filled with olives (Conservolea CV) and a 6% acid‐brine. Constant thermophysical properties of fluid remained static over the processing period except for the viscosity (temperature dependent) and density (in buoyancy term). The comparison criteria, between the predicted and experimental figures, using to evaluate goodness of fit, namely the coefficient of correlation (r = .9933) and the root mean square error (RMSE = 3.6555) indicated that the model was valid. The warm‐low‐density fluid ascended through a thin boundary layer and exchanging the heat, at the top of the can, where it flowed downward through the pores between the olives. This natural convection displaced the Slowest Heating Zone (SHZ) towards the bottom of the can, and eventually stayed in a region that was about 10 ± 15% of the can height from the bottom. The inactivation time for the most‐heat‐resistant fungus (Monascus rubber) during the thermal processing of green olives at 100°C was 600 s based on SHZ. A comparison between heat transfer by conduction and conduction–convection showed that the rate of heat transfer in conduction–convection was much more than conduction, also the SHZ in conduction was displaced towards the top of the can.Practical ApplicationsComsol Multiphysic, was used in this research in order to predict temperature profile in olive pasteurized in a metal can so that the accurate location of cold point was evaluated and the process time can then be controlled to reduce overheating and nutritional reduction or under heating and safety problems. As the model was validated, all the model results would be accurate to use so that velocity pattern of the fluid was extracted and different assumptions of heat conduction also were considered. The importance of pasteurization in different industries and the significance of reducing energy consumption, reveal our research importance because the calculated‐process time was much lower than what is currently used in industry.

Prediction of Temperature Profiles for Catalytic Hydrotreating of Vegetable Oil with a Robust Dynamic Reactor Model

Prediction of Temperature Profiles for Catalytic Hydrotreating of Vegetable Oil with a Robust Dynamic Reactor Model

2D Model of Transfer Processes for Water Boiling Flow in Microchannel

The modeling of transfer processes is a step in the generalization and interpretation of experimental data on heat transfer. The developed two-dimensional model is based on a homogeneous mixture model for boiling water flow in a microchannel with a new evaporation submodel. The outcome of the simulation is the distribution of velocity, void fraction and temperature profiles in the microchannel. The predicted temperature profile is consistent with the experimental literature data.

Simulation of Temperature in Gas Drilling with Gradient Method

Accurate prediction of gas temperature profile is critical for designing gas drilling operations. Traditionally the gas temperature distribution in wellbore is assumed to equal the static formation temperature where the influences of fluid flow and Joule-Thomson cooling effect have not been considered. This work presents a gradient equation method to predict the gas temperature profile in the wellbore with considerations of gas flow and Joule-Thomson local cooling at drill bit. The method applies equations of pressure, temperature, density and velocity to gas flow inside drill string and annulus. The solution of the gradient equations takes the form of the fourth order Runge-Kutta equation. Bottom hole temperature measurement from depth 700m to 2000m in a real well is found consistent with the temperature values predicted by the gradient method, both showing 20℃ ~ 30℃ temperature drop due to the Joule-Thomson cooling effect at the bit nozzles. Result of sensitivity analyses with the gradient method indicates that the temperature drops due to the Joule-Thomson cooling effect can be up to 50℃, depending on nozzle size. The steady temperature profile can be established within minutes of gas circulation. Because of the influences of gas flow and Joule-Thomson cooling, the gas temperature in the wellbore significantly deviates from the formation geothermal temperature in flowing conditions. The gas temperature in the drill string increases with the well depth and then decreases rapidly toward the bottom of hole. As the gas flows up the annulus, the gas temperature first jumps up, overshoots the formation temperature, and then gradually decreases along the geothermal gradient trend.

Model-based deep learning for additive manufacturing: New frontiers and applications

Artificial intelligence has created disruptive possibilities in additive manufacturing towards smarter design, process control, and quality assurance. Nonetheless, the scarcity of data in additive manufacturing significantly limits the wide adoption of artificial intelligent techniques. In this work, we propose the deployment of a novel artificial intelligent structure called model-based deep learning in the context of additive manufacturing which can address scenarios with scarce data but an available underlying iterative mathematical/inference model. Several immediate applications of this technique in the additive manufacturing research as well as a proof of concept on temperature profile prediction in metal AM process are presented.

Transient study of a solar pond under heat extraction from non-convective and lower convective zones considering finite effectiveness of exchangers

The transient performance of a salt gradient solar pond, which experiences heat extraction from both gradient and storage regions has been studied for annual performance via accounting for the overall heat transfer coefficient across heat exchangers. The proposed method after reduced complexities is satisfactorily validated with theory and experiment-based results of pertinent literature. The novelty of the work lies in the fact that, the temperature drop across both non-convective and lower-convective zone exchanger surfaces has been accounted by using a local time and space dependent heat transfer coefficient. Here, it takes into the consideration free convection from the pond to the exchanger surface, and forced convection from the exchanger surface to flowing working fluid. Further, temperature variation of thermo-fluidic parameters of water involving, viscosity, density and thermal conductivity is considered. Detailed numerical investigation reveals that by neglecting this coefficient which is assumed in conventional studies, it can lead to significant errors in the prediction of transient temperature profiles in the pond and the exchangers. Calculations reveal an error of about +69% in the annual extraction efficiency and about −9% in entropy production if the conventional assumption is used as opposed to realistic technique presented herein. Interestingly, the present study reveals that there is an optimum radius of exchanger pipe in each of non-convective and lower convective zones that maximises the annual extraction efficiency. This work presents a useful analysis to assess multi zone extraction from solar ponds under transient state in a more practical and accurate manner.

A flux estimation method for three‐phase dual‐active‐bridge DC/DC converters

This study proposes an estimation method of flux density in high-frequency transformers of three-phase dual-active-bridge transformers. The steady-state analysis shows that the flux in the converter is different in Buck and Boost modes and depending on load and voltage gain condition. From that, a closed-form formula is proposed to estimate the flux swing in the core. Unlike the conventional estimation technique, the proposed one represents the flux as a function of input and output voltages and the phase shift value. Finite element analysis confirms that the proposed formula can predict the flux with less than 4% error. The proposed formula can also help to improve the prediction of temperature profile of the transformers with a high accuracy of less than 1 degree Celsius in the mid- to high-power range as validated in experiments. The proposed flux estimation method can be used in designing the converter or in predicting the converter behaviour under critical conditions such as load changing or starting, and so forth, to help protect the transformers from saturation or DC bias.

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.

Experimental and Simulation Analysis of Effects of Laser Bending on Microstructures Applied to Advanced Metallic Alloys

The aim of this work is the analysis of laser beam forming (LBF) in the bending of two relevant materials used in the transportation industry—interstitial-free (IF) steel and AA6013 high-strength aluminum alloy. Our experiments and numerical simulations consider two different operating scenarios achieved by varying the laser beam scanning velocity using linear paths. The material behavior during this process is described via a coupled thermomechanical-plasticity-based formulation that allows prediction of temperature profiles and bending angles. Metallography, glow discharge optical emission spectroscopy, and X-ray diffraction are used for microstructure characterization. In addition, microstress analyses are performed in order to study the stress behavior of the irradiated zones. It is found that LBF mainly induces grain growth and melting in the case of high surface temperatures. Before melting, the materials developed compressive stresses that could be useful in preventing cracking failures. The resulting bending angles are predicted and experimentally validated, indicating the robustness of the model to estimate LBF effects on advanced alloys. The present analysis relating bending angles together with temperature and microstructure profiles along the thickness of the sheets is the main original contribution of this work, highlighting the need for further modeling refinement of the effects of LBF on advanced alloys to include more microstructural properties, such as grain boundary diffusion and surface roughness.

Development of a Surrogate Turbulent Transport Model and Its Usefulness in Transport Simulations

For accelerating a transport simulation with an advanced physics turbulent transport model like TGLF, we have been developing a surrogate model that mimics the behavior of the model based on a neural network model. With a steady-state transport solver GOTRESS used, the surrogate model has shown its ability to successfully predict temperature profiles almost equivalent to those by TGLF. The performance of the surrogate model is improved by optimizing hyperparameters and eliminating outliers from training data. Extrapolability of the optimized model is examined by changing the normalized temperature gradient. The objective is to better investigate the nature of the model in addition to measuring its utility in transport simulations. The versatile model, which has been trained with data of multiple cases, is developed applicable to many situations. It shows the same reproducibility as the model specific to each individual case, a fact which unveils great potential of the surrogate model in transport simulations.

Thermal reduction of iron–manganese oxide particles in a high-temperature packed-bed solar thermochemical reactor

The reduction of iron–manganese oxide particles in a high-temperature packed-bed solar thermochemical reactor is investigated using an advanced transient three-dimensional heat and mass transfer model. The model couples the reaction kinetics and fluid flow to conductive, convective, and radiative heat transfer. A reactor prototype that features a reaction tube confining the packed particles and a surrounding diffuse reflective cavity is tested under simulated high-flux solar irradiation to validate the model. The numerically predicted temperature profiles and oxygen generation rates are in good agreement with the experimental data. The validated model is applied to evaluate the thermochemical performance of the reactor. The calculated temperature profiles indicate that uniform temperature distribution in the reactive packed particles is achieved from the onset of the reaction. An energy rate balance analysis shows the instantaneous peak solar-to-chemical energy efficiency reaches 9.3%.

Experimental Assessment of the Lumped Lithium Ion Battery Model at Different Operating Conditions

This study investigates the electrical and thermal characteristics of a cylindrical lithium-ion cell with an axisymmetric two-dimensional lumped model. The cell is completely discharged at 0.5, 1 and 1.5 C rates under 0, 20 and 50 °C operating temperatures. Both the open circuit voltage values and the average specific heat value of the cell are measured and used as an input to the model. The model uses the variable internal resistance approach to evaluate the voltage variation of the cell that is obtained from experimental data. A cylindrical lithium-ion cell has a spiral construction that involves multiple layers. However, these layers are assumed as a uniform material in the lumped model. The lumped model in COMSOL Multiphysics couples the heat transfer and lumped battery interfaces so it allows predicting the surface temperature of the cell during discharging processes. The experimental results point out that the operating temperature inversely affects the internal resistance and the heat generation within the cell during a discharging process. Furthermore, it is found that the capacity of the cell significantly decreases at low operating temperatures. Finally, the predicted temperature profile follows the same trend with the experimental data and is consistent at each operating condition.

Prediction of Temperature and Concentration Profiles in an Industrial Polymerization Fluidized Bed Reactor under Condensed-Mode Operation

Condensed mode operation of gas-phase ethylene polymerization adds more complexity to the hydrodynamics of fluidized bed reactors (FBRs). This study aims at the development of an industrial ethylen...

IMPLEMENTATION AND VALIDATION OF A FISSION GAS RELEASE MODEL FOR CTFFUEL USING THE NEA/OECD IFPE DATABASE

The nuclear reactors themselves are complex systems whose responses are driven by interactions between different physics phenomena within the reactor core. Traditionally, the different physics phenomena have been analyzed separately and its interaction considered via boundary conditions or closure models. However, in parallel with the development of computational technology, multi-physics coupled simulations are being used to obtain accurate predictions thanks to the consideration of the feedback effects on the fly (on-line). In the nuclear systems the fuel temperature is an important feedback parameter used to obtain the nuclear cross sections at given conditions by the neutron kinetics codes. An accurate prediction of temperature profile within the fuel rod involve several physics such as neutron kinetics, mechanics, material behavior and properties, heat transfer, thermal-hydraulics, and even chemistry. The pellet to clad gap conductance is possibly the most important source of uncertainty in the solution of conductivity equation in the fuel rod and the fuel temperature prediction. The gap conductance depends on two effects: the pellet to gap distance and the conductivity of the gas species that fill the gap. In this research work, the authors are focused on improving of the prediction of the gap gas conductivity in CTFFuel by implementing a fission gas release model in the code. The objective of this contribution is the implementation of a transient fission gas release model in CTFFuel and its validation using the experimental data available in the OECD/NEA International Fuel Performance Experiments (IFPE) database. CTFFuel is an isolated fuel heat transfer capability within the framework of CTF code, the state-of-the-art version of the Coolant Boiling in Rod Arrays Code – Two-Fluid (COBRA-TF) sub-channel thermal-hydraulic code. The code is being jointly developed by North Carolina State University (NCSU) and Oak Ridge National Laboratory (ORNL) within the US Department of Energy (DOE) Consortium for Advanced Simulation of LWRs (CASL).

Experimental study of the effects of ullage height on fire plume centerline temperature with a new virtual origin model

Investigating the evolution of the fire plume centerline temperature has both fundamental and practical importance to process safety, risk analysis and disaster control. In practice, ullage height (distance between the fuel surface and the container upper rim) is a common boundary condition that affects the burning intensity and the flaming characteristics. In this study, a series of experiments were conducted to reveal the effects of ullage height on the evolution of the heptane fire plume centerline temperature. The tested ullage height was systematically changed from zero to the value at which the flame self-extinguished. Results showed that, as ullage height increased, the fire plume centerline temperature first decreased and then slightly increased. By comparing the model predictions of previous classic models with the tested results, this study proved that previous classic models are not capable in predicting temperature profiles under high ullage height conditions. Based on physical and dimensionless analysis, a new virtual origin model was established to quantify the effects of ullage height on the virtual origin. A more universal plume centerline temperature model, which is capable to predict the centerline temperature profiles with any ullage height conditions, was proposed and validated by literature experimental data.