Predicted Temperature Profiles Research Articles

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.

CFD simulation and experimental validation of in-container thermal processing in Fesenjan stew.

The purpose of the study was to develop and validate the CFD model for predicting temperature profile and determine the slowest heating zone (SHZ) during the thermal processing of Fesenjan stew. The temperature profile was recorded experimentally at a point where it was predicted to be the SHZ. The results showed that the SHZ was located at the geometric center of the containers, and the temperature reached 120.3 °C. The average temperatures of experiment versus predicted temperatures at the same positions, no significant difference were observed (p < .01). The RMSE calculation showed good agreement between simulation and experimental data (RMSE = 1.36). The container geometrical center F0 was measured 17.68 min and indicated that an additional process was applied. An 11.5 min reduction in holding time was estimated to optimize the process since the F0 of some meat and sauce‐based food is about 6–7.65 min.

Thermal Performance and Numerical Simulation of the 1-Pyrene Carboxylic-Acid Functionalized Graphene Nanofluids in a Sintered Wick Heat Pipe

Experimental and numerical modeling of a heat pipe included with a phase change heat transfer was developed to assess the effects of three parameters of nanofluid, heat pipe inclination angles, and input heating power. Distilled water (DW) and 1-pyrene carboxylic-acid (PCA)-functionalized graphene nanofluid (with concentrations of 0.06 wt%) were used as working fluids in the heat pipe. A computational fluid dynamic (CFD) model was developed for evaluation of the heat transfer and two-phase flow through the steady-state process of the heat pipe. It was found that inclination significantly affects the heat transfer of the heat pipe. Maximum increment of thermal performance in the heat pipe reached 49.4% by using 0.06 wt% of PCA-functionalized graphene as working fluids. The result associated with this comparison indicates that the highest deviation is less than 6%, consequently confirming that the CFD model was successful in reproducing the heat and mass transfer processes in the DW and nanofluids charged heat pipe. The results of CFD simulation have good agreement between predicted temperature profiles and experimental data.

Computational modelling of survival of Aspergillus flavus in peanut kernels during hot air-assisted radio frequency pasteurization

In recent years, radio frequency (RF) heating is getting popular as an alternative pasteurization method for agricultural commodities and low moisture foods. Computer simulation is an effective way to help understand RF interactions with food components and predict temperature distributions among food samples after RF treatments. In this study, a computer model based on Joule heating and thermal inactivation kinetic of A. flavus was established to predict both temperature distribution and microbial reduction among peanut kernels after RF processing. For the process validation, three 2-g peanut samples inoculated with 40 μL A. flavus were placed at three representative locations among 2.17 kg peanut kernels and subjected to various processing conditions in a 27.12 MHz, 6 kW RF heating unit together with hot air system. Results showed that the average difference of the sample temperature and microbial reduction between simulation and experiment was small with RMSE values of 0.009 °C and 0.012 °C, and 0.31 log CFU/g and 0.42 log CFU/g for peanut moisture contents of 7.56% and 12.02% w. b., respectively. Nonuniform RF heating resulted in the least lethality of A. flavus at the cold spot. The validated computer model was further used to estimate microbial reduction distributions at other target temperatures based on predicted temperature profiles. This computer model may help design the RF pasteurization protocols for peanut kernels without extensive experiments in food industry.

A model-based strategy for scaling-up traditional packed-bed bioreactors for solid-state fermentation based on measurement of O2 uptake rates

• O 2 uptake profiles are used to obtain the kinetics of metabolic heat generation. • The kinetics are incorporated in a two-phase heat and mass transfer model. • The model is validated with temperature profiles in a pilot-scale bioreactor. • The model is then used to explore packed-bed design and operation at large scale. • The approach is superior to previously published scale-up strategies for packed-beds. Model-based strategies for scaling up traditional packed-bed bioreactors used in solid-state fermentation have been available for over two decades. However, these strategies are theoretical, not being based on experimental data. A recently proposed strategy did use experimental data; however, it was based on data from a slow-growing fungus and also considered heat transfer in both the axial and radial directions, ignoring the fact that radial heat transfer is negligible in large-scale packed-bed bioreactors. The current work demonstrates a scale-up strategy based on a model describing heat transfer only in the axial direction. The strategy is demonstrated for the cultivation of Aspergillus niger , a fast-growing filamentous fungus. In the model, the kinetics of production of metabolic heat are based on an experimental time-course profile for the O 2 uptake rate. The model is validated by comparing its predictions for temperature profiles in the bed with temperatures measured experimentally in a pilot-scale bioreactor. Finally, the validated model is used to explore strategies for optimizing the performance of large-scale traditional packed-bed bioreactors. We conclude that a key strategy is to decrease the temperature of the inlet air during periods of peak heat generation.

Eigenfunctions and genetic algorithm based improved strategies for performance analysis and geometric optimization of a two-zone solar pond

In this paper, concept of a modified two-zone solar pond consisting lateral extraction from an extended length of convection free zone combined with thermoelectric generators for electrical power generation is proposed. In this work, a new transient-state analytical model to predict the performance of such a system is developed. Fully closed-form transient solution for performance analysis during extended length lateral heat extraction from solar ponds is not yet available in the literature. The presence of two source terms, one of which is solution dependent and mixed boundary conditions makes the present study complex to be solved analytically. The methodology based on the expansion of Eigenfunctions is proposed to predict temperature profiles within the solar pond having dual layer heat extraction. Using the temperature field, net energy output from the system for a specified period is obtained. The variation in power output and net energy for various cases of design and operating parameters during the maturation phase of the solar pond and after achieving the steady-state is analysed. The sensitivity of different operating and design parameters on the output of the system is also evaluated. Further, an inverse optimization analysis is also carried out to search for possible combinations of various design and operating parameters in the system towards meeting a specified net energy output from the thermoelectric generators. It was observed that using the inversely obtained set of parameters can significantly reduce the volume, base area, and total height of the solar pond concerning the existing convention.

Thermal integrity testing of cast in situ piles: An alternative interpretation approach

Integrity testing of deep cast in situ concrete foundations is challenging due to the intrinsic nature of how these foundations are formed. Several integrity test methods have been developed and are well established, but each of these have strengths and weaknesses. A relatively recent integrity testing method is thermal integrity testing. The fundamental feature is the early age concrete release of heat during curing; anomalies such as voids, necking, bulging and/or soil intrusion inside the concrete body result in local temperature variations. Temperature sensors installed on the reinforcement cage collect detailed temperature data along the entire pile during concrete curing to allow empirical identification of these temperature variations. This article investigates a new approach to the interpretation of the temperature variations from thermal integrity testing of cast in situ concrete piles and presents a field case study of this approach. The approach uses the heat of hydration and heat transfer theory and employs numerical modelling using the finite element method. The finite element model can be customised for different concrete mixes and pile geometries. The predicted temperature profile from the numerical model is then compared, in a systematic manner, to the field test temperature data. Any temperature discrepancies indicate potential anomalies of the pile structure. The proposed new interpretation approach could potentially reduce construction costs and increase the anomaly detection accuracy compared to traditional interpretation methods.

Prediction of Effective Steam Sterilization Times for Wine Barrels Using a Mathematical Modeling Approach

Oak barrels are often used to give wine, beer, and distilled beverages flavors and colors. However, microorganisms can penetrate into the wood, making cleaning and sterilization difficult. This can lead to undesired microbial growth in the contents of the barrel during subsequent use. Information on heat penetration into barrel staves during steam treatment and associated thermal inactivation rates is scarce. Therefore, we incorporated heat transfer and Arrhenius-type death kinetics to build a mathematical model to predict the killing rate of the microorganisms when steam sterilizing a wooden barrel. First, we use this model to predict temperature profiles in barrel staves as a function of distance from the steamed surface and time of steaming. Next we evaluated the thermal inactivation of microorganisms at 0.8 cm into the stave (the maximum wine penetration depth into a stave) to calculate the time needed to achieve 5-log reduction in live cells. Using this approach, we found that the required sterilization times for Brettanomyces bruxellensis, Saccharomyces cerevisiae, and Leuconostoc mesenteroides are 9 minutes, 12 minutes, and 200 minutes, respectively. This result is useful for winemakers to determine how long they desire to steam a barrel to prevent growth and contamination of key microbes in their wine.

Estimation of heat and mass transfer coefficients in a pilot packed-bed solid-state fermentation bioreactor

Although mathematical models of heat and water transfer in packed-bed bioreactors for solid-state fermentation have been available for several decades, there have been few efforts to determine the heat and mass transfer coefficients between the solids and air phases experimentally. In the current work, we undertook drying, cooling and heating experiments in a pilot packed-bed bioreactor, using a solid substrate prepared as for a fermentation, but uninoculated. We then used the solids-air heat and mass transfer coefficients as fitting parameters to adjust a heat and mass transfer model to temperature data obtained at different bed heights. The model fit the data well, but only if the air flow rate was allowed to vary as a function of position, indicating that the air flow in the bed was not uniform. In a sensitivity analysis, the air flow rate had a greater effect on the predicted temperature profiles than did the solids-air heat and mass transfer coefficients themselves. However, despite the lesser effect of these coefficients, it is not possible to treat the solids and air phases as being in thermal and hydric equilibrium. Rather, it is essential to use models that recognize the solids and air as separate phases and use expressions based on driving forces to describe evaporation and convective heat removal. The experimentally determined heat and mass transfer coefficients can be substituted into a mathematical model that also describes microbial growth and metabolic heat production and this model can be used to guide the scale-up from our pilot bioreactor to larger scales.

Processing maps based on polymerization modelling of thick methacrylic laminates

Control of the in-situ polymerization of thick methacrylic laminates is essential to minimize cavitation due to monomer boiling. To this end, a computationally efficient thermochemical model is developed to predict the temperature and degree of monomer conversion during polymerization of a methyl methacrylate (MMA)-based resin in a fibrous preform. The model couples the self-accelerating exothermic free-radical bulk polymerization of MMA (gel effect) with the heat transfer within the preform and to the environment. The predicted temperature profiles are in excellent agreement with experimental data measured during a series of in-situ polymerization tests with different heating conditions and preform thicknesses. Processing diagrams are constructed to reveal the regimes of interest for the production of thick fiber-reinforced methacrylic composites without voids induced by monomer boiling.