Dynamic Temperature Profiles Research Articles

Monitoring the changes in UHT whole milk during storage under dynamic and constant temperature profiles

Monitoring the changes in UHT whole milk during storage under dynamic and constant temperature profiles

Heat transfer study on a stator-permanent magnet electric motor: A hybrid estimation model for real-time temperature monitoring and predictive maintenance

When electrical machines operate without a specific cooling system, the surrounding environment plays a crucial role in the rise of temperature and the duty cycle of operation. More clearly, a natural convection cooling system with a low value of heat transfer coefficient carries the risk of thermal breakdown, insufficient safety, and reliability. This paper studies the heat transfer aspects of a low-power flux switching permanent magnet (FSPM) motor under natural convection cooling to implement a novel real-time sensor-less temperature monitoring system. Thermal and electromagnetic experiments are carried out to create foundations for transient and steady-state numerical models. A data-driven, deep learning algorithm estimates the core and permanent magnet (PM) eddy current losses in real-time, besides the already available copper and friction losses. Subsequently, a two-node thermal equivalent circuit in a hybrid model with a feed-forward neural network estimates the dynamic temperature profile of windings and PMs. It is indicated that the worst-case estimation error is below 7.5%, and the configuration is applicable under a wide range of operation states and environmental conditions. Lastly, the system, including the power source, FSPM motor, and hybrid temperature estimation unit, will be implemented in MATLAB/Simulink to investigate the fault prediction and operation management capabilities.

Unsteady-state modeling and analysis of a spray-assisted low-temperature desalination system

The spray-assisted low-temperature desalination (SLTD) technology driven by low-grade heat sources has demonstrated excellent thermodynamic and economic performances. However, existing studies are based on steady-state analyses and cannot describe dynamic system performance under fluctuations of the operating conditions. This work presents an unsteady-state analysis of the SLTD system driven by low-grade waste-heat sources. A dynamic model is developed and validated against experimental data. The model is observed to predict dynamic temperature profiles accurately with a maximum absolute error of ±0.3 °C. Then, the model is applied to analyze the responses of the SLTD system to different disturbances. Results show that the first evaporator is the most sensitive to the heat source temperature fluctuation, while locations farther away from the disturbance have longer response times and smaller fluctuations. When the heat source temperature for a 4-effect system fluctuates by 10 %, the response time and temperature fluctuation amplitude of the 1st effect are 579 s and 8 %, respectively, while those of the 4th effect are 1007 s and 3 %, respectively. Besides, the systems with smaller heat transfer areas, fewer numbers of effect, and higher heat source temperatures have faster response speeds. The results provide useful information for system operation and control strategy exploitation.

Demonstration of Inappropriate Validation Method for a Cracker Baking Process Using Predictive Modeling

Validation of baking processes for the inactivation of Salmonella is complicated by the combined effects of product heating and drying. The goal of this study was to quantitatively evaluate a previously disseminated approach to validating baking processes utilizing a predictive model developed using only isothermal and single-moisture inactivation data for the initially formulated dough. A simple cracker dough was formulated using flour inoculated with a five-strain cocktail of Salmonella. Side-by-side isothermal and baking experiments were performed to estimate Salmonella inactivation kinetics and to quantify survivors in a dynamic environment, respectively. Isothermal, single-moisture inactivation experiments were performed with cracker dough (water activity, aw = 0.956 ± 0.002; moisture content = 0.50 ± 0.01 dry basis) at three temperatures (56, 60, or 63°C) with ≥6 time intervals. Baking experiments were performed in a convection oven at 177°C with samples pulled every 30 s up to 360 s, with an endpoint product aw (25°C) of 0.45. The Salmonella isothermal, single-moisture inactivation kinetics in cracker dough resulted in D60°C and z−values of 4.6 min and 4.9°C, respectively; this model was then integrated over the dynamic product temperature profiles from the baking experiments. In the baking experiments, an average of 5-log reductions of Salmonella was achieved by 150 s of treatment; however, >100-log reductions were predicted by the dough-based models at that time point. This fail-dangerous overestimation of Salmonella lethality in crackers explicitly demonstrated that single-level moisture-based prediction models are inappropriate for describing inactivation in a process with both dynamic temperature and moisture, and that model-based validations must incorporate moisture/aw. Furthermore, end-users should exercise caution when utilizing unvalidated models to validate preventive control processes.

Numerical simulation of heat transfer during meat ball cooking and microbial food safety enhancement.

This study was conducted to apply the finite volume method (FVM) to solve the partial differential equation (PDE) governing the heat transfer process during meat cooking with convective surface conditions. For a one-dimensional, round-shaped food, such as meat balls, the domain may be divided into shells of equal thickness, with energy balance established for each adjacent shell using in the finite difference scheme (FDS) to construct a set of finite difference equations, which were then solved simultaneously using the FORTRAN language and the IVPAG subroutine of the International Mathematics and Statistics Library. The FDS is flexible for temperature-dependent physical properties of foods, such as thermal conductivity (k), specific heat (Cp ), thermal diffusivity (α), and boundary conditions, for example, surface heat transfer coefficient (h), to predict the dynamic temperature profiles in beef and chicken meat balls cooked in an oven. Once the FVM model was established and validated, it was used to simulate the dynamic temperature profiles during cooking, which were then used in combination with the general method to evaluate the thermal lethality of Shiga toxin-producing Escherichia coli and Salmonella spp. using D and z values in ground meats during cooking. The method can be applied to design cooking processes that effectively inactivate foodborne pathogens while maintaining the quality of cooked meats and evaluate the adequacy of a cooking process. PRACTICAL APPLICATION: The temperature dependences of thermal conductivity (k) and thermal diffusivity (α) of raw ground beef and ground chicken meats were measured. These thermal properties were then used in numerical simulation to predict the dynamic heating temperature profile and thermal lethality of ground beef and chicken meat balls. The numerical simulation method may be used to optimize and evaluate thermal processes and ensure the inactivation of pathogens in meat products during cooking.

Mathematical Modeling of the Dynamic Temperature Profile in Geothermal-Energy-Heated Natural Gas Hydrate Reservoirs

An analytical model was developed in this study for predicting the dynamic temperature profile in natural gas hydrate (NGH) reservoirs that receive heat energy from a geothermal layer for accelerating gas production. The analytical model was validated by a comparison of its result to the result given by a numerical model. The expression of the analytical model shows that, for a given system, the heat transfer is proportional to the mass flow rate and the temperature drop along the heat dissipator wellbore. Applying the analytical model to the NGH reservoir in the Shenhu area, Northern South China Sea, allowed for predicting the dynamic temperature profile in the NGH reservoir. The model result reveals that the NGH reservoir temperature should increase quickly at any heat-affected point, but it should propagate slowly in the radial direction. It should take more than two years to dissociate NGH within 20 m of the heat dissipator wellbore due to only thermal stimulation. Therefore, the geo-thermal stimulation method should be used as a technique for accelerating gas production with a depressurization scheme. The formation of gas phase due to the NGH dissociation should reduce the thermal conductivity of the NGH reservoir, while the water phase that dropped out from the dissociation should increase the thermal conductivity. The resultant effect should be investigated in the future in laboratories and/or numerical simulation of the dynamic water-gas two-phase flow coupled with a heat–transfer mechanism.

Mathematical Modeling of Heat Transfer from Geothermal Zones to Natural Gas Hydrate Reservoirs

An analytical model was developed in this study for predicting the dynamic temperature profile in natural gas hydrate (NGH) reservoirs that receive heat energy from a geothermal layer for accelerating gas production. The analytical model was validated by a comparison of its result to the result given by a numerical model. The expression of the analytical model shows that, for a given system, the heat transfer is proportional to the mass flow rate and the temperature drop along the heat dissipator wellbore. Applying the analytical model to the NGH reservoir in the Shenhu area, Northern South China Sea, allowed for predicting the dynamic temperature profile in the NGH reservoir. The model result reveals that the NGH reservoir temperature should rise quickly at any heat-affected point, but it should propagate slowly in the radial direction. It should take more than two years to dissociate NGH within 20 m of the heat dissipator wellbore due to only thermal stimulation. Therefore, the geo- thermal stimulation method should be used as a technique for accelerating gas production with depressurization scheme. The formation of gas phase due to the NGH dissociation should reduce the thermal conductivity of the NGH reservoir, while the water phase dropped out from the dissociation should increase the thermal conductivity. The resultant effect should be investigated in the future in laboratories and/or numerical simulation of the dynamic water-gas two-phase flow coupled with heat-transfer mechanism.

One-step dynamic analysis of growth kinetics of Bacillus cereus from spores in simulated fried rice – Model development, validation, and Marko Chain Monte Carlo simulation

Bacillus cereus is a spore-forming pathogen capable of producing an emetic toxin and several diarrheal enterotoxins that may cause outbreaks of foodborne illness often associated with rice-based and other farinaceous foods. Therefore, the objective of this study was to investigate the growth kinetics of B. cereus from spores in simulated egg fried rice.The growth of B. cereus was observed under dynamic conditions. Three independent growth curves were analyzed simultaneously using a one-step dynamic analysis (OSDA) to determine the kinetic parameters. The results showed that the minimum, optimum, and maximum growth temperatures were 11.8, 40.8, and 50.6 °C, respectively, with an optimum specific growth rate of 2.4 per h. The root-mean-square-error (RMSE) of model development was 0.4 log CFU/g. Deterministic validation with another 3 independent dynamic temperature profiles showed a RMSE of 0.5 log CFU/g. With Markov Chain Monte Carlo simulation, the RMSE of prediction was only 0.3 log CFU/g.This study proved that OSDA is an effective and efficient method for quickly developing integrated predictive models and estimating kinetic parameters. The resulting integrated model can be used to accurately predict the growth of B. cereus and for managing its risks associated with egg fried rice. The developed kinetic models also can be used to guide restaurant owners and catering establishments to properly prepare and store egg fried rice and other related products to prevent the growth of B. cereus. According to the model, the growth of mesophilic B. cereus is unlikely to occur if the food is stored below 10 °C.

Microbiological Quality Assessment of Chicken Thigh Fillets Using Spectroscopic Sensors and Multivariate Data Analysis.

Fourier transform infrared spectroscopy (FT-IR) and multispectral imaging (MSI) were evaluated for the prediction of the microbiological quality of poultry meat via regression and classification models. Chicken thigh fillets (n = 402) were subjected to spoilage experiments at eight isothermal and two dynamic temperature profiles. Samples were analyzed microbiologically (total viable counts (TVCs) and Pseudomonas spp.), while simultaneously MSI and FT-IR spectra were acquired. The organoleptic quality of the samples was also evaluated by a sensory panel, establishing a TVC spoilage threshold at 6.99 log CFU/cm2. Partial least squares regression (PLS-R) models were employed in the assessment of TVCs and Pseudomonas spp. counts on chicken’s surface. Furthermore, classification models (linear discriminant analysis (LDA), quadratic discriminant analysis (QDA), support vector machines (SVMs), and quadratic support vector machines (QSVMs)) were developed to discriminate the samples in two quality classes (fresh vs. spoiled). PLS-R models developed on MSI data predicted TVCs and Pseudomonas spp. counts satisfactorily, with root mean squared error (RMSE) values of 0.987 and 1.215 log CFU/cm2, respectively. SVM model coupled to MSI data exhibited the highest performance with an overall accuracy of 94.4%, while in the case of FT-IR, improved classification was obtained with the QDA model (overall accuracy 71.4%). These results confirm the efficacy of MSI and FT-IR as rapid methods to assess the quality in poultry products.

Development and validation of a predictive model for the effect of temperature, pH and water activity on the growth kinetics of Bacillus coagulans in non-refrigerated ready-to-eat food products

A cardinal model (CM) for the effects of temperature (range: 32–59 °C), pH (range: 5.0–8.5) and water activity (aw) (range: 0.980–0.995) on Bacillus coagulans DSM 1 growth rate was developed in brain heart infusion broth (BHI), using the Bioscreen C method and further validated in selected food products. The estimated values for the cardinal parameters Tmin, Topt, Tmax, pHmin, pHopt, pHmax, awmin and awopt were 23.77 ± 0.19 °C, 52.89 ± 0.01 °C, 59.37 ± 0.07 °C, 4.70 ± 0.02, 6.43 ± 0.02, 8.56 ± 0.01, 0.969 ± 0.0007 and 0.998 ± 0.0011, respectively. The growth behaviour of B. coagulans was studied in five commercial non-refrigerated ready-to-eat food products under static conditions at 53 °C in order to estimate the optimum specific growth rate for each tested food product. The developed models were validated in the five selected food products under four different dynamic temperature profiles by comparing predicted and observed growth behaviour of B. coagulans. The validation results indicated a good performance of the model for all tested products with the overall Bias factor (Bf) and Accuracy factor (Af) estimated at 1.00 and 1.12, respectively. The developed model can be considered an effective tool in predicting B. coagulans growth and spoilage risks of non-refrigerated ready-to-eat food products during distribution and storage.

Data-Driven Modeling of a Pilot Plant Batch Reactor and Validation of a Nonlinear Model Predictive Controller for Dynamic Temperature Profile Tracking.

Batch process plays a very crucial and important role in process industries. The increased operational flexibility and trend toward high-quality, low-volume chemical production has put more emphasis on batch processing. In this work, nonlinearities associated with the batch reactor process have been studied. ARX and NARX models have been identified using open-loop data obtained from the pilot plant batch reactor. The performance of the batch reactor with conventional linear controllers results in aggressive manipulated variable action and larger energy consumption due to its inherent nonlinearity. This issue has been addressed in the proposed work by identifying the nonlinear model and designing a nonlinear model predictive controller for a pilot plant batch reactor. The implementation of the proposed method has resulted in smooth response of the manipulated variable as well as reactor temperature on both simulation and real-time experimentation.

On optimum dynamic temperature profiles for thermal inactivation kinetics determination.

Determination of inactivation kinetics, associated with thermal processing of foods and obtained from dynamic temperature experiments, requires carefully designed experiments, the primary element being the selection of the appropriate temperature profile along with a carefully planned sampling schedule. In the present work, a number of different dynamic temperature profiles were investigated in terms of their ability to generate accurate kinetic parameters with low confidence intervals (CIs). Although alternative models have been also tested, our work was concentrated on thermal inactivation kinetics that could be described by the classical D-z values. A pair of D and z values was assumed, and for each temperature profile tested, concentration data at different processing times were generated through the appropriate models. Next, an error (up to ±2.5% or ±5%) was introduced on these theoretical values to generate pseudo-experimental data, and the back-calculation of the assumed kinetic parameters by non-linear regression was performed. The accuracy and the 95% CIs of the estimated kinetic parameters were evaluated; joint confidence regions were also constructed to investigate parameters correlation. The effect of temperature profile pattern, level of error, number of experimental points, and reference temperature was assessed. A stepwise increasing and a single triangle-pattern temperature profile were the best profiles among those tested. As a general observation, based on different kinetic models investigated, temperature profiles and sampling intervals that result in concentration versus time diagrams having shapes as suggested by the primary model used when isothermally applied are not considered appropriate for parameter estimation.

Numerical Modeling of Temperature-Dependent Cell Membrane Permeability to Water Based on a Microfluidic System with Dynamic Temperature Control.

In order to describe temperature-dependent cell osmotic behaviors in a more reliable method, a novel mathematical mass transfer model coupled with dynamic temperature change has been established based on the combination of a time domain to temperature domain transformation equation and a constant temperature mass transfer model. This novel model is numerically simulated under multiple temperature changing rates and extracellular osmolarities. A microfluidic system that can achieve single-cell osmotic behavior observation and provide dynamic and swift on-chip temperature control was built and tested in this paper. Utilizing the temperature control system, the on-chip heating processes are recorded and then described as polynomial time-temperature relationships. These dynamic temperature changing profiles were performed by obtaining cell membrane properties by parameter fitting only one set of testing experimental data to the mathematical model with a constant temperature changing rate. The numerical modeling results show that predicting the osmotic cell volume change using selected dynamic temperature profiles is more suitable for studies concerning cell membrane permeability determination and cryopreservation process than tests using constant temperature changing rates.

Multiscale CFD-DEM model for the CO2 gasification reaction of carbon anode

The reactivity of carbon anodes with CO2 is one of the main concerns in aluminum smelters using the Hall–Héroult process. Such reactivity is undesirable because it increases the net carbon consumption and thus shortens anode lifetime. Anode overconsumption is affected by anode intrinsic reactivity and mass-transport phenomena. Herein, as a first step toward the simulation of anode gasification with CO2, an anode particle bed was considered. Numerical multiscale computational fluid dynamics (CFD)–discrete element method (DEM) model was developed based on an Eulerian–Lagrangian concept. The model includes an Eulerian finite-element method for the gas and solid particles, and a Lagrangian DEM for the particle phase. The was intended to capture the particle-shrinkage effect (movement of particles during gasification). The physical (e.g., porosity and specific surface area) and thermochemical (e.g., heat of reaction) properties of particles are ultimately tracked. Geometric changes in particles, heat and mass transfer, particle shrinkage, and chemical reactions are considered during anode gasification with CO2. The dynamic concentration and temperature profiles of the reactant and product gases, as well as the solid conversion, were modeled in the voids between the particles and the pores inside each particle. To validate the model, experimental tests were performed using a bed of anode particles.

Dynamic kinetic analysis of growth of Listeria monocytogenes in pasteurized cow milk

The objective of this study was to develop a dynamic model for predicting the growth of Listeria monocytogenes in pasteurized cow milk under fluctuating temperature conditions during storage and temperature abuse. Six dynamic temperature profiles that simulated random fluctuation patterns were designed to change arbitrarily between 4 and 30°C. The growth data collected from 3 independent temperature profiles were used to determine the kinetic parameters and construct a growth model combining the primary and secondary models using a 1-step dynamic analysis method. The results showed that the estimated minimum growth temperature and maximum cell concentration were 0.6 ± 0.2°C and 7.8 ± 0.1 log cfu/mL (mean ± standard error), with the root mean square error (RMSE) only 0.3 log cfu/mL for model development. The model and the associated kinetic parameters were validated using the data collected under both dynamic and isothermal conditions, which were not used for model development, to verify the accuracy of prediction. The RMSE of prediction was approximately 0.3 log cfu/mL for fluctuating temperature profiles, and it was between 0.2 and 1.1 log cfu/mL under certain isothermal temperatures (2-30°C). The resulting model and kinetic parameters were further validated using 3 growth curves at 4, 7, and 10°C arbitrarily selected from ComBase (www.combase.cc). The RMSE of prediction was 0.8, 0.4, and 0.5 log cfu/mL, respectively, for these curves. The validation results indicated the predictive model was reasonably accurate, with relatively small RMSE. The model was then used to simulate the growth of L. monocytogenes under a variety of continuous and square-wave temperature profiles to demonstrate its potential application. The results of this study showed that the model developed in this study can be used to predict the growth of L. monocytogenes in contaminated milk during storage.

Effect of Oil Mass Flow Rate on Temperature Profile in Oil Wells

In several design calculations including the development of programs to optimize production, engineers and scientists require accurate prediction of temperature drop due to flow in oil wells. The purpose of this research is to create mathematical models to predict the effect of oil mass flow rate on temperature distribution in oil wells. A numerical mathematical model is developed to study the parameters affecting the dynamic and static temperature profiles in oil wells in production and shutting operation. The temperature distribution of the oil from the reservoir to the surface and the temperature distribution in the wall tubing of the oil well and casing, cement sheaths, and surrounding formation is studied. The natural flow of oil wells in Alwahat area located 70 Kilometres south of Marada area east of Libya in the Zaggut field called (6Q1-59) is taken as a study case. In production case, different mass flow rates in winter and summer seasons are studied. The temperature profile in the horizontal direction is estimated at different depths. The Results show that the surface temperature of crude oil increases with the rise in mass flow rate.

Effect of stacking sequence and porosity on creep behavior of glass/epoxy and carbon/epoxy hybrid laminate composites

The creep behavior of hybrid carbon/glass laminate composites was investigated using a dynamic mechanical analyzer and different temperature profiles. Dynamic analysis and porosity were carried out prior to the creep test applying Findley's power law. The dynamic curves were not influenced by porosity but were affected by the architecture of the composites. The creep tests showed porosity dependence at temperatures below Tg while at higher temperatures, the architecture of the reinforcement plays a major role. Time-temperature superposition was conducted for long-term creep prediction and the results showed lower creep strain for the carbon and interleaved hybrid composites.

Introduction to Control Volume Based Transient Thermal Limit

<div class="section abstract"><div class="htmlview paragraph">Advancement in modern aircraft with the development of more dynamic and efficient technologies has led to these technologies increasingly operated near or at their operation limits. More comprehensive analysis methods based on high-fidelity models co-simulated in an integrated environment are needed to support the full utilization of these advanced technologies. Furthermore, the additional information provided by these new analyses needs to be correlated with updates to traditional metrics and specifications. One such case is the thermal limit requirement that sets the upper bound on a thermal system temperature. Traditionally, this bound is defined based on steady-state conditions. However, advanced thermal management systems experience dynamic events where the temperature is not static and may violate steady-state requirements for brief periods of time. Due to the large thermal time constants for many components, such transient violations may not represent system failure and an understanding of transient temperature limits is beneficial. To meet this need, this paper introduces the transient thermal limit via control volume representation. Instead of a constant thermal limit, the transient thermal limit approach generates a dynamic temperature profile limit by representing the thermal system with a control volume and scaling the input temperature profile such that the control volume does not exceed the steady-state temperature limit. This simple approach is based on a physical representation that is customizable to each system and can dynamically adjust the limit based on system conditions. Additionally, this control volume transient temperature limit methodology was developed to minimize information sharing between proprietary systems. The details of this transient limit generation methodology will be reviewed in this paper and illustrated through application on an example thermal system.</div></div>

Reconfigurable, Self-Sufficient Convective Heat Exchanger for Temperature Control of Microfluidic Systems

Here, we demonstrate a modular, reconfigurable, and self-sufficient convective heat exchanger for regulation of temperature in microfluidic systems. The heat exchanger consists of polymer tubes wrapped around a plastic pole and fully embedded in an elastomer block, which can be easily mounted onto the microfluidic structure. It is compatible with various microfluidic geometries and materials. Miniaturized, battery-powered piezoelectric pumps are utilized to drive the heat carrying liquid through the heat exchanger at desired flow rates and temperatures. Customized temperature profiles can be generated by changing the configuration of the heat exchanger with respect to the microfluidic structure. Tailored dynamic temperature profiles can be generated by changing the temperature of the heat carrying liquid in successive cycles. This feature is used to study the calcium signaling of endothelial cells under successive temperature cycles of 24 to 37 °C. The versatility, simplicity, and self-sufficiency of the heat exchanger makes it suitable for various microfluidic based cellular assays.

One-Step Analysis for Listeria monocytogenes Growth in Ready-to-Eat Braised Beef at Dynamic and Static Conditions

This study aimed to estimate the growth parameters of Listeria monocytogenes growth in ready-to-eat (RTE) braised beef by one-step dynamic and static kinetic analysis. The Baranyi model and cardinal parameters model were integrated into a dynamic and static model to estimate the kinetic parameters under one dynamic condition (-20 to 40.0°C) and eight static conditions (4, 8, 15, 20, 30, 35, 37, and 40°C). Based on the dynamic and static methods, the respective dynamic and static results for estimated growth boundaries of L. monocytogenes in RTE braised beef were from -2.5 and -2.7°C to 40.5 and 40.7°C with optimal specific growth rates of 1.078 and 0.913 per h at temperatures of 35.7 and 35.0°C. Temperature effects on the specific growth rate and lag period were developed and used to simulate the change of the physiological state of inocula during the bacterial growth. Subsequently, three additional dynamic temperature profiles were implemented for external validation. The root mean square error of the model developed by dynamic regression (0.19 log CFU/g) is slightly better than that of the model developed by static regression (0.23 log CFU/g). Comparing the validation results, one-step dynamic analysis might be a preferable method for prediction, especially when the growth approaches the stationary phase. Generally, both one-step dynamic and static analyses could be used to accurately predict L. monocytogenes growth in RTE braised beef under fluctuating temperatures.