Prediction Of Heat Capacity Research Articles

Prediction of heat capacity of ionic liquids: A simple group contribution method

A simple method based on the mathematical relational expression and group contributions is proposed to predict the heat capacity of ionic liquids (ILs). A comprehensive heat capacity database of 11873 measured data points for 310 ILs at pressure of one atmosphere, and the temperature and the heat capacity in the ranges of 167.70-524.87 K and 84-2040 J⋅mol−1∙K−1, respectively, is established. The optimal group contribution values and universal parameters are determined from fitting 10675 measured data with the Newton's central difference algorithm by the categorical method (M1) and the overall method (M2), where the difference is whether each class of ILs has independent group contribution parameters. Average absolute relative deviations (AARDs) between the predicted and the measured values are 2.36% and 3.20% for the M1 and M2 methods, respectively. The test set including 1198 data points for 62 ILs is used to test and evaluate the model and parameters which have been trained on the above training data set. The group contribution method proposed in this work achieves the accurate prediction of the heat capacity of a variety of ionic liquids over a wide temperature range, which provides new ideas for predicting the other physical properties of ionic liquid systems.

Making heat capacity prediction more reliable.

Making heat capacity prediction more reliable.

Specific heat capacity prediction of hybrid nanofluid using artificial neural network and its heat transfer application

Currently, many industries aim to improve the heat transfer system to reduce cost and carbon emission. For improving heat exchanger apparatus, the convectional working fluid such as water can be replaced by hybrid nanofluid. The hybrid nanofluid is a suspension of nanoparticles into conventional base fluids to improve its properties. Generally, different combinations of nanoparticle and base fluid can variably enhance the specific heat capacity for removing heat from the system. In this study, the artificial neural network was thus employed to develop the prediction model for predicting specific heat capacity using feedforward and cascade forward propagation networks with Levenberg–Marquardt learning algorithm. The best artificial neural network (ANN) model topology was selected to predict specific heat capacity of hybrid nanofluid with R value of 0.9919, 0.9473 and 0.9673 for training, validating and testing, respectively. Then, the specific heat capacity value from ANN model was applied to evaluate and analyze the heat transfer rates. The hybrid nanofluid with Al2O3 and CuO in water exhibited the best heat removing medium in heat exchanger application.

Theoretical prediction of Gibbs free energy and specific heat capacity of gaseous molecules

• Highly efficient equations for the prediction of molar Gibbs free energy and molar specific heat capacity are presented. • The equations are based on the improved generalized Pöschl-Teller oscillator. • The analytical models are applicable to large class of diatomic and triatomic molecules. • The equations require only three molecular constants of the molecule as input data. Using the improved generalized Pöschl-Teller oscillator to model the internal vibration of a diatomic molecule, analytical equations to predict molar Gibbs free energy and specific heat capacity at constant pressure were obtained. The equations were applied to ground-state Br 2 , Cl 2 diatomic molecules, and CO gaseous molecule. Average absolute deviations of 0.0755%, 0.0787%, and 0.3583%, were obtained using the equation of reduced molar Gibbs free energy. The expression of molar specific heat capacity yielded average absolute deviations of 4.5055%, 2.5308%, and 2.3665%, respectively from experimental data for the diatomic molecules. The results are in good agreement with existing literature data on the diatomic molecules.

Impact of data processing and robust machine learning process on accurate estimation of specific heat capacity property in energy storage applications

The specific heat capacity propery is a critical thermal property because it is directly related to heat storage and transfer. The need for better and novel thermal energy storage materials have led to the discovery of high temperature nanofluids with molten salts (nanosalts). The utilization of novel machine learning tools in accurately estimating the specific heat capacity of nanofluids, would further pave the way for more innovative studies on nanosalt, hence the prediction of specific heat capacity of hybrid nanofluids constitute a significant research area. Despite the few researches conducted in the area of prediction of these fluids, priority with regards to the robust analysis of input variables for different machine learning algorithms have not been given due consideration. Therefore, this present study aims to develop and validate a robust package of machine learning algorithms (twenty machine learning algorithms) in estimating the specific heat capacity of hybrid nanofluids, across different feature spaces. The different algorithm hyperparameters were adjusted to give optimum prediction. A total of 600 data points were retrieved and split into training, testing, and validation data set, for which the validation dataset was not used in the training and testing process. The input variables used in the training of the machine learning models are temperature, volume fraction, the specific heat capacity of the base fluid, the specific heat capacity of individual nanoparticles, and the mixture ratio of individual nanoparticles. The Model A, which constituted the seven (7) input variables gave the best prediction accuracy with validation score of 0.999703, while the Model C, which contained the feature variables of temperature, volume fraction and specific heat capacity of the base fluid gave the least prediction accuracy. The optimal machine algorithms retrieved for the Model A, B, C and D was the Ridge, Gradient Boost regressor, Passive Aggressive regressor, and Kernel ridge regressor. The combination of ensemble tree regressors and online learning algorithms can be coded and used in future prediction analysis of SHC, as they have both shown accurate prediction quality.

Ab initio molecular dynamics (AIMD) simulations of NaCl, UCl[formula omitted] and NaCl-UCl[formula omitted] molten salts

Ab initio molecular dynamics (AIMD) simulations are used to calculate select thermophysical and thermodynamic properties of NaCl, UCl3 and NaCl-UCl3 molten salts. Following established approaches, the AIMD simulations include a model for Van der Waals interactions. The Langreth & Lundqvist (vDW-DF2), DFT-D3, and density-dependent energy correction (DFT-dDsC) dispersion models are first tested for molten NaCl in order to assess their accuracy for density and heat capacity predictions across a range of temperatures. Based on the NaCl results, the vdW-DF2 and DFT-dDsC methods are extended to the UCl3 system and compared to available experimental data. Next, mixtures of NaCl-UCl3 are investigated and analyzed with respect to the deviation from ideal solution behavior. For the DFT-dDsC simulations, density deviates by up to 2% from an ideal mixture, with the maximum occurring close to the eutectic composition. The mixing energy also deviates from an ideal solution and exhibits a minimum of about -0.074 to eV per formula unit, again close to the eutectic composition. Finally, the compressibility and species diffusivity of the pure and mixed salt systems are calculated. The diffusivities are slightly reduced in the mixed compared to the pure systems and the compressibilities loosely follow a linear correlation as a function of the UCl3 composition. The trends observed for mixing properties are rationalized by correlating them to the coordination chemistry, which emphasizes the importance of maintaining the extended network formed by U and Cl ions as the NaCl concentration increases. The concentration at which breakdown of the extended network occurs, roughly coincides with the minimum of both the mixing energy and the deviation from ideal solution behavior for density.

Toward Addressing the Challenge to Predict the Heat Capacities of RDX and HMX Energetic Materials

AbstractAvailability of heat capacity as a function of pressure and temperature is an essential prerequisite for development of a computational multiscale strategy capable to address the evolution of microstructure and energy release in advanced high energy density materials. In the case of 1,3,5‐trinitro‐1,3,5‐triazinane (RDX) and 1,3,5,7‐tetranitro‐1,3,5,7‐tetrazocane (HMX) systems as two of the most studied energetic materials, there are substantial gaps in experimental data, with available heat capacities values distributed only in a region close to standard ambient conditions. In this study we demonstrate how these major experimental limitations can be addressed in the case of the RDX and HMX systems based on the combined use of classical and quantum mechanical calculations. We show that by considering ideal gas properties evaluated using quantum mechanical methods, and residual properties obtained from molecular simulations using fully flexible atomistic force field models, excellent agreement can be obtained for the predicted heat capacities to the most recent experimental values. An important advantage of the current computational methodology is that it allows evaluation of both constant‐volume and constant‐pressure heat capacities for a broad interval of temperatures and pressures, which encompasses solid and liquid phases conditions. In the case of the solid α and γ phases of RDX and the β phase of HMX, the predicted results follow closely both the available experimental data at standard ambient conditions and the results obtained using density functional theory calculations at high pressures, a regime where experimental data are not available. A perspective to expand the current methodology is also discussed.

A novel model for predicting the effective specific heat capacity of molten salt doped with nanomaterial for solar energy application

In order to establish instructions for high-performance molten salt preparation, this paper focuses on accurate prediction method on the effective heat capacity of nanomaterial-enhanced molten salt (NMS). With carbon nanotube (CNT) enhanced carbonate eutectic (Li2CO3-K2CO3) taken as the study subject, the microscopic mechanism of nanomaterial on the effective specific heat capacity enhancement of molten salt is revealed. The nanolayer content is calculated, and the specific heat capacity of CNT and nanolayer is quantified. Accordingly, the effective heat capacity prediction model is completed. In addition, the CNT-enhanced carbonate eutectic is experimentally prepared with a high-energy ball milling method. Its effective specific heat capacity is measured and used to validate the prediction model. The results show that, firstly, the thickness of nanolayer is 0.65 nm and 0.98 nm in the single-walled CNT case and multi-walled CNT case, respectively. Secondly, the specific heat capacity of CNT is 1.613 J·g−1·K−1 at 800 K, and the specific heat capacity of nanolayer increases with increasing the surface atom ratio. Finally, the effective specific heat capacity of CNT-enhanced carbonate salt is predicted and validated with experimental results. It is revealed that, in the cases of well-dispersed nanomaterial-enhanced molten salt, the prediction method presented in this research has considerable accuracy (error < 2.90%). Furthermore, the effective specific heat capacity of NMS doped with CNT of various diameter is given.

Improved prediction of thermal properties of refrigerated foods

Thermal properties are essential parameters for performing heat transfer calculations. The thermal properties of foods can have a strong dependence on temperature during thermal processing, particularly during freezing, which has posed a challenge to those attempting to develop generic models that can be applied to a wide range of foods. Ideally, thermal property models should not incorporate any parameters whose value would need to be determined by a physical measurement (since this may defeat the purpose of the model). Instead it is preferable to perform a prediction based only on composition data, in terms of the major food components. This study presents an evaluation of thermal property models requiring only composition data as inputs. It is recommended that the Additive model incorporating a new correlation equation of the specific heat capacity of water should be used for effective specific heat capacity predictions. The thermal conductivity predictions from a little-known model developed by Dul'nev and Novikov provided more accurate predictions on average than more widely known models that have been recommended in previous studies. • A comparison of different composition-based thermal property models is presented. • The Additive model is recommended for effective specific heat capacity predictions. • The Dul'nev and Novikov model shows the best thermal conductivity predictions.

First-principle investigation of doping effects on mechanical and thermodynamic properties of [formula omitted

We investigate the variation of elastic stiffness moduli and the thermodynamic properties of yttrium orthosilicate (Y2SiO5, YSO) under various doping concentrations of Eu3+ ions. The model is based on a low temperature approximation (T<<θD) , and the plane-wave density functional theory (DFT) is used to carry out the calculations. The results show that the Eu3+ ions primarily occupy the Y1 site of the basic molecule for all applied concentrations. The overall shear, bulk, and Young’s moduli exhibit a decreasing trend with increasing Eu3+ concentration. The overall anisotropy shows a very small increase with increasing concentration. The Debye temperature as well as the Grünesien parameter for each concentration are predicted. Lastly, the predicted heat capacity at constant volume is calculated and compared to experimental values. Our study reveals that there is almost linear relationship between concentration and mechanical properties of YSO. The decrease of the Grünesien parameter with concentration increase might decrease the anharmonic effects in YSO, although this effect is small. In addition, the change in heat capacity with concentration rise is negligible.

Prediction of Heat Capacity of Main Chain Polymer Based on Molecular Vibration Analysis

Prediction of Heat Capacity of Main Chain Polymer Based on Molecular Vibration Analysis

Prediction and verification of heat capacities for pure ionic liquids

The heat capacity of ionic liquids is an important physical property, and experimental measuring is usually used as a common method to obtain them. Owing to the huge number of ionic liquids that can be potentially synthesized, it is desirable to acquire theoretical predictions. In this work, the Conductor-like Screening Model for Real Solvents (COSMO-RS) was used to predict the heat capacity of pure ionic liquids, and an intensive literature survey was conducted for providing a database to verify the prediction of COSMO-RS. The survey shows that the heat capacity is available for 117 ionic liquids at temperatures ranging 77.66–520 K since 2004, and the 4025 data points in total with the values from 76.37 to 1484 J·mol−1·K−1 have been reported. The prediction of heat capacity with COSMO-RS can only be conducted at two temperatures (298 and 323 K). The comparison with the experimental data proves the prediction reliability of COSMO-RS, and the average relative deviation (ARD) is 8.54%. Based on the predictions at two temperatures, a linear equation was obtained for each ionic liquid, and the heat capacities at other temperatures were then estimated via interpolation and extrapolation. The acquired heat capacities at other temperatures were then compared with the experimental data, and the ARD is only 9.50%. This evidences that the heat capacity of a pure ionic liquid follows a linear equation within the temperature range of study, and COSMO-RS can be used to predict the heat capacity of ionic liquids reliably.

Direct observation of the specific heat of Majorana quasiparticles in superfluid3He-B

The existence of Majorana quasiparticles was predicted in the edge state in topological insulators, especially in the p-wave superfluid medium {^3}He-B. Due to its purity and coherent quantum state, {^3}He-B is an ideal platform for searching for Majorana fermions in condensed matter systems. In the limit of extremely low temperatures, the density of Bogolyubov quasiparticles and the heat capacity of {^3}He-B decrease exponentially. In this article, we present the first observation of the deviation of its heat capacity from exponential dependence in the limit of record low cooling. We found an additional heat capacity that more than doubled the heat capacity of bulk {^3}He-B and changes as T^2. The additional heat capacity is in good agreement with the predicted heat capacity of 2D gas of Majorana. This observation is a direct proof of the existence of Majorana quasiparticles in {^3}He-B.

Thermodynamics of monoclinic and tetragonal hafnium dioxide (HfO2) at ambient pressure

Accurate and precise thermodynamic models of oxide compounds and their phases are important for calculating the phase stability of oxide materials. We develop and use a coupled quantum mechanical and molecular dynamics approach to create thermodynamic models of hafnia (HfO2) polymorphs from 0 K to 3000 K at ambient pressure. The approach is based on the quasi-harmonic approximation below the Debye temperature and on ab-initio molecular dynamics calculations above the Debye temperature to predict constant pressure heat capacities (Cp). A Bayesian model provides interpolated values between these regimes. As a case study, we develop thermodynamic models of monoclinic and tetragonal HfO2 polymorphs. The predicted heat capacities are in excellent agreement with experiment, and the predicted temperature of the monoclinic to tetragonal phase transition (2173 K) is in good agreement with the experimental value (2078K). These results provide a comprehensive and accurate thermodynamic model of the monoclinic and tetragonal phases of hafnia on a broad range of temperatures and can serve as input to CALPHAD assessment of multi-component hafnia-based phase diagrams.

Analytical prediction of heat capacity of molten AgCl and CuCl with application to thermochemical Cu-Cl cycle for hydrogen production

To sustain our power-dependent world, there is a need for technological innovation in all aspects of science and engineering. Many times, thermophysical and material properties are not well defined for the specific application, which leads to implementing assumptions and approximations from the published data. In the thermochemical copper-chlorine (Cu-Cl) cycle for hydrogen (H2) production, heat is recovered from cuprous chloride (CuCl) molten salt and it is then reacted with hydrochloric acid (HCl) in stoichiometric proportions to produce the anolyte for the H2 production step of the cycle. However, the lack of precise thermophysical properties on CuCl heavily hinders the detailed investigations of heat recovery from the molten salt as it cools from 450 °C to 90 °C. In this paper a new method is developed to determine the thermophysical property of CuCl and silver chloride (AgCl) as the molten salts are changing phases to solid. This is achieved by correlating electrochemistry data with thermal data. A model that predicts the specific heat capacity during phase change process is developed based on the existing electromotive force (EMF) and thermal data from literature. Developed model shows the EMF derived specific heat capacity values of AgCl and CuCl are similar with a slight offset since they have similar EMF's at higher temperatures.

Modeling of liquid internal energy and heat capacity over a wide pressure–temperature range from first principles

Recently, there have been significant theoretical advances in our understanding of liquids and dense supercritical fluids based on their ability to support high frequency transverse (shear) waves. Here, we have constructed a new computer model using these recent theoretical findings (the phonon theory of liquid thermodynamics) to model liquid internal energy across a wide pressure–temperature range. We have applied it to a number of real liquids in both the subcritical regime and the supercritical regime, in which the liquid state is demarcated by the Frenkel line. Our fitting to experimental data in a wide pressure–temperature range has allowed us to test the new theoretical model with hitherto unprecedented rigor. We have quantified the degree to which the prediction of internal energy and heat capacity is constrained by the different input parameters: the liquid relaxation time (initially obtained from the viscosity), the Debye wavenumber, and the infinite-frequency shear modulus. The model is successfully applied to output the internal energy and heat capacity data for several different fluids (Ar, Ne, N2, and Kr) over a range of densities and temperatures. We find that the predicted heat capacities are extremely sensitive to the values used for the liquid relaxation time. If these are calculated directly from the viscosity data, then, in some cases, changes within the margins of the experimental error in the viscosity data can cause the heat capacity to exhibit a completely different trend as a function of temperature. Our code is computationally inexpensive, and it is available for other researchers to use.

Thermal modeling of a high-energy prismatic lithium-ion battery cell and module based on a new thermal characterization methodology

Thermal management is crucial for lithium-ion batteries to ensure safe operation, high performance, and long lifetime. In this regard, the thermophysical properties of the batteries are key parameters for developing reliable and accurate thermal models. This study presents a new, simple, and cost-effective method for determination of heat capacity and anisotropic thermal conductivity of a commercial high energy density (43 Ah) prismatic-shape lithium-ion battery. The influence of various operating temperatures on thermal parameters is investigated. The predicted heat capacity and thermal conductivity are then used to simulate the battery cell temperature at a high current charge and discharge rate using a lumped thermal network and a three-dimensional (3D) thermal model at different environment temperatures. Additionally, the thermal behavior of a 48 V battery module consisting of 12 cells is simulated using a 3D thermal model at room temperature. Both cell and module level simulations are validated with experimental results obtained by thermocouples and a thermal camera. The thermal characterization results reveal that the heat capacity of the battery slightly increases as the operating temperature increases, while the thermal conductivity remains independent of the temperature. The comparison of simulation and experimental results indicate that the developed method in this study is a quick and practical way to accurately predict the thermophysical properties of the battery and contribute towards the thermal management of Li-ion modules and packs.

Numerical and experimental study on thermal characteristics of louvered fin microchannel air preheaters

Microchannel heat exchangers have been gradually getting importance in industrial applications due to offering outstanding benefits. The current study has focused on the development of a numerical model to predict the thermal performance of the microchannel air preheaters (MCPH) for HVAC systems. An experimental study has been performed to validate the numerical model results. A louvered fin multiport microchannel heat exchanger has been employed as an air preheater in the experiments. The proposed model has been developed based on the segment-by-segment approach and calculated the outlet temperature and heat capacity of the MCPH. Different air velocities at the frontal face and varying mass flow rates in passes of the MCPH have been taken into consideration in the model. It has been concluded from experimental data that the model predicts the outlet temperature with an average absolute deviation within ±2% for all investigated test conditions. The proposed model shows high accuracy with respect to temperature calculation. Another conclusion is that the non-uniform air velocity approach improves the precision of the proposed model. The heat capacity predictions with the uniform air velocity approach indicate higher deviations than the non-uniform air velocity approach.

Estimation of heat capacities of gases, liquids and solids, and heat capacities of vaporization and of sublimation of organic chemicals at 298.15 K

We have constructed equations for the correlation and prediction of heat capacities of gases Cp(gas), heat capacities of liquids Cp(liq), heat capacities of solids Cp(solid), the heat capacity of vaporization ΔvapCp, as Cp(gas) − Cp(liq) and the heat capacity of sublimation ΔsubCp, as Cp(gas) − Cp(solid) for organic compounds and a selection of organometallic compounds. These equations are statistically as good as, or better than, previously reported equations. The same independent variables are used in all the equations, and predictions can be made using simple arithmetic. For some processes, an equation that uses the McGowan volume as the only independent variable is found to be useful as a predictive equation, and has the advantage that the McGowan volume can trivially be calculated from structure, no computer software being required.

Prediction of the heat capacity of main-chain-type polymers below the glass transition temperature

This study predicts the absolute values of heat capacities from the molecular formula per monomer for main-chain-type polymers below the glass transition temperature. The frequencies of the skeletal and group-vibration modes are calculated using the Tarasov and Einstein equations, respectively, and the heat-capacity differences at constant pressure and constant volume are used to correct the predicted heat capacity. The contributes of skeletal vibrations to the heat capacity can be expressed by one- and three-dimensional Tarasov equations, and the contribution of group vibrations can be determined by summing the group-vibration heat capacities for functional groups and atoms constituting the monomer as obtained from the Einstein equation. The absolute value of the heat capacity is predicted from this combination of equations. The heat capacities of poly(4-methyl-1-pentene) are predicted within an error range of 8.0% from 90 to 180 K and ±2.0% from 180 to 300 K. The heat capacities of poly(vinyl benzoate) are within ±2.0% agreement from 190 to 350 K, while for poly(1,4-butylene adipate), the agreement is within ±2.0% from 80 to 200 K. This study predicted the absolute values of the heat capacities from the molecular formula per monomer for a main-chain-type polymer below the Tg. The calculations combined the Tarasov equation, the Einstein equation, and the (Cp − CV) correction term, accounting for the degrees of freedom of the monomer unit. The difference of predicted and experimental heat capacities of poly(4-methyl-1-pentene) was within 8.0% from 90 to 180 K and within ±2.0% agreement from 180 to 300 K. For poly(vinyl benzoate), the values were within ±2.0% from 190 to 350 K, and for poly(1,4-butylene adipate), they were within ±2.0% from 80 to 200 K. The predicted heat capacity was accurate, especially in the high-temperature region above 180 K.