Thermodynamic Phenomena Research Articles

Thermodynamic Separation of Hydrogen Isotopes Using Hofmann-Type Metal-Organic Frameworks with High-Density Open Metal Sites.

Hydrogen isotope separation with nanoporous materials is a very challenging yet promising approach. To overcome the limitation of the conventional isotope separation strategy, quantum sieving-based separation using nanoporous materials has been investigated recently. In this study, to see the thermodynamic deuterium separation phenomena attributed to the chemical affinity quantum sieving effect, we examine Hofmann-type metal-organic frameworks (MOFs), Co(pyz)[M(CN)4] (pyz = pyrazine, M = Pd2+, Pt2+, and Ni2+), which have microporosity (4.0 × 3.9 Å2) and an extraordinarily high density of open metal sites (∼9 mmol/cm3). Owing to the preferential adsorption of D2 over H2 at strongly binding open metal sites, the Hofmann-type MOF, Co(pyz)[Pd(CN)4] exhibited a high selectivity (SD2/H2) of 21.7 as well as a large D2 uptake of 10 mmol/g at 25 K. This is the first study of Hofmann-type MOFs to report high selectivity and capacity, both of which are important parameters for the practical application of porous materials toward isotope separation.

Thermodynamic Characteristics of a Single Stage Pneumatically Driven Gifford–McMahon Refrigerator

Abstract At present, the demand for high cooling capacity Gifford–McMahon (GM) refrigerators is increasing to cool the high-temperature superconducting magnets in cryogenic temperature range. Therefore, it is necessary to enhance the cooling capacity of existing refrigerators, which needs a thorough understanding of related thermodynamic phenomena. In this paper, thermodynamic processes of a GM refrigerator are analyzed and its performance is compared with Carnot cycle, and Brayton cycle with and without work recovery. Subsequently, a mathematical model has been formulated for a pneumatically driven GM refrigerator by applying the fundamental principles of thermodynamics and mechanics. The model computes the influence of geometrical and operating parameters upon its refrigeration performance. Subsequently, the impact of valve opening intervals upon internal thermodynamic and dynamic processes is evaluated. The dynamic characteristics of the displacer motion, transient variation of mass flow, and pressure distribution are studied by the model for different values of idling angles. It is found that an increase in the idling angle of the rotary valve of GM refrigerator reduces its cooling capacity and enhances its specific cooling capacity. An experimental study has also been undertaken to corroborate the mathematical results.

A Thermodynamic Method for the Estimation of Free Gas Proportion in Depressurization Production of Natural Gas Hydrate

Free gas saturation is one of the key factors that affect the overall production behaviors of hydrate reservoirs. For example, different free gas contents could alter the thermal response of hydrate reservoirs to the artificial stimulation and hence change the gas production performance. To investigate whether and how much the hydrate reservoir contains free gas, we proposed a thermodynamic method to calculate the total heat consumption of hydrate dissociation throughout gas production and assess the free gas proportion. Based on the monitoring data of the first offshore hydrate production in Japan, we calculated the total heat consumption and analyzed the contributions of heat convection, heat conduction, and sensible heat during the entire test. The calculation results showed that there is likely to be a certain amount of free gas in the hydrate reservoir in the Eastern Nankai Trough. In addition, the analysis of different heat sources revealed the critical thermodynamic phenomenon in which the reservoir sensible heat was the main source for enthalpy of hydrate dissociation, which consistently contributed more than 95% of the total heat supply during the 6-day production test. The results of this work may help upgrade the production strategy for natural gas hydrates.

Beyond Energy Efficiency in Building Sustainability: A Review of Emergy and Information for Systemically Characterizing Building Performance

Sustainable buildings tend to maximize power and information rather than efficiency. The multidimensional concepts and tools provided by systems ecology and thermodynamics aid the understanding of building performance and sustainability as part of the global and complex thermodynamic phenomena in living systems—energy is not concentrated, but it flows, increasing the flow rate of useful energy. From such an extended macroscopic perspective, this paper addresses holistic eco-systemic criteria of building performance evaluation, focusing on emergy (spelled with an "m") and information—the two critical indices of extensive and intensive analysis. Emergy aggregates the utmost and upstream energetic impacts, whereas information evaluates the structural pattern of the energy-flow distribution. These indices are theoretically correlated under the principles of ecological energy transformation and are often practically compatible. To clarify the definitions and appropriate scientific contexts of the new indices for environmental building studies, we review information theory, ecological theorems, and a few pioneering studies. Emergy and information have a great potential for advanced environmental building analysis, but building-scale implementation of emergy, information, and system principles remains a scientific challenge. The findings call for further research into the improvement of building-specific emergy/information data and reliable evidence of the analogy between building and open living systems.

RESEARCH OF THE PROCESS OF HIGH VOLTAGE ELECTRIC DISCHARGE SINTERING OF P6M5 RAPID STEEL POWDER

Studies have been carried out to study the process of high-voltage electric-discharge sintering of powder of high-speed steel R6M5. The process of high-voltage electric-discharge sintering of powders is accompanied by a complex of electrophysical, thermodynamic and mechanical phenomena that affect the sintering processes of samples and their properties.  It was found that the process of high-voltage electric-discharge sintering of powders of P6M5 steel is accompanied by compaction of the powder as a result of sintering of particles, as well as by the action of compressive stresses on the sample from the powder. The shape and size of particles, the nature of surface films, the size and shape of the matrix have a great influence on the process of high-voltage electric-discharge sintering. Factors affecting the strength properties of samples include the particle size distribution of the powder, its initial electrical resistance, and technological processing parameters, such as discharge energy and discharge current density.

Computational Analysis of Shear Banding in Simple Shear Flow of Viscoelastic Fluid-Based Nanofluids Subject to Exothermic Reactions

We investigated the shear banding phenomena in the non-isothermal simple-shear flow of a viscoelastic-fluid-based nanofluid (VFBN) subject to exothermic reactions. The polymeric (viscoelastic) behavior of the VFBN was modeled via the Giesekus constitutive equation, with appropriate adjustments to incorporate both the non-isothermal and nanoparticle effects. Nahme-type laws were employed to describe the temperature dependence of the VFBN viscosities and relaxation times. The Arrhenius theory was used for the modeling and incorporation of exothermic reactions. The VFBN was modeled as a single-phase homogeneous-mixture and, hence, the effects of the nanoparticles were based on the volume fraction parameter. Efficient numerical schemes based on semi-implicit finite-difference-methods were employed in MATLAB for the computational solution of the governing systems of partial differential equations. The fundamental fluid-dynamical and thermodynamical phenomena, such as shear banding, thermal runaway, and heat transfer rate (HTR) enhancement, were explored under relevant conditions. Important novel results of industrial significance were observed and demonstrated. Firstly, under shear banding conditions of the Giesekus-type VFBN model, we observed remarkable HTR and Therm-C enhancement in the VFBN as compared to, say, NFBN. Specifically, the results demonstrate that the VFBN are less susceptible to thermal runaway than are NFBN. Additionally, the results illustrate that the reduced susceptibility of the Giesekus-type VFBN to the thermal runaway phenomena is further enhanced under shear banding conditions, in particular when the nanofluid becomes increasingly polymeric. Increased polymer viscosity is used as the most direct proxy for measuring the increase in the polymeric nature of the fluid.

A generalized method for calculating plasmoelectric potential in non-Mie-resonant plasmonic systems.

Since its first observation in 2014, plasmoelectric potential (PEP) has drawn a great deal of research interest in all-metal optoelectronics and photochemistry. As an optical thermodynamic phenomenon induced by the electron number dependent equilibrium temperature in plasmonic nanostructures, the early theoretical model developed for calculating PEP is only applicable to Mie-resonant nanostructures, such as a gold nanosphere on a conductive indium tin oxide (ITO) substrate, where the transfer efficiency of hot electrons from gold to ITO can beanalytically determined. Without the presence of the substrate, the temperature increase on the gold nanosphere induced by plasmonic absorption was calculated on the basis of thermal radiation in vacuum, which probably over-estimates the actual temperature increase in comparison to realistic experimental conditions. Here, we propose an equilibrium-thermodynamics computational method to quantify the actual efficiency of plasmon-induced electron transfer between a non-Mie-resonant metallic nanostructure and a conductive substrate and hence determine the resultant plasmoelectric potential. With a less than 2.5% relative error in predicting the steady-state temperature of a Mie-resonant nanoparticle in vacuum, and a more strict evaluation ofthe plasmonic local heating induced temperature increase in a single plasmonic nanostructure or an array of such structures under continuous-wave illumination (CWI), our generalized method provides a robust and accurate approach for quantifying PEP in various plasmonic-particle (array)-on-film nanocavities.

Assessing CDK5 as a Nanomotor for Chemotactic Drug Delivery

Enzyme chemotaxis is a thermodynamic phenomenon in which enzymes move along a substrate concentration gradient towards regions with higher substrate concentrations. Mathematical models have been developed to understand and define this phenomenon using Michaelis-Menten kinetics and substrate binding coefficients. One key application of this phenomenon is in the field of drug delivery, where chemotaxis can steer nanovehicles towards targets along natural substrate concentrations. Such gradients exist throughout the body, including around the blood-brain barrier, and thus targeted nanovehicles can bring therapeutics through such a gradient and target themselves at a given toxic species. In patients with Alzheimer’s disease, a gradient of tau protein forms in the bloodstream, and this protein is a substrate of the enzyme CDK5, a tau protein kinase that catalyzes the phosphorylation of tau protein. Through chemotaxis, CDK5 can travel along the tau protein gradient towards increasing concentrations of tau tangles and amyloid-beta proteins that are the cause of neuronal cell death in Alzheimer’s patients. In order to create the momentum necessary to propel a nanovehicle along the substrate concentration gradient, an enzyme’s binding affinity towards its substrate must overcome obstacles such as Brownian motion. We hypothesized that CDK5 would be able to overcome these barriers and developed a quantitative model using Michaelis-Menten kinetics to define the necessary parameters to confirm and characterize CDK5’s chemotactic behavior to establish its utility in drug delivery and other applications. Our results found that CDK5’s movement significantly outpaced random motion and characterized its movement for usage in drug delivery system design.

Thermodynamics of Eddington-inspired-Born-Infeld-AdS black holes with global monopole

It is well known that a black hole can be endowed with a topological charge coming as a result of phase transition(s) in the early universe. It has also recently been revealed that such an object might exist in one of the modified theory of gravity called the Eddington-inspired-Born-Infeld (EiBI) theory. Here we shall investigate the possibility of phase transitions, and in general thermodynamic phenomena, of EiBI-anti-de Sitter (AdS) black hole with (topologically-charged) global monopole. This is the first work that applies the full Euclidean action formalism in this model. We provide a counterterm to cancel infinities and argue that it is the most suitable among other possibilities. Our investigation reveals that the state variables obtained are found to obey the first law of black hole mechanics and Smarr's law for black holes with $\mathrm{\ensuremath{\Lambda}}\ensuremath{\ne}0$. Related to the second feature, we obtained the forbidden range of parameter space for EiBI AdS black hole with global monopole, which corresponds to $\frac{\ensuremath{-}(1\ensuremath{-}\mathrm{\ensuremath{\Delta}})}{\mathrm{\ensuremath{\Lambda}}}\ensuremath{\le}\ensuremath{\kappa}\ensuremath{\le}\ensuremath{-}\frac{1}{\mathrm{\ensuremath{\Lambda}}}$. The dependencies on $\mathrm{\ensuremath{\Lambda}}$, the EiBI constant $\ensuremath{\kappa}$ and the global monopole charge $\ensuremath{\eta}$ of the state variables and state functions obtained manifests in a Schwarzchild AdS-like phase transition for black holes with parameters below the lower bound of the forbidden range, and could also manifest in a Schwarzschild-flat like phase behavior for black holes with parameters above the higher bound of the forbidden range.

Measurement and analysis of interfacial tension of decane/water system pressurized with methane + ethane + propane gas mixture

Blockages in natural gas and oil pipelines can be caused by the aggregation of clathrate hydrate crystals which form at the oil-water-gas interfaces. Knowledge of interfacial tensions between components are crucial to understanding the interface physical chemistry and numerically simulating pipeline multiphase flow. This study measures the effect of the presence of natural gas components on the oil-water interfacial tension and its potential implications on flow assurance in oil-gas pipelines. Decane is used to mimic oil and is saturated with the gaseous mixture of methane, ethane, and propane as is consistent with simulations of multiphase dynamics. Water droplets were generated in decane and photographed through a window using pendant drop method in order to determine the oil-water interfacial tension. This study examined both mechanical and thermodynamic phenomena by considering interfacial tension under different pressure/temperature conditions covering the natural gas hydrate formation region. The effect of the gas mixture composition and pressure on the interfacial tension was determined at different temperatures in the range of pipeline operating conditions. The behavior was explained from both experimental and theoretical points of view. At lower pressures, the presence of the C2 and C3 natural gas components lowers the decane-water interfacial tension compared to the case of the decane-water-methane system. In particular, the pressure dependence of the interfacial tension was found to be almost five times higher than that in the absence of C2 and C3. By decreasing the interfacial tension, the presence of ethane and propane may facilitate the mixing of the water and oil phases in pipelines, therefore affecting the rate of hydrate formation.

Artificial neural networks for bio-based chemical production or biorefining: A review

Machine learning through artificial neural networks have emerged as vital tools to predict chemical behavior for many of the most recognized biomass valorization processes relevant to biorefineries for the purpose of optimization of desired products and reaction conditions. Until recently, these neural network methodologies have successfully been utilized in the petroleum industry where much more extensive databases are available for effective algorithm training. These systems provide compelling advantages for pattern recognition when interpreting the influence of ever-changing feedstock compositions for complex biomass conversion processes as they do not require any a prior knowledge of reaction mechanisms or thermodynamic phenomena. This has been revealed to be tremendously beneficial for real-time, dynamic control applications of biochemical processes for rapid parameter monitoring and regulation such as during fermentation or anaerobic digestion. This review aims to present and evaluate studies that have attempted to apply these neural network strategies to various aspects of biorefining and how these models address the common challenges that occur when relying on conventional mechanistic modelling approaches to estimate sophisticated, non-linear systems. Comparisons are then identified when implementing these artificial intelligence computing practices in traditional petroleum refineries where feedstock inconsistencies are not as paramount compared to biorefineries. Subsequently, the practicality of these neural networks is critically assessed and recommendations are presented on how to strengthen its applicability and predictability towards future bio-based chemical production. Mathematical models such as artificial neural networks will be an integral technology in the future bioeconomy for the realization of innovative biorefinery concepts as computational power continues to advance.

Multifidelity domain-aware learning for the design of re-entry vehicles

The multidisciplinary design optimization (MDO) of re-entry vehicles presents many challenges associated with the plurality of the domains that characterize the design problem and the multi-physics interactions. Aerodynamic and thermodynamic phenomena are strongly coupled and relate to the heat loads that affect the vehicle along the re-entry trajectory, which drive the design of the thermal protection system (TPS). The preliminary design and optimization of re-entry vehicles would benefit from accurate high-fidelity aerothermodynamic analysis, which are usually expensive computational fluid dynamic simulations. We propose an original formulation for multifidelity active learning that considers both the information extracted from data and domain-specific knowledge. Our scheme is developed for the design of re-entry vehicles and is demonstrated for the case of an Orion-like capsule entering the Earth atmosphere. The design process aims to minimize the mass of propellant burned during the entry maneuver, the mass of the TPS, and the temperature experienced by the TPS along the re-entry. The results demonstrate that our multifidelity strategy allows to achieve a sensitive improvement of the design solution with respect to the baseline. In particular, the outcomes of our method are superior to the design obtained through a single-fidelity framework, as a result of the principled selection of a limited number of high-fidelity evaluations.

Soaking in CO2 huff-n-puff: A single-nanopore scale study

CO2 Huff-n-Puff is a promising method for enhancing the oil recovery from unconventional reservoirs and sequestering CO2. Optimizing the operation of Huff-n-Puff requires a fundamental understanding of the thermodynamic and transport phenomena of CO2 and oil in nanopores. Here, we investigate the soaking step of CO2 Huff-n-Puff in a single, 4 nm-wide calcite pore using molecular dynamics simulations. We show that the CO2 molecules entering the pore can become adsorbed on pore walls and diffuse along with walls or are transported into the pore's interior as free CO2 molecules. Decane molecules are displaced from the pore walls and out of the bulk zone in the pore. Before reaching the pore's end, the movement of the density fronts of adsorbed and free CO2 molecules inside the pore obeys a t1/2 scaling law with effective diffusion coefficients ∼50% smaller than that of bulk CO2. Except at the very beginning period of soaking, the accumulation of adsorbed and free CO2 molecules occurs at a similar rate and follows the diffusive scaling law. These pore-scale results highlight the importance of surface adsorption in the storage and transport of CO2 during the soaking process in unconventional oil reservoirs.

AeroSolved: Computational fluid dynamics modeling of multispecies aerosol flows with sectional and moment methods

Computational modeling of multispecies aerosols generated from nucleating supersaturated vapors is a challenging task because of the complexity of the involved thermodynamic phenomena, non-existing validated and/or first principles-based models for the nucleation and condensation/evaporation processes, necessity for high computational effort, and, finally, lack of simulation data and software for validation. Here, we present our contribution towards tackling at least some of these challenges by developing AeroSolved, a publicly available open-source computational fluid dynamics code for simulation of multispecies evolving aerosols in an Eulerian framework. We present a consistent modeling approach to nucleation and condensation using a multispecies extension of the classical nucleation theory and a multispecies Stefan flow model for particle condensation, respectively. The internally mixed assumption is used, i.e., the species concentration partitioning is particle size independent and uniform across local particle size distribution. Instantaneous temperature equlibration is also assumed between the phases. Applied assumptions were tested for aerosol flows with mean diameter particle size in the range of micrometers. Applications beyond tested regimes (e.g., nanometer or sub-millimeter particle size ranges, thermodynamical) would need revisiting the assumptions and consequently modeling limitations with required inclusion of additional processes (e.g., Kelvin effect, Fuchs–Sutugin corrections or Marangoni flows influence). The developed computational models are tested in three separate scenarios simulating: uniform nucleation/condensation conditions, single-particle evaporation/condensation, and laminar flow diffusion chamber flows. Two distinct approaches are proposed to solve the population balance equation and calculate the particle size distribution. The moment method assumes a log-normal shape and fixed width of distribution, while the sectional method resolves the particle size distribution without constraints on its shape, thus being more accurate but also computationally expensive. These two methods are demonstrated as complementary tools for industrial real-case scenarios where complex aerosol flow is simulated in a simplified geometry of the capillary aerosol generator. The more accurate and detailed sectional method serves as a tuning tool for the less computationally demanding log-normal moment method, which is then practically used for parametric studies concerning system performance. The simulations presented here unravel details on particle formation and the sensitivity of the setup to thermodynamic conditions and pave the road towards engineering application of the developed methods.

Thermodynamic Analysis of CNG Fast Filling Process of Composite Cylinder Type IV

Due to ecological and economic advantages, natural gas is used as an alternative fuel in the transportation sector in the form of compressed natural gas (CNG) and liquefied natural gas (LNG). Development of infrastructure is necessary to popularize vehicles that use alternative fuels. Selected positive factors from EU countries supporting the development of the CNG market were discussed. The process of natural gas vehicle (NGV) fast filling is related to thermodynamic phenomena occurring in a tank. In this study, the first law of thermodynamics and continuity equations were applied to develop a theoretical model to investigate the effects of natural gas composition on the filling process and the final in-cylinder conditions of NGV on-board composite cylinder (type IV). Peng–Robinson equation of state (P-R EOS) was applied, and a lightweight composite tank (type IV) was considered as an adiabatic system. The authors have devised a model to determine the influence of natural gas composition on the selected thermodynamic parameters during fast filling: Joule–Thomson (J-T) coefficient, in-cylinder gas temperature, mass flow rate profiles, in-cylinder mass increase, natural gas density change, ambient temperature on the final natural gas temperature, influence of an ambient temperature on the amount of refueled natural gas mass. Results emphasize the importance of natural gas composition as an important parameter for the filling process of the NGV on-board composite tank (type IV).

Joule-Thomson expansion for hairy black holes

We study the Joule-Thomson (JT) expansion of hairy black hole in AdS5 spacetime. We first investigate the inversion temperature and isenthalpic curve in the P−T plane of the system. The isenthalpic curves are divided into two parts by the inversion curve, above which the system undergoes a cooling process while below which it is in heating process during JT expansion. We then investigate the ratio between the minimum inversion temperature and the pressure-volume (PV) critical temperature. It is found that the scalar hair suppresses the ratio, while the electric charge enhances it. This finding indicates that the scalar hair activates the effect of electric charge, because the ratio in the normal charged black hole is a constant, independent of the electric charge. Our study completes the thermodynamic phenomena in the extended thermodynamic of scalar hairy black hole.

How to create a Ni-free corrosion protecting deposit on basis of ZnFe-X

ABSTRACT ZnNi-deposits are currently used for corrosion protection especially in the automotive industry. With current REACH 1 regulations there is a demand for Ni-free coatings. The objective of this research project was the design of a REACH-compliant ternary zinc alloy based on ZnFe-X (X= Cu, In, Mn, Sn, Mo, Cr) with superior coating properties compared to Ni-free alternatives such as binary Zn-alloys. Using a model-based electrolyte development approach both thermodynamic and kinetic phenomena were included in the simulations. The implementation of a robot assisted experimental set-up enabled an effective, resource-saving research on different electrolyte systems in parallel and on choosing optimum pulse plating parameters. Optimum results were obtained by incorporating Mo at up to 1.5% into the ZnFeMo alloy.

Prediction of Joule–Thomson coefficients and inversion curves of natural gas by various equations of state

The Joule-Thomson effect is an important thermodynamic phenomenon for natural gas-related industries. Hence, accurate prediction of the Joule–Thomson coefficient (JTC) and the Joule–Thomson inversion curve (JTIC) are of great importance in various industries. In this contribution, we apply six equations of state (EOSs) including SAFT, PρT-SAFT, SAFT-CP, PC-SAFT, and I-PC-SAFT, as well as SRK, to predict JTC and JTIC of eight main pure components of natural gas including methane, ethane, propane, n-butane, isobutane, pentane, carbon dioxide, and nitrogen. We also predict the JTC and JTIC for four and six synthetic mixtures of natural gas, respectively. By comparing the results of the six EOSs for prediction of natural gas-related pure and mixtures JTC and JTIC with literature data, we can conclude that although all the EOSs were in reasonable agreement with the literature data, in general, to predict the JTC and JTIC for all studies systems, SAFT-CP and original SAFT equations of state had the best results.

Machine learning based predictive model for methanol steam reforming with technical, environmental, and economic perspectives

To overcome limitations of conventional H2 production approaches such as steam methane reforming (SMR) in a membrane reactor (MR) such as large CO2 emission and deactivation of catalyst and membrane, a promising alternative H2 production system of methanol steam reforming (MSR) in serial reactors and membrane filters is reported here, affording its high product yield and a compact design. In this study, technical, environmental, and economic feasibility according to 12 techno-economic parameters and detailed effects of each parameter for this H2 production system are comprehensively investigated with a machine learning (ML) based predictive model in the following steps: (1) process simulation using Aspen Plus® with detailed thermodynamic phenomena and environmental performance; (2) numerical model using MATLAB® based on technical and environmental performance from the process simulation results; (3) ML-based predictive model having outputs of H2 production rate, CO2 emission, and unit H2 production cost feasibility trained by 12,000 data sets from a numerical model. It is well noted from this study that # of reactors and operating temperature for technical performance, # of reactors and S/C ratio for environmental performance, and operating temperature, # of reactors, reactant, and labor for economic performance are reported as most influential factors.

Effect of agglomeration on the selective distribution of nanoparticles in binary polymer blends

The location of isolated SiO2 nanoparticles (NPs) grafted with silane or poly(methyl methacrylate) (PMMA) brushes and their agglomerates in a phase-separated PMMA/poly(styrene-acrylonitrile) (SAN) blend is investigated. The isolated NPs can be selectively distributed at the interface of PMMA/SAN blend, while their agglomerates are selectively distributed in one phase of blend matrix. Without the interference of uneven modification and kinetic factors, such abnormal thermodynamic distribution phenomenon can be well explained with the introduction of Wenzel's roughness and the wetting coefficient of NPs agglomerates might be remarkably amplified relative to that of isolated NPs, resulting in their different locations of isolated brush NPs and their agglomerates in the blend matrix. The thorough understanding of the entropic contribution to the selective distribution of isolated brush NPs and their agglomerates in the blend matrix may help us prepare nanocomposites with the desired microstructure.