Energy Transport Model Research Articles

Bridging existing energy and chemical transport models to enhance air quality policy assessment

Connecting changes in emissions to air quality is critical for evaluating the effects of a specific policy. Here, we introduce a methodology to aid in assessing the air quality impacts of changes in the energy system. A set of widely varying scenarios that describe alternative potential evolutions of the US energy system is constructed using the TIMES energy system model. For each scenario, an R script is used to communicate future emissions changes to the CMAQ photochemical air quality model. Example results are shown, and the development of the TIMES scenarios is described for users who wish to adapt them to alternate geographies. Possible use cases include evaluating the air quality effects of specific emissions reduction measures or of broad changes to dominant technologies in major sectors such as transportation.

TauFlowNet: Revealing latent propagation mechanism of tau aggregates using deep neural transport equations

Mounting evidence shows that Alzheimer's disease (AD) is characterized by the propagation of tau aggregates throughout the brain in a prion-like manner. Since current pathology imaging technologies only provide a spatial mapping of tau accumulation, computational modeling becomes indispensable in analyzing the spatiotemporal propagation patterns of widespread tau aggregates from the longitudinal data. However, current state-of-the-art works focus on the longitudinal change of focal patterns, lacking a system-level understanding of the tau propagation mechanism that can explain and forecast the cascade of tau accumulation. To address this limitation, we conceptualize that the intercellular spreading of tau pathology forms a dynamic system where each node (brain region) is ubiquitously wired with other nodes while interacting with the build-up of pathological burdens. In this context, we formulate the biological process of tau spreading in a principled potential energy transport model (constrained by brain network topology), which allows us to develop an explainable neural network for uncovering the spatiotemporal dynamics of tau propagation from the longitudinal tau-PET scans. Specifically, we first translate the transport equation into a GNN (graph neural network) backbone, where the spreading flows are essentially driven by the potential energy of tau accumulation at each node. Conventional GNNs employ a l2-norm graph smoothness prior, resulting in nearly equal potential energies across nodes, leading to vanishing flows. Following this clue, we introduce the total variation (TV) into the graph transport model, where the nature of system's Euler-Lagrange equations is to maximize the spreading flow while minimizing the overall potential energy. On top of this min-max optimization scenario, we design a generative adversarial network (GAN-like) to characterize the TV-based spreading flow of tau aggregates, coined TauFlowNet. We evaluate our TauFlowNet on ADNI and OASIS datasets in terms of the prediction accuracy of future tau accumulation and explore the propagation mechanism of tau aggregates as the disease progresses. Compared to the current counterpart methods, our physics-informed deep model yields more accurate and interpretable results, demonstrating great potential in discovering novel neurobiological mechanisms through the lens of machine learning.

Towards Energy-Balance Closure with a Model of Dispersive Heat Fluxes

In the last decades the energy-balance-closure problem has been thoroughly investigated from different angles, resulting in approaches to reduce but not completely close the surface energy balance gap. Energy transport through secondary circulations has been identified as a major cause of the remaining energy imbalance, as it is not captured by eddy covariance measurements and can only be measured additionally with great effort. Several models have already been developed to close the energy balance gap that account for factors affecting the magnitude of the energy transport by secondary circulations. However, to our knowledge, there is currently no model that accounts for thermal surface heterogeneity and that can predict the transport of both sensible and latent energy. Using a machine-learning approach, we developed a new model of energy transport by secondary circulations based on a large data set of idealized large-eddy simulations covering a wide range of unstable atmospheric conditions and surface-heterogeneity scales. In this paper, we present the development of the model and show first results of the application on more realistic LES data and field measurements from the CHEESEHEAD19 project to get an impression of the performance of the model and how the application can be implemented on field measurements. A strength of the model is that it can be applied without additional measurements and, thus, can retroactively be applied to other eddy covariance measurements to model energy transport through secondary circulations. Our work provides a promising mechanistic energy balance closure approach to 30-min flux measurements.

Duality for a boundary driven asymmetric model of energy transport

We study the asymmetric brownian energy, a model of heat conduction defined on the one-dimensional finite lattice with open boundaries. The system is shown to be dual to the symmetric inclusion process with absorbing boundaries. The proof relies on a non-local map transformation procedure relating the model to its symmetric version. As an application, we show how the duality relation can be used to analytically compute suitable exponential moments with respect to the stationary measure.

Energy Evaluation of Synthesis Gas in a Turbocharger System Employing CFD Tools

Renewable energy sources derived from biomass, such as synthesis gases, represent an opportunity to take advantage of available waste resources and contribute to global energy rationing. This study developed an analysis with computational fluid dynamics (CFD) to estimate the energy behavior of synthesis gases through a turbocharger system. The synthesis gas used to drive the turbocharger turbine was extracted from the gasification of biomass from the Colombian Caribbean. The application of models for rigid body motion, as well as models of momentum, turbulence, energy, and conservation transport of species, suggest that the energy potential available by the turbine ranges from 0.4 kW to 5.2 kW of power generation, concerning mass flow rates entering the simulated system. The main findings of the study were temperature profiles, speed profiles, rotational speed variation, torque, and mechanical power generated in the turbocharger. It is emphasized that the synthesis gas studied presents a good behavior to generate energy through a turbine system of a turbocharger device.

Analysis of Near-wall Coherent Structure of Spiral Flow in Circular Pipe Based on Large Eddy Simulation

Based on the large eddy simulation method, this study performed the three-dimensional transient numerical analysis of the near-wall flow field of the spiral flow in a circular pipe and applied the sub-grid model of the kinetic energy transport. The low-speed bands, streamwise vortices and hairpin vortices of the spiral flow in the near-wall region of the circular pipe are determined using the Q criterion. The ejection and sweeping of coherent structures are identified using the velocity vector of the near-wall region; moreover, the two methods of creating the hairpin vortices are established by the image time series. The results demonstrate that the development directions of the near-wall bands, streamwise vortices and hairpin vortices of the spiral flow in the circular pipe develop along the path of the spiral line. The average spanwise period of the low-speed bands in the near-wall region is approximately 120 wall units, the length is more than 900 wall units and the height is not more than 40 wall units. The separation distance of the streamwise vortices is about 119 wall units. It has a certain angle with the wall (approximately 22°). The average burst period of a hairpin vortices is less than 0.015 s.

Local thermal equilibrium constraints for energy transport equations for thermal oil recovery processes

The mathematical framework of reservoir thermal simulators typically relies on the assumption of local thermal equilibrium (LTE) when modeling energy transport through porous media for petroleum engineering applications, including Thermal Enhanced Oil Recovery (TEOR) processes. The LTE presupposes instantaneous exchange of energy between the injected hot fluids and the reservoir's rock and fluids. Nevertheless, it is essential to acknowledge that LTE's validity becomes questionable in specific scenarios, potentially resulting in an overestimation of oil recovery, e.g., when high fluid velocities arise. Under such conditions, the local thermal non-equilibrium (LTNE) formulation may offer a more accurate representation of the energy transport physics. In this study, we present the physical constraints that must be satisfied for the application of two distinct mathematical formulations (1) the one-equation model for the fluids and rock as a whole (LTE is assumed), and (2) the two-equation model, one equation assigned to the fluids and another one to the rock (pseudo-LTNE is assumed). Such constraints were derived by examining conductive and convective heat transport properties via the theory of the method of volume averaging, specifically applied to an oil-water-gas-rock system at the pore scale. It was found that the one-equation model requires that 18 physical constraints to be satisfied, while the pseudo-LTNE model needs to fulfill 9 physical constraints. A sensitivity analysis was conducted based on factor upon which accumulation, conduction and convection constraint equations depend, aiming to identify the conditions under which LTNE occurs within the porous medium. These influencing factors encompass the characteristic time, the Peclet number, the presence of external heat sources, the distribution of phases at the pore-scale, and the disparity between rock and fluid properties. The combinations of these factors can induce the existence of local thermal non-equilibrium. One significant property is the heat transfer coefficient in the reservoir. We recommend that the pseudo-LTNE approach is reliable as long as the rock-fluid heat transfer coefficients are at least one order of magnitude larger than the fluid-fluid heat transfer coefficients (1000−10000W/(m3K)). Contrarily, the OEM should be employed only when the heat transfer coefficients surpass a threshold value of 10000W/(m3K). When injector and producer wells are hydraulically interconnected, resulting in heightened fluid velocities, it is encouraged the use of the two-equations model. The predictability and reliability of the energy models were verified by comparison of predicted oil recovery against data from laboratory experiments. The equations presented in this study consistently demonstrate their utility in assessing the reliability of energy transport models for TEOR scenarios.

Wigner Equations for Phonons Transport and Quantum Heat Flux

Starting from the quantum Liouville equation for the density operator and applying the Weyl quantization, Wigner equations for the acoustic, optical and Z phonons are deduced. The equations are valid for any solid, including 2D crystals like graphene. With the use of Moyal’s calculus and its properties, the pseudo-differential operators are expanded up to the second order in ħ\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\hbar $$\\end{document}. An energy transport model is obtained by using the moment method with closure relations based on a quantum version of the Maximum Entropy Principle by employing a relaxation time approximation for the production terms of energy and energy flux. An explicit form of the thermal conductivity with quantum correction up to ħ2\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\hbar ^2$$\\end{document} order is obtained under a long-time scaling for the most relevant phonon branches.

Mesoscale multi-component energy density transport for natural convective microchip cooling with solid–liquid–gas phase changes

A design of microchip cooling with natural convection driven multi-phase coolant flows is proposed by using thermoelectric modules as heat sinks. A new multi-component multi-phase (MCMP) thermal energy transport model is built for convective heat transfer with solid–liquid–gas phase changes. Different from previous lattice Boltzmann models based on temperature distributions, the present model is based on MCMP energy density distributions, which track sensible and latent heat transport for each phase. The local MCMP heat fluxes are related to mesoscale relaxation times and phase volume fractions. Transient solid–liquid–gas interfaces and local temperatures are obtained through the dynamical balance of mesoscale forces and fluxes among all phases together with MCMP enthalpy–temperature distributions. Comparisons of the on and off states of the heat sink modules show that the chip temperature can drop from about 300 to 110 °C, with the coolant liquid–gas phase change temperature of 80 °C at equivalent heat source and sink intensities of 100 W/cm2. Results also show that increasing heat sink intensity to 300 W/cm2 or decreasing gas–liquid phase change temperature to 60 °C can further decrease the chip temperature to 90 °C. The effects of sizes of chips, modules, and cooling units are also illustrated. It is observed that gas bubbles attached on chips impede heat transfer due to the low gas phase thermal conductivity, which means future improvements should also focus on removing or degrading attached gas bubbles. This model provides a quantitative tool for immersion chip cooling performances.

A Stochastic Model of Anomalously Fast Transport of Heat Energy in Crystalline Bodies

In this work, a new method for constructing the infinite-dimensional Ornstein–Uhlenbeck stochastic process is introduced. The constructed process is used to perturb the harmonic system in order to model anomalously fast heat transport in one-dimensional nanomaterials. The introduced method made it possible to obtain a transition probability function that allows for a different approach to the analysis of equations with such a disturbance. This creates the opportunity to relax assumptions about temporal correlations for such a process, which may lead to a qualitatively different model of energy transport through vibrations of the crystal lattice and, as a result, to obtain the superdiffusion equation on a macroscopic scale with an order of the fractional Laplacian different from the value of 3/4 obtained so far in stochastic models. Simulations confirming these predictions are presented and discussed.

Microbial community ecosystem network model for chemical energy transport

Microorganisms thriving in low-energy ecosystems have evolved diverse strategies to sustain life, including individual-level energy conservation, optimizing energy utilization through interspecies competition, and mutually beneficial interspecies syntrophy. This study introduces a novel community-level strategy to enhance energy efficiency. We employed an oxidation-reduction (redox) reaction network model to capture the intricate metabolic interactions within microbial communities. Our findings highlight the importance of microbial functional diversity in facilitating metabolic handoffs, leading to an improved energy utilization efficiency. Moreover, the mutualistic division of labor and the resulting complexity of redox pathways actively facilitate material cycling, thereby enhancing energy exploitation. These findings provide new insights into the potential of self-organized ecological interactions to develop efficient energy utilization strategies, with significant implications for the functioning and evolution of microbial ecosystems.

Thermal analysis of magnetohydrodynamics (MHD) Casson fluid with suspended iron (II, III) oxide-aluminum oxide-titanium dioxide ternary-hybrid nanostructures

This study is carried out to enhance and analyze the thermal performance of non-Newtonian Casson fluid by immersing Ternary hybrid nanoparticles Fe3O4-Al2O3-TiO2 uniformly. To model the behaviour of such complex phenomena mathematically, a system of complex transport differential equations is developed by utilizing a non-Fourier heat transfer model for energy transport. The non-dimensional system of transport equations involving physical parameters is analyzed numerically by employing the Galerkin finite element methodology (G-FEM). The simulations predicted promising enhanced heat transfer characteristics in Ternary hybrid Casson fluid. The Casson fluid parameter has shown remarkable behavior on temperature distribution. Ternary hybrid nanoparticles have increased the overall thermal conductivity of the fluid and consequently, the temperature distribution is significantly raised. Hence ternary hybridity of nano-structures in Casson fluid is shown as an effective technique in improving the thermal performance of complex fluid.

Expected Changes in Evaporation in the Taiga Zone of European Russia in the XXI Century

Model estimates were made for changes in the actual evaporation of boreal forests in the taiga zone of European Russia caused by climate evolution in the XXI century. Climate changes were forecasted using the results of calculations by an MGO regional climate model. The space and time specification of the forecast data was made with the use of a model of the atmospheric boundary layer. Variations of the forecasted changes due to the use of different methods to calculate evaporation were studied. Calculations based on an additional (complementary) relationship, determining the actual evaporation with the use of standard meteorological characteristics were compared with observations and calculations based on a detail model of energy transport in a forest massif. The model values of the actual evaporation in the XXI century increase all over the European Russia. The effect of the intensity of dry periods on the evolution of actual evaporation is analyzed. It is shown that, despite the arid climate, the effect of dry periods on taiga forests will be moderately negative.

Quasi-Random Discrete Ordinates Method to Radiative Transfer Equation with Linear Anisotropic Scattering

The modeling of energy transport via radiative transfer is important to many practical high temperature engineering applications. Furthermore, the computation of solutions to the radiative transfer equation (RTE) plays a fundamental part in it. The quasi-random discrete ordinates method was developed as an alternative to mitigate the ray effect found in the classical discrete ordinates method solutions. The former method was originally developed for transport problems with isotropic scattering and it is here extended and tested to problems with linear anisotropic scattering. Its main idea is to approximate the integral term of the RTE by a quasi-Monte Carlo integration. The discrete system of differential equations arising from it can be solved by a variety of classical discretization methods, here computed with a SUPG finite element scheme. The novel developments are tested for selected manufactured solutions. The achieved good results indicate the potential of the novel method to be applied to the solution of radiative transfer problems with anisotropic scattering.

Pricing method of electric-thermal heterogeneous shared energy storage service

Energy storage system (ESS) has been considered as a pivotal technology enjoying a wide range of applications in different levels of power systems. However, the investment of ESS is still relatively high. Thus, shared energy storage (SES) is consequently recognized as a promising business model. Considering the dynamic virtual energy storage characteristics of heat network, this paper proposes a power-thermal heterogeneous SES model to meet the practical need of participants. Firstly, a quasi-dynamic model of heat energy transport is conducted to analyze the virtual energy storage potential of heat network. Then, the power-thermal heterogeneous SES model is developed to describe the formation mechanism of ESS supply and demand with heat network as the schedulable energy storage resource. Furthermore, a pricing method of electric-thermal heterogeneous SES is proposed. The upper-level problem of it targets at maximizing the benefits of the SES operator, while the lower-level problem aims to minimize the costs of users. Simultaneously, the service price and user purchase capacity can be obtained as optimization variables. Finally, case studies are adapted to verify the rationality and validity of the proposed model.

On a model-based approach to improve intranasal spray targeting for respiratory viral infections

The nasopharynx, at the back of the nose, constitutes the dominant initial viral infection trigger zone along the upper respiratory tract. However, as per the standard recommended usage protocol (“Current Use”, or CU) for intranasal sprays, the nozzle should enter the nose almost vertically, resulting in sub-optimal nasopharyngeal drug deposition. Through the Large Eddy Simulation technique, this study has replicated airflow under standard breathing conditions with 15 and 30 L/min inhalation rates, passing through medical scan-based anatomically accurate human airway cavities. The small-scale airflow fluctuations were resolved through use of a sub-grid scale Kinetic Energy Transport Model. Intranasally sprayed droplet trajectories for different spray axis placement and orientation conditions were subsequently tracked via Lagrangian-based inert discrete phase simulations against the ambient inhaled airflow field. Finally, this study verified the computational projections for the upper airway drug deposition trends against representative physical experiments on sprayed delivery performed in a 3D-printed anatomic replica. The model-based exercise has revealed a new “Improved Use” (or, IU) spray usage protocol for viral infections. It entails pointing the spray bottle at a shallower angle (with an almost horizontal placement at the nostril), aiming slightly toward the cheeks. From the conically injected spray droplet simulations, we have summarily derived the following inferences: (a) droplets sized between 7–17 μm are relatively more efficient at directly reaching the nasopharynx via inhaled transport; and (b) with realistic droplet size distributions, as found in current over-the-counter spray products, the targeted drug delivery through the IU protocol outperforms CU by a remarkable 2 orders-of-magnitude.

Intricate structure of the plasma Rayleigh–Taylor instability in shock tubes

Spikes and bubbles grow on unstable interfaces that are accelerated in high-energy-density conditions. If a shock propagates ahead of the interface, the plasma can be heated to extreme conditions where conduction and radiation fluxes influence the hydrodynamics. For example, a National Ignition Facility experiment found reduced single-mode nonlinear mixed-width growth in conditions scaled from a supernova explosion [Kuranz et al., Nat. Commun. 9, 1564 (2018)]. We present high-resolution two-dimensional radiation hydrodynamic simulations with the Flash code that quantitatively reproduce the experiment. Radiative fluxes are primarily responsible for ablating the spike and removing the mushroom caps. The ablated plasma increases the mixed mass and forms a low-density halo with spikes forming in both directions. This is considerably more complex than classical instability. The halo is sensitive to ablative physics, so radiographing it may aid in the verification of energy transport modeling. Although ablation changes the spike shape, it has little effect on the overall mixed width for these parameters. This is because ablation enhances the bubble velocity but it has the opposite effect on the spike. The radiation transport instead suppresses the growth via increasing the shocked foam density, thus decreasing the Atwood number. A terminal velocity model including the rarefaction expansion agrees with the experimental mixed-width growth.

Extended angular-spectrum modeling (EASM) of light energy transport in scattering media.

The exact modeling of light transport in scattering media is critical in biological imaging, free-space communication, and phosphor-converted lighting. Angular spectrum is proved to be a fast and effective approach to reconstructing the wavefront dynamics during the propagation in scattering media, however, finding it difficult in acquiring the wavefront and energy change simultaneously. Besides, conventional methods for energy tracing, such as the Monte Carlo method, are inefficient in speed and hard to simulate the wavefront change. Here, we propose an extended angular-spectrum modeling (EASM) approach using tenuous scattering approximate solutions to obtain a time-efficient and accurate method for reconstruction of energy and wavefront dynamics in various scattering media. The generality of our method is numerically simulated and experimentally verified with a set of scattering media with different properties. EASM has a time advantage under the guarantee of calculation accuracy, especially when calculating several thickness changes after the calculation model is established. Furthermore, multi-layered media can also be simulated by EASM with a good precision. The results suggest that EASM performs certain computations more efficiently than the conventional method and thus provides an effective and flexible calculation tool for scattering media.

Determining interchromophore effects for energy transport in molecular networks using machine-learning algorithms.

Nature uses chromophore networks, with highly optimized structural and energetic characteristics, to perform important chemical functions. Due to its modularity, predictable aggregation characteristics, and established synthetic protocols, structural DNA nanotechnology is a promising medium for arranging chromophore networks with analogous structural and energetic controls. However, this high level of control creates a greater need to know how to optimize the systems precisely. This study uses the system's modularity to produce variations of a coupled 14-Site chromophore network. It uses machine-learning algorithms and spectroscopy measurements to reveal the energy-transport roles of these Sites, paying particular attention to the cooperative and inhibitive effects they impose on each other for transport across the network. The physical significance of these patterns is contextualized, using molecular dynamics simulations and energy-transport modeling. This analysis yields insights about how energy transfers across the Donor-Relay and Relay-Acceptor interfaces, as well as the energy-transport pathways through the homogeneous Relay segment. Overall, this report establishes an approach that uses machine-learning methods to understand, in fine detail, the role that each Site plays in an optoelectronic molecular network.

Cooling of a periodic heat-generating solid element in an electronic cabinet using a non-Newtonian pseudoplastic nanofluid and a heat-conducting substrate

PurposeNowadays, the most important challenge in mechanical engineering, power engineering and electronics is a development of effective cooling systems for heat-generating units. Taking into account this challenge, this study aims to deal with computational investigation of thermogravitational energy transport of pseudoplastic nanoliquid in an electronic chamber with a periodic thermally producing unit placed on the bottom heat-conducting wall of finite thickness under an influence of isothermal cooling from vertical side walls.Design/methodology/approachThe control equations formulated using the Boussinesq approach, Ostwald–de Waele power law and single-phase nanofluid model with experimentally based correlations of Guo et al. for nanofluid dynamic viscosity and Jang and Choi for nanofluid thermal conductivity have been worked out by the in-house computational procedure using the finite difference technique. The impact of the Rayleigh number, nanoadditives concentration, frequency of the periodic heat generation from the local element and thickness of the bottom solid substrate on nanoliquid circulation and energy transport has been studied.FindingsIt has been found that a raise of the nanoadditives concentration intensifies the cooling of the heat-generating element, while a growth of the heat-generation frequency allows reducing the amplitude of the heater temperature.Originality/valueMathematical modeling of a pseudoplastic nanomaterial thermogravitational energy transport in an electronic cabinet with a periodic thermally generating unit, a heat-conducting substrate and isothermal cooling vertical surfaces to identify the possibility of intensifying heat removal from a heated surface.