Search

While the use of carbonated water in enhanced oil recovery (EOR) within the petroleum sector is well-documented, its applications in other fields remain relatively unexplored. This review aims to shed light on the versatile utility of carbonated water across various sectors, with the objective of stimulating further research to address sustainability challenges. Carbonated water can benefit industrial, agricultural, and domestic contexts by offering a sustainable method for utilizing waste CO2. This review examines the diverse applications of carbonated water, including its role in enhancing oil recovery, aiding medical and healthcare research, reducing carbon footprint in construction, influencing biofuel production and green chemistry, and contributing to the agricultural sector, household, and cleaning domains. The findings suggest that carbonated water could serve as a viable source for CO2 utilization, presenting significant advantages across various fields. Despite initial costs and infrastructure requirements, integrating carbonated water into existing practices - especially in agriculture and food production - offers clear benefits for offsetting carbon emissions. Continued research and development are essential to advance these technologies and promote sustainable and environmentally responsible practices. We assert that ongoing research and innovation are crucial to unlocking the full potential of carbonated water in various emerging applications.

Heat exchangers are essential for various thermal applications, and their performance could be improved by optimization of layout/operating conditions or materials. The layout/operating conditions optimization has been widely subjected to research and development, while the implementation of different materials has been limited to the available joining solutions. Joining by forming allows to circumvent the existent limitations, since it utilizes plastic deformation to produce multi-material mechanical joints between thin sheets and thin-walled tubes. Here, joining by forming is adopted for the first time, to manufacture multi-material transversal finned tube heat exchangers. The numerical-experimental analysis explores the production of tube compression beads obtained by plastic instability to fix and support fins, and therefore manufacture a novel design of finned heat exchanger with improved performance. A case study is carried out with the help of computational fluid dynamics simulations to find an optimal layout. Then, finite element simulations are employed to analyse the joining by forming process. A prototype model of transversal finned tube heat exchangers was finally manufactured and experimentally tested. Results show considerable gains in comparison with conventional finned tube heat exchangers made from steel tubes and fins, being the multi-material heat exchanger effectiveness doubled with respect to traditional designs.

Abstract The rising demand for sustainable wastewater management and high-value resource recovery is pressing industries involved in, e.g., textiles, metals, and food production, to adopt energy-efficient and flexible liquid separation methods. The current techniques often fall short in achieving zero liquid discharge and enhancing socio-economic growth sustainably. Osmotic membrane distillation (OMD) has emerged as a low-temperature separation process designed to concentrate valuable elements and substances in dilute feed streams. The efficacy of OMD hinges on the solvent’s migration from the feed to the draw stream through a hydrophobic membrane, driven by the vapor pressure difference induced by both temperature and concentration gradients. However, the intricate interplay of heat and mass processes steering this mechanism is not yet fully comprehended or accurately modeled. In this research, we conducted a combined theoretical and experimental study to explore the capabilities and thermodynamic limitations of OMD. Under diverse operating conditions, the experimental campaign aimed to corroborate our theoretical assertions. We derived a novel equation to govern water flux based on foundational principles and introduced a streamlined version for more straightforward application. Our findings spotlight complex transport-limiting and self-adjusting mechanisms linked with temperature and concentration polarization phenomena. Compared with traditional methods like membrane distillation and osmotic dilution, which are driven by solely temperature or concentration gradients, OMD may provide improved and flexible performance in target applications. For instance, we show that OMD—if properly optimized—can achieve water vapor fluxes 50% higher than osmotic dilution. Notably, OMD operation at reduced feed temperatures can lead to energy savings ranging between 5 and 95%, owing to the use of highly concentrated draw solutions. This study underscores the potential of OMD in real-world applications, such as concentrating lithium in wastewater streams. By enhancing our fundamental understanding of OMD’s potential and constraints, we aim to broaden its adoption as a pivotal liquid separation tool, with focus on sustainable resource recovery.

Abstract Lithium-ion batteries (LIBs) have become an essential technology for the green economy transition, as they are widely used in portable electronics, electric vehicles, and renewable energy systems. The solid-electrolyte interphase (SEI) is a key component for the correct operation, performance, and safety of LIBs. The SEI arises from the initial thermal metastability of the anode-electrolyte interface, and the resulting electrolyte reduction products stabilize the interface by forming an electrochemical buffer window. This article aims to make a first—but important—step towards enhancing the parametrization of a widely-used reactive force field (ReaxFF) for accurate molecular dynamics (MD) simulations of SEI components in LIBs. To this end, we focus on Lithium Fluoride (LiF), an inorganic salt of great interest due to its beneficial properties in the passivation layer. The protocol relies heavily on various Python libraries designed to work with atomistic simulations allowing robust automation of all the reparameterization steps. The proposed set of configurations, and the resulting dataset, allow the new ReaxFF to recover the solid nature of the inorganic salt and improve the mass transport properties prediction from MD simulation. The optimized ReaxFF surpasses the previously available force field by accurately adjusting the diffusivity of lithium in the solid lattice, resulting in a two-order-of-magnitude improvement in its prediction at room temperature. However, our comprehensive investigation of the simulation shows the strong sensitivity of the ReaxFF to the training set, making its ability to interpolate the potential energy surface challenging. Consequently, the current formulation of ReaxFF can be effectively employed to model specific and well-defined phenomena by utilizing the proposed interactive reparameterization protocol to construct the dataset. Overall, this work represents a significant initial step towards refining ReaxFF for precise reactive MD simulations, shedding light on the challenges and limitations of ReaxFF force field parametrization. The demonstrated limitations emphasize the potential for developing more versatile and advanced force fields to upscale ab initio simulation through our interactive reparameterization protocol, enabling more accurate and comprehensive MD simulations in the future.

The study of nanoparticle motion has fundamental relevance in a wide range of nanotechnology-based fields. Molecular dynamics simulations offer a powerful tool to elucidate the dynamics of complex systems and derive theoretical models that facilitate the invention and optimization of novel devices. This research contributes to this ongoing effort by investigating the motion of one-end capped carbon nanotubes within an aqueous environment through extensive molecular dynamics simulations. By exposing the carbon nanotubes to localized heating, propelled motion with velocities reaching up to ≈0.08 nm ps−1 was observed. Through systematic exploration of various parameters such as temperature, nanotube diameter, and size, we were able to elucidate the underlying mechanisms driving propulsion. Our findings demonstrate that the propulsive motion predominantly arises from a rocket-like mechanism facilitated by the progressive evaporation of water molecules entrapped within the carbon nanotube. Therefore, this study focuses on the complex interplay between nanoscale geometry, environmental conditions, and propulsion mechanisms in capped nanotubes, providing relevant insights into the design and optimization of nanoscale propulsion systems with various applications in nanotechnology and beyond.

Numerical prediction of material properties is attracting the attention of the scientific community and industry because of its usefulness in the design process. In the fields of fluid dynamics and microfluidics, several simulation methods have been proposed and adopted to evaluate the properties of surfaces and material interfaces, thanks to the increasing computational power available. However, despite the efforts made, a general and standardized methodology for implementing such methods is still lacking, thus requiring a trial-and-error approach for each new problem, making them difficult to implement and creating a bottleneck at the initial stage of surface design. Here, we report a validated protocol to evaluate the wettability of micro-structured surfaces with a phase-field model. Summarizing: • Simulating physical phenomena with multi-phase flows and moving contact lines can be a challenging task, due to the coupling among disparate length scales. • Using the Cahn-Hilliard diffuse-interface model, moving contact lines can be extensively investigated, although difficulties may arise when implementing numerical simulations, e.g., model parameter calibration, selection of boundary conditions, post-processing of fluid dynamics/equilibrium. • A method for employing this model and evaluating the physical consistency of the results is proposed here, considering the wettability of micro-structured surfaces as a case study.

Abstract Hybrid electric vehicles and portable electronic systems use supercapacitors for energy storage owing to their fast charging/discharging rates, long life cycle, and low maintenance. Specific capacitance is regarded as one of the most important performance-related characteristics of a supercapacitor’s electrode. In the current study, Machine Learning (ML) algorithms were used to determine the impact of various physicochemical properties of carbon-based materials on the capacitive performance of electric double-layer capacitors. Published experimental datasets from 147 references (4899 data entries) were extracted and then used to train and test the ML models, to determine the relative importance of electrode material features on specific capacitance. These features include current density, pore volume, pore size, presence of defects, potential window, specific surface area, oxygen, and nitrogen content of the carbon-based electrode material. Additionally, categorical variables as the testing method, electrolyte, and carbon structure of the electrodes are considered as well. Among five applied regression models, an extreme gradient boosting model was found to best correlate those features with the capacitive performance, highlighting that the specific surface area, the presence of nitrogen doping, and the potential window are the most significant descriptors for the specific capacitance. These findings are summarized in a modular and open-source application for estimating the capacitance of supercapacitors given, as only inputs, the features of their carbon-based electrodes, the electrolyte and testing method. In perspective, this work introduces a new wide dataset of carbon electrodes for supercapacitors extracted from the experimental literature, also giving an instance of how electrochemical technology can benefit from ML models.

Surfaces with tailored wettability have attracted considerable attention because of their wide range of potential applications. Wettability can be finely designed by controlling the chemistry and/or morphology of a surface. However, the commonly adopted analytical theories of Wenzel and Cassie-Baxter cannot describe a variety of intermediate and metastable states, being a thorough understanding of the combined chemical and morphological effect on surface wettability still lacking. Hence, the design and optimization of these surfaces is generally expensive and time-consuming. In this work, we propose a numerical method based on the phase-field model to predict the wettability of micro-structured surfaces and assist their design. First, we simulated the sessile droplet experiment on flat surfaces to calibrate model parameters. Second, we modelled several surface morphologies, intrinsic contact angles and droplet impact velocities. Finally, we produced and tested 3D printed flat and micro-structured samples to validate the phase-field model, obtaining a reasonable qualitative and quantitative agreement between numerical and experimental results. The validated model proposed here can help design and prototype surfaces with tailored wettability. Furthermore, integrated with atomistic/mesoscopic simulations, it represents the last step of a predictive multi-scale model, where both chemical and morphological features of surfaces can be designed a priori.

Abstract We focus on gas sorption within metal-organic frameworks (MOFs) for energy applications and identify the minimal set of crystallographic descriptors underpinning the most important properties of MOFs for CO2 and H2O. A comprehensive comparison of several sequential learning algorithms for MOFs properties optimization is performed and the role played by those descriptors is clarified. In energy transformations, thermodynamic limits of important figures of merit crucially depend on equilibrium properties in a wide range of sorbate coverage values, which is often only partially accessible, hence possibly preventing the computation of desired objective functions. We propose a fast procedure for optimizing specific energy in a closed sorption energy storage system with only access to a single water Henry coefficient value and to the specific surface area. We are thus able to identify hypothetical candidate MOFs that are predicted to outperform state-of-the-art water-sorbent pairs for thermal energy storage applications.

With the growing demand for adequate thermal comfort and increasingly sophisticated and interconnected energy systems, software tools able to easily and effectively study complex thermal systems need to be developed. Here, a novel acausal equation-based and object-oriented modeling (OOM) approach is used to model the seasonal performance of a residential heating system taken as a case study. To this end, a purpose-built library named College Thermal was developed using Modelica language in the Wolfram System Modeler simulation environment. In detail, dynamic simulations under varying operating conditions were performed to assess the ability of the system to ensure the design conditions, and to estimate the resulting energy consumption during the heating season. Then, the effect of key parameters of the thermal components on the overall energy performance of the system is analyzed. The developed OOM library, which is characterized by interoperability, modularity, and scalability features and thus could easily incorporate extensions/evolutions of the studied system, would be prospectively suitable for optimizing system layouts and testing control strategies in various thermal engineering applications.

Generation of ultra high frequency acoustic waves in water is key to nano resolution sensing, acoustic imaging and theranostics. In this context water immersed carbon nanotubes (CNTs) may act as an ideal optoacoustic source, due to their nanometric radial dimensions, peculiar thermal properties and broad band optical absorption. The generation mechanism of acoustic waves in water, upon excitation of both a single-wall (SW) and a multi-wall (MW) CNT with laser pulses of temporal width ranging from 5 ns down to ps, is theoretically investigated via a multiscale approach. We show that, depending on the combination of CNT size and laser pulse duration, the CNT can act as a thermophone or a mechanophone. As a thermophone, the CNT acts as a nanoheater for the surrounding water, which, upon thermal expansion, launches the pressure wave. As a mechanophone, the CNT acts as a nanopiston, its thermal expansion directly triggering the pressure wave in water. Activation of the mechanophone effect is sought to trigger few nanometers wavelength sound waves in water, matching the CNT acoustic frequencies. This is at variance with respect to the commonly addressed case of water-immersed single metallic nano-objects excited with ns laser pulses, where only the thermophone effect significantly contributes. The present findings might be of impact in fields ranging from nanoscale non-destructive testing to water dynamics at the meso to nanoscale.

The nature of the electrode–electrolyte interface has an impact on the performance and durability of lithium-ion batteries (LIBs). The initial electrolyte’s thermodynamic instability at the anode–electrolyte interface in LIBs results in the formation of a passivation layer, called solid electrolyte interphase (SEI). The initial dense and intact layer allows Li+ transport and restricts electron tunneling, thus preventing electrolyte decomposition and ensuring the electrochemical stability of a battery. However, the growth of this layer can reduce the availability of active lithium and electrolyte, and ultimately lead to an irreversible battery capacity fade. Investigating the transport phenomena of lithium ions within SEI is crucial for understanding its formation and growth. Nonetheless, accurately describing all relevant mechanisms is challenging due to its complex and multiscale nature. An overview of current computational efforts to study Li+ transport within SEI is given in this article, ranging from electronic/atomistic scale simulations to macroscopic models. The drawbacks and advantages of the proposed numerical approaches are summarized along with the obstacles that need to be overcome to obtain accurate experimental data, identified on the basis of the most recent literature evidence. We highlight collaboration gaps between modeling and experimental approaches, as well as the urgent need for new multiscale models, to gain a better understanding of such a crucial transport phenomenon.

This research addresses the need for a multiscale model for the determination of the thermophysical properties of nanofiller-enhanced thermoset polymer composites. Specifically, we analyzed the thermophysical properties of an epoxy resin containing bisphenol-A diglyceryl ether (DGEBA) as an epoxy monomer and dicyandiamide (DICY) and diethylene triamine (DETA) as cross-linking agents. The cross-linking process occurs at the atomistic scale through the formation of bonds among the reactive particles within the epoxy and hardener molecules. To derive the interatomic coarse-grained potential for the mesoscopic model and match the density of the material studied through atomic simulations, we employed the iterative Boltzmann inversion method. The newly developed coarse-grained molecular dynamics model effectively reproduces various thermophysical properties of the DGEBA-DICY-DETA resin system. Furthermore, we simulated nanocomposites made of the considered epoxy additivated with graphene nanofillers at the mesoscopic level and verified them against continuum approaches. Our results demonstrate that a moderate amount of nanofillers (up to 2 wt.%) increases the elastic modulus and thermal conductivity of the epoxy resin while decreasing the Poisson’s ratio. For the first time, we present a coarse-grained model of DGEBA-DICY-DETA/graphene materials, which can facilitate the design and development of composites with tunable thermophysical properties for a potentially wide range of applications, e.g., automotive, aerospace, biomedical, or energy ones.

Improving the efficiency and sustainability of water treatment technologies is crucial to reduce energy consumption and environmental pollution. Solar-driven devices have the potential to supply off-grid areas with freshwater through a sustainable approach. Passive desalination driven by solar thermal energy has the additional advantage to require only inexpensive materials and easily maintainable components. The bottleneck to the widespread diffusion of such solar passive desalination technologies is their lower productivity with respect to active ones. A completely passive, multi-effect membrane distillation device with an efficient use of solar energy and thus a remarkable enhancement in distillate productivity has been recently proposed. The improved performance of this distillation device comes from the efficient exploitation of low-temperature thermal energy to drive multiple distillation processes. In this work, we analyze the proposed distillation technology by a more in-depth thermodynamic detail, considering a Second Law analysis. We then report a detailed exergy analysis, which allows to get insights on the production of irreversibilities in each component of the assembly. These calculations provide guidelines for the possible optimization of the device, since simple changes in the original configuration may easily yield up to a 46% increase in the Second Law efficiency.

Abstract Taking inspiration from the structure of diatom algae frustules and motivated by the need for new detecting strategies for emerging nanopollutants in water, we analyze the potential of nanoporous silica tablets as metering devices for the concentration of biomolecules or nanoparticles in water. The concept relies on the different diffusion behavior that water molecules exhibit in bulk and nanoconfined conditions, e.g., in nanopores. In this latter situation, the self-diffusion coefficient of water reduces according to the geometry and surface properties of the pore and to the concentration of suspended biomolecules or nanoparticles in the pore, as extensively demonstrated in a previous study. Thus, for a given pore-liquid system, the self-diffusivity of water in nanopores filled with biomolecules or nanoparticles provides an indirect measure of their concentration. Using molecular dynamics and previous results from the literature, we demonstrate the correlation between the self-diffusion coefficient of water in silica nanopores and the concentration of proteins or nanoparticles contained therein. Finally, we estimate the time required for the nanoparticles to fill the nanopores, in order to assess the practical feasibility of the overall nano-metering protocol. Results show that the proposed approach may represent an alternative method for assessing the concentration of some classes of nanopollutants or biomolecules in water.

Colloidal suspensions of nanoparticles (nanofluids) are materials of interest for thermal engineering, because their heat transfer properties are typically enhanced as compared to the base fluid one. Effective medium theory provides popular models for estimating the overall thermal conductivity of nanofluids based on their composition. In this article, the accuracy of models based on the Bruggeman approximation is assessed. The sensitivity of these models to nanoscale interfacial phenomena, such as interfacial thermal resistance (Kapitza resistance) and fluid ordering around nanoparticles (nanolayer), is considered for a case study consisting of alumina nanoparticles suspended in water. While no significant differences are noticed for various thermal conductivity profiles in the nanolayer, a good agreement with experiments is observed with Kapitza resistance ≈10−9 m2K/W and sub-nanometer nanolayer thickness. These results confirm the classical nature of thermal conduction in nanofluids and highlight that future studies should rather focus on a better quantification of Kapitza resistance at nanoparticle-fluid interfaces, in order to allow bottom up estimates of their effective thermal conductivity. Keywords: Nanolayer, Kapitza resistance, Nanofluid, Effective medium approximation, Thermal conductivity

Thermal energy storage is a key technology to increase the global energy share of renewables—by matching energy availability and demand—and to improve the fuel economy of energy systems—by recovery and reutilization of waste heat. In particular, the negligible heat losses of sorption technologies during the storing period make them ideal for applications where long-term storage is required. Current technologies are typically based on the sorption of vapor sorbates on solid sorbents, requiring cumbersome reactors and components operating at below ambient pressure. In this work, we report the experimental characterization of working pairs made of various liquid sorbates (distilled water, ethanol and their mixture) and a 13X zeolite sorbent at ambient pressure. The sorbent hydration by liquid sorbates shows lower heat storage performance than vapor hydration; yet, it provides similar heat storage density to that obtainable by latent heat storage (40–50 kWh/m3) at comparable costs, robustness and simplicity of the system, while gaining the long-term storage capabilities of sorption-based technologies. As a representative application example of long-term storage, we verify the feasibility of a sorption heat storage system with liquid sorbate, which could be used to improve the cold-start of stand-by generators driven by internal combustion engines. This example shows that liquid hydration may be adopted as a simple and low-cost alternative to more efficient—yet more expensive—techniques for long-term energy storage.

Zeolite crystal with porous structure is predicted to be a good membrane material for water purification, but experiments show water uptake orders of magnitude smaller than the theory. Here, Fasano et al. attribute this disagreement to the additional diffusion resistance induced by surface defects.

Water pasteurization has the potential to overcome some of the drawbacks of more conventional disinfection techniques such as chlorination, ozonation and ultraviolet radiation treatment. However, the high throughput of community water systems requires energy-intensive processes, and renewable energy sources have the potential to improve the sustainability of water pasteurization plants. In case of water pasteurization by solar thermal treatment, the continuity of operation is limited by the intermittent availability of the solar irradiance. Here we show that this problem can be addressed by a proper design of the plant layout, which includes a thermal energy storage system and an auxiliary gas boiler. Based on a target pasteurization protocol validated by experiments, a complete lumped-component model of the plant is developed and used to determine the operating parameters and size of the components for a given delivery flow rate. Finally, we report an economic analysis of the proposed plant layout, which allows its optimization for different scenarios based on two design variables, namely the solar multiple and the duration of the thermal energy storage. Based on the analyzed cases, it is found that the proposed plant layouts may yield a unit cost of water treatment ranging from ≈32 EUR-cents m−3 to ≈25 EUR-cents m−3.

The efficient dissipation of localized heat flux by convection is a key request in several engineering applications, especially electronic ones. The recent advancements in manufacturing processes are unlocking the design and industrialization of heat exchangers with unprecedented geometric characteristics and, thus, performance. In this work, laser etching manufacturing technique is employed to develop metal surfaces with designed microstructured surface patterns. Such precise control of the solid-air interface (artificial roughness) allows to manufacture metal heat sinks with enhanced thermal transmittance with respect to traditional flat surfaces. Here, the thermal performance of these laser-etched devices is experimentally assessed by means of a wind tunnel in a fully turbulent regime. At the highest Reynolds number tested in the experiments ( R e L ≈ 16 , 500 ), elliptic scale-roughened surfaces show thermal transmittances improved by up to 81% with respect to heat sinks with flat surface. At similar testing conditions, cones patterns provide an enhancement in Nusselt number and thermal transmittance of up to 102% and 357%, respectively. The latter results are correlated with the main geometric and thermal fluid dynamics descriptors of the convective heat transfer process in order to achieve a predictive model of their performance. The experimental evidence shown in this work may encourage and guide a broader use of micro-patterned surfaces for enhancing convective heat transfer in heat exchangers.

Because of their high thermal conductivity, graphene nanoribbons (GNRs) can be employed as fillers to enhance the thermal transfer properties of composite materials, such as polymer-based ones. However, when the filler loading is higher than the geometric percolation threshold, the interfacial thermal resistance between adjacent GNRs may significantly limit the overall thermal transfer through a network of fillers. In this article, reverse non-equilibrium molecular dynamics is used to investigate the impact of the relative orientation (i.e., horizontal and vertical overlap, interplanar spacing and angular displacement) of couples of GNRs on their interfacial thermal resistance. Based on the simulation results, we propose an empirical correlation between the thermal resistance at the interface of adjacent GNRs and their main geometrical parameters, namely the normalized projected overlap and average interplanar spacing. The reported correlation can be beneficial for speeding up bottom-up approaches to the multiscale analysis of the thermal properties of composite materials, particularly when thermally conductive fillers create percolating pathways.

In recent years, various online tools and databases have been developed to assess the potential energy output of photovoltaic (PV) installations in different geographical areas. However, these tools generally provide a spatial resolution of a few kilometers and, for a systematic analysis at large scale, they require continuous querying of their online databases. In this article, we present a methodology for fast estimation of the yearly sum of global solar irradiation and PV energy yield over large-scale territories. The proposed method relies on a multiple-regression model including only well-known geodata, such as latitude, altitude above sea level and average ambient temperature. Therefore, it is particularly suitable for a fast, preliminary, offline estimation of solar PV output and to analyze possible investments in new installations. Application of the method to a random set of 80 geographical locations throughout Europe and Africa yields a mean absolute percent error of 4.4% for the estimate of solar irradiation (13.6% maximum percent error) and of 4.3% for the prediction of photovoltaic electricity production (14.8% maximum percent error for free-standing installations; 15.4% for building-integrated ones), which are consistent with the general accuracy provided by the reference tools for this application. Besides photovoltaic potentials, the proposed method could also find application in a wider range of installation assessments, such as in solar thermal energy or desalination plants.

Graphene and carbon nanotubes are promising materials for nanoelectromechanical systems. Among other aspects, a proper understanding of the sliding dynamics of parallel graphene sheets or concentric nanotubes is of crucial importance for the design of nano-springs. Here, we analytically investigate the sliding dynamics between two parallel, rigid graphene sheets. In particular, the analysis focuses on configurations in which the distance between the sheets is kept constant and lower than the equilibrium interlayer spacing of graphite (unstable configurations). The aim is to understand how the interlayer force due to van der Waals interactions along the sliding direction changes with the geometrical characteristics of the configuration, namely size and interlayer spacing. Results show metastable equilibrium positions with completely faced sheets, namely a null force along the sliding direction, whereas net negative/positive forces arise when the sheets are approaching/leaving each other. This behavior resembles a molecular spring, being able to convert kinetic into potential energy (van der Waals potential), and viceversa. The amplitude of both storable energy and entrance/exit forces is found to be proportional to the sheet size, and inversely proportional to their interlayer spacing. This model could also be generalized to describe the behavior of configurations made of concentric carbon nanotubes, therefore allowing a rational design of some elements of carbon-based nanoelectromechanical systems.

In this work, we derive different systems of mesoscopic moment equations for the heat-conduction problem and analyze the basic features that they must hold. We discuss two- and three-equation systems, showing that the resulting mesoscopic equation from two-equation systems is of the telegraphist’s type and complies with the Cattaneo equation in the Extended Irreversible Thermodynamics Framework. The solution of the proposed systems is analyzed, and it is shown that it accounts for two modes: a slow diffusive mode, and a fast advective mode. This latter additional mode makes them suitable for heat transfer phenomena on fast time-scales, such as high-frequency pulses and heat transfer in small-scale devices. We finally show that, if proper initial conditions are provided, the advective mode disappears, and the solution of the system tends asymptotically to the transient solution of the classical parabolic heat-conduction equation.

Plate Fin Heat Sinks (PFHS) are among the simplest and most widespread devices for electronics cooling. Because of the many design parameters to be considered, developing both cost and thermal effective PFHS is a critical issue. Here, a novel thermal model of PFHS is presented. The model has a broad field of applicability, being comprehensive of the effects of flow bypass, developing boundary layers, fin efficiency and spreading resistance. Experiments are then carried out to validate the proposed thermal model, and its good accuracy is demonstrated. Finally, an optimization methodology based on genetic algorithms is proposed for a cost-effective selection of the design parameters of PFHS, which is particularly effective with semi-active configurations. Such an optimization methodology is then tested on a commercial heat sink, resulting in a possible 53% volume reduction at fixed thermal performances.

In this paper, we present an alternative derivation of the entropy production in turbulent flows, based on a formal analogy with the kinetic theory of rarefied gas. This analogy allows for proving that the celebrated k - ϵ model for turbulent flows is nothing more than a set of coupled BGK (Bhatnagar–Gross–Krook)-like equations with a proper forcing. This opens a novel perspective on this model, which may help in sorting out the heuristic assumptions essential for its derivation, such as the balance between turbulent kinetic energy production and dissipation. The entropy production is an essential condition for the design and optimization of devices where turbulent flows are involved.