Hasil untuk "Thermodynamics"

P. Flory

K. Spiegler, O. Kedem

K. Pitzer, Janice J. Kim

A. Feld, Gallagher Mikrowellen-Anregung, unabhangige Grundgrö

A. Beris, B. Edwards

Y. Marcus

G. M. Wang, E. Sevick, E. Mittag et al.

G. Gallavotti

Aspects of the modern dynamical systems approach to thermodynamics of stationary states out of equilibrium with attention to the original conceptions which arose at the beginnings of Statistical Mechanics

M. Šilhavý

Peipei Zhou, Shuiming Cai

This paper is devoted to investigating the problem of finite-time (FnT) adaptive cluster synchronization for heterogeneous fractional-order dynamic networks (FODNs) with community structure and co-competition interactions. By designing a suitable adaptive controller and using reduction to absurdity, some sufficient conditions are derived to ensure the considered heterogeneous FODNs can achieve cluster synchronization over a FnT interval. Meanwhile, the cluster-synchronized setting times (CSSTs) are evaluated effectively by means of the monotonicity of the Mittag-Leffler function. It is indicated that the estimated CSSTs are associated with the order of the derivation and the control parameters. Finally, numerical simulations are carried out to validate the effectiveness of our theoretical results.

Kensaku Chida, Antoine Andrieux, Katsuhiko Nishiguchi

Abstract Heat transfer mediated by the Coulomb interaction reveals unconventional thermodynamic behavior and broadens thermodynamics research into fields such as quantum dynamics and information engineering. Although some experimental demonstrations of phenomena utilizing Coulomb-mediated heat transfer have been reported, estimations of their performance, such as efficiency, and their theoretical evaluations necessitate qualitative evaluation of the transfer mechanism itself, which remains challenging. We present an experiment investigating single-electron dynamics in two electrostatically coupled silicon nanodots to quantify Coulomb-mediated heat transfer at the nanoscale. By estimating the Coulomb interaction strength between the dots using the cross-correlation measurements of the single-electron dynamics, we convert the single-electron dynamics into the statistics of Coulomb-mediated heat transfer. Conducting the experiment at equilibrium enabled us to obtain a fluctuating net-zero heat transfer between the dots. These heat transfer statistics are essential for exploring device functionalities from the perspective of stochastic thermodynamics and for verifying universal relations in nonequilibrium states.

T. Hatano, S. Sasa

We study Langevin dynamics describing nonequilibirum steady states. Employing the phenomenological framework of steady-state thermodynamics constructed by Oono and Paniconi [Prog. Theor. Phys. Suppl. 130, 29 (1998)], we find that the extended form of the second law which they proposed holds for transitions between steady states and that the Shannon entropy difference is related to the excess heat produced in an infinitely slow operation. A generalized version of the Jarzynski work relation plays an important role in our theory.

S. Typel, G. Röpke, T. Klähn et al.

We investigate nuclear matter at a finite temperature and density, including the formation of light clusters up to the alpha particle (1<=A<=4). The novel feature of this work is to include the formation of clusters as well as their dissolution due to medium effects in a systematic way using two many-body theories: a microscopic quantum statistical (QS) approach and a generalized relativistic mean-field (RMF) model. Nucleons and clusters are modified by medium effects. While the nucleon quasiparticle properties are determined within the RMF model from the scalar and vector self-energies, the cluster binding energies are reduced because of Pauli blocking shifts calculated in the QS approach. Both approaches reproduce the limiting cases of nuclear statistical equilibrium (NSE) at low densities and cluster-free nuclear matter at high densities. The treatment of the cluster dissociation is based on the Mott effect due to Pauli blocking, implemented in slightly different ways in the QS and the generalized RMF approaches. This leads to somewhat different results in the intermediate density range of about 10{sup -3} to 10{sup -1} fm{sup -3}, which gives an estimate of the present accuracy of the theoretical predictions. We compare the numerical results of these models for cluster abundances andmore » thermodynamics in the region of medium excitation energies with temperatures T<=20 MeV and baryon number densities from zero to a few times saturation density. The effects of cluster formation on the liquid-gas phase transition and on the density dependence of the symmetry energy are studied. It is demonstrated that the parabolic approximation for the asymmetry dependence of the nuclear equation of state breaks down at low temperatures and at subsaturation densities because of cluster formation. Comparison is made with other theoretical approaches, in particular, those that are commonly used in astrophysical calculations. The results are relevant for heavy-ion collisions and astrophysical applications.« less

Nallappan Gunasekaran, Murugesan Manigandan, Seralan Vinoth et al.

This paper delves into a novel category of nonlocal boundary value problems concerning nonlinear sequential fractional differential equations, coupled with a unique form of generalized Riemann–Liouville fractional differential integral boundary conditions. For single-valued maps, we employ a transformation technique to convert the provided system into an equivalent fixed-point problem, which we then address using standard fixed-point theorems. Following this, we evaluate the stability of these solutions utilizing the Ulam–Hyres stability method. To elucidate the derived findings, we present constructed examples.

Nursultan Alzhanov, Eddie Y. K. Ng, Yong Zhao

This paper presents a novel hybrid approach that integrates computational fluid dynamics (CFD), physics-informed neural networks (PINN), and fluid–structure interaction (FSI) methods to simulate fluid flow in stenotic coronary artery trees and predict fractional flow reserve (FFR) in areas of stenosis. The primary objective is to utilize a 1D PINN model to accurately predict outlet flow conditions, effectively addressing the challenges of measuring or estimating these conditions within complex arterial networks. Validation against traditional CFD methods demonstrates strong accuracy while embedding physics-based training to ensure compliance with fundamental fluid dynamics principles. The findings indicate that the hybrid CFD PINN FSI method generates realistic outflow boundary conditions crucial for diagnosing stenosis, requiring minimal input data. By seamlessly integrating initial conditions established by the 1D PINN into FSI simulations, this approach enables precise assessments of blood flow dynamics and FFR values in stenotic regions. This innovative application of 1D PINN not only distinguishes this methodology from conventional data-driven models that rely heavily on extensive datasets but also highlights its potential to enhance our understanding of hemodynamics in pathological states. Ultimately, this research paves the way for significant advancements in non-invasive diagnostic techniques in cardiology, improving clinical decision making and patient outcomes.

Jingya Qian, Di Chen, Yizhong Zhang et al.

Ultrasound has been widely used as a green and efficient non-thermal processing technique to assist with enzymatic hydrolysis. Compared with traditional enzymatic hydrolysis, ultrasonic-pretreatment-assisted enzymatic hydrolysis can significantly improve the efficiency of enzymatic hydrolysis and enhance the biological activity of substrates. At present, this technology is mainly used for the extraction of bioactive substances and the degradation of biological macromolecules. This review is focused on the mechanism of enzymatic hydrolysis assisted by ultrasonic pretreatment, including the effects of ultrasonic pretreatment on the enzyme structure, substrate structure, enzymatic hydrolysis kinetics, and thermodynamics and the effects of the ultrasonic conditions on the enzymatic hydrolysis results. The development status of ultrasonic devices and the application of ultrasonic-assisted enzymatic hydrolysis in the food industry are briefly described in this study. In the future, more attention should be paid to research on ultrasound-assisted enzymatic hydrolysis devices to promote the expansion of production and improve production efficiency.

Ye Chen, Chenxi Zhao, Qiurui Huang et al.

The flow inside the aviation engine fire extinguishing system is complicated due to its transient, two-phase and compressible nature. Temperature is one of the important variables of the flow, because it reflects the thermal respect of the physics. However, detailed analysis on the temperature variation during the discharge process is in absence. In this article, factors influencing temperature were analyzed, based on experimental experiments using halon1301 and novec1230. The temperature variation is found to be mainly influenced by the following factors: (1) the pressure gradient in pipe and gas fraction of the flow, (2) the relative magnitude of pressure work rate and convection, (3) pressure undershoot. The negative pressure gradient in the pipe, combined with the raising of gas fraction, leads to a sharp temperature drop of about 50 °C/s in the pipe. This process stops when the magnitude of pressure work is diminished, then heat convection from the pipe wall starts to dominate. Pressure undershoot is observed only in experiments using halon1301. The temperature drops of 6.1–9.8 °C in 0.01 s during the pipe pressure undershoot, and drops 12–14.2 °C in 1 s during bottle pressure undershoot.

N. Hyttinen

<p>Atmospheric new particle formation is initiated by clustering of gaseous precursors, such as small acids and bases. The hygroscopic properties of those precursors therefore affect the hygroscopic properties of aerosol particles. In this work, the water uptake of different salts consisting of atmospheric small acids and amines was studied computationally using the conductor-like screening model for real solvents (COSMO-RS). This method allows for the prediction of water activities in atmospherically relevant salts that have not been included in other thermodynamics models. Water activities are reported here for binary aqueous salt solutions, as well as ternary solutions containing proxies for organic aerosol constituents. The order of the studied cation species regarding water activities is similar in sulfate, iodate, and methylsulfonate, as well as in bisulfate and nitrate. Predicted water uptake strengths (in mole fraction) conform to the following orders: tertiary <span class="inline-formula">&gt;</span> secondary <span class="inline-formula">&gt;</span> primary amines and guanidinos <span class="inline-formula">&gt;</span> amino acids. The addition of water-soluble organic to the studied salts generally leads to weaker water uptake compared to pure salts. On the other hand, water-insoluble organic likely phase separates with aqueous salt solutions, leading to minimal effects on water uptake.</p>

Daling Li Yi, Ke Fan, Shengping He

The area of Arctic winter sea ice growth (WSIG) has expanded dramatically since winter 2008. Yet the thermodynamic and dynamic contributions to the abrupt increase in WSIG remain unclear. Here using an ice concentration budget, we characterized quantitatively the increasing WSIG and revealed the relative contributions of dynamics during 1985–2021. Ice dynamics related to ice convergence/divergence are compared in two representative regions. The northern Laptev Sea is a freezing-dominated ice growth region and is competitively driven by the ice convergence. While in northwest Beaufort Gyre (BG), the combined effects of freezing and ice divergence have both enhanced since 2008, and the dynamics contribute 84% to the significant WSIG intensification since 2008. Comparison of thermodynamic and dynamic contributions emphasized that the winter sea-ice expansion is influenced not only by winter freeze, but also by convergence/divergence relative to newly formed thinner and mobile ice. Furthermore, the amplified summer Beaufort High in the mid-2000s and its long-lasting memory of the wind-driven strengthened BG are partially attributed to the abrupt increased WSIG since 2008.

O. Catoni

This monograph deals with adaptive supervised classification, using tools borrowed from statistical mechanics and information theory, stemming from the PACBayesian approach pioneered by David McAllester and applied to a conception of statistical learning theory forged by Vladimir Vapnik. Using convex analysis on the set of posterior probability measures, we show how to get local measures of the complexity of the classification model involving the relative entropy of posterior distributions with respect to Gibbs posterior measures. We then discuss relative bounds, comparing the generalization error of two classification rules, showing how the margin assumption of Mammen and Tsybakov can be replaced with some empirical measure of the covariance structure of the classification model.We show how to associate to any posterior distribution an effective temperature relating it to the Gibbs prior distribution with the same level of expected error rate, and how to estimate this effective temperature from data, resulting in an estimator whose expected error rate converges according to the best possible power of the sample size adaptively under any margin and parametric complexity assumptions. We describe and study an alternative selection scheme based on relative bounds between estimators, and present a two step localization technique which can handle the selection of a parametric model from a family of those. We show how to extend systematically all the results obtained in the inductive setting to transductive learning, and use this to improve Vapnik's generalization bounds, extending them to the case when the sample is made of independent non-identically distributed pairs of patterns and labels. Finally we review briefly the construction of Support Vector Machines and show how to derive generalization bounds for them, measuring the complexity either through the number of support vectors or through the value of the transductive or inductive margin.