Hasil untuk "Thermodynamics"

A. Bérut, A. Arakelyan, A. Petrosyan et al.

C. Jarzynski

J. Cox, D. Wagman, V. Medvedev

Y. Bulut, Haluk Aydın

D. Jou, J. Casas-Vazquez, G. Lebon

B. Coleman, M. Gurtin

M. Zuker, D. Mathews, D. Turner

I. Müller, T. Ruggeri

M. Wertheim

J. Pekola

Elyaakouby Yassine, Tilioua Amine

The study brings into consideration and enriches the areas of hydrology, energy engineering, and sustainable development by the enhancement of the environmental and energy performance of a reverse osmosis (RO) plant in Zagora, Morocco, which is treating brackish surface water, very thoroughly. The whole idea is to check IMSDesign software predictions' reliability against operational data of more than a year. In simulations with Hydranautics ESPA2-LD membranes, a feed flow of 125 m³/h under 9.1 bar was forecasted eventually giving rise to 93.75 m³/h of permeate and a recovery rate of 75%. On the other hand, actual field measurements over a period of more than a year indicated always lower results. For instance, the first RO train averaged a feed flow of 109.1 m³/h at 9.8 bar with a 63% recovery, while the second train performed not much differently. Seasonal changes in water conductivity, temperature changes, and membrane fouling were found to be the main reasons for the discrepancy. Also, the actual reject flows (22.1–23.8 m3/h) were less than the predicted 31.25 m³/h. The modeling tool is still considered useful for the initial system design; however, this research indicates the need of continuous monitoring, adaptive maintenance strategies, and pretreatment efficiency evaluation especially by improving the environmental performance and energy efficiency of inland water treatment infrastructure in arid regions in arid areas.

José Luis Díaz Palencia

Cosmic rays exhibit anomalous diffusion behaviors in the heliospheric environment that cannot be adequately described by classical diffusion models. In this paper, we develop a theoretical framework employing a fractional Fokker–Planck equation to model the anomalous transport of cosmic rays. This approach accounts for the observed non-Gaussian distributions, long-range correlations and memory effects in cosmic ray fluxes. We derive analytical solutions using the Adomian Decomposition Method and express them in terms of Mittag-Leffler functions and Lévy stable distributions. The model parameters, including the fractional orders <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mi>α</mi></semantics></math></inline-formula> and <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mi>μ</mi></semantics></math></inline-formula> and the entropic index <i>q</i>, are estimated by a short comparison between theoretical predictions and observational data from cosmic ray experiments. Our findings suggest that the integration of fractional calculus and non-extensive statistics can be employed for describing the cosmic ray propagation and the anomalous diffusion observed in the heliosphere.

Haimeng Zhao, Yuzhen Zhang, John Preskill

The energy cost of erasing quantum states depends on our knowledge of the states. We show that learning algorithms can acquire such knowledge to erase many copies of an unknown state at the optimal energy cost. This is proved by showing that learning can be made fully reversible and has no fundamental energy cost itself. With simple counting arguments, we relate the energy cost of erasing quantum states to their complexity, entanglement, and magic. We further show that the constructed erasure protocol is computationally efficient when learning is efficient. Conversely, under standard cryptographic assumptions, we prove that the optimal energy cost cannot be achieved efficiently in general. These results also enable efficient work extraction based on learning. Together, our results establish a concrete connection between quantum learning theory and thermodynamics, highlighting the physical significance of learning processes and enabling provably-efficient learning-based protocols for thermodynamic tasks.

Juulia-Gabrielle Moreau, Argo Jõeleht, Anna Losiak et al.

Sedimentary rocks often form the upper layers or the entire target rocks in impact events. Thermodynamic properties of sedimentary rocks related to porosity and water saturation affect the process of impact crater formation. The heterogeneous distribution of sedimentary facies can complicate the development and distribution of shock effects, especially in numerical modeling. This work focuses on the shock thermodynamic properties of carbonate rocks with differing porosity textures (e.g., isolated pores, interstitial porosity, elongated pores) and water saturation levels. Using mesoscale numerical modeling, we found that water saturation reduces shock temperatures compared to those in dry, porous carbonate rocks. The orientation of elongated pores and porosity lineations influences the shock temperature distribution and rock deformation at angles of 50-90° to the shock front. Additionally, due to complex shock wave interactions, interstitial porosity is key in creating temperature zonations around larger grains.

Osama A. Marzouk

This study investigates the Rankine vapor power thermodynamic cycle using steam/water as the working fluid, which is common in commercial power plants for power generation as the source of the rotary shaft power needed to drive electric generators. The four-process cycle version, which comprises a water pump section, a boiler/superheater section, a steam turbine section, and a condenser section, was considered. The performance of this thermodynamic power cycle depends on several design parameters. This study varied a single independent variable, the absolute pressure of the condenser, by a factor of 256, from 0.78125 to 200 kPa. The peak pressure and peak temperature in the cycle were fixed at 50 bar (5,000 kPa) and 600°C, respectively, corresponding to a base case with a base value for the condenser's absolute pressure of 12.5 kPa (0.125 bar). The analysis was performed using the thermodynamics software package Cantera as an extension of the Python programming language. The results suggest that over the range of condenser pressures examined, a logarithmic function can be deployed to describe the dependence of input heat, the net output work, and cycle efficiency on the absolute pressure of the condenser. Each of these three performance metrics decreases as the absolute pressure of the condenser increases. However, a power function is a better choice to describe how the steam dryness (steam quality) at the end of the turbine section increases as the absolute pressure of the condenser rises.

Chengbin Zhang, Xiangdong Liu

Salomé A. Sepúlveda-Fontaine, José M. Amigó

Since its origin in the thermodynamics of the 19th century, the concept of entropy has also permeated other fields of physics and mathematics, such as Classical and Quantum Statistical Mechanics, Information Theory, Probability Theory, Ergodic Theory and the Theory of Dynamical Systems. Specifically, we are referring to the classical entropies: the Boltzmann–Gibbs, von Neumann, Shannon, Kolmogorov–Sinai and topological entropies. In addition to their common name, which is historically justified (as we briefly describe in this review), another commonality of the classical entropies is the important role that they have played and are still playing in the theory and applications of their respective fields and beyond. Therefore, it is not surprising that, in the course of time, many other instances of the overarching concept of entropy have been proposed, most of them tailored to specific purposes. Following the current usage, we will refer to all of them, whether classical or new, simply as entropies. In particular, the subject of this review is their applications in data analysis and machine learning. The reason for these particular applications is that entropies are very well suited to characterize probability mass distributions, typically generated by finite-state processes or symbolized signals. Therefore, we will focus on entropies defined as positive functionals on probability mass distributions and provide an axiomatic characterization that goes back to Shannon and Khinchin. Given the plethora of entropies in the literature, we have selected a representative group, including the classical ones. The applications summarized in this review nicely illustrate the power and versatility of entropy in data analysis and machine learning.

Artur Stepniak, Marta Biernacka, Magdalena Malecka et al.

The aim of the research was to investigate and compare the interaction between flavanones (flavanone, 4-chloro-flavanone) with potential anticancer activity and selected cyclodextrins. Measurements were made using calorimetric (ITC, DSC) and spectrophotometric (UV-Vis spectroscopy, FT-IR, ¹H NMR) methods. The increase in the solubility in aqueous medium caused by the complexation process was determined by the Higuchi-Connors method. As a result of the study, the stoichiometry and thermodynamics of the complexation reaction were determined. The formation of stable inclusion complexes at a 1:1 M ratio between flavanone and 4-chloroflavanone and the cyclodextrins selected for research was also confirmed.

Mohamed Jasim Mohamed, Bashra Kadhim Oleiwi, Layla H. Abood et al.

The robotic manipulator is considered one of the complex systems that include multi-input, multi-output, non-linearity, and highly coupled. The uncertainty in the parameters and external disturbances have a negative influence on the performance of the system. Therefore, the controllers that will be designed for these systems must be able to deal with these complexities and difficulties. The Proportional, Integral, and Derivative (PID) controller is known to be simple and well robust, while the neural network has a solid ability to map complex functions. In this paper, we propose six control structures by combining the benefits of PID controller with integer and fractional order and the benefits of neural networks to produce hybrid controllers for a 2-Link Rigid Robot Manipulator (2-LRRM) handling with the problem of trajectory tracking. The Gorilla Forces Troops Optimization algorithm (GTO) was used to tune the parameters of the proposed controller schemes to minimize the Integral of Time Square Error (ITSE). In addition, the robustness of the performance of the suggested control systems is tested by altering the initial position, external disturbances and parameters and carried out using MATLAB. The best performance of the proposed controllers was the Neural Network Fractional Order Proportional Integral Derivative Controller (NNFOPID).

Hayder Salem, Adel Mohammedredha, Abdullah Alawadhi

Nowadays, wind energy is one of the most cost-effective and environmentally friendly energies in high demand due to shortages in fossil fuels and the necessity to reduce global carbon footprint. One of the main goals of wind turbine development is to increase the power output of the turbine either by increasing the turbine blade swept area or increasing the velocity of the wind. In this article, a proprietary augmentation system was introduced to increase the power output of vertical axis wind turbines (VAWT) by increasing the free stream velocity to more than two folds. The system comprises two identical airfoiled casings within which the turbine/turbines are seated. The results showed that the velocity slightly increases when decreasing the gap between the casing. It was also found that changing the angle of attack of the housing has more impact on the augmented airspeed. CFD technique was used to assess the velocity and flow of air around the system.