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arXiv 2023年

作者： Khurana, Madhur Chaudhary, Himanshu Mumtaz, Saadia Pacif, S.K.J. Mustafa, G. Department of Applied Physics Delhi Technological University Delhi110042 India Department of Applied Mathematics Delhi Technological University Delhi110042 India Sagara Odisha Sambalpur768224 India Department of Mathematics Shyamlal College University of Delhi Delhi110032 India Institute of Chemical Engineering and Technology University of the Punjab Quaid-e-Azam Campus Lahore54590 Pakistan Department of Physics Zhejiang Normal University Jinhua321004 China New Uzbekistan University Movarounnahr street 1 Tashkent100000 Uzbekistan

In this research paper, we explore a well-motivated parametrization of the time-dependent deceleration parameter, characterized by a cubic form in cosmic time t, in the context of late-time cosmic acceleration. The whole analysis of our work is within the framework of f(Q,T) gravity theory, where we consider the background metric as the homogeneous and isotropic one represented by the Friedmann–Lemaître–Robertson–Walker (FLRW) metric. We solve the field equations with the considered functional form of d(t). In order to find some best fit values of the model parameters and validate our obtained model, we constrain the model using some recent observational datasets, including cosmic chronometer (CC), Supernovae (SNIa), Baryon Acoustic Oscillation (BAO), Cosmic Microwave Background Radiation (CMB), Gamma-ray Burst (GRB), and Quasar (Q) datasets. The joint analysis of these datasets results in tighter constraints for the model parameters, enabling us to discuss both the physical and geometrical aspects of the model. Moreover, we determine the present values of the deceleration parameter (q0), the Hubble parameter (H0), and the transition redshift (zt) from deceleration to acceleration ensuring consistency with some recent results of Planck 2018. Our statistical analysis yields highly improved results, surpassing those obtained in previous investigations. Overall, this study presents valuable insights into the higher order q(t) model and its implications for late-time cosmic acceleration, shedding light on the nature of the late universe. Copyright © 2023, The Authors. All rights reserved.

关键词： Cosmology

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A First-Principles and Calphad-Assisted Phase-Field Model for Microstructure Evolution: Application to Mo-V Binary Alloy Systems

SSRN

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SSRN 2023年

作者： Thakur, Abhishek Kumar Kovacevic, Sasa Manga, Venkateswara Rao Deymier, Pierre Alix Muralidharan, Krishna Department of Materials Science and Engineering University of Arizona TucsonAZ85721 United States Department of Civil and Environmental Engineering Imperial College London LondonSW7 2AZ United Kingdom Lunar and Planetary Laboratory University of Arizona TucsonAZ85721 United States Graduate Interdisciplinary Program in Applied Mathematics India

A multiscale computational framework combining the first-principles calculations and the CALPHAD approach with the phase-field method is presented to simulate the microstructure evolution in multicomponent steel alloys. We demonstrate the potential of the framework by predicting the microstructural evolution in elastically periodic arrays of the Mo-V binary sub-system. The framework utilizes the first-principles calculations using special quasi-random structures. Hitherto unavailable thermodynamic and material properties of the alloy are obtained by employing the first-principles calculations and the CALPHAD approach and fed into the phase-field model to predict the microstructure evolution at different temperatures within the miscibility gap region. In addition to the temperature and cooling rates, the model incorporates the role of mechanical fields in decomposition kinetics in the Mo-V binary alloy system. Regimes for temperatures and cooling rates at which spinodal decomposition occurs are identified. Applying external loading leads to directional phase separation in the Mo-V binary system. The elastic inhomogeneity in terms of material properties between the two phases initiates the directional alignment while eigenstrains and applied external loading control the degree of alignment. The framework developed is general and extendable to higher multicomponent sub-systems in steel alloys. © 2023, The Authors. All rights reserved.

关键词： Temperature

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Liquid-Liquid Transition in Water from First Principles

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Physical Review Letters 2022年第25期129卷 255702-255702页

作者： Thomas E. Gartner, III Pablo M. Piaggi Roberto Car Athanassios Z. Panagiotopoulos Pablo G. Debenedetti Department of Chemistry Princeton University Princeton New Jersey 08544 USA Department of Physics Princeton University Princeton New Jersey 08544 USA Program in Applied and Computational Mathematics Princeton University Princeton New Jersey 08544 USA Princeton Institute for the Science and Technology of Materials Princeton University Princeton New Jersey 08544 USA Department of Chemical and Biological Engineering Princeton University Princeton New Jersey 08544 USA

A long-standing question in water research is the possibility that supercooled liquid water can undergo a liquid-liquid phase transition (LLT) into high- and low-density liquids. We used several complementary molecular simulation techniques to evaluate the possibility of an LLT in an ab initio neural network model of water trained on density functional theory calculations with the SCAN exchange correlation functional. We conclusively show the existence of a first-order LLT and an associated critical point in the SCAN description of water, representing the first definitive computational evidence for an LLT in water from first principles.

关键词： Classical statistical mechanics First order phase transitions First-principles calculations Liquid-liquid phase transition Phase transitions Thermodynamics Deep learning Density functional theory Molecular dynamics

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Structural properties of hyperuniform Voronoi networks

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Physical Review E 2025年第3期111卷 034123-034123页

作者： Eli Newby Wenlong Shi Yang Jiao Reka Albert Salvatore Torquato Department of Physics Pennsylvania State University University Park Pennsylvania 16802 USA Materials Science and Engineering Arizona State University Tempe Arizona 85287 USA Department of Physics Arizona State University Tempe Arizona 85287 USA Department of Chemistry Princeton University Princeton New Jersey 08544 USA Department of Physics Princeton University Princeton New Jersey 08544 USA Princeton Institute of Materials Princeton University Princeton New Jersey 08544 USA Program in Applied and Computational Mathematics Princeton University Princeton New Jersey 08544 USA

Disordered hyperuniform many-particle systems are recently discovered exotic states of matter, characterized by the complete suppression of normalized infinite-wavelength density fluctuations, as in perfect crystals, while lacking conventional long-range order, as in liquids and glasses. In this work, we begin a program to quantify the structural properties of nonhyperuniform and hyperuniform networks. In particular, large two-dimensional (2D) Voronoi networks (graphs) containing approximately 10,000 nodes are created from a variety of different point configurations, including the antihyperuniform hyperplane intersection process (HIP), nonhyperuniform Poisson process, nonhyperuniform random sequential addition (RSA) saturated packing, and both non-stealthy and stealthy hyperuniform point processes. We carry out an extensive study of the Voronoi-cell area distribution of each of the networks by determining multiple metrics that characterize the distribution, including their average areas and corresponding variances as well as higher-order cumulants (i.e., skewness γ1 and excess kurtosis γ2). We show that the HIP distribution is far from Gaussian, as evidenced by a high skewness (γ1=3.16) and large positive excess kurtosis (γ2=16.2). The Poisson (with γ1=1.07 and γ2=1.79) and non-stealthy hyperuniform (with γ1=0.257 and γ2=0.0217) distributions are Gaussian-like distributions, since they exhibit a small but positive skewness and excess kurtosis. The RSA (with γ1=0.450 and γ2=−0.0384) and the highest stealthy hyperuniform distributions (with γ1=0.0272 and γ2=−0.0626) are also non-Gaussian because of their low skewness and negative excess kurtosis, which is diametrically opposite of the non-Gaussian behavior of the HIP. The fact that the cell-area distributions of large, finite-sized RSA and stealthy hyperuniform networks (e.g., with N≈10,000 nodes) are narrower, have larger peaks, and smaller tails than a Gaussian distribution implies that in the thermodynamic limit th

关键词： Gaussian distribution

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NONLINEAR DISCRETE-TIME OBSERVERS WITH PHYSICS-INFORMED NEURAL NETWORKS

arXiv

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arXiv 2024年

作者： Alvarez, Hector Vargas Fabiani, Gianluca Kevrekidis, Ioannis G. Kazantzis, Nikolaos Siettos, Constantinos Modelling Engineering Risk and Complexity Scuola Superiore Meridionale Naples80138 Italy Department of Chemical and Biomolecular Engineering Johns Hopkins University BaltimoreMD21218 United States Department of Chemical Engineering Worcester Polytechnic Institute WorcesterMA01609 United States Dipartimento di Matematica e Applicazioni "Renato Caccioppoli" Università degli Studi di Napoli Federico II Naples80126 Italy Department of Applied Mathematics and Statistics Johns Hopkins University BaltimoreMD21218 United States Department of Urology School of Medicine Johns Hopkins University BaltimoreMD21218 United States

We use Physics-Informed Neural Networks (PINNs) to solve the discrete-time nonlinear observer state estimation problem. Integrated within a single-step exact observer linearization framework, the proposed PINN approach aims at learning a nonlinear state transformation map by solving a system of inhomogeneous functional equations. The performance of the proposed PINN approach is assessed via two illustrative case studies for which the observer linearizing transformation map can be derived analytically. We also perform an uncertainty quantification analysis for the proposed PINN scheme and we compare it with conventional power-series numerical implementations, which rely on the computation of a power series *** Codes 37N30, 68T05, 93C55, 65D15 Copyright © 2024, The Authors. All rights reserved.

关键词： Uncertainty analysis

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Model-based control algorithms for the quadruple tank system: An experimental comparison

arXiv

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arXiv 2022年

作者： Andersen, Anders H.D. Ritschel, Tobias K.S. Hørsholt, Steen Huusom, Jakob Kjøbsted Jørgensen, John Bagterp Department of Applied Mathematics and Computer Science Technical University of Denmark Kgs. LyngbyDK-2800 Denmark Department of Chemical and Biochemical Engineering Technical University of Denmark Kgs. LyngbyDK-2800 Denmark

We compare the performance of proportional-integral-derivative (PID) control, linear model predictive control (LMPC), and nonlinear model predictive control (NMPC) for a physical setup of the quadruple tank system (QTS). We estimate the parameters in a continuous-discrete time stochastic nonlinear model for the QTS using a prediction-error-method based on the measured process data and a maximum likelihood (ML) criterion. In the NMPC algorithm, we use this identified continuous-discrete time stochastic nonlinear model. The LMPC algorithm is based on a linearization of this nonlinear model. We tune the PID controller using Skogestad’s IMC tuning rules using a transfer function representation of the linearized model. Norms of the the observed tracking errors and the rate of change of the manipulated variables are used to compare the performance of the control algorithms. The LMPC and NMPC perform better than the PID controller for a predefined time-varying setpoint trajectory. The LMPC and NMPC algorithms have similar performance. Copyright © 2022, The Authors. All rights reserved.

关键词： Model predictive control

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Tailoring Charge Donor-Acceptor Interaction in CsPbBr3 Perovskite Nanocrystals through Ligand Exchange

arXiv

引用

arXiv 2024年

作者： Shah, Syed Abdul Basit Ghimire, Sushant Lesyuk, Rostyslav Diamanti, Maria Vittoria Lughi, Vanni Klinke, Christian Department of Engineering and Architecture Università degli studi di Trieste Trieste Italy Institute of Physics University of Rostock Rostock18059 Germany Munich80539 Germany Pidstryhach Institute for Applied Problems of Mechanics and Mathematics of NAS of Ukraine Lviv Ukraine Department of Chemistry Materials and Chemical Engineering "Giulio Natta" Politecnico di Milano Italy Department of Chemistry Swansea University Swansea United Kingdom Department Life Light & Matter University of Rostock Rostock18059 Germany

The surface ligands in colloidal metal halide perovskites influence not only their intrinsic optoelectronic properties but also their interaction with other materials and molecules. We explore donor-acceptor interactions of CsPbBr3 perovskite nanocrystals with TiO2 nanoparticles and nanotubes by replacing long-chain oleylamine ligands with short-chain butylamines. Through post-synthesis ligand exchange, we functionalize the nanocrystals with butylamine ligands while maintaining their intrinsic properties. In solution, butylamine-capped nanocrystals exhibit reduced photoluminescence intensity with increasing TiO2 concentration but without any change in photoluminescence lifetime. Intriguingly, the Stern-Volmer plot depicts different slopes at low and high TiO2 concentrations, suggesting mixed static and sphere-of-action quenching interactions. Oleylamine-capped nanocrystals in solution, on the other hand, show no interaction with TiO2, as indicated by consistent photoluminescence intensities and lifetimes before and after TiO2 addition. In films, both types exhibit decreased photoluminescence lifetime with TiO2, indicating enhanced donor-acceptor interactions, which are discussed in terms of trap state modification and electron transfer. TiO2 nanotubes enhance nonradiative recombination more in butylamine-capped CsPbBr3 perovskite nanocrystals, emphasizing the role of ligand chain length. © 2024, CC BY-NC-ND.

关键词： Quenching

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Early Adoption of Generative Artificial Intelligence in Computing Education: Emergent Student Use Cases and Perspectives in 2023

arXiv

引用

arXiv 2024年

作者： Smith, C. Estelle Shiekh, Kylee Cooreman, Hayden Rahman, Sharfi Zhu, Yifei Siam, Md Kamrul Ivanitskiy, Michael Ahmed, Ahmed M. Hallinan, Michael Grisak, Alexander Fierro, Gabe Colorado School of Mines Department of Computer Science GoldenCO United States Colorado School of Mines Department of Physics GoldenCO United States Colorado School of Mines Applied Mathematics & Statistics GoldenCO United States Colorado School of Mines Department of Geophysics GoldenCO United States Colorado School of Mines Chemical & Biological Engineering GoldenCO United States Colorado School of Mines Department of Mechanical Engineering GoldenCO United States

Because of the rapid development and increasing public availability of Generative Artificial Intelligence (GenAI) models and tools, educational institutions and educators must immediately reckon with the impact of students using GenAI. Yet there is limited prior research on computing students’ use and perceptions of GenAI. We surveyed all computer science majors in a small engineering-focused R1 university in order to: (1) capture a baseline assessment of how GenAI has been immediately adopted by aspiring computer scientists;(2) describe computing students’ GenAI-related needs and concerns for their education and careers;and (3) provide recommendations for GenAI policy development and educational tooling. We present an exploratory qualitative analysis of this data and discuss the impact of our findings on the emerging conversation around GenAI and education. © 2024, CC BY.

关键词： Adversarial machine learning

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Enhanced Gaussian Process Regression for Diagnosing Wind Energy Conversion Systems

引用

IFAC-PapersOnLine 2022年第6期55卷 673-678页

作者： Majdi Mansouri Radhia Fezai Mohamed Trabelsi Hajji Mansour Hazem Nounou Mohamed Nounou Department of Mathematics and Sciences Prince Sultan University Riyadh 11586 Saudi Arabia Research Laboratory of Automation Signal Processing and Image National Engineering School of Monastir 5019 Tunisia Electronic and Communications Engineering Department Kuwait College of Science and Technology P. O. Box 27235 Kuwait Higher Institute of Applied Sciences and Technology of Kasserine Kairouan University Tunisia Chemical Engineering Program Texas A&M University at Qatar Doha Qatar

Fault detection and diagnosis techniques are increasingly important to ensure robust and resource efficient operation of Wind Energy Conversion (WEC) systems. In this context, this paper presents a Reduced Enhanced Gaussian Process Regression (REGPR)-based Random Forest (RF) technique (REGPR-RF) to identify and diagnose faults occurring in a nonlinear WEC systems. The proposed technique uses REGPR technique for features extraction and selection from raw sensor data. Then, these selected features are fed to RF classifier to reliably detect and classify faults. The use of REGPR to learn features avoid the dimension problems and improves the classification performance significantly with a small number of training data. The results obtained by REGPR-RF are compared to those obtained with other conventional classifiers (Support Vector Machines (SVM), Naive Bayes (NB), ...). The results show that the developed REGPR-RF technique achieve higher accuracy (99.99%) with small data sets.

关键词： Gaussian Process Regression (GPR) Noise-Signal-Dependence Random Forest (RF) Feature Extraction Selection Fault Detection Diagnosis (FDD) Wind Energy Conversion (WEC) Systems

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Past, present, and futures of non destructive techniques

Past, present, and futures of non destructive techniques

引用

IEEE Metrology for Aerospace

作者： Ciro Tortora Fabiana Graziano Veronica Vespini Sara Coppola Simonetta Grilli Pietro Ferraro Department of engineering of industrial product and chemical processes CNR-ISASI Institute of Applied Sciences and Intelligent Systems “E. Caianiello” University of Naples “Federico II” Napoli Italy Department of Mathematics and Physics CNR-ISASI Institute of Applied Sciences and Intelligent Systems “E. Caianiello” University of Campania “Luigi Vanvitelli” Caserta Italy CNR-ISASI Institute of Applied Sciences and Intelligent Systems “E. Caianiello” Pozzuoli Italy

Recent progress in manufacturing techniques has led to a wide range of new materials and components. Advance in material development has resulted in a wide range of new kind of materials as carbon composite and carbon fiber reinforced. The need, already consolidated, to investigate its properties (mechanical, electrical, thermal, etc.) arises and characterize the possible innovations by industrial sector such as aeronautics and aerospace, through the use of analysis techniques non-destructive (NDT). Common NDT techniques can be classified into seven major categories: visual (visual inspection), penetrating radiation (X-ray, and neutron imaging), magnetic-electrical (magnetic particle), mechanical vibration (ultrasonic, acoustic emission, and tapping), chemical/electrochemical (chemical spot testing), thermal (infrared thermography) and other optical methods (interferometry, holography, and shearography). More traditional non-destructive techniques (NDT) are not always effective as expected, and there is therefore a long-term need to use innovative NDT methods capable of provide reliable and quantitative information regarding damage status and integrity of materials. In the present work we focus on the development of specific tools of NDT for assuring the quality related to the fabrication of industrial components. The potential tool that will be take into consideration for inspection and detection of defects will mainly based on the following two main methods: ‐full-field optical techniques (such as profilometry, interferometry, sherography and thermography); ‐Laser ultrasound testing (LUT); The integrated and combinated use of different NDT (thermography, shearography and ultrasound) was necessary for a complete characterization of some types of composite materials.

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