A5100709551- Fluid Dynamics and Turbulent Flows
- Quantum Information and Cryptography
- Computational Fluid Dynamics and Aerodynamics
- Quantum Mechanics and Applications
- Fluid Dynamics and Vibration Analysis
- Quantum Computing Algorithms and Architecture
- Model Reduction and Neural Networks
- Laser-Plasma Interactions and Diagnostics
- Lattice Boltzmann Simulation Studies
- Aerodynamics and Acoustics in Jet Flows
- Plasma and Flow Control in Aerodynamics
- Fluid Dynamics Simulations and Interactions
- Aerodynamics and Fluid Dynamics Research
- Guidance and Control Systems
- Quantum optics and atomic interactions
- Combustion and Detonation Processes
- Particle Dynamics in Fluid Flows
- Meromorphic and Entire Functions
- Fluid Dynamics and Heat Transfer
- Molecular Sensors and Ion Detection
- Heat Transfer and Optimization
- Nanofluid Flow and Heat Transfer
- Vibration and Dynamic Analysis
- Combustion and flame dynamics
- Electromagnetic Launch and Propulsion Technology
Nanjing University of Science and Technology
2015-2024
Lanzhou Veterinary Research Institute
2024
Chinese Academy of Agricultural Sciences
2024
Lanzhou University
2024
Shandong Youth University of Political Science
2024
University of Jinan
2024
Fujian Institute of Research on the Structure of Matter
2024
Chinese Academy of Sciences
2024
Wuhan University
2014-2024
Fujian Normal University
2005-2023
We introduce an intuitive measure of genuine multipartite entanglement which is based on the well-known concurrence. show how lower bounds this can be derived that also meet important characteristics measure. These are experimentally implementable in a feasible way enabling quantification broad variety cases.
With the increasing scale and complexity of network, network attack technology is also changing, such as malicious program attack, Trojan horse, distributed denial service worm, virus, web code injection, botnet, other new tools emerge in large numbers. As core hotspot information security, security situational awareness has received more attention. The traditional way prediction relatively single. Usually, only one algorithm used for perception prediction, its accuracy limited. To explore...
This paper applies deep reinforcement learning (DRL) on the synthetic jet control of flows over an NACA (National Advisory Committee for Aeronautics) 0012 airfoil under weak turbulent condition. Based proximal policy optimization method, appropriate strategy controlling mass rate a is successfully obtained at Re=3000. The effectiveness DRL based active flow (AFC) method first demonstrated by studying problem with constant inlet velocity, where remarkable drag reduction 27.0% and lift...
This paper introduces a novel surrogate model for two-dimensional adaptive steady-state thermal convection fields based on deep learning technology. The proposed aims to overcome limitations in traditional frameworks caused by network types, such as the requirement extensive training data, accuracy loss due pixelated preprocessing of original and inability predict information near boundaries with precision. We propose new framework that consists primarily physical-informed neural (PINN)...
In recent years, the realm of deep learning has witnessed significant advancements, particularly in object detection algorithms. However, unique challenges posed by remote sensing images, such as complex backgrounds, diverse target sizes, dense distribution, and overlapping or obscuring targets, demand specialized solutions. Addressing these challenges, we introduce a novel lightweight algorithm based on Yolov5s to enhance performance while ensuring rapid processing broad applicability. Our...
In this article, we propose an unsteady data-driven reduced order model (ROM) (surrogate model) for predicting the velocity field around airfoil. The network applies a convolutional neural (CNN) as encoder and deconvolutional (DCNN) decoder. constructs mapping function between temporal evolution of pressure signal on airfoil surface surrounding field. For improving performance, input matrix is designed to further incorporate information Reynolds number, geometry airfoil, angle attack. DCNN...
Incompatible observables can be approximated by compatible in joint measurement or measured sequentially, with constrained accuracy as implied Heisenberg's original formulation of the uncertainty principle. Recently, Busch, Lahti, and Werner proposed inaccuracy trade-off relations based on statistical distances between probability distributions outcomes [P. Busch et al., Phys. Rev. Lett. 111, 160405 (2013); P. A 89, 012129 (2014)]. Here we reformulate their theoretical framework, derive an...
In this paper, we propose a neural network based reduced-order model for predicting the unsteady flow field over single/multiple cylinders. The constructs mapping function between temporal evolution of pressure signal on cylinder surface and surrounding velocity field, where Convolutional Neural Network (CNN) layers are used as encoder deconvolutional decoder. Compared with fully connected (FC) decoder, deconvolution (DC) decoder is good capturing reconstructing spatial relationships...
We develop a deep neural network-based reduced-order model (ROM) for rapid prediction of the steady-state velocity field with arbitrary geometry and various boundary conditions. The input matrix network is composed nearest wall signed distance function (NWSDF), which contains more physical information than (SDF) binary map; conditions are represented by specifically designed values fused NWSDF. architecture comprises convolutional transpose-convolutional layers, layers employed to encode...
A data-driven model for rapid prediction of the steady-state heat conduction a hot object with arbitrary geometry is developed. Mathematically, can be described by Laplace's equation, where (spatial) diffusion term dominates governing equation. As intensity only depends on gradient temperature field, distribution displays strong features locality. Therefore, convolution neural network-based proposed, which good at capturing local (sub-invariant) thus treated as numerical discretization in...
In the interdisciplinary field of data-driven models and computational fluid mechanics, reduced-order model for flow prediction is mainly constructed by a convolutional neural network (CNN) in recent years. However, standard CNN only applicable to data with Euclidean spatial structure, while non-Euclidean properties can be convolved after pixelization, which usually leads decreased accuracy. this work, novel framework based on graph convolution (GCN) proposed allow operator predict dynamics...
This paper presents a novel reduced-order model for internal and external flow field estimations based on sparse convolution neural network. Since traditional network requires “rectangular” matrixes as input, the convolutional operation is computationally inefficient when applied to these problems with input matrix having information. In our approach, we innovatively introduce (SCNN) collect spatial information geometries that are inherently sparse, e.g., in thin pipelines much larger domain...
To alleviate the computational burden associated with fluid dynamics (CFD) simulation stage and improve aerodynamic optimization efficiency, this work develops an innovative procedure for airfoil shape optimization, which is implemented through coupling genetic algorithm (GA) optimizer coefficients prediction network (ACPN) model. The ACPN established using a fully connected neural geometry as input output. results show that ACPN's mean accuracy lift drag coefficient high up to about 99.02%....
90Sr, with a long half-life and strong radioactivity, will pose huge threat to the ecological security once exposed environment. Its high solubility easy mobility, coupled with...
Genuine-multipartite-entanglement (GME) concurrence is a measure of genuine multipartite entanglement that generalizes the well-known notion concurrence. We define an observable for GME The permits us to avoid full state tomography and leads different analytic lower bounds. By means explicit examples we show criteria based on bounds have better performance with respect known methods.
Entanglement monotones, such as the concurrence, are useful tools to characterize quantum correlations in various physical systems. The computation of concurrence involves, however, difficult optimizations and only for simplest case two qubits a closed formula was found by Wootters [Phys. Rev. Lett. 80, 2245 (1998)]. We show how this approach can be generalized, resulting lower bounds on higher dimensional systems well multipartite demonstrate that certain families states our results...