Behavior of a quantum particle in contact with a classical heat bath

1989 ◽  
Vol 55 (3-4) ◽  
pp. 745-767 ◽  
Author(s):  
P. Nielaba ◽  
J. L. Lebowitz ◽  
H. Spohn ◽  
J. L. Vall�s
Keyword(s):  
Quantum Particle ◽  
Heat Bath ◽  
Particle In Contact

BLACKBODY RADIATION: ROSETTA STONE OF HEAT BATH MODELS

2007 ◽  
Vol 07 (04) ◽  
pp. L483-L490 ◽  
Author(s):  
R. F. O'CONNELL
Keyword(s):  
Radiation Effects ◽  
Quantum Particle ◽  
Heat Bath ◽  
Zero Point ◽  
Equation Of Motion ◽  
Blackbody Radiation ◽  
Harmonic Oscillators ◽  
Temperature Dependent ◽  
Rosetta Stone ◽  
Quantum Thermodynamic

The radiation field can be regarded as a collection of independent harmonic oscillators and, as such, constitutes a heat bath. Moreover, the known form of its interaction with charged particles provides a "rosetta stone" for deciding on and interpreting the correct interaction for the more general case of a quantum particle in an external potential and coupled to an arbitrary heat bath. In particular, combining QED with the machinery of stochastic physics, enables the usual scope of applications to be widened. We discuss blackbody radiation effects on: the equation of motion of a radiating electron (obtaining an equation of motion which is free from runaway solutions), anomalous diffusion, the spreading of a Gaussian wave packet, and decoherence effects due to zero-point oscillations. In addition, utilizing a formula we obtained for the free energy of an oscillator in a heat bath, enables us to determine all the quantum thermodynamic functions of interest (particularly in the areas of quantum information and nanophysics where small systems are involved) and from which we obtain temperature dependent Lamb shifts, quantum effects on the entropy at low temperature and implications for Nernst's law.

Fault Diagnosis of Wind Turbine Gearbox Based on Improved QPSO Algorithm

2019 ◽  
Vol 12 (3) ◽  
pp. 277-283
Author(s):  
Jiatang Cheng ◽  
Yan Xiong
Keyword(s):  
Particle Swarm Optimization ◽  
Fault Diagnosis ◽  
Wind Turbine ◽  
Bp Neural Network ◽  
Quantum Particle ◽  
Particle Swarm ◽  
Wind Turbine Gearbox ◽  
Bp Network ◽  
Swarm Optimization ◽  
Quantum Particle Swarm Optimization

Background: The effective diagnosis of wind turbine gearbox fault is an important means to ensure the normal and stable operation and avoid unexpected accidents. Methods: To accurately identify the fault modes of the wind turbine gearbox, an intelligent diagnosis technology based on BP neural network trained by the Improved Quantum Particle Swarm Optimization Algorithm (IQPSOBP) is proposed. In IQPSO approach, the random adjustment scheme of contractionexpansion coefficient and the restarting strategy are employed, and the performance evaluation is executed on a set of benchmark test functions. Subsequently, the fault diagnosis model of the wind turbine gearbox is built by using IQPSO algorithm and BP neural network. Results: According to the evaluation results, IQPSO is superior to PSO and QPSO algorithms. Also, compared with BP network, BP network trained by Particle Swarm Optimization (PSOBP) and BP network trained by Quantum Particle Swarm Optimization (QPSOBP), IQPSOBP has the highest diagnostic accuracy. Conclusion: The presented method provides a new reference for the fault diagnosis of wind turbine gearbox.

scholarly journals Entanglement content of quantum particle excitations. Part II. Disconnected regions and logarithmic negativity

2019 ◽  
Vol 2019 (11) ◽  
Author(s):  
Olalla A. Castro-Alvaredo ◽  
Cecilia De Fazio ◽  
Benjamin Doyon ◽  
István M. Szécsényi
Keyword(s):  
Quantum Particle ◽  
Logarithmic Negativity

Research on disruption management of urgent arrival in job shop with deteriorating effect

2021 ◽  
pp. 1-13
Author(s):  
Ning Tao ◽  
Duan Xiaodong ◽  
An Lu ◽  
Gou Tao
Keyword(s):  
Prospect Theory ◽  
Job Shop ◽  
Job Shop Scheduling ◽  
Quantum Particle ◽  
Particle Swarm ◽  
Cumulative Prospect Theory ◽  
Disruption Management ◽  
Particle Swarm Algorithm ◽  
Shop Scheduling ◽  
Management Method

A disruption management method based on cumulative prospect theory is proposed for the urgent with deteriorating effect arrival in flexible job shop scheduling problem (FJSP). First, the mathematical model of problem is established with minimizing the completion time of urgent order, minimizing the total process time of the system and minimizing the total cost as the target. Then, the cumulative prospect theory equation of the urgent arrival in job shop scheduling process is induced designed. Based on the selected model, an optimized multi-phase quantum particle swarm algorithm (MQPSO) is proposed for selecting processing route. Finally, using Solomon example simulation and company Z riveting shop example as the study object, the performance of the proposed method is analyzed. It is compared with the current common rescheduling methods, and the results verify that the method proposed in this paper not only meets the goal of the optimized objects, but improves the practical requirements for the stability of production and processing system during urgent arrival. Lastly, the optimized multiphase quantum particle swarm algorithm is used to solve disruption management of urgent arrival problem. Through instance analysis and comparison, the effectiveness and efficiency of urgent arrival disruption management method with deteriorating effect are verified.

Measuring the electrostatic charges of a single particle in contact electrification

2021 ◽  
Author(s):  
Chuanxi Xu ◽  
Weiwei Zhang ◽  
Shui Hu ◽  
Peng Li ◽  
Shengyuan Jiang ◽  
...  
Keyword(s):  
Single Particle ◽  
Electrostatic Charges ◽  
Contact Electrification ◽  
Particle In Contact

scholarly journals Vibrational heat-bath configuration interaction

2021 ◽  
Vol 154 (7) ◽  
pp. 074104
Author(s):  
Jonathan H. Fetherolf ◽  
Timothy C. Berkelbach
Keyword(s):  
Configuration Interaction ◽  
Heat Bath

scholarly journals A Multimodal Smart Quantum Particle Swarm Optimization for Electromagnetic Design Optimization Problems

Energies ◽  
2021 ◽  
Vol 14 (15) ◽  
pp. 4613
Author(s):  
Shah Fahad ◽  
Shiyou Yang ◽  
Rehan Ali Khan ◽  
Shafiullah Khan ◽  
Shoaib Ahmed Khan
Keyword(s):  
Particle Swarm Optimization ◽  
Optimization Problems ◽  
Quantum Particle ◽  
Particle Swarm ◽  
Continuous Variables ◽  
Design Problems ◽  
Swarm Optimization ◽  
Electromagnetic Design ◽  
Quantum Particle Swarm Optimization ◽  
Engineering Design Problems

Electromagnetic design problems are generally formulated as nonlinear programming problems with multimodal objective functions and continuous variables. These can be solved by either a deterministic or a stochastic optimization algorithm. Recently, many intelligent optimization algorithms, such as particle swarm optimization (PSO), genetic algorithm (GA) and artificial bee colony (ABC), have been proposed and applied to electromagnetic design problems with promising results. However, there is no universal algorithm which can be used to solve engineering design problems. In this paper, a stochastic smart quantum particle swarm optimization (SQPSO) algorithm is introduced. In the proposed SQPSO, to tackle the premature convergence problem in order to improve the global search ability, a smart particle and a memory archive are adopted instead of mutation operations. Moreover, to enhance the exploration searching ability, a new set of random numbers and control parameters are introduced. Experimental results validate that the adopted control policy in this work can achieve a good balance between exploration and exploitation. Finally, the SQPSO has been tested on well-known optimization benchmark functions and implemented on the electromagnetic TEAM workshop problem 22. The simulation result shows an outstanding capability of the proposed algorithm in speeding convergence compared to other algorithms.

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