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Journal articles on the topic 'Stochastic heating'

To see the other types of publications on this topic, follow the link: Stochastic heating.

Author: Grafiati

Published: 4 June 2021

Last updated: 31 January 2023

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1

Pavlovski, Georgi, and Edward C. D. Pope. "Stochastic heating of cooling flows." Monthly Notices of the Royal Astronomical Society 399, no. 4 (November 11, 2009): 2195–200. http://dx.doi.org/10.1111/j.1365-2966.2009.15424.x.

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2

Zhao, Guo-Qing, Heng-Qiang Feng, De-Jin Wu, Qiang Liu, Yan Zhao, and Zhan-Jun Tian. "On Mechanisms of Proton Perpendicular Heating in the Solar Wind: Test Results Based on Wind Observations." Research in Astronomy and Astrophysics 22, no. 1 (January 1, 2022): 015009. http://dx.doi.org/10.1088/1674-4527/ac3413.

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Abstract The solar wind protons undergo significant perpendicular heating when they propagate in the interplanetary space. Stochastic heating and cyclotron resonance heating due to kinetic Alfvén waves (KAWs) are two proposed mechanisms. Which mechanism accounts for the perpendicular heating is still an open question. This paper performs tests for the two mechanisms based on Wind observations during 2004 June and 2019 May. Results show that heating rates in terms of stochastic heating theory considerably depend on the parameter of plasma β. For the solar wind with moderately high β, the theoretical heating rates are comparable to or larger than empirical heating rates, suggesting that the stochastic heating could be a powerful mechanism. For the solar wind with low β, on the contrary, the majority of data have theoretical heating rates much lower than empirical heating rates, showing that the stochastic heating seems to be weak in this case. On the other hand, it is found that, when the propagation angles of KAWs are around 70°, theoretically predicted damping wavenumbers of KAWs are equal to the observed wavenumbers at which magnetic energy spectra become significantly steep. This may imply that resonance heating due to cyclotron damping of KAWs could be another mechanism if KAWs have propagation angles around 70°.

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3

Galinsky, V. L., and V. I. Shevchenko. "A stochastic mechanism of electron heating." Physics of Plasmas 19, no. 8 (August 2012): 082506. http://dx.doi.org/10.1063/1.4742988.

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4

Nomura, Yasuyuki. "Statistical properties of the stochastic heating map." Kakuyūgō kenkyū 62, no. 3 (1989): 201–15. http://dx.doi.org/10.1585/jspf1958.62.201.

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5

Antonov, A. N., V. A. Buts, A. G. Zagorodny, E. A. Kornilov, V. G. Svichensky, and D. V. Tarasov. "Stochastic Heating of Plasma in Plasma Cavity." Физические основы приборостроения 3, no. 3 (September 15, 2014): 72–85. http://dx.doi.org/10.25210/jfop-1403-072085.

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6

Klimov, O. V., and A. A. Tel’nikhin. "Stochastic heating in a plasma-beam system." Technical Physics 43, no. 11 (November 1998): 1318–22. http://dx.doi.org/10.1134/1.1259191.

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7

PATIN, D., E. LEFEBVRE, A. BOURDIER, and E. D'HUMIÈRES. "Stochastic heating in ultra high intensity laser-plasma interaction: Theory and PIC code simulations." Laser and Particle Beams 24, no. 2 (June 2006): 223–30. http://dx.doi.org/10.1017/s0263034606060320.

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In the first part, the theoretical model of the stochastic heating effect is presented briefly. Then, a numerical resolution of the Hamilton equations highlights the threshold of the stochastic effect. Finally, Particle-In-Cell (PIC) code simulations results, for experimentally relevant parameters, are presented in order to confirm the acceleration mechanism predicted by the one-particle theoretical model. This paper gives the conditions on the different experimental parameters in order to have an optimization of the stochastic heating.

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8

BOURDIER, A., D. PATIN, and E. LEFEBVRE. "Stochastic heating in ultra high intensity laser-plasma interaction." Laser and Particle Beams 25, no. 1 (February 28, 2007): 169–80. http://dx.doi.org/10.1017/s026303460707022x.

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Stochastic instabilities are studied considering the motion of one particle in a very high intensity wave propagating along a constant homogeneous magnetic field, and in a high intensity wave propagating in a nonmagnetized medium perturbed by one or two low intensity traveling waves. Resonances are identified and conditions for resonance overlap are studied. The part of chaos in the electron acceleration is analyzed. PIC code simulation results confirm the stochastic heating.

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9

Golovanivsky, K. S. "ECRIS plasmas: stochastic heating or Langmuir caviton collapses?" Plasma Sources Science and Technology 2, no. 4 (November 1, 1993): 240–50. http://dx.doi.org/10.1088/0963-0252/2/4/003.

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10

Khazanov, G., A. Tel'nikhin, and A. Krotov. "Stochastic ion heating by the lower-hybrid waves." Radiation Effects and Defects in Solids 165, no. 2 (February 2010): 165–76. http://dx.doi.org/10.1080/10420150903516684.

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11

Kaganovich, I. D., V. I. Kolobov, and L. D. Tsendin. "Stochastic electron heating in bounded radio‐frequency plasmas." Applied Physics Letters 69, no. 25 (December 16, 1996): 3818–20. http://dx.doi.org/10.1063/1.117115.

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12

Schulze, J., B. G. Heil, D. Luggenhölscher, R. P. Brinkmann, and U. Czarnetzki. "Stochastic heating in asymmetric capacitively coupled RF discharges." Journal of Physics D: Applied Physics 41, no. 19 (September 19, 2008): 195212. http://dx.doi.org/10.1088/0022-3727/41/19/195212.

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13

Antonov, A. N., V. A. Buts, O. F. Kovpik, E. A. Kornilov, O. V. Manuilenko, V. G. Svichenskii, K. N. Stepanov, and Yu A. Turkin. "Stochastic heating of plasma at electron cyclotron resonance." Journal of Experimental and Theoretical Physics Letters 69, no. 11 (June 1999): 851–57. http://dx.doi.org/10.1134/1.568101.

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14

Hanby, V. I., and A. J. Dil. "Stochastic modelling of building heating and cooling systems." Building Services Engineering Research and Technology 16, no. 4 (November 1995): 199–205. http://dx.doi.org/10.1177/014362449501600404.

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15

Gao, Xinliang, Quanming Lu, Mingyu Wu, and Shui Wang. "Ion stochastic heating by obliquely propagating magnetosonic waves." Physics of Plasmas 19, no. 6 (June 2012): 062111. http://dx.doi.org/10.1063/1.4731707.

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16

de Angelis, U., A. V. Ivlev, G. E. Morfill, and V. N. Tsytovich. "Stochastic heating of dust particles with fluctuating charges." Physics of Plasmas 12, no. 5 (May 2005): 052301. http://dx.doi.org/10.1063/1.1889446.

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17

Kostyukov, I. Yu, and J. M. Rax. "Stochastic heating in field-reversed low pressure discharges." Physics of Plasmas 7, no. 1 (January 2000): 185–92. http://dx.doi.org/10.1063/1.873793.

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18

Geletka, Vladimír, and Anna Sedláková. "Predicting Annual Heating Demand under Sensitivity Analysis." Applied Mechanics and Materials 330 (June 2013): 911–15. http://dx.doi.org/10.4028/www.scientific.net/amm.330.911.

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The quality of most buildings may be affected during the initial phase of architectural design. It is therefore to optimize input parameters, which significantly influence energy efficiency. In principle it is possible to speak of a deterministic approach, which consider the input parameters to be fixed or a stochastic approach, which takes a wider set of input parameters into account. A single-storey house is evaluated in terms of energy performance in the initial phase of building design, where input parameters are changed in order to determine a correlation coefficient. The methodology is based on a sensitivity analysis (SA) and MonteCarlo simulation based on a stochastic random selection. Regression (RA) were written to express the impact architectural design has on energy performance. Feedback from the regression model estimates annual heating demand of single storey house.

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19

Kostyukov, I. Yu. "Stochastic heating and stochastic outer ionization of an atomic cluster in a laser field." Journal of Experimental and Theoretical Physics 100, no. 5 (May 2005): 903–10. http://dx.doi.org/10.1134/1.1947314.

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20

Khalilzadeh, Elnaz, Mohammad Jafar Jafari, Amir Chakhmachi, Somayeh Rezaei, and Zohreh Dehghani. "Stochastic heating threshold of electrons in field-ionized plasma." Optik 245 (November 2021): 167725. http://dx.doi.org/10.1016/j.ijleo.2021.167725.

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21

Volodina, Victoria, Edward Wheatcroft, and Henry Wynn. "Comparing district heating options under uncertainty using stochastic ordering." Sustainable Energy, Grids and Networks 30 (June 2022): 100634. http://dx.doi.org/10.1016/j.segan.2022.100634.

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22

McChesney, J. M., P. M. Bellan, and R. A. Stern. "Observation of fast stochastic ion heating by drift waves." Physics of Fluids B: Plasma Physics 3, no. 12 (December 1991): 3363–78. http://dx.doi.org/10.1063/1.859768.

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23

McChesney, J. M., R. A. Stern, and P. M. Bellan. "Observation of fast stochastic ion heating by drift waves." Physical Review Letters 59, no. 13 (September 28, 1987): 1436–39. http://dx.doi.org/10.1103/physrevlett.59.1436.

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24

Czarnetzki, Uwe. "Kinetic model for stochastic heating in the INCA discharge." Plasma Sources Science and Technology 27, no. 10 (October 19, 2018): 105011. http://dx.doi.org/10.1088/1361-6595/aadeb9.

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25

Bourdier, A., D. Patin, and E. Lefebvre. "Stochastic heating in ultra high intensity laser-plasma interaction." Physica D: Nonlinear Phenomena 206, no. 1-2 (June 2005): 1–31. http://dx.doi.org/10.1016/j.physd.2005.04.017.

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26

PATIN, D., A. BOURDIER, and E. LEFEBVRE. "Stochastic heating in ultra high intensity laser-plasma interaction." Laser and Particle Beams 23, no. 4 (October 2005): 599. http://dx.doi.org/10.1017/s0263034605059987.

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27

Kawamura, E., M. A. Lieberman, and A. J. Lichtenberg. "Stochastic heating in single and dual frequency capacitive discharges." Physics of Plasmas 13, no. 5 (May 2006): 053506. http://dx.doi.org/10.1063/1.2203949.

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28

Dostov, A. I. "Stochastic dynamics of boiling on the heating element surface." Thermophysics and Aeromechanics 25, no. 2 (March 2018): 225–36. http://dx.doi.org/10.1134/s0869864318020087.

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29

Mikhailov, Yu A., L. A. Nikitina, G. V. Sklizkov, A. N. Starodub, and M. A. Zhurovich. "Stochastic heating of electrons in focused multimode laser fields." Journal of Russian Laser Research 28, no. 4 (July 2007): 345–56. http://dx.doi.org/10.1007/s10946-007-0023-6.

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30

Mortensen, R. E., and K. P. Haggerty. "A stochastic computer model for heating and cooling loads." IEEE Transactions on Power Systems 3, no. 3 (August 1988): 1213–19. http://dx.doi.org/10.1109/59.14584.

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31

Peñarrubia, Jorge. "Stochastic tidal heating by random interactions with extended substructures." Monthly Notices of the Royal Astronomical Society 484, no. 4 (February 1, 2019): 5409–36. http://dx.doi.org/10.1093/mnras/stz338.

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32

Xiang, Lv, Li Yi, and Wang Shui. "Stochastic Heating of Ions by Linear Polarized Alfvén Waves." Chinese Physics Letters 24, no. 7 (June 28, 2007): 2010–13. http://dx.doi.org/10.1088/0256-307x/24/7/062.

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33

Goedde, C. G., A. J. Lichtenberg, and M. A. Lieberman. "Self‐consistent stochastic electron heating in radio frequency discharges." Journal of Applied Physics 64, no. 9 (November 1988): 4375–83. http://dx.doi.org/10.1063/1.341286.

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34

Oliveira Panão, Marta J. N., and André Penas. "Building Stock Energy Model: Towards a Stochastic Approach." Energies 15, no. 4 (February 15, 2022): 1420. http://dx.doi.org/10.3390/en15041420.

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This work uses the outcome of a computational tool that performs Energy Performance Certification (EPC) data processing and transforms raw data into comparable data. Multi-correlation among variables results in probability distributions for the most relevant form and fabric building parameters. The model consistently predicts the distributions for heating and cooling energy needs for the Lisbon Metropolitan Area, with an error below 7% for the first, second and third quartiles. Differences in the energy needs estimation are below 6% when comparing the seasonal steady-state with the resistance-capacitance (RC) model, which proved to be a robust alternative algorithm capable of modeling hourly user profiles. The RC model calculates electricity consumption for actual, adequate, and minimum thermal comfort scenarios corresponding to different user profiles. The actual scenario, built from statistics and a previous survey, defines a reference to evaluate other scenarios for the mean electricity consumption for space heating and cooling in the building units with those systems. The results show that the actual mean electricity consumption for heating (610 kWh/y) is slightly above the minimum (512 kWh/y), with 37% of building units potentially under heated. The electricity consumption (108 kWh/y) for cooling is below the minimum (129 kWh/y).

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35

Sardeshmukh, Prashant D., and Philip Sura. "Multiscale Impacts of Variable Heating in Climate." Journal of Climate 20, no. 23 (December 1, 2007): 5677–95. http://dx.doi.org/10.1175/2007jcli1411.1.

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Abstract While it is obvious that the mean diabatic forcing of the atmosphere is crucial for maintaining the mean climate, the importance of diabatic forcing fluctuations is less evident in this regard. Such fluctuations do not appear directly in the equations of the mean climate but affect the mean indirectly through their effects on the time-mean transient-eddy fluxes of heat, momentum, and moisture. How large are these effects? What are the effects of tropical phenomena associated with substantial heating variations such as ENSO and the MJO? To what extent do variations of the extratropical surface heat fluxes and precipitation affect the mean climate? What are the effects of the rapid “stochastic” components of the heating fluctuations? Most current climate models misrepresent ENSO and the MJO and ignore stochastic forcing; they therefore also misrepresent their mean effects. To what extent does this contribute to climate model biases and to projections of climate change? This paper provides an assessment of such impacts by comparing with observations a long simulation of the northern winter climate by a dry adiabatic general circulation model forced only with the observed time-mean diabatic forcing as a constant forcing. Remarkably, despite the total neglect of all forcing variations, the model reproduces most features of the observed circulation variability and the mean climate, with biases similar to those of some state-of-the-art general circulation models. In particular, the spatial structures of the circulation variability are remarkably well reproduced. Their amplitudes, however, are progressively underestimated from the synoptic to the subseasonal to interannual and longer time scales. This underestimation is attributed to the neglect of the variable forcing. The model also excites significant tropical variability from the extratropics on interannual scales, which is overwhelmed in reality by the response to tropical heating variability. It is argued that the results of this study suggest a role for the stochastic, and not only the coherent, components of transient diabatic forcing in the dynamics of climate variability and the mean climate.

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36

Wielen, Roland, and Burkhard Fuchs. "Dynamical evolution of the Galactic disk." Symposium - International Astronomical Union 106 (1985): 481–90. http://dx.doi.org/10.1017/s0074180900242976.

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After some general remarks on the dynamical evolution of the galactic disk, we review mechanisms which may affect the velocities of disk stars: stochastic heating, deflections, adiabatic cooling or heating. We compare the observed velocities of nearby disk stars with theoretical predictions based on the diffusion of stellar orbits.

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37

Stasiewicz, Krzysztof, and Bengt Eliasson. "Stochastic and Quasi-adiabatic Electron Heating in Quasi-parallel Shocks." Astrophysical Journal 904, no. 2 (December 2, 2020): 173. http://dx.doi.org/10.3847/1538-4357/abbffa.

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38

Martinović, Mihailo M., Kristopher G. Klein, and Sofiane Bourouaine. "Radial Evolution of Stochastic Heating in Low-β Solar Wind." Astrophysical Journal 879, no. 1 (July 2, 2019): 43. http://dx.doi.org/10.3847/1538-4357/ab23f4.

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39

Wang, Haichao, Risto Lahdelma, and Pekka Salminen. "Stochastic multicriteria evaluation of district heating systems considering the uncertainties." Science and Technology for the Built Environment 24, no. 8 (April 19, 2018): 830–38. http://dx.doi.org/10.1080/23744731.2018.1457399.

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40

Rozmus, W., W. Tighe, A. A. Offenberger, and Kent Estabrook. "Parametric instabilities in underdense plasma and stochastic heating of electrons." Physics of Fluids 28, no. 3 (1985): 920. http://dx.doi.org/10.1063/1.865063.

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41

Krajewski, Karol L., Witold F. Krajewski, and Forrest M. Holly. "Real‐Time Optimal Stochastic Control of Power Plant River Heating." Journal of Energy Engineering 119, no. 1 (April 1993): 1–18. http://dx.doi.org/10.1061/(asce)0733-9402(1993)119:1(1).

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42

Pavlyuchenkov, Ya N., D. S. Wiebe, V. V. Akimkin, M. S. Khramtsova, and Th Henning. "Stochastic grain heating and mid-infrared emission in protostellar cores." Monthly Notices of the Royal Astronomical Society 421, no. 3 (February 10, 2012): 2430–41. http://dx.doi.org/10.1111/j.1365-2966.2012.20480.x.

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43

Liu, Hai-Feng, Shi-Qing Wang, Ke-Hua Li, and Chang-Jian Tang. "Nonresonant and stochastic heating of ions by low-frequency wave." Physics Letters A 378, no. 48 (November 2014): 3614–16. http://dx.doi.org/10.1016/j.physleta.2014.05.050.

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44

Deeskow, P., K. Elsässer, and F. Jestczemski. "Critical wave spectra for stochastic heating of a magnetized plasma." Physics of Fluids B: Plasma Physics 2, no. 7 (July 1990): 1551–64. http://dx.doi.org/10.1063/1.859480.

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45

Chen, Long-Fei, Qiang Chang, and Hong-Wei Xi. "Effect of stochastic grain heating on cold dense clouds chemistry." Monthly Notices of the Royal Astronomical Society 479, no. 3 (June 11, 2018): 2988–3001. http://dx.doi.org/10.1093/mnras/sty1525.

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46

Wang, Bin, C. B. Wang, P. H. Yoon, and C. S. Wu. "Stochastic heating and acceleration of minor ions by Alfvén waves." Geophysical Research Letters 38, no. 10 (May 2011): n/a. http://dx.doi.org/10.1029/2011gl047729.

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47

Gozhev, D. A., S. G. Bochkarev, N. I. Busleev, A. V. Brantov, S. I. Kudryashov, A. B. Savel’ev, and V. Yu Bychenkov. "Laser-triggered stochastic volumetric heating of sub-microwire array target." High Energy Density Physics 37 (November 2020): 100856. http://dx.doi.org/10.1016/j.hedp.2020.100856.

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48

Stasiewicz, Krzysztof. "Stochastic ion and electron heating on drift instabilities at the bow shock." Monthly Notices of the Royal Astronomical Society: Letters 496, no. 1 (May 15, 2020): L133—L137. http://dx.doi.org/10.1093/mnrasl/slaa090.

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Abstract The analysis of the wave content inside a perpendicular bow shock indicates that heating of ions is related to the lower hybrid drift (LHD) instability, and heating of electrons is related to the electron cyclotron drift (ECD) instability. Both processes represent stochastic acceleration caused by the electric field gradients on the electron gyroradius scales, produced by the two instabilities. Stochastic heating is a single-particle mechanism where large gradients break adiabatic invariants and expose particles to direct acceleration by the direct current and wave fields. The acceleration is controlled by function $\chi = m_iq_i^{-1} B^{-2}$div(E), which represents a general diagnostic tool for processes of energy transfer between electromagnetic fields and particles, and the measure of the local charge non-neutrality. The identification was made with multipoint measurements obtained from the Magnetospheric Multiscale spacecraft. The source for the LHD instability is the diamagnetic drift of ions, and for the ECD instability the source is ExB drift of electrons. The conclusions are supported by laboratory diagnostics of the ECD instability in Hall ion thrusters.

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49

Permyakova, Evelina V., and Denis S. Goldobin. "Stochastic parametric excitation of Rayleigh-Bénard convection." ВЕСТНИК ПЕРМСКОГО УНИВЕРСИТЕТА. ФИЗИКА, no. 4 (2022): 34–44. http://dx.doi.org/10.17072/1994-3598-2022-4-34-44.

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The paper is devoted to the problem of the thermal convection excitation in a horizontal layer with isothermal free boundaries caused by a random modulation of the gravity acceleration. Equations for the stochastic dynamics of the amplitude of small perturbations of the temperature field and the stream function are derived for the system. For these equations, the conditions for the growth of mean-square values are derived; these conditions are used as a criterion for the excitation of convective motions in the system. The excitation of flows is considered both for the case of heating from below and for heating from above. It is verified that, for any parameter values, the obtained modes of the fastest growth of mean-square values lie within the physically meaningful domain of the phase space. In contrast to the case of high-frequency periodic vibrations, white Gaussian noise always exerts a destabilizing effect on the heat-conducting state of the system. The cases of white Gaussian noise and harmonic high-frequency vibrations are also compared in a general form, without reference to a particular form of thermal convection equations.

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50

Fujiwara, S., and S. Hasegawa. "Estimation of the heating rate of ions due to laser fluctuations when implementing quantum algorithms." Quantum Information and Computation 7, no. 7 (September 2007): 573–83. http://dx.doi.org/10.26421/qic7.7-1.

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We analyze numerically the heating of trapped ions due to laser intensity and phase fluctuations when implementing Grover's algorithm and the Quantum Fourier Transform. For a simpler analysis we assume that the stochastic processes are white noise processes and average over each noise as in [Phys. Rev. A. \textbf{57}, 3748, (1998)]. We investigate the fidelity and the heating rate for these algorithms using parameters estimated from experiments, and we can see the order of magnitude difference in the heating rate depending on the quantum algorithms.

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