Browsing by Author "Liu, X."

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DIII-D research advancing the physics basis for optimizing the tokamak approach to fusion energy
(2022-04-01) Fenstermacher, M. E.; Abbate, J.; Abe, S.; Abrams, T.; Adams, M.; Adamson, B.; Aiba, N.; Akiyama, T.; Aleynikov, P.; Allen, E.; Allen, S.; Anand, H.; Anderson, J.; Andrew, Y.; Andrews, T.; Appelt, D.; Arbon, R.; Ashikawa, N.; Ashourvan, A.; Aslin, M.; Asnis, Y.; Austin, M.; Ayala, D.; Bak, J.; Bandyopadhyay, I.; Banerjee, S.; Barada, K.; Bardoczi, L.; Barr, J.; Bass, E.; Battaglia, D.; Battey, A.; Baumgartner, W.; Baylor, L.; Beckers, J.; Beidler, M.; Belli, E.; Berkery, J.; Bernard, T.; Bertelli, N.; Beurskens, M.; Bielajew, R.; Bilgili, S.; Biswas, B.; Blondel, S.; Boedo, J.; Bogatu, I.; Boivin, R.; Bolzonella, T.; Bongard, M.; Bonnin, X.; Bonoli, P.; Bonotto, M.; Bortolon, A.; Bose, S.; Bosviel, N.; Bouwmans, S.; Boyer, M.; Boyes, W.; Bradley, L.; Brambila, R.; Brennan, D.; Bringuier, S.; Brodsky, L.; Brookman, M.; Brooks, J.; Brower, D.; Brown, G.; Brown, W.; Burke, M.; Burrell, K.; Butler, K.; Buttery, R.; Bykov, I.; Byrne, P.; Cacheris, A.; Callahan, K.; Callen, J.; Campbell, G.; Candy, J.; Canik, J.; Cano-Megias, P.; Cao, N.; Carayannopoulos, L.; Carlstrom, T.; Carrig, W.; Carter, T.; Cary, W.; Casali, L.; Cengher, M.; Cespedes Paz, G.; Chaban, R.; Chan, V.; Chapman, B.; Char, I.; Chattopadhyay, A.; Chen, R.; Chen, J.; Chen, X.; Chen, M.; Chen, Z.; Choi, M.; Choi, W.; Choi, G.; Chousal, L.; Chrobak, C.; Chrystal, C.; Chung, Y.; Churchill, R.; Cianciosa, M.; Clark, J.; Clement, M.; Coda, S.; Cole, A.; Collins, C.; Conlin, W.; Cooper, A.; Cordell, J.; Coriton, B.; Cote, T.; Cothran, J.; Creely, A.; Crocker, N.; Crowe, C.; Crowley, B.; Crowley, T.; Cruz-Zabala, D.; Cummings, D.; Curie, M.; Curreli, D.; Dal Molin, A.; Dannels, B.; Dautt-Silva, A.; Davda, K.; De Tommasi, G.; De Vries, P.; Degrandchamp, G.; Degrassie, J.; Demers, D.; Denk, S.; Depasquale, S.; Deshazer, E.; Diallo, A.; Diem, S.; Dimits, A.; Ding, R.; Ding, S.; Ding, W.; Do, T.; Doane, J.; Dong, G.; Donovan, D.; Drake, J.; Drews, W.; Drobny, J.; Du, X.; Du, H.; Duarte, V.; Dudt, D.; Dunn, C.; Duran, J.; Dvorak, A.; Effenberg, F.; Eidietis, N.; Elder, D.; Eldon, D.; Ellis, R.; Elwasif, W.; Ennis, D.; Erickson, K.; Ernst, D.; Fasciana, M.; Fedorov, D.; Feibush, E.; Ferraro, N.; Ferreira, J.; Ferron, J.; Fimognari, P.; Finkenthal, D.; Fitzpatrick, R.; Fox, P.; Fox, W.; Frassinetti, L.; Frerichs, H.; Frye, H.; Fu, Y.; Gage, K.; Galdon Quiroga, J.; Gallo, A.; Gao, Q.; Garcia, A.; Garcia Munoz, M.; Garnier, D.; Garofalo, A.; Gattuso, A.; Geng, D.; Gentle, K.; Ghosh, D.; Giacomelli, L.; Gibson, S.; Gilson, E.; Giroud, C.; Glass, F.; Glasser, A.; Glibert, D.; Gohil, P.; Gomez, R.; Gomez, S.; Gong, X.; Gonzales, E.; Goodman, A.; Gorelov, Y.; Graber, V.; Granetz, R.; Gray, T.; Green, D.; Greenfield, C.; Greenwald, M.; Grierson, B.; Groebner, R.; Grosnickle, W.; Groth, M.; Grunloh, H.; Gu, S.; Guo, W.; Guo, H.; Gupta, P.; Guterl, J.; Guttenfelder, W.; Guzman, T.; Haar, S.; Hager, R.; Hahn, S.; Halfmoon, M.; Hall, T.; Hallatschek, K.; Halpern, F.; Hammett, G.; Han, H.; Hansen, E.; Hansen, C.; Hansink, M.; Hanson, J.; Hanson, M.; Hao, G.; Harris, A.; Harvey, R.; Haskey, S.; Hassan, E.; Hassanein, A.; Hatch, D.; Hawryluk, R.; Hayashi, W.; Heidbrink, W.; Herfindal, J.; Hicok, J.; Hill, D.; Hinson, E.; Holcomb, C.; Holland, L.; Holland, C.; Hollmann, E.; Hollocombe, J.; Holm, A.; Holmes, I.; Holtrop, K.; Honda, M.; Hong, R.; Hood, R.; Horton, A.; Horvath, L.; Hosokawa, M.; Houshmandyar, S.; Howard, N.; Howell, E.; Hoyt, D.; Hu, W.; Hu, Y.; Hu, Q.; Huang, J.; Huang, Y.; Hughes, J.; Human, T.; Humphreys, D.; Huynh, P.; Hyatt, A.; Ibanez, C.; Ibarra, L.; Icasas, R.; Ida, K.; Igochine, V.; In, Y.; Inoue, S.; Isayama, A.; Izacard, O.; Izzo, V.; Jackson, A.; Jacobsen, G.; Jaervinen, A.; Jalalvand, A.; Janhunen, J.; Jardin, S.; Jarleblad, H.; Jeon, Y.; Ji, H.; Jian, X.; Joffrin, E.; Johansen, A.; Johnson, C.; Johnson, T.; Jones, C.; Joseph, I.; Jubas, D.; Junge, B.; Kalb, W.; Kalling, R.; Kamath, C.; Kang, J.; Kaplan, D.; Kaptanoglu, A.; Kasdorf, S.; Kates-Harbeck, J.; Kazantzidis, P.; Kellman, A.; Kellman, D.; Kessel, C.; Khumthong, K.; Kim, E.; Kim, H.; Kim, J.; Kim, S.; Kim, K.; Kim, C.; Kimura, W.; King, M.; King, J.; Kinsey, J.; Kirk, A.; Kiyan, B.; Kleiner, A.; Klevarova, V.; Knapp, R.; Knolker, M.; Ko, W.; Kobayashi, T.; Koch, E.; Kochan, M.; Koel, B.; Koepke, M.; Kohn, A.; Kolasinski, R.; Kolemen, E.; Kostadinova, E.; Kostuk, M.; Kramer, G.; Kriete, D.; Kripner, L.; Kubota, S.; Kulchar, J.; Kwon, K.; La Haye, R.; Laggner, F.; Lan, H.; Lantsov, R.; Lao, L.; Lasa Esquisabel, A.; Lasnier, C.; Lau, C.; Leard, B.; Lee, J.; Lee, R.; Lee, M.; Lee, Y.; Lee, C.; Lee, S.; Lehnen, M.; Leonard, A.; Leppink, E.; Lesher, M.; Lestz, J.; Leuer, J.; Leuthold, N.; Li, X.; Li, K.; Li, E.; Li, G.; Li, L.; Li, Z.; Li, J.; Li, Y.; Lin, Z.; Lin, D.; Liu, X.; Liu, J.; Liu, Y.; Liu, T.; Liu, C.; Liu, Z.; Liu, A.; Liu, D.; Loarte-Prieto, A.; Lodestro, L.; Logan, N.; Lohr, J.; Lombardo, B.; Lore, J.; Luan, Q.; Luce, T.; Luda Di Cortemiglia, T.; Luhmann, N.; Lunsford, R.; Luo, Z.; Lvovskiy, A.; Lyons, B.; Ma, X.; Madruga, M.; Madsen, B.; Maggi, C.; Maheshwari, K.; Mail, A.; Mailloux, J.; Maingi, R.; Major, M.; Makowski, M.; Manchanda, R.; Marini, C.; Marinoni, A.; Maris, A.; Markovic, T.; Marrelli, L.; Martin, E.; Mateja, J.; Matsunaga, G.; Maurizio, R.; Mauzey, P.; Mauzey, D.; McArdle, G.; McClenaghan, J.; McCollam, K.; McDevitt, C.; McKay, K.; McKee, G.; McLean, A.; Mehta, V.; Meier, E.; Menard, J.; Meneghini, O.; Merlo, G.; Messer, S.; Meyer, W.; Michael, C.; Michoski, C.; Milne, P.; Minet, G.; Misleh, A.; Mitrishkin, Y.; Moeller, C.; Montes, K.; Morales, M.; Mordijck, S.; Moreau, D.; Morosohk, S.; Morris, P.; Morton, L.; Moser, A.; Moyer, R.; Moynihan, C.; Mrazkova, T.; Mueller, D.; Munaretto, S.; Munoz Burgos, J.; Murphy, C.; Murphy, K.; Muscatello, C.; Myers, C.; Nagy, A.; Nandipati, G.; Navarro, M.; Nave, F.; Navratil, G.; Nazikian, R.; Neff, A.; Neilson, G.; Neiser, T.; Neiswanger, W.; Nelson, D.; Nelson, A.; Nespoli, F.; Nguyen, R.; Nguyen, L.; Nguyen, X.; Nichols, J.; Nocente, M.; Nogami, S.; Noraky, S.; Norausky, N.; Nornberg, M.; Nygren, R.; Odstrcil, T.; Ogas, D.; Ogorman, T.; Ohdachi, S.; Ohtani, Y.; Okabayashi, M.; Okamoto, M.; Olavson, L.; Olofsson, E.; Omullane, M.; Oneill, R.; Orlov, D.; Orvis, W.; Osborne, T.; Pace, D.; Paganini Canal, G.; Pajares Martinez, A.; Palacios, L.; Pan, C.; Pan, Q.; Pandit, R.; Pandya, M.; Pankin, A.; Park, Y.; Park, J.; Parker, S.; Parks, P.; Parsons, M.; Patel, B.; Pawley, C.; Paz-Soldan, C.; Peebles, W.; Pelton, S.; Perillo, R.; Petty, C.; Peysson, Y.; Pierce, D.; Pigarov, A.; Pigatto, L.; Piglowski, D.; Pinches, S.; Pinsker, R.; Piovesan, P.; Piper, N.; Pironti, A.; Pitts, R.; Pizzo, J.; Plank, U.; Podesta, M.; Poli, E.; Poli, F.; Ponce, D.; Popovic, Z.; Porkolab, M.; Porter, G.; Powers, C.; Powers, S.; Prater, R.; Pratt, Q.; Pusztai, I.; Qian, J.; Qin, X.; Ra, O.; Rafiq, T.; Raines, T.; Raman, R.; Rauch, J.; Raymond, A.; Rea, C.; Reich, M.; Reiman, A.; Reinhold, S.; Reinke, M.; Reksoatmodjo, R.; Ren, Q.; Ren, Y.; Ren, J.; Rensink, M.; Renteria, J.; Rhodes, T.; Rice, J.; Roberts, R.; Robinson, J.; Rodriguez Fernandez, P.; Rognlien, T.; Rosenthal, A.; Rosiello, S.; Rost, J.; Roveto, J.; Rowan, W.; Rozenblat, R.; Ruane, J.; Rudakov, D.; Ruiz Ruiz, J.; Rupani, R.; Saarelma, S.; Sabbagh, S.; Sachdev, J.; Saenz, J.; Saib, S.; Salewski, M.; Salmi, A.; Sammuli, B.; Samuell, C.; Sandorfi, A.; Sang, C.; Sarff, J.; Sauter, O.; Schaubel, K.; Schmitz, L.; Schmitz, O.; Schneider, J.; Schroeder, P.; Schultz, K.; Schuster, E.; Schwartz, J.; Sciortino, F.; Scotti, F.; Scoville, J.; Seltzman, A.; Seol, S.; Sfiligoi, I.; Shafer, M.; Sharapov, S.; Shen, H.; Shepard, T.; Shi, S.; Shibata, Y.; Shin, G.; Shiraki, D.; Shousha, R.; Si, H.; Simmerling, P.; Sinclair, G.; Sinha, J.; Sinha, P.; Sips, G.; Sizyuk, T.; Skinner, C.; Sladkomedova, A.; Slendebroek, T.; Slief, J.; Smirnov, R.; Smith, J.; Smith, S.; Smith, D.; Snipes, J.; Snoep, G.; Snyder, A.; Snyder, P.; Solano, E.; Solomon, W.; Song, J.; Sontag, A.; Soukhanovskii, V.; Spendlove, J.; Spong, D.; Squire, J.; Srinivasan, C.; Stacey, W.; Staebler, G.; Stagner, L.; Stange, T.; Stangeby, P.; Stefan, R.; Stemprok, R.; Stephan, D.; Stillerman, J.; Stoltzfus-Dueck, T.; Stonecipher, W.; Storment, S.; Strait, E.; Su, D.; Sugiyama, L.; Sun, Y.; Sun, P.; Sun, Z.; Sun, A.; Sundstrom, D.; Sung, C.; Sungcoco, J.; Suttrop, W.; Suzuki, Y.; Suzuki, T.; Svyatkovskiy, A.; Swee, C.; Sweeney, R.; Sweetnam, C.; Szepesi, G.; Takechi, M.; Tala, T.; Tanaka, K.; Tang, X.; Tang, S.; Tao, Y.; Tao, R.; Taussig, D.; Taylor, T.; Teixeira, K.; Teo, K.; Theodorsen, A.; Thomas, D.; Thome, K.; Thorman, A.; Thornton, A.; Ti, A.; Tillack, M.; Timchenko, N.; Tinguely, R.; Tompkins, R.; Tooker, J.; Torrezan De Sousa, A.; Trevisan, G.; Tripathi, S.; Trujillo Ochoa, A.; Truong, D.; Tsui, C.; Turco, F.; Turnbull, A.; Umansky, M.; Unterberg, E.; Vaezi, P.; Vail, P.; Valdez, J.; Valkis, W.; Van Compernolle, B.; Van Galen, J.; Van Kampen, R.; Van Zeeland, M.; Verdoolaege, G.; Vianello, N.; Victor, B.; Viezzer, E.; Vincena, S.; Wade, M.; Waelbroeck, F.; Wai, J.; Wakatsuki, T.; Walker, M.; Wallace, G.; Waltz, R.; Wampler, W.; Wang, L.; Wang, H.; Wang, Y.; Wang, Z.; Wang, G.; Ward, S.; Watkins, M.; Watkins, J.; Wehner, W.; Wei, Y.; Weiland, M.; Weisberg, D.; Welander, A.; White, A.; White, R.; Wiesen, S.; Wilcox, R.; Wilks, T.; Willensdorfer, M.; Wilson, H.; Wingen, A.; Wolde, M.; Wolff, M.; Woller, K.; Wolz, A.; Wong, H.; Woodruff, S.; Wu, Y.; Wukitch, S.; Wurden, G.; Xiao, W.; Xie, R.; Xing, Z.; Xu, X.; Xu, C.; Xu, G.; Yan, Z.; Yang, X.; Yang, Seongmoo; Yokoyama, T.; Yoneda, R.; Yoshida, M.; You, K.; Younkin, T.; Yu, J.; Yu, M.; Yu, G.; Yuan, Q.; Zaidenberg, L.; Zakharov, L.; Zamengo, A.; Zamperini, S.; Zarnstorff, M.; Zeger, E.; Zeller, K.; Zeng, L.; Zerbini, M.; Zhang, L.; Zhang, X.; Zhang, R.; Zhang, B.; Zhang, J.; Zhao, L.; Zhao, B.; Zheng, Y.; Zheng, L.; Zhu, B.; Zhu, J.; Zhu, Y.; Zsutty, M.; Zuin, M.; Wu, Mingfu; Sheng, Zhicai
A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
DIII-D physics research addresses critical challenges for the operation of ITER and the next generation of fusion energy devices. This is done through a focus on innovations to provide solutions for high performance long pulse operation, coupled with fundamental plasma physics understanding and model validation, to drive scenario development by integrating high performance core and boundary plasmas. Substantial increases in off-axis current drive efficiency from an innovative top launch system for EC power, and in pressure broadening for Alfven eigenmode control from a co-/counter-I p steerable off-axis neutral beam, all improve the prospects for optimization of future long pulse/steady state high performance tokamak operation. Fundamental studies into the modes that drive the evolution of the pedestal pressure profile and electron vs ion heat flux validate predictive models of pedestal recovery after ELMs. Understanding the physics mechanisms of ELM control and density pumpout by 3D magnetic perturbation fields leads to confident predictions for ITER and future devices. Validated modeling of high-Z shattered pellet injection for disruption mitigation, runaway electron dissipation, and techniques for disruption prediction and avoidance including machine learning, give confidence in handling disruptivity for future devices. For the non-nuclear phase of ITER, two actuators are identified to lower the L-H threshold power in hydrogen plasmas. With this physics understanding and suite of capabilities, a high poloidal beta optimized-core scenario with an internal transport barrier that projects nearly to Q = 10 in ITER at ∼8 MA was coupled to a detached divertor, and a near super H-mode optimized-pedestal scenario with co-I p beam injection was coupled to a radiative divertor. The hybrid core scenario was achieved directly, without the need for anomalous current diffusion, using off-axis current drive actuators. Also, a controller to assess proximity to stability limits and regulate β N in the ITER baseline scenario, based on plasma response to probing 3D fields, was demonstrated. Finally, innovative tokamak operation using a negative triangularity shape showed many attractive features for future pilot plant operation.
Multiwavelength Temporal Variability of the Blazar PKS 1510–089
(2023-08-01) Yuan, Q.; Kushwaha, Pankaj; Gupta, Alok C.; Tripathi, Ashutosh; Wiita, Paul J.; Zhang, M.; Liu, X.; Lähteenmäki, Anne; Tornikoski, Merja; Tammi, Joni; Ramakrishnan, Venkatessh; Cui, L.; Wang, X.; Gu, M. F.; Bambi, Cosimo; Volvach, A. E.
A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
We perform correlation and periodicity search analyses on long-term multiband light curves of the flat-spectrum radio quasar PKS 1510−089 observed by the space-based Fermi-Large Area Telescope in γ-rays, the SMARTS and Steward Observatory telescopes in optical and near-infrared (NIR), and the 13.7 m radio telescope in Metsähovi Radio Observatory between 2008 and 2018. The z-transform discrete correlation function method is applied to study the correlation and possible time lags among these multiband light curves. Among all pairs of wavelengths, the γ-ray versus optical/NIR and optical versus NIR correlations show zero time lags; however, both the γ-ray and optical/NIR emissions precede the radio radiation. The generalized Lomb–Scargle periodogram, weighted wavelet z-transform, and REDFIT techniques are employed to investigate the unresolved core emission–dominated 37 GHz light curve and yield evidence for a quasi period around 1540 days, although given the length of the whole data set it cannot be claimed to be significant. We also investigate the optical/NIR color variability and find that this source shows a simple redder-when-brighter behavior over time, even in the low-flux state.
Structural phase transition of monochalcogenides investigated with machine learning
(2022-03-28) Zhang, J.; Zhang, Feng; Wei, D.; Liu, L.; Liu, X.; Wang, Dangqi; Zhang, G. X.; Chen, X.; Wang, D.
A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
As machine learning becomes increasingly important in science and engineering, it holds the promise to provide a universal approach applicable to various systems to investigate their crystalline phase transitions. Here, we build and train accurate artificial neural networks that can distinguish tiny energy difference, which is crucial to predict the crystalline phase transitions. Employing the trained artificial neural networks in Monte Carlo simulations as the surrogate energy function, we apply this approach to monochalcogenides, including bulk and two-dimensional monolayer SnTe and GeTe, investigating their crystalline phase transitions. The machine-learning approach, when viewed as providing a universal mathematical structure, can be transferred to the investigation of other materials when the training data set generated with ab initio methods are available. Moreover, the machine-learning approach resolves the difficulties associated with constructing the effective Hamiltonian for monochalcogenides, showing great potential with its accuracy and efficiency.