اشترك بالحزمة الذهبية واحصل على وصول غير محدود شمرا أكاديميا
تسجيل مستخدم جديدImportance of theory, computation and predictive modeling in the US magnetic fusion energy strategic plan
59
0
0.0
(
0
)
اسأل ChatGPT حول البحث
ﻻ يوجد ملخص باللغة العربية
Based on the community input at the National Academy of Sciences (NAS) Madison and Austin workshops in July and December 2017, respectively, this whitepaper was prepared and submitted to the NAS in the category of US fusion theory and computation. This whitepaper was submitted to NAS as one of five community-approved whitepapers. The revised version was also submitted for the Knoxville American Physical Society Division of Plasma Physics Community Planning Process (APS-DPP-CPP) workshop in September 2019.
قيم البحث
اقرأ أيضاً
This document is the final report of the Community Planning Process (CPP) that describes a comprehensive plan to deliver fusion energy and to advance plasma science. The CPP was initiated by the executive committee of the American Physical Society Di
vision of Plasma Physics (APS DPP) to help the Fusion Energy Sciences Advisory Committee (FESAC) fulfill a charge from the U.S. Department of Energy (DOE) to develop a strategic plan for the DOE Office of Fusion Energy Sciences (FES). In this charge, dated Nov 30, 2018, DOE Deputy Director for Science Dr. Stephen Binkley requested that FESAC undertake a new long range strategic planning activity for the Fusion Energy Sciences (FES) program. The strategic planning activity to encompass the entire FES research portfolio (namely, burning plasma science and discovery plasma science) should identify and prioritize the research required to advance both the scientific foundation needed to develop a fusion energy source, as well as the broader FES mission to steward plasma science. The CPP represents the first phase in developing a long range strategic plan for FES, and will serve as the basis for the second phase activity conducted by FESAC. It is worth noting that enacting the full scope of the recommendations in the strategic plan in this document will require suitable partnerships with other offices and governmental agencies, as well as with private industry and international partners.
The quest to understand the fundamental building blocks of nature and their interactions is one of the oldest and most ambitious of human scientific endeavors. Facilities such as CERNs Large Hadron Collider (LHC) represent a huge step forward in this
quest. The discovery of the Higgs boson, the observation of exceedingly rare decays of B mesons, and stringent constraints on many viable theories of physics beyond the Standard Model (SM) demonstrate the great scientific value of the LHC physics program. The next phase of this global scientific project will be the High-Luminosity LHC (HL-LHC) which will collect data starting circa 2026 and continue into the 2030s. The primary science goal is to search for physics beyond the SM and, should it be discovered, to study its details and implications. During the HL-LHC era, the ATLAS and CMS experiments will record circa 10 times as much data from 100 times as many collisions as in LHC Run 1. The NSF and the DOE are planning large investments in detector upgrades so the HL-LHC can operate in this high-rate environment. A commensurate investment in R&D for the software for acquiring, managing, processing and analyzing HL-LHC data will be critical to maximize the return-on-investment in the upgraded accelerator and detectors. The strategic plan presented in this report is the result of a conceptualization process carried out to explore how a potential Scientific Software Innovation Institute (S2I2) for High Energy Physics (HEP) can play a key role in meeting HL-LHC challenges.
One of the main diagnostic tools for measuring electron density profiles and the characteristics of long wavelength turbulent wave structures in fusion plasmas is Beam Emission Spectroscopy (BES). The increasing number of BES systems necessitated an
accurate and comprehensive simulation of BES diagnostics, which in turn motivated the development of the RENATE simulation code that is the topic of this paper. RENATE is a modular, fully three-dimensional code incorporating all key features of BES systems from the atomic physics to the observation, including an advanced modeling of the optics. Thus RENATE can be used both in the interpretation of measured signals and the development of new BES systems. The most important components of the code have been successfully benchmarked against other simulation codes. The primary results have been validated against experimental data from the KSTAR tokamak.
We present an ultrafast neural network (NN) model, QLKNN, which predicts core tokamak transport heat and particle fluxes. QLKNN is a surrogate model based on a database of 300 million flux calculations of the quasilinear gyrokinetic transport model Q
uaLiKiz. The database covers a wide range of realistic tokamak core parameters. Physical features such as the existence of a critical gradient for the onset of turbulent transport were integrated into the neural network training methodology. We have coupled QLKNN to the tokamak modelling framework JINTRAC and rapid control-oriented tokamak transport solver RAPTOR. The coupled frameworks are demonstrated and validated through application to three JET shots covering a representative spread of H-mode operating space, predicting turbulent transport of energy and particles in the plasma core. JINTRAC-QLKNN and RAPTOR-QLKNN are able to accurately reproduce JINTRAC-QuaLiKiz T i,e and n e profiles, but 3 to 5 orders of magnitude faster. Simulations which take hours are reduced down to only a few tens of seconds. The discrepancy in the final source-driven predicted profiles between QLKNN and QuaLiKiz is on the order 1%-15%. Also the dynamic behaviour was well captured by QLKNN, with differences of only 4%-10% compared to JINTRAC-QuaLiKiz observed at mid-radius, for a study of density buildup following the L-H transition. Deployment of neural network surrogate models in multi-physics integrated tokamak modelling is a promising route towards enabling accurate and fast tokamak scenario optimization, Uncertainty Quantification, and control applications.
سجل دخول لتتمكن من نشر تعليقات
التعليقات
جاري جلب التعليقات
سجل دخول لتتمكن من متابعة معايير البحث التي قمت باختيارها
