Henrice Jr et al., 2021 - Google Patents
Online identification of trips caused by the external proton source in an ADS reactorHenrice Jr et al., 2021
- Document ID
- 10486561477247208136
- Author
- Henrice Jr E
- Palma D
- Gonçalves A
- Publication year
- Publication venue
- Nuclear Engineering and Design
External Links
Snippet
New fields in the domain of signal analysis began with the improvement of the techniques that gave rise to Wavelet Transforms in the late 1980s and early 1990s. Another important element that allowed the development and use of new methods in signal analysis was the …
- 238000000034 method 0 abstract description 32
Classifications
-
- G—PHYSICS
- G21—NUCLEAR PHYSICS; NUCLEAR ENGINEERING
- G21C—NUCLEAR REACTORS
- G21C17/00—Monitoring; Testing ; Maintaining
- G21C17/02—Devices or arrangements for monitoring coolant or moderator
- G21C17/022—Devices or arrangements for monitoring coolant or moderator for monitoring liquid coolants or moderators
-
- G—PHYSICS
- G21—NUCLEAR PHYSICS; NUCLEAR ENGINEERING
- G21C—NUCLEAR REACTORS
- G21C17/00—Monitoring; Testing ; Maintaining
- G21C17/10—Structural combination of fuel element, control rod, reactor core, or moderator structure with sensitive instruments, e.g. for measuring radioactivity, strain
-
- G—PHYSICS
- G21—NUCLEAR PHYSICS; NUCLEAR ENGINEERING
- G21C—NUCLEAR REACTORS
- G21C3/00—Reactor fuel elements and their assemblies; Selection of substances for use as reactor fuel elements
- G21C3/02—Fuel elements
-
- G—PHYSICS
- G21—NUCLEAR PHYSICS; NUCLEAR ENGINEERING
- G21D—NUCLEAR POWER PLANT
- G21D3/00—Control of nuclear power plant
- G21D3/001—Computer implemented control
- G21D2003/002—Core design; Core simulations
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
- G06F17/20—Handling natural language data
-
- G—PHYSICS
- G21—NUCLEAR PHYSICS; NUCLEAR ENGINEERING
- G21C—NUCLEAR REACTORS
- G21C19/00—Arrangements for treating, for handling, or for facilitating the handling of, fuel or other materials which are used within the reactor, e.g. within its pressure vessel
-
- G—PHYSICS
- G21—NUCLEAR PHYSICS; NUCLEAR ENGINEERING
- G21D—NUCLEAR POWER PLANT
- G21D3/00—Control of nuclear power plant
- G21D3/04—Safety arrangements
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02E—REDUCTION OF GREENHOUSE GASES [GHG] EMISSION, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
- Y02E30/00—Energy generation of nuclear origin
- Y02E30/30—Nuclear fission reactors
- Y02E30/40—Other aspects relating to nuclear fission
-
- G—PHYSICS
- G21—NUCLEAR PHYSICS; NUCLEAR ENGINEERING
- G21D—NUCLEAR POWER PLANT
- G21D3/00—Control of nuclear power plant
- G21D3/08—Regulation of any parameters in the plant
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Yao et al. | Uncertainty-aware deep learning for reliable health monitoring in safety-critical energy systems | |
| EP3061100B1 (en) | A method for monitoring boron dilution during a reactor outage | |
| Pirouzmand et al. | Estimation of relative power distribution and power peaking factor in a VVER-1000 reactor core using artificial neural networks | |
| Zeng et al. | Uncertainty quantification and propagation of multiphysics simulation of the pressurized water reactor core | |
| Buffa et al. | Fuzzy FMECA analysis of radioactive gas recovery system in the SPES experimental facility | |
| Antonello et al. | Insights in the safety analysis of an early microreactor design | |
| Rivas et al. | Propagating neutronic uncertainties for FFTF LOFWOS Test# 13 | |
| Cui et al. | Development and verification of a three-dimensional spatial dynamics code for molten salt reactors | |
| Liu et al. | Deep learning driven inverse solving method for neutron diffusion equations and three-dimensional core power reconstruction technology | |
| Henrice Jr et al. | Online identification of trips caused by the external proton source in an ADS reactor | |
| Garcia et al. | Integration of facility modeling capabilities for nuclear nonproliferation analysis | |
| Ho et al. | Numerical investigation of the random arrangement effect of coated fuel particles on the criticality of HTTR fuel compact using MCNP6 | |
| Siefman et al. | Uncertainty Quantification of a Light Water Pulsed-Neutron Die-Away Experiment to Thermal Neutron Scattering Laws | |
| Tikhomirov et al. | Independent testing of new generation codes of the «Proryv» project | |
| Kim et al. | Feasibility study on AI-based prediction for CRUD induced power shift in PWRs | |
| Souza et al. | A method to identify an accidental control rod drop with inverse distance weighted interpolation | |
| Mesquita et al. | On-line monitoring of the IPR-R1 TRIGA reactor neutronic parameters | |
| Liu et al. | An integrated assessment method of real-time source term for high temperature gas-cooled reactor | |
| Stewart et al. | Utilizing a Virtual Sodium-Cooled Fast Reactor Digital Twin to Aid in Diversion Pathway Analysis for International Safeguards Applications | |
| Talon et al. | Continuous mapping of nuclear reactor core power using artificial neural network even in the presence of inactive detectors | |
| Tran et al. | A multi-group neutron noise simulator for fast reactors | |
| Meunier et al. | Modeling, verification and validation of multiple PWR depletion cycles with DRAGON and PARCS | |
| Salmassian et al. | Diagnosing core local flow blockages in a VVER-1000/446 reactor using ex-core detectors and neural networks | |
| Nissan | Intelligent technologies for nuclear power systems: heuristic and neural tools | |
| Choi et al. | Optimization of water chemistry to mitigate corrosion products in nuclear power plants using big data and multiple linear regression in machine learning |