RELAP-7 Theory Manual

Berry, Ray A.; Peterson, John W.; Zhang, Hongbin; Martineau, Richard; Zhao, Haihua; Zou, Ling; Andrš, David

doi:10.2172/1239866

Cited by 18 publications

(4 citation statements)

References 64 publications

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“…• A single representative fuel cooling mechanism preserving the main hydraulic parameters of the collection of all the fuel cooling channels (flow area, hydraulic diameter, etc.) is modeled with RELAP-7 [11] with an inlet coolant temperature of 300 K. The H 2 propellant is assumed to be an ideal gas with the specific heat capacity adjusted to match the expected enthalpy rise over a temperature range of 50-2700 K, as was done in Ref. [5].…”

Section: Iiib Simplified Modelmentioning

confidence: 99%

Hybrid Temperature-Reactivity PID Controllers for Nuclear Thermal Propulsion Startup

Laboure,

Terlizzi,

Schunert

2023

Nuclear and Emerging Technologies for Space (NETS 2023)

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In this work, a novel temperature-reactivity controller is introduced to rotate control drums based on a chamber temperature signal. It consists of two components: (1) a reactivitydriven proportional controller converting the temperature demand into a reactivity signal, and (2) a temperature-driven proportional-derivative controller which relies on a derivative term to anticipate sudden changes in demand and limit temperature overshoot. A weighting function is proposed to continuously transition from one component to the other.

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Section: Iiib Simplified Modelmentioning

confidence: 99%

Hybrid Temperature-Reactivity PID Controllers for Nuclear Thermal Propulsion Startup

Laboure,

Terlizzi,

Schunert

2023

Nuclear and Emerging Technologies for Space (NETS 2023)

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“…However, most existing JFNK research mainly focuses on the reactor core and primary circuit problem. While for the secondary circuit coupling issue, the traditional coupling methods [11] are still mainly used, and only a few attempts have been made to use the JFNK method, such as Relap7 [12]. The main contribution of this work is to fully realize the coupling calculation of the secondary circuit within the JFNK framework, which is an important part of the whole HTR nuclear power plant coupling calculation.…”

Section: Introductionmentioning

confidence: 99%

A Modified JFNK for Solving the HTR Steady State Secondary Circuit Problem

Jiang

Zhang

et al. 2023

Energies

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A nuclear power plant is a complex coupling system, which features multi-physics coupling between reactor physics and thermal-hydraulics in the reactor core, as well as the multi-circuit coupling between the primary circuit and the secondary circuit by the shared steam generator (SG). Especially in the pebble-bed modular HTR nuclear power plant, different nuclear steam supply modules are further coupled together through the shared main steam pipes and the related equipment in the secondary circuit, since the special configuration of multiple reactor modules connects to a steam turbine. The JFNK (Jacobian-Free Newton–Krylov) method provides a promising coupling framework to solve the whole HTR nuclear power plant problem, due to its excellent convergence rate and strong robustness. In this work, the JFNK method was modified and applied to the steady-state calculation of the HTR secondary circuit, which plays an important role in simultaneous solutions for the whole HTR nuclear power plant. The main components in the secondary circuit included SG, steam turbine, condenser, feed pump, high/low-pressure heat exchanger, deaerator, as well as the extraction steam from the steam turbine. The results showed that the JFNK method can effectively solve the steady state issue of the HTR secondary circuit. Moreover, the JFNK method could converge well within a wide range of initial values, indicating its strong robustness.

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“…Simulation and control needs are clarified and a set of candidate analysis tools is discussed. The possible use of modeling and simulation languages and environments such as RELAP5-3D, 2 RELAP-7, 3 RAVEN, 4 Multi-physics Object Oriented Simulation Environment (MOOSE), 5 MATLAB ® /Simulink ® and its related toolboxes, 6,7 Modelica, 8 Aspen, 9 Ptolemy, 10 Power System Computer Aided Design (PSCAD) and Real Time Digital Simulators (RTDS ® ), 11 Dymola 12 was considered. Appendix A summarizes the characteristics of each tool considered.…”

Section: Introductionmentioning

confidence: 99%

Strategy and gaps for modeling, simulation, and control of hybrid systems

Rabiti

García

Hovsapian

et al. 2015

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This report establishes a strategy for modeling, simulation, and control of candidate hybrid energy systems. Modeling, simulation, and control are necessary to design, evaluate, and optimize the systems' technical and economic performance. This report first establishes modeling requirements to analyze candidate hybrid systems (a strict definition of "hybrid system" will be also provided). Modeling fidelity levels are based on the temporal scale, real and synthetic data availability or needs, solution accuracy, and output parameters needed to evaluate case-specific figures of merit (FOMs). The associated computational and co-simulation resources needed are established, including physical models when needed, code assembly and integrated solutions platforms, mathematical solvers, and data processing. This report first describes the FOMs, systems requirements, and constraints necessary to characterize the grid and hybrid system behaviors and market interactions. Grid reliability assessment metrics and effective cost of energy (ECE), as opposed to the standard levelized cost of electricity (LCOE), are introduced as technical and economic indices for integrated energy system evaluations. Financial assessment methods are subsequently introduced for evaluating nontraditional, hybrid energy systems. Algorithms for coupled and iterative evaluation of the technical and economic performance are subsequently discussed.This report further defines modeling objectives, computational tools, solution approaches, and real-time data collection and processing (in some cases using real test units) that will be required to model, control, co-simulate, and optimize:(1) energy system's components (e.g., power generation unit, chemical process, electricity management unit), (2) system domains (e.g., thermal, electrical or chemical energy generation, conversion, and transport), and (3) system control modules. Controlling and co-simulating complex, tightly coupled, dynamic energy systems requires multiple controls and simulation tools, potentially developed in several programing languages and resolved on separate time scales. Whereas further investigation and development of hybrid concepts will provide a more complete understanding of the joint computational and physical modeling and control needs, this report highlights areas where control and co-simulation capabilities are warranted. The current development status, quality assurance, availability, and maintainability of control and simulation tools available for hybrid systems modeling are presented. Existing gaps in the modeling, simulation, and control toolsets and development needs are subsequently discussed. This work will feed into broader efforts to design, develop, and demonstrate hybrid energy systems.

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RELAP-7 Theory Manual

Cited by 18 publications

References 64 publications

Hybrid Temperature-Reactivity PID Controllers for Nuclear Thermal Propulsion Startup

Hybrid Temperature-Reactivity PID Controllers for Nuclear Thermal Propulsion Startup

A Modified JFNK for Solving the HTR Steady State Secondary Circuit Problem

Strategy and gaps for modeling, simulation, and control of hybrid systems

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