RAVEN User Manual

Rabiti, Cristian; Alfonsi, Andrea; Cogliati, Joshua J.; Mandelli, Diego; Kinoshita, Robert; Sen, Sonat; Wang, Congjian; Talbot, Paul; Maljovec, Dan

doi:10.2172/1466671

Cited by 22 publications

(15 citation statements)

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“…The necessary relevant input/output variables must be identified so that ROMs can produce accurate physical results by relating, through simplified relationships, the output space from an input space. As RAVEN contains many different ROM generation methods, the appropriate method must be selected when producing ROMs for FORCE analyses [8]. The ROM input will likely center around economic system demands, and the output must include relevant economic values as well as relevant physical constraint data.…”

Section: Reduced Order Modelsmentioning

confidence: 99%

Delivery of Dynamic Thermal Energy Storage Models and Advanced Reactor Concept Models to the HYBRID Repository

Mikkelson

Shigrekar

Hancock

et al. 2022

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This publication details newly created energy storage and reactor models developed within the HYBRID modeling repository as part of the Department of Energy (DOE) Office of Nuclear Energy Integrated Energy Systems (IES) program, led by Idaho National Laboratory (INL).The model development included creating dynamic systems-level models of a pebble-bed high-temperature gas-cooled reactor (HTGR), a sodium fast reactor (SFR), compressed air energy storage (CAES), liquid air energy storage (LAES) and Modelica standard library (MSL)-based two-tank sensible heat storage (SHS) in the IES-based HYBRID repository. These models were developed using the latest publicly available data and incorporating the possibility of control strategy inclusion for use with the existing IES modeling, analysis, and optimization toolset. Simulations showcase the capability of each technology to flexibly operate in ways consistent with IES operation expectations.When these models are available, they can be utilized within different integrated energy park concepts to understand optimal system operation, control, and dispatching. Moreover, given the generic nature of the models, industrial partner technologies can be quickly added to the repository by using the existing models as a basis. Additional dynamic models for thermal energy storage (TES) concepts can be developed and added to the HYBRID repository as needed. Also detailed in this report are future development goals for the HYBRID repository, including adding suites of steady-state models, economic costing information, and reduced-order models (ROMs).

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Section: Reduced Order Modelsmentioning

confidence: 99%

Delivery of Dynamic Thermal Energy Storage Models and Advanced Reactor Concept Models to the HYBRID Repository

Mikkelson

Shigrekar

Hancock

et al. 2022

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show abstract

“…RAVEN began in 2012 as a project to create a modernized risk evaluation framework as a part of the Risk-Informed Safety Margin Characterization (RISMC) work, now known as Risk-Informed Systems Analysis (RISA) [3]. These research paths were developed as a part of the Light Water Reactor Sustainability (LWRS) program at the DOE Nuclear Energy Office (NE).…”

Section: Overview Of Ravenmentioning

confidence: 99%

“…An increased computational capacity enabled the ability to bring a more intimate coupling of high-low systems between popular nuclear energy simulation codes. The recently developed software framework called the Risk Analysis Virtual Environment (RAVEN) enables increasingly flexible statistically driven analysis of traditional simulation codes and advanced new codes [3]. The potential use cases are rapidly growing as the capabilities of RAVEN increase.…”

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confidence: 99%

Uncertainty Quantification in High-Low Dynamic System Coupling Using RAVEN and TRANSFORM

Kumar¹,

Wet²,

Gurecky³

2022

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This report demonstrates new functionality and applications enabled by the development of high-fidelity to low-fidelity (high-low) coupling for system simulations. Additionally, this development allows further exploration of the capabilities of the Risk Analysis Virtual Environment (RAVEN), a novel software framework, in the performance of uncertainty quantification in these high-low coupled system models. The work builds on previous work on high-low coupling that utilized COBRA-TF (CTF), the high-fidelity subchannel analysis code, with a low-fidelity model built in the system analysis code TRANSFORM, utilizing the Functional Mock-up Interface (FMI). Steady-state and transient analysis examples using the high-low coupled models generated from CTF and TRANSFORM/FMI are investigated. The workflows for in-memory and out-of-memory coupling are shown with specific applications to nuclear systems' uncertainty and system characterization studies. This work elucidates some of the potential benefits and future needs of using RAVEN for high-low system coupling analysis of energy systems.

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“…Additionally, ADETS can compute the dose at various distances of the reactor using a point gamma source, whose characteristics are computed by ORIGEN-ARP. In addition, ADETS is fully coupled with the Uncertanty Quantification, Probabilistic Risk Assessment, Parameter Optimization and Data Analysis code RAVEN (see [1], [2], [3]) (developed at the Idaho National Laboratory as well). RAVEN is released with ADETS but MCNP and SCALE (ORIGEN-ARP) are not included.…”

Section: Adets User Manualmentioning

confidence: 99%

ADETS User Manual

Alfonsi

2020

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RAVEN User Manual

Cited by 22 publications

References 0 publications

Delivery of Dynamic Thermal Energy Storage Models and Advanced Reactor Concept Models to the HYBRID Repository

Delivery of Dynamic Thermal Energy Storage Models and Advanced Reactor Concept Models to the HYBRID Repository

Uncertainty Quantification in High-Low Dynamic System Coupling Using RAVEN and TRANSFORM

ADETS User Manual

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