Cristian Rabiti

Generalized Perturbation Theory-Free Sensitivity Analysis for Eigenvalue Problems

Generalized perturbation theory (GPT) has been recognized as the most computationally efficient approach for performing sensitivity analysis for models with many input parameters, which renders forward sensitivity analysis computationally overwhelming. In critical systems, GPT involves the solution of the adjoint form of the eigenvalue problem with a response-dependent fixed source. Although conceptually simple to implement, most neutronics codes that can solve the adjoint eigenvalue problem do not have a GPT capability unless envisioned during code development. We introduce in this manuscript a reduced-order modeling approach based on subspace methods that requires the solution of the fundamental adjoint equations but allows the generation of response sensitivities without the need to set up GPT equations, and that provides an estimate of the error resulting from the reduction. Moreover, the new approach solves the eigenvalue problem independently of the number or type of responses. This allows for an efficient computation of sensitivities when many responses are required. This paper introduces the theory and implementation details of the GPT-free approach and describes how the errors could be estimated as part of the analysis. The applicability is demonstrated by estimating the variations in the flux distribution everywhere in the phase space of a fast critical sphere and a high-temperature gas-cooled reactor prismatic lattice. The variations generated by the GPT-free approach are benchmarked to the exact variations generated by direct forward perturbations.

Comparison of the PHISICS/RELAP5-3D Ring and Block Model Results for Phase I of the OECD/NEA MHTGR-350 Benchmark

Abstract The Parallel and Highly Innovative Simulation for INL Code System (PHISICS) has been under development at Idaho National Laboratory since 2010. It consists of several modules providing improved coupled core simulation capability: INSTANT (Intelligent Nodal and Semi-structured Treatment for Advanced Neutron Transport) (three-dimensional nodal transport core calculations); MRTAU (Multi-Reactor Transmutation Analysis Utility) (depletion and decay heat generation); and modules performing criticality searches, fuel shuffling, and generalized perturbation. Coupling of the PHISICS code suite to the thermal-hydraulic system code RELAP5-3D was finalized in 2013, and as part of the verification and validation effort, the first phase of the Organisation for Economic Co-operation and Development/Nuclear Energy Agency (OECD/NEA) MHTGR-350 benchmark has now been completed. The theoretical basis and latest development status of the coupled PHISICS/RELAP5-3D tool are described in more detail in a concurrent paper. This paper provides an overview of the OECD/NEA MHTGR-350 benchmark and presents the results of exercises 2 and 3 defined for phase I. Exercise 2 required the modeling of a stand-alone thermal fluids solution at the end of equilibrium cycle for the Modular High Temperature Gas-Cooled Reactor (MHTGR). The RELAP5-3D results of four subcases are discussed, consisting of various combinations of coolant bypass flows and material thermophysical properties. Exercise 3 required a coupled neutronics and thermal fluids solution, and the PHISICS/RELAP5-3D code suite was used to calculate the results of two subcases. The main focus of this paper is a comparison of results obtained with the traditional RELAP5-3D “ring” model approach against a much more detailed model that includes kinetics feedback on individual “block” level and thermal feedbacks on a triangular submesh. The higher fidelity that can be obtained by this block model is illustrated with comparison results on the temperature, power density, and flux distributions. It is shown that the ring model leads to significantly lower fuel temperatures (up to 10%) when compared with the higher-fidelity block model and that the additional model development and run-time efforts are worth the gains obtained in the improved spatial temperature and flux distributions.

Framework for the Economic Analysis of Hybrid Systems Based on Exergy Consumption

Starting from an overview of the dynamic behavior of the electricity market the need of the introduction of energy users that will provide a damping capability to the system is derived as also a qualitative analysis of the impact of uncertainty, both in the demand and supply side, is performed. Then it follows an introduction to the investment analysis methodologies based on the discounting of the cash flow, and then work concludes with the illustration and application of the exergonomic principles to provide a sound methodology for the cost accounting of the plant components to be used in the cash flow analysis.

Rethinking sensitivity analysis of nuclear simulations with topology

In nuclear engineering, understanding the safety margins of the nuclear reactor via simulations is arguably of paramount importance in predicting and preventing nuclear accidents. It is therefore crucial to perform sensitivity analysis to understand how changes in the model inputs affect the outputs. Modern nuclear simulation tools rely on numerical representations of the sensitivity information - inherently lacking in visual encodings - offering limited effectiveness in communicating and exploring the generated data. In this paper, we design a framework for sensitivity analysis and visualization of multidimensional nuclear simulation data using partition-based, topology-inspired regression models and report on its efficacy. We rely on the established Morse-Smale regression technique, which allows us to partition the domain into monotonic regions where easily interpretable linear models can be used to assess the influence of inputs on the output variability. The underlying computation is augmented with an intuitive and interactive visual design to effectively communicate sensitivity information to nuclear scientists. Our framework is being deployed into the multipurpose probabilistic risk assessment and uncertainty quantification framework RAVEN (Reactor Analysis and Virtual Control Environment). We evaluate our framework using a simulation dataset studying nuclear fuel performance.

BWR Station Blackout: A RISMC Analysis Using RAVEN and RELAP5-3D

Abstract The existing fleet of nuclear power plants is in the process of having its lifetime extended and having the power generated from these plants increased via power uprates and improved operations. In order to evaluate the impact of these factors on the safety of the plant, the Risk-Informed Safety Margin Characterization (RISMC) pathway aims to provide insights to decision makers through a series of simulations of the plant dynamics for different initial conditions and accident scenarios. This paper presents a case study in order to show the capabilities of the RISMC methodology to assess the impact of power uprate of a boiling water reactor system during a station blackout accident scenario. We employ a system simulator code, RELAP5-3D, coupled with RAVEN, which performs the stochastic analysis. Our analysis is performed by (a) sampling values from a set of parameters from the uncertainty space of interest, (b) simulating the system behavior for that specific set of parameter values, and (c) analyzing the outcomes from the set of simulation runs.

Probabilistic analysis of hybrid energy systems using synthetic renewable and load data

This paper focuses on probabilistic analysis of hybrid energy systems (HES), which integrate multiple energy inputs and multiple energy outputs for effective management of variability in renewable energy and grid demand. To characterize the volatility, a statistical model combining Fourier series and autoregressive moving average (ARMA) is used to generate synthetic weather condition (e.g., wind speed) and grid demand data. Specifically, Fourier series is used to model the seasonal trends in historical data, while ARMA is applied to characterize the autocorrelation in residue time series (e.g., measurements with seasonal trends subtracted). The synthetic data is shown to have same statistic characteristics with historical measurements, but possesses different temporal profile. The probabilistic analysis of a particular HES configuration is then performed, which consists of nuclear power plant, wind farm, battery storage, and desalination plant. Requirements on component ramping rate, and the effects of deploying different sizes of batteries in smoothing renewable variability, are all investigated.

Simulation of AER-DYN-002 and AER-DYN-003 Control Rod Ejection Benchmarks by RELAP5-3D/PHISICS Coupled Codes

Abstract ENEA “Casaccia” Research Center is collaborating with Idaho National Laboratory performing activities devoted to the validation of the Parallel and Highly Innovative Simulation for INL Code System (PHISICS) neutron simulation code. In such framework, the AER-DYN-002 and AER-DYN-003 control rod (CR) ejection benchmarks were used to validate the coupled codes RELAP5-3D/PHISICS. The AER-DYN-002 benchmark provides a test case of a CR ejection accident in a VVER-440 at hot-zero-power and end-of-cycle conditions assuming an adiabatic fuel and taking into account only the fuel temperature feedback. The AER-DYN-003 benchmark is based on the same problem; however, the moderator density feedback and the coolant heat removal are also considered. A RELAP5-3D core channel-by-channel, thermal-hydraulic nodalization was developed and coupled, first with the RELAP5-3D internal neutronic routine NESTLE and then with the PHISICS code. Analysis of the AER-DYN-002 results shows that the steady-state solutions are in good agreement with the other participants’ average solution, while some differences are shown in the transient simulations. In the AER-DYN-003 benchmark, however, both steady-state and transient results are in good agreement with the average solution.

A Flooding Induced Station Blackout Analysis for a Pressurized Water Reactor Using the RISMC Toolkit

In this paper we evaluate the impact of a power uprate on a pressurized water reactor (PWR) for a tsunami-induced flooding test case. This analysis is performed using the RISMC toolkit: the RELAP-7 and RAVEN codes. RELAP-7 is the new generation of system analysis codes that is responsible for simulating the thermal-hydraulic dynamics of PWR and boiling water reactor systems. RAVEN has two capabilities: to act as a controller of the RELAP-7 simulation (e.g., component/system activation) and to perform statistical analyses. In our case, the simulation of the flooding is performed by using an advanced smooth particle hydrodynamics code called NEUTRINO. The obtained results allow the user to investigate and quantify the impact of timing and sequencing of events on system safety. In addition, the impact of power uprate is determined in terms of both core damage probability and safety margins.

Light Water Reactor Sustainability Program Status of Adaptive Surrogates within the RAVEN framework

................................................................................................................................................ iii FIGURES ....................................................................................................................................................... v TABLES ....................................................................................................................................................... vi ACRONYMS .............................................................................................................................................. vii

RAVEN Beta Release

This documents the release of the Risk Analysis Virtual Environment (RAVEN) code. A description of the RAVEN code is provided, and discussion of the release process for the M2LW-16IN0704045 milestone. The RAVEN code is a generic software framework to perform parametric and probabilistic analysis based on the response of complex system codes. RAVEN is capable of investigating the system response as well as the input space using Monte Carlo, Grid, or Latin Hyper Cube sampling schemes, but its strength is focused toward system feature discovery, such as limit surfaces, separating regions of the input space leading to system failure, using dynamic supervised learning techniques. RAVEN has now increased in maturity enough for the Beta 1.0 release.