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At least 1,459 records · Page 81
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Self-consistent equilibrium and transport simulations for NSTX-U plasmas enhanced via machine learning surrogate models

The Control-Oriented Transport SIMulator (COTSIM) is an advanced equilibrium and transport code designed for simulating tokamak discharges at computational speeds suitable for control applications. COTSIM’s modular framework enables users to select models that balance accuracy with speed according to specific needs, allowing the code to operate from fast to faster-than-real-time performance levels. This work presents recent enhancements to COTSIM’s predictive accuracy for NSTX-U scenarios, achieved by integrating neural-network-based surrogate models and self-consistent equilibrium calculations. To improve source deposition predictions, a surrogate model for NUBEAM has been incorporated. Additionally, a surrogate model for the Multi-Mode Module (MMM) now supports predictions of anomalous thermal, momentum, and particle diffusivities—key factors for modeling the evolution of temperature and rotation. Each surrogate model was specifically trained for the NSTX-U operational regime to enhance COTSIM’s accuracy while maintaining computational efficiency. Moreover, COTSIM now couples fixed-boundary equilibrium solvers with its transport solvers, enabling self-consistent predictions of plasma profiles and equilibrium evolution over the discharge. Simulation results demonstrate strong agreement between COTSIM and TRANSP predictions for NSTX-U discharges. These substantial advancements expand COTSIM’s utility in model-based control applications for NSTX-U. Potential applications include simultaneous optimization of equilibrium and transport scenarios, integration into digital twins, real-time profile estimation (e.g., temperature and rotation) from limited or noisy measurements, and advanced feedback-based scenario control.

Equilibrium and transport modeling↗
DOE OSTI2020

One-Step Ahead Prediction of Thermal Mixing Tee Sensors with Long Short Term Memory (LSTM) Neural Networks

High-temperature advanced reactors under development, such as sodium fast reactors (SFR) and molten salt cooled reactors (MSCR), are expected to offer lower levelized cost of energy (LCOE) compared to existing light water reactor (LWR’s). In the existing light water reactors (LWR’s), operation and maintenance (O&M) expenses constitute the largest fraction of the total operating cost. Some of the O&M costs are related maintenance of sensors which can fail due to exposure to harsh environment in a reactor. The O&M costs of Advanced Reactor (AR)’s are expected to constitute a significant fraction of the total cost as well, because of high temperature and radiation level in AR are likely to cause material fatigue and premature failure of sensors and components. The O&M costs in AR’s could be reduced through integration of advanced informatics of performance-related sensors into a digital twin designed for reactor monitoring. For example, machine learning (ML) could be employed for real-time validation and correction of performance-related sensors, and reducing the number of performance-related physical sensor units through virtual sensing. As part of the effort, we investigate real-time validation of thermal hydraulic sensors through one-step ahead forecasting of sensor values using long short-term memory (LSTM) recurrent neural networks (RNN). The sensors are installed in a flow loop containing a thermal mixing tee, which is a common experimental model to study thermal fatigue in a thermal hydraulic loop. In addition, nonlinear transients generated in a thermal mixing tee constitute a good challenge data set for training and validation of ML algorithms. Sensors in this study include thermocouples, flow meters, and optical fibers for distributed temperature sensing. In one experiment, measurement data sets were obtained for a loop was filled with water, and in another experiment, measurements were performed on a loop filled with liquid metal Galinstan. We have also conducted preliminary investigation of one-step ahead prediction of fiber optics-based distributed temperature sensing with LSTM networks. In predicting fiber-based temperature measurements, we treated each gauge pitch of the fiber as an independent sensor. Accuracy of one-step ahead forecasting was estimated by calculating root mean square error (RMSE) for the test segment of time series of each sensor. RMSE’s for temperature sensors in water loop were, for the most part, lower than for the same sensors in Galinstan loop. The RMSE’s for flow meters were similar for both loops. The RMSE’s for distributed temperature measured with the fiber optic sensor were similar to those of the point sensors. Results of this study demonstrated the capability of LSTM one-step ahead forecasting with RMSE comparable to uncertainty in sensor measurements.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2024

Central Asia Seismic Hazard Assessment (CASHA): A Probabilistic Seismic Hazard Assessment for Kazakhstan, Kyrgyzstan and Tajikistan

Probabilistic seismic hazard assessments (PSHA) underpin the calculation of earthquake loads in most building codes around the world. In Central Asia, the building codes are slowly being updated to incorporate some of the contemporary concepts of seismic hazard representation. There is also a regional desire to coordinate hazard assessments and building code modernization. However, some challenges remain. Expertise in the region related to seismic hazard assessments is still largely compartmentalised, requiring a significant amount of training and capacity building in seismic hazard assessment related topics. In addition, there are vast amounts of seismic data (bulletin and waveforms), both from analogue and digital eras, that the region’s countries stored but until recently did not use or share among themselves or with the broader seismological community around the world. Finally, after the collapse of the Soviet Union in the 1990s, many of the countries’ seismic networks suffered a major setback with the lack of attention and budget to update existing equipment and installation of new instruments. In order to address these issues, the United States Department of Energy through Lawrence Livermore National Laboratory (LLNL) initiated a project in 2016 to engage and train local scientists in Central Asia to install new equipment, to enhance the quality of seismic monitoring and reporting, to improve and harmonise the regional earthquake catalogue, and to conduct national probabilistic seismic hazard assessments using the new and improved datasets. To achieve the seismic hazard assessment related goals, a series of workshops were held in Almaty, Kazakhstan; Bishkek, Kyrgyzstan; and Dushanbe, Tajikistan from 2016 until 2020. During the time that the COVID-19 pandemic restricted travel, workshops continued online (22 online workshops were hosted in two years). Finally, in May 2022, an in-person workshop in Istanbul, Turkey brought together all project participants along with civil engineers engaged with building code activities in their respective countries, providing a platform to discuss the implementation of the hazard models into updates of building codes in each country, as well as to discuss model parameters, sensitivity analyses and model results in terms of hazard maps, uniform hazard spectra and hazard deaggregations. The workshops were a combination of lectures and hands-on exercises, and included international participation as well as local scientists and engineers. The workshops served several purposes, including training, coordination of data collection, interactions between local earth scientists and engineers, and brainstorming and knowledge exchange among local and international experts. This report outlines the new earthquake catalogue compilation effort and the PSHA project undertaken in Kyrgyzstan, Tajikistan, and Kazakhstan as part of this initiative. The southern part of this region is tectonically active with moderate to high levels of both shallow crustal seismic activity and occurrence of deeper earthquakes under the Hindu Kush and Pamir mountain ranges. Deeper earthquakes also occur near southwestern Kazakhstan, under the eastern Greater Caucasus and Caspian Sea. Large portions of central and northern Kazakhstan, on the other hand, are in stable continental regions with low levels of seismic activity. This study systematically compiles and improves all available data on local seismicity, active faults, and ground motion attenuation characteristics of the region; and builds a framework to enable a contemporary PSHA to be carried out with the engagement of local scientists. While the project was regional, the seismic hazard assessments are primarily driven by the countries’ own national preferences and understanding of data collection, interpretation, and validation of results.

58 GEOSCIENCES↗
DOE OSTI2020

Near-Real-Time Forecast of Satellite-Based Soil Moisture Using Long Short-Term Memory with an Adaptive Data Integration Kernel

Nowcasts, or near-real-time (NRT) forecasts, of soil moisture based on the Soil Moisture Active and Passive (SMAP) mission could provide substantial value for a range of applications including hazards monitoring and agricultural planning. To provide such a NRT forecast with high fidelity, we enhanced a time series deep learning architecture, long short-term memory (LSTM), with a novel data integration (DI) kernel to assimilate the most recent SMAP observations as soon as they become available. The kernel is adaptive in that it can accommodate irregular observational schedules. Testing over the CONUS, this NRT forecast product showcases predictions with unprecedented accuracy when evaluated against subsequent SMAP retrievals. It showed smaller error than NRT forecasts reported in the literature, especially at longer forecast latency. The comparative advantage was due to LSTM’s structural improvements, as well as its ability to utilize more input variables and more training data. The DI-LSTM was compared to the original LSTM model that runs without data integration, referred to as the projection model here. We found that the DI procedure removed the autocorrelated effects of forcing errors and errors due to processes not represented in the inputs, for example, irrigation and floodplain/lake inundation, as well as mismatches due to unseen forcing conditions. The effects of this purely data-driven DI kernel are discussed for the first time in the geosciences. Furthermore, this work presents an upper-bound estimate for the random component of the SMAP retrieval error.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Non-intrusive reduced-order modeling for dynamical systems with spatially localized features

This work presents a non-intrusive reduced-order modeling framework for dynamical systems with spatially localized features characterized by slow singular value decay. The proposed approach builds upon two existing methodologies for reduced and full-order non-intrusive modeling, namely Operator Inference (OpInf) and sparse Full-Order Model (sFOM) inference. We decompose the domain into two complementary subdomains that exhibit fast and slow singular value decay. The dynamics of the subdomain exhibiting slow singular value decay are learned with sFOM while the dynamics with intrinsically low dimensionality on the complementary subdomain are learned with OpInf. The resulting, coupled OpInf-sFOM formulation leverages the computational efficiency of OpInf and the high resolution of sFOM, and thus enables fast non-intrusive predictions for conditions beyond those sampled in the training data set. A novel regularization technique with a closed-form solution based on the Gershgorin disk theorem is introduced to promote stable sFOM and OpInf models. We also provide a data-driven indicator for subdomain selection and ensure solution smoothness over the interface via a post-processing interpolation step. We evaluate the efficiency of the approach in terms of offline and online speedup through a quantitative, parametric computational cost analysis. We demonstrate the coupled OpInf-sFOM formulation for two test cases: a one-dimensional Burgers’ model for which accurate predictions beyond the span of the training snapshots are presented, and a two-dimensional parametric model for the Pine Island Glacier ice thickness dynamics, for which the OpInf-sFOM model achieves an average prediction error on the order of 1% with an online speedup factor of approximately 8$\times$ compared to the numerical simulation.

42 ENGINEERING↗
DOE OSTI2024

Trustworthiness modeling and evaluation for a nearly autonomous management and control system

The Nearly Autonomous Management and Control (NAMAC) system supports the advanced reactor operation by recommending control actions to operators based on real-time measurements and digital twins (DTs) learning from the knowledge base. To enable the safe and reliable use of autonomous technologies, NAMAC and its recommendations should be trustworthy to operators and regulators at both the design and operation stages. This study proposes a NAMAC trustworthiness modeling and evaluation framework supported by trustworthiness ontologies and evidence-based approaches. The development-time and run-time ontologies are separately constructed and then converted to Bayesian networks to quantitatively evaluate the NAMAC trustworthiness. This evaluation is demonstrated by collecting and characterizing evidence from NAMAC practices, such as the development and assessment of the NAMAC system, data coverage assessment, and the training and optimizations of neural-network-based DTs. Our proposed approach can aggregate various trustworthiness attributes of complex artificial-intelligence-supported systems for safety-critical applications. It also considers the interaction between different DTs and extends beyond the trustworthiness evaluation of a single DT. In conclusion, the evidence-based method enhances the transparency of the trustworthiness modeling and evaluation processes and helps identify uncertainties and subjectivity involved in the processes.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2025

Travelling wave‐based fault detection and location in a real low‐voltage DC microgrid

Abstract This paper discusses a device‐level implementation of a travelling wave (TW) protection device (PD) designed for a real low‐voltage DC microgrid. The TWPD fault detection and location algorithm is executed on a commercial digital signal processor (DSP) board, involving signal sampling at 1 MHz via the DSP board's analog‐to‐digital converter (ADC). The analogue input card measures positive pole, negative pole and pole‐to‐pole voltages at the TWPD location. Upon a successful fault detection using a second‐order high‐pass filter, the voltage data is normalised and multi‐resolution analysis (MRA) is performed on a 128‐sample buffer around the TW arrival time. MRA employs the discrete wavelet transform (DWT) to capture high‐frequency voltage patterns, and then the Parseval's energy theorem quantifies these TW characteristics by computing the energy of reconstructed wavelet coefficients. These energy values per decomposed frequency band are the basis for training a random forest classifier that predicts fault location and type. The TWPD is fully implemented and connected to a real DC microgrid in Albuquerque, NM, USA, for validation, and results are shown for field tests verifying the performance under faults.

Paruthiyil, Sajay Krishnan [Department of Electric↗
DOE OSTI2026

Modeling Multi-View Impedance-Based Cross-Geometry SOH Estimator for Li-ion Batteries

Abstract: Accurately estimating battery’s State of Health (SOH) remains challenging when models must generalize across cell designs and operating conditions. Most Electrochemical Impedance Spectroscopy (EIS)-based approaches either (i) hand-engineer a few Nyquist-plot features for shallow models—fast but does not generalize across geometries—or (ii) learn directly from Nyquist plots with deep networks, which removes manual feature extraction, yet still limited to a single plot type. As a result, cross-geometry robustness and deployability on constrained Internet of Things (IoT) devices remain open problems. We propose a compact Convolutional Neural Network (CNN) (∼ 10k parameters) that takes multi-representation EIS inputs—Nyquist (real/imaginary) and phase–magnitude (|Z|/ϕ) stacked as four channels, so the model can learn complementary degradation signatures while remaining small enough for fast inference. We build a dataset from cyclic aging of two geometries (LG INR18650MJ1 cylindrical cells and LIR2032 coin cells), acquire EIS every ten cycles from 10 kHz to 10 mHz (10 points/decade), and evaluate with leave-one-cell-out testing strategy. We further study fusion vs. single-representation inputs and assess feasibility for on-device deployment (e.g., NVIDIA Jetson device). The results show that training on multiple EIS representations improves SOH estimation accuracy and cross-geometry generalization compared to single-representation models, which uses only Nyquist or phase–magnitude plots. This design targets accurate, generalizable SOH prediction without manual feature engineering while enabling practical real-time use.

Bakr, Ahmed [The University of Alabama (UA)]↗
DOE OSTI2021

Magnetic Field Measurements of First Pre-series Full -Length 4.2 m Quadrupole MQXFA03 Using PCB Rotating Coils for the Hi Hi-Lumi LHC Project

The U.S. Hi-Lumi LHC Accelerator Upgrade Project (AUP) and CERN have joined efforts to develop high field quad-rupoles for the Hi-Lumi LHC upgrade. The US national laborato-ries in the AUP project will deliver 10 magnets and each cryostat has two 4.2 m high gradient quadrupoles in it. These magnets are made of Nb3Sn conductors, with large aperture (150 mm) and in-tegrated gradient of 556.9 T. This paper reports on magnetic measurements performed during the vertical test at Brookhaven National Laboratory (BNL) in 2019-2020. A warm measurement Z-Scan (+/- 15 A) with 42 Z-positions before cool-down was per-formed at BNL. The results were directly compared to field data measured at LBNL during magnet assembly. Measured harmonics and magnetic center offsets (ΔX and ΔY) have provided timely and informative diagnostics on the magnet structure’s shape at both warm and cold temperatures. A new centering fixture was de-signed and added to better center the warm bore tube which con-tains the rotating coil probe. After the quench training to 16.47 kA was achieved, a complete set of cold measurements (Z-Scan at 16.47 kA and I-Scan from 960 A to 16.47 kA and back to 960 A) was made. Periodic axial variation of allowed and nonallowed harmonics was observed which is related to the coil radial and/or mid-plane variations along the magnet axis. Overall, the average harmonics in the straight section are within the required field boundaries.

43 PARTICLE ACCELERATORS↗
DOE OSTI2026

An On-Demand Electric Transit Case Study of New Rochelle, New York

Here, this article characterizes the performance and ridership patterns of an on-demand transit (ODT) service utilizing lightweight electric vehicles (EVs) in New Rochelle, New York. Ridership sociodemographics, travel patterns (both temporal and spatiotemporal), and energy use from the service were explored using travel and survey data from September 2019 through December 2023. The ODT service was found to be used more by women (nearly 60%) and younger demographics (>65% under the age of 42), with peak use in the middle of the day and a grocery store as a top origin and destination. The service was utilized primarily for short trips (86% under 2 mi), with approximately one-third of riders using the ODT service to connect to a train or bus. The costs associated with fueling/charging were compared for different types of fleet vehicles, and the small, right-sized EVs were found to have annual charging costs that were roughly half of the refueling costs for conventional hybrid vans, and 24 times lower than a fleet of diesel buses. Evaluating the vehicle fleet and mapping current socio-spatial travel demand can inform system performance, guide service area development, and support future planning such as expansion to nearby communities and transit hubs. The findings in this case study suggest that on-demand electric transit may be a significant and growing space for advancing highly valued public mobility services. Public transport interventions that consider right-sized, electric, on-demand vehicles can help improve mobility access and reduce energy use and refueling costs.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2021

Assessment of Aging Degradation Mechanisms of Alloy 709 for Sodium Fast Reactors

This project was initiated in October 2015 to investigate the creep and creep-fatigue behavior of Alloy 709 for structural components in Gen IV sodium cooled fast spectrum reactors. The goal of this project is to develop an understanding of the mechanisms responsible for the behavior of austenitic stainless steel alloy 709 under accelerated testing conditions to confidently predict long term creep and creep-fatigue behavior at the times (500,000 hours) and temperatures (550°C) of interest for fast reactor structural applications. Alloy 709 has exhibited significantly better creep resistance than Alloy 316H, which is currently specified in design of fast reactor structural components. However, the alloy has not been code qualified for elevated temperature nuclear design. The elevated temperature microstructure stability and creep and creep-fatigue behavior need to be better understood so service life can be accurately predicted. Alloy 709 was developed for fossil boiler applications, which operate at a higher temperature than the nuclear components. Thus, there is a strong need to characterize its creep and creep-fatigue behavior at these lower temperatures and corresponding longer service lifetimes. The overall objective of this project is to develop sufficient understanding of the mechanisms responsible for behavior of Alloy 709 under accelerated testing conditions to confidently predict long term creep and creep-fatigue behavior at the times (500,000 hours) and temperatures (550°C) of interest for fast reactor structural applications. The following report summarizes the findings of the creep-fatigue and creep behavior with respect to the research questions identified during the course of the project. Additionally, the training and professional development that occurred through the project and the products resulting from the project are summarized.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Study of The Compression of an Imploding Plasma and the Magnetic- Field Flux Using Advanced, Non-Intrusive, Spectroscopic Techniques

The main motivation of the project was to measure the magnetic field in pulsed power driven plasma compression using advanced non-intrusive spectroscopic techniques. The strength of the project is that it encompasses a combination of laboratory experiments with diagnostics for both the magnetic field and plasma properties and computational modeling. The following issues were addressed: (1) Plasma and magnetic field dynamics, (2) Field compression and diffusion, (3) Energy balance in the system, (4) Onset of instabilities, and (5) Correlation of the plasma and field evolutions with the initial conditions. The unique feature of the research was the use of advanced spectroscopy to measure both the magnetic field and various plasma properties in multiple locations using both side-on and end-on views, simultaneously. The experimental system allowed for imploding different gas mixtures (which facilitated local measurements) and for flexibility in modifying the initial conditions. Two current generators were used with significantly different current rise times (200 ns and 1.6 µs) but at the same current level of 400 kA. The slower pulsed power generator (at rise time 1.6 µs) was based at the Weizmann Institute of Science (WIS) and imploded plasma into which an axial magnetic field (up to 1 T) was initially embedded. The faster current rise time generator (at rise time 200 ns) based at University of California San Diego (UCSD) was used for plasma parameter studies initially, with collaborative efforts we successfully implemented the advanced spectroscopy techniques at UC San Diego. One graduate student (Nick Aybar) and a postdoctoral scholar (Maylis Dozeries) were trained. They both are based at the Lawrence Livermore National Laboratory now.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2025

Driver Identification Midyear Report

First, we create a profile for each authorized driver based on their existing driving data. We then train a machine learning model on the driving data from this profile, yielding an individualized model for each driver. Finally during a drive, we pass the Controller Area Network (CAN) data to the model and authen ticate the driver’s identity in real-time. This verification or lack thereof could be used to alert supervisors of threats to their drivers or transported materials. Deviations from their normal driving behavior could indicate high-risk situations, medical events, or even insider threats.

98 NUCLEAR DISARMAMENT, SAFEGUARDS, AND PHYSICAL P↗
DOE OSTI2026

Empirical Comparison of Machine Learning Approaches for Black-Box Modeling of Power Conversion System Dynamics

Inverter-based resources are key components in modern power systems, but accurately modeling their complex behavior can be challenging. Standard, generic converter models often oversimplify inverter dynamics, leading to significant errors in predicting performance. In this work, we compare several data-driven machine learning (ML) approaches for inverter modeling, performing experiments on power conversion systems, systematically varying input conditions, and recording the resulting voltages and currents. The ML models were then trained on this measured data to capture the inverter's dynamic response and to predict the inverter's output current. A performance comparison between the four ML models under study is conducted, laying the foundation for future work on hardware implementation for real-time inference.

30 DIRECT ENERGY CONVERSION↗
DOE OSTI2020

RockFlow: Fast Generation of Synthetic Source Rock Images Using Generative Flow Models

Image-based evaluation methods are a valuable tool for source rock characterization. The time and resources needed to obtain images has spurred development of machine-learning generative models to create synthetic images of pore structure and rock fabric from limited image data. While generative models have shown success, existing methods for generating 3D volumes from 2D training images are restricted to binary images and grayscale volume generation requires 3D training data. Shale characterization relies on 2D imaging techniques such as scanning electron microscopy (SEM), and grayscale values carry important information about porosity, kerogen content, and mineral composition of the shale. Here, we introduce RockFlow, a method based on generative flow models that creates grayscale volumes from 2D training data. We apply RockFlow to baseline binary micro-CT image volumes and compare performance to a previously proposed model. We also show the extension of our model to 2D grayscale data by generating grayscale image volumes from 2D SEM and dual modality nanoscale shale images. The results show that our method underestimates the porosity and surface area on the binary baseline datasets but is able to generate realistic grayscale image volumes for shales. With improved binary data preprocessing, we believe that our model is capable of generating synthetic porous media volumes for a very broad class of rocks from shale to carbonates to sandstone.

58 GEOSCIENCES↗
DOE OSTI2021

Inception Based Deep Convolutional Neural Network for Remaining Useful Life Estimation of Turbofan Engines

Accurate estimation of the remaining useful life (RUL) is a key component of condition based maintenance (CBM) and prognosis and health management (PHM). Data-based models for the estimation of RUL are of particular interest because expert knowledge of systems is not always available and physical modeling is often not feasible. In this paper, a deep convolutional neural network (CNN) architecture is investigated for its ability to estimate the RUL of turbofan engines. The input to the model is a window of time series data collected from the engine under test. Inputting raw sensor data allows features to be learned instead of manually determined. To incorporate the ability to detect features of differing lengths, inception modules are used in the neural network architecture. The model is trained and tested using the new Commercial Modular Aero-Propulsion System Simulation (N-CMAPSS) data set and high prognosis accuracy was achieved. The developed model was used in the 2021 PHM Society Data Challenge and received second place, further validating its ability to accurately estimate RUL.

DeVol, Nathaniel↗
DOE OSTI2021

Constrained Block Nonlinear Neural Dynamical Models

Neural network modules conditioned by known priors can be effectively trained and combined to represent systems with nonlinear dynamics. This work explores a novel formulation for data-efficient learning of deep control-oriented nonlinear dynamical models by embedding local model structure and constraints. The proposed method consists of neural network blocks that represent input, state, and output dynamics with constraints placed on the network weights and system variables. For handling partially observable dynamical systems, we utilize a state observer neural network to estimate the states of the system's latent dynamics. We evaluate the performance of the proposed architecture and training methods on system identification tasks for three nonlinear systems: a continuous stirred tank reactor, a two tank interacting system, and an aerodynamics body. Models optimized with a few thousand system state observations accurately represent system dynamics in open loop simulation over thousands of time steps from a single set of initial conditions. Experimental results demonstrate an order of magnitude reduction in open-loop simulation mean squared error for our constrained, block-structured neural models when compared to traditional unstructured and unconstrained neural network models.

Skomski, Elliott↗
DOE OSTI2024

Uncertainty Quantification and Sensitivity Analysis of Non-Nuclear Advanced Controls Testbed Reactor Mockup

The research presented in this report describes our progress in applying stochastic methods and uncertainty quantification, parametric study, and variance-based sensitivity analysis (also known as Sobol sensitivity analysis) to a full-core model of a nuclear thermal propulsion (NTP) system simulated with Griffin, with the goal of developing a reduced order (surrogate) model which can be rapidly sampled while perturbing multiple input parameters. In this NTP system, reactivity and power feedback affect the rotation of control drums, which are controlled by a hybrid proportional, integral and derivative (PID) controller, actuated by the power demand and reactivity feedback from the numerical model. This model uses reactor kinetic feedback (mean generation time and $\beta$ from a transient Griffin simulation executed with the improved quasi-static method to provide the kinetic parameters) as inputs to functions which control the CD rotation angle. Using a number of stochastic method approaches, we developed a dual purpose training-surrogate model of the NTP system using polynomial regression. The trained model can be rapidly sampled while simultaneously perturbing various input parameters of the model, such as coefficients on the PID control, or temperature (directly affect the neutron cross section). The surrogate model delivers accurate results orders-of-magnitude faster (minutes, not days) than the base model. Once the base model has been trained, distributions of the uncertain parameters can be changed at will to investigate the effects of perturbing multiple inputs and their effect on the output. For example, coefficients used in the PID control system may vary due to some physical interference, or there may be uncertainty in the temperature of the neutron cross sections in various regions of the reactor. A distribution can be placed on these parameters and operational boundaries can be determined. The goal of this work is to support development of an advanced control system to operate CDs in a functioning NTP system.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗