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All sourcesNASA NTRSDOE OSTI
DOE OSTI2018

Technologies to Reactors: Enabling Accelerated Deployment of Nuclear Energy Systems Workshop Report

Nuclear energy provides almost 20% of the electric power in the United States, producing power with high availability while accounting for 63% of our carbon-free energy production. While we have the capacity to expand this clean power source within the United States, we have not built any advanced reactor plant designs in our rapidly evolving electricity generation market, where cost is a major driver. With construction cost and plant safety being major elements of cost for new nuclear, advanced reactor technologies could provide lower cost, safer, carbon-free, grid-resilient energy sources for the US electricity grid. When the current fleet of nuclear reactors was originally designed, many of today’s science and technology (S&T) advances were not available. Today these advances show great promise for transforming our national approach to nuclear reactor design. Other industries are capitalizing on rapid innovations in materials, manufacturing, sensors and control systems, and high-fidelity modeling and simulation (M&S), along with data analytics to provide lower costs in manufacturing and extended ranges of operations. In the nuclear industry, these innovations can translate into similar benefits as well as rapid design and qualification of advanced nuclear fuels. Effectively implementing these types of innovations could position American expert leadership to compete more fully in the international nuclear energy market. In July of 2018, the US Department of Energy (DOE) Office of Nuclear Energy sponsored a workshop to address these possibilities: “Technologies to Reactors: Enabling Accelerated Deployment of Nuclear Energy Systems.” Workshop participants explored how modern and emerging S&T can be used to transform the approach to nuclear reactor design, licensing, and operation to accelerate deployment of advanced nuclear energy systems. More than 80 experts from federal agencies, academia, and industry participated in the workshop to develop consensus on priority technology directions (PTDs) and recommend technology and deployment initiatives to explore and demonstrate key benefits for the nuclear community.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2014

Improving Heat Pump Water Heater Effeciency by Avoiding Electric Resistance Heater Use

Heat pump water heaters (HPWHs) are a promising technology that can decrease the domestic hot water energy consumption over an electric resistance storage water heater by up to 50%. Heat pump water heaters are really two water heaters in one; they can heat water by using a heat pump or by using electric resistance elements. During large water draw events the HPWHs will use the resistance elements that decrease the overall efficiency of the units. ORNL proposed and tested an advanced control algorithm that anticipates the large water draw events and appropriately sets-up the temperature of the tank water using only the heat pump. With sufficient energy stored in the tank at the elevated temperature, the large water draw is provided for and electric resistance use is avoided. Simulations using a validated heat pump water heater model, and measured water draw data from 25 homes, show average yearly energy savings of 9% for the advanced control algorithm. If the advanced control algorithm perfectly predicts the large water draw events then the savings increase to 19%. This discrepancy could be due to a lack of predictability of water draw patterns in some homes, or the water draw forecasting algorithm could be improved.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2026

Crowdsourcing the Frontier: Advancing Hybrid Physics‐ML Climate Simulation via a $\$$50,000 Kaggle Competition

Subgrid machine-learning (machine learning [ML]) parameterizations have the potential to introduce a new generation of climate models that incorporate the effects of higher-resolution physics without incurring the prohibitive computational cost associated with more explicit physics-based simulations. However, important issues, ranging from online instability to inconsistent online performance, have limited their operational use for long-term climate projections. To more rapidly drive progress in solving these issues, domain scientists and ML researchers opened up the offline aspect of this problem to the broader ML and data science community with the release of ClimSim, a NeurIPS Data sets and Benchmarks publication, and an associated Kaggle competition. This paper reports on the downstream results of the Kaggle competition by coupling emulators inspired by the winning teams' architectures to an interactive climate model (including full cloud microphysics, a regime historically prone to online instability) and systematically evaluating their online performance. Our results demonstrate that online stability in the low-resolution real-geography setting is reproducible across multiple diverse architectures, which we consider a key milestone. All tested architectures exhibit strikingly similar offline and online biases, though their responses to architecture-agnostic design choices (e.g., expanding the list of input variables) can differ significantly. Multiple Kaggle-inspired architectures achieve state-of-the-art results on certain metrics such as zonal mean bias patterns and global Root Mean Squared Error, indicating that crowdsourcing the essence of the offline problem is one path to improving online performance in hybrid physics-AI climate simulation.

Environmental sciences↗
DOE OSTI2019

California Wave Power Technologies Open Water Demonstration - Budget Period 1 reports

The objective of the project is to advance the Technology Readiness Level (TRL) of the Wave Energy Converter (WEC) developed by CalWave Wave Power Technologies Inc (CalWave) through advanced numerical simulations, dynamic hardware tests, and ultimately a scaled open water demonstration deployment while continuing to exceed DOE's target ACE threshold of 3m/M$. The outcomes of Budget Period 1 will be a detailed design of the scaled demonstration unit and bench testing of the critical hardware components.

16 TIDAL AND WAVE POWER↗
DOE OSTI2019

Hierarchical sensitivity analysis for simulating barrier island geomorphologic responses to future storms and sea-level rise

This paper presents a new application of an advanced hierarchical sensitivity analysis of a new climate model of barrier island geomorphological evolution. The implemented sensitivity analysis in this study integrates a hierarchical uncertainty framework with a variance-based global sensitivity analysis to decompose the different model input uncertainties. The analysis can provide quantitative and accurate measurements for the relative importance of uncertain model input factors while considering their dependence relationships. The climate model used in this research was the barrier island profile (BIP) model, which is a new computer code developed to simulate barrier island morphological evolution over periods ranging from years to decades under the impacts of accelerated future sea-level rise and long-term changes in the storm climate. In the application of the model, the BIP model was used to evaluate the responses of a series of barrier island cross-sections derived for Santa Rosa Island, Florida, to random storm events and five potential accelerated rates of sea-level rise projected over the next century. The uncertain model input factors thus include the scenario uncertainty caused by alternative future sea-level rise scenarios and the parametric uncertainties of random storm parameters and dune characteristics. The study results reveal that the occurrence of storms is the most important factor for the evolution of sand dunes on the barrier island and the impact of sea-level rise is essential to the morphological change of the island backshore environment. The analysis can provide helpful insights for coastal management and planning. This hierarchical sensitivity analysis is mathematically general and rigorous and can be applied to a wide range of climate models.

Dai, Heng↗
DOE OSTI2017

Center for Extended Magnetohydrodynamics Modeling

This researcher participated in the DOE-funded Center for Extended Magnetohydrodynamics Modeling (CEMM), a multi-institutional collaboration led by the Princeton Plasma Physics Laboratory with Dr. Stephen Jardin as the overall Principal Investigator. This project developed advanced simulation tools to study the non-linear macroscopic dynamics of magnetically confined plasmas. The collaborative effort focused on the development of two large numerical simulation codes, M3D-C1 and NIMROD, and their application to a wide variety of problems. Dr. Ramos was responsible for theoretical aspects of the project, deriving consistent sets of model equations applicable to weakly collisional plasmas and devising test problems for verification of the numerical codes. This activity was funded for twelve years.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2016

X-ray metrology and performance of a 45-cm long x-ray deformable mirror

We describe experiments with a 45-cm long x-ray deformable mirror (XDM) that have been conducted in End Station 2, Beamline 5.3.1 at the Advanced Light Source. A detailed description of the hardware implementation is provided. We explain our one-dimensional Fresnel propagation code that correctly handles grazing incidence and includes a model of the XDM. This code is used to simulate and verify experimental results. Initial long trace profiler metrology of the XDM at 7.5 keV is presented. The ability to measure a large (150-nm amplitude) height change on the XDM is demonstrated. The results agree well with the simulated experiment at an error level of 1 μrad RMS. Lastly, direct imaging of the x-ray beam also shows the expected change in intensity profile at the detector.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2023

Classroom aerosol dispersion modeling: experimental assessment of a low-cost flow simulation tool

The purpose of this study was to assess the utility of a low-cost flow simulation tool for an indoor air modeling application by comparing its outputs with the results of a physical experiment, as well as those from a more advanced computational fluid dynamics (CFD) software package. In this study, five aerosol dispersion tests were performed in two different classrooms by releasing a CO 2 tracer gas from six student locations. Resultant steady-state concentrations were monitored at 13 locations around the periphery of the room. Subsequently, the experiments were modeled using both a low-cost tool (SolidWorks Flow Simulation) and a more sophisticated tool (STAR-CCM+). Models were evaluated based on their ability to predict the experimentally measured concentrations at the 13 monitoring locations by calculating four performance parameters commonly used in the evaluation of dispersion models: fractional mean bias (FB), normalized mean-square error (NMSE), fraction of predicted value within a factor of two (FAC2), and normalized absolute difference (NAD). The more sophisticated model performed better in 15 of the 20 possible cases (five tests at four parameters each), with parameters meeting acceptance criteria in 19 of 20 cases. However, the lower-cost tool was only slightly worse, with parameters meeting acceptance criteria in 18 of 20 cases, and it performed better than the other tool in 3 of 20 cases. Because it provides useful results at a fraction of the monetary and training cost and is already widely accessible to many institutions, such a tool may be worthwhile for many indoor aerosol dispersion applications, especially for students or researchers just beginning CFD modeling.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Modeling Time-Dependent Surrogates of Additive-Manufactured Nuclear Fuels Processes

Additive manufacturing (AM) technology is being increasingly adopted in a wide variety of application areas for its ability to rapidly produce, prototype, and customize designs. Recently, a hybrid AM technique was successfully developed at Idaho National Laboratory (INL) to manufacture nuclear fuels [1]. Despite the advantages, this AM technique needs optimization due to defects from a highly complex melting and sintering process. The complex metallurgical phenomena during AM processes are strongly related to parameters such as applied laser power, traveling speed, and scan style, which could lead to differences in density, residual stress, crystallographic texture, and mechanical properties. In addition, stochastic variations in laser energy interaction and associated multiscale/multiphysics phenomena cause variations in microstructure evolution and mechanical properties. Currently, researchers at INL are focusing on developing a comprehensive modeling framework, leveraging INL’s simulation tools MOOSE/MARMOT/BISON/RAVEN [2-4] to describe all steps of this AM process across multiple length scales. Although this advanced framework plays a critical role in enabling enhancements to traditional trial and error approaches for design and optimization of nuclear fuel materials, it remains computationally intense, limiting its use in sensitivity and optimization analysis. In this case, an accurate and inexpensive surrogate becomes an effective tool for providing a tractable approximation of the underlying underline physics. Surrogate models generally not based on the physics of a system are purely mathematical models used to capture the relationships between specific system inputs and outputs. Popular approaches, including neural networks [5], response surfaces [6], and subspace-based reduced order models [7], have been applied to a wide range of disciplines, such as nuclear reactor design, aerospace design and automotive design. In this summary, we employ advanced time-dependent surrogate models such as high-dimensional model representation (HDMR) [8] and physics-informed deep neural network (PINNs) [9] to accelerate the design and optimization of AM process.

42 ENGINEERING↗
DOE OSTI2024

Weak shock compaction on granular salt

This study conducted integrated experiments and computational modeling to investigate the speeds of a developing shock within granular salt and analyzed the effect of various impact velocities up to 245 m/s. Experiments were conducted on table salt utilizing a novel setup with a considerable bore length for the sample, enabling visualization of a moving shock wave. Experimental analysis using particle image velocimetry enabled the characterization of shock velocity and particle velocity histories. Mesoscale simulations further enabled advanced analysis of the shock wave’s substructure. In simulations, the shock front’s precursor was shown to have a heterogeneous nature, which is usually modeled as uniform in continuum analyses. The presence of force chains results in a spread out of the shock precursor over a greater ramp distance. With increasing impact velocity, the shock front thickness reduces, and the precursor of the shock front becomes less heterogeneous. Furthermore, mesoscale modeling suggests the formation of force chains behind the shock front, even under the conditions of weak shock. This study presents novel mesoscale simulation results on salt corroborated with data from experiments, thereby characterizing the compaction front speeds in the weak shock regime.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2015

Introducing Enabling Computational Tools to the Climate Sciences: Multi-Resolution Climate Modeling with Adaptive Cubed-Sphere Grids

The research investigates and advances strategies how to bridge the scale discrepancies between local, regional and global phenomena in climate models without the prohibitive computational costs of global cloud-resolving simulations. In particular, the research explores new frontiers in computational geoscience by introducing high-order Adaptive Mesh Refinement (AMR) techniques into climate research. AMR and statically-adapted variable-resolution approaches represent an emerging trend for atmospheric models and are likely to become the new norm in future-generation weather and climate models. The research advances the understanding of multi-scale interactions in the climate system and showcases a pathway how to model these interactions effectively with advanced computational tools, like the Chombo AMR library developed at the Lawrence Berkeley National Laboratory. The research is interdisciplinary and combines applied mathematics, scientific computing and the atmospheric sciences. In this research project, a hierarchy of high-order atmospheric models on cubed-sphere computational grids have been developed that serve as an algorithmic prototype for the finite-volume solution-adaptive Chombo-AMR approach. The foci of the investigations have lied on the characteristics of both static mesh adaptations and dynamically-adaptive grids that can capture flow fields of interest like tropical cyclones. Six research themes have been chosen. These are (1) the introduction of adaptive mesh refinement techniques into the climate sciences, (2) advanced algorithms for nonhydrostatic atmospheric dynamical cores, (3) an assessment of the interplay between resolved-scale dynamical motions and subgrid-scale physical parameterizations, (4) evaluation techniques for atmospheric model hierarchies, (5) the comparison of AMR refinement strategies and (6) tropical cyclone studies with a focus on multi-scale interactions and variable-resolution modeling. The results of this research project demonstrate significant advances in all six research areas. The major conclusions are that statically-adaptive variable-resolution modeling is currently becoming mature in the climate sciences, and that AMR holds outstanding promise for future-generation weather and climate models on high-performance computing architectures.

58 GEOSCIENCES↗
DOE OSTI2025

Kinetic Plasma Simulation Capabilities in the MOOSE Framework: Verification of Particle-Particle Collisions

High-fidelity simulations of complex plasma systems allow researchers to gain key insights into and understanding of these systems. To facilitate massively parallel high-fidelity plasma simulations, finite-element-based particle-in-cell capabilities are being developed within the open-source Multiphysics Object-Oriented Simulation Environment (MOOSE) based framework called Software for Advanced Large-scale Analysis of MAgnetic confinement for Numerical Design, Engineering & Research (SALAMANDER). While SALAMANDER’s primary objective is modeling edge plasmas and plasma-facing components in fusion devices, the particle-in-cell capabilities being developed are general and will support modeling low-temperature plasmas as well. Previously, collisionless magnetostatic simulation capabilities have been verified with the two-stream and Dorey-Guest-Harris instabilities, and single particle motion. Collisions were implemented using the direct simulation Monte Carlo method, and verification of this capability will be presented here several verification problems: relaxation of a randomly initialized gas to a Maxwellian distribution, Fourier heat flow, and comparison of reaction rates to both analytic calculations and those calculated using a multi-term Boltzmann solver.

70 - PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2026

Assessing the Costs of Hydropower at Non-Powered Dams Using a Reference Site Model

Hydropower capacity in the United States currently stands at approximately 103 GW, and there are remaining water resources that could help meet the rapidly increasing demand for electricity and ancillary grid services. Existing dams that do not generate power, known as non-powered dams, are a near-term solution to enhance the contribution of hydropower to the US power grid. However, there are thousands of such sites, and the lack of detailed information about their specific characteristics and associated costs presents significant challenges for stakeholders. This study addresses these challenges by developing a reference site model to evaluate the potential costs of hydropower at non-powered dams using currently available technologies. An application of the model reveals a wide range of estimates for capacity, capital costs, levelized cost of electricity, and cost components. While many sites are competitive with current technologies, the majority would need cost-reducing innovations to be viable. Despite the limited available information, the model offers valuable insights into the relative competitiveness of hydropower projects at non-powered dams. The simulation results highlight the need for continued technological advancements in hydropower and provide a basis for evaluating the benefits of new innovations.

cost drivers↗
DOE OSTI2016

WE-H-BRA-04: Biological Geometries for the Monte Carlo Simulation Toolkit TOPASNBio

Purpose: New advances in radiation therapy are most likely to come from the complex interface of physics, chemistry and biology. Computational simulations offer a powerful tool for quantitatively investigating radiation interactions with biological tissue and can thus help bridge the gap between physics and biology. The aim of TOPAS-nBio is to provide a comprehensive tool to generate advanced radiobiology simulations. Methods: TOPAS wraps and extends the Geant4 Monte Carlo (MC) simulation toolkit. TOPAS-nBio is an extension to TOPAS which utilizes the physics processes in Geant4-DNA to model biological damage from very low energy secondary electrons. Specialized cell, organelle and molecular geometries were designed for the toolkit. Results: TOPAS-nBio gives the user the capability of simulating biological geometries, ranging from the micron-scale (e.g. cells and organelles) to complex nano-scale geometries (e.g. DNA and proteins). The user interacts with TOPAS-nBio through easy-to-use input parameter files. For example, in a simple cell simulation the user can specify the cell type and size as well as the type, number and size of included organelles. For more detailed nuclear simulations, the user can specify chromosome territories containing chromatin fiber loops, the later comprised of nucleosomes on a double helix. The chromatin fibers can be arranged in simple rigid geometries or within factual globules, mimicking realistic chromosome territories. TOPAS-nBio also provides users with the capability of reading protein data bank 3D structural files to simulate radiation damage to proteins or nucleic acids e.g. histones or RNA. TOPAS-nBio has been validated by comparing results to other track structure simulation software and published experimental measurements. Conclusion: TOPAS-nBio provides users with a comprehensive MC simulation tool for radiobiological simulations, giving users without advanced programming skills the ability to design and run complex simulations.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2024

A data-driven framework for predicting machining stability: employing simulated data, operational modal analysis, and enhanced transfer learning

Chatter, a self-excited vibration phenomenon, presents a significant challenge in machining operations, particularly in high-speed milling, where it can degrade tool life, reduce material removal efficiency, and compromise workpiece quality. Addressing this challenge requires a reliable predictive model that can accommodate the complex dynamics of various machining scenarios. This study introduces a novel, data-driven approach to predicting machining stability, leveraging over 140,000 simulated datasets and employing advanced techniques such as operational modal analysis (OMA), enhanced transfer learning (TL), and receptance coupling substructure analysis (RCSA). By integrating these methodologies, the framework effectively classifies and predicts chatter across diverse operational modes, achieving robust and accurate outcomes. Our model utilizes a Random Forest (RF) classifier trained with the comprehensive dataset, which demonstrates substantial improvements in both predictive accuracy and robustness. Specifically, the RF model achieved an accuracy rate of 85%, an area under the curve (AUC) of 0.90, and an F1 score of 0.88, underscoring its capability to adapt to varying machining configurations. These results highlight the framework’s potential to enhance operational efficiency and machining quality by providing reliable chatter predictions across a broad range of machining parameters. In conclusion, this research thus offers a significant advancement in predictive maintenance for machining processes, enabling more stable and efficient manufacturing operations.

42 ENGINEERING↗
DOE OSTI2020

Multi-GPU immersed boundary method hemodynamics simulations

Large-scale simulations of blood flow that resolve the 3D deformation of each comprising cell are increasingly popular owing to algorithmic developments in conjunction with advances in compute capability. Among different approaches for modeling cell-resolved hemodynamics, fluid structure interaction (FSI) algorithms based on the immersed boundary method are frequently employed for coupling separate solvers for the background fluid and the cells within one framework. GPUs can accelerate these simulations; however, both current pre-exascale and future exascale CPU-GPU heterogeneous systems face communication challenges critical to performance and scalability. In this paper, we describe, to our knowledge, the largest distributed GPU-accelerated FSI simulations of high hematocrit cell-resolved flows with over 17 million red blood cells. We compare scaling on a fat node system with six GPUs per node and on a system with a single GPU per node. Through comparison between the CPU- and GPU-based implementations, we identify the costs of data movement in multiscale multi-grid FSI simulations on heterogeneous systems and show it to be the greatest performance bottleneck on the GPU.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2018

Fine-scale application of WRF-CAM5 during a dust storm episode over East Asia: Sensitivity to grid resolutions and aerosol activation parameterizations

An advanced online-coupled meteorology and chemistry model WRF-CAM5 has been applied to East Asia using triple-nested domains at different grid resolutions (i.e., 36-, 12-, and 4-km) to simulate a severe dust storm period in spring 2010. Analyses are performed to evaluate the model performance and investigate model sensitivity to different horizontal grid sizes and aerosol activation parameterizations and to examine aerosol-cloud interactions and their impacts on the air quality. A comprehensive model evaluation of the baseline simulations using the default Abdul-Razzak and Ghan (AG) aerosol activation scheme shows that the model can well predict major meteorological variables such as 2-m temperature (T2), water vapor mixing ratio (Q2), 10-m wind speed (WS10) and wind direction (WD10), and shortwave and longwave radiation across different resolutions with domain-average normalized mean biases typically within±15%. The baseline simulations also show moderate biases for precipitation and moderate-to-large underpredictions for other major variables associated with aerosol-cloud interactions such as cloud droplet number concentration (CDNC), cloud optical thickness (COT), and cloud liquid water path (LWP) due to uncertainties or limitations in the aerosol-cloud treatments. The model performance is sensitive to grid resolutions, especially for surface meteorological variables such as T2, Q2, WS10, and WD10, with the performance generally improving at finer grid resolutions for those variables. Comparison of the sensitivity simulations with an alternative (i.e., the Fountoukis and Nenes (FN) series scheme) and the default (i.e., AG scheme) aerosol activation scheme shows that the former predicts larger values for cloud variables such as CDNC and COT across all grid resolutions and improves the overall domain-average model performance for many cloud/radiation variables and precipitation. Sensitivity simulations using the FN series scheme also have large impacts on radiations, T2, precipitation, and air quality (e.g., decreasing O3) through complex aerosol-radiation-cloud-chemistry feedbacks. The inclusion of adsorptive activation of dust particles in the FN series scheme has similar impacts on the meteorology and air quality but to lesser extent as compared to differences between the FN series and AG schemes. Compared to the overall differences between the FN series and AG schemes, impacts of adsorptive activation of dust particles can contribute significantly to the increase of total CDNC (~45%) during dust storm events and indicate their importance in modulating regional climate over East Asia.

WRF-CAM5, Aerosol activation, adsorptive activatio↗