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DOE OSTI2024

Sensor discriminators and methods for detecting electrical property changes in a metal organic framework

A sensor discriminator for detecting a gaseous substance includes a power source, a discrimination module, a sensor simulator that simulates a metal organic framework under at least one simulation condition, a simulation circuitry electrically coupling the sensor simulator to the power source and the discrimination module, and a discriminator circuitry that electrically couples the power source and the discrimination module to a gas capture probe. The discrimination module compares a discrimination pulse and a simulation pulse from the power source after the discrimination pulse passes through a metal organic framework of the gas capture sensor and the simulation pulse passes through a simulation component of the sensor simulator. The discrimination module causes a discriminator output that includes the comparison of the discrimination pulse to the simulation pulse. An electrical property of the discrimination pulse depends on an electrical parameter of the metal organic framework that is augmented by the gaseous substance.

Christensen, Daniel A.↗

DOE OSTI2025

Failure Analysis–Informed Risk Assessment Framework for Geological Carbon Storage Using Numerical Simulation and Machine Learning

Geological carbon storage (GCS) is recognized as a critical technology for achieving large-scale reductions in anthropogenic carbon dioxide (CO 2 ) emissions. Ensuring long-term containment and safety requires robust risk assessment frameworks that account for geological uncertainty and identify potential failure scenarios. Among various indicators, the area of review (AoR) serves as a key metric for evaluating storage performance, regulatory compliance, and monitoring design, as it delineates the spatial extent impacted by pressure buildup and plume migration. However, conventional AoR-based risk assessments typically perturb parameters within narrow uncertainty bounds, potentially overlooking rare but high-impact events arising from extreme geological conditions. In this study, we present a failure analysis–informed risk assessment framework for large-scale GCS projects to improve site prescreening and monitoring design. A suite of 300 numerical simulations was generated using stochastic geological models that vary five key parameters: net-to-gross ratio, anisotropy azimuth, porosity multiplier, permeability multiplier, and vertical-to-horizontal permeability ratio. Among these, 200 realizations represent normal geological uncertainty, while 100 additional cases explore extreme yet plausible conditions for failure-case analysis. The AoR was simulated and computed from pressure and CO 2 saturation fields, where the baseline AoR boundary, representing the extent predicted under typical geological uncertainty, was defined as the union of 200 normal-range simulations, and failure was identified when extreme-range cases exceeded this baseline. Results show that incorporating broader parameter uncertainty produces significantly larger AoR extents, underscoring the potential underestimation of risk under conventional uncertainty ranges. Furthermore, spatial probability maps derived from failure-induced AoR exceedance identify regions requiring enhanced monitoring attention. Various machine learning (ML)–based classifiers were developed to predict failure occurrence from geological parameters, with the random forest model achieving the highest performance (F1-score of 0.986). Consistent findings from correlation coefficient, feature importance, and Sobol sensitivity analyses reveal that low net-to-gross ratios and permeability multipliers are the dominant risk drivers, reflecting reduced reservoir connectivity and limited pressure dissipation. Altogether, these results provide a novel framework for risk-informed site prescreening and monitoring design that explicitly considers rare but high-impact geological scenarios in GCS projects.

25 ENERGY STORAGE↗

DOE OSTI2017

Status Report on the High-Temperature Steam Electrolysis Plant Model Developed in the Modelica Framework (FY17)

This report has been prepared as part of an effort to design and build a modeling and simulation (M&S) framework to assess the economic viability of a nuclear-renewable hybrid energy system (N-R HES). In order to facilitate dynamic M&S of such an integrated system, research groups in multiple national laboratories have been developing various subsystems as dynamic physics-based components using the Modelica programming language. In fiscal year 2015 (FY15), Idaho National Laboratory (INL) performed a dynamic analysis of two region-specific N-R HES configurations, including the gas-to-liquid (natural gas to Fischer-Tropsch synthetic fuel) and brackish water reverse osmosis desalination plants as industrial processes. In FY16, INL developed two additional subsystems in the Modelica framework: (1) a high-temperature steam electrolysis (HTSE) plant as a high priority industrial plant to be integrated with a light water reactor (LWR) within an N-R HES and (2) a gas turbine power plant as a secondary energy supply. In FY17, five new components (i.e., a feedwater pump, a multi-stage compression system, a sweep-gas turbine, flow control valves, and pressure control valves) have been incorporated into the HTSE system proposed in FY16, aiming to better realistically characterize all key components of concern. Special attention has been given to the controller settings based on process models (i.e., direct synthesis method), aiming to improve process dynamics and controllability. A dynamic performance analysis of the improved LWR/HTSE integration case was carried out to evaluate the technical feasibility (load-following capability) and safety of such a system operating under highly variable conditions requiring flexible output. The analysis (evaluated in terms of the step response) clearly shows that the FY17 model resulted in superior output responses with much smaller settling times and less oscillatory behavior in response to disturbances in the electric load than those observed with the FY16 model. Simulation results involving several case studies show that the suggested control scheme could maintain the controlled variables (including the steam utilization factor, cathode stream inlet composition, and temperatures and pressures of the process streams at various locations) within desired limits under various plant operating conditions. The results also indicate that the proposed HTSE plant could provide operational flexibility to participate in energy management at the utility scale by dynamically optimizing the use of excess plant capacity within an N-R HES.

08 HYDROGEN↗

DOE OSTI2022

Layer Time Control for Large Scale Additive Manufacturing Using High Performance Computing

This work proposes to optimize an additive manufacturing AM process to reduce energy and printing cost. The polymer AM process is inherently dependent on the time-temperature history of each layer to maintain geometric tolerances and mechanical integrity. Our preliminary study shows that regression-based layer time control model using thermal images could result in up to 30% build time reduction for simple geometries. This proposed work would use high-performance computing (HPC) to couple the data-driven model with thermal simulation for better predicting layer temperature profiles, improving throughput of large-scale additive manufacturing, and reducing its energy cost. We have developed a method to optimize a layer deposition time (a.k.a. layer time) for large-scale AM via physics-based simulations. A long layer time leads to an over-cooled surface on which a new layer is deposited, and therefore, it may result in a weak bonding or debonding between layers, cracking, or warping. A short layer time leads to a high temperature of the structure due to insufficient cooling, and therefore, the structure may not be stiff enough and may collapse during manufacturing. Therefore, it is important to estimate the optimal layer time in additive manufacturing for a high-quality product. The temperature of a top layer right before deposition is recommended to be slightly higher than the glass temperature of the material. A temperature cooling was approximated to an exponential function of time, and the optimized layer time was obtained based on a target temperature while maintaining a minimal printing time. The material used is carbon fiber-reinforced polycarbonate (CF/PC), and the large-scale deposition system used is LSAM TM from Thermwood Corporation. Three different layer time cases were used for experiments, and a series of thermal images were obtained via an infra-red (IR) camera during the entire AM processes. AM process simulations were performed using a finite element method and the temperature profiles from the simulation were in good agreements with those from experiments. The layer time optimization was performed based on the temperature profiles from the simulations. A layer temperature with the optimal layer time was confirmed as the target temperature through simulation. In addition to the development of a layer time optimization method, we have developed a numerical framework for AM simulation with element activations in sync with toolpath, based on an open source finite element framework, DEAL.II. A major portion of this work was presented at SAMPE 2022 Conference and Exhibition on May 2022, and published in Proceedings of SAMPE 2022.

42 ENGINEERING↗

DOE OSTI2022

Ingrained: An Automated Framework for Fusing Atomic-Scale Image Simulations into Experiments

To fully leverage the power of image simulation to corroborate and explain patterns and structures in atomic resolution microscopy, an initial correspondence between the simulation and experimental image must be established at the outset of further high accuracy simulations or calculations. Furthermore, if simulation is to be used in context of highly automated processes or high-throughput optimization, the process of finding this correspondence itself must be automated. In this work, "ingrained," an open-source automation framework which solves for this correspondence and fuses atomic resolution image simulations into the experimental images to which they correspond, is introduced. Here, the overall "ingrained" workflow, focusing on its application to interface structure approximations, and the development of an experimentally rationalized forward model for scanning tunneling microscopy simulation are described.

36 MATERIALS SCIENCE↗

DOE OSTI2016

Exploring the impacts of physics and resolution on aqua-planet simulations from a nonhydrostatic global variable-resolution modeling framework

Advances in computing resources are gradually moving regional and global numerical forecasting simulations towards sub-10 km resolution, but global high resolution climate simulations remain a challenge. The non-hydrostatic Model for Prediction Across Scales (MPAS) provides a global framework to achieve very high resolution using regional mesh refinement. Previous studies using the hydrostatic version of MPAS (H-MPAS) with the physics parameterizations of Community Atmosphere Model version 4 (CAM4) found notable resolution dependent behaviors. This study revisits the resolution sensitivity using the non-hydrostatic version of MPAS (NH-MPAS) with both CAM4 and CAM5 physics. A series of aqua-planet simulations at global quasi-uniform resolutions ranging from 240 km to 30 km and global variable resolution simulations with a regional mesh refinement of 30 km resolution over the tropics are analyzed, with a primary focus on the distinct characteristics of NH-MPAS in simulating precipitation, clouds, and large-scale circulation features compared to H-MPAS-CAM4. The resolution sensitivity of total precipitation and column integrated moisture in NH-MPAS is smaller than that in H-MPAS-CAM4. This contributes importantly to the reduced resolution sensitivity of large-scale circulation features such as the inter-tropical convergence zone and Hadley circulation in NH-MPAS compared to H-MPAS. In addition, NH-MPAS shows almost no resolution sensitivity in the simulated westerly jet, in contrast to the obvious poleward shift in H-MPAS with increasing resolution, which is partly explained by differences in the hyperdiffusion coefficients used in the two models that influence wave activity. With the reduced resolution sensitivity, simulations in the refined region of the NH-MPAS global variable resolution configuration exhibit zonally symmetric features that are more comparable to the quasi-uniform high-resolution simulations than those from H-MPAS that displays zonal asymmetry in simulations inside the refined region. Overall, NH-MPAS with CAM5 physics shows less resolution sensitivity compared to CAM4. These results provide a reference for future studies to further explore the use of NH-MPAS for high-resolution climate simulations in idealized and realistic configurations.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2023

Preparations for quantum simulations of quantum chromodynamics in 1+1 dimensions. II. Single-baryon β-decay in real time

Here, a framework for quantum simulations of real-time weak decays of hadrons and nuclei in a two-flavor lattice theory in one spatial dimension is presented. A single generation of the Standard Model is found to require 16 qubits per spatial lattice site after mapping to spin operators via the Jordan-Wigner transformation. Both quantum chromodynamics and flavor-changing weak interactions are included in the dynamics, the latter through four-Fermi effective operators. Quantum circuits that implement time evolution in this lattice theory are developed and run on Quantinuum’s H1-1 20-qubit trapped ion system to simulate the β-decay of a single baryon on one lattice site. These simulations include the initial state preparation and are performed for both one and two Trotter time steps. The potential intrinsic error-correction properties of this type of lattice theory are discussed, and the leading lattice Hamiltonian required to simulate 0νββ-decay of nuclei induced by a neutrino Majorana mass term is provided.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

DOE OSTI2026

Transfer Learning Meets Embedded Correlated Wavefunction Theory for Chemically Accurate Molecular Simulations: Application to Calcium Carbonate Ion Pairing

Achieving chemical accuracy for molecular simulations remains a central challenge in computational chemistry. Here, we present an embedded correlated wavefunction transfer learning (ECW-TL) framework for accurately simulating molecular dynamics in the condensed phase. ECW-TL incorporates high-level electron exchange and correlation effects in ECW theory while preserving the training and computational efficiency of machine-learned interatomic potentials. We demonstrate the framework on Ca 2+ –CO 3 2– ion pairing in aqueous solution, a key process underlying CO 2 mineralization in seawater. As proof of principle, we first show that fine-tuning a DFT-revPBE-D3(BJ) baseline model with embedded-DFT-SCAN data reproduces the DFT-SCAN free-energy surface within 1 kcal/mol across all solvation states. Extending the framework to embedded MP2 and localized natural-orbital CCSD(T) further refines the free-energy profile, revealing the crucial role of exact electron exchange and correlation in determining ion-pair stability and structure. The computed ion-pair association free energy is in quantitative agreement with experimental measurements, further validating the accuracy of the ECW-TL framework. ECW-TL thus provides a general, data-efficient route for transferring CW accuracy to efficient simulations of complex aqueous and interfacial chemical processes.

cluster chemistry↗

DOE OSTI2020

Simulated JWST Data Sets for Multispectral and Hyperspectral Image Fusion

The James Webb Space Telescope (JWST) will provide multispectral and hyperspectral infrared images of a large number of astrophysical scenes. Multispectral images will have the highest angular resolution, while hyperspectral images (e.g., with integral field unit spectrometers) will provide the best spectral resolution. This paper aims at providing a comprehensive framework to generate an astrophysical scene and to simulate realistic hyperspectral and multispectral data acquired by two JWST instruments, namely, NIRCam Imager and NIRSpec IFU. We want to show that this simulation framework can be resorted to assess the benefits of fusing these images to recover an image of high spatial and spectral resolutions. To do so, we make a synthetic scene associated with a canonical infrared source, the Orion Bar. We develop forward models including corresponding noises for the two JWST instruments based on their physical features. JWST observations are then simulated by applying the forward models to the aforementioned synthetic scene. We test a dedicated fusion algorithm we developed on these simulated observations. We show that the fusion process reconstructs the high spatio-spectral resolution scene with a good accuracy on most areas, and we identify some limitations of the method to be tackled in future works. The synthetic scene and observations presented in the paper can be used, for instance, to evaluate instrument models, pipelines, or more sophisticated algorithms dedicated to JWST data analysis. Besides, fusion methods such as the one presented in this paper are shown to be promising tools to fully exploit the unprecedented capabilities of the JWST.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2024

NEAMS-Multiphysics MOOSE End Year Framework Activities FY20

The Multiphysics Object Oriented Simulation Environment (MOOSE) is a general finite element package meant for high performance solution of multiphysics problems in science and engineering. In this report, we present additions and enhancements to MOOSE funded by the Nuclear Engineering Advanced Modeling and Simulation (NEAMS) program. NEAMS-funded MOOSE framework library improvements include: expansion of support for multi-level multi-application restart; enhancement of coupling between native MOOSE applications and external libraries; addition of a sparse automatic differentiation (AD) container enabling non-local degree of freedom coupling; block-specific quadrature rules; further development of an eigenvalue executioner; faster setup of periodic boundary conditions; and creation of a mesh meta-data system streamlining simulation startup. Besides developing the framework library, NEAMS funds were used to overhaul a swath of important MOOSE infrastructure in order to substantially improve user experience. These critical infrastructure changes included adapting MOOSE to python 3, improving the test harness to catch race conditions, and most importantly transitioning MOOSE to use Conda, a globally known packaging system, that greatly eases software adoption.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2025

Detailed simulations of the first deuterium-tritium-filled double shell implosions on the National Ignition Facility

The first indirectly driven, liquid DT-filled double shell inertial confinement fusion (ICF) implosions have recently been successfully performed on the National Ignition Facility (NIF). Double shells are a class of alternative designs that use a low-Z outer shell to compress a foam cushion that accelerates a high-Z inner shell to efficiently compress a liquid DT core. Double shells are challenging to fabricate, field, and model. Important engineering features enabling double shell fabrication include a fill-tube penetrating all shells and a carefully designed and very narrow (few μm) step-joint in the ablator. Due to the higher density materials involved, high Atwood number instabilities are also important at many material interfaces. In this paper, numerical simulations of double shell implosions using the Los Alamos National Laboratory multi-physics radiation-hydrodynamics code xRAGE will be discussed. An extensive effort has been under way for several years to develop the code capabilities for ICF simulations in a common modeling framework to allow ease of simulation setup and standardization of the computational methodology. This paper will present a wide range of simulation results capturing, quantifying, and comparing the impact of all these degradation mechanisms on implosion performance. Brief comparisons with recent experimental results and suggestions for future improvements will also be discussed. Our results suggest that capsule surface roughness and the step-joint gap have the largest impact on implosion performance. Initial experimental data may suggest that the sensitivity to the step-joint gap could provide the dominant explanation for DT-filled double shell experiments that have been fielded on NIF thus far.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DOE OSTI2018

Calculations of Beam-Beam Effect and Luminosity for Crab Dynamics Simulations in JLEIC

Crab crossing is an integral part of the Jefferson Lab Electron-Ion Collider (JLEIC) design to achieve high luminosity while meeting the detection and physics pro-gram requirements. The crab crossing scheme provides a head-on beam-beam collision for beams with a nonzero crossing angle. Simulations of crabbing dynamics currently do not include beam-beam effects. We describe a framework for accurate simulation of beam-beam effects on crabbing dynamics by applying a numerical calculation of the Bassetti-Erskine analytic solution to symplectic particle tracking codes. The numerical calculation is benchmarked against the analytic solution by calculating the luminosity reduction for several colliding beam scenarios. Benchmarking results show good agreement be-tween the numerical calculation and analytic solution, paving the way for implementation of the beam-beam kick to Elegant tracking simulations.

Huang, He↗

DOE OSTI2025

Modeling a Sodium Heat Pipe Experiment at SPHERE Using Sockeye

The Single Primary Heat Extraction and Rejection Emulator (SPHERE) facility at Idaho Na- tional Laboratory was recently utilized to generate data for the startup and steady operation of a high-performance, sodium heat pipe over the course of 1000 hours, as a test of detrimental, long-term effects of heat pipe operation. The setup consists of a single, sodium heat pipe enclosed in a stainless-steel vacuum chamber, heated radiatively via a cylindrical ceramic fiber heater configuration and cooled via a water-cooled calorimeter. Measurements include temperatures at several axial locations along the outer surface of the heat pipe, the power provided to the heaters, and the heat removal rate of the calorimeter. In this work, this data is utilized to validate heat pipe models in the heat pipe application Sockeye, which is based upon the Multiphysics Object-Oriented Simulation Environment (MOOSE) framework. Sockeye provides various heat pipe models at an engineering scale appropriate for the multiphysics simulation of microreactors, which may feature several hundred heat pipes. This work details models of this experiment in SPHERE using various heat pipe models with Sockeye, including heat conduction-based models and compressible flow models of the heat pipe interior. These models are compared to the experimental data to assess the accuracy of several aspects of heat pipe modeling, including frozen startup, the effect of non-condensable gases, and the coupling of the heat pipe to its environment.

97 - MATHEMATICS AND COMPUTING↗

DOE OSTI2025

Modeling a Sodium Heat Pipe Experiment at SPHERE Using Sockeye

The Single Primary Heat Extraction and Rejection Emulator (SPHERE) facility at Idaho National Laboratory was recently utilized to generate data for the startup and steady operation of a high-performance, sodium heat pipe over the course of 1,000 hours to test the detrimental, long-term effects of heat pipe operation. The setup consisted of a single, sodium heat pipe enclosed in a stainless-steel vacuum chamber, heated radiatively via a cylindrical ceramic-fiber heater configuration and cooled via a water-cooled calorimeter. Measurements included temperatures at several axial locations along the outer surface of the heat pipe, the power provided to the heaters, and the heat removal rate of the calorimeter. In this work, we use this data to validate heat pipe models in Sockeye, a heat pipe application based on the Multiphysics Object-Oriented Simulation Environment (MOOSE) framework. Sockeye provides various heat pipe models at an engineering scale appropriate for the multiphysics simulation of microreactors, which may feature several hundred heat pipes. This work details models of this experiment at SPHERE using various heat pipe models with Sockeye, including heat-conduction-based and compressible flow models of the heat pipe interior.

97 - MATHEMATICS AND COMPUTING↗

DOE OSTI2016

Uncertainty quantification in LES of channel flow

Here, in this paper, we present a Bayesian framework for estimating joint densities for large eddy simulation (LES) sub-grid scale model parameters based on canonical forced isotropic turbulence direct numerical simulation (DNS) data. The framework accounts for noise in the independent variables, and we present alternative formulations for accounting for discrepancies between model and data. To generate probability densities for flow characteristics, posterior densities for sub-grid scale model parameters are propagated forward through LES of channel flow and compared with DNS data. Synthesis of the calibration and prediction results demonstrates that model parameters have an explicit filter width dependence and are highly correlated. Discrepancies between DNS and calibrated LES results point to additional model form inadequacies that need to be accounted for.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2018

Status Report on the Component Models Developed in the Modelica Framework: Reverse Osmosis Desalination Plant & Thermal Energy Storage

This report has been prepared as part of an effort to design and build a Modeling and Simulation (M&S) framework to assess the economic viability of a Nuclear-Renewable Hybrid Energy System (N-R HES). In order to facilitate dynamic M&S of such an integrated system, research groups in multiple national laboratories and universities have been developing various subsystems as dynamic physics-based components using the Modelica programming language. In Fiscal Years (FYs) 2015, Idaho National Laboratory (INL) performed a dynamic analysis of two region-specific N-R HES configurations, including the gas-to-liquid (natural gas to Fischer-Tropsch synthetic fuel) and brackish water Reverse Osmosis (RO) desalination plants as industrial processes. In FYs 2016–2017, INL developed two additional subsystems in the Modelica framework: (1) a high-temperature steam electrolysis plant as a high priority industrial plant to be integrated with a light water reactor within an N-R HES and (2) a gas turbine power plant as a secondary energy supply. In FY 2018, the RO desalination system model developed in FY 2015 has been updated such that the model is compatible with the most recent version of the ThermoPower library. Special attention has been given to the controller settings based on process models, aiming to improve process dynamics and controllability. A dynamic performance analysis of the updated RO desalination plant was carried out to evaluate the technical feasibility (load-following capability) of such a system operating under highly variable conditions requiring flexible output. Simulation results involving several case studies show that the suggested control scheme could maintain the controlled variables (including the variable electrical load and RO feed pressure) within desired limits under various plant operating conditions. The results also indicate that the proposed RO plant could provide operational flexibility to participate in energy management at the utility scale by dynamically optimizing the use of excess plant capacity within an N-R HES. For a small-scale energy storage system, a sensible Thermal Energy Storage (TES) model has been developed in the Modelica Framework in FY 2018.

25 ENERGY STORAGE↗

DOE OSTI2025

Learning to Simulate Aerosol Dynamics with Graph Neural Networks

Aerosol effects on climate, weather, and air quality depend on characteristics of individual particles, which are tremendously diverse and change in time. Particle-resolved models are the only models able to capture this diversity in particle physiochemical properties, and these models are computationally expensive. As a strategy for accelerating particle-resolved microphysics models, we introduce Graph-based Learning of Aerosol Dynamics (GLAD) and use this model to train a surrogate of the particle-resolved model PartMC-MOSAIC. GLAD implements a Graph Network-based Simulator (GNS), a machine learning framework that has been used to simulate particle-based fluid dynamics models. In GLAD, each particle is represented as a node in a graph, and the evolution of the particle population over time is simulated through learned message passing. Here, we demonstrate our GNS approach on a simple aerosol system that includes condensation of sulfuric acid onto particles composed of sulfate, black carbon, organic carbon, and water. A graph with particles as nodes is constructed, and a graph neural network (GNN) is then trained using the model output from PartMC-MOSAIC. The trained GNN can then be used for simulating and predicting aerosol dynamics over time. Results demonstrate the framework's ability to accurately learn chemical dynamics and generalize across different scenarios, achieving efficient training and prediction times. We evaluate the performance across four scenarios, highlighting the framework's robustness and adaptability in modeling aerosol microphysics and chemistry.

aerosol chemistry dynamics↗

DOE OSTI2021

Signatures of electronic and nuclear coherences in ultrafast molecular x-ray and electron diffraction

Femtosecond x-ray and electron diffraction hold promise to image the evolving structures of single molecules. We present a unified quantum-electrodynamical formulation of diffraction signals, based on the exact many-body nuclear + electronic wavefunction that can be extracted from quantum chemistry simulations. This gives a framework for analyzing various approximate molecular dynamics simulations. We show that the complete description of ultrafast diffraction signals contains interesting contributions involving mixed elastic and inelastic scattered photons that are usually masked by other larger contributions and are neglected. These terms include overlaps of nuclear wavepackets between different electronic states that provide an electronic decoherence mechanism and are important for the time-resolved imaging of conical intersections.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗