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At least 361 records · Page 20

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DOE OSTI2025

Immersive Scientific Visualization of Molten-Salt Reactor Waste Characteristics Using Virtual Reality

Immersive visualization is changing how we explore, communicate, and understand complex scientific systems. In nuclear energy, an area in which data are often multidimensional, time-dependent, and difficult to interpret, virtual reality (VR) represents a powerful and intuitive informational medium. This work introduces a VR-based platform that visualizes the post-shutdown behavior and waste management lifecycle of molten-salt reactors (MSRs), a next-generation reactor type with unique operational and safety characteristics. The platform, built in Unity, is streamed on the Meta Quest 3 headset. It transforms high-fidelity simulation data into an interactive, immersive experience. Users can explore time-dependent reactor characteristics such as nuclide decay, which is a key factor for evaluating reactor waste strategies. The datasets were generated using the MOOSE (Multiphysics Object-Oriented Simulation Environment) framework and then processed through ParaView scripting for smooth integration into Unity. From a visualization standpoint, the platform emphasizes spatial storytelling, temporal exploration, and user-centered interaction. Users can navigate 3D reactor geometries, slice through volumetric data, and manipulate time to observe how physical phenomena evolve. Real-scale rendering and embodied interaction make the experience feel tangible. The interface is designed to be accessible, even to those without nuclear or simulation expertise. This lowers the barrier for stakeholders, policymakers, and the general public, while still supporting expert analysis and collaborative decision-making. This work shows how immersive visualization can function as both a scientific tool and a communication interface. By integrating simulation, processing, and visualization into a cohesive workflow, we offer a scalable framework for immersive scientific storytelling. The modular design supports future extensions to other reactor types and lifecycle stages, from shutdown to long-term storage, making the platform adaptable for both research and outreach.

99 - GENERAL AND MISCELLANEOUS↗

DOE OSTI2024

System Modeling of Frost/Defrost Cycles in Heat Pumps

Accurately modeling frost/defrost cycles is crucial for guiding heat exchanger design, appropriately sizing the compressor, and quantifying seasonal energy consumption for heating. While previous modelling studies of frosting cycle have predominantly focused on the component level of fin-and-tube coils, there has been limited development of models for system defrosting cycles. This study enhances the existing knowledge by improving a detailed, segment-to-segment heat exchanger model to simulate frost/defrost cycles for both fin-and-tube coils and microchannel heat exchangers. Furthermore, it integrates this component-level model into a steady-state/quasi-steady-state system modeling framework to simulate complete frost/defrost processes, i.e. the ORNL Heat Pump Design Model (HPDM). This comprehensive approach will unveil differences in coil types, and their interactions with the outdoor fan. It will also predict energy consumption patterns and time intervals associated with these processes.

Shen, Bo↗

DOE OSTI2025

CHARGE-MAP: An integrated framework to study the multicriteria EV charging infrastructure expansion problem

The widespread adoption of electric vehicles (EVs) in recent years has necessitated the development of effective charging infrastructures. However, charging infrastructure expansion is a multifaceted problem that requires careful consideration of the existing infrastructure, spatiotemporal distribution of charging demands, power-grid capacity, and budget constraints. Here, to approach this complex problem, we present CHARGE-MAP, a data-driven simulation-optimization framework, focused on ensuring meaningful charging experience for individual EV owners. CHARGE-MAP integrates three modules: an agent-based simulation module that estimates spatiotemporal distribution of charging demands by modeling EV adopter mobility and charging behavior; an optimization module that determines optimal new charging station/charger locations and capacities, while minimizing expected detour distances and wait-times with a limited number of new stations; and a power module that determines how to connect the stations to the power grid while maintaining its stability. Using the state of Virginia (consisting of 95 counties and 38 independent cities) as a case study, our results show that CHARGE-MAP can meet the demand of ~198,600 predicted EVs with 1,305 new public charging stations and 2,164 new chargers. It reduces average detour distances for charging by 66% and wait-times at stations by 72% compared to the existing infrastructure. Furthermore, transformer capacity requirement analysis reveals that only 1.8% of residential transformers require upgrades, while over 80% of commercial charging locations can be supported with modest transformer infrastructure (25 to 50 kVA). This indicates that targeted investments can facilitate cost-effective EV integration. Consequently, CHARGE-MAP provides policymakers and urban planners with crucial data-driven insights for effective EV charging infrastructure expansion. Sign up for PNAS alerts.

charging infrastructure↗

NASA NTRSDate not supplied

Large-Scale Simulation of a Distributed Sensing Network Supporting Regional Urban Air Mobility Operations

Urban Air Mobility (UAM) is set to transform transportation in densely populated regions like the San Francisco Bay Area. This paper introduces an innovative simulation approach to explore large-scale UAM scenarios, emphasizing the use of distributed sensing to enhance operational efficiency and safety. The Revolutionary Vertical Lift Technology (RVLT) model is employed as the framework for simulating complex interactions among multiple vehicles within urban landscapes. Strategically deployed ground sensor nodes enable distributed sensing, enhancing situational awareness and operational effectiveness. By integrating empirical data and geographical realism, the simulations provide a systematic analysis of the feasibility, efficiency, and safety considerations associated with UAM deployment in urban environments. Factors such as air traffic density and infrastructural requirements are thoroughly examined, offering actionable insights for policymakers and industry stakeholders. This paper aims to refine the structure and scenarios for large-scale simulations based on distributed sensing, thereby contributing to the advancement of UAM operations.

UAM↗

NASA NTRSDate not supplied

Large-Scale Simulation of a Distributed Sensing Network Supporting Regional Urban Air Mobility Operations

Aircraft Mobility↗

NASA NTRS1993

An RL10A-3-3A rocket engine model using the rocket engine transient simulator (ROCETS) software

Steady-state and transient computer models of the RL10A-3-3A rocket engine have been created using the Rocket Engine Transient Simulation (ROCETS) code. These models were created for several purposes. The RL10 engine is a critical component of past, present, and future space missions; the model will give NASA an in-house capability to simulate the performance of the engine under various operating conditions and mission profiles. The RL10 simulation activity is also an opportunity to further validate the ROCETS program. The ROCETS code is an important tool for modeling rocket engine systems at NASA Lewis. ROCETS provides a modular and general framework for simulating the steady-state and transient behavior of any desired propulsion system. Although the ROCETS code is being used in a number of different analysis and design projects within NASA, it has not been extensively validated for any system using actual test data. The RL10A-3-3A has a ten year history of test and flight applications; it should provide sufficient data to validate the ROCETS program capability. The ROCETS models of the RL10 system were created using design information provided by Pratt & Whitney, the engine manufacturer. These models are in the process of being validated using test-stand and flight data. This paper includes a brief description of the models and comparison of preliminary simulation output against flight and test-stand data.

Binder, Michael↗

NASA NTRSDate not supplied

Transonic Lift and Drag Predictions using Wall Modelled Large Eddy Simulations

Wall-modelled Large Eddy Simulations (WMLES) of the NASA Common Research Model (CRM) at transonic conditions and various angles of attacks leading up to and including shock-induced flow separation are performed using the LAVA computational framework. The simulations are shown to accurately predict the lift curve slope and the onset of separation characterized by the break in the pitching moment. Furthermore, careful assessment of skin-friction drag at cruise condition and its subsequent decrease with increasing angles of attack are shown to be in agreement with viscous sublayer resolving Reynolds Averaged Navier Stokes(RANS) simulations. The small differences between WMLES and RANS appear to be of the same order as differences seen between two RANS models at the cruise-point. Some sensitivity is observed to the coefficient used in the subgrid scale model, although this can be reconciled by noting the low chord- and shock-incidence Reynolds numbers (Rex≈106) seen in the outboard regions of the wing, along with the uncertainties associated with tripping and the numerical transition that occurs near the leading edge. Among the primary drawbacks seen in the WMLES predictions is the subdued increase in wave-drag with increasing angle-of-attack and the shock intensity when compared with experimental data. Preliminary assessment suggests that additional span- and stream-wise grid resolution is likely needed in mid- and out-board portions of the wing to better resolve the shock-induced separated flow and to further investigate prediction accuracy of unsteady temporal characteristics of the problem.

ARMD↗

NASA NTRS1991

Numerical simulations of thermal detection of disbonds in lap joints

A 2D time-dependent FEM heat-transfer algorithm that is unconditionally stable and implicit is presently used to solve for the time-dependence of a structure's temperature. By examining data-reduction techniques in the framework of simulation data, the contrast enhancement afforded by a given technique can be evaluated without regard to experimental error. The present computational simulations demonstrate that the time-derivative technique can clearly delineate the substructure of lap-joint geometries; in addition, histograms of the front surface images are able to quantitatively measure contrast-enhancements for various inspection and data-reduction techniques.

Howell, P. A.↗

NASA NTRS2018

Active and Passive Radiative Transfer Modeling of the Olympic Mountains Experiment

Sensor forward models are an important tool for interpreting remote sensing observations of geophysical phenomena. By implementing a three-dimensional framework, we can simulate and analyze observations from various sensors on disparate platforms. To demonstrate our model framework, we simulate observations from the Olympic Mountains Experiment (OLYMPEX). The use of cloud model simulations allows us to understand sensor response to cloud ice, falling snow, and other processes and features, and the application of model tools to observations allows us to quantify precipitation.MIIST 3D Forward ModelThe Multi-Instrument Inverse Solver Testbed(MIIST) uses the Atmospheric Radiative TransferSimulator (ARTS) for solving the vector radiativetransfer (RT) equation in up to three spatialdimensions within a spherical geometry• Gas absorptiono Line-by-line calculationso Fast transmittance tables• Hydrometeor scattering solverso Discrete ordinateo RT4 (Evans, 1D)o Radar Single Scattering (1D or 3D)o Monte Carlo (3D)Scattering TablesHigh-fidelity hydrometeor scatteringtables are necessary for accurateand consistent forward modeling ofmulti-frequency observations• Requires full Stokes matriceso And absorption vector• Randomly oriented particleso Discrete Dipole Approximationo Characteristic Basis Function Method(coming soon)• Horizontally-oriented plateso Invariant Imbedding T-matrix MethodCloud Resolving SimulationsCloud resolving simulations (e.g.,NU-WRF) supply output consistentwith ARTS needs• Atmospheric Informationo Temperatureo Pressure / heighto Water vapor• Hydrometeor Profileso ARTS architecture ripe for explicit binmicrophysics• Examples use Morrison 2M schemeThe Olympic Mountains Experiment (OLYMPEX)Validation for GPM of mid-latitudefrontal systems approaching nearcoastalmountains from the ocean• Large collection of ground-based andairborne sensorso Radarso Radiometerso In situ• Contemporaneous with RADEXo Two sets of radar at same frequenciesRadiometer Simulation (3 km NUWRF, 20151203, 15:00)2018.12.14 7Simulate 166 GHz polarizationdifference• Corresponds to the presence of aligned icecrystals• Look at trends for both simulations andobservations• Simulations can tolerate lower resolutiono Larger domainSimulations from Observations: OLYMPEXSimulate sensor response usinggeophysical retrievals as input• Single frequency radar retrievals• Multiple scattering enhancementapparent at W band• Spatially dependent phenomenonModeling Application: 1D Retrievals03 December 2015• DC-8 and ER-2 flightso Focus on APR-3 (DC-8)• Citationo Stacked microphysics legso Qualitative comparisonso Range of frozen habitso Presence of supercooledliquid cloudsResults• Retrievals match probeso Good qualitative match• Bands of increasedreflectivity correspond tolarge Dm and highaggregate fraction• Significant amounts ofsupercooled liquid water

Adams, Ian S.↗

DOE OSTI2024

An integrated modeling framework with open architecture for phase field simulation of multi-component alloys

An integrated modeling framework (PanPhaseField) has been developed, which enables a direct and fast coupling between CALPHAD calculations and large-scale phase field simulations for multi-component alloys. Further, it adopts an open architecture allowing for integration of user-defined phase field models in a plug-and-play manner by taking full advantage of the user-friendly graphical interface of Pandat software. The developed modeling platform becomes an enabling tool that can be used to simulate the evolution of spatially varying microstructures of industrial complex alloys for various engineering applications.

36 MATERIALS SCIENCE↗

DOE OSTI2026

An uncertainty visualization framework for large-scale cardiovascular flow simulations: A case study on aortic stenosis

We present a generalizable uncertainty quantification (UQ) and visualization framework for lattice Boltzmann method simulations of high Reynolds number vascular flows, demonstrated on a patient-specific stenosed aorta. The framework combines EasyVVUQ for parameter sampling with large-eddy simulation turbulence modeling in HemeLB, and executes ensembles on the Frontier exascale supercomputer. Spatially resolved metrics, including entropy and isosurface-crossing probability, are used to map uncertainty in pressure and wall shear stress fields directly onto vascular geometries. Two sources of model variability are examined: inlet peak velocity and the Smagorinsky constant. Inlet velocity variation produces high uncertainty downstream of the stenosis where turbulence develops, while upstream regions remain stable. Smagorinsky constant variation has little effect on the large-scale pressure field but increases WSS uncertainty in localized high-shear regions. In both cases, the stenotic throat manifests low entropy, indicative of robust identification of elevated WSS. By linking quantitative UQ measures to three-dimensional anatomy, the framework improves interpretability over conventional 1D UQ plots and supports clinically relevant decision-making, with broad applicability to vascular flow problems requiring both accuracy and spatial insight.

Hemodynamics↗

DOE OSTI2025

Molecular Simulation of Functionalized Covalent Organic Framework Membranes for Inorganic Salt Separation

Covalent organic frameworks (COFs) enable molecular-level design of nanochannels for selective separation in pressure-driven membrane processes. Through variation of building blocks and, subsequently, the pore structure and chemistry, membrane performance can be tailored. This study employs nonequilibrium molecular dynamics simulations to theoretically demonstrate the tunable selectivity of COF membranes through a bottom-up functionalization approach. Water and salt transport are evaluated for six β-ketoenamine-linked COFs with varying multilayer thicknesses. For the thinnest multilayer (0.64–0.72 nm), all COFs exhibit low sodium sulfate (Na 2 SO 4 ) rejection (55–66%). However, 20 stacked sheets (6.4–7.2 nm) provide 67–98% rejection, with the sulfonated COF providing the highest Na 2 SO 4 rejection. Analysis of time-resolved ion density profiles reveals that solute rejection is primarily governed by interfacial exclusion arising from pore size and functional group chemistry. Although increasing salt rejection compromises water permeance, the permeance of all COF membranes is at least two orders of magnitude greater than that of a commercially available nanofiltration membrane. Overall, this work guides the rational design of COF membranes for aqueous salt separation.

Nanofiltration↗

DOE OSTI2025

Development of a River Dynamical Core for E3SM to simulate compound flooding on Exascale-class heterogeneous supercomputers

Flooding events pose significant risk to human life, property, and infrastructure. Physically-consistent quantification of altered flood risks in global models requires hyper-resolution (~1 km) or fine flood simulations using two-dimensional (2D) physics schemes, both of which are unavailable in the current generation Earth System Models. Here, in this work, we have developed the River Dynamical Core (RDycore), which is an open-source, 2D shallow water equation (SWE) library for the U.S. Department of Energy's Energy Exascale Earth System Model (E3SM). RDycore uses PETSc and libCEED libraries that allows it to run efficiently on CPUs and GPUs, as well as select a time-integration algorithm at runtime without requiring any code modifications. RDycore achieves spatial error convergence rates for problems with analytical and manufactured solutions similar to those reported previously in the literature, or consistent with the implemented first-order spatial discretization scheme. RDycore's accuracy in predicting flooding for a well-studied dam break problem is comparable to existing SWE models. For a problem with 471 million grid cells, RDycore achieves a speedup of 6.6x and 7.6x on GPUs compared to CPUs when using 320 compute nodes on DOE's Perlmutter and Frontier supercomputers, respectively. The one-way coupling of the RDycore library within E3SM is demonstrated by performing multiple 5-day flooding simulations during Hurricane Harvey driven by five precipitation datasets. The E3SM--RDycore simulations at 30 m spatial resolution accurately simulate maximum water height during the hurricane when benchmarked against a previously published study and achieve a speedup of 15x (Perlmutter) and 21x (Frontier) on GPUs relative to CPUs. The work presented here is the foundational step in providing hardware and algorithmic portability framework for simulating kilometer-scale river dynamics within E3SM.

Flood Simulation↗

NASA NTRS1990

Radiatively driven stratosphere-troposphere interactions near the tops of tropical cloud clusters

Results are presented of two numerical simulations of the mechanism involved in the dehydration of air, using the model of Churchill (1988) and Churchill and Houze (1990) which combines the water and ice physics parameterizations and IR and solar-radiation parameterization with a convective adjustment scheme in a kinematic nondynamic framework. One simulation, a cirrus cloud simulation, was to test the Danielsen (1982) hypothesis of a dehydration mechanism for the stratosphere; the other was to simulate the mesoscale updraft in order to test an alternative mechanism for 'freeze-drying' the air. The results show that the physical processes simulated in the mesoscale updraft differ from those in the thin-cirrus simulation. While in the thin-cirrus case, eddy fluxes occur in response to IR radiative destabilization, and, hence, no net transfer occurs between troposphere and stratosphere, the mesosphere updraft case has net upward mass transport into the lower stratosphere.

Churchill, Dean D.↗

NASA NTRS1993

Gas turbine system simulation: An object-oriented approach

A prototype gas turbine engine simulation has been developed that offers a generalized framework for the simulation of engines subject to steady-state and transient operating conditions. The prototype is in preliminary form, but it successfully demonstrates the viability of an object-oriented approach for generalized simulation applications. Although object oriented programming languages are-relative to FORTRAN-somewhat austere, it is proposed that gas turbine simulations of an interdisciplinary nature will benefit significantly in terms of code reliability, maintainability, and manageability. This report elucidates specific gas turbine simulation obstacles that an object-oriented framework can overcome and describes the opportunity for interdisciplinary simulation that the approach offers.

Drummond, Colin K.↗

NASA NTRS1990

Electron heating in quasi-perpendicular shocks - A Monte Carlo simulation

To study the problem of electron heating in quasi-perpendicular shocks, under the combined effects of 'reversible' motion, in the shock electric potential and magnetic field, and wave-particle interactions a diffusion equation was derived, in the drift (adiabatic) approximation and it was solved by using a Monte Carlo method. The results show that most of the observations can be explained within this framework. The simulation has also definitively shown that the electron parallel temperature is determined by the dc electromagnetic field and not by any wave particle induced heating. Wave-particle interactions are effective in smoothing out the large gradients in phase space produced by the 'reversible' motion of the electrons, thus producing a 'cooling' of the electrons. Some constraints on the wave-particle interaction process may be obtained from a detailed comparison between the simulation and observations. In particular, it appears that the adiabatic approximation must be violated in order to explain the observed evolution of the perpendicular temperature.

Veltri, Pierluigi↗

DOE OSTI2024

Rethinking materials simulations: Blending direct numerical simulations with neural operators

Abstract Materials simulations based on direct numerical solvers are accurate but computationally expensive for predicting materials evolution across length- and time-scales, due to the complexity of the underlying evolution equations, the nature of multiscale spatiotemporal interactions, and the need to reach long-time integration. We develop a method that blends direct numerical solvers with neural operators to accelerate such simulations. This methodology is based on the integration of a community numerical solver with a U-Net neural operator, enhanced by a temporal-conditioning mechanism to enable accurate extrapolation and efficient time-to-solution predictions of the dynamics. We demonstrate the effectiveness of this hybrid framework on simulations of microstructure evolution via the phase-field method. Such simulations exhibit high spatial gradients and the co-evolution of different material phases with simultaneous slow and fast materials dynamics. We establish accurate extrapolation of the coupled solver with large speed-up compared to DNS depending on the hybrid strategy utilized. This methodology is generalizable to a broad range of materials simulations, from solid mechanics to fluid dynamics, geophysics, climate, and more.

36 MATERIALS SCIENCE↗

DOE OSTI2024

Simulating Crystallization in a Colloidal System Using State Predictive Information Bottleneck Based Enhanced Sampling

Here, we investigate crystal nucleation in supersaturated colloid suspensions using enhanced molecular dynamics simulations augmented with machine learning techniques. The simulations reveal that crystallization in the model colloidal system studied here, with particles interacting through a repulsive screened Coulomb Yukawa potential, proceeds from vapor to dense liquid droplet to crystalline phases across multiple high barriers. Employing a one-dimensional reaction coordinate derived from the State Predictive Information Bottleneck framework, our simulations capture back-and-forth phase transitions across multiple barriers effectively in biased metadynamics simulations. We obtain relative free energy differences between different phases and also quantify the roles of different molecular level features in driving the phase changes.

Chemistry↗