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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 973 records · Page 54
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Acoustic Codes in 2D Spherical Coordinate

Finite-difference methods are widely used to simulate infrasound propagation in the atmosphere. Flexibility of finite-difference scheme allows implementation of highly heterogeneous media for sound propagation as well as complex source models for sound generation. While full 3-D finite-difference methods have been utilized for local infrasound propagation with pronounced topography, 2-D modeling approach has been preferred for regional and global propagation as full 3-D methods generally require enormous computational resources. Infrasound propagation is often simulated with a second-order finite difference scheme. This lowest-order finite-difference scheme is robust and straightforward to implement complex boundary conditions, but the solution includes large error with numerical dispersion and dissipation. This large numerical error may make the second-order finite-difference not optimal for long range infrasound propagation modeling as the numerical dispersion degrades the accuracy of the solution unacceptably. Here, we developed a high-order finite-difference solver for long-range infrasound simulation. The high-order scheme is particularly popular for linear wave modeling in aeroacoustics owing to its low-dispersive and low-dissipative behavior. We develop and evaluate a high-order finite difference scheme in 2-D axisymmetric coordinates. The axisymmetry allows to approximate 3-D spherical sound propagation and amplitude attenuation by a 2-D method. AC2Dr is developed to simulate infrasound propagation in realistic atmosphere, but can be used for linear acoustic waves in general materials with background flow. AC2Dr in an axisymmetric coordinates allows for spherical radiation of acoustic waves from compact sources.

Sjogreen, Bjorn↗
DOE OSTI2024

AIRES-NODE

This software package performs time series forecasting for engineering systems using neural controlled differential equations. Neural controlled differential equations are continuous time analogues to recurrent neural networks. AIRES-NODE is particularly well suited for dynamic systems which are subject to external forcing terms and time-dependent boundary conditions. AIRES-NODE provides an encoder-decoder architecture which allows for variable-length forecasts to be made and is not limited to a fixed time step size when forecasting. Additionally, by utilizing a novel combination of a variational autoencoder and neural controlled differential equations, the AIRES-NODE package is capable of producing probabilistic future forecasts.

Gurecky, William [Oak Ridge National Laboratory (O↗
DOE OSTI2024

Pipeline Hydrogen Decarbonization and Repurposing Analyzers (P-HyDRAs)

The Pipeline Hydrogen Decarbonization and Repurposing Analyzers (P-HyDRAs) are a set of prototype computational tools for simulating and optimizing midstream natural gas pipeline system operations subject to location and time-dependent hydrogen blending. The models can accurately resolve dynamic gas flows through large-scale pipeline networks using non-ideal gas equations of state. The codes can be used as decision support for planning and design decisions involving intra-day energy flow schedules as well as spatiotemporal economic values of natural gas, hydrogen, and net energy delivered to consumers while ensuring that pipeline hydraulic limitations, gas compressor station constraints, operational factors, and pre-existing shipping contracts are satisfied. The inputs to the codes are a model of the pipeline system as well as time-series data that specify boundary conditions on the network. For optimization, the code module requires price and quantity offers for natural gas and hydrogen and price and quantity bids for energy, which are used as time-dependent constraints in an optimal control problem. The outputs are time-series data that provide a predictive simulation of gas flows, mass fractions, and pressures, or with additional degrees of freedom give an approximately optimal solution for gas injections/withdrawals, compressor settings, and sensitivities to the objective function that provide locational values of energy.

Zlotnik, Anatoly↗
DOE OSTI2021

Temperature controlled friction extrusion process

Processing temperature is decisive to the properties and performance of the product in friction extrusion. However, the constantly varying thermal boundary conditions result in an unstable processing temperature even fixed die rotational speed and feed rate were implemented, which extensively disturb the stability of the process and the quality of the product. It is key to maintain the process temperature to ensure the properties of the product is uniform. What's more, a controlled processing temperature enables the properties of the product to be manipulatable between parts and between different locations of one part

Li, Xiao↗
DOE OSTI2023

pyNuMAD v.0.1

SAND2024-08606O The pyNuMAD software is used for managing wind turbine blade model data. pyNuMAD specializes in defining the geometry, materials, and boundary conditions for structural analysis of wind turbine blades. This includes loading in data files and providing an interface for users to make updates to the model. The software also features meshing functionality, which takes the blade model and creates a shell or brick mesh for use in finite element analysis. A typical user workflow might be: load in blade information from a yaml file, make adjustments to the blade properties, update the blade based on the adjustments, create a mesh of the blade, export this blade to another software for structural analysis. Sandia National Laboratories is a multimission laboratory managed and operated by National Technology & Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International Inc., for the U.S. Department of Energy’s National Nuclear Security Administration under contract DE-NA0003525.

Paquette, Joshua↗
DOE OSTI2024

DualSPHysics-INL

Funded by the DOE's Bioenergy Technology Office through the Feedstock-Conversion Interface Consortium, INL researchers developed this code to model biomass freestock flow in various handling equipment, such as hoppers and augers. Built on top of an existing open-source code DualSPHysics (https://dual.sphysics.org/) , the enrichment includes modification of the mass conservation equation that switching tracking density to void ratio, adding a hypoplastic constitutive law to better capture the flow physics of this type of material and implementing a novel boundary condition that can handle the dynamic contact between material and equipment. All implementation were realized via Nvidia CUDA, so GPU accelaration can be leveraged to signifiantly speed up the computational process.

Jin, Wencheng [Idaho National Laboratory (INL), Id↗
DOE OSTI2026

HydraGNN v5.0

HydraGNN v5.0 expands the code base into a more portable, scalable, and flexible framework for scientific graph learning, with particular strength in atomistic machine-learning interatomic potentials and large-scale distributed training. The release adds Fully Sharded Data Parallel (FSDP) support alongside existing DDP and DeepSpeed paths, including FSDP-aware checkpointing and optimizer integration, and introduces a configurable multi-precision training workflow supporting FP32, BF16, and FP64 across GPUs and Intel XPUs. For atomistic modeling, HydraGNN v5.0 strengthens its MLIP capabilities through dynamic graph construction at every forward pass, energy-conserving force prediction via automatic differentiation, and per-atom energy loss formulations, while extending EGNN models to properly handle periodic boundary conditions. The release also broadens model expressiveness through graph-level attribute conditioning, adds new multi-task and model-parallel extensions such as MACE support and encoder/decoder branch optimization, and expands application coverage with integrated examples for datasets including OC25, Nabla2-DFT, QCML, Open Polymers 2026, and OPF. In parallel, HydraGNN v5.0 improves production readiness through performance optimizations for large-scale runs, stratified sampling and linear-regression preprocessing utilities, and tested installation scripts for DOE supercomputers including Frontier, Aurora, Perlmutter, and Andes. Overall, the release advances HydraGNN as a robust software platform for scalable graph neural networks across materials science, chemistry, and scientific machine learning workflows

Lupo Pasini, Massimiliano [Oak Ridge National Labo↗
DOE OSTI2026

A Concept of a Convection–Cloud Chamber to Study Aerosol–Cloud–Drizzle Interactions

Understanding and quantifying the full chain of processes from aerosol activation to drizzle formation, and the associated feedbacks to the aerosol chemical and physical properties, all within a turbulent cloud are some of the toughest challenges in atmospheric chemistry and physics and are keys to the cloud–precipitation puzzle. This paper describes a concept for a new type of research facility consisting of a cloud chamber plus associated instrumentation and computational models, to explore aerosol–cloud interactions and processing, cloud optical properties, entrainment–cloud interactions, and quantitative assessment of drizzle onset. The envisioned design is for a 3 m × 3 m × 9 m chamber, such that the height is sufficient to achieve long lifetimes for aerosol processing and for significant drizzle growth by collision and coalescence. A suite of computational tools for simulating microphysical properties in the chamber provides a digital twin for designing the chamber and a range of example experiments. Theory and test results from novel remote sensing systems for exploring chemical and physical interactions and evolution of aerosols, cloud droplets, and drizzle within turbulent clouds are described. Testing of technology needed for the operation of a large-volume chamber, including aerosol generation methods and novel materials for water vapor boundary conditions, is described. Simulations suggest that spatially uniform turbulence and microphysical properties can be sustained in a steady state, with reasonable aerosol and water vapor fluxes, and that substantial drizzle can be produced through collision and coalescence of cloud droplets. Remaining challenges for more detailed engineering design and a discussion of possible first-light experiments are described.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Examining the Impacts of Great Lakes Temperature Perturbations on Simulated Precipitation in the Northeastern United States

Most inland water bodies are not resolved by general circulation models, requiring that lake surface temperatures be estimated. Given the large spatial and temporal variability of the surface temperatures of the North American Great Lakes, such estimations can introduce errors when used as lower boundary conditions for dynamical downscaling. Lake surface temperatures (LSTs) influence moisture and heat fluxes, thus impacting precipitation within the immediate region and potentially in regions downwind of the lakes. For this study, the Advanced Research version of the Weather Research and Forecasting Model (WRF-ARW) was used to simulate precipitation over the six New England states during a 5-yr historical period. The model simulation was repeated with perturbed LSTs, ranging from 10°C below to 10°C above baseline values obtained from reanalysis data, to determine whether the inclusion of erroneous LST values has an impact on simulated precipitation and synoptic-scale features. Results show that simulated precipitation in New England is statistically correlated with LST perturbations, but this region falls on a wet–dry line of a larger bimodal distribution. Wetter conditions occur to the north and drier conditions occur to the south with increasing LSTs, particularly during the warm season. Additionally, the precipitation differences coincide with large-scale anomalous temperature, pressure, and moisture patterns. Care must therefore be taken to ensure reasonably accurate Great Lakes surface temperatures when simulating precipitation, especially in southeastern Canada, Maine, and the mid-Atlantic region.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Convectively Forced Diurnal Gravity Waves in the Maritime Continent

Long-lived, zonally propagating diurnal rainfall disturbances are a highly pronounced and common feature in the Maritime Continent (MC). A recent study argues that these disturbances can be explained as diurnally phase-locked gravity waves. Here we explore the origins of these waves through regional cloud-permitting numerical model experiments. The gravity waves are reproduced and isolated in the model framework through the combined use of realistic geography and diurnally cyclic lateral boundary conditions representative of both characteristic easterly and westerly background zonal flow regimes. These flow regimes are characteristic of the Madden–Julian oscillation (MJO) suppressed and active phase in the MC, respectively. Tests are conducted wherein Borneo, Sumatra, or both islands and/or their orography are removed. These tests imply that the diurnal gravity waves are excited and maintained directly by latent heating from the vigorous mesoscale convective systems (MCSs) that form nocturnally in both Borneo and Sumatra. Removing orography has only a secondary impact on both the MCSs and the gravity waves, implying that it is not critical to these waves. We therefore hypothesize that diurnal gravity waves are fundamentally driven by mesoscale organized deep convection, and are only sensitive to orography to the measure that the convection is affected by the orography and its mesoscale flows. Factor separation further reveals that the nonlinear interaction of synchronized diurnal cycles in Sumatra and Borneo slightly amplifies this gravity wave mode compared to if either island existed in isolation. This nonlinear feedback appears most prominently at longitudes directly between the two islands.

Ruppert, Jr., James H.↗
DOE OSTI2021

Response of U.S. West Coast Mountain Snowpack to Local Sea Surface Temperature Perturbations: Insights from Numerical Modeling and Machine Learning

Sea surface temperature (SST) significantly modulates the precipitation and temperature over land, with important consequences on land surface processes such as snowpack. Compared to the impact of remote SST, the effect of nearshore/local SST is less well understood. In this study, the impact of local SST on the mountain snowpack of the U.S. West Coast is investigated using two 6-km regional climate simulations driven by the same lateral boundary conditions but with time-varying versus time-invariant and warmer local SSTs during 2003–15. Results show that local SST warming leads to warmer winter with more precipitation over the mountains. Meanwhile, the removal of SST temporal variability results in reduced temperature variability but increased precipitation variability. As a result, winter snow accumulation decreases by ~200 mm per season in the Cascade Mountains in the north but increases by ~100 mm per season in the Sierra Nevada in the south. Such a dipole response results from the competing effects of precipitation and temperature change at different elevations and are amplified by the enhanced atmospheric river moisture transport. To further delineate the relative contributions of different meteorological factors to the snowpack response, two neural network models were developed to predict the snow behaviors at seasonal and monthly scales. These models reveal the dominant influence of the total amount and the average temperature of precipitation on the snowpack response. Furthermore, these findings highlight the sensitivity of mountain snowpack to local SST in the western United States and underscore the importance of local SST and atmospheric rives to accurate snowpack estimations for water management.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Extreme Miller cycle with high intake boost for improved efficiency and emissions in heavy-duty diesel engines

This study experimentally investigates the impact of extreme Miller cycle strategies paired with high intake manifold pressures on the combustion process, emissions, and thermal efficiency of heavy-duty diesel engines. Well-controlled experiments isolating the effect of Miller cycle strategies on the combustion process were conducted at constant engine speed and load (1160 rpm, 1.76 MPa net IMEP) on a single cylinder research engine equipped with a fully-flexible hydraulic valve train system. Late intake valve closing (LIVC) timing strategies were compared to a conventional intake valve profile under either constant cylinder composition, constant engine-out NO x emission, or constant overall turbocharger efficiency (η TC ) to investigate the operating constraints that favor Miller cycle operation over the baseline strategy. Furthermore, utilizing high boost with conventional intake valve closing timing resulted in improved fuel consumption at the expense of sharp increases in peak cylinder pressures, engine-out NO x emissions, and reduced exhaust temperatures. Miller cycle without EGR at constant λ demonstrated LIVC strategies effectively reduce engine-out NO x emissions by up to 35%. However, Miller cycle associated with very aggressive LIVC timings led to fuel consumption penalties due to increased pumping work and exhaust enthalpy. LIVC strategies allowed for increased charge dilution at the baseline NO x constraint of 3.2 g/kWh, resulting in significant fuel consumption benefits over the baseline case without compromising exhaust temperatures or peak cylinder pressures. As Miller cycle implementation was shown to affect the boundary conditions dictating η TC , the LIVC and conventional IVC cases were studied at an equivalent η TC point representative of high boost operation. With high boost, LIVC yielded reduced NO x emissions, reduced peak cylinder pressures, and elevated exhaust temperatures compared to the conventional IVC case without compromising fuel consumption.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2024

Role of interfaces on the mechanical response of accumulative roll bonded nanometallic laminates investigated via dislocation dynamics simulations

Unraveling the effects of continuous dislocation interactions with interfaces, particularly at the nanometer length scales, is key to a broader understanding of plasticity, to material design and to material certification. To this end, this work proposes a novel discrete dislocation dynamics-based model for dislocation interface interactions tracking the fate of residual dislocation on interfaces. This new approach is used to predict the impact of dislocation/interface reactions on the overall mechanical behavior of accumulative roll bonded nanometallic laminates. The framework considers the dynamic evolution of the interface concurrent with a large network of dislocations, thus, accounting for the local short and long range effects of the dislocations under the external boundary conditions. Specifically, this study focuses on two-phase Fe/Cu nanometallic laminates, and investigates the role of the underlying elastic and plastic contrast of the Fe and the Cu layers on the composite response of the material. Moreover, the role of initial microstructures, resulting from processing is also investigated. Subsequently, the model is used to examine the effect of layer thickness and interface orientation relationship on the residual stresses of the relaxed microstructure. The associated mechanical response of these laminates are compared when loaded under normal direction compression, as well as shear compression. Finally, this work predicts a dominant effect of the layer thickness, as compared to the interface orientation relationship, on the macroscopic response and on the residual stresses of these nanolaminates, while the local dislocation transmission propensity through the interface is significantly influenced by the corresponding orientation relationship.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Extension of the PINN diffusion model to k-eigenvalue problems

This paper extends our recent work on the Physics-Informed Neural Networks (PINN) approach for the fixed source diffusion models and applies it to the diffusion theory based k-eigenvalue problems. To make the PINN equitable for the eigenvalue problems, we introduce a novel integral regularization term to the loss function in the framework, and allow the direct inference of the principal eigenvalue and the associated eigenfunction. The regularization term enforces a pre-defined value on the integration of the model predictions, and this value can be directly related to a physical property of the system. We also introduce an additional learnable parameter to approximate the principal eigenvalue. As a proof of principle, we solve the one-group two-dimensional k-eigenvalue neutron diffusion equation in this work. We then provide two numerical examples to demonstrate the applicability of the PINN approach. In each example, we solve the k-eigenvalue diffusion equation in a multi-region configuration constrained with a set of Robin boundary conditions for generality. We use a FEM solution based on the power-iteration method to verify the results of the PINN solution. The results showed relative percentage error in the predicted eigenvalue of about 0.77% and about 1.2% for example 1 and example 2, respectively. The mean absolute error in the predicted flux for example 1 is ∼ 0.002 and for example 2 is ∼ 0.0024. These results indicate some preliminary successes of the PINN application to k-eigenvalue problems. (authors)

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2024

Uncertainty Propagation from Experiment Measurements to Modeling Approaches: A Case for SMR Steam Entrainment Testing

To license new and advanced reactor designs, regulators must be convinced that their unique safety cases—relative to existing large scale reactors—have been adequately addressed by the designed reactor protection systems. In water cooled small modular reactors (SMRs), droplet entrainment in steam flow has significant implications on the progression of accident scenarios due to its compact design features, which requires representative test data applicable to SMR designs. Computer code, modeling and simulation (M&S) tools and models require adequate verification, assessment, and qualification. This includes M&S results validation against scaled empirical data within allowable uncertainty bands to gain regulatory approvals during the various stages of reactor system design, demonstration, and commercialization. However, measurement uncertainty within the empirical datasets and test data applicability ranges requires careful consideration of M&S inputs (i.e., boundary conditions, and initial conditions), and verification and validation efforts. This study focuses on uncertainty quantification in designing scaled test facilities for SMR applications with appropriate measurements and a standard data-reduction method to estimate thermal hydraulics characteristics parameters that incorporate physics phenomena of interest. In addition, this study supports the evaluation model development and assessment process using M&S that interfaces with advanced computing tools and digital twin capabilities. This will allow synchronization between experiment and modeling approaches for droplet entrainment testing and analysis, improving diagnostics, prognostics, and decision-making to accelerate regulatory approval.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2021

RISE: robust iterative surface extension for sub-nanometer X-ray mirror fabrication

Precision optics have been widely required in many advanced technological applications. X-ray mirrors, as an example, serve as the key optical components at synchrotron radiation and free electron laser facilities. They are rectangular silicon or glass substrates where a rectangular Clear Aperture (CA) needs to be polished to sub-nanometer Root Mean Squared (RMS) to keep the imaging capability of the incoming X-ray wavefront at the diffraction limit. The convolutional polishing model requires a CA to be extended with extra data, from which the dwell time is calculated via deconvolution. However, since deconvolution is very sensitive to boundary errors and noise, the existing surface extension methods can hardly fulfill the sub-nanometer requirement. On one hand, the figure errors in a CA were improperly modeled during the extension, leading to continuity issues along the boundary. On the other hand, uncorrectable high-frequency errors and noise were also extended. In this study, we propose a novel Robust Iterative Surface Extension (RISE) method that resolves these problems with a data fitting strategy. RISE models the figure errors in a CA with orthogonal polynomials and ensures that only correctable errors are fit and extended. Combined with boundary conditions, an iterative refinement of dwell time is then proposed to compensate the errors brought by the extension and deconvolution, which drastically reduces the estimated figure error residuals in a CA while the increase of total dwell time is negligible. To our best knowledge, RISE is the first data fitting-based surface extension method and is the first to optimize dwell time based on iterative extension. An experimental verification of RISE is given by fabricating two elliptic cylinders (10 mm × 80 mm CAs) starting from a sphere with a radius of curvature around 173 m using ion beam figuring. The figure errors in the two CAs greatly improved from 204.96 nm RMS and 190.28 nm RMS to 0.62 nm RMS and 0.71 nm RMS, respectively, which proves that RISE is an effective method for sub-nanometer level X-ray mirror fabrication.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Self-supervised physics-informed generative networks for phase retrieval from a single X-ray hologram

X-ray phase contrast imaging significantly improves the visualization of structures with weak or uniform absorption, broadening its applications across a wide range of scientific disciplines. Propagation-based phase contrast is particularly suitable for time- or dose-critical in vivo/in situ/operando (tomography) experiments because it requires only a single intensity measurement. However, the phase information of the wave field is lost during the measurement and must be recovered. Conventional algebraic and iterative methods often rely on specific approximations or boundary conditions that may not be met by many samples or experimental setups. In addition, they require manual tuning of reconstruction parameters by experts, making them less adaptable for complex or variable conditions. Here we present a self-learning approach for solving the inverse problem of phase retrieval in the near-field regime of Fresnel theory using a single intensity measurement (hologram). A physics-informed generative adversarial network is employed to reconstruct both the phase and absorbance of the unpropagated wave field in the sample plane from a single hologram. Unlike most state-of-the-art deep learning approaches for phase retrieval, our approach does not require paired, unpaired, or simulated training data. This significantly broadens the applicability of our approach, as acquiring or generating suitable training data remains a major challenge due to the wide variability in sample types and experimental configurations. The algorithm demonstrates robust and consistent performance across diverse imaging conditions and sample types, delivering quantitative, high-quality reconstructions for both simulated data and experimental datasets acquired at beamline P05 at PETRA III (DESY, Hamburg), operated by Helmholtz-Zentrum Hereon. Furthermore, it enables the simultaneous retrieval of both phase and absorption information.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Surrogate modeling of Cellular-Potts agent-based models as a segmentation task using the U-Net neural network architecture

The Cellular-Potts model is a powerful and ubiquitous framework for developing computational models for simulating complex multicellular biological systems. Cellular-Potts models (CPMs) are often computationally expensive due to the explicit modeling of interactions among large numbers of individual model agents and diffusive fields described by partial differential equations (PDEs). In this work, we develop a convolutional neural network (CNN) surrogate model using a U-Net architecture that accounts for periodic boundary conditions. We use this model to accelerate the evaluation of a mechanistic CPM previously used to investigate in vitro vasculogenesis. The surrogate model was trained to predict 100 computational steps ahead (Monte-Carlo steps, MCS), accelerating simulation evaluations by a factor of 562 times compared to single-core CPM code execution on CPU. Over short timescales of up to 3 recursive evaluations, or 300 MCS, our model captures the emergent behaviors demonstrated by the original Cellular-Potts model such as vessel sprouting, extension and anastomosis, and contraction of vascular lacunae. This approach demonstrates the potential for deep learning to serve as a step toward efficient surrogate models for CPM simulations, enabling faster evaluation of computationally expensive CPM simulations of biological processes.

97 MATHEMATICS AND COMPUTING↗