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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 55 records · Page 3
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Computational and Experimental Investigation of Discrete “W” Turbulators for sCO2 Turbine Internal Cooling

Direct-fired supercritical CO2 (sCO2 ) cycles are of particular interest for power generation due to the potential for higher thermal efficiencies with inherent CO2 capture. Direct fired sCO2 turbines must withstand high temperatures and the density of the working fluid is much higher than conventional Brayton cycles. Based on conventional Brayton cycle applications, internal serpentine cooling channels with ribbed turbulator strips are a common approach to achieve the required cooling, while minimizing coolant utilization. Most of the previous studies of coolant channel heat transfer enhancements have focused on serpentine channels using air as the cooling media at Reynold’s numbers typical of Brayton cycles. This paper investigates the Nusselt number augmentation of coolant channels with no features, ribbed turbulators, and discrete “W” turbulators with surface roughness effects included and CO2 as the coolant. The numerical results are compared to experimental results using CO2 as the cooling media at Reynold’s numbers ranging from 100,000 to 300,000. The results show the Nusselt number augmentation plateaus with increasing Reynolds number, in contrast to the decay observed in prior publications with air as the coolant media. Additionally, the CFD results indicate the discrete “W” geometry provides a 10% increase in Nusselt number augmentation relative to the angled ribbed turbulators.

computational fluid dynamics (CFD)↗
DOE OSTI2025

Computing virtual dark-field X-ray microscopy images of complex discrete dislocation structures from large-scale molecular dynamics simulations

Dark-field X-ray microscopy (DFXM) is a novel diffraction-based imaging technique that non-destructively maps the local deformation from crystalline defects in bulk materials. While studies have demonstrated that DFXM can spatially map 3D defect geometries, it is still challenging to interpret DFXM images of the high-dislocation-density systems relevant to macroscopic crystal plasticity. This work develops a scalable forward model to calculate virtual DFXM images for complex discrete dislocation structure(s) (DDS) obtained from atomistic simulations. Our new DDS-DFXM model integrates a non-singular formulation for calculating the local strain from the DDS and an efficient geometrical optics algorithm for computing the DFXM image from the strain field. We apply the model to complex DDS obtained from a large-scale mol­ecular dynamics simulation of compressive loading on single-crystal silicon. Simulated DFXM images exhibit prominent contrast for dislocation features between the multiple slip systems, demonstrating the potential of DFXM to resolve features from dislocation multiplication. In conclusion, the integrated DDS-DFXM model provides a toolbox for DFXM experimental design and image interpretation in the context of bulk crystal plasticity for a range of measurements across shock plasticity and the broader materials science community.

X-ray imaging↗
DOE OSTI2025

New electric force and charge exchange modules in discrete element model enables particle dynamics simulation in electric field

Discrete element modeling (DEM) is an important technique for particle dynamics simulation. The field of metal additive manufacturing often utilizes DEM to simulate the rheological behaviors of powder. Standard contact and short-range interactions are sufficient in most cases but insufficient to describe the particle dynamics with the influence of an electric field. Modeling such a system requires additional physics to describe the particle–field interactions. The relevant physics has been experimentally understood but is not yet available in DEM. Here, we develop a charge exchange and an electric force module. The electric force module governs particle response to the electric field, while the charge exchange module enables particles to acquire proper charge during contact with charged geometries. We validate the modules against analytical calculations and high-speed videos of electrostatic powder deposition experiments. Notably, the model struggles to capture the initial particle levitation. We later deploy a modified electric field, as supported by static electric field simulation, to better approximate the electric field penetration into the powder layer. This modification improves the model’s capability of simulating realistic particle levitation. The results highlight the challenges of modeling particle behaviors in the electric field while demonstrating the feasibility of obtaining quantitative results, which are difficult to measure experimentally.

charge exchange↗
DOE OSTI2024

Modeling powder spreadability in powder-based processes using the discrete element method

Powder-bed fusion (PBF) processes refer to a subset of Additive Manufacturing (AM) techniques where powder is spread on the build-plate before melting (by a laser or electron beam). While PBF processes are attractive due to their ability for realizing complex structures that are either difficult or impossible to create through conventional means, the parts fabricated with these techniques can exhibit defects such as pores, inclusions, and excessive surface roughness. To minimize these defects, much research has been dedicated towards process maturation by optimizing laser or electron beam parameters. However, these developmental efforts typically do not address the recoating process where achieving dense and uniform layers of powder is a necessity for ensuring process repeatability and part quality. While the recoating process can be studied through experimentation, the dynamics of particle movement are difficult to analyze experimentally. Therefore, here, in this study, powder spreading in PBF was simulated through the Discrete Element Method (DEM) to elucidate the mechanisms that control powder-bed quality. Utilizing the Buckingham Pi theorem, a dimensionless metric referred to as the spreading index is developed that combines powder-bed density, roughness, and particle size to assess the quality of powder layers. The formulated spreading index is then related to several dimensionless quantities that provide insight into the mechanisms dominating powder spreading in PBF. The DEM simulations conducted in this work focused on the scenario where powder is spread onto an existing powder bed and revealed that a reduction in the recoating velocity causes an increase in the spreading index while little to no impact on the spreading index was observed when varying layer thickness from 30 μm to 75 μm.Particle size effects on the powder-bed quality were also investigated.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Predicting the evolution of biomass bulk density through feedstock preprocessing: Discrete element modeling, regression analysis, and pilot-scale validation

Bulk density is an important material property of biomass feedstocks, influencing handling, storage, transport costs, and conversion efficiency. In this study, predictive regression models for loose and tapped bulk densities of Alamo and Cave-in-Rock switchgrass are developed using a comprehensive dataset generated via calibrated bonded-sphere discrete element method (DEM) simulations. Here, a key contribution of this study is the use of a DEM-based approach, which correlates density with moisture content and particle size distribution parameters and enables analysis across a continuous particle size range, overcoming limitations of purely experimental data. For comparison, regression models are also developed using only experimental data from pilot-scale runs at the Biomass Feedstock National User Facility at Idaho National Laboratory. Validation against pilot-scale data showed reasonable prediction accuracy for both model types, particularly for smaller particle sizes (post-secondary grinding). While the experimental model showed slightly better performance matching the validation data in some cases, the DEM-based model benefits from a much larger dataset, reduced predictor multicollinearity, and continuous parameter coverage, highlighting the utility of validated simulation models for developing robust predictive tools for biomass preprocessing applications.

09 - BIOMASS FUELS↗
DOE OSTI2025

Discrete fracture network model benchmarks developed and applied in a DECOVALEX-2023 repository performance assessment study

This study presents newly developed benchmarks for modeling flow and transport within discrete fracture networks (DFNs) and useful methods for analyzing the results. The new benchmarks are designed to test modeling approaches for use in probabilistic performance assessment models of deep geologic repositories in fractured rock. The benchmarks simulate flow and transport through a 1 km 3 block of fractured rock. The first simulates migration of a short pulse of tracer through a simple network of four intersecting fractures. The second adds 1089 stochastically generated fractures. The third changes the pulse to a continuous point source. Evaluation of model performance relies on moment analysis and comparison of the results of different models. The expected nondimensional first moment of the conservative tracer for each benchmark is 1. The benchmarks were simulated by teams from Canada, Czechia, Germany, Korea, Sweden, Taiwan, and the United States as part of a DECOVALEX-2023 study (decovalex.org). The teams used various approaches, including explicit DFN modeling, DFN upscaling to an equivalent continuous porous medium (ECPM), and a combination of both methods. Transport mechanisms are modeled using either the advection-dispersion equation or particle tracking. Results demonstrate strong agreement among the models in breakthrough behavior up to the 75th percentile. Significant deviations in first moments and well-clustered outputs led to the identification of inaccuracies in several models. Such findings exemplify the benefit of exercising these benchmarks and using the presented methods to test DFN flow and transport models.

Benchmark↗
DOE OSTI2024

Sensitivity Analysis in the Presence of Intrinsic Stochasticity for Discrete Fracture Network Simulations

Abstract Large‐scale discrete fracture network (DFN) simulators are standard fare for studies involving the sub‐surface transport of particles since direct observation of real world underground fracture networks is generally infeasible. While these simulators have successfully been used in several engineering applications, estimates of output quantities of interest (QoI) — such as breakthrough time of particles reaching the edge of the system — suffer from two distinct types of uncertainty. A run of a DFN simulator requires several parameters to be set that dictate the placement and size of fractures, the density of fractures, and the overall permeability of the system; uncertainty on the proper parameters will lead to uncertainty in the QoI, called epistemic uncertainty. Furthermore, since these input settings to DFN simulators control the stochastic processes which place fractures and govern flow, understanding how this randomness affects the QoI requires several runs of the simulator at distinct random seeds. The uncertainty in the QoI attributed to different realizations (i.e., different seeds) of the same random process (i.e., identical input parameters) leads to a second type of uncertainty, called aleatoric uncertainty. In this paper, we perform a Sensitivity Analysis, which directly attributes the uncertainty observed in the QoI to the epistemic uncertainty from each input parameter and to the aleatoric uncertainty. Beyond the specific takeaways on which input variables influence uncertainty in the QoI the most, a major contribution of this paper is the introduction of a statistically rigorous workflow for characterizing the uncertainty in DFN flow simulations that exhibit heteroskedasticity.

58 GEOSCIENCES↗
DOE OSTI2026

Active learning of a crystal plasticity flow rule from discrete dislocation dynamics simulations

Continuum-scale material deformation models, such as crystal plasticity (CP), can significantly enhance their predictive accuracy by incorporating input from lower-scale (i.e. mesoscale) models. The procedure to generate and extract the relevant information is however typically complex and ad hoc, involving decision and intervention by domain experts, leading to long development times. In this study, we develop a principled approach for calibration of continuum-scale models using lower scale information by representing a CP flow rule as a Gaussian process model. This representation allows for efficient parameter space exploration, guided by the uncertainty embedded in the model through a process known as Bayesian optimization (BO). We demonstrate a semi-autonomous BO loop which instantiates discrete dislocation dynamics simulations whose initial conditions are automatically chosen to optimize the uncertainty of a model CP flow rule. Our self-guided computational pipeline efficiently generated a dataset and corresponding model whose error, uncertainty, and physical feature sensitivities were validated with comparison to an independent dataset four times larger, demonstrating a valuable and efficient active learning implementation readily transferable to similar material systems.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Algebraic discrete quantum harmonic oscillator with dynamic resolution scaling

We develop an algebraic formulation for the discrete quantum harmonic oscillator (DQHO) from the Hamiltonian for two, coupled QHOs and provide a physical picture for the Kravchuk function eigenstates of the oscillator. The familiar $\mathfrak{su}(2)$ structure of the coupled QHO Hamiltonian divides its spectrum into sets corresponding to the DQHO at different resolutions. In addition to energy ladder operators, the formulation allows for the introduction of resolution ladder operators connecting all DQHOs with different resolutions, thus enabling the dynamic scaling of the resolution of finite degree-of-freedom quantum simulations. The coherent state of the DQHO is constructed, and its expected position is proven to oscillate as a classical harmonic oscillator. The DQHO coherent state recovers that of the quantum harmonic oscillator at large resolution.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Coarse Graining Discrete Element Method Information in Particle-in-Cell Length Scales Using a Machine Learning Approach

This report details the development of a machine learning (ML)-driven framework to coarse-grain inter-particle collision dynamics from high-fidelity Discrete Element Method (DEM) simulations to Particle-in-Cell (PIC) scales for gas-solid systems. Traditional PIC models, while computationally efficient, rely on empirical granular stress formulations that fail to capture the full complexity of collision physics, particularly the heterogeneity in particle dynamics. This study adopts a bottom-up approach, integrating insights from DEM simulations to improve the physical fidelity and interpretability of PIC-scale models.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Discrete-Element and Material-Point Method (DEM and MPM) Based Solvers for Sustainable Technologies

We present the use of discrete element method (DEM) and material point method (MPM) in three relevant green technology applications that include biomass feedstock handling, lithium-ion battery manufacturing, and high-pressure reverse osmosis. Our open-source DEM and MPM solvers are developed using performance portable grid and particle management library, AMReX, thus enabling superior performance on NVIDIA and AMD GPUs with > 100 million particles. Our DEM solver resolves the motion of individual particles in a granular system and includes a bonded sphere method for modeling non-spherical particles along with Hertzian and liquid bridge-based contact models. We simulate highly variable biomass feedstock flows in large-scale hoppers for biofuel production and electrode calendering in battery manufacturing using DEM. Our simulations predict flow blockage in large scale biomass hoppers and electrode microstructure variations, thus providing valuable information for biofuel and battery manufacturers, respectively. The second half of the talk will be on MPM and its application towards pore resolved simulations of reverse osmosis membranes under compressive loads. We present a validation study of our MPM simulations with membrane microscopy imaging thus providing useful insights on membrane stability under high pressure conditions. We also present a spectral stability analysis of using linear hat, quadratic and cubic spline basis in MPM indicating regions of numerical stability.

BIOMASS FUELS,MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Entanglement Renormalization for Quantum Field Theories with Discrete Wavelet Transforms

We propose an adaptation of Entanglement Renormalization for quantum field theories that, through the use of discrete wavelet transforms, strongly parallels the tensor network architecture of the Multiscale Entanglement Renormalization Ansatz (a.k.a. MERA). Our approach, called wMERA, has several advantages of over previous attempts to adapt MERA to continuum systems. In particular, (i) wMERA is formulated directly in position space, hence preserving the quasi-locality and sparsity of entanglers; and (ii) it enables a built-in RG flow in the implementation of real-time evolution and in computations of correlation functions, which is key for efficient numerical implementations. As examples, we describe in detail two concrete implementations of our wMERA algorithm for free scalar and fermionic theories in (1+1) spacetime dimensions. Possible avenues for constructing wMERAs for interacting field theories are also discussed.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2024

Numerical analysis of a time discretized method for nonlinear filtering problem with Lévy process observations

Abstract In this paper, we consider a nonlinear filtering model with observations driven by correlated Wiener processes and point processes. We first derive a Zakai equation whose solution is an unnormalized probability density function of the filter solution. Then, we apply a splitting-up technique to decompose the Zakai equation into three stochastic differential equations, based on which we construct a splitting-up approximate solution and prove its half-order convergence. Furthermore, we apply a finite difference method to construct a time semi-discrete approximate solution to the splitting-up system and prove its half-order convergence to the exact solution of the Zakai equation. Finally, we present some numerical experiments to demonstrate the theoretical analysis.

Mathematics↗
DOE OSTI2025

Direct interpolative construction of the discrete Fourier transform as a matrix product operator

The quantum Fourier transform (QFT), which can be viewed as a reindexing of the discrete Fourier transform (DFT), has been shown to be compressible as a low-rank matrix product operator (MPO) or quantized tensor train (QTT) operator. However, the original proof of this fact does not furnish a construction of the MPO with a guaranteed error bound. Meanwhile, the existing practical construction of this MPO, based on the compression of a quantum circuit, is not as efficient as possible. We present a simple closed-form construction of the QFT MPO using the interpolative decomposition, with guaranteed near-optimal compression error for a given rank. This construction can speed up the application of the QFT and the DFT, respectively, in quantum circuit simulations and QTT applications. We also connect our interpolative construction to the approximate quantum Fourier transform (AQFT) by demonstrating that the AQFT can be viewed as an MPO constructed using a different interpolation scheme.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Simulation of gas mixture dynamics in a pipeline network using explicit staggered-grid discretization

Here we develop an explicit staggered finite difference discretization scheme for simulating the transport of highly heterogeneous gas mixtures through pipeline networks. This study is motivated by the proposed blending of hydrogen into natural gas pipelines to reduce end use carbon emissions while using existing pipeline systems throughout their planned lifetimes. Our computational method accommodates an arbitrary number of constituent gases with very different physical properties that may be injected into a network with significant spatiotemporal variation. In this setting, the gas flow physics are highly location- and time- dependent, so that local composition and nodal mixing must be accounted for. The resulting conservation laws are formulated in terms of pressure, partial densities and flows, and volumetric and mass fractions of the constituents. We include non-ideal equations of state that employ linear approximations of gas compressibility factors, so that the pressure dynamics propagate locally according to a variable wave speed that depends on mixture composition and density. We derive compatibility relationships for network edge boundary values that are more complex than for a homogeneous gas. The simulation method is evaluated on initial boundary value problems for a single pipe and a small network, is cross-validated with a lumped element simulation, and used to demonstrate a local monitoring and control policy for maintaining allowable concentration levels.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Insights into the soft brittle-to-ductile transition from discrete dislocation dynamics

The Brittle-to-ductile transition (BDT) in body centered cubic metals exhibits a soft transition wherein the fracture toughness gradually rises to before the onset of ductility. The resultant brittle-to-ductile transition temperature can be described with an Arrhenius relationship whose activation energy is related to plasticity in the material. To provide further insight into the nature of the BDTT, in this work we utilized a discrete dislo- cation dynamics model with a crack to simulate the BDT and how it depends on the thermally activated nature of plasticity. The interrelationship between the BDT activation energy and the dislocation mobility parameters were determined via the calculation of first order sensitivity coefficients. This analysis allows us to demonstrate that the activation energy for the BDT is directly related to the activation energy for plasticity through an effective stress that defines this relationship. This effective stress physically is the average stress on the dislocations that move out of the crack. Lastly, we are able to show that this effective stress is dictated by the low temperature fracture toughness or cleave energy of the material and the source position, the latter of which can be affected by processing. Collectively, these results provide new insight into what controls the thermal activation of the BDT and what are the important parameters to control it.

36 MATERIALS SCIENCE↗
DOE OSTI2024

All-fiber, discrete arbitrary picket-pulse shaping

Here, a novel all-fiber, hybrid pulse-shaping system based on the temporal stacking of discrete optical picket pulses combined with continuously shaped optical pulses provides 100-ps resolution with the high dynamic range and flexibility required for laser fusion.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗