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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 271 records · Page 15
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Nuclear Quality Assurance Certification of the MCNP-ORIGEN Activation Automation tool

The MCNP-ORIGEN Activation Automation (MOAA) tool couples MCNP and ORIGEN-S, allowing to support ATR and TREAT experiments by predicting their composition and activity before their irradiation takes place. MOAA is developed and maintained by the Irradiation Experiment Neutronics Analysis group (C-150) at INL. This poster presents the motivation behind the development of MOAA, its main workflow, as well as the software quality assurance (SQA) efforts required by LWP-13620, "Managing Information Technology Assets."

07 ISOTOPE AND RADIATION SOURCES↗
DOE OSTI2018

Numerical and experimental comparison of tracer particle and averaging techniques for particle velocities in a fluidized bed

Particle tracking methods using emitted radiation show potential for following motion in opaque systems such as granular materials. Leading examples are Positron Emission Particle Tracking (PEPT) and Radioactive Particle Tracking (RPT). The application of such techniques sometimes requires the use of tracer particles which differ in size, density and/or shape from the particles of interest. This study investigates the extent to which such differences affect the result of the study by using the open source MFIX-DEM software to model particle motion in the travelling fluidized bed experiments. The results are compared with previously reported experimental studies using both PEPT and RPT. Consistent numerical results were obtained for both PEPT and RPT tracer particles. In determining averaged velocities using such techniques, there is a choice to be made between averaging velocities of particles crossing a virtual plane over a period of time (the “face-average” approach) or those passing through a defined volume over time (the “volume-average” approach). The differences between results obtained with these two methods are shown to be significant in this case, for both computation and experiment.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2019

Hydrogenolysis of Dinuclear PCN R Ligated Pd II μ‐Hydroxides and Their Mononuclear Pd II Hydroxide Analogues

Abstract The hydrogenolysis of mono‐ and dinuclear Pd II hydroxides was investigated both experimentally and computationally. It was found that the dinuclear μ‐hydroxide complexes {[(PCN R )Pd] 2 (μ‐OH)}(OTf) (PCN H =1‐[3‐[(di‐ tert ‐butylphosphino)methyl]phenyl]‐1 H ‐pyrazole; PCN Me =1‐[3‐[(di‐ tert ‐butylphosphino)methyl]phenyl]‐5‐methyl‐1 H ‐pyrazole) react with H 2 to form the analogous dinuclear hydride species {[(PCN R )Pd] 2 (μ‐H)}(OTf). The dinuclear μ‐hydride complexes were fully characterized, and are rare examples of structurally characterized unsupported singly bridged μ‐H Pd II dimers. The {[(PCN Me )Pd] 2 (μ‐OH)}(OTf) hydrogenolysis mechanism was investigated through experiments and computations. The hydrogenolysis of the mononuclear complex (PCN H )Pd‐OH resulted in a mixed ligand dinuclear species [(PCN H )Pd](μ‐H)[(PCC)Pd] (PCC=a dianionic version of PCN H bound through phosphorus P, aryl C, and pyrazole C atoms) generated from initial ligand “rollover” C−H activation. Further exposure to H 2 yields the bisphosphine Pd 0 complex Pd[(H)PCN H ] 2 . When the ligand was protected at the pyrazole 5‐position in the (PCN Me )Pd−OH complex, no hydride formed under the same conditions; the reaction proceeded directly to the bisphosphine Pd 0 complex Pd[(H)PCN Me ] 2 . Reaction mechanisms for the hydrogenolysis of the monomeric and dimeric hydroxides are proposed.

Bailey, Wilson D.↗
DOE OSTI2022

Multi-fidelity information fusion with concatenated neural networks

Recently, computational modeling has shifted towards the use of statistical inference, deep learning, and other data-driven modeling frameworks. Although this shift in modeling holds promise in many applications like design optimization and real-time control by lowering the computational burden, training deep learning models needs a huge amount of data. This big data is not always available for scientific problems and leads to poorly generalizable data-driven models. This gap can be furnished by leveraging information from physics-based models. Exploiting prior knowledge about the problem at hand, this study puts forth a physics-guided machine learning (PGML) approach to build more tailored, effective, and efficient surrogate models. For our analysis, without losing its generalizability and modularity, we focus on the development of predictive models for laminar and turbulent boundary layer flows. In particular, we combine the self-similarity solution and power-law velocity profile (low-fidelity models) with the noisy data obtained either from experiments or computational fluid dynamics simulations (high-fidelity models) through a concatenated neural network. We illustrate how the knowledge from these simplified models results in reducing uncertainties associated with deep learning models applied to boundary layer flow prediction problems. The proposed multi-fidelity information fusion framework produces physically consistent models that attempt to achieve better generalization than data-driven models obtained purely based on data. While we demonstrate our framework for a problem relevant to fluid mechanics, its workflow and principles can be adopted for many scientific problems where empirical, analytical, or simplified models are prevalent. In line with grand demands in novel PGML principles, this work builds a bridge between extensive physics-based theories and data-driven modeling paradigms and paves the way for using hybrid physics and machine learning modeling approaches for next-generation digital twin technologies.

42 ENGINEERING↗
DOE OSTI2019

High Fidelity Computational Model for Fluidized Bed Experiments (Final Report)

MFiX was integrated with preconditioners and linear solver packages in Trilinos via MFIX, Fortran, C, and C++ wrappers. The MFIX wrapper interpreted the structure of the matrix and vector from MFiX while the Fortran wrapper transfers this information to a low-level language, C. C-wrapper transforms the memory semantic between Fortran and C++ language. C++ wrapper passes the matrix and vector to a Trilinos package and gets the solution. This solution was transferred to MFiX via C++, C, Fortran, MFIX wrapper. The framework is used to integrate first, second and third generation linear solvers in Trilinos with MFiX.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

High Fidelity Computational Model for Fluidized Bed Experiments (Final Report)

MFiX was integrated with preconditioners and linear solver packages in Trilinos via MFIX, Fortran, C, and C++ wrappers. The MFIX wrapper interpreted the structure of the matrix and vector from MFiX while the Fortran wrapper transfers this information to a low-level language, C. C-wrapper transforms the memory semantic between Fortran and C++ language. C++ wrapper passes the matrix and vector to a Trilinos package and gets the solution. This solution was transferred to MFiX via C++, C, Fortran, MFIX wrapper. The framework is used to integrate first, second and third generation linear solvers in Trilinos with MFiX.

20 FOSSIL-FUELED POWER PLANTS↗
DOE OSTI2018

High Fidelity Computational Model for Fluidized Bed Experiments

MFiX was integrated with preconditioners and linear solver packages in Trilinos via MFiX, Fortran, C, and C++ wrappers. The MFiX wrapper interpreted the structure of the matrix and vector from MFiX while the Fortran wrapper transfered this information to a low-level language, C. The C-wrapper transformed the memory semantic between Fortran and C++ language. The C++ wrapper passed the matrix and vector to a Trilinos package and got the solution. This solution was transferred to MFiX via C++, C, Fortran, MFiX wrapper. The framework was used to integrate first, second, and third generation linear solvers in Trilinos with MFiX.

20 FOSSIL-FUELED POWER PLANTS↗
DOE OSTI2021

Modelling the complex evaporated gas flow and its impact on particle spattering during laser powder bed fusion

The additive manufacturing (AM) of metals is becoming an increasingly important production process with the potential to replace traditional techniques such as casting. Laser Powder Bed Fusion (LPBF) is used in many applications to print metal parts from powder. The metal powder is heated locally with sufficient laser radiation that the liquid melt easily reaches its boiling temperature, which leads to a metallic vapour jet that can entrain both powder bed particles and molten droplets. The small size of laser-matter interaction site makes a detailed experimental analysis of the process challenging. Synchrotron X-ray imaging experiments are one of the few methods which can capture the dynamic melting and solidification processes. Comparing such experiments with computer simulations of the process is an important approach in order to better understand the manufacturing process and to analyse the influence of process parameters on the evaporated gas jet and the subsequent impact on particle ejection, leading to potentially reduced AM component quality. The melting and solidification of the metal powder is simulated using an Eulerian multiphase approach based on a control volume discretization of powder bed and substrate and a volume of liquid separation from melt and gas phase. The gas phase modelled as an ideal gas reaches velocities up to 100 m/s. Lagrangian particle tracking in the simulation demonstrates that the velocity fields calculated by the Eulerian multi-phase approach in combination with a standard drag-force model lead to particle accelerations in good agreement with those measured experimentally. In order to avoid numerical laborious Lagrangian calculations, a direct method to compare an Eulerian multiphase simulation with synchrotron X-ray experiments was introduced and validated. This approach is used to analyse the influence of process parameters including laser power and laser speed on the maximal acceleration of particles from the melt pool area. While the particle acceleration increases linearly with line energy in the conduction mode, a linear decrease of the acceleration with increasing line energy can be found in the transition mode before the accel-eration increases again with line energy in the keyhole mode.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Rapid determination of supercritical CO 2 and brine relative permeability using an unsteady-state flow method

In this study, relative permeability of supercritical CO 2 (scCO 2 ) and brine was determined in reactive and non-reactive rock cores using a combination of unsteady-state methodology and computed tomography. Experiments were conducted using a medical grade CT scanner to determine saturation using a custom Python script. The saturation and differential pressure across the core were then used to derive four empirical constants to calculate relative permeability. This methodology increases temporal efficiency while reducing experimental complexity. Additionally, we show that the method can be used to determine scCO 2 relative permeability in a wide range of lithologies and flow rates, and with the ability to account for matrix dissolution during scCO 2 flooding.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Bayesian Conavigation: Dynamic Designing of the Material Digital Twins via Active Learning

Scientific advancement is universally based on the dynamic interplay between theoretical insights, modeling, and experimental discoveries. However, this feedback loop is often slow, including delayed community interactions and the gradual integration of experimental data into theoretical frameworks. This challenge is particularly exacerbated in domains dealing with high-dimensional object spaces, such as molecules and complex microstructures. Hence, the integration of theory within automated and autonomous experimental setups, or theory in the loop-automated experiment, is emerging as a crucial objective for accelerating scientific research. The critical aspect is to use not only theory but also on-the-fly theory updates during the experiment. Furthermore, we introduce a method for integrating theory into the loop through Bayesian conavigation of theoretical model space and experimentation. Our approach leverages the concurrent development of surrogate models for both simulation and experimental domains at the rates determined by latencies and costs of experiments and computation, alongside the adjustment of control parameters within theoretical models to minimize epistemic uncertainty over the experimental object spaces. This methodology facilitates the creation of digital twins of material structures, encompassing both the surrogate model of behavior that includes the correlative part and the theoretical model itself. While being demonstrated here within the context of functional responses in ferroelectric materials, our approach holds promise for broader applications, such as the exploration of optical properties in nanoclusters, microstructure-dependent properties in complex materials, and properties of molecular systems.

Microscopy↗
DOE OSTI2024

Development and Experimental Optimization of High-Temperature Modeling Tools and Methods for Concentrated Solar Power Particle - Systems

A novel, open-source radiative modeling toolset was developed to extend the functionality of particle-based modeling software (e.g. discrete element method (DEM)) to environmental conditions relevant to concentrated solar power applications. This toolset was optimized for deployment on desktop workstations instead of high-performance computing systems, to render such tools more accessible to the research community. Both particle-based modeling and radiative exchange modeling are computationally expensive and often require specialized programming expertise, making these methods cumbersome to use. Recent developments in DEM software by DCS Computing have greatly reduced these challenges, providing a graphical-user-interface based platform and modeling optimization for desktop workstations, HPCs, and cloud computing. The University of Dayton leveraged the experience of DCS Computing in developing a user-friendly, open-source radiative heat transfer expansion for DEM modeling. The University of Dayton DEM+ radiative modeling toolset was developed using a combination of fundamental experimental measurements, modeling, and simplified flow experiments over a range of temperatures and flow conditions. The toolset provides researchers with access to multiple radiative models including an accelerated Monte-Carlo Ray Tracing (application agnostic, highly computationally expensive), an expanded database of distance-based approximations (application limited, computationally light), and a weighted blending of the two methods capable of achieving over 90% reduction in computation time with equivalent accuracy compared to Monte-Carlo Ray Tracing. Through a graphical user interface, users can customize the radiative models to match their desired accuracy and available computational resources, improving access to particle based modeling for the research community. Ceramic sintered bauxite proppants were used in modeling and experimentally as a baseline. Both the radiative heat transfer and flow properties for particulate systems were investigated at elevated temperatures up to 800 °C. The major accomplishments for this work include a verified, open-source radiative modeling toolset to be distributed amongst the research community and the fabrication of three small-scale test facilities to investigate particle behavior and tune DEM flow properties for operation up to 800 °C. The findings have been shared with the research community via conference modeling workshops, deployment of the tools in DCS Computing Aspherix®, and open-source access to the developed radiative modeling tool. The development of next-generation CSP facilities and thermal energy storage systems based on ceramic particles requires providing access to computationally efficient and accurate modeling tools. Particles will experience a wide range of environments (20-800 °C) and handling conditions (dilute curtains or dense packing), requiring specially designed and optimized equipment. Optimizing solid particle physics models and establishing best-practices for particle modeling in CSP environments will assist researchers with designing optimized equipment, accelerating the deployment of more economically-competitive CSP facilities.

14 SOLAR ENERGY↗
DOE OSTI2025

Quantifying how the cis/trans ratio of N,N -dimethyl-3,5-dimethylpiperidinium hydroxide impacts the growth kinetics, composition and local structure of SSZ-39

This work integrates experiments and computational methods to quantify how the cis/trans ratio of the OSDA used in SSZ-39 synthesis impacts the crystallization kinetics, material properties, and final product composition. The crystallization kinetics increase by 30% when increasing the trans isomer content from 14% to 80%. Per prior work, in all cases based on the synthesis gel composition and product yield aluminum is the limiting reagent, and the absence of any amorphous material detected in the time resolved PXRD studies leads us to conclude that FAU dissolution is the rate limiting step in the formation of SSZ-39 in this synthesis protocol. The TGA and NMR results suggest that the trans isomer of OSDA is selectively incorporated into the product. The NMR binding studies, and corresponding DFT-based results show that the trans isomer binds to FAU more strongly than the cis isomer, providing one possible explanation for this enhancement in kinetics and preferential uptake of the trans isomer. The EDS analysis indicates that the Si/Al ratios are between 7.7 and 8.6 at low and high trans OSDA content, indicating zeolite composition is mildly sensitive to the trans isomer content. EDS results show this decrease in aluminum content leads to a corresponding decrease in sodium uptake. DFT-based calculations confirm OSDA–sodium interactions cannot explain any decrease in sodium uptake, reinforcing lower aluminum content as the cause of lower sodium uptake. Preliminary cobalt titration experiments show a surprisingly low cobalt uptake but also show a clear dependence of the cobalt uptake on the solution pH.

Cui, Zheng [Tulane University, New Orleans, LA (Un↗
DOE OSTI2024

Augmenting a Simulation Campaign for Hybrid Computer Model and Field Data Experiments

The Kennedy and O’Hagan (KOH) calibration framework uses coupled Gaussian processes (GPs) to meta-model an expensive simulator (first GP), tune its “knobs” (calibration inputs) to best match observations from a real physical/field experiment and correct for any modeling bias (second GP) when predicting under new field conditions (design inputs). There are well-established methods for placement of design inputs for data-efficient planning of a simulation campaign in isolation, that is, without field data: space-filling, or via criterion like minimum integrated mean-squared prediction error (IMSPE). Analogues within the coupled GP KOH framework are mostly absent from the literature. Here, in this study, we derive a closed form IMSPE criterion for sequentially acquiring new simulator data for KOH. We illustrate how acquisitions space-fill in design space, but concentrate in calibration space. Closed form IMSPE precipitates a closed-form gradient for efficient numerical optimization. We demonstrate that our KOH-IMSPE strategy leads to a more efficient simulation campaign on benchmark problems, and conclude with a showcase on an application to equilibrium concentrations of rare earth elements for a liquid–liquid extraction reaction.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Data Science and Computation for Rapid and Dynamic Compression Experiment Workflows at Experimental Facilities (Workshop Overview) [Slides]

Motivation for this workshop stems from these items: Compression experiments are important to LANL's core mission. The scientific community is sometimes frustrated at the pace of discovery. Data analytics for other experimental regimes are advancing. Now is the time as facilities are upgraded and coming online. Advances in data analytics and computational resources could push compression experiment discoveries to a new level.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Computing for the DUNE Long-Baseline Neutrino Oscillation Experiment

This is a talk given at Computers in High Energy Physics in Adelaide, South Australia, Australia in November 2019. It is partially intended to explain the context of DUNE Computing for computing specialists. The DUNE collaboration consists of over 180 institutions from 33 countries. The experiment is in preparation now with commissioning of the first 10kT fiducial volume Liquid Argon TPC expected over the period 2025-2028 and a long data taking run with 4 modules expected from 2029 and beyond. An active prototyping program is already in place with a short test beam run with a 700T, 15,360 channel prototype of single-phase readout at the neutrino platform at CERN in late 2018 and tests of a similar sized dual-phase detector scheduled for mid-2019. The 2018 test beam run was a valuable live test of our computing model. The detector produced raw data at rates of up to ~2GB/s. These data were stored at full rate on tape at CERN and Fermilab and replicated at sites in the UK and Czech Republic. In total 1.2 PB of raw data from beam and cosmic triggers were produced and reconstructed during the six week test beam run. Baseline predictions for the full DUNE detector data, starting in the late 2020's are 30-60 PB of raw data per year. In contrast to traditional HEP computational problems, DUNE's Liquid Argon TPC data consist of simple but very large (many GB) 2D data objects which share many characteristics with astrophysical images. This presents opportunities to use advances in machine learning and pattern recognition as a frontier user of High Performance Computing facilities capable of massively parallel processing.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2019

Active Mixing of Disparate Inks for Multimaterial 3D Printing

3D printing of multimaterial parts relies upon efficient mixing of the ink components and a rapid response to composition changes. However, at low Reynolds numbers and large Peclet numbers, mixing disparate viscosity and density inks poses a challenge. In this study, the performance of active micromixers for disparate non-Newtonian inks is evaluated using both experiments and computational fluid dynamics simulations. The mixing efficiencies are compared with scaling relationships for active micromixers. Using detailed simulation results, multiple factors are identified that can impact the micromixer response time during a composition change. Finally, an active micromixer is proposed and evaluated to efficiently mix arbitrary multimaterial ink compositions and produce fine composition gradients within printed parts.

3D printing↗
DOE OSTI2023

Nanostructure‐Derived Antireflectivity in Leafhopper Brochosomes

Understanding how insect‐derived biomaterials interact with light has led to new advances and interdisciplinary insights in entomology and physics. Leafhoppers are insects that coat themselves with highly ordered biological nanostructures known as brochosomes. Brochosomes are thought to provide a range of protective properties to leafhoppers, such as hydrophobicity and antireflectivity, which has inspired the development of synthetic brochosomes that mimic their structures. Despite recent progress, the high antireflective properties of brochosome structures are not fully understood. Herein, a combination of experiments and computational modeling is used to understand the structure‐, material‐, and polarization‐dependent optical properties of brochosomes modeled on the geometries found in three leafhopper species. The results qualitatively represent that light interference interaction with nanostructures naturally occurring in brochosomes is responsible for the spectral tuning and the asymmetric line shape of the reflectance spectra. Whereas prior work has focused on the computational modeling of idealized pitted particles, this work shows that light–matter interactions with brochosome structures can be tuned by varying the geometry of their cage‐like nanoscale features and by changing the arrangement of multiparticle assemblies. Broadly, this work establishes principles for the guided design of new optically active materials inspired by these unique insect nanostructures.

Banerjee, Progna↗