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DOE OSTI2020

Towards Interactive, Reproducible Analytics at Scale on HPC Systems

The growth in scientific data volumes has resulted in a need to scale up processing and analysis pipelines using High Performance Computing (HPC) systems. These workflows need interactive, reproducible analytics at scale. The Jupyter platform provides core capabilities for interactivity but was not designed for HPC systems. In this paper, we outline our efforts that bring together core technologies based on the Jupyter Platform to create interactive, reproducible analytics at scale on HPC systems. Our work is grounded in a real world science use case-applying geophysical simulations and inversions for imaging the subsurface. Our core platform addresses three key areas of the scientific analysis workflow-reproducibility, scalability, and interactivity. We describe our implemention of a system, using Binder, Science Capsule, and Dask software. We demonstrate the use of this software to run our use case and interactively visualize real-Time streams of HDF5 data.

containers↗

NASA NTRS2010

A History of Collapse Factor Modeling and Empirical Data for Cryogenic Propellant Tanks

One of the major technical problems associated with cryogenic liquid propellant systems used to supply rocket engines and their subassemblies and components is the phenomenon of propellant tank pressurant and ullage gas collapse. This collapse is mainly caused by heat transfer from ullage gas to tank walls and interfacing propellant, which are both at temperatures well below those of this gas. Mass transfer between ullage gas and cryogenic propellant can also occur and have minor to significant secondary effects that can increase or decrease ullage gas collapse. Pressurant gas is supplied into cryogenic propellant tanks in order to initially pressurize these tanks and then maintain required pressures as propellant is expelled from these tanks. The net effect of pressurant and ullage gas collapse is increased total mass and mass flow rate requirements of pressurant gases. For flight vehicles this leads to significant and undesirable weight penalties. For rocket engine component and subassembly ground test facilities this results in significantly increased facility hardware, construction, and operational costs. "Collapse Factor" is a parameter used to quantify the pressurant and ullage gas collapse. Accurate prediction of collapse factors, through analytical methods and modeling tools, and collection and evaluation of collapse factor data has evolved over the years since the start of space exploration programs in the 1950 s. Through the years, numerous documents have been published to preserve results of studies associated with the collapse factor phenomenon. This paper presents a summary and selected details of prior literature that document the aforementioned studies. Additionally other literature that present studies and results of heat and mass transfer processes, related to or providing important insights or analytical methods for the studies of collapse factor, are presented.

deQuay, Laurence↗

DOE OSTI2021

The Cell Utilized Partitioning Model as a Predictive Tool for Optimizing Counter-Current Chromatography Processes

Counter-current chromatography (CCC) is capable of unique elution modes that isolate analytes using the movement of the stationary phase in addition to moving the mobile phase. These modes include elution-extrusion CCC (EECCC) and dual-mode CCC (DM CCC) that are not possible in traditional solid-liquid chromatography systems. Although EECCC and DM CCC are widely used to recover highly retained components, to our knowledge, optimizing the elution process in these modes with predictive models has not been reported. To address this gap, we developed a predictive model for CCC dubbed the Cell Utilized Partitioning (CUP) model. The CUP model accurately predicts the effluents of multicomponent separations in EECCC and DM CCC modes when compared to experimental data. Furthermore, CUP model simulations were extended to investigate the influence of operating and intrinsic parameters on the yield and productivity, and to compare the separation performances of EECCC and DM CCC in various conditions. The results demonstrate that low distribution constants, usually a KD less than 1, and a selectivity > 1.3, under specific flowrate ranges, increase both productivity and yield. From these results, generalized optimization and scaleup guidelines are proposed that can apply to research settings and to industrial processes to maximize preparative CCC performance.

BIOMASS FUELS,INORGANIC, ORGANIC, PHYSICAL, AND AN↗

DOE OSTI2025

Nanotomography for Quantitative 3D Particle Reconstruction

Particulates are ubiquitous across fuel cycle operations and carry critical information about particle formation, processing, and potential proliferation-related activities. Traditional analytical techniques, including micro-Raman spectroscopy and standard electron microscopy, are often limited in spatial resolution or dimensionality, particularly when used to examine metallic or submicron-scale features. Understanding particle morphology, phase distribution, and internal porosity is essential for constraining formation conditions, thermodynamic environments, and material transport behavior. In this report, we demonstrate the application of plasma focused ion beam nanotomography to reconstruct micron-scale particulates at nanoscale resolution. Using high-resolution backscattered electron imaging and Avizo software, we obtained 3D reconstructions that enabled quantitative analysis of particle morphology, phase composition, and internal voids. Representative examples include a Ta particle with a large central void and a composite particle with embedded tetrahedral crystalline structures. These reconstructions reveal structural and compositional details that are inaccessible through conventional 2D imaging. The results demonstrate that nanotomography provides both qualitative and quantitative insights into particle formation and behavior. Using nanotomography, porosity and phase distributions can be quantified to inform models of particle density, transport, and solidification conditions. Beyond technical insights, the workflow developed here establishes a transferable capability for analyzing heterogeneous particles and has potential applications in bulk materials studies via x-ray computed tomography or other volumetric imaging modalities. Ongoing efforts are focused on optimizing the workflow to process multiple particles simultaneously, increasing throughput and statistical robustness. Overall, this work illustrates the power of nanotomography as a tool for connecting particulate morphology to formation mechanisms, composition, and transport, thereby strengthening analytical capabilities for nuclear forensics, fuel cycle analysis, and related scientific investigations.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

NASA NTRS1975

Zinc depolarized electrochemical CO2 concentration

Two zinc depolarized electrochemical carbon dioxide concentrator concepts were analytically and experimentally evaluated for portable life support system carbon dioxide (CO2) removal application. The first concept, referred to as the zinc hydrogen generator electrochemical depolarized CO2 concentrator, uses a ZHG to generate hydrogen for direct use in an EDC. The second concept, referred to as the zinc/electrochemical depolarized concentrator, uses a standard EDC cell construction modified for use with the Zn anode. The Zn anode is consumed and subsequently regenerated, thereby eliminating the need to supply H2 to the EDC for the CO2 removal process. The evaluation was based primarily on an analytical evaluation of the two ZnDCs at projected end item performance and hardware design levels. Both ZnDC concepts for PLSS CO2 removal application were found to be noncompetitive in both total equivalent launch weight and individual extravehicular activity mission volume when compared to other candidate regenerable PLSS CO2 scrubbers.

Woods, R. R.↗

DOE OSTI2022

A novel approach for produced water treatment: Supercritical water oxidation and desalination

Produced water generated from oil and gas operations contains significant quantities of environmentally hazardous hydrocarbons, organic chemicals, and inorganic salts. Environmental regulations with impaired water disposal mandates produced water treatment and desalination. Industry standard desalination technologies are energy-intensive and mandate extensive produced water pre-treatment including removal of hydrocarbons constituents for efficient operations. Herein this study investigates a novel process intensification approach integrating supercritical water oxidation (SCWO) unit operation with a supercritical water desalination (SCWD) process for energy efficient produced water treatment and desalination. The coupled SCWD-SCWO process is found to be very energy-efficient and cost effective than existing industry standard desalination technologies. For energy efficient operation, the heat generated from the SCWO of hydrocarbons present in the produced water is used for power generation using a high efficiency and low-cost re-heat Rankine cycle process. The cost of treating produced water with proposed SCWO-SCWD process is independent of the salt concentration. The proposed process has the potential to offer step-change improvements over conventional processes in both the efficiency and cost of produced water treatment: for example, a cost and energy saving of 70% and 100% is calculated for the treatment of produced water containing 250 g/l salt and 10.6 g/l hydrocarbon.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2020

Characterizing Variability in Lignocellulosic Biomass: A Review

Feedstock variability is a significant barrier to the scale-up and commercialization of lignocellulosic biofuel technologies. Variability in feedstock characteristics and behavior creates numerous challenges to the biorefining industry by affecting continuous operation and biofuels yields. Recently, feedstock variability is understood and explained largely on the basis of chemical composition. Physical and mechanical properties and behavior of lignocellulosic feedstock in various unit operations, studied through advanced analytical methods, can further explain variability. Such studies will enable us in developing processes and designing equipment to improve operation and conversion performance. In this perspective, we review several advanced analytical methods that measure density, moisture content, thermal properties, flowability, grindability, rheology properties, and micromorphological characteristics. We also discuss the correlations and interactions among these properties that reflect the complexity of lignocellulosic biomass as a feedstock and the associated quality metrics and logistics of supplying consistent quality feedstock to a biorefinery. We further examine methods that have not traditionally been used to characterize lignocellulosic feedstocks but have the potential to bridge the gap in our explanation of feedstock variability.

09 BIOMASS FUELS↗

NASA NTRS2004

Verification of RSRM Nozzle Thermal Models With ETM-3 Aft Exit Cone In-depth Temperature Measurements

One of the goals of the Engineering Test Motor (ETM-3) static test was to verify analytical models through the use of instrumentation that provide real-time transient temperature response of ablating phenolic liners. Accurate measurement of in-depth temperature is critical for validating the analytical models and assessing design safety margins for nozzle insulation materials. Recent developments of in-depth thermocouple plugs have been made to more accurately measure temperature response of nozzle Liners. Thermocouple plugs were installed at two axial stations (four plugs circumferentially at each station) near the end of the ETM-3 nozzle aft exit cone to gather temperature histories during and after motor operation. The thermocouple plugs were placed at depths that bounded the reusable solid rocket motor nominal measured char depth in order to portray the carbon phenolic temperature response during the charring process. The data were used to verify the analytical models during motor operation and to better define char penetration during heat soak after motor burn out.

Maw, Joel F.↗

NASA NTRS1983

Double-injection, deep-impurity switch development

The overall objective of this program is the development of device design and process techniques for the fabrication of a double-injection, deep-impurity (DI)(2) silicon switch that operates in the 1-10 kV range with conduction current of 10 and 1A, respectively. Other major specifications include a holding voltage of 0 to 5 volts at 1 A anode current, 10 microsecond switching time, and power dissipation of 50 W at 75 C. This report describes work that shows how the results obtained at the University of Cincinnati under NASA Grant NSG-3022 have been applied to larger area and higher voltage devices. The investigations include theoretical, analytical, and experimental studies of device design and processing. Methods to introduce deep levels, such as Au diffusion and electron irradiation, have been carried out to "pin down' the Fermi level and control device-switching characteristics. Different anode, cathode, and gate configurations are presented. Techniques to control the surface electric field of planar structures used for (DI)(2) switches are examined. Various sections of this report describe the device design, wafer-processing techniques, and various measurements which include ac and dc characteristics, 4-point probe, and spreading resistance.

Selim, F. A.↗

DOE OSTI2024

Modeling of Sagging for 3D Printed Layers During the Curing Process

Here, a printed layer of silica-enforced poly(dimethylsiloxane)-co-(diphenylsiloxane) is modeled as a two-phase system consisting of air and polymer with an interface set up during the printing process. The structural geometry changes mostly due to the action of surface tension, while all material properties are strongly temperature dependent. Polymer flow is described using equations of the extended Herschel–Bulkley model, with parameters strongly dependent on temperature and degree of curing. Parameters of the model are determined using flow sweep measurements and separate experiments with vertical structure sagging at different temperatures. The curing process is modeled using dependencies between the curing rate, degree of curing, and temperature obtained in studies by differential scanning calorimetry. The developed model is used for simulations of printed structure deformations with different initial and boundary conditions.

36 MATERIALS SCIENCE↗

NASA NTRS1985

Nonlinear astrophysical Alfven waves - Onset and outcome of the modulational instability

The nonlinear development of Alfven waves is numerically studied, with applications to Alfven waves in astrophysical plasmas. It is found that amplitude-modulated Alfven wave packets undergo a collapse instability in which the wave packets become more intense and of smaller spatial extent. The wave packet steepening is eventually halted in a process most aptly described as soliton formation. A simple analytic model based on the method of characteristics can account for many of the results of the numerical calculations. The instability probably cannot prevent particle pitch angle isotropization due to self-generated Alfven waves. Nonlinear effects of the collapse may modify the process by which energetic electrons are reaccelerated by plasma turbulence. The model calculations can semiquantitatively account for properties of shock-associated Alfven waves in the solar system.

Spangler, S. R.↗

NASA NTRS1995

Micromechanical models for textile composites

Numerical and analytical micromechanical models are presented to predict the thermoelastic behavior of a textile composite. In the numerical model, the unit-cell is discretized with finite elements, and periodic boundary conditions are imposed between opposite faces of the unit-cell. For a thin textile composite, stress gradients effects through the thickness are demonstrated. The consequent difference in the stiffness and strength behavior of thick and thin composites are discussed. The numerical model is implemented to predict 3-D thermo-elastic constants for a thick textile composite, and the plate thermo-mechanical properties for a thin textile composite. The numerical model is extended to compute the thermal residual microstresses due to processing to predict the composite failure envelopes. An analytical model - Selective Averaging Method (SAM) - is proposed, which is based on a judicious combination of stiffness and compliance averaging to estimate the 3-D elastic constants. Both the models are tested and verified for several examples by comparing the stiffness properties with elasticity solutions and available results.

Sankar, Bhavani V.↗

DOE OSTI2021

Analytic phase solutions of three-wave interactions

Closed-form analytic phase solutions of three-wave interactions are presented for the first time. The cases from simple second harmonic generation to most general three wave interactions without any constraints are considered. The phase amplification or deamplification behavior in the phase-sensitive parametric process is illustrated using the results obtained here. The analytic solutions agree with the results from direct numerical integration. The validity range of an approximate phase solution is discussed.

47 OTHER INSTRUMENTATION↗

DOE OSTI2023

Influence of fluorescent dopants on the vat photopolymerization of acrylate-based plastic scintillators for application in neutron/gamma pulse shape discrimination

Plastic scintillators, a class of solid-state materials used for radiation detection, were additively manufactured with vat photopolymerization. The photopolymer resins consisted of a primary dopant and a secondary dopant dissolved in a bisphenol A ethoxylate diacrylate-based matrix. The absorptive dopants significantly influence important print parameters, for example, secondary dopants decrease the light penetration depth by a factor > 12×. The primary dopant 2,5-diphenyloxazole had minimal impact on the printing process even when loaded at 25 % by mass of the resin. Working curve measurements, which relate energy dose to cure depth, were performed as a function of feature size to further assess the influence of dopants. Photopatterns smaller than 150 µm width had apparent increases in critical energy dose compared to larger photopatterns, while all resins maintained printed features in line gratings with 50 µm of separation. Printed scintillator monoliths were compared to scintillators cast by traditional molding, demonstrating that the layer-by-layer printing process does not decrease scintillation response. In conclusion, a maximum light output of 31 % of a benchmark plastic scintillator (EJ-200) and successful pulse shape discrimination were achieved with 20 % by mass 2,5-diphenyloxazole as the primary dopant and 0.1 % by mass 9,9-dimethyl-2,7-distyrylfluorene as the secondary dopant in printed scintillator samples.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2022

Bayesian Active Learning for Scanning Probe Microscopy: From Gaussian Processes to Hypothesis Learning

Recent progress in machine learning methods and the emerging availability of programmable interfaces for scanning probe microscopes (SPMs) have propelled automated and autonomous microscopies to the forefront of attention of the scientific community. However, enabling automated microscopy requires the development of task-specific machine learning methods, understanding the interplay between physics discovery and machine learning, and fully defined discovery workflows. This, in turn, requires balancing the physical intuition and prior knowledge of the domain scientist with rewards that define experimental goals and machine learning algorithms that can translate these to specific experimental protocols. Here, we discuss the basic principles of Bayesian active learning and illustrate its applications for SPM. We progress from the Gaussian process as a simple data-driven method and Bayesian inference for physical models as an extension of physics-based functional fits to more complex deep kernel learning methods, structured Gaussian processes, and hypothesis learning. These frameworks allow for the use of prior data, the discovery of specific functionalities as encoded in spectral data, and exploration of physical laws manifesting during the experiment. Here, the discussed framework can be universally applied to all techniques combining imaging and spectroscopy, SPM methods, nanoindentation, electron microscopy and spectroscopy, and chemical imaging methods and can be particularly impactful for destructive or irreversible measurements.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2023

Recursive Use of the Short-Time Fast Fourier Transform for Signature Analysis in Continuous Processes

Although a nuclear reactor is a hostile environment for sensing and electrical communications, the reactor core is amenable to acoustic communication. An acoustic measurement infrastructure (AMI) has been installed in the Advanced Test Reactor (ATR) to record acoustic signals that can capture its different operating regimes. AMI uses coolant pumps as continuous signal sources, coolant and structural components as transmission lines, and accelerometers to capture system motion. A recursive signal processing technique based on the short-time fast Fourier transform (STFFT) for continuous processes provides unique signatures for diagnostic and prognostic analyses from the system motion data. Here this article presents a recursive STFFT methodology that processes acoustic signals from continuous industrial processes. The article first discusses the initial STFFT use with simulated data to elucidate the basic principles necessary to understand and interpret the STFFT results from actual pump vibration data. Each repetitive use of the STFFT on pump vibration data using the results from the prior STFFT processing will generate additional complimentary time-frequency-based signatures. These signatures are generated by the coolant pumps operating under different process conditions. After each use of the STFFT, the resulting signatures provide exemplary examples of the diversity and intuitive nature of recursively using the STFFT. This article focuses on recursively using the STFFT to provide numerous complimentary and diverse signatures that will ultimately be inputs for machine learning algorithms that provide predictive data analytics. The intuitive nature of the information and signatures from recursive STFFT processing will also bring intuitive interpretation capabilities to machine learning and predictive data analytic techniques.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

NASA NTRS1983

New trends in combustion research for gas turbine engines

Research on combustion is being conducted to provide improved analytical models of the complex flow and chemical reaction processes which occur in the combustor of gas turbine engines, in order to enable engine manufacturers to reduce the development time of these concepts. The elements of the combustion fundamentals program is briefly discussed with examples of research projects described more fully. Combustion research will continue to emphasize the development of analytical models and the support of these models with fundamental flow experiments to assess the models accuracy and shortcomings.

Mularz, E. J.↗

NASA NTRS1983

New trends in combustion research for gas turbine engines

Mularz, E. J.↗