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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 235 records · Page 13
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Calculation of Dynamical Response Functions Using a Bound-State Method

Abstract We investigate a method to extract response functions (dynamical polarisabilities) directly from a bound-state approach applied to calculations of perturbation-induced reactions. The use of a square-integrable basis leads to a response in the form of a sum of $$\delta $$ δ functions. We integrate this over energy and fit a smooth function to the resulting stepwise-continuous one. Its derivative gives the final approximation to the physical response function. We show that the method reproduces analytical results where known, and analyse the details for a variety of models. We apply it to some simple models, using the stochastic variational method as the numerical method. Albeit we find that this approach, and other numerical techniques, have some difficulties with the threshold behavior in coupled-channel problems with multiple thresholds, its stochastic nature allows us to extract robust results even for such cases.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2025

Reversible parts-per-trillion-level detection of perfluorooctane sulfonic acid in tap water using field-effect transistor sensors

Widespread, persistent and toxic per- and polyfluoroalkyl substances (PFAS) pose a major threat to water systems and human health. Current detection methods are relatively expensive, slow and complex, underscoring the need for more accessible alternatives to meet increasingly stringent PFAS regulations. Here, in this study, we present an ultrasensitive sensing platform for perfluorooctane sulfonic acid detection in tap water with a reporting limit ( ~ 250 parts per quadrillion) lower than the US Environmental Protection Agency’s regulatory standard (4 parts per trillion), using a remote gate field-effect transistor featuring β-cyclodextrin (β-CD)-modified reduced graphene oxide as the sensing membrane. The sensor exhibits excellent selectivity against common inorganic ions, natural organic matter and select organic pollutants in tap water. The reversible and rapid response ( < 2 min) indicates the potential of remote gate field-effect transistor for continuous in-line monitoring. Mechanistic studies using quartz crystal microbalance and molecular dynamics simulations reveal key roles of analyte adsorption and charge properties in sensing performance and offer insights for designing more selective PFAS capture probes.

Wang, Yuqin [University of Chicago, IL (United Sta↗
DOE OSTI2022

Performance Testing of Software Upgrade for SRNL CPC Instrument at the Nuclear Material Laboratory, Office of Safeguards Analytical Services, International Atomic Energy Agency

This report summarizes the performance testing for the Software upgrade for the Savannah River National Laboratory (SRNL) Controlled Potential Coulometry (CPC) instrument. This upgrade is specifically to meet the needs of the Nuclear Material Laboratory, Office of Safeguards Analytical Services, International Atomic Energy Agency at Seibersdorf, Austria. The instrument hardware was upgraded in 2010, but the software continued to operate on a Windows platform using a High Tech Basic (HT Basic) application. HT Basic usage is declining. The IAEA requested an upgrade to the existing coulometer software to Laboratory Virtual Instrument Engineering Workbench (LabVIEW), an object-oriented programming language in wide use and one the NML staff is familiar with. The software was developed by SRNL Software Engineer, with assistance from Electrical Engineer and Scientist. Once developed, the new LabVIEW software was validated, tested with Iron (surrogate for plutonium) and plutonium. Once testing was completed, the software was installed at the International Atomic Energy Agency Nuclear Material Laboratory (IAEA NML) coulometer, and SRNL provided hands on training to NML staff on using the new software.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2021

Multisensor Agile Adaptive Sampling of Convective Storms Driven by Real-time Analytics

Convective storms vertically transport water vapor and condensate from Earth’s surface to the upper troposphere. Life on Earth is fundamentally linked to this transport which determines the hydrological cycle, and the intensity of severe weather responsible for the destruction of life and property. Despite advances in high-resolution modeling and better observational capabilities, the scientific community continues to be confronted with knowledge gaps about convective storms that limit our predictive capabilities. The ongoing developments in the high-resolution Energy Exascale Earth System Model (E3SM), large eddy simulations, and AI-based analytics to evaluate uncertainties are expected to provide a comprehensive framework for new scientific discovery. The model-experiment (MODEX) approach suggests that the aforementioned advancements in model development and AI-based inference techniques should be complemented by similar advancements in the experimental (observational) side so that the former does not outstrip the ability of the latter to provide meaningful constraints. What are the recent advancements in observations that will provide the necessary leap forward in improving our predictive capabilities? To address this question, we propose a new experimental paradigm called Multisensor Agile Adaptive Sampling (MAAS) that capitalizes on advancements in communications (5G), computational resources (edge/fog computing), sensor capabilities, and machine learning (ML) and AI techniques (Kollias et al., 2020). The MAAS framework allows for the collection of higher spatiotemporal resolution and quality observations of convective storms than is traditionally possible. The MAAS framework is scalable and applicable to atmospheric observatories such as those operated by the Department of Energy (DoE) Atmospheric Radiation Measurement (ARM) facility.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

The role of pre-existing heterogeneities in materials under shock and spall

There has been a challenge for many decades to understand how heterogeneities influence the behavior of materials under shock loading, eventually leading to spall formation and failure. Experimental, analytical, and computational techniques have matured to the point where systematic studies of materials with complex microstructures under shock loading and the associated failure mechanisms are feasible. This is enabled by more accurate diagnostics as well as characterization methods. As interest in complex materials grows, understanding and predicting the role of heterogeneities in determining the dynamic behavior becomes crucial. Early computational studies, hydrocodes, in particular, historically preclude any irregularities in the form of defects and impurities in the material microstructure for the sake of simplification and to retain the hydrodynamic conservation equations. Contemporary computational methods, notably molecular dynamics simulations, can overcome this limitation by incorporating inhomogeneities albeit at a much lower length and time scale. This review discusses literature that has focused on investigating the role of various imperfections in the shock and spall behavior, emphasizing mainly heterogeneities such as second-phase particles, inclusions, and voids under both shock compression and release. Pre-existing defects are found in most engineering materials, ranging from thermodynamically necessary vacancies, to interstitial and dislocation, to microstructural features such as inclusions, second phase particles, voids, grain boundaries, and triple junctions. This literature review explores the interaction of these heterogeneities under shock loading during compression and release. Systematic characterization of material heterogeneities before and after shock loading, along with direct measurements of Hugoniot elastic limit and spall strength, allows for more generalized theories to be formulated. Further, continuous improvement toward time-resolved, in situ experimental data strengthens the ability to elucidate upon results gathered from simulations and analytical models, thus improving the overall ability to understand and predict how materials behave under dynamic loading.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Parametric dependencies of resonant layer responses across linear, two-fluid, drift-MHD regimes

Non-axisymmetric magnetic fields arising in a tokamak either by external or internal perturbations can induce complex non-ideal MHD responses in their resonant surfaces while remaining ideally evolved elsewhere. This layer response can be characterized in a linear regime by a single parameter called the inner-layer Δ, which enables outer-layer matching and the prediction of torque balance to non-linear island regimes. In this work, we follow strictly one of the most comprehensive analytic treatments including two-fluid and drift MHD effects and keep the fidelity of the formulation by incorporating the numerical method based on the Riccati transformation when quantifying the inner-layer Δ. The proposed scheme reproduces not only the predicted responses in essentially all asymptotic regimes but also with continuous transitions as well as improved accuracies. In particular, the Δ variations across the inertial regimes with viscous or semi-collisional effects have been further resolved, in comparison with additional analytic solutions. The results imply greater shielding of the electromagnetic torque at the layer than what would be expected by earlier work when the viscous or semi-collisional effects can compete against the inertial effects, and also due to the intermediate regulation by kinetic Alfvén wave resonances as rotation slows down. These are important features that can alter the non-axisymmetric plasma responses including the field penetration by external fields or island seeding process in rotating tokamak plasmas.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2022

Parametric dependencies of resonant layer responses across linear, two-fluid, drift-MHD regimes

Non-axisymmetric magnetic fields arising in a tokamak either by external or internal perturbations can induce complex non-ideal MHD responses in their resonant surfaces while remaining ideally evolved elsewhere. This layer response can be characterized in a linear regime by a single parameter called the inner-layer Delta, which enables outer-layer matching and the prediction of torque balance to non-linear island regimes. Here, we follow strictly one of the most comprehensive analytic treatments including two-fluid and drift MHD effects and keep the fidelity of the formulation by incorporating the numerical method based on the Riccati transformation when quantifying the inner-layer Delta. The proposed scheme reproduces not only the predicted responses in essentially all asymptotic regimes but also with continuous transitions as well as improved accuracies. In particular, the Delta variations across the inertial regimes with viscous or semi-collisional effects have been further resolved, in comparison with additional analytic solutions. The results imply greater shielding of the electromagnetic torque at the layer than what would be expected by earlier work when the viscous or semi-collisional effects can compete against the inertial effects, and also due to the intermediate regulation by kinetic Alfven wave resonances as rotation slows down. These are important features that can alter the nonaxisymmetric plasma responses including the field penetration by external fields or island seeding process in rotating tokamak plasmas.

MHD instability↗
DOE OSTI2020

NREL's Biochemical Conversion Pilot Plant Capabilities

NREL's biochemical conversion plant is a flexible pilot plant for testing a wide range of bio-based technologies for producing renewable fuels and byproducts. The biochemical conversion pilot plant is housed in the Integrated Biorefinery Research Facility located on the National Renewable Energy Laboratory's (NREL's) main campus. The facility provides industry partners the opportunity to test and develop their own biorefining technology using NREL equipment or rental equipment that can be brought into the plant. The pilot plant can process materials up to one dry ton per day in an integrated fashion using a variety of operating modes including batch, fed-batch, and continuous processing.

biochemical↗
DOE OSTI2021

Cyber-Physical System Implementation for Manufacturing With Analytics in the Cloud Layer

Effective and efficient modern manufacturing operations require the acceptance and incorporation of the fourth industrial revolution, also known as Industry 4.0. Traditional shop floors are evolving their production into smart factories. To continue this trend, a specific architecture for the cyber-physical system is required, as well as a systematic approach to automate the application of algorithms and transform the acquired data into useful information. This work makes use of an approach that distinguishes three layers that are part of the existing Industry 4.0 paradigm: edge, fog, and cloud. Each of the layers performs computational operations, transforming the data produced in the smart factory into useful information. Trained or untrained methods for data analytics can be incorporated into the architecture. A case study is presented in which a real-time statistical control process algorithm based on control charts was implemented. The algorithm automatically detects changes in the material being processed in a computerized numerical control (CNC) machine. The algorithm implemented in the proposed architecture yielded short response times. The performance was effective since it automatically adapted to the machining of aluminum and then detected when the material was switched to steel. The data were backed up in a database that would allow traceability to the line of g-code that performed the machining.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

The role of biochar in the degradation of sugars during fast pyrolysis of biomass

With appropriate pretreatment, sugars can be a major product from fast pyrolysis of lignocellulosic biomass. Analytical pyrolysis of pure cellulose can produce up to 60 wt% yield of levoglucosan although yields are significantly lower in continuous pyrolysis at larger scales. Secondary reactions of vaporized levoglucosan are thought to be responsible for this loss of sugar yield, suggesting changes in the design and operation of pyrolysis reactors to minimize these reactions. Micropyrolysis experiments were performed to better understand the mechanism of sugar degradation in the presence of biochar. A 57% loss in levoglucosan yield was observed for cellulose overlain with untreated biochar powder compared to the pure cellulose control sample. The addition of biochar derived from pyrolysis of untreated corn stover to a fluidized bed pyrolyzer reduced sugar yields from cellulose from 61.3 wt% to 21.3 wt% and 41.5–11.6 wt% for conventional and autothermal operation, respectively. The significant drop in sugar yield due to biochar interaction inspired change in feeder configuration for the fluidized bed pyrolyzer to reduce vapor-char interactions. Biomass feeding was changed from in-bed to above-bed injection, which allowed significant devolatilization to occur above the layer of biochar that exists at the surface of the bed. Finally, by reducing secondary reactions, bio-oil and sugar yields increased by 7.9% and 14%, respectively, for autothermal pyrolysis.

09 BIOMASS FUELS↗
DOE OSTI2021

Driven-dissipative phase transition in a Kerr oscillator: From semiclassical PT symmetry to quantum fluctuations

We study a minimal model that has a driven-dissipative quantum phase transition, namely a Kerr non-linear oscillator subject to driving and dissipation. Using mean-field theory, exact diagonalization, and the Keldysh formalism, we analyze the critical phenomena in this system, showing which aspects can be captured by each approach and how the approaches complement each other. Then critical scaling and finite-size scaling are calculated analytically using the quantum Langevin equation. Furthermore, the physics contained in this simple model is surprisingly rich: it includes a continuous phase transition, Z 2 symmetry breaking, PT symmetry, state squeezing, and critical fluctuations. Due to its simplicity and solvability, this model can serve as a paradigm for exploration of open quantum many-body physics.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2022

MatLab Package for Whispering Gallery Mode Data

Dye-doped whispering gallery mode resonator (WGMR) microspheres yield highly structured emission spectra that are extremely sensitive to their environment and are of intense interest for use in a variety of sensing applications. Efforts to leverage the unique sensitivities of WGMRs have relied on stringent experimental requirements to correlate specific spectral shifts/changes to an analyte/stimulus such as 1) precise positional knowledge, 2) reference spectra for each microsphere, and 3) high mechanical stability. Consequently, these can hinder adequate mixing or incorporation of analytes and creates challenges for remote sensing. The MATLAB codes provided here are to be used in conjunction with a continuous flow technique for measuring WGM spectra of dye-doped microspheres suspended in solution. One MATLAB script, smooths the data, automatically baseline corrects it to isolate the whispering gallery modes (WGM) from the unwanted bulk emission, and assesses the similarity of each spectrum to aid in selecting a set of unique WGM spectra for further analysis. The next script is designed to analyze WGM spectra to determine the size of the resonator and the refractive index (RI) of its local environment without a priori knowledge of the individual microsphere. The final script allows the user to cluster spheres based on the product of their RI and radius and the contrast ratio of the RI of the sphere material and that of its environment by using a shared nearest neighbor spectral clustering algorithm.

Lilley, Laura↗
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 ↗
DOE OSTI2024

Time series methods for the analysis of soundscapes and other cyclical ecological data

Biodiversity monitoring has entered an era of ‘big data’, exemplified by a near-continuous collection of sounds, images, chemical and other signals from organisms in diverse ecosystems. Such data streams have the potential to help identify new threats, assess the effectiveness of conservation interventions, as well as generate new ecological insights. However, appropriate analytical methods are often still missing, particularly with respect to characterizing cyclical temporal patterns. Here, we present a framework for characterizing and analysing ecological responses that represent nonstationary, complex temporal patterns and demonstrate the value of using Fourier transforms to decorrelate continuous data points. In our example, we use a framework based on three approaches (spectral analysis, magnitude squared coherence, and principal component analysis) to characterize differences in tropical forest soundscapes within and across sites and seasons in Gabon. By reconstructing the underlying, cyclic behaviour of the soundscape for each site, we show how one can identify circadian patterns in acoustic activity. Soundscapes in the dry season had a complex diel cycle, requiring multiple harmonics to represent daily variation, while in the wet season there was less variance attributable to the daily cyclic patterns. Our framework can be applied to most continuous, or near-continuous ecological data collected at a fine temporal resolution, allowing ecologists to explore patterns of temporal autocorrelation at multiple levels for biologically meaningful trends. Such methods will become indispensable as biological big data are used to understand the impact of anthropogenic pressures on biodiversity and to inform efforts to mitigate them.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Mapping the Path Forward to Next Generation Algal Technologies: Workshop on Understanding the Rules of Life and Complexity in Algal Systems

Algal technology offers enormous potential as a source of renewable biomass and bioproducts for fuels, biochemicals, nutrients, and numerous other applications. While tremendous opportunities exist, significant challenges are also present in harnessing and utilizing this valuable and potentially sustainable bioproduction platform. Funded by a joint partnership between the Bioenergy Technologies Office (BETO) at the Department of Energy (DOE) and the National Science Foundation (NSF), the recent virtual workshop, "Understanding the Rules of Life: Complexity in Algal Systems Workshop," was organized to bring together thought leaders from academia, national laboratories and other government agencies, and industry to identify key challenges and propose research strategies to overcome these barriers. Two parallel sessions were undertaken: 1) algae strain and toolkit development and 2) algal ecosystem and microbiome dynamics, in order to advance algae engineering and cultivation efforts. In each session, attendees discussed the most pressing challenges from the perspective of model systems and omics analysis. Themes such as genetic engineering toolboxes, translation from the laboratory to outdoor cultivation, functional genomics, metabolomics, algal ecology, consortial design, microbiome dynamics, mathematical models, and data analytics emerged from the discussion. Overall, this workshop has served to elucidate the key hurdles and many opportunities present as we continue to advance algal science and technology from the laboratory and into the commercial realm.

09 BIOMASS FUELS↗
DOE OSTI2023

An integrated data management and informatics framework for continuous drug product manufacturing processes: A case study on two pilot plants

The pharmaceutical industry continuously looks for ways to improve its development and manufacturing efficiency. In recent years, such efforts have been driven by the transition from batch to continuous manufacturing and digitalization in process development. To facilitate this transition, integrated data management and informatics tools need to be developed and implemented within the framework of Industry 4.0 technology. Here, in this regard, the work aims to guide the data integration development of continuous pharmaceutical manufacturing processes under the Industry 4.0 framework, improving digital maturity and enabling the development of digital twins. This paper demonstrates two instances where a data integration framework has been successfully employed in academic continuous pharmaceutical manufacturing pilot plants. Details of the integration structure and information flows are comprehensively showcased. Approaches to mitigate concerns in incorporating complex data streams, including integrating multiple process analytical technology tools and legacy equipment, connecting cloud data and simulation models, and safeguarding cyber-physical security, are discussed. Critical challenges and opportunities for practical considerations are highlighted.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Workflow for evaluating enzyme immobilization and performance for continuous flow manufacturing

Enzymes have shown promise in various industries due to their functional specificity, catalytic efficiency, and environmental sustainability. These biological catalysts can be a pivotal component of manufacturing pipelines like continuous flow chemistry. For this, there exists a need to robustly immobilize enzymes on solid supports and assess the effects of the solid supports on catalytic performance and stability. Here, we use an industrially relevant model enzyme, C. ensiformis (Jack bean) urease, to demonstrate immobilization and assess performance in the context of continuous flow manufacturing. Various immobilization strategies were screened focusing on immobilization efficiency, protocol simplicity, and urease biocatalyst kinetics. Based on this, CDI-agarose and NHS-agarose resins were identified as the best-performing immobilization strategies for urease. CDI-agarose-urease and NHS-agarose-urease were then scaled up and applied to a large-scale continuous flow reactor to evaluate product yields, operational stability, and long-term stability. These experiments identified differences in stability and performance depending on the immobilization method tested. This highlights the importance of screening immobilization methods and subsequent enzyme performance for each candidate biocatalyst used in manufacturing to promote optimal performance and stability. As such, this work provides a framework for evaluating enzyme biocatalyst immobilization approaches to improve performance and enable transition into industrial processes.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

HARD: A performance portable radiation hydrodynamics code based on FleCSI framework

Hydrodynamics And Radiation Diffusion (HARD) is an open-source application for high-performance simulations of compressible hydrodynamics with radiation-diffusion coupling. Built on the FleCSI (Bergen et al., 2021 [1]) (Flexible Computational Science Infrastructure) framework, HARD expresses its computational units as tasks whose execution can be orchestrated by multiple back-end runtimes, including Legion (Bauer et al., 2012 [2]), MPI (Forum, 1994 [3]), and HPX (Kaiser et al., 2020 [4]). Node-level parallelism is handled through Kokkos (Edwards et al., 2014 [5]), providing a single-source, portable code base that runs efficiently on laptops, small homogeneous clusters, and the largest heterogeneous supercomputers currently available. To ensure scientific reliability, HARD includes a regression test suite that automatically reproduces canonical verification problems such as the Sod and LeBlanc shock tubes, and the Sedov blast wave, comparing numerical solutions against known analytical results. The project is distributed under an OSI-approved license, hosted on GitHub, and accompanied by reproducible build scripts and continuous integration workflows. This combination of performance portability, verification infrastructure, and community-focused development makes HARD a sustainable platform for advancing radiation hydrodynamics research across multiple domains.

97 MATHEMATICS AND COMPUTING↗