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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 109 records · Page 6
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Gas‐Phase Ion‐Molecule Interactions in a Collision Reaction Cell with ICP‐MS / MS : Investigations with CO 2 as the Reaction Gas

Carbon dioxide (CO 2 ) was used as a reaction gas to investigate the gas‐phase ion‐molecule interactions using the Agilent 8900 ICP‐MS/MS. A solution containing forty‐five elements representative of the periodic table was used to supply the ions to react with CO 2 in the collision/reaction cell (CRC). The only significant product ions formed were monoxides. The general reactivity was shown to be consistent with density functional theory (DFT)‐predicted reaction enthalpies, such that all predicted exothermic reactions produced product ions at levels of at least 1% of the unreacted ion. Most endothermic reactions observed had sufficient kinetic energy in excess of the reaction enthalpies. Our results suggest that reaction enthalpy is a reasonable predictor of reactivity with CO 2 on the timescales of the interactions in non‐thermal ICP‐MS/MS systems. The ease and rapidity of data collection with the ICP‐MS/MS and DFT calculations using the NWChem suite has value given the scarcity of thermochemical data of CO 2 reactions in the literature. These studies are especially useful for the identification of targeted reaction chemistries to be leveraged for analytical method development, such as for the inline separation of isobaric interferences from analytes of interest.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Development of Microwave Resonant Cavity Transducer Recalibration Procedures

We are investigating a microwave resonant cavity transducer for flow sensing in the vessel of a high temperature fluid advanced reactor (AR), such as a molten salt cooled reactor (MSCR) or a sodium fast reactor (SFR). This transducer is a hollow metallic cylindrical cavity, with one of the flat walls of the cylinder flexible enough to undergo microscopic deflection due to dynamic fluid pressure. Membrane deflection leads to a shift in the microwave resonant frequency, which can be detected with a spectrum analyzer. We have performed a preliminary proof-of-principle test of flow sensing in high temperature liquid sodium in environment. The liquid sodium setup consists of a cylindrical vessel with a center feed line, where a transducer inserted through the top cap of the vessel measures velocity of the impinging liquid jet. For this test, we have developed a cylindrical resonator, which was machined from stainless steel 316 and electroplated with silver on the interior surfaces. Inner diameter of the cylindrical cavity is 22.2 mm, and the thin wall is approximately 200µm thick. The cavity was excited through a WR-42 waveguide through a subwavelength hole on the side of the wall of the cylinder in the TE 011 mode with resonant frequency f ≈ 17.8GHz. Sodium flow rate sensing study was performed in a liquid sodium vessel at 340°C temperature and ambient pressure. The flow rate was changed by varying sodium pump power, and a frequency shift of several hundred KHz was observed. From the measurements, we observed significant scatter in high temperature liquid sodium at low flow velocity values. Our hypothesis is that this is caused by temperature drift in the liquid, which results in thermal expansion of the cavity and a drift in the resonator frequency. In this report, we investigate temperature dependence of the resonant frequency through development of analytic models and preliminary analysis of experimental data. In addition, we develop a procedure for compensation for temperature drift during flow sensing.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2022

A machine learning approach for clinker quality prediction and nonlinear model predictive control design for a rotary cement kiln

Abstract Cement manufacturing is energy‐intensive (5Gj/t) and comprises a significant portion of the energy footprint of concrete systems. Incorporating modern monitoring, simulation and control systems will allow lower energy use, lower environmental impact, and lower costs of this widely used construction material. One of the goals of the CESMII roadmap project on the Smart Manufacturing of Cement included developing an analytical process model for clinker quality that includes the chemistry of the kiln feed and accounts for critical process variables. This predictive model will be used in nonlinear model predictive control system designed to significantly reduce process energy use while maintaining or improving product quality. In the cement manufacturing plant used in this study, the kiln feed (meal) is tested every 12 h and used to estimate the mineral composition of the cement kiln output (clinker) using the stoichiometry‐based Bogue's model and the expertise of the plant operators. During kiln operation, kiln output (clinker) is sampled and tested every 2 h to measure its chemical and mineral composition. The predicted and measured values of the clinker composition are used by the plant operators to adjust the kiln input stream and the production process characteristics to maintain stable operation and uniform product quality. However, the time delay between prediction and testing, along with inaccuracies inherent in the Bogue's model have made any process changes designed to minimize energy use problematic, especially in‐light of potential clinker quality issues that process changes often pose. A new analytical model that integrates quality information and process operation information has been developed from data collected from 2 years of production from an operating cement facility. To make the model fuel‐type‐independent, consumed heat energy was computed in the model instead of fuel type and amount. A Feedforward Network was trained and tailored from collected data. Many data‐based simulations were conducted to quantitatively evaluate the proposed model and the 5‐fold cross‐validation procedure was used to test the models. The resulting predictive model was shown to have a low root mean square error (MSE) with respect to the estimated clinker mineral composition compared to that using the industry standard “Bogue’ model”. The end goal of this work was to develop a single machine learning tool that allows the use of quality control data and process control variables to improve energy efficiency of the process in a continuous fashion. The proposed nonlinear model predictive control system (NMPC) can generate predicted kiln production characteristics based on manipulated variables in manner that accurately follows the target product quality values. Simulation results also show that the proposed model produced accurate predictions of kiln outputs that fell within the required constraints, while manipulating control variables within typical operational ranges.

Ali, Asem M.↗
DOE OSTI2020

Analytic model of electron transport through and over non-linear barriers

Tunneling barriers are an essential component of electron sources, sensors, detectors, and vacuum nanoelectronics, and a pivotal factor in their performance, but the barriers themselves routinely depart from the analytic models used to model their behavior. Here, a new formalism is developed to analytically and accurately model emission through and over barriers associated with depletion layers, nanotip barriers, and MIM structures. The transmission probability for depletion layer and metal-insulator-metal (MIM) and metaloxide-semiconductor (MOS) barriers is accurately modeled as the electron energy exceeds the barrier height using approaches designed for rapid implementation demanded by simulation codes, and extensible to general barriers. The models supersede conventional thermal and field models in depletion and MIM/MOS barrier studies. Thermal-field methods are used to treat the transmission probability, and shape factor methods to treat the tunneling factor. Analytic formulae for current density are obtained. The methods ease device simulation and characterization of current-voltage relations for emerging technologically interesting barriers with better accuracy.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2022

Lignin Utilization

Valorization of lignin has massive potential economic and sustainability benefits for the lignocellulosic biorefinery. However, challenges remain to realize lignin conversion to coproducts, especially related to selective, high yield depolymerization to monomers and quantitative analytics on lignin, the latter of which is critical for accurate process modeling. Towards these goals, the Lignin Utilization project focuses on catalytic lignin deconstruction chemistries for C-O and C-C bond cleavage, development of analytical chemistry techniques to quantitatively characterize lignin, and syntheses of requisite model compounds for understanding lignin transformations. This work is done with and supports multiple BETO projects, including the Biological Lignin Valorization (BLV) project, the Separations Consortium, and others. Outcomes of Lignin Utilization for analytics and synthesis include 1) development of new mass spectrometry (MS) methods to characterize lignin dimers/oligomers in process streams, 2) deployment of a computational-experimental tool (with Biochemical Process Modeling and Simulation) to identify lignin-derived compounds with high fidelity from MS, and 3) delivery of >40 unique compounds. From a catalysis perspective, we have developed new oxidative approaches to cleave C-O and C-C bonds and produce >50% bio-available aromatic monomers for biological funneling for the BLV project and have developed recoverable bases for base-catalyzed deconstruction of lignin.

BIOMASS FUELS↗
DOE OSTI2026

Control Parameter Sensitivity Study for Inverter-Based-Resource Dominated Grids: A Small Signal Stability Approach and Framework

The growing adoption of renewable energy is driving the prevalence of inverter-based resources (IBRs) within power grids. Future power grids will integrate both grid-following IBRs (GFM-IBRs) and grid-forming IBRs (GFL-IBRs) alongside synchronous generators. Therefore, it is crucial to perform stability studies that account for all components and especially control interactions related to IBRs. Extensive research has performed to study the IBR-related stability, however, the sensitivity study of IBRs' control parameters on system stability has not been adequately studied yet, especially from a systematic way. Therefore, this paper conducts a small signal stability analysis for a generic grid with multiple types of resources and develops an analytical framework for assessing the sensitivity of control parameters affecting stability margins. To achieve that, the non-autonomous reduced-order non-linear dynamic model is developed for a generic power system with multiple synchronous generator-based resources (SGBRs), GFM-IBRs, and GFL-IBRs. Based on the analytic model, a systematic framework for parametric sensitivity on systems' asymptotic stability is developed. A parameter sensitivity analysis based on eigenvalue methods is proposed. The impact of the droop controllers of GFM-IBRs, PQ-dispatch and the PLL controller of GFL-IBR on the system asymptotic stability is discussed. This sensitivity study is aiming to provide deep insights on control parameters' impact on system stability, and gives direction for parameter tuning in case of instability.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2020

NREL's Capabilities in Analytical Sciences Supporting Research and Development (R&D)

NREL researchers perform biomass characterization in dedicated laboratories working with over $5 million in state-of-the-art analytical equipment. A dedicated team of highly skilled analytical chemists have over 100 cumulative years of experience in characterization. They have three specialized laboratories with dedicated sample processing and advanced analytical equipment. Core capabilities include biomass and biochemical characterization and quantification, publicly available laboratory analytical procedures (LAPs), and near-infrared rapid analysis.

analysis↗
DOE OSTI2026

Multibody for Everybody (M4E): A Symbolic Dynamics Modeling Tool with Applications in Simulation, Control, and Optimization

Developing the analytical model of a multibody system is often the initial step in control and optimization. The analytical model (equations of motion) describes a system’s time evolution under specified forcing conditions. Although developing these equations is easy for simple systems, this process becomes more complex for systems composed of multiple bodies. Deriving equations of motion for complex multibody systems requires specialized expertise in multibody dynamics, is time-consuming, and is susceptible to error. To address this issue, this paper presents an open-source, easy-to-use, systematic framework to derive symbolic equations of motion in both Python and MATLAB using the joint coordinate formulation. This formulation results in a set of ordinary differential equations that use the minimum set of coordinates needed to model a system. The symbolic representation provides better insight into the influence of design parameters on system performance, facilitates sensitivity analysis and parameter studies, and supports direct implementation of control and optimization routines. The tool enables numerical simulation for specified parameter sets, is modular for straightforward integration with other tools and libraries, and allows incorporation of hydrodynamics, mooring, and other external forces. The result is a reproducible, extensible pipeline for modeling, simulation, and design of complex multibody systems. The proposed tool is versatile and can be applied to domains such as robotics, control, and design. In addition, we integrated external libraries that provide capabilities for modeling offshore systems such as underwater robots and marine energy converters.

16 TIDAL AND WAVE POWER↗
DOE OSTI2020

An analytical expression for temperature in a thermodynamically consistent model with a Mie-Gruneisen equation for pressure

The objective of this paper is to develop an analytical expression for temperature in a thermodynamically consistent model with a Mie-Gruneisen equation for pressure. This expression for the temperature is obtained for both constant and variable specific heat models and can be used in common models for the material yield strength that include thermal softening due to increase in temperature. Additionally, the material parameters in the model can easily be calibrated to match known experimental data and the model has been used to determine the value of the shock pressure at the onset of melting during shock loading.

42 ENGINEERING↗
DOE OSTI2023

Spatiotemporal measurements of striations in a glow discharge’s positive column using laser-collisional induced fluorescence

Here we have observed the behavior of striations caused by ionization waves propagating in low-pressure helium DC discharges using the non-invasive laser-collision induced fluorescence (LCIF) diagnostic. To achieve this, we developed an analytic fit of collisional radiative model (CRM) predictions to interpret the LCIF data and recover quantitative two-dimensional spatial maps of the electron density, n e , and the ratios of LCIF emission states that can be correlated with T e with the use of accurate distribution functions at localized positions within striated helium discharges at 500 mTorr, 750 mTorr, and 1 Torr. To our knowledge, these are the first spatiotemporal, laser-based, experimental measurements of n e in DC striations. The n e and 447:588 ratio distributions align closely with striation theory. Constriction of the positive column appears to occur with decreased gas pressure, as shown by the radial n e distribution. We identify a transition from a slow ionization wave to a fast ionization wave between 750 mTorr and 1 Torr. These experiments validate our analytic fit of n e , allowing the implementation of an LCIF diagnostic in helium without the need to develop a CRM.

42 ENGINEERING↗
DOE OSTI2024

Precision Computations in Strongly Coupled Conformal Field Theories (Final Technical Report)

Conformal Field Theories (CFTs) are quantum field theories that are invariant under the conformal symmetry group (which includes translations and rotations, but also local rescalings of spacetime). They are building blocks of general quantum field theories, and appear in many areas of physics, including statistical physics, condensed matter physics, particle physics, and quantum gravity. Because of their extra symmetries, the mathematical structure of CFTs is tightly constrained, and this leads to the idea of the ``conformal bootstrap," which is to use these mathematical structures to constrain, and in some cases determine, CFT observables. A new numerical implementation of the conformal bootstrap idea appeared in 2008 with the work of Rattazzi, Rychkov, Tonni, and Vichi. Their observation was that certain bootstrap constraints (conformal symmetry and unitarity) could be combined to yield a convex optimization problem that constraints CFT data. By solving this convex optimization problem on a computer, one could obtain bounds on observables like critical exponents and operator product expansion (OPE) coefficients. Over the course of this award, the PI has improved numerical bootstrap techniques by optimizing known algorithms and finding new ones for performing the required convex optimization computations. The PI has applied these techniques to compute high-precision observables in several important strongly-coupled systems. The PI has also explored both analytical and numerical bootstrap methods for constraining the space of low energy effective field theories of quantum gravity, and developed new analytical techniques for CFT and QFT more broadly.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2026

Multi-system analysis of offshore geologic carbon storage: a review of open-source data science solutions

Geologic carbon storage projects are maturing worldwide and the footprint of deployment in the offshore is expanding. At present, there are ten projects in operation or that have been completed, more than 50 in construction and development, and dozens of characterization studies completed or underway. Offshore geologic carbon storage offers potential benefits over onshore geologic carbon storage. These offshore projects are generally remote in location, distant from population centers, and avoid complicated pore space rights while having abundant prospective storage potential. Some offshore fields targeted for carbon storage have comparatively fewer prior borehole penetrations except for areas that have been explored for petroleum production, minimizing potential issues such as pressure interference and infrastructure impacts. Yet offshore geologic carbon storage projects face distinctive technical and economic challenges, such as seafloor geohazards (e.g., seabed instability), expensive maritime transport, and meteorological-oceanographic conditions that can damage infrastructure and impact operations. Analytical capabilities and improved computational speeds have advanced engineering, earth and energy sciences in the wake of the arrival of modern data science over the last decade. These advancements have created an opportunity for integrated, multi-systems modeling approaches utilizing artificial intelligence and machine learning that are no longer limited by computational issues. Analytical tools developed alongside this advancement in data science can be leveraged to calibrate the potential advantages and challenges of carbon storage operations in the offshore. New methods and approaches that incorporate data science to analyze multiple aspects of engineered and natural systems can provide insights that complement the characterization and onsite engineering that traditional commercial and operational software addresses. These new methods and approaches can potentially improve the outcome of energy operations and carbon storage. Providing multi-system, science-driven data analytics enhances the knowledge base that offshore developers, operators, and regulatory bodies may draw from to improve offshore site selection and operational efficiency. Here, we provide a brief synopsis of geologic carbon storage efforts to date, an overview of the engineered and natural systems involved in offshore geologic carbon storage, and a review of publicly available, open-source, offshore and/or carbon storage related data- and science-driven tools developed by 2010 or later that are suitable for screening and assessing regions for offshore geologic carbon storage.

artificial intelligence↗
DOE OSTI2021

A vortex sheet based analytical model of the curled wake behind yawed wind turbines

Motivated by the need for compact descriptions of the evolution of non-classical wakes behind yawed wind turbines, we develop an analytical model to predict the shape of curled wakes. Interest in such modelling arises due to the potential of wake steering as a strategy for mitigating power reduction and unsteady loading of downstream turbines in wind farms. We first estimate the distribution of the shed vorticity at the wake edge due to both yaw offset and rotating blades. By considering the wake edge as an ideally thin vortex sheet, we describe its evolution in time moving with the flow. Vortex sheet equations are solved using a power series expansion method, and an approximate solution for the wake shape is obtained. The vortex sheet time evolution is then mapped into a spatial evolution by using a convection velocity. Apart from the wake shape, the lateral deflection of the wake including ground effects is modelled. Our results show that there exists a universal solution for the shape of curled wakes if suitable dimensionless variables are employed. For the case of turbulent boundary layer inflow, the decay of vortex sheet circulation due to turbulent diffusion is included. Finally, we modify the Gaussian wake model by incorporating the predicted shape and deflection of the curled wake, so that we can calculate the wake profiles behind yawed turbines. Model predictions are validated against large-eddy simulations and laboratory experiments for turbines with various operating conditions.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2026

Robust and High Throughput Tools for Characterization of Wet Cellulosic Biogas Feedstocks and Co-Products: Cooperative Research and Development Final Report, CRADA Number CRD-21-21472

This project will develop standardized analytical procedures to support understanding of the chemical composition of wet manure feedstocks, and the digestate formed following anaerobic digestion of organic waste to produce renewable natural gas (RNG). The developed methods will be validated to improve component mass closure, eliminate ambiguities around key constituents, and document the precision and accuracy of the methods. Following validation of the analytical laboratory methods for characterization of wet organic waste feedstocks and digestate, NLR will use the same samples to build and validate near-infrared (NIR) models for high throughput analysis, potentially deployable at commercial facilities, enabling increased production of low carbon intensity RNG from wet organic wastes.

09 BIOMASS FUELS↗
DOE OSTI2020

An Integrated Platform for Collaborative Data Analytics

While collaboration among data scientists is a key to organizational productivity, data analysts face significant barriers to achieving this end, including data sharing, accessing and configuring the required computational environment, and a unified method of sharing knowledge. Each of these barriers to collaboration is related to the fundamental question of knowledge management “how can organizations use knowledge more effectively?”. In this paper, we consider the problem of knowledge management in collaborative data analytics and present ShareAL, an integrated knowledge management platform, as a solution to that problem. The ShareAL platform consists of three core components: a full stack web application, a dashboard for analyzing streaming data and a High Performance Computing (HPC) cluster for performing real time analysis. Prior research has not applied knowledge management to collaborative analytics or developed a platform with the same capabilities as ShareAL. ShareAL overcomes the barriers data scientists face to collaboration by providing intuitive sharing of data and analytics via the web application, a shared computing environment via the HPC cluster and knowledge sharing and collaboration via a real time messaging application.

Oesch, T↗
DOE OSTI2022

GradientGraph

Under this SBIR Phase II, Reservoir Labs has developed G2 Analytics, a new technology that allows network operators to analyze bottleneck and flow performance with high precision. G2 delivers a new analytical approach and framework to resolve a variety of key problems found in modern communication networks, including: traffic engineering, routing, flow scheduling, network design, capacity planning, resiliency analysis, network slicing, or service level agreement (SLA) management, among others. G2 leverages the bottleneck structure of congestion-controlled communication networks, a recent mathematical discovery by the Reservoir team [RL19b, RL20a, RL20b, RL21a]. Bottleneck structures reveal how perturbations on flows and links propagate through the network, providing an analytical framework to measure (qualitatively and quantitatively) the ripple effects induced as they traverse the network. Leveraging the mathematics of bottleneck structures, Reservoir Labs is developing the G2 technology to provide network operators with a framework to design, optimize and troubleshoot network performance. This delivery includes the G2 software stack.

Yellamraju, Sruthi↗
DOE OSTI2021

Critical Roles of Information, Analysis, Research, and Operations in the Cyber Realm

PNNL has developed an adaptive cyber integration framework (ACIF) to facilitate the timely sharing of cyber threat information along with the advancement of situational awareness tools to enhance protection against and respond to critical infrastructure cybersecurity threats. The ACIF comprises components implemented iteratively to achieve research and mission goals. The ACIF components include data generation technologies, analytic tools development and maturation, data enrichment and fusion, trust building with stakeholders, investigative research, analytic rigor, production, and dissemination. Each component, its importance to the ACIF, and how they can be adopted and applied across other information-sharing sectors and domains are discussed in this paper.

Cyber Threat Intelligence, Cyber Security, Data En↗
DOE OSTI2020

Mixed-strategy approach to band-edge analysis and modeling in semiconductors

In semiconductor physics, the Urbach rule describes an exponential dependence of the UV-Vis absorption coefficient on photon energy at the band edge. The rule is so ubiquitously applied that a single common mechanism, variability in the local potential, is generally considered to be the cause of exponential band edges. Variability in the local potential may be caused by alloy inhomogeneity, structural defects, impurities, thermal excitation, or any other mechanism that interrupts ideal crystallinity and creates disorder. A crucial point of the Urbach rule is its development for band edges which approach linearity when plotted on a logarithmic scale. The rule was not developed in the context of nonlinear band edges with complex fine structure. Even so, this model has been applied extensively to band edges with such features. Using differential analysis and deconvolution, we develop an analytical technique capable of separating the components responsible for the fine structure observed. This approach reduces challenging signal-artifact convolution problems to a simple linear combination. Furthermore, we develop a model that can be applied to the nonlinear band-edge signals extracted. In this work, we show that in the limiting case of linearity, our model is consistent with and produces the same results as the linear Urbach model. Our model is capable of bypassing “tailing” as an indirect measure of the spread in the local potential and gives a direct measure of this quantity.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗