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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 91 records · Page 5
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

An Applied Strategy for Using Empirical and Hybrid Models in Online Monitoring

The monitoring of plant equipment for failure prediction is one of the key contributors to operation and maintenance (O&M) costs for a nuclear power plant (NPP) because O&M monitoring depends on labor-intensive activities that are required to meet high equipment reliability standards. These activities rely primarily on humans for information gathering, condition diagnosis, and predictive analysis. Online monitoring aims to automate these activities by relying on sensors to replace human information gathering and machine learning to replace human analysis and decision making. To facilitate automated monitoring, a systematic strategy for anomaly detection is needed to optimally use the available sensor data, empirical models, and physics-supported models. This strategy is essential to provide credible reasoning on why and when an empirical (i.e., purely data-driven) versus hybrid (i.e., physics-supported) approach should be used and to determine the ideal mix of these two approaches for a defined anomaly detection scope. The extant methods usually adopt an ad hoc trial-and-error approach that, in addition to being time-consuming and costly, is also highly subjective; it is impacted by the background and the skill set of the personnel making the decisions. Thus, such an approach cannot guarantee an optimum outcome. This represents the motivation of the current research effort, which is focused on devising a scientifically supported strategy for the optimum selection of anomaly detection methods. This report presents a detailed assessment of the main anomaly detection techniques within the empirical or hybrid method streams. Empirical methods include pattern, statistical, and causal inference. Hybrid methods include the use of physics models to train and test data methods, reduce data dimensionality, reduce data-model complexity, augment data, and reduce empirical uncertainty; hybrid methods also include the use of data to tune physics models. The listed techniques within these two streams represent the vast majority of techniques performed for anomaly detection. Using the techniques as outcomes, a strategy was developed to enable a systematic decision-making process to lead to one of these techniques. The strategy is driven by key decision points related to data relevance, simple modeling feasibility, data inference, physics-modeling value, data dimensionality, physics knowledge, method of validation, performance, data availability and suitability for training and testing, cause-effect, entropy inference, and model fitting. Each of these decision points in the strategy is explained in detail in this report with examples, along with the scientific basis behind the decisions and outcomes in common and simplified terminology. The strategy is developed for use by any NPP staff with basic engineering or science knowledge. A user-friendly graphical state flow diagram was also developed as a visual presentation of the strategy. The strategy was tested and demonstrated through two pilot projects for the application of anomaly detection at an NPP. Each pilot had two use cases: an initial case in which certain decisions were made that resulted in one or more empirical techniques and a revised use case where one or more key decisions were modified resulting in using a set of hybrid methods.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2021

Current status of biogas upgrading for direct biomethane use: A review

Anaerobic digestion produces biogas, a mixture of CH 4 and CO 2 , where CH 4 is a low cost, environmentally friendly, and renewable energy source. The application of biogas production is increasing rapidly as a means of reducing the pollution impact of organic biomasses. However, biogas contains unwanted elements such as hydrogen sulfide, carbon monoxide, siloxanes, and carbon dioxide. To remove these elements, several biogas upgrading technologies like water scrubbing, amine scrubbing, pressure swing adsorption, and membrane separation have been developed and are being used at various commercial scales. Problems with these methods are high energy consumption, the use of expensive chemicals, and high operating cost. Therefore, a major effort is currently underway to improve the design of existing methods as well as developing innovative new upgrading technologies such as cryogenic separation and biological upgrading. Here, this review intends to provide a comprehensive overview of the limitations with the existing upgrading technologies along with recent advances in physical, chemical, and biological biogas upgrading technologies (e.g., pressure swing adsorption, membrane separation, biochar adsorption and CO 2 conversion by biological organisms) and further into possible future solutions, such as hybrid systems. Comparative studies of process complexities and associated economic concerns are also provided, and future perspectives that may facilitate research into sustainable biogas upgrading technologies are discussed, focusing in particular on cryogenic separation, novel biological techniques, biochar based upgrading and hybrid technologies incorporating two or more different methods seamlessly integrated.

09 BIOMASS FUELS↗
DOE OSTI2026

Classical-Quantum Algorithm for Solving Stochastic Programs

Stochastic programming provides a rigorous mathematical framework for making decisions under uncertainty in a risk-aware manner. Two-stage stochastic programming is, perhaps, the simplest form of this framework. Here the first-stage variables represent decisions that must be made "here and now" in the face of uncertainty, while the second-stage variables are decisions made after uncertain events. However, the broad adoption of stochastic programming has been hindered by computational challenges caused by the two-stage stochastic programming formulation which requires solving an ensemble of optimization problems. Using quantum amplitude estimation (QAE), quantum computers have shown the theoretic ability to compute expectations with Monte-Carlo methods with quadratically fewer samples than classical methods. In this work, we present a quantum algorithm for computing the expectation term using QAE for given first-stage decisions. Further, we detail methods of computing gradient information from the quantum calculation enabling the application of classical gradient-based optimization techniques. The result is a classical-quantum hybrid method of solving two-stage stochastic programs. These techniques are demonstrated with computational experiments based an engineering optimization problem.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Scalable Hybrid Classification-Regression Solution for High-Frequency Nonintrusive Load Monitoring

Residential buildings with the ability to monitor and control their net-load (sum of load and generation) can provide valuable flexibility to power grid operators. We present a novel multiclass nonintrusive load monitoring (NILM) approach that enables effective net-load monitoring capabilities at high-frequency with minimal additional equipment and cost. The proposed machine learning based solution provides accurate multiclass state predictions while operating at a faster timescale (able to provide a prediction for each 60- Hz ac cycle used in US power grid) without relying on event-detection techniques. We also introduce an innovative hybrid classification-regression method that allows for the prediction of not only load on/off states but also individual load operating power levels. A test bed with eight residential appliances is used for validating the NILM approach. Results show that the overall method has high accuracy, good scaling and generalization properties.

feature extraction↗
DOE OSTI2022

Hybrid interferometric diagnostic for the refractive index measurement across nearly discrete shock waves

The measurement of high temperature gas properties is key for characterizing high speed flows. Nearly discrete changes in density across shock waves, in particular, are difficult to resolve through traditional fringe-counting interferometric methods. Existing techniques for estimating large fringe jumps are either resolution limited or require specialized window configurations. In this Letter, we describe a unique hybrid interferometric technique that combines narrowband fringes for high resolution and broadband fringes as an absolute reference to measure changes in refractive index with a resolution of up to 7 × 10 −8 across nearly discrete index changes of up to 1.5 × 10 −4 . By capturing fringes with an ultrahigh-speed camera, the refractive index changes across discrete shock fronts can be estimated inside a shock tube with high accuracy and time resolution. First, a novel hybrid calibration method for tracking finite fringes is discussed. Next, this technique is used to measure the post-initial-shock refractive indices for Mach 2.7 to 4.2 flows (pressures from 90.4 to 228.4 kPa). Results are then compared with theoretical values showing agreement within 2%.

Wang, Gwendolyn T. (ORCID:000000016271893X)↗
DOE OSTI2022

Evolution of Programmatic Asset Lifecycle Planning at MESA

Early on in 2018 Sandia recognized the Microsystems Engineering, Science and Applications (MESA) Programmatic Asset Lifecycle Planning capability to be unpredictable, inconsistent, reactive, and unable to provide strong linkage to the sponsor's needs. The impetus for this report is to share learnings from MESA's journey towards maturing this capability. This report describes re-building the foundational elements of MESA's Programmatic Asset Lifecycle Planning capability using a risk-based, Multi-Criteria Decision Analysis (MCDA) approach. To begin, MESA's decades-old Piano Chart + Ad Hoc Hybrid Methodology is described with a narrative of its strengths and weaknesses. Then its replacement, the MCDA /Analytical Hierarchy Process, is introduced with a discussion of its strengths and weaknesses. To generate a realistic Programmatic Asset Lifecycle Planning budget outlook, MESA used its rolling 20-year Extended Life Program Plan (MELPP) as a baseline. The new MCDA risk-based prioritization methodology implements DOE/NNSA guidelines for prioritization of DOE activities and provides a reliable, structured framework for combining expert judgement and stakeholder preferences according to an established scientific technique. An in-house Hybrid Decision Support System (HDSS) software application was developed to facilitate production of several key deliverables. The application enables analysis of the prioritization decisions with charts to display and provide linkage of MESA's funding requests to the stakeholders' priorities, strategic objectives, nuclear deterrence programs, MESA priorities, and much more.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2021

Equation of State Measurements on Iron Near the Melting Curve at Planetary Core Conditions by Shock and Ramp Compressions

The outer core of the Earth is composed primarily of liquid iron, and the inner core boundary is governed by the intersection of the melt line and the geotherm. While there are many studies on the thermodynamic equation of state for solid iron, the equation of state of liquid iron is relatively unexplored. In this work, we use dynamic compression to diagnose the high-pressure liquid equation of state of iron by utilizing the shock-ramp capability at Sandia National Laboratories’ Z-Machine. This technique enables measurements of material states off the Hugoniot by initially shocking samples and subsequently driving a further, shockless compression. Planetary studies benefit greatly from isentropic, off-Hugoniot experiments since they can cover pressure-temperature (P-T) conditions that are close to adiabatic profiles found in planetary interiors. We used this method to drive iron to P-T conditions similar to those of the Earth’s outer-inner core boundary, along an elevated-temperature isentrope in the liquid from 275 GPa to 400 GPa. We derive the equation of state using a hybrid backward integration – forward Lagrangian technique on particle velocity traces to determine the pressure-density history of the sample. Our results are in excellent agreement with SESAME 92141, a previously published equation of state table. With our data and previous experimental data on liquid iron we provide new information on the iron melting line and derive new parameters for a Vinet-based equation of state. The table and our parameterized equation of state are applied to provide an updated means of modeling the pressure, mass, and density of liquid iron cores in exoplanetary interiors.

58 GEOSCIENCES↗
DOE OSTI2021

Predicting concrete compressive strength using hybrid ensembling of surrogate machine learning models

This study aims to implement a hybrid ensemble surrogate machine learning technique in predicting the compressive strength (CS) of concrete, an important parameter used for durability design and service life prediction of concrete structures in civil engineering projects. For this purpose, an experimental database consisting of 1030 records has been compiled from the machine learning repository of the University of California, Irvine. The database was used to train and validate four conventional machine learning (CML) models, namely Artificial Neural Network (ANN), Linear and Non-Linear Multivariate Adaptive Regression Splines (MARS-L and MARS-C), Gaussian Process Regression (GPR), and Minimax Probability Machine Regression (MPMR). Subsequently, the predicted outputs of CML models were combined and trained using ANN to construct the Hybrid Ensemble Model (HENSM). It is observed that the proposed HENSM produces higher predictive accuracy compared to the CML models used in the present study. The predictive performance of all models for CS prediction was compared using the testing dataset and it is found that the HENSM model attained the highest predictive accuracy in both phases. Based on the experimental results, the newly constructed HENSM model is very potential to be a new alternative in handling the overfitting issues of CML models and hence, can be used to predict the concrete CS, including the design of less polluting and more sustainable concrete constructions.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Nonlinear optics from hybrid dispersive orbits

In this paper we expand the technique of characterizing nonlinear optics from off-energy closed orbits (NOECO) to cover harmonic sextupoles in storage rings. The existing NOECO technique has been successfully used to correct the chromatic sextupole errors on the MAX-IV machine [Olsson et al., Phys. Rev. Accel. Beams 23, 102803], however, it does not account for harmonic sextupoles, which are still widely used on many other machines. By generating vertical dispersion with chromatic skew quadrupoles, a measurable chromatic function dependence on harmonic sextupoles can be observed on hybrid dispersive orbits. Proof of concept of this technique was accomplished by simulations and beam measurements on the National Synchrotron Light Source II (NSLS-II) storage ring.

43 PARTICLE ACCELERATORS↗
DOE OSTI2020

Quantum computing based hybrid solution strategies for large-scale discrete-continuous optimization problems

Quantum computing (QC) has gained popularity due to its unique capabilities that are quite different from that of classical computers in terms of speed and methods of operations. This paper proposes hybrid models and methods that effectively leverage the complementary strengths of deterministic algorithms and QC techniques to overcome combinatorial complexity for solving large-scale mixed-integer programming problems. Four applications, namely the molecular conformation problem, job-shop scheduling problem, manufacturing cell formation problem, and the vehicle routing problem, are specifically addressed. Large-scale instances of these application problems across multiple scales ranging from molecular design to logistics optimization are computationally challenging for deterministic optimization algorithms on classical computers. To address the computational challenges, hybrid QC-based algorithms are proposed and extensive computational experimental results are presented to demonstrate their applicability and efficiency. The proposed QC-based solution strategies enjoy high computational efficiency in terms of solution quality and computation time, by utilizing the unique features of both classical and quantum computers.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

A Hybrid Anomaly Detection Approach for Obfuscated Malware

With the rapid evolution of malicious software, cyber threats have become increasingly sophisticated, employing advanced obfuscation techniques to evade traditional detection methods. This study presents a hybrid anomaly detection approach applied to obfuscated malware. Even though there is a large body of research in this field, existing malware detection techniques have some drawbacks, such as requiring large amounts of data, trustworthiness (imprecise results) of algorithms, and advanced obfuscation. To overcome these challenges, there is a need to employ solid and efficient techniques for malware detection. This paper proposes a hybrid approach, combining an autoencoder with traditional machine-learning methods to create an efficient malware detection framework. We used the malware memory dataset (MalMemAnalysis-2022) to evaluate this framework. The results indicate that our proposed approach can detect obfuscated malware when a deep autoencoder used for feature learning is combined with logistic regression, and it is extremely fast with an Accuracy, Detection Rate (DR), Matthew Correlation Coefficient(MCC), and Statistical Parity Difference

malware detection, Hybrid Anomly Detection, Obfusc↗
DOE OSTI2022

Integrating a ponderomotive guiding center algorithm into a quasi-static particle-in-cell code based on azimuthal mode decomposition

High fidelity modeling of plasma based acceleration (PBA) requires the use of three dimensional, fully nonlinear, and kinetic descriptions based on the particle-in-cell (PIC) method. In PBA an intense particle beam or laser (driver) propagates through a tenuous plasma whereby it excites a plasma wave wake. Three-dimensional PIC algorithms based on the quasi-static approximation (QSA) have been successfully applied to efficiently model the interaction between relativistic charged particle beams and plasma. In a QSA PIC algorithm, the plasma response to a charged particle beam or laser driver is calculated based on forces from the driver and self-consistent forces from the QSA form of Maxwell's equations. These fields are then used to advance the charged particle beam or laser forward by a large time step. Since the time step is not limited by the regular Courant-Friedrichs-Lewy (CFL) condition that constrains a standard 3D fully electromagnetic PIC code, a 3D QSA PIC code can achieve orders of magnitude speedup in performance. Recently, a new hybrid QSA PIC algorithm that combines another speedup technique known as an azimuthal Fourier decomposition has been proposed and implemented. This hybrid algorithm decomposes the electromagnetic fields, charge and current density into azimuthal harmonics and only the Fourier coefficients need to be updated, which can reduce the algorithmic complexity of a 3D code to that of a 2D code. Modeling the laser-plasma interaction in a full 3D electromagnetic PIC algorithm is very computationally expensive due the enormous disparity of physical scales to be resolved. In the QSA the laser is modeled using the ponderomotive guiding center (PGC) approach. We describe how to implement a PGC algorithm compatible for the QSA PIC algorithms based on the azimuthal mode expansion. Here this algorithm permits time steps orders of magnitude larger than the cell size and it can be asynchronously parallelized. Details on how this is implemented into the QSA PIC code that utilizes an azimuthal mode expansion, QPAD, are also described. Benchmarks and comparisons between a fully 3D explicit PIC code (OSIRIS), as well as a few examples related to laser wakefield acceleration, are presented.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Importance of Superstructure in Stabilizing Oxygen Redox in P3‐Na 0.67 Li 0.2 Mn 0.8 O 2

Abstract Activation of oxygen redox represents a promising strategy to enhance the energy density of positive electrode materials in both lithium and sodium‐ion batteries. However, the large voltage hysteresis associated with oxidation of oxygen anions during the first charge represents a significant challenge. Here, P3‐type Na 0.67 Li 0.2 Mn 0.8 O 2 is reinvestigated and a ribbon superlattice is identified for the first time in P3‐type materials. The ribbon superstructure is maintained over cycling with very minor unit cell volume changes in the bulk while Li ions migrate reversibly between the transition metal and Na layers at the atomic scale. In addition, a range of spectroscopic techniques reveal that a strongly hybridized Mn 3d–O 2p favors ligand‐to‐metal charge transfer, also described as a reductive coupling mechanism, to stabilize reversible oxygen redox. By preparing materials under three different synthetic conditions, the degree of ordering between Li and Mn is varied. The sample with the maximum cation ordering delivers the largest capacity regardless of the voltage windows applied. These findings highlight the importance of cationic ordering in the transition metal layers, which can be tuned by synthetic control to enhance anionic redox and hence energy density in rechargeable batteries.

36 MATERIALS SCIENCE↗
DOE OSTI2021

NanoSIP: NanoSIMS Applications for Microbial Biology

High-resolution imaging with secondary ion mass spectrometry (nanoSIMS) has become a standard method in systems biology and environmental biogeochemistry and is broadly used to decipher ecophysiological traits of environmental microorganisms, metabolic processes in plant and animal tissues, and cross-kingdom symbioses. When combined with stable isotope-labeling—an approach we refer to as nanoSIP—nanoSIMS imaging offers a distinctive means to quantify net assimilation rates and stoichiometry of individual cell-sized particles in both low- and high-complexity environments. While the majority of nanoSIP studies in environmental and microbial biology have focused on nitrogen and carbon metabolism (using 15 N and 13 C tracers), multiple advances have pushed the capabilities of this approach in the past decade. The development of a high-brightness oxygen ion source has enabled high-resolution metal analyses that are easier to perform, allowing quantification of metal distribution in cells and environmental particles. New preparation methods, tools for automated data extraction from large data sets, and analytical approaches that push the limits of sensitivity and spatial resolution have allowed for more robust characterization of populations ranging from marine archaea to fungi and viruses. Further, NanoSIMS studies continue to be enhanced by correlation with orthogonal imaging and ‘omics approaches; when linked to molecular visualization methods, such as in situ hybridization and antibody labeling, these techniques enable in situ function to be linked to microbial identity and gene expression. Here we present an updated description of the primary materials, methods, and calculations used for nanoSIP, with an emphasis on recent advances in nanoSIMS applications, key methodological steps, and potential pitfalls.

07 ISOTOPE AND RADIATION SOURCES↗
DOE OSTI2020

Fabrication and Radio Frequency Properties of 3-GHz SRF Cavities Coated with MgB 2

Magnesium diboride (MgB 2 ) is considered a potential material for superconducting radio frequency cavities. MgB 2 coated Cu cavity will allow for a higher operational temperature than a bulk Nb cavity because of the higher transition temperature of MgB 2 and the high thermal conductivity of Cu. Using the hybrid physical chemical vapor deposition technique, MgB 2 coatings were successfully achieved on the inner wall of 3-GHz Cu cavities. The surface and superconducting properties of the coatings were characterized using small samples on Cu plugs mounted at different locations of mock cavities. RF measurement of a MgB 2 coated single-cell 3-GHz test cavity was carried out and it showed superconducting transition at 36 K. Here, the quality factor of this test cavity was lower than expected due to poor connectivity and inclusion of Mg-Cu alloy in the MgB 2 coating.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2025

Multi-physics melt pool modeling and process optimization for laser direct energy deposition of Nb-based refractory C103: Defect formation, geometric precision, and process mapping

Recent developments in additive manufacturing (AM) technology have reignited interest in the fabrication of the Nb-based refractory C103 alloy offering solutions to the challenges posed by traditional manufacturing methods. However, the limited numerical and experimental studies on laser direct energy deposition (DED) of C103 have hindered the understanding of the relationships between process parameters and build quality. This has made it challenging to consistently produce parts with the desired quality and microstructure suitable for critical applications. In this study, we focus on optimizing the laser DED process for C103 by employing a hybrid approach that combines experimental techniques and computational fluid dynamics (CFD). This approach facilitates the development of process maps for defect detection and geometric precision. To achieve this, multi-layer C103 samples were fabricated using laser DED under various process parameters, enabling the creation of a process map for defect detection. Additionally, a multi-physics, multiphase simulation framework was developed within a high-performance computing (HPC) environment to establish process maps for geometric precision. Using these process maps, printability windows were identified for achieving both the desired geometric accuracy and defect-free prints. It was observed that prints with a power-to-velocity (P/V) ratio close to unity resulted in defect-free outcomes. This study provides a foundation for reducing design lead time and rejected parts, ultimately optimizing the laser DED process for C103.

Defect formation and geometric precision↗
DOE OSTI2020

Phenomenological mechanisms of hybrid organic–inorganic perovskite thin film deposition by RIR-MAPLE

Resonant infrared, matrix-assisted pulsed laser evaporation (RIR-MAPLE) is a promising technique for the physical vapor deposition of hybrid organic–inorganic perovskites. The approach already has been used to deposit both three-dimensional and two-dimensional hybrid perovskites with material quality comparable to those synthesized by solution processing. However, the phenomenological mechanisms of hybrid perovskite film formation by RIR-MAPLE have not been articulated. Therefore, this work presents a careful investigation of film formation mechanisms of three-dimensional methylammonium lead halide perovskites by considering the temporal evolution of morphology, crystallinity, and optical properties of films deposited by RIR-MAPLE.

Barraza, E. T. (ORCID:0000000328681496)↗
DOE OSTI2021

Combined microwave and laser Rayleigh scattering diagnostics for pin-to-pin nanosecond discharges

In this work, the temporal decay of electrons produced by an atmospheric pin-to-pin nanosecond discharge operating in the spark regime was measured via a combination of microwave Rayleigh scattering (MRS) and laser Rayleigh scattering (LRS). Due to the initial energy deposition of the nanosecond pulse, a variation in the local gas density occurs on the timescale of electron decay. Thus, the assumption of a constant collisional frequency is no longer applicable when electron number data are extracted from MRS measurements. Furthermore, to recalibrate MRS measurements throughout the electron decay period, temporally resolved LRS measurements of the local gas density were performed over the event duration. The local gas density was calculated to be 30% of the ambient level during the later stages of electron decay, and it recovers at about 1 ms after discharge. A shock front traveling approximately 500 m/s was additionally observed. Coupled with plasma volume calibration via temporally resolved intensified charge-coupled device imaging, the corrected decay curves of the electron number and electron number density are presented with a measured peak electron number density of 4.5 × 1015 cm -3 and a decay rate of ~(0.1–0.35) × 10 7 s -1 . A hybrid MRS and LRS diagnostic technique can be applied for a broad spectrum of atmospheric-pressure microplasmas where a variation in the gas number density is expected due to energy deposition in the discharge.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗