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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 1,369 records · Page 76
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Sensor impact evaluation and verification for fault detection and diagnostics in building energy systems: A review

Sensors are the key information source for fault detection and diagnostics (FDD) in buildings. However, sensors are often not properly designed, installed, calibrated, located, and maintained, which negatively impacts FDD performance. Several sensor-related FDD topics have been widely studied, covering a wide range of fault types and applications. However, it is difficult to get a clear picture of the technical development of sensor-related topics in FDD. A systematic review of sensor topics is needed to summarize the existing research in a logical way, draw conclusions on the current development, and predict the future development of sensors in building FDD. To address this gap, we conducted a comprehensive literature review of more than 100 FDD-sensor-related papers. In this article, we subdivide the FDD tasks into building-level, system-level, and component-level FDD, and review sensor-related topics in each category. Our major conclusions are: (a) current data-driven FDD research focuses more on FDD algorithms than sensors, (b) sensor “hardware” research topics are less studied than sensor “software” topics, (c) very few papers focus on sensor engineering as an integral aspect of FDD development, and (d) some important sensor topics, such as sensor cost-effectiveness and sensor schema/layout/location, are not well studied. Finally, we discuss the need for a systematic framework of FDD sensors and models to integrate sensor design/selection, sensor data analysis/mining, feature selection, physics-based or data-driven algorithm development, sensor fault detection, sensor calibration, and sensor maintenance. Finally, expert interviews are conducted to validate the above findings and conclusions.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2025

Beyond Epitaxy: Ion Implantation as a Tool for Orbital Engineering

Manipulating electronic orbital states in quantum materials provides a powerful means of controlling their physical properties and technological functionality. Here, we demonstrate that orbital populations in strongly correlated oxide thin films can be continuously and reversibly tuned through postsynthesis He ion implantation. Using LaNiO 3 as a model system, we show that the orbital preference can be systematically adjusted from favoring in-plane d x 2 –y 2 occupation toward out-of-plane d z 2 states through precise control of ion fluence. Unlike conventional heteroepitaxial approaches that lock in orbital configurations during growth, this strain-doping technique enables continuous orbital tuning and the selective modification of specific film regions after device fabrication. We demonstrate the practical impact of this control by achieving a 7-fold enhancement in oxygen reduction reaction catalysis. This work establishes ion implantation as a powerful approach for orbital engineering that complements existing synthesis-based strategies while offering unique advantages for both basic research and device development.

Doping↗
DOE OSTI2025

Solving sparse finite element problems on neuromorphic hardware

The finite element method (FEM) is one of the most important and ubiquitous numerical methods for solving partial differential equations (PDEs) on computers for scientific and engineering discovery. Applying the FEM to larger and more detailed scientific models has driven advances in high-performance computing for decades. Here we demonstrate that scalable spiking neuromorphic hardware can directly implement the FEM by constructing a spiking neural network that solves the large, sparse, linear systems of equations at the core of the FEM. We show that for the Poisson equation, a fundamental PDE in science and engineering, our neural circuit achieves meaningful levels of numerical accuracy and close to ideal scaling on modern, inherently parallel and energy-efficient neuromorphic hardware, specifically Intel’s Loihi 2 neuromorphic platform. We illustrate extensions to irregular mesh geometries in both two and three dimensions as well as other PDEs such as linear elasticity. Our spiking neural network is constructed from a recurrent network model of the brain’s motor cortex and, in contrast to black-box deep artificial neural network-based methods for PDEs, directly translates the well-understood and trusted mathematics of the FEM to a natively spiking neuromorphic algorithm.

Applied mathematics↗
DOE OSTI2024

Application of functional genomics for domestication of novel non-model microbes

Abstract With the expansion of domesticated microbes producing biomaterials and chemicals to support a growing circular bioeconomy, the variety of waste and sustainable substrates that can support microbial growth and production will also continue to expand. The diversity of these microbes also requires a range of compatible genetic tools to engineer improved robustness and economic viability. As we still do not fully understand the function of many genes in even highly studied model microbes, engineering improved microbial performance requires introducing genome-scale genetic modifications followed by screening or selecting mutants that enhance growth under prohibitive conditions encountered during production. These approaches include adaptive laboratory evolution, random or directed mutagenesis, transposon-mediated gene disruption, or CRISPR interference (CRISPRi). Although any of these approaches may be applicable for identifying engineering targets, here we focus on using CRISPRi to reduce the time required to engineer more robust microbes for industrial applications. One-Sentence Summary The development of genome scale CRISPR-based libraries in new microbes enables discovery of genetic factors linked to desired traits for engineering more robust microbial systems.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2019

Summary of Grizzly Development for Advanced Reactor Structural Materials

This summary report contains an overview of work performed during Fiscal Year 2019 under the Nuclear Engineering Advanced Modeling and Simulation (NEAMS) Fuels Product Line work package entitled "MS-19IN020106 - GRIZZLY - INL". Grizzly is a code based on the MOOSE framework that has been under development for several years funded by the US Department of Energy’s Light Water Reactor Sustainability (LWRS) program. Prior to 2019, Grizzly development was focused on development of capabilities needed to simulate the effects of aging mechanisms in light water reactor (LWR) components. The goal of this LWR-focused work has been to develop tools that can be used to assess the ability of existing LWRs to perform safety in long term operation (LTO) scenarios. This LWR-focused work has primarily focused on the effects of reactor pressure vessel (RPV) embrittlement and aging of reinforced concrete structures. Grizzly development to support LWR degradation needs is still ongoing, but starting in 2019, Grizzly’s capabilities are being expanded to also meet growing needs for a tool to address structural component integrity and degradation in advanced reactors. A simulation capability for advanced reactor components has many of the same basic requirements that one for LWRs does, such as the ability to solve coupled systems of partial differential equations that arise from the fact that many of the problems of interest involve multiple coupled physics. The materials issues also inherently involve multiple length scales, with the response at the engineering scale being strongly affected by mechanisms occurring at atomistic and mesoscopic scales.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2021

Performance Comparison of Clipping Detection Techniques in AC Power Time Series: Preprint

In this research, a variety of methods were developed to detect clipping periods in AC power time series. Novel logic-based and machine learning (ML) approaches were developed to classify time series values as clipping or non-clipping. These approaches were compared to the RdTools method for detecting clipping periods. The logic-based and ML XGBoost approaches achieved F-scores of 82.6 and 74.4, respectively, as compared to the current RdTools approach (F-score of 56.4), indicating a significant improvement at detecting clipping periods. Additionally, the effects of using more accurate clipping filters when evaluating system degradation rates will be assessed in our final manuscript.

clipping↗
DOE OSTI2010

PROGRESS TOWARDS MODELING OF FISCHER TROPSCH SYNTHESIS IN A SLURRY BUBBLE COLUMN REACTOR

The Hybrid Energy Systems Testing (HYTEST) Laboratory is being established at the Idaho National Laboratory to develop and test hybrid energy systems with the principal objective to safeguard U.S. Energy Security by reducing dependence on foreign petroleum. A central component of the HYTEST is the slurry bubble column reactor (SBCR) in which the gas-to-liquid reactions will be performed to synthesize transportation fuels using the Fischer Tropsch (FT) process. SBCRs are cylindrical vessels in which gaseous reactants (for example, synthesis gas or syngas) is sparged into a slurry of liquid reaction products and finely dispersed catalyst particles. The catalyst particles are suspended in the slurry by the rising gas bubbles and serve to promote the chemical reaction that converts syngas to a spectrum of longer chain hydrocarbon products, which can be upgraded to gasoline, diesel or jet fuel. These SBCRs operate in the churn-turbulent flow regime which is characterized by complex hydrodynamics, coupled with reacting flow chemistry and heat transfer, that effect reactor performance. The purpose of this work is to develop a computational multiphase fluid dynamic (CMFD) model to aid in understanding the physico-chemical processes occurring in the SBCR. Our team is developing a robust methodology to couple reaction kinetics and mass transfer into a four-field model (consisting of the bulk liquid, small bubbles, large bubbles and solid catalyst particles) that includes twelve species: (1) CO reactant, (2) H2 reactant, (3) hydrocarbon product, and (4) H2O product in small bubbles, large bubbles, and the bulk fluid. Properties of the hydrocarbon product were specified by vapor liquid equilibrium calculations. The absorption and kinetic models, specifically changes in species concentrations, have been incorporated into the mass continuity equation. The reaction rate is determined based on the macrokinetic model for a cobalt catalyst developed by Yates and Satterfield [1]. The model includes heat generation due to the exothermic chemical reaction, as well as heat removal from a constant temperature heat exchanger. Results of the CMFD simulations (similar to those shown in Figure 1) will be presented.

10 SYNTHETIC FUELS↗
DOE OSTI2023

Concepts Relevant for the Kinetic Analysis of Reversible Reaction Systems

The net rate of a reversible chemical reaction is the difference between unidirectional rates of traversal along forward and reverse reaction paths. In a multistep reaction sequence, the forward and reverse trajectories, in general, are not the microscopic reverse of one another; rather, each unidirectional route is comprised of distinct rate-controlling steps, intermediates, and transition states. Consequently, traditional descriptors of rate (e.g., reaction orders) do not reflect intrinsic kinetic information but instead conflate unidirectional contributions determined by (i) the microscopic occurrence of forward/reverse reactions (i.e., unidirectional kinetics) and (ii) the reversibility of reaction (i.e., nonequilibrium thermodynamics). This review aims to provide a comprehensive resource of analytical and conceptual tools which deconvolute the contributions of reaction kinetics and thermodynamics to disambiguate unidirectional reaction trajectories and precisely identify rate- and reversibility-controlling molecular species and steps in reversible reaction systems. The extrication of mechanistic and kinetic information from bidirectional reactions is accomplished through equation-based formalisms (e.g., De Donder relations) grounded in principles of thermodynamics and interpreted in the context of theories of chemical kinetics developed in the past 25 years. Here, the aggregate of mathematical formalisms detailed herein is general to thermochemical and electrochemical reactions and encapsulates a diverse body of scientific literature encompassing chemical physics, thermodynamics, chemical kinetics, catalysis, and kinetic modeling.

42 ENGINEERING↗
DOE OSTI2021

Deep Analysis Net with Causal Embedding for Coal-fired Power Plant Fault Detection and Diagnosis (DANCE4CFDD)

Fault detection and diagnosis is critical to power plant operation to ensure attaining high reliability while reducing operation cost. As more renewable power is introduced to the power grid, traditional fossil power plants take on the extra burden of excessive load cycling to compensate the generation variability from renewable power. Such load cycling will pose more reliability challenges to power plant operation. There are a number of challenges faced by today’s asset health management system in coal- fired (or gas) power plants: 1) high-dimensional nonlinear interaction among multiple time series measurements; 2) high measurement variance induced by operational conditions/modes; 3) variation among asset types and plant configurations; and 4) a small number of faulty events to learn from. To cope with these challenges, today’s fielded asset health management systems rely heavily on manual efforts from domain experts and hand-crafted features or rules based on domain knowledge. Despite its role in plant reliability, such a practice is costly and hinders its scalability and sustainability, particularly when a plant undergoes modifications. The objective of this project is to develop a novel end-to-end AI learning system that is trainable (i.e., the AI representation of a complex system behavior can be directly learned from properly labeled data) for accurate fault detection and root cause analysis. The ability to create a fault detection model directly from time series could alleviate the efforts associated with today’s asset management solution development. In the course of this project, we have achieved the following: Created an AI model development environment incorporating state-of-the-art neural network architectures for rapid model development and evaluation; Developed novel learning strategies for training of fault detection model; Developed special-purpose neural network architecture embedded with variable association graph aiming for better interpretability; Developed a learning strategy to leverage a small number of faulty events for enhanced fault detection capability; Conducted detailed experimental study based on public benchmark datasets and demonstrated the effectiveness of the proposed solution; and Validated the developed system with data from both a coal-fired plant boiler dynamic simulation model and real-world coal-fired power plant covering multiple asset and fault types. Overall, the project attained a technology readiness level of TRL 5 from TRL 2 at the beginning of the project.

20 FOSSIL-FUELED POWER PLANTS↗
DOE OSTI2016

Orbital Engineering in Nickelate Heterostructures Driven by Anisotropic Oxygen Hybridization rather than Orbital Energy Levels

We used resonant inelastic x-ray scattering to investigate the electronic origin of orbital polarization in nickelate heterostructures taking LaTiO 3 -LaNiO 3 -3×(LaAlO 3 ), a system with exceptionally large polarization, as a model system. Furthermore, we find that heterostructuring generates only minor changes in the Ni 3d orbital energy levels, contradicting the often-invoked picture in which changes in orbital energy levels generate orbital polarization. Instead, O K-edge x-ray absorption spectroscopy demonstrates that orbital polarization is caused by an anisotropic reconstruction of the oxygen ligand hole states. This also provides an explanation for the limited success of theoretical predictions based on tuning orbital energy levels and implies that future theories should focus on anisotropic hybridization as the most effective means to drive large changes in electronic structure and realize novel emergent phenomena.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2021

Heat Exchanger Design Optimization for Mitigation of Diesel Engine Exhaust Fouling

Abstract The design optimization of a diesel exhaust-coupled heat and mass exchanger that drives a 2.71 kW cooling capacity absorption heat pump is presented in this study. Fouling layer thermal resistance and pressure drops from single-tube experiments are used to develop a thermodynamic, heat transfer, and pressure drop model for the exhaust-coupled desorber. A parametric study is performed to select a desorber design that meets system performance while minimizing footprint. Experimental heat duties and pressure drops are within 10% and 3%, respectively, of the model predictions. Thus, large data sets from single-tube experiments with representative geometries are successful in accounting for fouling effects at the component level. Desorber design optimization based on this approach ensures continued heat pump performance after fouling. This study, along with the single-tube experiments, presents a systematic approach to design exhaust-coupled heat exchangers while considering the effects of fouling. These results are applicable for a wide range of waste-heat recovery applications, and this method can be extended to different geometries and operating conditions.

Energy & Fuels↗
DOE OSTI2021

A Transported Livengood–Wu Integral Model for Knock Prediction in Computational Fluid Dynamics Simulation

This work describes the development of a transported Livengood–Wu (L–W) integral model for computational fluid dynamics (CFD) simulation to predict autoignition and engine knock tendency. The currently employed L–W integral model considers both single-stage and two-stage ignition processes, thus can be generally applied to different fuels such as paraffin, olefin, aromatics, and alcohol. The model implementation is first validated in simulations of homogeneous charge compression ignition (HCCI) combustion for three different fuels, showing good accuracy in prediction of autoignition timing for fuels with either single-stage or two-stage ignition characteristics. Then, the L–W integral model is coupled with G-equation model to indicate end-gas autoignition and knock tendency in CFD simulations of a direct-injection spark-ignition engine. This modeling approach is about 10 times more efficient than the ones that based on detailed chemistry calculation and pressure oscillation analysis. Two fuels with same Research Octane Number (RON) but different octane sensitivity are studied, namely, Co-Optima alkylate and Co-Optima E30. Feed-forward neural network model in conjunction with multivariable minimization technique is used to generate fuel surrogates with targets of matched RON, octane sensitivity, and ethanol content. The CFD model is validated against experimental data in terms of pressure traces and heat release rate for both fuels under a wide range of operating conditions. The knock tendency—indicated by the fuel energy contained in the autoignited region—of the two fuels at different load conditions correlates well with the experimental results and the fuel octane sensitivity, implying the current knock modeling approach can capture the octane sensitivity effect and can be applied to further investigation on composition of octane sensitivity.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2017

Algorithms and analyses for stochastic optimization for turbofan noise reduction using parallel reduced-order modeling

Simulation-based optimization of acoustic liner design in a turbofan engine nacelle for noise reduction purposes can dramatically reduce the cost and time needed for experimental designs. Because uncertainties are inevitable in the design process, a stochastic optimization algorithm is posed based on the conditional value-at-risk measure so that an ideal acoustic liner impedance is determined that is robust in the presence of uncertainties. A parallel reduced-order modeling framework is developed that dramatically improves the computational efficiency of the stochastic optimization solver for a realistic nacelle geometry. The reduced stochastic optimization solver takes less than 500 s to execute. Additionally, well-posedness and finite element error analyses of the state system and optimization problem are provided.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Data Requirements for Application of Risk-Based Dynamic Contingency Analysis to Evaluate Hurricane Impact to Electrical Infrastructure in Puerto Rico

This paper presents a risk-based dynamic contingency analysis framework that was used to evaluate the hurricane impact to electrical infrastructure in Puerto Rico. PNNL developed a scalable risk-based framework for identifying high-voltage transmission resilience improvements by classifying and prioritizing high-risk power grid contingencies (system failures) under hurricane impact. The risk-based framework is founded on grid outage definitions with their associated probabilities of occurrence from hurricane events, in combination with an impact assessment derived from detailed dynamic cascading failure analysis. This paper focuses on a discussion around data requirements for transmission resilience planning for hurricane events, derived from the development of the risk-based framework and its application to Puerto Rico. This paper launches an important first step in encouraging the engineering community and power system industry to move towards establishing resilience planning as a routine practice. Since actual results for Puerto Rico contain sensitive information, sample simulation results will be used to illustrate the data requirements and risk-based dynamic cascading framework on the Puerto Rico power grid, as well as demonstrate the potential for such a simulation framework. The paper includes a discussion on the lessons learned, importance and need for improved datasets that are not usually considered in traditional power system planning. The paper will also elaborate on how the scalable simulation framework and datasets might be expanded to larger footprints and leveraged for modelling other types of natural disasters.

DCAT, Puerto Rico, hurricane, Power System Stabili↗
DOE OSTI2018

Chapter 2: Advances in Coarse Discrete Particle Methods with Industrial Applications

This chapter provides the full description of a coarse-grained discrete particle method based on a novel hard-sphere contact model for the simulation of industrial-scale fluidized bed reactors. This method is based on simple models that are easy to understand and implement in numerical codes. This technique is verified and validated for several small-scale fluidized systems where numerical data based on finer methods as well as experimental data are available. The speed of execution of this method is increased several orders of magnitudes compared to particle-based discrete methods, which allows for thousands of seconds of flow, heat, and mass transfer simulations of industrial reactors such as fluidized catalytic cracking regenerator, Methanol to Olefins reactor, and Rare Earth Elements leaching reactor, achieved in just few days using commonly available computer resources. It is now possible for the common engineer to conduct simulations of large-scale fluid–particle reactors to understand, design, and troubleshoot, as well as optimize the performance of these complex multiphase flow systems.

Lu, Liqiang↗
DOE OSTI2017

Realization of a Hole-Doped Mott Insulator on a Triangular Silicon Lattice

The physics of doped Mott insulators is at the heart of some of the most exotic physical phenomena in materials research including insulator-metal transitions, colossal magnetoresistance, and high-temperature superconductivity in layered perovskite compounds. Advances in this field would greatly benefit from the availability of new material systems with a similar richness of physical phenomena but with fewer chemical and structural complications in comparison to oxides. In this paper, using scanning tunneling microscopy and spectroscopy, we show that such a system can be realized on a silicon platform. The adsorption of one-third monolayer of Sn atoms on a Si(111) surface produces a triangular surface lattice with half filled dangling bond orbitals. Modulation hole doping of these dangling bonds unveils clear hallmarks of Mott physics, such as spectral weight transfer and the formation of quasiparticle states at the Fermi level, well-defined Fermi contour segments, and a sharp singularity in the density of states. These observations are remarkably similar to those made in complex oxide materials, including high-temperature superconductors, but highly extraordinary within the realm of conventional $sp$-bonded semiconductor materials. Finally, it suggests that exotic quantum matter phases can be realized and engineered on silicon-based materials platforms.

36 MATERIALS SCIENCE↗
DOE OSTI2019

Development of Community, Capabilities and Understanding Through Unmanned Aircraft-Based Atmospheric Research: The LAPSE-RATE Campaign

Members of the international community developing unmanned aircraft for atmospheric research conducted a highly-successful coordinated flight week in Colorado during summer 2018 to collect measurements, develop capabilities, and enhance community in the field. Because unmanned aircraft systems (UAS) offer new perspectives on the atmosphere, their use in atmospheric science is expanding rapidly. In support of this growth, the International Society for Atmospheric Research using Remotely-piloted Aircraft (ISARRA) has been developed and have convened annual meetings and 'flight weeks'. The 2018 flight week, dubbed the Lower Atmospheric Profiling Studies at Elevation - a Remotely-piloted Aircraft Team Experiment (LAPSE-RATE), involved a one-week deployment to Colorado's San Luis Valley. Between 14-20 July 2018 over 100 students, scientists, engineers, pilots and outreach coordinators conducted an intensive field operation using unmanned aircraft and ground-based assets to develop datasets, community, and capabilities. In addition to a coordinated 'community day' which offered a chance for groups to share their aircraft and science with the San Luis Valley community, LAPSE-RATE participants conducted nearly 1300 research flights totaling over 250 flight hours. The measurements collected have been used to advance capabilities (instrumentation, platforms, sampling techniques and modeling tools), conduct a detailed system intercomparison study, develop new collaborations, and foster community support for the use of UAS in atmospheric science.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Environmental Protection Coating System for Refractory Metal Alloys (EPCS for RMAs)

As part of ARPA-E’s Ultra High Temperature Impervious Materials Advancing Turbine Efficiency (UITIMATE) program, this project aims to develop an environmental protection coating system (EPCS) for refractory metal alloys (RMAs) – which will provide radical improvement in long term protection for ultra-high temperature refractory metal components in harsh gas-turbine environments. The drive for higher fuel efficiency and higher core power of gas turbines used in electric power generation and in aircraft propulsion requires higher peak operation temperatures. The temperature capability of state-of-the-art materials cannot meet the new requirements. Refractory metal alloys have desirable capabilities for achieving the required peak temperatures, but most of them are subject to oxidation in the gas turbine environment. Thus, there is a strong need to develop novel environmental protective coatings that enable the refractory metals to operate in an ultra-high temperature engine environment. This project is composed of four major technical innovations that can provide a potentially transformational solution in providing environmental protection for refractory metal alloys over a wide range of temperature. 1) Multi-layer self-healing environmental barrier coating (EBC) that provides protection against combustion gas environment. 2) Oxidation resistant diffusion barrier (DB) on the alloy surface to enhance coating stability. 3) Synergistic integration of the multi-layer self-healing EBC with the diffusion barrier to extend coating life. 4) Guidance of physics-based modeling to accelerate coating development and optimization. Our team has successfully developed a multi-layer self-healing environmental protection coating system (EPCS) that can provide oxidation protection for commercially available molybdenum based refractory metal alloys over a wide temperature range of up to 1600 °C for fuel-efficient gas turbine applications. The team also developed a multi-layer high temperature coating design tool to accelerate coating development and optimization based on physics-based modeling. The team also engaged RTX business unite, notable Pratt and Whitney, for future commercialization of this technology. The successful completion of this Phase I effort led to the selection of performing Phase II of the program. In Phase II, the team plans to apply the EPCS in the protection of a newly discovered molybdenum-based metal alloy which has the potential of superior high temperature properties to the commercial products. Th coating will be evaluated under test conditions and environment that mimic gas turbine operation.

36 MATERIALS SCIENCE↗