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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 649 records · Page 36
All sourcesNASA NTRSDOE OSTI
DOE OSTI2018

Harnessing expert knowledge: Defining a Bayesian network decision model with limited data-Model structure for the vibration qualification problem

As systems become more complex, systems engineers rely on experts to inform decisions. There are few experts and limited data in many complex new technologies. This challenges systems engineers as they strive to plan activities such as qualification in an environment where technical constraints are coupled with the traditional cost, risk, and schedule constraints. Bayesian network (BN) models provide a framework to aid systems engineers in planning qualification efforts with complex constraints by harnessing expert knowledge and incorporating technical factors. By quantifying causal factors, a BN model can provide data about the risk of implementing a decision supplemented with information on driving factors. This allows a systems engineer to make informed decisions and examine “what-if” scenarios. This paper discusses a novel process developed to define a BN model structure based primarily on expert knowledge supplemented with extremely limited data (25 data sets or less). The model was developed to aid qualification decisions—specifically to predict the suitability of six degrees of freedom (6DOF) vibration testing for qualification. The process defined the model structure with expert knowledge in an unbiased manner. Finally, validation during the process execution and of the model provided evidence the process may be an effective tool in harnessing expert knowledge for a BN model.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

On-Demand Column Joining for High Energy Physics

As the Large Hadron Collider (LHC) transitions into the High-Luminosity LHC (HL-LHC) era, the volume of data to be processed is expected to increase significantly. The CMS Experiment currently utilizes various data formats, including AOD, MiniAOD, and NanoAOD, each with different levels of detail and storage requirements. This paper addresses the challenges of data duplication and storage inefficiencies in high-energy physics (HEP) analyses by proposing an on-demand column-joining solution. This approach aims to reduce data duplication by enabling the dynamic combination of NanoAOD data with auxiliary information from larger data tiers, such as MiniAOD. The proposed solution leverages Trino, a high-performance distributed SQL query engine, to perform efficient and scalable data joins. Benchmarks using CMS OpenData demonstrate the feasibility of this approach, showing that it can handle large datasets with low latency. Integration with the scikit-hep ecosystem and the coffea analysis framework is also discussed, highlighting the potential for seamless end-to-end data processing and analysis. Ongoing and future work focuses on expanding benchmarks, integrating ServiceX for data transformation, and exploring the use of native object storage solutions.

Manganelli, Nicholas [Northeastern U.]↗
DOE OSTI2021

Advanced Health Information Technology Analytic Framework and Application to Hazard Detection

Health Information Technology (HIT) aims to improve healthcare outcomes by organizing and analyzing various health-related data. With data accumulating at a staggering rate, the importance of real-time analytics has been increasing dramatically, shifting the focus of informatics from batch processing to streaming analytics. HIT is also facing unprecedented challenges in adapting to this new requirement and leveraging advanced IT technologies. This paper introduces a HIT data and compute platform that supports multi-granularity real-time analytics from heterogeneous data sources. The paper first identifies functional requirements and proposes a framework that satisfies the requirements using state-of-the-art big data technologies including Apache Kafka, Spark Structured Streaming Engine, and Delta Lake. To demonstrate its capability to support data analytics in multiple time granularities analytics, a statistical process control-based hazard detection algorithm has been implemented on top of the framework to detect unexpected hazards from order cancellation data of the Department of US Veterans Affairs (VA) in near real-time.

Kumar, Mohit↗
DOE OSTI2018

Assessment of Methane Emissions – Impact of Using Natural Gas Engines in Unconventional Resource Development

Researchers at the Center for Alternative Fuels, Engines, and Emissions (CAFEE) completed a multi-year program under DE-FE0013689 entitled, “Assessing Fugitive Methane Emissions Impact Using Natural Gas Engines in Unconventional Resource Development.” When drilling activity was high and industry sought to lower operating costs and reduce emissions they began investing in dual fuel and dedicated natural gas engines to power unconventional well equipment. From a review of literature we determined that the prime-movers (or major fuel consumers) of unconventional well development were the service trucks (trucking), horizontal drilling rig (drilling) engines, and hydraulic stimulation pump (fracturing) engines. Based on early findings from on-road studies we assessed that conversion of prime movers to operate on natural gas could contribute to methane emissions associated with unconventional wells. As such, we collected significant in-use activity data from service trucks and in-use activity, fuel consumption, and gaseous emissions data from drilling and fracturing engines. Our findings confirmed that conversion of the prime movers to operate as dual fuel or dedicated natural gas – created an additional source of methane emissions. While some gaseous emissions were decreased from implementation of these technologies – methane and CO 2 equivalent emissions tended to increase, especially for non-road engines. The increases were highest for dual fuel engines due to methane slip from the exhaust and engine crankcase. Dedicated natural gas engines tended to have lower exhaust methane emissions but higher CO 2 emissions due to lower efficiency. Therefore, investing in currently available natural gas technologies for prime movers will increase the greenhouse gas footprint of the unconventional well development industry.

03 NATURAL GAS↗
DOE OSTI2025

Data Qualification Report: SRNL Glass Composition-Properties (ComPro) Database

The Savannah River National Laboratory Glass Composition-Properties (ComPro) database is an extensive database containing pertinent composition and durability data to support the accelerated clean-up mission at the Defense Waste Processing Facility. The activities described in this data qualification report were performed to support the information contained in the database. There were two objectives of the original data qualification process. The first objective was to review supporting documentation to determine if DOE/RW-0333P Quality Assurance Requirements and Description had been implemented during the original work. If the DOE/RW-0333P Quality Assurance Requirements and Description had not been directly implemented during the original work, the second objective was to determine if the controls that were used were adequate to meet the intent of the DOE/RW-0333P Quality Assurance Requirements and Description. The results of these two objectives and the activities performed to support these decisions are described in this document. An assessment of each dataset was made to determine if the data were RW-0333P Compliant, RW-0333P Equivalent or Non-RW-0333P Compliant. The original data qualification was performed in accordance with E7, Conduct of Engineering Manual, Procedure 3.70, Revision 4, Qualification of Data. The specific method that was used was Equivalent Controls as described in E7, 3.70. Revision 2 of this document adds supporting information for the RW-0333P Compliant datasets added to Revision 3 of the database.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2015

Analysis of Thermal and Chemical Effets on Negative Valve Overlap Period Energy Recovery for Low-Temperature Gasoline Combustion

A central challenge for efficient auto-ignition controlled low-temperature gasoline combustion (LTGC) engines has been achieving the combustion phasing needed to reach stable performance over a wide operating regime. The negative valve overlap (NVO) strategy has been explored as a way to improve combustion stability through a combination of charge heating and altered reactivity via a recompression stroke with a pilot fuel injection. The study objective was to analyze the thermal and chemical effects on NVO-period energy recovery. The analysis leveraged experimental gas sampling results obtained from a single-cylinder LTGC engine along with cylinder pressure measurements and custom data reduction methods used to estimate period thermodynamic properties. The engine was fueled by either iso-octane or ethanol, and operated under sweeps of NVO-period oxygen concentration, injection timing, and fueling rate. Gas sampling at the end of the NVO period was performed via a custom dump-valve apparatus, with detailed sample speciation by in-house gas chromatography. The balance of NVO-period input and output energy flows was calculated in terms of fuel energy, work, heat loss, and change in sensible energy. Experiment results were complemented by detailed chemistry single-zone reactor simulations performed at relevant mixing and thermodynamic conditions, with results used to evaluate ignition behavior and expected energy recovery yields. For the intermediate bulk-gas temperatures present during the NVO period (900-1100 K), weak negative temperature coefficient behavior with iso-octane fueling significantly lengthened ignition delays relative to similar ethanol fueled conditions. Faster ethanol ignition chemistry led to lower recovered fuel intermediate yields relative to similar iso-octane fueled conditions due to more complete fuel oxidation. From the energy analysis it was found that increased NVO-period global equivalence ratio, either from lower NVOperiod oxygen concentrations or higher fueling rates, in general led to a greater fraction of net recovered fuel energy and work as heat losses were minimized. These observations were supported by complementary single-zone reactor model results, which further indicated that kinetic time-scales favor chemical energy-consuming exothermic oxidation over slower endothermic reformation. Nonetheless, fuel energy recovery close to the thermodynamic equilibrium solution was achieved for baseline conditions that featured 4% NVO-period oxygen concentration.

ethanol↗
DOE OSTI2023

AL-ASMR: Active Learning of Atomistic Surrogate Models for Rare Events

Atomistic simulation with artificial intelligence (AI) is an emerging tool for understanding materials' properties and behaviors and predicting novel materials with optimized/targeted properties. Neural network potentials (NNPs) are outstanding in this field as they have shown a comparable accuracy to ab initio electronic structure calculations for reproducing potential energy surfaces while being several orders of magnitude faster. However, such NNPs can perform poorly outside of their training domain and typically fail catastrophically in the prediction of rare events in molecular dynamics (MD) simulations. For effective AL loops to distinguish the informative data from enhanced sampled configurations, we developed a decision engine by configurational similarity and uncertainty quantification (UQ) with data augmentation.

Jung, Gang Seob↗
DOE OSTI2015

Disentangling multidimensional spatio-temporal data into their common and aberrant responses

With the advent of high-throughput measurement techniques, scientists and engineers are starting to grapple with massive data sets and encountering challenges with how to organize, process and extract information into meaningful structures. Multidimensional spatio-temporal biological data sets such as time series gene expression with various perturbations over different cell lines, or neural spike trains across many experimental trials, have the potential to acquire insight about the dynamic behavior of the system. For this potential to be realized, we need a suitable representation to understand the data. A general question is how to organize the observed data into meaningful structures and how to find an appropriate similarity measure. A natural way of viewing these complex high dimensional data sets is to examine and analyze the large-scale features and then to focus on the interesting details. Since the wide range of experiments and unknown complexity of the underlying system contribute to the heterogeneity of biological data, we develop a new method by proposing an extension of Robust Principal Component Analysis (RPCA), which models common variations across multiple experiments as the lowrank component and anomalies across these experiments as the sparse component. We show that the proposed method is able to find distinct subtypes and classify data sets in a robust way without any prior knowledge by separating these common responses and abnormal responses. Thus, the proposed method provides us a new representation of these data sets which has the potential to help users acquire new insight from data.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2015

Recovery Act. Demonstration of a Pilot Integrated Biorefinery for the Efficient, Direct Conversion of Biomass to Diesel Fuel

The Renewable Energy Institute International, in collaboration with Greyrock Energy and Red Lion Bio-Energy (RLB) has successfully demonstrated operation of a 25 ton per day (tpd) nameplate capacity, pilot, pre-commercial-scale integrated biorefinery (IBR) plant for the direct production of premium, “drop-in”, synthetic fuels from agriculture and forest waste feedstocks using next-generation thermochemical and catalytic conversion technologies. The IBR plant was built and tested at the Energy Center, which is located in the University of Toledo Medical Campus in Toledo, Ohio.

03 NATURAL GAS↗
DOE OSTI2022

Combustion machine learning: Principles, progress and prospects

Progress in combustion science and engineering has led to the generation of large amounts of data from large-scale simulations, high-resolution experiments, and sensors. This corpus of data offers enormous opportunities for extracting new knowledge and insights—if harnessed effectively. Machine learning (ML) techniques have demonstrated remarkable success in data analytics, thus offering a new paradigm for data-intense analyses and scientific investigations through combustion machine learning (CombML). While data-driven methods are utilized in various combustion areas, recent advances in algorithmic developments, the accessibility of open-source software libraries, the availability of computational resources, and the abundance of data have together rendered ML techniques ubiquitous in scientific analysis and engineering. This article examines ML techniques for applications in combustion science and engineering. Starting with a review of sources of data, data-driven techniques, and concepts, we examine supervised, unsupervised, and semi-supervised ML methods. Various combustion examples are considered to illustrate and to evaluate these methods. Next, we review past and recent applications of ML approaches to problems in combustion, spanning fundamental combustion investigations, propulsion and energy-conversion systems, and fire and explosion hazards. Challenges unique to CombML are discussed and further opportunities are identified, focusing on interpretability, uncertainty quantification, robustness, consistency, creation and curation of benchmark data, and the augmentation of ML methods with prior combustion-domain knowledge.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2015

Degradation of transparent conductive oxides: Interfacial engineering and mechanistic insights

Transparent conductive oxides (TCOs) are a known failure mode in a variety of thin film photovoltaic (PV) devices, through mechanisms such as resistivity increase and delamination. Degradation science studies of these materials, as well as most PV systems, have primarily utilized industry standard qualification protocols, which are not designed to be used as lifetime prediction tests. Here, this work applies a data science approach to this engineering challenge, utilizing commercially available TCOs and subjecting them to an array of stressors, including environmental and material stressors. Optical, electrical and surface sensitive TCO property metrics were monitored and analyzed en mass. Different degradation mechanisms and modes were observed when different stressor combinations were applied; TCO surfaces are sensitive to the proportion of water and light in an exposure, yellowing of the TCO only occurs when humidity and UV light are combined, and PEDOT:PSS (poly(3,4-ethylenedioxythiophene) poly(styrenesulfonate)) application results in hazing and roughening of aluminum-doped zinc oxide (AZO). Using multi-variate analytics and plotting critical material properties against one another in a mechanistic plot, trade-offs between properties and the activation of different degradation mechanisms become readily apparent. In addition to a survey of failure modes of TCOs, a possible solution to the degradation of AZO was examined: the application of an organofunctional silane layer. The application of a thin APTES (3-aminopropyltriethoxysilane) film nearly eliminated the observed edge effects and greatly reduced the resistivity increase caused by damp heat exposure of AZO.

14 SOLAR ENERGY↗
DOE OSTI2023

MHTLS Cross-flow Heat Exchanger Temperature Performance Data

The presentation and supporting information provide data on the performance of an engineering-scale cross-flow heat exchanger used in hydrothermal liquefaction (HTL). Temperature profiles are provide for the process of waste water treatment sludges and a food waste. The data can be used to model larger-scale heat exchangers used in HTL.

Bioenergy Technologies Tags Heat Exchanger HTL MHT↗
DOE OSTI2020

Southeast Regional CO 2 Utilization and Storage Acceleration Partnership (SECARB-USA): Data Quality Methodology (4.2.1)

SECARB-USA Deliverable 4.2.1 utilizes the outputs from the Needs Assessment (Subtask 2.1) to develop a data quality methodology. Many types of data are needed to evaluate a site for technical and financial viability. Several inventories of data types have been produced (for example NETL, 2010). The objective here is to organize the data types so that the needs met are specified. From this cross index (Table 1 in the Appendix), it will be possible in future tasks to (1) determine, on a site-specific basis, how much of each data type is required at each stage of a project to meet the need, and conversely (2) to further specify and define the data collection methods applied such that the data are tailored to fit that need. Derivative tables can then be developed to semi-quantitively evaluate the extent to which need is critical for early go/no decision points, or if it is more important than average, requiring faster or larger capitalization and spend to meet the need. In an additional step, the current availability of data for a site can be semi-quantitively assessed. From the table of data criticality and the table of data availability, site-specific cost for meeting the data needs can be determined, and allow a pre-permit spend estimated for a portfolio of projects. All of the data needs to evaluate a site are somewhat interconnected. We used criterion (2) above to determine if the data collection design would have to be modified to meet the need; if this was commonly true a linkage was shown. The application of this cross index to sites in the subsequent tasks will demonstrate that the demand for data types varies site-to-site and project-to-project. Examples of factors to be considered are the complexity of the geology, the injection goals such as rate and duration of the injection, and the types of risk and risk tolerance of key stakeholders. In future tasks the team will compare the demand for data with existing data availability. This will, in turn, determine when data needs to be acquired to support project development. For example, in a project area with complex structure, 3-D seismic data may be needed earlier and more urgently than in an area with simple rock body geometries. In some locations, a 3-D seismic survey has already been collected and can be purchased. In other locations the project developer will need to collect these data. For another example, a project near an urban area or near to a park may generate earlier and more substantive public concern than a site that is developed in mined lands. A calculation using the derivative from table 1 will show the different investment needs. Project cost will vary corresponding to data criticality and data availability.

42 ENGINEERING↗
DOE OSTI2022

Performance Assessment of High Efficiency Variable Speed Air-Source Heat Pump in Cold Climate Applications

This project was part of an effort by ComEd's emerging technology program to evaluate the energy saving potential of new energy efficiency technologies. The focus of this technology assessment was to determine energy and peak demand savings potentials of a high efficiency variable speed, air-source, split system heat pump designed for cold climate applications. The results of this technology assessment will be used by CLEAResult to develop a new energy efficiency measure for Commonwealth Edison Company's incentive programs. The project utilized the National Renewable Energy Laboratory's (NREL) Thermal Test Facility to experimentally characterize cooling and heating performance of a high efficiency heat pump split system under varying outdoor climate conditions. The selected climate conditions represented summer and low temperature winter conditions in ComEd's service territory. The laboratory experimentation results were used to develop equipment performance curves required by EnergyPlus hourly simulation engine. Using typical meteorological year 3 weather data for the Chicago O'Hare airport, hourly building simulations (using the EnergyPlus engine) was utilized to estimate the annual energy savings of the high efficiency heat pump in comparison to a standard efficiency unit in the following U.S. Department of Energy (DOE) building codes program energy prototypes: Single-family residence; Strip mall; and Low-rise office.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2023

DME-Propane Ignition Delay Time Measurements at Mixing Controlled Compression Ignition Engine-Relevant Conditions

The blend of dimethyl ether (DME, CH 3 OCH 3 ) and propane (C 3 H 8 ) is a potentially renewable fuel mixture that has the potential to replace diesel in compression ignition engines. The combination can potentially reduce particulate and greenhouse gas emissions compared to a conventional diesel engine operating under similar conditions. However, detailed conceptual and simulation studies must be conducted before adopting a new fuel on a compression ignition engine. For these simulations, accurate chemical kinetic models are necessary. However, the validity of chemical kinetic mechanisms in the literature is unknown for mixing controlled compression ignition (MCCI) engine operating conditions. Hence, in this work, we studied the ignition of dimethyl ether (DME) and propane blends in a shock tube at MCCI engine conditions. Ignition delay time (IDT) data was collected behind the reflected shock for DME-propane mixtures for heavy-duty compression ignition (CI) engine parameters. Undiluted experiments spanning temperatures of 700 to 1100 K and pressures of 55 to 84 bar for various blends (100% CH 3 OCH 3 / 0% C 3 H 8, 100% C 3 H 8 / 0% CH 3 OCH 3 , 60% CH 3 OCH 3 / 40% C 3 H 8 ) of DME and propane were combusted in synthetic air (21% O 2 / 79% N 2 ). Some experiments were conducted at higher pressures (90-120 bar) to understand model performance at these conditions. Comparisons of IDT were made with the predictions of recent chemical kinetic mechanisms for DME-propane mixture, including the Aramco3.0, NUIG, and Dames et al. mechanisms. All mechanisms overpredicted IDT compared to experimental values. Sensitivity analysis was conducted with Dames et al. model, and critical reactions sensitive to IDT of DME-propane mixture near 100 bar are outlined.

Mohammed, Zuhayr Pasha↗
DOE OSTI2019

3-D Modeling of the CFR Engine for the Investigation of Knock on Natural Gas

Engine knock and misfire are barriers to pathways leading to higher efficiency natural gas (NG) engines, where the general tendency to knock is highly dependent on engine operating conditions and the fuel reactivity. The problem is further complicated by low emissions limits and the wide range of chemical reactivity in pipeline quality NG, making the understanding of fuel-engine interactions essential to achieving high efficiencies in spark-ignited NG engines. The present work highlights a three-dimensional computational fluid dynamics (CFD) model of the Cooperative Fuel Research (CFR) engine to investigate knock behavior when running on NG. Using a Converge CFD model of the CFR engine fuel-engine interactions that lead to knock with NG, including effects of fuel reactivity, engine operating parameters, and exhaust gas recirculation, are being investigated. A GT-Power model is used to estimate appropriate initial conditions in the Converge CFD model, including cylinder wall, piston, valves, and port surface temperatures as well as residual gas fraction. In order to validate the model, in-cylinder pressure data taken with a CFR engine is compared to model predictions while varying compression ratio, CA50, and BMEP.

Bestel, Diego↗
DOE OSTI2019

3-D Modeling of the CFR Engine for the Investigation of Knock on Natural Gas

Abstract: Engine knock and misfire are barriers to pathways leading to higher efficiency natural gas (NG) engines, where the general tendency to knock is highly dependent on engine operating conditions and the fuel reactivity. The problem is further complicated by low emissions limits and the wide range of chemical reactivity in pipeline quality NG, making the understanding of fuel- engine interactions essential to achieving high efficiencies in spark-ignited NG engines. The present work highlights a three-dimensional computational fluid dynamics (CFD) model of the Cooperative Fuel Research (CFR) engine to investigate knock behavior when running on NG. Using a Converge CFD model of the CFR engine fuel-engine interactions that lead to knock with NG, including effects of fuel reactivity, engine operating parameters, and exhaust gas recirculation, are being investigated. A GT-Power model is used to estimate appropriate initial conditions in the Converge CFD model, including cylinder wall, piston, valves, and port surface temperatures as well as residual gas fraction. In order to validate the model, in-cylinder pressure data taken with a CFR engine is compared to model predictions while varying compression ratio, CA50, and BMEP.

CFR Engine, Converge CFD, knock, Natural Gas↗
DOE OSTI2021

Real-Time Health Monitoring for Gas Turbine Components Using Online Learning and High-Dimensional Data

Capital-intensive turbomachinery, such as gas turbines and combined cycle plants, are constantly being monitored for performance anomalies, faults, and physical degradation. Although these power-generating assets are equipped with hundreds of sensors, existing monitoring tools can only handle moderate-sized data. As a result, only a handful of aggregate metrics are used to monitor machine health. At the same time, developing advanced tools suitable for large datasets have been restricted by the lack of appropriate data. The objective of this proposal was to demonstrate a Big Data analytics framework for fault detection and diagnosis in gas turbine applications. We develop a predictive analytics framework methodology guided by these experimental data, industrial data from our collaborators, and physics-based models with engineering domain knowledge. Our analytics framework consists of four key components (1) a data curation process that addresses data storage, data quality assessments, and integrity checks, (2) a feature engineering component that utilizes statistical methods and transformation algorithms guided by physics-based models to extract high-fidelity fault features that can be leveraged for fault detection and classifying fault severities, (3) a Machine Learning-based fault detection and diagnostics algorithms for detecting operational and hardware faults in the combustion and the turbines section. We utilize two industry-class gas turbine component test rigs to generate first of its kind data for critical gas turbine faults with varying severity levels. Advanced gas turbine test facilities will be interrogated using state-of-the-art instrumentation techniques to build fault signatures and data trends for key combustor and turbine faults. Data generated from a combustor test rig (Georgia Tech) and a turbine test rig (Penn State) during both normal operation and with seeded faults serve as the basis for the Big Data sets. The test conditions in the two test facilities include common, critical events that occur in the operation. Utilizing the combustor test rig, we examine two common combustor faults: lean blowout and centerbody degradation. For the turbine section we develop analytic models for monitoring cooling faults in the gas turbine

03 NATURAL GAS↗