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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 685 records · Page 38
All sourcesNASA NTRSDOE OSTI
NASA NTRS2016

A Simple Engineering Analysis of Solar Particle Event High Energy Tails and Their Impact on Vehicle Design

The mathematical models for Solar Particle Event (SPE) high energy tails are constructed with several di erent algorithms. Since limited measured data exist above energies around 400 MeV, this paper arbitrarily de nes the high energy tail as any proton with an energy above 400 MeV. In order to better understand the importance of accurately modeling the high energy tail for SPE spectra, the contribution to astronaut whole body e ective dose equivalent of the high energy portions of three di erent SPE models has been evaluated. To ensure completeness of this analysis, simple and complex geometries were used. This analysis showed that the high energy tail of certain SPEs can be relevant to astronaut exposure and hence safety. Therefore, models of high energy tails for SPEs should be well analyzed and based on data if possible.

Singleterry, Robert C., Jr.↗
DOE OSTI2021

National Technology & Engineering Solutions of Sandia, LLC Technical Assistance With Energy Cloud (Final CTAP Report)

Sandia provided technical assistance to Energy Cloud to support the assessment of Energy Cloud’s technology for air filtration of the SARS-CoV-2 virus. The Energy Cloud system was developed to filter the SARS-CoV-2 virus from commercial HVAC systems, ducts, and enclosed rooms without generating ozone. The project included a 3-week experimental series with aerosolized SARS-CoV-2 virus at the University of Southern California’s biosafety level 3 (BSL-3) laboratory. USC provided the facility as well as experimentally derived measurements and assessment of the Energy Cloud system efficacy for SARSCoV-2 filtration from air handling systems.

42 ENGINEERING↗
NASA NTRS1986

Systems Engineering of Electric and Hybrid Vehicles

Technical paper notes systems engineering principles applied to development of electric and hybrid vehicles such that system performance requirements support overall program goal of reduced petroleum consumption. Paper discusses iterative design approach dictated by systems analyses. In addition to obvious peformance parameters of range, acceleration rate, and energy consumption, systems engineering also considers such major factors as cost, safety, reliability, comfort, necessary supporting infrastructure, and availability of materials.

Kurtz, D. W.↗
DOE OSTI2020

Special Issue from the Advances in Fossil Energy R&D topical sessions held at the 2019 American Institute of Chemical Engineers Annual Fall Meeting

This special issue entitled “Catalysis for Energy Related Activities” represents a collection of research works presented during the Advances in Fossil Energy R&D topical sessions at the 2019 American Institute of Chemical Engineers (AIChE) Annual Meeting in Orlando, Florida November 10–15, 2019.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
NASA NTRS1991

Lewis Research Center R and D Facilities

The NASA Lewis Research Center (LeRC) defines and develops advanced technology for high priority national needs. The work of the Center is directed toward new propulsion, power, and communications technologies for application to aeronautics and space, so that U.S. leadership in these areas is ensured. The end product is knowledge, usually in a report, that is made fully available to potential users--the aircraft engine industry, the energy industry, the automotive industry, the space industry, and other NASA centers. In addition to offices and laboratories for almost every kind of physical research in such fields as fluid mechanics, physics, materials, fuels, combustion, thermodynamics, lubrication, heat transfer, and electronics, LeRC has a variety of engineering test cells for experiments with components such as compressors, pumps, conductors, turbines, nozzles, and controls. A number of large facilities can simulate the operating environment for a complete system: altitude chambers for aircraft engines; large supersonic wind tunnels for advanced airframes and propulsion systems; space simulation chambers for electric rockets or spacecraft; and a 420-foot-deep zero-gravity facility for microgravity experiments. Some problems are amenable to detection and solution only in the complete system and at essentially full scale. By combining basic research in pertinent disciplines and generic technologies with applied research on components and complete systems, LeRC has become one of the most productive centers in its field in the world. This brochure describes a number of the facilities that provide LeRC with its exceptional capabilities.

Source record↗
NASA NTRS1994

The NASA-JPL advanced propulsion program

The NASA Advanced Propulsion Concepts (APC) program at the Jet Propulsion Laboratory (JPL) consists of two main areas: The first involves cooperative modeling and research activities between JPL and various universities and industry; the second involves research at universities and industry that is directly supported by JPL. The cooperative research program consists of mission studies, research and development of ion engine technology using C-60 (Buckminsterfullerene) propellant, and research and development of lithium-propellant Lorentz-force accelerator (LFA) engine technology. The university/industry- supported research includes research (modeling and proof-of-concept experiments) in advanced, long-life electric propulsion, and in fusion propulsion. These propulsion concepts were selected primarily to cover a range of applications from near-term to far-term missions. For example, the long-lived pulsed-xenon thruster research that JPL is supporting at Princeton University addresses the near-term need for efficient, long-life attitude control and station-keeping propulsion for Earth-orbiting spacecraft. The C-60-propellant ion engine has the potential for good efficiency in a relatively low specific impulse (Isp) range (10,000 - 30,000 m/s) that is optimum for relatively fast (less than 100 day) cis-lunar (LEO/GEO/Lunar) missions employing near-term, high-specific mass electric propulsion vehicles. Research and modeling on the C-60-ion engine are currently being performed by JPL (engine demonstration), Caltech (C-60 properties), MIT (plume modeling), and USC (diagnostics). The Li-propellant LFA engine also has good efficiency in the modest Isp range (40,000 - 50,000 m/s) that is optimum for near-to-mid-term megawatt-class solar- and nuclear-electric propulsion vehicles used for Mars missions transporting cargo (in support of a piloted mission). Research and modeling on the Li-LFA engine are currently being performed by JPL (cathode development), Moscow Aviation Institute (engine testing), Thermacore (electrode development), as well as at MIT (plume modeling), and USC (diagnostics). Also, the mission performance of a nuclear-electric propulsion (NEP) Li-LFA Mars cargo vehicle is being modeled by JPL (mission analysis; thruster and power processor modeling) and the Rocketdyne Energy Technology and Engineering Center (ETEC) (power system modeling). Finally, the fusion propulsion research activities that JPL is supporting at Pennsylvania State University (PSU) and at Lawrenceville Plasma Physics (LPP) are aimed at far-term fast (less than 100 day round trip) piloted Mars missions and, in the very far term, interstellar missions.

Frisbee, Robert H.↗
DOE OSTI2020

Stochastic multi-period optimal dispatch of energy storage in unbalanced distribution feeders

This report presents a convex, multi-period, AC-feasible Optimal Power Flow (OPF) framework that robustly dispatches flexible demand-side resources in unbalanced distribution feeders against uncertainty in very-short timescale solar Photo-Voltaic (PV) forecasts. This is valuable for power systems with significant behind-the-meter solar PV generation as their operation is affected by uncertainty from forecasts of demand and solar PV generation. The aim of this work is then to ensure the feasibility and reliability of distribution system operation under high solar PV penetration. We develop and present a novel, robust OPF formulation that accounts for both the nonlinear power flow constraints and the uncertainty in forecasts. This is achieved by linearizing an optimal trajectory and using first-order methods to systematically tighten voltage bounds. Case studies on a realistic distribution feeder shows the effectiveness of a receding-horizon implementation.

25 ENERGY STORAGE↗
DOE OSTI2020

Summary of Structural Material Modeling Development for the NEAMS Program in Fiscal Year 2020

This report summarizes work performed during Fiscal Year (FY) 2020 at Idaho National Laboratory (INL) for the U.S. Department of Energy?s Nuclear Engineering Advanced Modeling and Simulation (NEAMS) program for the Structural Materials and Chemistry Technical Area in the work package entitled "MS- 20IN050104 - Structural Materials - INL." The Structural Materials and Chemistry Technical Area is a relatively new component of the NEAMS program, and is currently focusing on developing simulation capabilities to support the deployment of nuclear energy in the areas of molten salt reactor chemistry, light water reactor (LWR) structural material degradation, and structural material behavior for advanced reactor applications. INL performed work for to advance capabilities for simulation of structural material behavior in both LWR and advanced reactor applications in the work described here.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2025

Including Physics-Informed Atomization Constraints in Neural Networks for Reactive Chemistry

Machine learning interatomic potentials (MLIPs) have emerged as powerful tools for investigating atomistic systems with high accuracy and a relatively low computational cost. However, a common and unaddressed challenge with many current neural network (NN) MLIP models is their limited ability to accurately predict the relative energies of systems containing isolated or nearly isolated atoms, which appear in various reactive processes. To address this limitation, we present a mathematical technique for modifying any existing atom-centered NN architecture to account for the energies of isolated atoms. The result produces a consistent prediction of the atomization energy (AE) of a system using minimal constraints on the model. Using this technique, we build a model architecture that we call hierarchically interacting particle neural network (HIP-NN)-AE, an AE-constrained version of the HIP-NN, as well as ANI-AE, the AE-constrained version of the accurate NN engine for molecular energies (ANI). Our results demonstrate AE consistency of AE-constrained models, which drastically improves the AE predictions for the models. We compare the AE-constrained approach to unconstrained models as well as models from the literature in other scenarios, such as bond dissociation energies, bond dissociation pathways, and extensibility tests. These results show that the constraints improve the model performance in some of these tasks and do not negatively affect the performance on any tasks. The AE constraint approach thus offers a robust solution to the challenges posed by isolated atoms in energy prediction tasks.

74 ATOMIC AND MOLECULAR PHYSICS↗
DOE OSTI2022

Technoeconomic optimization of superalloy supercritical CO 2 microtube shell-and-tube-heat exchangers

Heat exchangers are critical components of aerospace systems and improve efficiency of operation by providing waste heat recovery. Supercritical CO has emerged as a promising candidate working fluid due to its potential in increasing the power density of heat exchangers. In this paper, a generalized costing model is developed to estimate the capital costs incurred to manufacture microtube shell-and-tube heat exchangers. This model is utilized in conjunction with an accurate and efficient 2D numerical shell-and-tube heat exchanger performance prediction model to conduct optimization studies with two key objectives – minimization of cost and maximization of heat exchanger power density – on supercritical CO microtube heat exchangers utilizing superalloy Haynes 282 as the solid material. A methodology is then demonstrated to optimize these heat exchangers using Particle Swarm Optimization for aerospace applications and highly compact and cost-effective optimal designs with power density around 20 and cost per conductance less than 5 $ ∙ K/W are obtained.

42 ENGINEERING↗
DOE OSTI2021

Who is marginalized in energy justice? Amplifying community leader perspectives of energy transitions in Ghana

There is a divide in energy access studies, between technologically-focused modeling papers in engineering and economics, and energy justice frameworks and principles grounded in social sciences. Quantitative computational models are necessary when analyzing energy, and more specifically electricity, systems, as they are technologically-complex systems that can diverge from intuitive patterns. To assure energy justice, these models must be reflective of, and informative to, a wide range of stakeholders, including households and communities alongside utilities, governments, and others. Yet, moving from a qualitative understanding of preferences to quantitative modeling is challenging. In this perspective piece, we pilot the use of the value-focused thinking framework to inform stakeholder engagement. The result is a strategic objective hierarchy that highlights the tradeoffs and the social, economic and technological factors that need to be measured in models. We apply the process in Ghana, using a survey, stakeholder workshops, and follow-up interviews to uncover key tradeoffs and stakeholder-derived objectives. We discuss three key areas that have been rarely, if ever, well-represented in energy models: (1) the relationship between the dynamics of electricity end-use and the technology and economic structure of the system; (2) explicit tradeoffs between electricity access, cost, and reliability as defined by stakeholders; and (3) the definition of new objectives, such as minimizing hazards related to theft. We conclude that this model of engagement provides an opportunity to tie together rigorous qualitative analysis and stakeholder engagement with crucial quantitative models of the electricity system.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2020

Dicarboxylic acid emissions from a GDI engine equipped with a catalytic gasoline particulate filter

Dicarboxylic acids play an important role in atmospheric chemistry, yet their emissions from primary sources, such as internal combustion engines, has not been extensively studied. In this paper, KOH impregnated quartz filters were loaded with exhaust gases from a gasoline direct injection (GDI) engine equipped with a catalytic gasoline particulate filter (GPF). All filters were analyzed for carboxylic and dicarboxylic acids using a derivatized gas chromatography-mass spectroscopy method. Exhaust gas was sampled from pre-GPF and post-GPF locations to determine the performance of the GPF regarding acid conversion. Lean and stoichiometric engine modes were considered with non-oxygenated gasoline and 10% splash blended ethanol in gasoline (E10) to examine the impact of stoichiometry and fuel type. Acid emissions represented as much as 0.51% of total unburned hydrocarbon emissions for total monocarboxylic acids and as much as 0.40% for total dicarboxylic acids. Individual acid concentrations were as high as 38 mg/kg-fuel for monocarboxylic acids and as high as 29 mg/kg-fuel for dicarboxylic acids. Overall, the study found that fuel oxygenates had mixed impact on the acid emissions. Engine-out monocarboxylic acids were reduced when using the E10 fuel by approximately 30–45% for the stoichiometric condition and increased marginally for the lean condition. Dicarboxylic acid emissions were generally insensitive to ethanol content. However, the engine condition significantly affected the acid emissions. Lean operation produced a factor of two to an order of magnitude higher emissions rates of both monocarboxylic and dicarboxylic acids than the stoichiometric condition. The catalytic GPF eliminated between 80 and 92% of the acids emitted from the engine, allowing some acids to be emitted into the environment.

42 ENGINEERING↗
DOE OSTI2020

Assessment of CO 2 trapping mechanisms in partially depleted oil-bearing sands

The goal of this work is to evaluate various CO 2 sequestration mechanisms occurring in the Morrow B Sandstone in the Farnsworth Unit. A history-matched numerical simulation model was created using extensive geological, petrophysical, and operational data collected from the field. The numerical model is competent to investigate the impact of residual, structural-stratigraphic, solubility, and mineral trapping mechanisms on the fluid transportation dynamics and petrophysical property variations. The model forecasts the field response of 20 years of WAG injections. Afterward, all wells were shut-in, and the reservoir was allowed to evolve for 1000 years to investigate the fate of injected CO 2 . In this paper, we assess the impacts of various trapping mechanisms on oil recovery and CO 2 storage efficacy. By analyzing the results reported from the numerical simulation model, the in-situ fluid composition and mineralogy changes are also investigated. More importantly, we seek to confirm the petrophysical property variations due to the CO 2 injection with observations from laboratory measurements. The experiences gained from this study provide valuable insights regarding physiochemical storage induced by the CO 2 injection activities and serve as a benchmark case for future CO 2 enhanced oil recovery (EOR) projects involving reactive solute transport.

42 ENGINEERING↗
DOE OSTI2020

A machine learning model for predicting the minimum miscibility pressure of CO 2 and crude oil system based on a support vector machine algorithm approach

CO 2 enhanced oil recovery (EOR) is a potential way for carbon capture, utilization and storage (CCUS). Though, the effect of CO 2 injection is greatly influenced by the reservoir conditions. Typically, Minimum miscible pressure (MMP) is selected as one of the key parameters for the screening and evaluation of prospective CO 2 flooding. Conventional slim tube test is both accurate and widely accepted but it is inefficient. Existing empirical formulas for MMPs are easy to be used but have been proved inaccurate and unreliable. Machine learning-based methods have great advantages in predicting MMP. However, only predication accuracy is discussed for most models without the screening of the main control factors and further validation of the model reliability. In this paper, a new prediction model based on support vector machine (SVM) was developed for pure/impure CO 2 and crude oil system. This study was based on 147 sets of MMP data from the literature with full information on reservoir temperature, oil composition and gas composition. The main control factors were screened by several statistical methods. Unlike the conventional prediction models that verified by only prediction accuracy, learning curve and single factor control variable analysis are further validated to obtain the optimum model.

02 PETROLEUM↗
DOE OSTI2021

Machine learning assisted rediscovery of methane storage and separation in porous carbon from material literature

Porous carbon (PC) has been widely regarded as one of the most promising absorbents for methane storage. Studies show that its uptake capacity and selectivity highly depend on textural structures. Although much effort has been made, unveiling their detailed structure-performance relationship remains a challenge. Here, we propose an innovative study where, with the assistance of machine learning, the hidden relationship of the textural structures of PC with the methane uptake and separation can be derived from existing data in material literature. Machine learning models were trained by the data, including specific surface area, micropore volume, mesopore volume, temperature, and pressure as the input variables and methane uptake as the output variable for prediction. Among the tested models, the multilayer perceptron (MLP) shows the highest accuracy in predicting the methane uptake. In addition, the model enables to automatically construct a uptake performance map in terms of micropore volume and mesopore volume. The obtained MLP model was also extended to explore the CO 2 /CH 4 selectivity by retraining it with the data collected from literature of PC for the CO 2 uptake. Finally, the constructed 2D selectivity map shows that the high selectivity can be achieved in the low CH 4 uptake region.

42 ENGINEERING↗
DOE OSTI2022

Development of simplified model for injection rate prediction of diesel injectors during transient and steady operation

Determining the injection rate of the diesel injector during the transient short injection operations is a critical matter to enhance the combustion process by improving air/fuel mixture formation. In this regard, numerous injection models have been suggested in the literature to predict the injection rate accurately. However, such algorithms have not precisely predicted the rate of injection (ROI) in transient short injection events. In addition, most of those models require many input parameters to characterize hydraulic and mechanical subsystems in the injector, which can make them inconvenient to tune for different injector specifications, in other words not suitable for universal usage. Therefore, our study aims to develop a simple injection model that can predict injection rate precisely in the short and long injection durations considering relatively fewer input parameters. The model traces the pressure variation inside the sac based on the bulk modulus theory to predict nozzle exit velocity, thus the injection rate of the injector. In the algorithm, the bulk modulus of fuel (E) and discharge coefficients of the nozzle hole and needle seat (C d,H and C d,N ) have been considered to vary rather than set constant during the injection process since such flow parameters would have a critical impact on the flow development at the transient stage, thus affecting the injection rate of diesel injectors. Here with this modelling strategy, the accuracy of the new model on ROI prediction was confirmed with acceptable model error for both short and long injection durations by validating against experimental results. To understand the effect of the new model scheme on ROI prediction, ROI results with the new model have been discussed by comparing it with the ROI results predicted with constant flow parameters.

42 ENGINEERING↗
DOE OSTI2022

The effect of original and initial saturation on residual nonwetting phase capillary trapping efficiency

Injection of supercritical carbon dioxide (CO 2 ) into geological formations is a strategy for both atmospheric greenhouse gas reduction (climate change mitigation) and enhanced oil recovery. To understand CO 2 trapping efficiency, the capillary trapping behaviors that immobilize subsurface fluids were analyzed at the pore-scale using pairs of proxy fluids representing the range of in situ nonwetting and wetting fluid properties encountered in geologic storage reservoirs. The pairs of fluids were cycled through imbibition and drainage processes using a flow cell apparatus containing a sintered glass bead column. Computed x-ray microtomography (microCT) was used to identify immobilized nonwetting fluid after imbibition and drainage events. From microCT images, the trapped residual (post-secondary imbibition) nonwetting phase was spatially correlated to both the original (post-primary imbibition) and the initial (post-primary drainage) nonwetting phase; this relationship is referred to here as the original saturation dependence (SO-dependence) and initial saturation dependence (SI-dependence), respectively. Significant trends of decreasing SO- and SI-dependence with increasing wetting and nonwetting fluid phase viscosities were observed. This finding implies that the amount of CO 2 injected and ultimately trapped is dependent on the nonwetting phase (e.g. oil or gas) already present in the formation, as well as on the manner in which supercritical CO 2 is initially injected, which are factors not considered in current trapping models. To explore the potential effect that varying viscosity and interfacial tension (IFT) might have on this process, we report on a variety of fluid pairs with different viscosities and IFT.

42 ENGINEERING↗