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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 721 records · Page 40
All sourcesNASA NTRSDOE OSTI
NASA NTRS1979

Identification of a STOL propulsion plant model from flight data

Accurate dynamic models for propulsion system behavior are critical to successful synthesis of STOL autothrottle/autoland control systems. This paper describes the development of a nonlinear dynamic model for the response of a STOL aircraft in the landing/approach configuration. The model is based upon an analysis of the throttle, governor, and turbofan engine components. Model parameters are directly identified from data acquired during a flight of the NASA Augmentor Wing Jet STOL research aircraft. The accuracy of the model and a comparison of results for two engines are described. Various approaches to modeling propulsion system response for complex systems representative of current and near-term aircraft are discussed.

De Hoff, R. L.↗
NASA NTRS2017

Geomagnetically Induced Currents: Science, Engineering, and Applications Readiness

This paper is the primary deliverable of the very first NASA Living With a Star Institute Working Group, Geomagnetically Induced Currents (GIC) Working Group. The paper provides a broad overview of the current status and future challenges pertaining to the science, engineering, and applications of the GIC problem. Science is understood here as the basic space and Earth sciences research that allows improved understanding and physics-based modeling of the physical processes behind GIC. Engineering, in turn, is understood here as the ''impact'' aspect of GIC. Applications are understood as the models, tools, and activities that can provide actionable information to entities such as power systems operators for mitigating the effects of GIC and government agencies for managing any potential consequences from GIC impact to critical infrastructure. Applications can be considered the ultimate goal of our GIC work. In assessing the status of the field, we quantify the readiness of various applications in the mitigation context. We use the Applications Readiness Level (ARL) concept to carry out the quantification.

Geomagnetically induced currents↗
NASA NTRS2004

Strategy Developed for Selecting Optimal Sensors for Monitoring Engine Health

Sensor indications during rocket engine operation are the primary means of assessing engine performance and health. Effective selection and location of sensors in the operating engine environment enables accurate real-time condition monitoring and rapid engine controller response to mitigate critical fault conditions. These capabilities are crucial to ensure crew safety and mission success. Effective sensor selection also facilitates postflight condition assessment, which contributes to efficient engine maintenance and reduced operating costs. Under the Next Generation Launch Technology program, the NASA Glenn Research Center, in partnership with Rocketdyne Propulsion and Power, has developed a model-based procedure for systematically selecting an optimal sensor suite for assessing rocket engine system health. This optimization process is termed the systematic sensor selection strategy. Engine health management (EHM) systems generally employ multiple diagnostic procedures including data validation, anomaly detection, fault-isolation, and information fusion. The effectiveness of each diagnostic component is affected by the quality, availability, and compatibility of sensor data. Therefore systematic sensor selection is an enabling technology for EHM. Information in three categories is required by the systematic sensor selection strategy. The first category consists of targeted engine fault information; including the description and estimated risk-reduction factor for each identified fault. Risk-reduction factors are used to define and rank the potential merit of timely fault diagnoses. The second category is composed of candidate sensor information; including type, location, and estimated variance in normal operation. The final category includes the definition of fault scenarios characteristic of each targeted engine fault. These scenarios are defined in terms of engine model hardware parameters. Values of these parameters define engine simulations that generate expected sensor values for targeted fault scenarios. Taken together, this information provides an efficient condensation of the engineering experience and engine flow physics needed for sensor selection. The systematic sensor selection strategy is composed of three primary algorithms. The core of the selection process is a genetic algorithm that iteratively improves a defined quality measure of selected sensor suites. A merit algorithm is employed to compute the quality measure for each test sensor suite presented by the selection process. The quality measure is based on the fidelity of fault detection and the level of fault source discrimination provided by the test sensor suite. An inverse engine model, whose function is to derive hardware performance parameters from sensor data, is an integral part of the merit algorithm. The final component is a statistical evaluation algorithm that characterizes the impact of interference effects, such as control-induced sensor variation and sensor noise, on the probability of fault detection and isolation for optimal and near-optimal sensor suites.

Source record↗
DOE OSTI2021

Model predictive combustion control of a Gasoline Compression Ignition engine

Gasoline Compression Ignition is a novel combustion concept that derives its superiority from the high compression ratio of a compression ignition engine as well as the properties of gasoline fuel, such as longer ignition delay and higher volatility compared to diesel. Here, this combustion concept was experimentally tested on a 12.4L Class 8 truck engine. Based on these experimental data, prior efforts by the authors focused on the development of an engine model for a heavy-duty engine operating on a low-reactivity fuel. This engine model was leveraged within this study to investigate a combustion control strategy at different engine conditions and injection methods and was augmented to incorporate cycle-to-cycle combustion variations. State estimation is performed by means of a Kalman filter which feeds into a model predictive controller. The model predictive controller chooses control actions based on a predefined cost function under consideration of bounds reflecting physical constraints. The engine model was utilized to establish a state-space model that serves the Kalman filter and model predictive controller for estimation and prediction. A comparative study investigating control actions and engine behavior was performed with and without limiting in-cylinder peak pressure as well as combustion noise, which is of particular interest for early pilot injection strategies. In addition, the proposed control architecture was investigated at two different levels of cycle-to-cycle variations and compared to the performance of a control structure with input disturbance rejection. For increased cycle-to-cycle variations, disturbance estimation reduces state fluctuations and control effort. In general, this investigation highlights control aspects specific to a compression-ignited combustion regime with low-reactivity fuel. The control algorithm is able to maintain the desired references for brake mean effective pressure and combustion phasing while controlling peak in-cylinder pressure and combustion noise.

42 ENGINEERING↗
NASA NTRS1997

Remote Sensing of Surficial Process Responses to Extreme Meteorological Events

Changes in the frequency and magnitude of extreme meteorological events are associated with changing environmental means. Such events are important in human affairs, and can also be investigated by orbital remote sensing. During the course of this project, we applied ERS-1, ERS-2, Radarsat, and an airborne sensor (AIRSAR-TOPSAR) to measure flood extents, flood water surface profiles, and flood depths. We established a World Wide Web site (the Dartmouth Flood Observatory) for publishing remote sensing-based maps of contemporary floods worldwide; this is also an online "active archive" that presently constitutes the only global compilation of extreme flood events. We prepared an article for EOS concerning SAR imaging of the Mississippi Valley flood; an article for the International Journal of Remote Sensing on measurement of a river flood wave using ERS-2, began work on an article (since completed and published) on the Flood Observatory for a Geoscience Information Society Proceedings volume, and presented lectures at several Geol. Soc. of America Natl. Meetings, an Assoc. of Amer. Geographers Natl. Meeting, and a Binghamton Geomorphology Symposium (all on SAR remote sensing of the Mississippi Valley flood). We expanded in-house modeling capabilities by installing the latest version of the Army Corps of Engineers RMA two-dimensional hydraulics software and BYU Engineering Graphics Lab's Surface Water Modeling System (finite elements based pre- and post-processors for RMA work) and also added watershed modeling software. We are presently comparing the results of the 2-d flow models with SAR image data. The grant also supported several important upgrades of pc-based remote sensing infrastructure at Dartmouth. During work on this grant, we collaborated with several workers at the U.S. Army Corps of Engineers, Remote Sensing/GIS laboratory (for flood inundation mapping and modeling; particularly of the Illinois River using the AIRSAR/TOPSAR/ERS-2 combined data), with Dr. Karen Prestegaard at the University of Maryland (geomorphological responses to the extreme 1993 flood along the Raccoon drainage in central Iowa), and with Mr Tim Scrom of the Albany National Weather Service River Forecast Center (initial planning for the use of Radarsat and ERS-2 for flood warning). The work thus initiated with this proposal is continuing.

Brakenridge, G. Robert↗
NASA NTRS1990

A conceptual model for megaprogramming

Megaprogramming is component-based software engineering and life-cycle management. Magaprogramming and its relationship to other research initiatives (common prototyping system/common prototyping language, domain specific software architectures, and software understanding) are analyzed. The desirable attributes of megaprogramming software components are identified and a software development model and resulting prototype megaprogramming system (library interconnection language extended by annotated Ada) are described.

Tracz, Will↗
DOE OSTI2025

Enabling Low-Temperature (LTP) Ignition Technologies for Multi-Mode Engines through the Development of a Validated High-Fidelity LTP Model for Predicative Simulations Tools

The goal of multi-mode engine architectures is to extend current lean-burn dilution limits with renewable fuels, which requires spark plugs to deposit high energies (hundreds of mJ) in order to initiate ignition and complete combustion. At elevated energy deposition rates, spark plugs experience increased electrode erosion and thermal losses, which ultimately shortens the spark-plug lifetime and lowers ignition efficiency. As such, in order to safeguard the efficiency gains of multi-mode concepts, new and improved ignition technologies are required. Recently, non-equilibrium low-temperature plasmas (LTP) have been shown to promote energy-efficient ignition via quenching and transport of electronically excited atoms and molecules, selective radical production and fast heating of hydrocarbon/air mixtures [1-2]. Thus, LTP is seen as a technology that can potentially improve the energy extraction efficiency of fuels, while enabling kinetically controlled combustion modes towards fuel leaner conditions to realize current DOE VTO goals of improving the sustainability of future mobility [3]. Although many previous studies have demonstrated the efficacy of plasma-assisted ignition to enhance combustion, the detailed enhancement mechanisms remain largely unknown, especially for oxygenated fuels and at elevated pressures that are most relevant to practical engine conditions. These barriers hinder the development of accurate and comprehensive numerical models that seek to describe LTP-based ignition in existing engine design software tools and methods. Current state-of-the-art simulation capabilities for LTP ignition systems are in need of improvements since they deliver qualitative results only due to important limitations of existing approaches. Firstly, validated kinetic models with elementary steps for plasma discharges in oxygenated fuel/air mixtures of relevance to the transportation sector are required. Such kinetic models do not exist at present and will be developed and validated within this project. Secondly, plasma discharges and reactive mixture ignition are multi-scale, unsteady processes requiring high-performance numerical methods and software that execute efficiently on DOE supercomputers. Such software does not exist at present and will be developed and applied to practical LTP ignition scenarios as part of this project. Thirdly, experimental databases that are tailored to serve as benchmark in support of the development of predictive computational models of LTP ignition do not exist and will be part of this project.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2013

30-kW Maintenance-Free Stirling Engine for High-Performance Dish Concentrating Solar

This final report summarizes the final results of the 30-kW Maintenance-Free Stirling Engine for High-Performance Dish Concentrating Solar project performed by Infinia Corporation for the U.S. Department of Energy under contract DE-FC36-08GO18032 during the project period of March 1, 2008 – March 31, 2012. Over the course of the project, Infinia Corporation achieved substantial progress in the design, fabrication, assembly, testing and evaluation of a 30-kW multi-cylinder, free-piston Stirling engine (MCFPSE) prototype. In this effort, prior MCFPSE conceptual design approaches were adapted for the development of a 30-kW design, thereby producing a cohesive design package and detailed performance specifications. Numerical analysis using the Stirling Engine Analysis and Optimization Code (SAGE) was completed culminating in the selection of a fully balanced 6-cylinder configuration composed of two 3-cylinder engine modules. The model was refined throughout the program to include all wall conduction losses, piston and rod seal losses, appendix, shuttle and pumping losses, and piston radiation loss to closely represent the real system. Performance mapping of the engine was then completed using the detailed model. Based on knowledge and experience from its existing commercial products, and using the results from the extensive numerical modeling discussed above, Infinia successfully fabricated, assembled and tested two sets of 30-kW engine modules. One of these configurations was driven by an external power supply and operated as a cooler in order to verify the mechanical integrity of the system. In the engine configuration, induction heaters provided the energy to the heater head to operate the engine. Considerable effort was expended on correcting problems associated with contact and rubbing of internal components, identification and subsequent elimination of internal leaks, and undesirable amounts of piston bias. Many details concerning the fabrication and testing efforts are considered proprietary. As a result, those findings are not disclosed herein but rather can be found in proprietary reports supplied to the DOE on a periodic basis. The prototype did not fully demonstrate performance nor did it realize the projected cost targets. Furthermore, the basis of the mechanical and electrical architecture of the 30-kW multi-cylinder prototype design is now at least two generations old. Alternatively, Infinia’s 8-kW Bonneville generator, which was successfully demonstrated in April 2012, and is slated to enter commercial production in late 2013, offers immediate benefits of lower cost and significantly higher power density such that the generator will likely meet DOE’s Sunshot LCOE target goal of $\$$0.07-0.10/kWh by 2015 and $\$$0.05-0.07/kWh by 2020. Therefore, the design of a 30-kW Stirling engine as disclosed in the project will not be pursued further. However, Infinia remains keenly interested in developing higher power Stirling machines in the future based on the new Bonneville mechanical design.

14 SOLAR ENERGY↗
NASA NTRS2017

Modeling for Battery Prognostics

For any battery-powered vehicles (be it unmanned aerial vehicles, small passenger aircraft, or assets in exoplanetary operations) to operate at maximum efficiency and reliability, it is critical to monitor battery health as well performance and to predict end of discharge (EOD) and end of useful life (EOL). To fulfil these needs, it is important to capture the battery's inherent characteristics as well as operational knowledge in the form of models that can be used by monitoring, diagnostic, and prognostic algorithms. Several battery modeling methodologies have been developed in last few years as the understanding of underlying electrochemical mechanics has been advancing. The models can generally be classified as empirical models, electrochemical engineering models, multi-physics models, and molecular/atomist. Empirical models are based on fitting certain functions to past experimental data, without making use of any physicochemical principles. Electrical circuit equivalent models are an example of such empirical models. Electrochemical engineering models are typically continuum models that include electrochemical kinetics and transport phenomena. Each model has its advantages and disadvantages. The former type of model has the advantage of being computationally efficient, but has limited accuracy and robustness, due to the approximations used in developed model, and as a result of such approximations, cannot represent aging well. The latter type of model has the advantage of being very accurate, but is often computationally inefficient, having to solve complex sets of partial differential equations, and thus not suited well for online prognostic applications. In addition both multi-physics and atomist models are computationally expensive hence are even less suited to online application An electrochemistry-based model of Li-ion batteries has been developed, that captures crucial electrochemical processes, captures effects of aging, is computationally efficient, and is of suitable accuracy for reliable EOD prediction in a variety of operational profiles. The model can be considered an electrochemical engineering model, but unlike most such models found in the literature, certain approximations are done that allow to retain computational efficiency for online implementation of the model. Although the focus here is on Li-ion batteries, the model is quite general and can be applied to different chemistries through a change of model parameter values. Progress on model development, providing model validation results and EOD prediction results is being presented.

Prognostics↗
NASA NTRS2002

Cosmic-Ray Background Flux Model Baed on a Gamma-Ray Large Area Space Telescope Baloon Flight Engineering

Cosmic-ray background fluxes were modeled based on existing measurements and theories and are presented here. The model, originally developed for the Gamma-ray Large Area Space Telescope (GLAST) Balloon Experiment, covers the entire solid angle (4(pi) sr), the sensitive energy range of the instrument ((approx) 10 MeV to 100 GeV) and abundant components (proton, alpha, e(sup -), e(sup +), (mu)(sup -), (mu)(sup +) and gamma). It is expressed in analytic functions in which modulations due to the solar activity and the Earth geomagnetism are parameterized. Although the model is intended to be used primarily for the GLAST Balloon Experiment, model functions in low-Earth orbit are also presented and can be used for other high energy astrophysical missions. The model has been validated via comparison with the data of the GLAST Balloon Experiment.

COSMIC RADIATION↗
DOE OSTI2021

An Experimental Study of Uncertainty Considerations Associated with Predicting Auto-ignition Timing using Livengood-Wu Integral Method

The application of the Livengood-Wu (LW) integral method as a tool to estimate knock onset in spark ignited (SI) engines and combustion phasing in advanced compression ignition (ACI) engines has been demonstrated through simulations several times. In this study, the effect of uncertainties associated with parameters required for the LW integral method, when used as a tool for model based control of ignition timing in an ACI engine, were experimentally studied using five full boiling range gasoline fuels. As a first step, the method was applied to experimental data from a rapid compression machine and it was found that the ability of the LW integral method to predict ignition timing was very sensitive to the performance of the chemical kinetic model of each fuel. The method was subsequently applied to experimental data from a single-cylinder gasoline engine with simple approximations for the LW integral input parameters, and it was found that the predicted time of ignition was significantly different from the actual start of combustion. Systematic evaluation of various parametric uncertainties conducted thereafter showed that the uncertainty in cylinder charge temperature has the greatest influence. Improved methods of estimating cylinder charge temperature are proposed to account for the previously determined corrections, to enable the use of the LW integral method for model based control of ignition timing.

Compression Ignition↗
DOE OSTI2024

Latent Pitfalls in Microstructure-Based Modeling for Thermally Aged 9Cr-1Mo-V Steel (Grade 91)

A case study was conducted on a mechanistic model development that predicted tensile strength deterioration with thermal aging of 9Cr-1Mo-V steel in supporting the 60-year design life expected for advanced nuclear reactors. For property prediction beyond practical testing times, mechanistic modeling is highly desired, as it taps into the physics of structure–property relationships and therefore can generate reliable results for extrapolation. Meanwhile, as mechanistic models are often complicated, reflecting the intricacy of microstructure and strengthening mechanisms, pitfalls that are difficult to detect often exist. Here, this paper discusses latent pitfalls that are common in mechanistic modeling or specific in this 9Cr-1Mo-V case development through using the American Society of Mechanical Engineers verification and validation in computational solid mechanics (ASME V&V 10) standard for evaluating credibility of modeling in materials engineering. Suggestions are also made for enhancing reliability of microstructure-based modeling.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Report on High-Fidelity Dynamic Modeling of a Coal-Fired Steam Power Plant

As a result of the growth of renewables including solar and wind energy with fluctuating production, fossil fuel power plants are being required to cycle between high and low power production. This cycling is both at a greater frequency and over a wider range than in the past. In many cases, power plants are not designed for this type of cycling operation but can nonetheless endure these challenging operation requirements under the right conditions. In this project, optimal solution for enhanced flexible operations are being investigated using model based estimation and control techniques. To support the development of model-based estimator and model-based controls at GE Global Research, GE Steam Power configured a dynamic model using a reference steam plant design including the boiler, turbine, and water/steam conditioning systems as well as the controls needed for plant cycling with stability and reliability. The dynamic model was built using the APROS® software from VTT, and then calibrated to multiple load conditions from full load (100%TMCR) to partial loads (75%TMCR, 50%TMCR and 25%TMCR) based on internally developed steady state heat balance models at the unit level. These internal heat balance models are based on first principles and extensive engineering experiences from GE Steam Power as an OEM and a services provider. This topical report presents the structure of the unit level dynamic model, the tuning process, and representative simulation results from typical load cycling simulations using the dynamic model.

01 COAL, LIGNITE, AND PEAT↗
DOE OSTI2019

Surface Engineering by Simultaneous Action of Multiple External Fields (Final Technical Report)

This research project aims at enabling surface engineering strategies based on the fundamental understanding and predictive modeling of the surface morphological response of solid materials subjected to the combined action of multiple external forces. Surface morphological response to mismatch strain in strained-layer heterostructures also is analyzed. Our research focuses on the surface morphological evolution and stability of electrically conducting (metallic, such as Cu) and semiconducting (such as Si) solids under the simultaneous application of mechanical stresses, electric fields, and temperature gradients. We emphasize on identifying the conditions under which the multiply driven surface morphology is stable and on exploring the complexity of the corresponding various morphologically stable surface patterns. We also emphasize on the driven evolution of nanoscale features aiming at stabilizing and controlling nanoscale patterns on surfaces by their manipulation through simultaneously applied multiple external forces. Specifically, we address systematically the morphological response to the combined action of multiple external forces of bulk solid surfaces, surfaces of thin films grown epitaxially on thick or thin substrates, as well as nanoscale surface features such as coherently strained islands grown epitaxially on substrate surfaces. The research is based on a modeling approach that combines theoretical analyses of surface morphological stability with self-consistent dynamical simulations of surface evolution based on properly parameterized continuum and multiscale surface transport models.

36 MATERIALS SCIENCE↗
DOE OSTI2016

CFD Predictions of Severe Accident Natural Circulation Flows in a Combustion Engineering PWR

Computational fluid dynamics (CFD) is used to predict natural circulation flows between a reactor vessel upper plenum and a steam generator during specific severe accidents in a pressurized-water reactor (PWR). The predictions are based on a model for a simplified Combustion Engineering (CE) design. This work is part of a larger study to investigate the potential for severe accident induced steam generator (SG) tube failures in PWRs being conducted by the U.S. Nuclear Regulatory Commission Office of Nuclear Regulatory Research. The CFD model builds upon the lessons learned from previous studies of a generic Westinghouse PWR under similar conditions. The CE SG is significantly larger than the Westinghouse design and geometric differences are expected to reduce the mixing and increase the potential thermal challenge to the SG tubes. The CFD predictions are utilized to improve the basis for simplified flow and mixing models applied in one-dimensional severe accident codes. Parameters are determined to support system code modeling of hot leg and SG inlet plenum mixing, hot leg and SG tube bundle flow rates, and tube inlet temperatures. This updated modeling uses boundary conditions obtained from system analysis code predictions of severe accident behavior in a CE PWR. Model refinements include an improved tube bundle representation and improvements to the thermal boundary conditions. Results indicate less mixing and higher tube inlet temperatures for the CE design compared to previous Westinghouse studies. (author)

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
NASA NTRS1991

Expert systems for assembly sequence evaluation

The objective is to identify delivered orbital subassemblies derived from a phase A conceptual space vehicle design while minimizing on-orbit assembly complexity. The topics covered are presented in viewgraph form and include the following: an orbital assembly scenario; subassemblies; subassembly identification and simulation model; estimating subassembly interfaces by engineering functional allocation using MBR; representative data base of interface connections; vehicle decomposition model; connection interface estimation model results; knowledge-source 1 and 2 results; knowledge-source 3 -- connection-index results (Boeing NTR-2016 CI-profile); KS3 algorithm, and KS3 flight manifest results.

Jolly, Steve↗
DOE OSTI2018

A predictive 0-D HCCI combustion model for ethanol, natural gas, gasoline, and primary reference fuel blends

Homogeneous Charge Compression Ignition (HCCI) is a promising advanced combustion concept with high thermal efficiency and low exhaust emissions. This work purposes a computationally-efficient, zero-dimensional (0-D) HCCI combustion model that does not need to solve any differential equations. To develop the burn rate correlations, experimental HCCI data of ethanol, natural gas, E10-gasoline, and Primary Reference Fuel (PRF) blends was collected on a CFR engine. The burn rate model is built based on the individual cycle mass fraction burned (MFB) curves calculated from the experimental data. CA0 can be predicted from an ignition delay correlation. Once CA0 is known, CA50 and CA90 can be predicted based on CA0 and the charge-mass equivalence ratio Φ'. Then, with CA0, 50 and 90 known, a Wiebe function can be constructed to represent the MFB curve and burn rate. For PRF blends, low-temperature and high-temperature heat releases (LTHR and HTHR) are modeled by two Wiebe functions with Φ' and PRF number dependence. The fitted model has high accuracy compared to the experimental data with R 2 values generally greater than 0.97. Finally, various ignition delay correlations from the literature are also tested against the experimentally collected data and some modifications to the correlations are suggested.

42 ENGINEERING↗