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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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Design and parametric data for the hydrogen oxygen engine used in the orbiter transfer vehicle are presented. The engine design is analyzed and minimum engine performance and man rating requirements are discussed. Candidate cycle performance, weight and tradeoff analysis, and cost estimates are reported.
Fracture of Czochralski silicon wafers during processing is an important factor in solar cell yield and cost. A fracture-mechanics test and analysis program was developed to evaluate fracture strength changes in the in-process wafer-to-cell processing at different stages on a manufacturer's production line. The strength data were described by Weibull statistical analysis and can be interpreted with the surface-flaw distribution of each of the process steps.
The effect of population and certain environmental characteristics on the availability of land for siting nuclear power plants was assessed. The study area, consisting of the 48 contiguous states, was divided into 5 kilometer (km) square grid cells yielding a total of 600,000 cells. Through the use of a modern geographic information system, it was possible to provide a detailed analysis of a quite large area. Numerous maps and statistical tables were produced, the detail of which were limited only by available data. Evaluation issues included population density, restricted lands, seismic hardening, site preparation, water availability, and cost factors.
The maximum likelihood estimator has been used to extract stability and control derivatives from flight data for many years. Most of the literature on aircraft estimation concentrates on new developments and applications, assuming familiarity with basic estimation concepts. Some of these basic concepts are presented. The maximum likelihood estimator is briefly discussed and the aircraft equations of motion that the estimator uses. The basic concepts of minimization and estimation are examined for a simple computed aircraft example. The cost functions that are to be minimized during estimation are defined and discussed. Graphic representations of the cost functions are given to help illustrate the minimization process. Finally, the basic concepts are generalized, and estimation from flight data is discussed. Some of the major conclusions for the computed example are also developed for the analysis of flight data.
The concept of commonality to enhance cost savings, as applied to NASA's Space Station Common Module (CM), is explored. The equipment to be included in the CM is organized by subsystems of structure, power, thermal, command and data handling, environmental control and life support, and crew station. The weight, volume, and quantity of each instrument item will be subsequently added to support a cost model. The CM concept, its reference configuration, power distribution and management, and cost sensitivity options are discussed in detail. Some computer programs are outlined, stressing the importance of the existing capabilities of the STS and the optimum commonality case.
The orbiting space station being developed by NASA will have many thermal sources and sinks as well as requirements for the transport of thermal energy through large distances. The station is also expected to evolve over twenty or more years from an initial design. As the station evolves, thermal management will become more difficult. Thus, analysis techniques to evaluate the effects of changing various thermal loads and the methods utilized to control temperature distributions in the station are essential. Analysis techniques, including a user-friendly computer program, were developed which should prove useful to thermal designers and system analysts working on the space station. The program uses a data base and user input to compute costs, sizes, and power requirements for individual components and complete systems. User input consists of selecting mission parameters, selecting thermal acquisition configurations, transport systems and distances, and thermal rejection configurations. The capabilities of the program may be expanded by including additional thermal models as subroutines.
To further investigate nozzle flow, numerical computations are employed. The computations produce complete flow velocity and temperature fields within the nozzle. As a check, these results can be compared with experimental data at the wall. Once an accurate numerical scheme has been validated, it can be used as a design tool to predict the performance of other nozzle designs without the cost of experimental testing. Typically, the numerical analysis assumes either a laminar boundary layer or a fully turbulent boundary layer which is steady and two-dimensional. Boundary layer transition is not considered. Computing both the completely laminar boundary layer and the completely turbulent boundary layer conditions gives the minimum and maximum wall heat flux possible for a specified geometry. When the experimental heat flux measurements lie between these two values, the nature of the boundary layer is unknown. The boundary layer may have transitioned from laminar to turbulent; three-dimensional structures may be present in the boundary layer, or the inlet flow conditions may not be correctly specified in the computation.
Integrated Computational Materials Engineering (ICME) has recently received widespread attention due to its promises in reducing dependence on physical testing for engineering design by relying on simulation, reducing both time and cost to market for various applications. ICME however requires validated multiscale material models, which is heavily dependent on available test data with full material and test pedigree, including material processing, test and measurement equipment, raw data collection, and analysis methodology and results. Populating searchable information management systems with such rich data sets is often burdensome for data producers, resulting in a lack of findable data for modelers to validate and verify their models. To overcome these cultural barriers to ICME, NASA has developed of various database-integration toolsets that perform both data management activities within the organization’s best practices with additional functionality that relieves the effort of the data producer and promotes adoption of information management system. One such tool currently under development is Py MILab, an automatic framework for automatic capturing, analysis, maintenance, and storage of material test data. Py MILab uses a modular approach for capturing raw data, analyzing the data, and storing the data in a database, interfaced by neutral file structures, to promote plug-and-play capabilities for various analysis types. TMAnalysis is a Python-based tool that performs automatic data reduction and analysis of uniaxial thermomechanical test data. The TMAnalysis toolset can be implemented within the Analysis module of Py MILab, and thus requires a populated neutral file form the Raw Data Module of Py MILab and outputs a Analysis neutral file compatible with the Database Module of Py MILab. TMAnalysis is able to perform automatic segmentation of multistage tests and perform data analysis and reduction, including determination of point-wise properties in tension, compression, and shear, analysis of stress relaxation tests, creep analysis and zone identification, and combination of these stage types for tests with complex loading histories. The TMAnalysis code is accompanied with a graphical user interface (GUI) that allows users to easily analyze test data in bulk, verify the automatic, consistent analysis performed by the backend code, and edit stage segmentation if necessary before producing the output neutral files, ensuring data is properly analyzed and maintained with full traceability.
The use of data derived from LANDSAT is facilitated when special products or computer enhanced images can be analyzed. However, the facilities required to produce and analyze such products prevent many users from taking full advantages of the LANDSAT data. A simple, low-cost method is presented by which users can make their own specially enhanced composite images from the four band black and white LANDSAT images by using the diazo process. The diazo process is described and a detailed procedure for making various color composites, such as color infrared, false natural color, and false color, is provided. The advantages and limitations of the diazo process are discussed. A brief discussion interpretation of diazo composites for land use mapping with some typical examples is included.
The design and development of helicopter rotors is subject to the many design variables and their interactions that effect rotor operation. Until recently, selection of rotor design variables to achieve specified rotor operational qualities has been a costly, time consuming, repetitive task. For the past several years, Kaman Aerospace Corporation has successfully applied multiple linear regression analysis, coupled with optimization and sensitivity procedures, in the analytical design of rotor systems. It is concluded that approximating equations can be developed rapidly for a multiplicity of objective and constraint functions and optimizations can be performed in a rapid and cost effective manner; the number and/or range of design variables can be increased by expanding the data base and developing approximating functions to reflect the expanded design space; the order of the approximating equations can be expanded easily to improve correlation between analyzer results and the approximating equations; gradients of the approximating equations can be calculated easily and these gradients are smooth functions reducing the risk of numerical problems in the optimization; the use of approximating functions allows the problem to be started easily and rapidly from various initial designs to enhance the probability of finding a global optimum; and the approximating equations are independent of the analysis or optimization codes used.
This project directly supports the Geothermal Technologies Office (GTO) objectives outlined in the Multi-Year Program Plan (MYPP) by advancing two key research areas: “Exploration and Characterization” and “Data, Modeling, and Analysis.” This project has successfully demonstrated a pre-drilling ability to image and characterize the distribution and connectivity of subsurface faults and fractures, key parameters for identifying permeable pathways that enable geothermal fluids to circulate and produce energy. Specifically, we developed and implemented innovative machine learning methodologies to enhance geothermal exploration. Large-scale faults were detected using a Convolutional Neural Network (CNN), while small-scale fractures were characterized using a novel Double-Beam Neural Network (DBNN). These tools have proven both technically effective and cost-efficient by reducing reliance on expensive exploratory drilling. Through collaboration with our geothermal industry partner, this research has significantly advanced techniques for identifying hidden geothermal systems and extending the productive lifespan of existing geothermal fields. We applied our methods to two geothermal fields—Soda Lake (Nevada) and Lightning Dock (New Mexico)—to identify shallow steam-charged fracture zones and characterize deep faults at depths of 1.5-2 km. The steam zone identified at the Soda Lake geothermal field showed excellent agreement with prior drilling data, validating the effectiveness of our approaches. In addition, the analysis revealed three new prospective drilling targets for further development and verification. The outcomes of this project improve our scientific understanding of geothermal reservoir behavior, enhance exploration efficiency, extend the economic life of existing geothermal plants. Ultimately, these advancements contribute to GTO’s goal of achieving more sustainable, affordable, and data-driven geothermal energy development across the United States.
LANDSAT images for three snowmelt seasons were utilized to map and analyze snowcover depletion on a small river basin in southeastern Wyoming. Results indicate that snowcover-runoff curves established from repetitive LANDSAT coverage may be used in conjunction with streamflow data to provide low-cost seasonal runoff forecasts having a high degree of accuracy. Additionally, detectable variations within a snowpack might provide temporal estimates of peak flows.
The decisions made in the conceptual design phase have large impacts on the resulting vehicle capability and cost. With the large and varied design space of Small Launch Vehicles(SLVs), it is even more important to have the data necessary to make informed design choices during the conceptual design phase. To enable extensive exploration of the SLV design space, a multi-disciplinary design framework was created. The design framework consists of trajectory, aerodynamics, propulsion, and structures disciplines. The framework then integrates and automates these tools, thus allowing for rapid design space exploration at the conceptual design phase. The outputs of the framework provide preliminary information on SLV size and structural configurations as well as propulsion and aerodynamic characteristics. Finally, the framework provides information necessary for the designer to make informed decisions on what variables lead to the desired performance characteristics and what segment of the design space would benefit from more detailed exploration.
Supported by the U.S. DOE NETL Award DE-FE0032043, this project was a collaborative effort. Project participants included the University of Kentucky Institute for Decarbonization and Energy Advancement (UK IDEA), Biosystems and Agricultural Engineering department (UK BAE), and Wabash Valley Resources, LLC. The goal of this final technical project report is to comprehensively summarize the work conducted on project DE-FE0032043. In accordance with the Statement of Project Objectives (SOPO), the University of Kentucky (UK) (Project Prime Recipient) has developed and studied a biomass/plastic fuel with a hydrophobic surface area less than 10 m 2 /m 3 that is suitable for oxygen-blown entrained flow gasification with slurry feed. The project involved the utilization of an existing thermogravimetric analysis (TGA)-mass spectrometer (MS), 1.5” drop tube furnace, 1 ton per day (TPD) coal gasifier, and high-pressure extruder operated at UK. The pilot-scale production of blended material was done at the Polymers Technology Center in Charlotte, North Carolina. Parametric testing and solid fuel blend slurry performance validation was completed using the UK entrained flow gasifier with multiple opposed burners to narrow the major near-term technical gaps that impede gasification of biomass and carbonaceous mixed wastes such as plastics in order to achieve net-negative CO 2 emissions. Project results validated the UK approach to address the major technical challenges on the biomass/plastic pretreatment and gasification. Previously, this has been limited in application to fluidized-type or moving bed-type gasifiers due to the high-water uptake of porous biomass containing hydroxyl groups during the conventional slurry preparation, resulting in a highly viscous, un-pumpable slurry. The biomass pretreatment with plastic developed for this project demonstrates advantages in cost and flexibility, which include: 1) the development of a blended solid fuel slurry with 55-60 wt% solids and comparable heating value to 100% coal-based water slurry; 2) the collection of gasification kinetic data and identification of preliminary operating conditions by performing thermogravimetric analysis, gasification experiments by using a 1.5” drop tube furnace; and finally 3) the demonstrated gasification of the blended solid fuel in the UK entrained flow gasifier with a long-lasting stable solid fuel blend slurry, dataset detailing operating conditions, and characterization of slag phase formation and solidification. The lab-scale data and experience obtained during this project encourages the development of technologies and commercial approaches to enable a hydrogen-based energy economy while achieving net-negative CO 2 emissions through gasification of coal, biomass, and carbonaceous mixed wastes such as plastics.
A number of new aircraft concepts have recently been proposed which tightly couple the propulsion system design and operation with the overall vehicle design and performance characteristics. These concepts include propulsion technology such as boundary layer ingestion, hybrid electric propulsion systems, distributed propulsion systems and variable cycle engines. Initial studies examining these concepts have typically used a traditional decoupled approach to aircraft design where the aerodynamics and propulsion designs are done a-priori and tabular data is used to provide inexpensive look ups to the trajectory ana-ysis. However the cost of generating the tabular data begins to grow exponentially when newer aircraft concepts require consideration of additional operational parameters such as multiple throttle settings, angle-of-attack effects on the propulsion system, or propulsion throttle setting effects on aerodynamics. This paper proposes a new modeling approach that eliminated the need to generate tabular data, instead allowing an expensive propulsion or aerodynamic analysis to be directly integrated into the trajectory analysis model and the entire design problem optimized in a fully coupled manner. The new method is demonstrated by implementing a canonical optimal control problem, the F-4 minimum time-to-climb trajectory optimization using three relatively new analysis tools: Open M-DAO, PyCycle and Pointer. Pycycle and Pointer both provide analytic derivatives and Open MDAO enables the two tools to be combined into a coupled model that can be run in an efficient parallel manner that helps to cost the increased cost of the more expensive propulsion analysis. Results generated with this model serve as a validation of the tightly coupled design method and guide future studies to examine aircraft concepts with more complex operational dependencies for the aerodynamic and propulsion models.
Rationale Nd and Sm isotope ratios play an important role in geological dating and as nuclear forensic signatures; however, the overlap of the respective 144, 148, 150 Nd/Sm isobars requires prior separations to be performed before analysis on typical MS platforms. The work presented here overcomes these isobaric interferences using ultrahigh‐mass resolution to alleviate interference without prior chemical separations. Methods A liquid sampling‐atmospheric pressure glow discharge ion source was coupled to a standard, QExactive Focus Orbitrap mass spectrometer, providing a mass resolution of ~80 k. A Spectroswiss FTMS booster X2 data acquisition package was used to collect extended transients, providing much higher mass resolution; ~230 k and ~600 k are employed here for Nd and Sm isotopes. Results While the standard Orbitrap resolution is far greater than typical “atomic” MS platforms, it was insufficient to alleviate all isobars. The use of a resolution of ~230 k resulted in baseline separation across the entire isotopic envelope for both Nd and Sm. Isotope ratios obtained from Nd:Sm mixtures using high‐resolution were equivalent to those found for individual‐element solutions, while isotope ratios obtained at a resolution of ~80 k (standard for the OEM data system) showed large deviations. Conclusions Use of ultrahigh‐resolution is an attractive alternative to extensive chemical separations to alleviate severe isobaric interferences. Sufficient mass resolution greatly reduces/eliminates the need for sample manipulations (separations) before analysis while reducing costs and total analysis times.
The research and advanced development work is reported on a ballistic-mode, outer planet spacecraft using radioisotope thermoelectric generator (RTG) power. The Thermoelectric Outer Planet Spacecraft (TOPS) project was established to provide the advanced systems technology that would allow the realistic estimates of performance, cost, reliability, and scheduling that are required for an actual flight mission. A system design of the complete RTG-powered outer planet spacecraft was made; major technical innovations of certain hardware elements were designed, developed, and tested; and reliability and quality assurance concepts were developed for long-life requirements. At the conclusion of its active phase, the TOPS Project reached its principal objectives: a development and experience base was established for project definition, and for estimating cost, performance, and reliability; an understanding of system and subsystem capabilities for successful outer planets missions was achieved. The system design answered long-life requirements with massive redundancy, controlled by on-board analysis of spacecraft performance data.
In order to reduce costs and accelerate vehicle turnaround, a single automated system will be developed to support shuttle launch site operations, replacing a multiplicity of systems used in previous programs. The Launch Processing System will provide real-time control, data analysis, and information display for the checkout, servicing, launch, landing, and refurbishment of the launch vehicles, payloads, and all ground support systems. It will also provide real-time and historical data retrieval for management and sustaining engineering (test records and procedures, logistics, configuration control, scheduling, etc.).