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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 883 records · Page 49

A technique for mass spectrometer measurements of atomic and molecular oxygen in the lower thermosphere

A neutral mass spectrometer with a quasi-open ion source was flown on each of the Atmosphere Explorer (AE) C, D, and E satellites. The three instruments provided an opportunity to study the effects of different source insert materials on the source surface chemistry. It was found that, after a period of conditioning in space, the recombination coefficient of atomic oxygen on gold appears to be substantially lower than it is on Nichrome V. The lower recombination coefficient on gold allows the spectrometer to directly measure a significant fraction of the incident atomic oxygen, making it possible to distinguish between ambient O and O2. Equations are developed to calculate the atomic and molecular oxygen densities. Preliminary measurements of molecular oxygen densities obtained by this technique agree well with measurements taken in the fly-through mode of operation.

Kayser, D. C.↗

New Class of Flow Batteries for Terrestrial and Aerospace Energy Storage Applications

Future sustainable energy generation technologies such as photovoltaic and wind farms require advanced energy storage systems on a massive scale to make the alternate (green) energy options practical. The daunting requirements of such large-scale energy systems such as long operating and cycle life, safety, and low cost are not adequately met by state-of-the-art energy storage technologies such as vanadium flow cells, lead-acid, and zinc-bromine batteries. Much attention is being paid to redox batteries specifically to the vanadium redox battery (VRB) due to their simplicity, low cost, and good life characteristics compared to other related battery technologies. NASA is currently seeking high-specific- energy and long-cycle-life rechargeable batteries in the 10-to-100-kW range to support future human exploration missions, such as planetary habitats, human rovers, etc. The flow batteries described above are excellent candidates for these applications, as well as other applications that propose to use regenerative fuel cells. A new flow cell technology is proposed based on coupling two novel electrodes in the form of solvated electron systems (SES) between an alkali (or alkaline earth) metal and poly aromatic hydrocarbons (PAH), separated by an ionically conducting separator. The cell reaction involves the formation of such SES with a PAH of high voltage in the cathode, while the alkali (or alkaline earth metal) is reduced from such an MPAH complex in the anode half-cell. During recharge, the reactions are reversed in both electrodes. In other words, the alkali (alkaline earth) metal ion simply shuttles from one M-PAH complex (SES) to another, which are separated by a metal-ion conducting solid or polymer electrolyte separator. As an example, the concept was demonstrated with Li-naphthalene//Li DDQ (DDQ is 2,3-Dichloro-5,6-dicyano- 1,4-benzoquinone) separated by lithium super ion conductor, either ceramic or polymer (solid polymer or gel polymer) electrolytes. The reactants are Li-naphthalene dissolved in tetrahydrofuran (THF) with a lithium salt of 1M LiBF4 (lithium tetra fluoroborate) in the anode compartment, and DDQ again dissolved in THF and also containing 1M LiBF4 salt in the cathode half-cell. The solid electrolyte separator used in the first set of experiments is a ceramic solid electrolyte, available from a commercial source. The open circuit voltage of the cells is close to 3.0 V, as expected from the individual half-cell voltages of Li-naphthalene and Li-DDQ. Upon discharge, the cell shows steady discharge voltage of 2.7 V, which confirms that the electrochemical processes do involve lithium ion shuttling from the anodic compartment to the cathode half-cell. The reversibility or rechargeability is demonstrated by charging the partially discharged cells (i.e., with lithium present in the DDQ half). Once again, a steady voltage close to 3.0 V was observed during charge, indicating that the system is quite reversible. In the subsequent concept-demonstration studies, the ceramic electrolyte has been replaced with a gel polymer electrolyte, e.g., PVDF-HFP (poly vinylene difluoride hexafluoropropene) gel, which has several advantages such as high ionic conductivity (almost comparable to liquid electrolyte and about 2 orders of magnitude better than the ceramic equivalent), lower cost, and possibly higher chemical stability at the anode. In addition, it can be bonded to the electrode by thermal fusion to form membrane electrode assemblies (MEAs), as is done in fuel cells.

Bugga, Ratnakumar V.↗

Performance of the Dot Product Function in Radiative Transfer Code SORD

The successive orders of scattering radiative transfer (RT) codes frequently call the scalar (dot) product function. In this paper, we study performance of some implementations of the dot product in the RT code SORD using 50 scenarios for light scattering in the atmosphere-surface system. In the dot product function, we use the unrolled loops technique with different unrolling factor. We also considered the intrinsic Fortran functions. We show results for two machines: ifort compiler under Windows, and pgf90 under Linux. Intrinsic DOT_PRODUCT function showed best performance for the ifort. For the pgf90, the dot product implemented with unrolling factor 4 was the fastest. The RT code SORD together with the interface that runs all the mentioned tests are publicly available from ftp:maiac.gsfc.nasa.govpubskorkinSORD_IP_16B (current release) or by email request from the corresponding (first) author.

polarized radiative transfer↗

Modeling Turboshaft Engines for the Revolutionary Vertical Lift Technology Project

Turboshaft engine performance and weight models were developed to support conceptual propulsion and vehicle mission design and performance under the Revolutionary Vertical Lift Technology (RVLT) Project. These models were developed using open data sources, assuming current and advanced technology levels, and range from 650 to 7,500 shaft output horsepower (485 to 5,600 kW). Documenting the methodology, assumptions, and resulting performance realizes important benefits NASA and the aviation community. NASA concept vehicle efforts using these propulsion models can be more readily shared among the government, industry and university community as common baselines to support current and future work. Assessing the benefits of advanced technologies and new configurations can be facilitated using these models, which helps guide technology investment. As the various modeling conceptual vehicle and mission analysis environments advanced, these models can be used directly for broader systems analysis studies, including optimization within the propulsion model itself. To perform this effort, the turboshaft engine is briefly discussed, highlighting the specific components and their expected performance characteristics over the power range and technology levels considered. Engine configurations will also be discussed as they will vary based on power output and assumed technology level. Engine performance, such as airflow, power output and weight will be reported, noting trends that are important for system studies. The effect of advanced propulsion technologies on RVLT concept vehicles are also reported. Finally, potential future propulsion modeling work will be proposed.

Snyder, Christopher A.↗

Modeling Turboshaft Engines for the Revolutionary Vertical Lift Technology Project

Turboshaft engine performance and weight models were developed to support conceptual propulsion and vehicle mission design in support of the National Aeronautics and Space Administration's (NASA) Aeronautics Mission Research Directorate's (ARMD) Revolutionary Vertical Lift Technology (RVLT) Project. These models were developed using open data sources, assuming current and advanced technology levels, and range from 650 to 7,500 shaft output horsepower (485 to 5,600 kilowatts). Documenting the methodology, assumptions, and resulting performance realizes important benefits for NASA and the aviation community. NASA concept vehicle efforts using these propulsion models can more readily shared among the government, industry and university community as common baselines to support current and future work. Assessing the benefits of advanced technologies and new configurations can be facilitated using these models, which helps guide technology investment. As the various modeling conceptual vehicle and mission analysis environments advance, these models can be used directly for broader systems analysis studies, including optimization within the propulsion model itself. To perform this effort, the turboshaft engine is briefly discussed, highlighting the specific components and their expected performance characteristics over the power range and technology levels considered. Engine configurations will also be discussed as they will vary based on power output and assumed technology level. Engine performance, such as airflow, power output and weight will be reported, noting trends that are important for system studies. The effect of advanced propulsion technologies on RVLT-concept vehicles are also reported. Finally, potential future propulsion modeling work will be proposed.

Snyder, Christopher A.↗

Modeling Turboshaft Engines for the Revolutionary Vertical Lift Technology Project, Expanded

Turboshaft engine performance and weight models were developed to support conceptual propulsion and vehicle mission design and performance under the Revolutionary Vertical Lift Technology (RVLT) Project in 2019 by Snyder in Ref. 1. These models were developed using open data sources, assuming present and future technology levels, and range from 650 to 5,000 output shaft horsepower (485 to 3,730 kW). This paper expands on the previous research, extending the power ranges from 200 to 15000 output shaft horsepower (150 to 11,200 kW) and documenting the methodology, assumptions, and engine performance realizes important benefits for NASA and the aviation community. NASA concept vehicle study efforts using these baseline propulsion models can be more readily shared among the government, industry, and university community to support present and future work. Assessing the benefits of advanced technologies and new configurations can be facilitated using these models, which helps guide technology investment. As the various modeling conceptual vehicle and mission analysis environments advance, these models can be used directly for broader systems analysis studies, including optimization within the propulsion model itself. To expand on the previous effort, the turboshaft engine is briefly discussed, highlighting the specific components, and expected performance characteristics over the updated power range and technology levels considered. Additional engine configurations will also be discussed as they vary based on power output and assumed technology level. Engine performance, such as airflow, power output and weight will be updated, noting trends that are important for system studies. Finally, the effect of advanced propulsion technologies on RVLT concept vehicles are also reported along with the tools and software methods used to complete the analysis.

VTOL↗

Modeling Turboshaft Engines for the Revolutionary Vertical Lift Technology Project, Expanded

Turboshaft engine performance and weight models were developed to support conceptual propulsion and vehicle mission design and performance under the Revolutionary Vertical Lift Technology (RVLT) Project in 2019 by Snyder in Ref. 1. These models were developed using open data sources, assuming present and future technology levels, and range from 650 to 5,000 output shaft horsepower (485 to 3,730 kW). This paper expands on the previous research, extending the power ranges from 200 to 15000 output shaft horsepower (150 to 11,200 kW) and documenting the methodology, assumptions, and engine performance realizes important benefits for NASA and the aviation community. NASA concept-vehicle study efforts using these baseline propulsion models can be more readily shared among the government, industry, and university community to support present and future work. Assessing the benefits of advanced technologies and new configurations can be facilitated using these models, which helps guide technology investment. As the various modeling conceptual vehicle and mission analysis environments advance, these models can be used directly for broader systems analysis studies, including optimization within the propulsion model itself. To expand on the previous effort, the turboshaft engine is briefly discussed, highlighting the specific components, and expected performance characteristics over the updated power range and technology levels considered. Additional engine configurations will also be discussed as they vary based on power output and assumed technology level. Engine performance, such as airflow, power output and weight will be updated, noting trends that are important for system studies. Finally, the effect of advanced propulsion technologies on RVLT concept vehicles are also reported along with the tools and software methods used to complete the analysis

VTOL↗

Modeling Turboshaft Engines for the Revolutionary Vertical Lift Technology Project, Expanded

Turboshaft engine performance and weight models were developed to support conceptual propulsion and vehicle mission design and performance under the Revolutionary Vertical Lift Technology (RVLT) Project in 2019 by Snyder in Reference 7. These models were developed using open data sources, assuming present and future technology levels, and range from 650 to 5,000 output shaft horsepower (485 to 3,730 kW). This paper expands on the previous research, extending the power ranges from 200 to 15000 output shaft horsepower (150 to 11,200 kW) and documenting the methodology, assumptions, and engine performance realizes important benefits for NASA and the aviation community. NASA concept-vehicle study efforts using these baseline propulsion models can be more readily shared among the government, industry, and university community to support present and future work. Assessing the benefits of advanced technologies and new configurations can be facilitated using these models, which helps guide technology investment. As the various conceptual vehicle and mission analysis simulations are developed, these models can be used directly for broader systems analysis studies, including optimization within the propulsion model itself. To expand on the previous effort, the turboshaft engine is briefly discussed, highlighting the specific components, and expected performance characteristics over the updated power range and technology levels considered. Additional engine configurations will also be discussed as they vary based on power output and assumed technology level. Engine performance, such as airflow, power output and weight are updated, noting important trends for system studies. Finally, the effect of advanced propulsion technologies on public reference models including RVLT concept vehicles are reported along with the tools and software methods used to complete the analysis.

VTOL↗

arcjetCV: automating recession extraction from video

Arc jet Computer Vision (arcjetCV)[1][2] is a software application built to automate time-resolved recession tracking of edges in test videos, specifically for tracking material recession and the shock-material standoff. This provides a new capability to resolve and validate new physics associated with non-linear processes and an essential step to reduce testing, modeling, and validation uncertainties for heatshield material performance. ArcticCV uses several types of machine learning (convolutional neural net: CNN, decision tree: DT, k-means unsupervised clustering: KM) to automate the video processing pipeline. These include inferring the start/stop of time segments of interest (1D CNN), measuring the time-dependent 2D recession of the material samples (2D CNN, DT), measuring the time-dependent shock standoff distance (2D CNN, DT), and post-processing cleaning of the recession data (KM). The software also provides a graphical user interface for ease of use. The results of using this tool on arc jet videos show non-linear time-dependent effects can be important for certain materials and characterizing certain failure modes.

Recession tracking↗

ArcjetCV: Automating Recession Tracking

Arc jet Computer Vision (arcjetCV) is a software application built to automate time-resolved recession tracking of edges in test videos, specifically for tracking material recession and the shock-material standoff. This provides a new capability to resolve and validate new physics associated with non-linear processes and an essential step to reduce testing, modeling, and validation uncertainties for heatshield material performance. ArcticCV uses several types of machine learning (convolutional neural net: CNN, decision tree: DT, k-means unsupervised clustering: KM) to automate the video processing pipeline. These include inferring the start/stop of time segments of interest (1D CNN), measuring the time-dependent 2D recession of the material samples (2D CNN, DT), measuring the time-dependent shock standoff distance (2D CNN, DT), and post-processing cleaning of the recession data (KM). The software also provides a graphical user interface for ease of use. The results of using this tool on arc jet videos show non-linear time-dependent effects can be important for certain materials and characterizing certain failure modes.

Recession tracking↗

ArcjetCV: Automating Arc Jet Analysis

Arc jet Computer Vision (arcjetCV) is a software application built to automate time-resolved recession tracking of edges in test videos, specifically for tracking material recession and the shock-material standoff. This provides a new capability to resolve and validate new physics associated with non-linear processes and an essential step to reduce testing, modeling, and validation uncertainties for heatshield material performance. ArcticCV uses several types of machine learning (convolutional neural net: CNN, decision tree: DT, k-means unsupervised clustering: KM) to automate the video processing pipeline. These include inferring the start/stop of time segments of interest (1D CNN), measuring the time-dependent 2D recession of the material samples (2D CNN, DT), measuring the time-dependent shock standoff distance (2D CNN, DT), and post-processing cleaning of the recession data (KM). The software also provides a graphical user interface for ease of use. The results of using this tool on arc jet videos show non-linear time-dependent effects can be important for certain materials and characterizing certain failure modes.

Recession tracking↗