Minimum mass bar for axial vibration at specified natural frequency.
Minimum mass bar design for axial vibration of beam with load distribution at specified natural frequency
VIBRATION MODE↗
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File Specification for the 7-km GEOS-5 Nature Run, Ganymed Release Non-Hydrostatic 7-km Global Mesoscale Simulation
This document describes the gridded output files produced by a two-year global, non-hydrostatic mesoscale simulation for the period 2005-2006 produced with the non-hydrostatic version of GEOS-5 Atmospheric Global Climate Model (AGCM). In addition to standard meteorological parameters (wind, temperature, moisture, surface pressure), this simulation includes 15 aerosol tracers (dust, sea-salt, sulfate, black and organic carbon), O3, CO and CO2. This model simulation is driven by prescribed sea-surface temperature and sea-ice, daily volcanic and biomass burning emissions, as well as high-resolution inventories of anthropogenic sources. A description of the GEOS-5 model configuration used for this simulation can be found in Putman et al. (2014). The simulation is performed at a horizontal resolution of 7 km using a cubed-sphere horizontal grid with 72 vertical levels, extending up to to 0.01 hPa (approximately 80 km). For user convenience, all data products are generated on two logically rectangular longitude-latitude grids: a full-resolution 0.0625 deg grid that approximately matches the native cubed-sphere resolution, and another 0.5 deg reduced-resolution grid. The majority of the full-resolution data products are instantaneous with some fields being time-averaged. The reduced-resolution datasets are mostly time-averaged, with some fields being instantaneous. Hourly data intervals are used for the reduced-resolution datasets, while 30-minute intervals are used for the full-resolution products. All full-resolution output is on the model's native 72-layer hybrid sigma-pressure vertical grid, while the reduced-resolution output is given on native vertical levels and on 48 pressure surfaces extending up to 0.02 hPa. Section 4 presents additional details on horizontal and vertical grids. Information of the model surface representation can be found in Appendix B. The GEOS-5 product is organized into file collections that are described in detail in Appendix C. Additional details about variables listed in this file specification can be found in a separate document, the GEOS-5 File Specification Variable Definition Glossary. Documentation about the current access methods for products described in this document can be found on the GEOS-5 Nature Run portal: http://gmao.gsfc.nasa.gov/projects/G5NR. Information on the scientific quality of this simulation will appear in a forthcoming NASA Technical Report Series on Global Modeling and Data Assimilation to be available from http://gmao.gsfc.nasa.gov/pubs/tm/.
GEOS-5↗
GES DISC Datalist Enables Easy Data Selection For Natural Phenomena Studies
In order to investigate and assess natural hazards such as tropical storms, winter storms, volcanic eruptions, floods, and drought in a timely manner, the NASA Goddard Earth Sciences Data and Information Services Center (GES DISC) has been developing an efficient data search and access service. Called "Datalist," this service enables users to acquire their data of interest "all at once," with minimum effort. A Datalist is a virtual collection of predefined or user-defined data variables from one or more archived data sets. Datalists are more than just data. Datalists effectively provide users with a sophisticated integrated data and services package, including metadata, citation, documentation, visualization, and data-specific services (e.g., subset and OPeNDAP), all available from one-stop shopping. The predefined Datalists, created by the experienced GES DISC science support team, should save a significant amount of time that users would otherwise have to spend. The Datalist service is an extension of the new GES DISC website, which is completely data-driven. A Datalist, also known as "data bundle," is treated just as any other data set. Being a virtual collection, a Datalist requires no extra storage space.
natural hazards↗
Assessment and Improvement of RANS-based Transition Models based on Experimental Data of the Common Research Model with Natural Laminar Flow
Transition models based on auxiliary transport equations augmenting the Reynolds-averaged Navier-Stokes (RANS) framework often rely upon the correlations that were derived from a limited number of low-speed experiments and do not account for all of the transition mechanisms and/or their variation with the significant flow parameters. Available data from a recent experiment in the National Transonic Facility at the NASA Langley Research Center are used to assess the current transition modeling capability in NASA's OVERFLOW 2.2o code for a swept wing configuration at transonic cruise conditions. Specifically, the OVERFLOW solutions are used together with detailed stability analysis of the boundary layer flow over the new Common Research Model with Natural Laminar Flow (CRM-NLF) to evaluate the accuracy and the robustness of the transport-equation-based transition models, with the goal of proposing improvements that would help to strengthen the physical basis of these models for the important class of flows involving the combined effects of crossflow and flow compressibility. Results highlight the significant underprediction of the laminar flow extent within the inboard region of the wing, wherein the onset of transition may be attributed to a gradual amplification of Tollmien-Schlichting instabilities.
Boundary layer transition↗
Natural Language Processing Methods for Air Traffic Management Text and Speech Data
This presentation discusses two efforts of the NARI AI/ML Intern team during the Fall 2021 OSTEM Internship term. For Letters of Agreement (LoA), we have studied how LoAs are structured and explored the question ‘What is an LoA constraint?’ To do this, our approach is data-driven, iterative, and assisted by machine learning when available. In this presentation, we will walk through our tasks of manually scanning through documents, performing a preliminary entity labelling task, and our unsupervised analysis on LoA procedures sections. After this research phase, we define the smallest constraint unit in an LoA, and start to perform entity extraction. Looking towards constraint extraction, we are also exploring the use of a one-class support vector machine (OneClassSVM) model to identify patterns within the data. The second effort of our team this term is focused on Air Traffic Control System Command Center (ATCSCC) advisory meetings, and the subsequent advisory documents that get published from their content. These advisory documents are important to give readily accessible summaries of daily operations, so that data centers, airline officials, and other stakeholders can easily understand the context of these meetings in real time. In applying machine learning to this scenario, two natural language processing tasks are used. First is developing machine learning models to convert the meeting speech data into text. With this text, use of extractive and abstractive text summarization models are used to automatically generate preliminary versions of the advisory documents.
Natural Language Processing↗
Natural Gas/Oxygen Burner Rig at The NASA Glenn Materials Research Laboratory
This technical memorandum describes the development of a new natural gas/oxygen (NG/O2) fueled burner rig to be used for high-temperature environmental durability studies of advanced materials and components at atmospheric pressure. The burner simulates the high-temperature, high-heat flux, and high-velocity thermal environments encountered in aerospace applications. It will be used to study environmental effects such as water vapor interactions relevant to advanced gas turbine engine materials such as ceramic matrix composites with protective environmental barrier coatings. The highest sample temperature achieved to date in a study of the oxidation and recession of monolithic silicon carbide is 3000 °F.
Burner Rig↗
Natural Language Understanding and Extraction of Flight Constraints Recorded in Letters of Agreement
This paper presents an automated information extraction and inference technique using natural language processing for extracting flight operational procedures and constraints embedded in heritage air traffic management documents. The extracted flight constraints can be digitized and fit into existing airspace information exchange models such as the Aeronautical Information Exchange Model (AIXM). This approach offers a digitized solution to disseminate airspace operating conditions to diverse air users and stakeholders in the National Airspace System (NAS). Furthermore, the digitized flight procedures can provide operational flexibility for emerging advanced air mobility providers and reduce traffic controller workload while maintaining current safety standards. To demonstrate this process, 1,972 Letters of Agreement (LOAs) have been selected for processing, named entity extraction, constraint identification and extraction. This dataset is derived from a subset of documents related to Air Route Traffic Control Centers (ARTCC) operations. We experimented with various traditional information extraction techniques, state-of-the-art machine learning and deep learning models to perform named entity recognition and pattern recognition on our dataset. We present the results from our experiments and demonstrate 99.0% F-1 score for named entity recognition, and a 96.6% accuracy for our entire workflow up to named entity recognition. We also discuss constraint definitions using generic patterned templates and extensions to this work in applying entity linking to digitally extracting relevant constraints.
Natural Language Processing↗
Natural Language Understanding and Extraction of Flight Constraints Recorded in Letters of Agreement
This paper presents an automated information extraction and inference technique using natural language processing for extracting flight operational procedures and constraints embedded in heritage air traffic management documents. The extracted flight constraints can be digitized and fit into existing airspace information exchange models such as the Aeronautical Information Exchange Model (AIXM). This approach offers a digitized solution to disseminate airspace operating conditions to diverse air users and stakeholders in the National Airspace System (NAS). Furthermore, the digitized flight procedures can provide operational flexibility for emerging advanced air mobility providers and reduce traffic controller workload while maintaining current safety standards. To demonstrate this process, 1,972 Letters of Agreement (LOAs) have been selected for processing, named entity extraction, constraint identification and extraction. This dataset is derived from a subset of documents related to Air Route Traffic Control Centers (ARTCC) operations. We experimented with various traditional information extraction techniques, state-of-the-art machine learning and deep learning models to perform named entity recognition and pattern recognition on our dataset. We present the results from our experiments and demonstrate 99.0% F-1 score for named entity recognition, and a 96.6% accuracy for our entire workflow up to named entity recognition. We also discuss constraint definitions using generic patterned templates and extensions to this work in applying entity linking to digitally extracting relevant constraints.
Natural Language Processing↗
Design and Experimental Results for a Low-Speed, Flapped, Slotted, Natural-Laminar-Flow Airfoil
A 12.91-percent-thick, flapped, slotted, natural-laminar-flow (SNLF) airfoil, the S702, intended for a low-speed, fixed-wing aircraft has been designed and analyzed theoretically and verified experimentally in The Pennsylvania State University Low-Speed, Low- Turbulence Wind Tunnel. The two primary objectives of high maximum lift, insensitive to roughness, and low profile drag have been achieved. The airfoil exhibits a sharp stall that is less abrupt than the stalls of earlier SNLF airfoils, which meets the design objective. The constraint on the pitching moment has been satisfied. Comparisons of the theoretical and experimental results show reasonably good agreement overall, given the complexity of the configuration.
slotted airfoil↗
Progress Towards the Design of a Natural Laminar Flow Wing for a Low-Boom Concept using CDISC
The X-59 is being developed to evaluate the ability to reduce sonic boom loudness to acceptable levels, with the ultimate goal of enabling supersonic flight overland. If successful, one of the future research challenges will be focused on reducing fuel burn. In this paper, Natural Laminar Flow (NLF) is proposed as a technology to decrease fuel burn for a low-boom configuration. This will require an evaluation of the compatibility of NLF wings for a low-boom configuration. As a first step towards this goal, the CATNLF method was employed using the CDISC design module to perform NLF design on an isolated wing from a low-boom configuration. The results show that the NLF wing features laminar flow over 46% of the wing upper surface, providing a potential 8.3 drag count reduction relative to the design with no laminar flow. Off-design analyses were additionally performed to evaluate the performance of the NLF wing for changes in angle of attack and Mach number. The results showed that the NLF wing offers a performance benefit over the entire range of off-design conditions considered, with most of the laminar flow maintained for near cruise conditions. Future work will consider the full configuration, which will enable an assessment of the impact of the NLF wing design on sonic boom loudness. Additionally, attachment line transition will be addressed, which was predicted to result in a loss of laminar flow on the inboard 50% span for this work. Finally, future research will focus on developing a method for effectively choosing the frequency-beta pairs used for the stability analysis to improve transition prediction and provide a more accurate estimate of the performance benefit.
Supersonic↗
Progress Towards the Design of a Natural Laminar Flow Wing for a Low-Boom Concept Using CDISC
The X-59 is being developed to evaluate the ability to reduce boom loudness to acceptable levels, with the ultimate goal of enabling supersonic flight overland. If successful, one of the future research challenges will be focused on reducing fuel burn. In this paper, Natural Laminar Flow (NLF) is proposed as a technology to decrease fuel burn for a low-boom configuration. This will require an evaluation of the compatibility of NLF wings for a low-boom configuration. As a first step towards this goal, the CATNLF method was employed using the CDISC design module to perform NLF design on an isolated wing from a low-boom configuration. The results show that the NLF wing features laminar flow over 46\% of the wing upper surface, providing a potential 8.3 drag count reduction relative to the design with no laminar flow. Off-design analyses were additionally performed to evaluate the performance of the NLF wing for changes in angle of attack and Mach number. The results showed that the NLF wing offers a performance benefit over the entire range of off-design conditions considered, with the majority of the laminar flow maintained for near cruise conditions. Future work will consider the full configuration, which will enable an assessment of the impact of the NLF wing design on sonic boom loudness. Additionally, attachment line transition will be addressed, which was predicted to result in a loss of laminar flow on the inboard 50\% span for this work. Finally, future research will focus on developing a method for effectively choosing the frequency-beta pairs used for the stability analysis to improve transition prediction and provide a more accurate estimate of the performance benefit.
Supersonic↗
Aerodynamic Design Optimization for Natural Laminar Flow Airfoils
Natural laminar flow technology is a passive laminar flow control (LFC) strategy that seeks to delay the onset of boundary-layer transition (BLT) through shape optimization to reduce the drag of the aerodynamic vehicle. Adjoint-based design optimization for LFC is proposed in an integrated multidisciplinary framework, which includes the computational fluid dynamics (CFD), geometry and grid deformation, and linear stability analysis (LSA) for transition prediction. In particular, the BLT location is predicted using the dual N-factor method that is based on a linear stability theory (LST) eigenvalue problem. The dual N-factor criterion accounts for the amplification of planar Tollmien-Schlichting (TS) and stationary crossflow (CF) boundary-layer instabilities to predict the transition location in three-dimensional boundary-layer flows. The adjoint-based shape optimization procedure is based on an iteratively coupled CFD and LSA methodology to converge the transition location and flow solutions, as well as to calculate the sensitivities of the aerodynamic metrics of interest with respect to the flow and shape design parameters. The RAE 2822 airfoil at 0 and 30 degrees yaw angles, an angle of attack of 0.72 degrees, and subsonic conditions (M∞ = 0.19, Rec = 5.6 × 106 ) are used as baseline configurations for design optimization. The angle of attack and the vertical displacement of free-form-deformation control points are used as design variables to reduce the drag coefficient while reaching a specified lift coefficient. The optimized unswept airfoil designs achieve a 30% drag reduction accompanied by a downstream shift of the transition locations over both suction and pressure sides of the airfoil. The initial design iterations for the swept case also show a favorable trend in the drag reduction with transition delay over both sides.
Transition↗
Assessing Effects of Climate and Technology Uncertainties in Large Natural Resource Allocation Problems
The productivity of the world's natural resources is critically dependent on a variety of highly uncertain factors, which obscure individual investors and governments that seek to make long-term, sometimes irreversible, investments in their exploration and utilization. These dynamic considerations are poorly represented in disaggregated resource models, as incorporating uncertainty into large-dimensional problems presents a challenging computational task. In this paper, we apply the SCEQ algorithm (Cai and Judd, 2023) to solve a large-scale dynamic stochastic global land resource use problem with stochastic crop yields due to adverse climate impacts and limits on further technological progress. For the same model parameters and bounded shocks, the range of land conversion is considerably smaller for the dynamic stochastic model than for deterministic scenario analysis.
numerical methods↗
Manufacturing and Scale-Up of Natural Fiber Composite eVTOL Propeller Skin and C-Channel
This report presents the manufacturing and scale-up of natural fiber composites for aerospace applications, conducted within NASA’s Convergent Aeronautics Solutions portfolio to advance aircraft capabilities and efficiency. The effort focused on the development and demonstration of two proof-of-concept articles, an Electric Vertical Take-off and Landing (eVTOL) propeller skin and a C-channel, produced through an iterative design and process optimization approach. Best practices and guidance on addressing key challenges with bio-based composite materials and manufacturing is provided. Ideally suited for non-load-bearing structural components, these emerging materials offer potential for weight reduction, vibration damping, noise mitigation, interference-free communication, and cost savings, without compromising the performance of safety-critical primary structures.
Natural fibers↗
Natural Fiber Simulations and Analysis for Composites
As the U.S. government and the aviation industry strive to achieve net-zero carbon emissions by 2050, the Sustainable Manufacturing of Aircraft (SUMAC) project, supported by the Convergent Aeronautics Solutions (CAS) program of the National Aeronautics and Space Administration (NASA), aims to develop sustainably derived thermoplastic composites and manufacturing technologies for future aviation. A multidisciplinary team of experts drives this initiative through a collective effort. The team’s key focus areas are sustainably derived thermoplastic resins, natural fiber reinforcement, composite manufacturing, structural health monitoring, computational materials modeling, and systems analysis. This presentation will give an overview of the SUMAC project. Fiber-scale microscopy analysis characterized fiber cross sections finding that hemp and flax fibers are very deformable and non-circular (circularities of 0.2 were observed), and diameters range from submicron to 20 μm. Fiber shapes range from flattened, with aspect ratios Rlong/Rshort up to 5, to round, Rlong/Rshort =1. And Fiber core perimeters, which likely drives deformability, were calculated Pcore=7 μm. Explicit-fiber yarn simulations, parameterized from single-fiber measurements, resolved and identified slack as a cause for yarn strain-hardening tensile behavior. The bonded network model of PLA, PHA and PA11 resins captures tensile behavior, and can model an infused weave. Computational models of the weave unit cells matched the pattern (including plain, twill and triaxial braid), yarn ply (1to 2), and yarn twist from optical microscopy.
thermoplastic↗
Natural Fiber Simulations and Analysis for Composites
As the U.S. government and the aviation industry strive to achieve net-zero carbon emissions by 2050, the Sustainable Manufacturing of Aircraft (SUMAC) project, supported by the Convergent Aeronautics Solutions (CAS) program of the National Aeronautics and Space Administration (NASA), aims to develop sustainably derived thermoplastic composites and manufacturing technologies for future aviation. A multidisciplinary team of experts drives this initiative through a collective effort. The team’s key focus areas are sustainably derived thermoplastic resins, natural fiber reinforcement, composite manufacturing, structural health monitoring, computational materials modeling, and systems analysis. This presentation will give an overview of the SUMAC project. Fiber-scale microscopy analysis characterized fiber cross sections finding that hemp and flax fibers are very deformable and non-circular (circularities of 0.2 were observed), and diameters range from submicron to 20 μm. Fiber shapes range from flattened, with aspect ratios Rlong/Rshort up to 5, to round, Rlong/Rshort =1. And Fiber core perimeters, which likely drives deformability, were calculated Pcore=7 μm. Explicit-fiber yarn simulations, parameterized from single-fiber measurements, resolved and identified slack as a cause for yarn strain-hardening tensile behavior. The bonded network model of PLA, PHA and PA11 resins captures tensile behavior, and can model an infused weave. Computational models of the weave unit cells matched the pattern (including plain, twill and triaxial braid), yarn ply (1to 2), and yarn twist from optical microscopy.
thermoplastic↗