Results for “future vision” — Page 38

NASA NTRS1989

Supercomputer requirements for selected disciplines important to aerospace

Speed and memory requirements placed on supercomputers by five different disciplines important to aerospace are discussed and compared with the capabilities of various existing computers and those projected to be available before the end of this century. The disciplines chosen for consideration are turbulence physics, aerodynamics, aerothermodynamics, chemistry, and human vision modeling. Example results for problems illustrative of those currently being solved in each of the disciplines are presented and discussed. Limitations imposed on physical modeling and geometrical complexity by the need to obtain solutions in practical amounts of time are identified. Computational challenges for the future, for which either some or all of the current limitations are removed, are described. Meeting some of the challenges will require computer speeds in excess of exaflop/s (10 to the 18th flop/s) and memories in excess of petawords (10 to the 15th words).

Peterson, Victor L.↗

NASA NTRS1995

Integrated Flight and Propulsion Controls for Advanced Aircraft Configurations

The research vision of the NASA Lewis Research Center in the area of integrated flight and propulsion controls technologies is described. In particular the Integrated Method for Propulsion and Airframe Controls developed at the Lewis Research Center is described including its application to an advanced aircraft configuration. Additionally, future research directions in integrated controls are described.

Merrill, Walter↗

NASA NTRS1996

Integrated Flight and Propulsion Controls for Advanced Aircraft Configurations

The research vision of the NASA Lewis Research Center in the area of integrated flight and propulsion controls technologies is described. In particular, the integrated method for propulsion and airframe controls developed at the Lewis Research Center is described including its application to an advanced aircraft configuration. Additionally, future research directions in integrated controls are described.

Merrill, Walter↗

NASA NTRS2008

Beagle 2 the Moon: An Experimental Package to Measure Polar Ice and Volatiles in Permanently Shadowed Areas or Beneath the Lunar Surface

NASA has announced the selection of several Lunar Science Sortie Concept Studies for potential scientific payloads with future Lunar Missions. The Beagle 2 scientific package was one of those chosen for study. Near the beginning of the next decade will see the launch of scientific payloads to the lunar surface to begin laying the foundations for the return to the moon in the Vision for Space Exploration. Shortly thereafter, astronauts will return to the lunar surface with the ability to place scientific packages on the surface that will provide information about lunar resources and compositions of materials in permanently shadowed regions of the moon (1). One of the important questions which must be answered early in the program is whether there are lunar resources which would facilitate "living off the land" and not require the transport of resources and consumables from Earth (2). The Beagle science package developed to seek the signatures of life on Mars is the ideal payload (3) to use on the lunar surface for determining the nature of hydrogen, water and lunar volatiles found in the polar regions which could support the Vision for Space Exploration.

Gibson, E. K.↗

NASA NTRSDate not supplied

Ceres Planetary Mission Concept Study: Exploration of Ceres' Habitability

Dwarf planet Ceres is a compelling target as an evolved ocean world with recent and potentially ongoing geological activity. As the most water-rich body in the inner solar system after Earth (in absolute abundance), it is a representative of the population of planetesimals that brought volatiles and organics to the inner solar system. And, situated in the Main Belt of Asteroids, it is accessible enough to return a sample from within the resources of a typical medium-class (New Frontiers) NASA mission. Habitability is addressed via a combination of geological, geophysical, and compositional investigations. Past and ongoing activity is addressed via high-resolution imaging and gravity measurements from orbit near landforms interpreted as volcanic structures based on the Dawn observations. The depth of deep brines would be probed via electromagnetic sounding. Sample return would enable a vast range of experimental techniques to be applied with sensitivities and accuracies beyond those feasible with in situ instruments and would benefit from future facilities developments. Sample return could be executed with a single flight system with solar electric propulsion. The baseline design of the sample return capsule is inherited from the OSIRIS-REx mission. This concept also relies on the availability of key technologies: an enhanced landing vision system leveraging investments for Mars 2020; retractable/redeployable solar arrays, which have been demonstrated on the International Space Station but not at Ceres’ gravity; and a new design for sample transfer from the pneumatic sampling system to the sample return capsule based off Honeybee Robotics’ design for the MMX mission. The authors acknowledge support from NASA’s Planetary Mission Concept Study program. The information in this abstract is pre-decisional and is provided for planning and discussion purposes only.

K. E. Miller↗

NASA NTRSDate not supplied

Visualizing Corridors in Terminal Airspace using Trajectory Clustering

Context: Advances in battery and automation technology have made routine air taxi and cargo transport in urban areas a business model that can be attained by emerging aviation innovators. The community vision and work to enable these novel operations is discussed using the term ‘Urban Air Mobility’ or UAM. Small, piloted, airspace vehicles that fly with a few passengers do operate in urban areas today, and these vehicles can be studied as an early proxy for this future UAM traffic. Aim: We seek to identify corridors already in daily operation and their properties. Method: We applied DBSCAN and HDBSCAN to Dallas Forth-Worth TRACON flight data to identify corridors in use, their density, and devised a method to annotate landing sites used in these corridors with site metadata. Results: While DBSCAN was unable to group similar trajectories, we we were able to successfully identify corridors using HDBSCAN, measure their density and annotate them. Conclusion: The applied method can successfully identify corridors in daily operation with additional metadata to help domain expert understand the intent of UAM corridors.

UAM, Trajectory, TRACON, Clustering, DBSCAN, HDBSC↗

NASA NTRSDate not supplied

Visualizing Corridors in Terminal Airspace Using Trajectory Clustering

Context: Advances in battery and automation technology have made routine air taxi and cargo transport in urban areas a business model that can be attained by emerging aviation innovators. The community vision and work to enable these novel operations is discussed using the term ‘Urban Air Mobility’ or UAM. Small, piloted, airspace vehicles that fly with a few passengers do operate in urban areas today, and these vehicles can be studied as an early proxy for this future UAM traffic. Aim: We seek to identify corridors already in daily operation and their properties. Method: We applied DBSCAN and HDBSCAN to Dallas Forth-Worth TRACON flight data to identify corridors in use, their density, and devised a method to annotate landing sites used in these corridors with site metadata. Results: While DBSCAN was unable to group similar trajectories, we we were able to successfully identify corridors using HDBSCAN, measure their density and annotate them. Conclusion: The applied method can successfully identify corridors in daily operation with additional metadata to help domain expert understand the intent of UAM corridors.

UAM Trajectory, TRACON, Clustering, DBSCAN, HDBSCA↗

NASA NTRS2022

Low-Speed Performance Enhancement Using Localized Active Flow Control: Localized Active Flow Control Simulations on a Reference Aircraft (2/4)

A study of the potential implementations of localized active flow control (AFC) technology onto future airplanes is presented. This collaborative investigation addresses key objectives of the NASA Advanced Air Transport Technology (AATT) Project, in terms of reduction in fuel consumption and lower emission. It specifically targets the goals set forth in a roadmap developed by the NASA/Boeing team. The roadmap is a result of a series of meetings held between the two parties over the years and it represents a shared vision for practical implementations, leading up to flight demonstrations of localized flow control. If successful, localized flow control may lead to important ramifications for next generation airplanes from both the economic and environmental perspectives. Under this contract localized AFC has been used to improve aerodynamic performance during high-lift operations using Computational Fluid Dynamics (CFD). Specifically, AFC has been applied at the aileron and at various location in the wing leading edge (LE) regions. The applications target reduced drag and enhanced lift over the range of practical angles of attack, including stall. These benefits translate to airplane performance improvements, such as longer range or larger payload. The CFD results are used to quantify potential aerodynamic benefits, as well as the input required for actuation. This helps identify the most promising candidates, which potentially provide material net airplane level enhancements using onboard fluidic sources. The airplane configuration selected for the CFD study is a representative of a future short/medium-range twin-engine airplane dubbed the Reference Aircraft. A slew of AFC applications has been explored and their aerodynamic performance enhancements were benchmarked against the baseline Reference Aircraft. Promising AFC candidates have been deemed practical and potentially suitable for both the aileron and the wing LE implementations. The findings on the Reference Aircraft are used to guide the development of the AFC-enhanced aileron for the CRM-HL. The wind-tunnel model of the CRM-HL will be used by NASA to validate the AFC concepts, complementing the CFD-based analysis and the integration study (final report document #3).

CFD↗

DOE OSTI2021

Autonomous experimentation systems for materials development: A community perspective

Solutions to many of the world's problems depend upon materials research and development. However, advanced materials can take decades to discover and decades more to fully deploy. Humans and robots have begun to partner to advance science and technology orders of magnitude faster than humans do today through the development and exploitation of closed-loop, autonomous experimentation systems. This review discusses the specific challenges and opportunities related to materials discovery and development that will emerge from this new paradigm. Our perspective incorporates input from stakeholders in academia, industry, government laboratories, and funding agencies. We outline the current status, barriers, and needed investments, culminating with a vision for the path forward. We intend the article to spark interest in this emerging research area and to motivate potential practitioners by illustrating early successes. We also aspire to encourage a creative reimagining of the next generation of materials science infrastructure. To this end, we frame future investments in materials science and technology, hardware and software infrastructure, artificial intelligence and autonomy methods, and critical workforce development for autonomous research.

36 MATERIALS SCIENCE↗

DOE OSTI2025

Fleet-Level Energy and Emissions Analysis of the US Off-Road Sector with VISION: Off-Road

In the United States (US), the off-road sector (i.e., agriculture, construction, etc.) contributes to approximately 10% of the country’s transportation greenhouse gas (GHG) emissions, similar to the aviation sector. The off-road sector is extremely diverse; as the EPA MOVES model classifies it into 11 sub-sectors, which include 85 different types of equipment. These equipment types have horsepower ranging from 1 to greater than 3000 and have very different utilization, which makes decarbonization a complex endeavor. To address this, Argonne’s on-road vehicle fleet model, VISION, has been expanded to the off-road sector. The GHG emission factors for several energy carriers (biofuels, electricity, and hydrogen) have been incorporated from Argonne’s GREET model for a sector-wide well-to-wheel (WTW) GHG emissions analysis of the present and future fleet. Several technology adoption and energy decarbonization scenarios were modeled to better understand the appropriate actions required to drive towards net-zero emissions of the off-road sector. Results show that WTW decarbonization up to 67% can be achieved from 2023 to 2050 in a business-as-usual scenario. But with aggressive sales increases of electric and hydrogen powertrains, WTW decarbonization up to 77% can be achieved, which can further increase to 85% if electricity production is aggressively decarbonized by 2035.

lifecycle↗

NASA NTRS1996

Field Geology/Processes

The field geology/process group examined the basic operations of a terrestrial field geologist and the manner in which these operations could be transferred to a planetary lander. Four basic requirements for robotic field geology were determined: geologic content; surface vision; mobility; and manipulation. Geologic content requires a combination of orbital and descent imaging. Surface vision requirements include range, resolution, stereo, and multispectral imaging. The minimum mobility for useful field geology depends on the scale of orbital imagery. Manipulation requirements include exposing unweathered surfaces, screening samples, and bringing samples in contact with analytical instruments. To support these requirements, several advanced capabilities for future development are recommended. Capabilities include near-infrared reflectance spectroscopy, hyper-spectral imaging, multispectral microscopy, artificial intelligence in support of imaging, x ray diffraction, x ray fluorescence, and rock chipping.

Allen, Carlton↗

NASA NTRS2015

Who's Got the Bridge? - Towards Safe, Robust Autonomous Operations at NASA Langley's Autonomy Incubator

NASA aeronautics research has made decades of contributions to aviation. Both aircraft and air traffic management (ATM) systems in use today contain NASA-developed and NASA sponsored technologies that improve safety and efficiency. Recent innovations in robotics and autonomy for automobiles and unmanned systems point to a future with increased personal mobility and access to transportation, including aviation. Automation and autonomous operations will transform the way we move people and goods. Achieving this mobility will require safe, robust, reliable operations for both the vehicle and the airspace and challenges to this inevitable future are being addressed now in government labs, universities, and industry. These challenges are the focus of NASA Langley Research Center's Autonomy Incubator whose R&D portfolio includes mission planning, trajectory and path planning, object detection and avoidance, object classification, sensor fusion, controls, machine learning, computer vision, human-machine teaming, geo-containment, open architecture design and development, as well as the test and evaluation environment that will be critical to prove system reliability and support certification. Safe autonomous operations will be enabled via onboard sensing and perception systems in both data-rich and data-deprived environments. Applied autonomy will enable safety, efficiency and unprecedented mobility as people and goods take to the skies tomorrow just as we do on the road today.

Allen, B. Danette↗

NASA NTRS1997

Autonomy Enables New Science Missions

The challenge of space flight in NASA's future is to enable smaller, more frequent and intensive space exploration at much lower total cost without substantially decreasing mission reliability, capability, or the scientific return on investment. The most effective way to achieve this goal is to build intelligent capabilities into the spacecraft themselves. Our technological vision for meeting the challenge of returning quality science through limited communication bandwidth will actually put scientists in a more direct link with the spacecraft than they have enjoyed to date. Ultimately, new classes of exploration missions will be enabled.

autonomy↗

NASA NTRS2021

A Guide for Aircraft Certification by Analysis

Analysis-based means of compliance for airplane and engine certification, commonly known as "Certification by Analysis" (CbA), provides a strong motivation for the development and maturation of current and future flight and engine modeling technology. The many benefits of CbA include streamlined product certification testing programs at lower cost while maintaining equivalent levels of safety. Flight and engine testing represent a substantial cost to airplane and engine development programs. This report provides a far-reaching vision for CbA and presents a research roadmap for computational technology development to enable CbA.

certification by analysis↗

NASA NTRSDate not supplied

BEAST: Expanding Sustainable Data Infrastructure for High-Enthalpy Facilities

Reproducible, data-driven thermal protection system (TPS) research requires that experimental records from high-enthalpy testing be consistently structured, traceable, and accessible across campaigns and institutions. In practice, however, arcjet and plasma facilities data remain largely fragmented: raw diagnostics are stored in ad hoc formats, material sample histories are disconnected from test conditions, and metadata standards are absent, precluding systematic cross-campaign analysis and long-term reuse. BEAST (Backend for Experiment Analysis, Storage, and Traceability) is an open-source, web-based platform that addresses these limitations by providing a unified, queryable infrastructure for high-enthalpy ground-test data [1]. First presented at the 15th Ablation Workshop [2], BEAST has since undergone significant development. The platform ingests and structures multi-channel time-series diagnostics, facility configurations, and material property records within a common provenance model, ensuring end-to-end traceability from raw sensor acquisition to reduced experimental quantities. A versioned material library links specimen identity and processing history to the specific runs in which each sample was tested. An integrated modeling workbench enables training and evaluation of regression models directly on archived experimental data, supporting condition interpolation and the construction of empirical material response databases. Beyond its original deployment at NASA Ames Research Center, BEAST has been designed to be facility-agnostic, with ongoing efforts to extend its adoption to other facilities. Its modular architecture accommodates heterogeneous diagnostic setups and facility types, and its future open-source distribution allows institutions to build on a common data standard rather than maintaining isolated, bespoke solutions. BEAST is further integrated within a broader ecosystem of companion tools: arcjetCV [3] extracts recession rates and shock standoff distances from high-speed video using computer vision, and miniSTARscan [4] provides sub-minute, portable photogrammetric surface reconstruction of test articles before and after exposure. All tools share a common data schema, enabling seamless ingestion of surface geometry, imagery, and time-series data into a single, coherent experimental record.

Database↗

NASA NTRSDate not supplied

BEAST: Expanding Sustainable Data Infrastructure for High-Enthalpy Facilities

Reproducible, data-driven thermal protection system (TPS) research requires that experimental records from high-enthalpy testing be consistently structured, traceable, and accessible across campaigns and institutions. In practice, however, arcjet and plasma facilities data remain largely fragmented: raw diagnostics are stored in ad hoc formats, material sample histories are disconnected from test conditions, and metadata standards are absent, precluding systematic cross-campaign analysis and long-term reuse. BEAST (Backend for Experiment Analysis, Storage, and Traceability) is an open-source, web-based platform that addresses these limitations by providing a unified, queryable infrastructure for high-enthalpy ground-test data [1]. First presented at the 15th Ablation Workshop [2], BEAST has since undergone significant development. The platform ingests and structures multi-channel time-series diagnostics, facility configurations, and material property records within a common provenance model, ensuring end-to-end traceability from raw sensor acquisition to reduced experimental quantities. A versioned material library links specimen identity and processing history to the specific runs in which each sample was tested. An integrated modeling workbench enables training and evaluation of regression models directly on archived experimental data, supporting condition interpolation and the construction of empirical material response databases. Beyond its original deployment at NASA Ames Research Center, BEAST has been designed to be facility-agnostic, with ongoing efforts to extend its adoption to other facilities. Its modular architecture accommodates heterogeneous diagnostic setups and facility types, and its future open-source distribution allows institutions to build on a common data standard rather than maintaining isolated, bespoke solutions. BEAST is further integrated within a broader ecosystem of companion tools: arcjetCV [3] extracts recession rates and shock standoff distances from high-speed video using computer vision, and miniSTARscan [4] provides sub-minute, portable photogrammetric surface reconstruction of test articles before and after exposure. All tools share a common data schema, enabling seamless ingestion of surface geometry, imagery, and time-series data into a single, coherent experimental record.

Database↗

DOE OSTI2024

Beyond Binary: Automated PLC Memory Forensics through RGB Image Analysis and Deep Learning

The introduction of Industry 4.0 and the evolution of industrial control systems (ICS) to adopt Internet-based technologies enhanced productivity, but have inadvertently increased their vulnerability to cyber-based malicious attacks. When an ICS system is compromised, security analysts need to identify the root cause quickly to start the recovery process and develop mitigation strategies to safeguard against future instances. Memory forensics is critical in the analysis process to ascertain what occurred. To date, approaches to analyze the persistent memory in ICS devices are limited, and almost nonexistent for volatile memory. This paper proposes an automated methodology, COMA, for PLC memory dump analysis using computer vision and deep learning techniques. Specifically, COMA converts the sequences of bytes in a PLC memory dump to RGB pixels and creates a deep learning model that learns the underlying patterns and features of pre-labeled forensic artifacts in images and segments them into distinct regions. COMA then uses the trained model to automatically segment new memory images and extract forensic artifacts. We evaluate COMA on a Schneider Electric Modicon M221 PLC involving two cyber-based attack scenarios: (i) code injection and (ii) code modification. The empirical results show that COMA can successfully detect attack artifacts in memory dumps in both scenarios.

Asmar Awad, Rima↗

NASA NTRS2003

High End Computing Technologies for Earth Science Applications: Trends, Challenges, and Innovations

Earth science applications of the future will stress the capabilities of even the highest performance supercomputers in the areas of raw compute power, mass storage management, and software environments. These NASA mission critical problems demand usable multi-petaflops and exabyte-scale systems to fully realize their science goals. With an exciting vision of the technologies needed, NASA has established a comprehensive program of advanced research in computer architecture, software tools, and device technology to ensure that, in partnership with US industry, it can meet these demanding requirements with reliable, cost effective, and usable ultra-scale systems. NASA will exploit, explore, and influence emerging high end computing architectures and technologies to accelerate the next generation of engineering, operations, and discovery processes for NASA Enterprises. This article captures this vision and describes the concepts, accomplishments, and the potential payoff of the key thrusts that will help meet the computational challenges in Earth science applications.

Parks, John↗