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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 397 records · Page 22
All sourcesNASA NTRSDOE OSTI
NASA NTRS2017

An N+3 Technology Level Reference Propulsion System

An N+3 technology level engine, suitable as a propulsion system for an advanced single-aisle transport, was developed as a reference cycle for use in technology assessment and decision-making efforts. This reference engine serves three main purposes: it provides thermodynamic quantities at each major engine station, it provides overall propulsion system performance data for vehicle designers to use in their analyses, and it can be used for comparison against other proposed N+3 technology-level propulsion systems on an equal basis. This reference cycle is meant to represent the expected capability of gas turbine engines in the N+3 timeframe given reasonable extrapolations of technology improvements and the ability to take full advantage of those improvements.

Jones, Scott M.↗
NASA NTRS2017

MIL-HDBK-338: Environmental Conversion Table Correction

In reliability analysis, especially for launch vehicles, limited data is frequently a problem. Component data from other environments must be used. MIL-HBK-338 has a matrix showing the conversation between environments. Due to round off the conversions are not commutative, converting from A to B will not equal converting from B to A. Agenda: Introduction to environment conversions; Original table; Original table with edits; How big is the problem?; First attempt at correction; Proposed solution.

Hark, Frank↗
NASA NTRS2017

MIL-HDBK-338-Environmental Conversion Table Correction

In reliability analysis for space launch vehicles, limited data is frequently a challenge due to the pure number of launches. A common solution is to use surrogate historical data of similar components from other industries (military data). The operating environment of the common data may be different from that of the necessary target analysis. The military electronic design handbook (MIL-HDBK-338) has a table for converting Mean Time Between Failure (MTBF) data from one environment to another. However, the table has some discrepancies and rounding of complementary conversions; namely going from environment A to B does not given the same result as going from B to A. This presentation will show the discrepancies in the original conversation table, the greater than expected magnitude, the problem with the updated published table and a suggested corrected table to reference when doing MTBF data environment conversion.

Hark, Frank↗
NASA NTRSDate not supplied

Aftbody Aeroheating Wind Tunnel Measurements on a Hypersonic Inflatable Aerodynamic Decelerator

The aftbody aeroheating environment of a Hypersonic Inflatable Aerodynamic Decelerator (HIAD) was investigated in the NASA Langley 31-Inch Mach 10 Air Tunnel. The wind tunnel model geometry was representative of the Low-Earth Orbit Flight Test of an Inflatable Decelerator (LOFTID) vehicle. Aeroheating data were obtained for a range of Reynolds numbers and angles-of-attack using global phosphor thermography. Aeroheating data were also obtained using a developmental IR thermography technique for evaluation of data quality.

Elizabeth F Rieken↗
NASA NTRSDate not supplied

SIRIUS: Simulation Infrastructure for Research on Interoperating Unmanned Systems

This paper presents the Simulation Infrastructure for Research on Interoperating Unmanned Systems (SIRIUS), a research framework for simulation and analysis of future conceptual Urban Air Mobility (UAM) operations. SIRIUS is being developed under the auspices of the NASA Air Traffic Management eXploration project, UAM subproject (ATM-X UAM). SIRIUS provides an intuitive, highly configurable graphical user interface to design complex traffic scenarios and airspace configurations representative of conceptual UAM operations. Aircraft simulated with SIRIUS can be equipped with flight-tested capabilities for detect and avoid (DAA), geofencing, distributed merging and spacing, path conformance, and path planning while executing time-constrained, 4D trajectories generated by a UAM ground operations system. Central to the design of the SIRIUS simulation framework is the capability to evaluate the integration and interoperability of ground-based separation services (e.g., strategic separation) with extended DAA functionality (e.g., path monitoring, separation provision, merging and spacing, etc.) The simulation environment also supports modelling of wind, navigation, and sensor uncertainties, as well as communication delays. SIRIUS enables distributed simulation of large-scale scenarios. An interactive graphical analysis capability helps isolate, visualize, and compare relevant vehicle state data and widely used measures of performance metrics across multiple scenarios.

Andrew Peters↗
NASA NTRSDate not supplied

Trajectory Reconstruction of the Low-Earth Orbit Flight Test of an Inflatable Decelerator

The Low-Earth Orbit Flight Test of an Inflatable Decelerator (LOFTID) project conducted a flight test of a 6m inflatable aeroshell. The LOFTID test article was a secondary payload on an Atlas V launcher that carried the Joint Polar Satellite System-2 (JPSS-2) as its primary payload. The vehicle launched on November 10th, 2022. After reaching orbit, the LOFTID test article inflated the aeroshell, separated from the upper stage on an entry trajectory, and entered the atmosphere to splash down in the Pacific Ocean under parachutes. The test concept of operations is shown in Figure 1. The test article was instrumented with a variety of sensors to be used for post-flight evaluation of vehicle performance. Data from one of the key sensors for trajectory reconstruction, the Inertial Measurement Unit (IMU), was not captured in the data recorder due to a malfunction. Data from the nose cone mounted Flush Air Data Sensing (FADS) system were successfully acquired. The layout of the FADS sensors and the measured pressures during atmospheric entry are shown in Figure 2. The FADS data were combined with a Newtonian flow pressure model [1, 2] to produce estimates of the atmospheric relative trajectory. A Mach number anchoring technique given in [2] was used to stabilize estimates in high speed flight conditions. Since no IMU data were available, a trajectory simulation was used to provide the Mach number time history. The resulting estimates of the atmospheric-relative trajectory are shown in Figures 3. Given the loss of the IMU data, alternate methods for trajectory reconstruction are being explored. One approach under investigation is the use of the on-board video recorder data to be analyzed to reconstruct attitude motion. This approach is currently under investigation and will be reported on in the final paper. The Newtonian flow pressure model for the FADS analysis will also be updated with a CFD-based pressure model.

Christopher D Karlgaard↗
NASA NTRSDate not supplied

Trajectory Reconstruction of the Low-Earth Orbit Flight Test of an Inflatable Decelerator

The Low-Earth Orbit Flight Test of an Inflatable Decelerator (LOFTID) project conducted a flight test of a 6m inflatable aeroshell. The LOFTID test article was a secondary payload on an Atlas V launcher that carried the Joint Polar Satellite System-2 (JPSS-2) as its primary payload. The vehicle launched on November 10th, 2022. After reaching orbit, the LOFTID test article inflated the aeroshell, separated from the upper stage on an entry trajectory, and entered the atmosphere to splash down in the Pacific Ocean under parachutes. The test concept of operations is shown in Figure 1. The test article was instrumented with a variety of sensors to be used for post-flight evaluation of vehicle performance. Data from one of the key sensors for trajectory reconstruction, the Inertial Measurement Unit (IMU), was not captured in the data recorder due to a malfunction. Data from the nose cone mounted Flush Air Data Sensing (FADS) system were successfully acquired. The layout of the FADS sensors and the measured pressures during atmospheric entry are shown in Figure 2. The FADS data were combined with a Newtonian flow pressure model [1, 2] to produce estimates of the atmospheric relative trajectory. A Mach number anchoring technique given in [2] was used to stabilize estimates in high speed flight conditions. Since no IMU data were available, a trajectory simulation was used to provide the Mach number time history. The resulting estimates of the atmospheric-relative trajectory are shown in Figures 3. Given the loss of the IMU data, alternate methods for trajectory reconstruction are being explored. One approach under investigation is the use of the on-board video recorder data to be analyzed to reconstruct attitude motion. This approach is currently under investigation and will be reported on in the final paper. The Newtonian flow pressure model for the FADS analysis will also be updated with a CFD-based pressure model.

Christopher D. Karlgaard↗
DOE OSTI2021

Method of controlling a vehicle to adjust perception system energy usage

A method of controlling a vehicle includes determining a current operating situation of the vehicle, and identifying a subset of a plurality of sensors of the vehicle needed to provide data to enable a vehicle control function for the current operating situation of the vehicle. A remainder of the plurality of sensors is disengaged to reduce electric energy usage by the vehicle while the vehicle is operating in the current operating situation of the vehicle. A sampling rate for the selected subset of sensors may be reduced to further reduce energy usage of the vehicle. Additionally, an energy reduction processing strategy may be implemented to reduce a processor frequency or a voltage of a computing device used to provide the vehicle control function to further reduce energy usage of the vehicle.

Hu, Yuxiao↗
DOE OSTI2020

Demand-adaptive Transit Design for Urban Transportation Hubs

In this study, we proposed a novel three-stage framework for planning the optimal demand-adaptive transit (DAT) at urban transportation hubs. Given the potential trip demand and road traffic condition, the proposed framework sequentially generates the optimal set of candidate routes, combines the outgoing routes and incoming routes at the hub, and derives the optimal fleet size and corresponding route frequency under the fixed budget. In particular, we build the route generation algorithm which maximizes passenger demand coverage with travel time deviation constraint. And a heuristic algorithm is further developed which yields near-optimal operation routes for real-time demand. The fleet optimization problem is formulated to minimize the weighted cost of energy savings, operation cost and trip revenue. We conduct comprehensive numerical experiments for planning DAT with electric buses at JFK airport in NYC using NYC taxi and for-hire vehicle trip data and GoogleMap speed data. The results show the superior performance of the proposed route generation algorithm which is able to cover citywide passenger demand with only 61 DAT routes. The results also suggest that the proposed DAT planning framework may serve over 47% of existing taxi and FHV demand by operating 18 routes using the fleet of 62 electric buses.

demand adaptive↗
DOE OSTI2023

Quantifying and Understanding the Access Time to Dockless Micromobility: A Case Study in Washington, D.C.

Micromobility has been widely deployed in many cities. Similar as how access time/distance affects the travel demand to use public transit and informs transit system design, access time/distance to micromobility service measures its service efficiency and also serves as an equity indicator to inform city agencies from a regulation perspective. Though there is an increasing need to understand it, access to micromobility has not been sufficiently studied. This paper developed a framework to quantity the access time to dockless micromobility service (i.e., the minimum time needed to walk to reach the closest dockless micromobility vehicles). Based on the real-time vehicle location data collected from Washington, DC, this research quantified the access time to dockless micromobility, analyzed its spatial and temporal variation patterns and investigated its relationship with socio-demographic variables (i.e., population density, employment density and low-income population). The results revealed that the access time to dockless micromobility ranges between 0 to 4 minutes with the most frequently observed range of 0.5 to 1 minutes, and the city center area tends to have shorter access time than the outskirts areas. Results also indicate a quite stable access time level in DC with access time standard deviation of 0.2 to 0.5 minutes. After correlating the access time at census-block-group level with socio-demographic data, it was discovered that shorter access time usually aligns with larger population and employment density, and the proportion of low-income population was found not helpful with explaining the access time variation, which indicating a relatively equitable micromobility program.

access time↗
NASA NTRS1994

Computational study of generic hypersonic vehicle flow fields

The geometric data of the generic hypersonic vehicle configuration included body definitions and preliminary grids for the forebody (nose cone excluded), midsection (propulsion system excluded), and afterbody sections. This data was to be augmented by the nose section geometry (blunt conical section mated with the noncircular cross section of the forebody initial plane) along with a grid and a detailed supersonic combustion ramjet (scramjet) geometry (inlet and combustor) which should be merged with the nozzle portion of the afterbody geometry. The solutions were to be obtained by using a Navier-Stokes (NS) code such as TUFF for the nose portion, a parabolized Navier-Stokes (PNS) solver such as the UPS and STUFF codes for the forebody, a NS solver with finite rate hydrogen-air chemistry capability such as TUFF and SPARK for the scramjet and a suitable solver (NS or PNS) for the afterbody and external nozzle flows. The numerical simulation of the hypersonic propulsion system for the generic hypersonic vehicle is the major focus of this entire work. Supersonic combustion ramjet is such a propulsion system, hence the main thrust of the present task has been to establish a solution procedure for the scramjet flow. The scramjet flow is compressible, turbulent, and reacting. The fuel used is hydrogen and the combustion process proceeds at a finite rate. As a result, the solution procedure must be capable of addressing such flows.

Narayan, Johnny R.↗
NASA NTRS2016

Liftoff and Time Equivalent Duration Data Evaluation of Exploration Flight Test 1 Orion Multi-Purpose Crew Vehicle

The liftoff phase induces high acoustic loading over a broad frequency range for a launch vehicle. These external acoustic environments are used in the prediction of the internal vibration responses of the vehicle and components. There arises the question about time equivalent (Teq) duration of the liftoff phase and similarity to other launch vehicles. Vibroacoustic engineers require the fatigue-weighted time duration values for qualification testing inputs. In order to determine the Teq for the Space Launch System, NASA's newest launch vehicle, the external microphone data from the Exploration Flight Test 1 (EFT-1) flight of the Orion Multi-Purpose Crew Vehicle (MPCV) was evaluated. During that evaluation, a trend was observed in the data and the origin of that trend is discussed in this paper. Finally, the Teq values for the EFT-1 Orion MPCV are presented.

Houston, Janice↗
NASA NTRS1980

Remotely piloted vehicles. Citations from the International Aerospace abstracts data base

These citations from the international literature cover various aspects of remotely piloted vehicles. Included are articles concerning aircraft design, flight tests, aircraft control, cost effectiveness, automatic flight control, automatic pilots, and data links. Civil aviation applications are included, although military uses of remotely piloted vehicles are stressed. This updated bibliography contains 224 citations, 43 of which are new additions to the previous edition.

Mauk, S. C.↗
NASA NTRSDate not supplied

A Concept of Operations (ConOps) of an In-time Aviation Safety Management System (IASMS) for Advanced Air Mobility (AAM)

The growth of new emerging operations involving Advanced Air Mobility (AAM) necessitates developing a perspective for an In-time Aviation Safety Management System (IASMS). This perspective advances from the National Academies report on IASMS and its recommendation for developing a Concept of Operations (ConOps) for IASMS. A ConOps has been developed for In-time System-Wide Safety Assurance (ISSA) from which the IASMS ConOps pivots to provide a robust scope commensurate with the broad vision defined by the National Academies. The IASMS ConOps focuses on emerging operations and spans innovations in Unmanned Aircraft System (UAS) and an increasingly complex ecosystem comprised of a widening mix of vehicles and technologies, Urban Air Mobility (UAM) with industry-federated services, traditional operations, as well as new supersonic aircraft and space launch systems.The challenge for the IASMS ConOps is to be broad to encompass innovations in the coming years and decades while agile to ensure levels of safety compatible with operational and certification requirements of the National Airspace System (NAS). The IASMS ConOps interweaves increasing complexity of operational safety capabilities and unlocking UAS Maturity Levels (UMLs). The relationships between increased complexity of automation and automated systems, fewer operators who are not as traditionally higher skilled, more complex operational environments, and aviation operations management with mixed aircraft and equipage pose a multi-dimensional space for IASMS capabilities essential for safety assurance and risk management. Instantiating IASMS capabilities and how they would be integral to AAM operations and increasing maturity of UAM could be accomplished through a series of Safety Demonstrators. These Safety Demonstrators could provide increased understanding and insight into use of controls for risk mitigation, means of compliance for certification, and operational experience with safety services such as in relation to contingency management. The IASMS capabilities can be viewed as initially residing with the vehicle, airspace, and Supplemental Data Service Provider (SDSP). For example, vehicle capabilities include communications including the command and control link, Remote Identification (ID), conflict advisory/alerting, and UAS system monitoring. These capabilities monitor and assess data such as battery health, aircraft state, and human performance. Complexity of ISAMS capabilities depends on a number of factors. These factors are intendedonly as a notional categorization with the purpose being to reflect the complexity of the AAM ecosystem that would drive up the complexity of ISSA capabilities including systems, sensors, models, standards, and controls. Factors could include the Vehicle Flight Management, Environment, Airspace, and Contingency Management. Each of these factors can be comprised of multiple sub-factors that contribute to increasing complexity. For example, Airspace at a lower level of complexity could be dedicated to UTM operations that are unmonitored, and at a higher level of complexity could involve mixed UTM and ATM operations. The IASMS concept includes safety services that provide data and information to different participants in AAM. The roles and responsibilities of participants can be defined using the Responsible-Accountable-Consulted-Informed (RACI) analysis. For example, for the safety service involving the Remote ID, the Operator would be accountable for providing the data, the Vehicle would be responsible for transmitting it, and the USS, SDSP, Vertiports, FIMS (FAA), and Public Entities such as safety services would be informed by receiving the data. The IASMS ConOps identifies the capabilities needed for risk mitigation and safety assurance in the increasingly complex national airspace. The ConOps serves as a pathway for engaging with industry to gain operational experience including through the Safety Demonstrator series, the RACI analysis, and operational complexity factors. The ConOps serves to integrate these different perspectives to build a cohesive and cogent approach to an AAM safety management system.

In-Time Aviation Safety Management System↗
NASA NTRSDate not supplied

AeroFusion: Data Fusion and Uncertainty Quantification for Entry Vehicles

AeroFusion is a NASA Langley initiative to incorporate advances in data science into the aerodynamic modeling process to improve efficiency. The effort can largely be categorized in three components: reduced-order modeling techniques, surrogate modeling techniques, and uncertainty quantification. By combining various methods from these categories, AeroFusion aims to reduce the development cost of aerodynamic models, both in terms of time and money.

Steven Snyder↗