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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 19 records
All sourcesNASA NTRSDOE OSTI
NASA NTRS2004

Integrated Mode Choice, Small Aircraft Demand, and Airport Operations Model User's Guide

A mode choice model that generates on-demand air travel forecasts at a set of GA airports based on changes in economic characteristics, vehicle performance characteristics such as speed and cost, and demographic trends has been integrated with a model to generate itinerate aircraft operations by airplane category at a set of 3227 airports. Numerous intermediate outputs can be generated, such as the number of additional trips diverted from automobiles and schedule air by the improved performance and cost of on-demand air vehicles. The total number of transported passenger miles that are diverted is also available. From these results the number of new aircraft to service the increased demand can be calculated. Output from the models discussed is in the format to generate the origin and destination traffic flow between the 3227 airports based on solutions to a gravity model.

Yackovetsky, Robert E.↗
NASA NTRS2006

Projecting Future Scheduled Airline Demand, Schedules and NGATS Benefits Using TSAM

The Transportation Systems Analysis Model (TSAM) developed by Virginia Tech s Air Transportation Systems Lab and NASA Langley can provide detailed analysis of the effects on the demand for air travel of a full range of NASA and FAA aviation projects. TSAM has been used to project the passenger demand for very light jet (VLJ) air taxi service, scheduled airline demand growth and future schedules, Next Generation Air Transportation System (NGATS) benefits, and future passenger revenues for the Airport and Airway Trust Fund. TSAM can project the resulting demand when new vehicles and/or technology is inserted into the long distance (100 or more miles one-way) transportation system, as well as, changes in demand as a result of fare yield increases or decreases, airport transit times, scheduled flight times, ticket taxes, reductions or increases in flight delays, and so on. TSAM models all long distance travel in the contiguous U.S. and determines the mode choice of the traveler based on detailed trip costs, travel time, schedule frequency, purpose of the trip (business or non-business), and household income level of the traveler. Demand is modeled at the county level, with an airport choice module providing up to three airports as part of the mode choice. Future enplanements at airports can be projected for different scenarios. A Fratar algorithm and a schedule generator are applied to generate future flight schedules. This paper presents the application of TSAM to modeling future scheduled air passenger demand and resulting airline schedules, the impact of NGATS goals and objectives on passenger demand, along with projections for passenger fee receipts for several scenarios for the FAA Airport and Airway Trust Fund.

Dollyhigh, Samuel↗
NASA NTRSDate not supplied

Passenger Aggregation Network with Very Efficient Listing (PANVEL) Ridesharing Model for Urban Air Mobility

This paper introduces a ridesharing model useful for examining the role of ridesharing in advancing Urban Air Mobility (UAM) operations. Although the success of UAM will likely require a host of new technologies in aircraft design, airspace management, autonomy, and more, operational innovations like ridesharing may also be key, especially to lower operating costs and attract a broader market. The ridesharing algorithm introduced, termed the Passenger Aggregation Network with Very Efficient Listing (PANVEL) model, estimates trip numbers and passenger occupancy aboard particular aircraft within a UAM network in a metropolitan area. This algorithm is applied within a transportation computational framework to determine the impact of ridesharing on mode choice, passenger travel patterns, and transportation costs. The algorithm aggregates passengers traveling between identical origin-destination pairs, factoring in individual-specific metrics, such as value of time and alternative mode options, to generate high load factors on UAM aircraft. The computational framework simulates passenger mode-choice selections using an effective-cost approach to determine the number of ridesharing UAM-preferred trips, and results with ridesharing are representative of a rough upper bound on UAM ridership potential for a given metro area. A case study in the Cleveland metro area is presented to demonstrate the algorithm and assess ridesharing potential.

advanced air mobility↗
NASA NTRSDate not supplied

Passenger Aggregation Network with Very Efficient Listing (PANVEL) Ridesharing Model for Urban Air Mobility

This presentation introduces a ridesharing model useful for examining the role of ridesharing in advancing Urban Air Mobility (UAM) operations. Although the success of UAM will likely require a host of new technologies in aircraft design, airspace management, autonomy, and more, operational innovations like ridesharing may also be key, especially to lower operating costs and attract a broader market. The ridesharing algorithm introduced, termed the Passenger Aggregation Network with Very Efficient Listing (PANVEL) model, estimates trip numbers and passenger occupancy aboard particular aircraft within a UAM network in a metropolitan area. This algorithm is applied within a transportation computational framework to determine the impact of ridesharing on mode choice, passenger travel patterns, and transportation costs. The algorithm aggregates passengers traveling between identical origin-destination pairs, factoring in individual-specific metrics, such as value of time and alternative mode options, to generate high load factors on UAM aircraft. The computational framework simulates passenger mode-choice selections using an effective-cost approach to determine the number of ridesharing UAM-preferred trips, and results with ridesharing are representative of a rough upper bound on UAM ridership potential for a given metro area. A case study in the Cleveland metro area is presented to demonstrate the algorithm and assess ridesharing potential.

advanced air mobility↗
NASA NTRS1975

Mode shift strategies in intercity transportation and their effect on energy consumption

Policies are examined which, if implemented, could lead to significant energy savings in intercity travel in the northeast corridor arena, without restricting the traveler's freedom of mode choice. The effects on arena energy consumption of introducing new, more energy-efficient aircraft are investigated; and several strategies unrelated to the implementation of new aircraft are introduced to yield reductions in overall intercity energy use. In both parts of this analysis, resulting changes in patronage (modal share) and energy use are demonstrated, leading to new insights into the effectiveness of different potential policies for achieving energy conservation. Some observations on induced demand trends that could be associated with certain strategies and the resultant potential effect on energy conservation are provided.

Sokolsky, S.↗
NASA NTRS1978

Cargo Logistics Airlift Systems Study (CLASS). Volume 3: Cross impact between the 1990 market and the air physical distribution systems, book 2

Book 2 of this volume is divided into the following sections: (1) commodities and system networks; (2) future mode choice decisions and commodity air eligibility; (3) comparative cargo transportation costs - air, truck, rail and water; (4) elasticities of demand; (5) operating cost; (6) operating profit, rate making, and returns; (7) importance of rate and service on future aircraft; (8) potential market demand for new aircraft; (9) scenario of events affecting system/market growth; and (10) future study and technology requirements.

Burby, R. J.↗
NASA NTRS2002

Analysis of Small Aircraft as a Transportation System

An analysis was conducted to examine the market viability of small aircraft as a transportation mode in competition with automobile and scheduled commercial air travel by estimating the pool of users that would potentially switch to on-demand air travel due to cost/time savings. The basis for the analysis model was the Integrated Air Transportation System Evaluation Tool (IATSET) which was developed under contract to NASA by the Logistics Management Institute. IATSET is a macroeconomic model that predicts at a National level the mode choice between automobile, scheduled air, and on-demand air travel based on the value of a travelers time and monetary cost of the trip. A number of modifications are detailed to the original IATSET to better model the changing small aircraft environment. The potential trip market was modeled for the Eclipse 500 operated as a corporate jet and as an air taxi for the business travel market. The Cirrus 20R and a $80K single engine piston aircraft (based on automobile manufacturing technology) are evaluated in the pleasure and personal business travel market.

Dollyhigh, Samuel M.↗
NASA NTRS2006

NAS Demand Predictions, Transportation Systems Analysis Model (TSAM) Compared with Other Forecasts

The current work incorporates the Transportation Systems Analysis Model (TSAM) to predict the future demand for airline travel. TSAM is a multi-mode, national model that predicts the demand for all long distance travel at a county level based upon population and demographics. The model conducts a mode choice analysis to compute the demand for commercial airline travel based upon the traveler s purpose of the trip, value of time, cost and time of the trip,. The county demand for airline travel is then aggregated (or distributed) to the airport level, and the enplanement demand at commercial airports is modeled. With the growth in flight demand, and utilizing current airline flight schedules, the Fratar algorithm is used to develop future flight schedules in the NAS. The projected flights can then be flown through air transportation simulators to quantify the ability of the NAS to meet future demand. A major strength of the TSAM analysis is that scenario planning can be conducted to quantify capacity requirements at individual airports, based upon different future scenarios. Different demographic scenarios can be analyzed to model the demand sensitivity to them. Also, it is fairly well know, but not well modeled at the airport level, that the demand for travel is highly dependent on the cost of travel, or the fare yield of the airline industry. The FAA projects the fare yield (in constant year dollars) to keep decreasing into the future. The magnitude and/or direction of these projections can be suspect in light of the general lack of airline profits and the large rises in airline fuel cost. Also, changes in travel time and convenience have an influence on the demand for air travel, especially for business travel. Future planners cannot easily conduct sensitivity studies of future demand with the FAA TAF data, nor with the Boeing or Airbus projections. In TSAM many factors can be parameterized and various demand sensitivities can be predicted for future travel. These resulting demand scenarios can be incorporated into future flight schedules, therefore providing a quantifiable demand for flights in the NAS for a range of futures. In addition, new future airline business scenarios are investigated that illustrate when direct flights can replace connecting flights and larger aircraft can be substituted, only when justified by demand.

Viken, Jeff↗
NASA NTRS2006

Utilizing Traveler Demand Modeling to Predict Future Commercial Flight Schedules in the NAS

The current work incorporates the Transportation Systems Analysis Model (TSAM) to predict the future demand for airline travel. TSAM is a multi-mode, national model that predicts the demand for all long distance travel at a county level based upon population and demographics. The model conducts a mode choice analysis to compute the demand for commercial airline travel based upon the traveler s purpose of the trip, value of time, cost and time of the trip,. The county demand for airline travel is then aggregated (or distributed) to the airport level, and the enplanement demand at commercial airports is modeled. With the growth in flight demand, and utilizing current airline flight schedules, the Fratar algorithm is used to develop future flight schedules in the NAS. The projected flights can then be flown through air transportation simulators to quantify the ability of the NAS to meet future demand. A major strength of the TSAM analysis is that scenario planning can be conducted to quantify capacity requirements at individual airports, based upon different future scenarios. Different demographic scenarios can be analyzed to model the demand sensitivity to them. Also, it is fairly well know, but not well modeled at the airport level, that the demand for travel is highly dependent on the cost of travel, or the fare yield of the airline industry. The FAA projects the fare yield (in constant year dollars) to keep decreasing into the future. The magnitude and/or direction of these projections can be suspect in light of the general lack of airline profits and the large rises in airline fuel cost. Also, changes in travel time and convenience have an influence on the demand for air travel, especially for business travel. Future planners cannot easily conduct sensitivity studies of future demand with the FAA TAF data, nor with the Boeing or Airbus projections. In TSAM many factors can be parameterized and various demand sensitivities can be predicted for future travel. These resulting demand scenarios can be incorporated into future flight schedules, therefore providing a quantifiable demand for flights in the NAS for a range of futures. In addition, new future airline business scenarios are investigated that illustrate when direct flights can replace connecting flights and larger aircraft can be substituted, only when justified by demand.

Viken, Jeff↗
NASA NTRS1963

Vehicle Design for Mars Landing and Return to Mars Orbit

This paper briefly describes three modes for accomplishing the Mars landing mission and compares them on a gross basis to indicate their probable order of merit and to identify design requirements placed on the Mars-excursion module (MEM) by the choice of mode. The paper shows that a flyby-rendezvous mode requiring low weight in earth orbit requires the MEM to enter the Mars atmosphere at velocities ranging from 20,000 to 30,000 ft/sec. The MEM for the flyby-rendezvous mode is not covered in this paper but merits further study. The MEM for the other modes of mission accomplishment begins its active operational sequence in Mars orbit and need not be greatly influenced by the method of delivery to Mars orbit. Parametric studies of the entry problem for two vehicles typifying a ballistic-type and a lifting-body-type were conducted to identify the problems associated with design of a MEM to accommodate the extremes of Mars atmospheric density presently predicted. This brief study indicates that: (a) the presently predicted density extremes of the Mars atmosphere present no serious design problems for a MEM which can operate across the entire band of predicted densities; (b) details of operational requirements and mission objectives will control the choice of configuration rather than entry requirements; and (c) the ballistic-type MEM is lighter and simpler but has less operational flexibility than a high L/D MEM.

Hammock, D. M.↗
NASA NTRS1989

Advanced power sources for space missions

Approaches to satisfying the power requirements of space-based Strategic Defense Initiative (SDI) missions are studied. The power requirements for non-SDI military space missions and for civil space missions of the National Aeronautics and Space Administration (NASA) are also considered. The more demanding SDI power requirements appear to encompass many, if not all, of the power requirements for those missions. Study results indicate that practical fulfillment of SDI requirements will necessitate substantial advances in the state of the art of power technology. SDI goals include the capability to operate space-based beam weapons, sometimes referred to as directed-energy weapons. Such weapons pose unprecedented power requirements, both during preparation for battle and during battle conditions. The power regimes for these two sets of applications are referred to as alert mode and burst mode, respectively. Alert-mode power requirements are presently stated to range from about 100 kW to a few megawatts for cumulative durations of about a year or more. Burst-mode power requirements are roughly estimated to range from tens to hundreds of megawatts for durations of a few hundred to a few thousand seconds. There are two likely energy sources, chemical and nuclear, for powering SDI directed-energy weapons during the alert and burst modes. The choice between chemical and nuclear space power systems depends in large part on the total duration during which power must be provided. Complete study findings, conclusions, and eight recommendations are reported.

Gavin, Joseph G., Jr.↗
NASA NTRS1989

Multibody dynamics: Modeling component flexibility with fixed, free, loaded, constraint, and residual modes

The assumed-modes method in multibody dynamics allows the elastic deformation of each component in the system to be approximated by a sum of products of spatial and temporal functions commonly known as modes and modal coordinates respectively. The choice of component modes used to model articulating and non-articulating flexible multibody systems is examined. Attention is directed toward three classical Component Mode Synthesis (CMS) methods whereby component normal modes are generated by treating the component interface (I/F) as either fixed, free, or loaded with mass and stiffness contributions from the remaining components. The fixed and free I/F normal modes are augmented by static shape functions termed constraint and residual modes respectively. A mode selection procedure is outlined whereby component modes are selected from the Craig-Bampton (fixed I/F plus constraint), MacNeal-Rubin (free I/F plus residual), or Benfield-Hruda (loaded I/F) mode sets in accordance with a modal ordering scheme derived from balance realization theory. The success of the approach is judged by comparing the actuator-to-sensor frequency response of the reduced order system with that of the full order system over the frequency range of interest. A finite element model of the Galileo spacecraft serves as an example in demonstrating the effectiveness of the proposed mode selection method.

Spanos, John T.↗
NASA NTRS2009

Impedance Eduction in Ducts with Higher-Order Modes and Flow

An impedance eduction technique, previously validated for ducts with plane waves at the source and duct termination planes, has been extended to support higher-order modes at these locations. Inputs for this method are the acoustic pressures along the source and duct termination planes, and along a microphone array located in a wall either adjacent or opposite to the test liner. A second impedance eduction technique is then presented that eliminates the need for the microphone array. The integrity of both methods is tested using three sound sources, six Mach numbers, and six selected frequencies. Results are presented for both a hardwall and a test liner (with known impedance) consisting of a perforated plate bonded to a honeycomb core. The primary conclusion of the study is that the second method performs well in the presence of higher-order modes and flow. However, the first method performs poorly when most of the microphones are located near acoustic pressure nulls. The negative effects of the acoustic pressure nulls can be mitigated by a judicious choice of the mode structure in the sound source. The paper closes by using the first impedance eduction method to design a rectangular array of 32 microphones for accurate impedance eduction in the NASA LaRC Curved Duct Test Rig in the presence of expected measurement uncertainties, higher order modes, and mean flow.

Watson, Willie R.↗
NASA NTRS1972

Investigation of some parameters affecting the stability of a hingeless helicopter blade in hover

Equations of motion are used to investigate the effects of the choice of the mode shape and built-in coning angle on the stability boundaries of hingeless blades in hover. The results obtained indicate that the stability boundaries are dependent upon the mode shape to a considerable degree. It was also found that positive built-in coning is usually destabilizing while a negative amount of built-in coning can be quite stabilizing.

Friedmann, P.↗
NASA NTRS1982

Appendix B: Principles of computer processing of LANDSAT data

Computer processing facilitates extraction of information from every pixel by executing a variety of functional operations, called processed algorithms, in general or specialized routines. The best results are obtained when data from more than one multispectral band are used together. Multivariate tatistical analysis, computer tape characteristics, processing modes, and a choice of systems (batch or interactive) are discussed. The major operations in computer processing elaborated include: preprocessing, enhancement, effects of rationing, and classification. Techniques for multisource data correlation are considered with emphasis on geobased systems.

Source record↗
NASA NTRS1983

Space flight experience with the Shuttle Orbiter control system

Experience gained through the Shuttle Orbital Flight Test program has matured the engineering understanding of the Shuttle on-orbit control system. The geneology of the control systems (called digital autopilots, or DAPs, and used by the Shuttle for on-orbit operations) is reviewed, the flight experience gained during the flight test program is examined within the context of preflight analysis and test results, and issues for the operational phase of the Shuttle, including constraints upon both operations and analysis still required to increase confidence in the Shuttle's ability to handle capabilities not experienced during the flight test program are addressed. Two orbital autopilots have resulted from computer memory and time constraints on a flight control system, with many different, flight phase unique requirements. The transition DAP, used for insertion and deorbit, has more active sensors and redundancy but a less complex data processing scheme excluding state estimation with fewer choices of operational mode.

Cox, K. J.↗