Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “Operational Efficiency”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 1,009 records · Page 56
All sourcesNASA NTRSDOE OSTI
NASA NTRS1996

Constraints in Genetic Programming

Genetic programming refers to a class of genetic algorithms utilizing generic representation in the form of program trees. For a particular application, one needs to provide the set of functions, whose compositions determine the space of program structures being evolved, and the set of terminals, which determine the space of specific instances of those programs. The algorithm searches the space for the best program for a given problem, applying evolutionary mechanisms borrowed from nature. Genetic algorithms have shown great capabilities in approximately solving optimization problems which could not be approximated or solved with other methods. Genetic programming extends their capabilities to deal with a broader variety of problems. However, it also extends the size of the search space, which often becomes too large to be effectively searched even by evolutionary methods. Therefore, our objective is to utilize problem constraints, if such can be identified, to restrict this space. In this publication, we propose a generic constraint specification language, powerful enough for a broad class of problem constraints. This language has two elements -- one reduces only the number of program instances, the other reduces both the space of program structures as well as their instances. With this language, we define the minimal set of complete constraints, and a set of operators guaranteeing offspring validity from valid parents. We also show that these operators are not less efficient than the standard genetic programming operators if one preprocesses the constraints - the necessary mechanisms are identified.

Janikow, Cezary Z.↗
NASA NTRS1994

Thermal Design of the Cassini Narrow Angle Camera

The Narrow Angle Camera (NAC) is one of two cameras in the Imaging Science Subsystem (ISS) on the Cassini Spacecraft (S/C), the second camera is a Voyager-inherited Wide Angle Camera (WAC). Cassini is currently planned to be launched in October 1997 and will arrive at Saturn for a four year tour in June 2004. The Narrow Angle Optics are a Ritchey Chretien type Optics, has a focal length of 2000 mm, a relative aperture of f/10.5, a spectral range of 200 to 1100 nm, 24 filters, a pixel field of view of 6.0 microradian/pixel, and has a field of view of 3.5 x 3.5 degrees. The sensor is a Charged Couple Device (CCD), 1024 x 1024 pixels with a pixel size of 12 x 12 um, a full well greater than 50,000 e-, on chip processing of up to 800,000 e- pixel summation, a dark current of less than 0.1 e-/pixel/sec at operating temperature, and a charge transfer efficiency of 0.99999 at operating temperature...

Hoffman, Pamela↗
NASA NTRS2008

Software Analyzes Complex Systems in Real Time

Expert system software programs, also known as knowledge-based systems, are computer programs that emulate the knowledge and analytical skills of one or more human experts, related to a specific subject. SHINE (Spacecraft Health Inference Engine) is one such program, a software inference engine (expert system) designed by NASA for the purpose of monitoring, analyzing, and diagnosing both real-time and non-real-time systems. It was developed to meet many of the Agency s demanding and rigorous artificial intelligence goals for current and future needs. NASA developed the sophisticated and reusable software based on the experience and requirements of its Jet Propulsion Laboratory s (JPL) Artificial Intelligence Research Group in developing expert systems for space flight operations specifically, the diagnosis of spacecraft health. It was designed to be efficient enough to operate in demanding real time and in limited hardware environments, and to be utilized by non-expert systems applications written in conventional programming languages. The technology is currently used in several ongoing NASA applications, including the Mars Exploration Rovers and the Spacecraft Health Automatic Reasoning Pilot (SHARP) program for the diagnosis of telecommunication anomalies during the Neptune Voyager Encounter. It is also finding applications outside of the Space Agency.

Source record↗
NASA NTRS2010

MCCx C3I Control Center Interface Emulator

This slide presentation reviews the project to develop and demonstrate alternate Information Technologies and systems for new Mission Control Centers that will reduce the cost of facility development, maintenance and operational costs and will enable more efficient cost and effective operations concepts for ground support operations. The development of a emulator for the Control Center capability will enable the facilities to conduct the simulation requiring interactivity with the Control Center when it is off line or unavailable, and it will support testing of C3I interfaces for both command and telemetry data exchange messages (DEMs).

Mireles, James R.↗
NASA NTRSDate not supplied

Quantifying Effects of Departure and Flight Time Uncertainty on Urban Air Mobility Operations

Demand capacity balancing is a key mechanism for maintaining safe and efficient Urban Air Mobility (UAM) operations. However, uncertainties such as departure delays and flight time variation may reduce the effectiveness of algorithms used for balancing and detrimentally impact the safety and efficiency of UAM operations. In this paper, the effects of these uncertainties on UAM operations are quantified by modeling a distribution of departure and flight time errors. A route network in the Dallas/Fort Worth metropolitan area was used to simulate traffic demand with and without uncertainty. Simulations were conducted with three main models of uncertainty – first uncertainty in departure time delay resulting in late takeoffs, second with uncertainty in flight times in addition to departure delays, and finally, uncertainty in departure times that cause either late or early takeoffs. Each of these simulations were performed using varied standard deviations to fully understand the effects of uncertainties. Results from these simulations were compared to a baseline simulation using the same parameters, but without any uncertainty. The results suggest that both safety and efficiency are significantly impacted by uncertainty even with relatively low uncertainty introduced. These results work towards quantifying the effects of uncertainty in flight scheduling for UAM. They will also aid in the further development of the demand capacity balancing algorithms for UAM operations and associated air traffic management.

Urban Air Mobility↗
NASA NTRSDate not supplied

Quantifying the Effects of Departure and Flight Time Uncertainty on Urban Air Mobility Operations

Demand capacity balancing is a key mechanism for maintaining safe and efficient Urban Air Mobility (UAM) operations. However, uncertainties such as departure delays and flight time variation may reduce the effectiveness of algorithms used for balancing and detrimentally impact the safety and efficiency of UAM operations. In this paper, the effects of these uncertainties on UAM operations are quantified by modeling a distribution of departure and flight time errors. A route network in the Dallas/Fort Worth metropolitan area was used to simulate traffic demand with and without uncertainty. Simulations were conducted with three main models of uncertainty – first uncertainty in departure time delay resulting in late takeoffs, second with uncertainty in flight times in addition to departure delays, and finally, uncertainty in departure times that cause either late or early takeoffs. Each of these simulations were performed using varied standard deviations to fully understand the effects of uncertainties. Results from these simulations were compared to a baseline simulation using the same parameters, but without any uncertainty. The results suggest that both safety and efficiency are significantly impacted by uncertainty even with relatively low uncertainty introduced. These results work towards quantifying the effects of uncertainty in flight scheduling for UAM. They will also aid in the further development of the demand capacity balancing algorithms for UAM operations and associated air traffic management.

Urban Air Mobility↗
NASA NTRS1982

Compression Ratio Adjuster

New mechanism alters compression ratio of internal-combustion engine according to load so that engine operates at top fuel efficiency. Ordinary gasoline, diesel and gas engines with their fixed compression ratios are inefficient at partial load and at low-speed full load. Mechanism ensures engines operate as efficiently under these conditions as they do at highload and high speed.

Akkerman, J. W.↗
NASA NTRS1983

Recent developments in EHF Satcom technology

The state-of-the-art in EHF Satcom technology is assessed and hardware samples are described. Travelling wave tube amplifiers provide up to 30 percent efficiency for 20 GHz spaceborne operations and up to 20 percent efficiency at 30/44 GHz in ground operations. Solid-state power amplifiers incorporate FET and IMPATT diode technologies for 20-44 GHz transmissions using GaAs FETs and Si or GaAs diodes. Noise is reduced with loss image-enhanced mixers coupled to an IF FET amplifier, resulting ultimately in 6-7 dB noise levels compared to 19 dB gain. Finally, high power varactor upconverters have been developed to provide up to 50 mW RF output at at least 10 percent efficiency at frequencies up to 50 GHz.

Okean, H. C.↗
NASA NTRS1991

Applied-field MPD thruster geometry effects

Eight MPD thruster configurations were used to study the effects of applied field strength, propellant, and facility pressure on thruster performance. Vacuum facility background pressures higher than approx. 0.12 Pa were found to greatly influence thruster performance and electrode power deposition. Thrust efficiency and specific impulse increased monotonically with increasing applied field strength. Both cathode and anode radii fundamentally influenced the efficiency specific impulse relationship, while their lengths influence only the magnitude of the applied magnetic field required to reach a given performance level. At a given specific impulse, large electrode radii result in lower efficiencies for the operating conditions studied. For all test conditions, anode power deposition was the largest efficiency loss, and represented between 50 and 80 pct. of the input power. The fraction of the input power deposited into the anode decreased with increasing applied field and anode radii. The highest performance measured, 20 pct. efficiency at 3700 seconds specific impulse, was obtained using hydrogen propellant.

Myers, Roger M.↗
NASA NTRS1991

Applied-field MPD thruster geometry effects

Eight MPD thruster configurations were used to study the effects of applied-field strength, propellant, and facility pressure on thruster performance. Vacuum facility background pressures higher than about 0.12 Pa were found to significantly influence thruster performance and electrode power deposition. Thrust efficiency and specific impulse increased monotonically with increasing applied field strength. Both cathode and anode radii fundamentally influenced the efficiency-specific impulse relationship, while their lengths influenced only the magnitude of the applied magnetic field required to reach a given performance level. At a given specific impulse, large electrode radii result in lower efficiencies for the operating conditions studied. For all test conditions, anode power deposition was the largest efficiency loss, and represented between 50 percent and 80 percent of the input power. The fraction of the input power deposited into the anode decreased with increasing applied field and anode radius. The highest performance measured, 20 percent efficiency at 3700 seconds specific impulse, was obtained using hydrogen propellant.

Myers, Roger M.↗
NASA NTRSDate not supplied

Electrified Aircraft Propulsion Integration Concepts: Primary Fuel Cells and Cryogenic Hydrogen Storage

This presentation leverages lessons learned from the multiple activities funded by the National Aeronautics and Space Agency (NASA) to convey development paths for implementing an Electrified Aircraft Propulsion (EAP) system powered by primary fuel cells systems and fueled by cryogenically stored hydrogen. To reduce greenhouse gas emissions associated with all aspects of hydrogen-fueled aviation, this work minimizes required energy and time per payload distance at take-off so as to reduce emissions associated with ground operations and the production, liquefaction, transfer, and storage of hydrogen. The approach targets efficient system integration, maximizing hydrogen utilization efficiency, maximizing power system efficiency, and viable operational concepts for delivered payload distances. The discussion outlines the performance and compliance challenges revealed by analyses of current fuel cell and cryogenic management technologies. The conclusion offers potential mitigation strategies to the identified the performance and compliance challenges.

Fuel Cell↗
NASA NTRS1984

Solar photochemical process engineering for production of fuels and chemicals

The engineering costs and performance of a nominal 25,000 scmd (883,000 scfd) photochemical plant to produce dihydrogen from water were studied. Two systems were considered, one based on flat-plate collector/reactors and the other on linear parabolic troughs. Engineering subsystems were specified including the collector/reactor, support hardware, field transport piping, gas compression equipment, and balance-of-plant (BOP) items. Overall plant efficiencies of 10.3 and 11.6% are estimated for the flat-plate and trough systems, respectively, based on assumed solar photochemical efficiencies of 12.9 and 14.6%. Because of the opposing effects of concentration ratio and operating temperature on efficiency, it was concluded that reactor cooling would be necessary with the trough system. Both active and passive cooling methods were considered. Capital costs and energy costs, for both concentrating and non-concentrating systems, were determined and their sensitivity to efficiency and economic parameters were analyzed. The overall plant efficiency is the single most important factor in determining the cost of the fuel.

Biddle, J. R.↗
NASA NTRS2011

Investigating the Nature of and Methods for Managing Metroplex Operations

A combination of traffic demand growth, Next Generation Air Transportation System (NextGen) technologies and operational concepts, and increased utilization of regional airports is expected to increase the occurrence and severity of coupling between operations at proximate airports. These metroplex phenomena constrain the efficiency and/or capacity of airport operations and, in NextGen, have the potential to reduce safety and prevent environmental benefits. Without understanding the nature of metroplexes and developing solutions that provide efficient coordination of operations between closely-spaced airports, the use of NextGen technologies and distribution of demand to regional airports may provide little increase in the overall metroplex capacity. However, the characteristics and control of metroplex operations have not received significant study. This project advanced the state of knowledge about metroplexes by completing three objectives: 1. developed a foundational understand of the nature of metroplexes; 2. provided a framework for discussing metroplexes; 3. suggested and studied an approach for optimally managing metroplexes that is consistent with other NextGen concepts

Atkins, Stephen↗
NASA NTRS2017

UAV Trajectory Modeling Using Neural Networks

Large amount of small Unmanned Aerial Vehicles (sUAVs) are projected to operate in the near future. Potential sUAV applications include, but not limited to, search and rescue, inspection and surveillance, aerial photography and video, precision agriculture, and parcel delivery. sUAVs are expected to operate in the uncontrolled Class G airspace, which is at or below 500 feet above ground level (AGL), where many static and dynamic constraints exist, such as ground properties and terrains, restricted areas, various winds, manned helicopters, and conflict avoidance among sUAVs. How to enable safe, efficient, and massive sUAV operations at the low altitude airspace remains a great challenge. NASA's Unmanned aircraft system Traffic Management (UTM) research initiative works on establishing infrastructure and developing policies, requirement, and rules to enable safe and efficient sUAVs' operations. To achieve this goal, it is important to gain insights of future UTM traffic operations through simulations, where the accurate trajectory model plays an extremely important role. On the other hand, like what happens in current aviation development, trajectory modeling should also serve as the foundation for any advanced concepts and tools in UTM. Accurate models of sUAV dynamics and control systems are very important considering the requirement of the meter level precision in UTM operations. The vehicle dynamics are relatively easy to derive and model, however, vehicle control systems remain unknown as they are usually kept by manufactures as a part of intellectual properties. That brings challenges to trajectory modeling for sUAVs. How to model the vehicle's trajectories with unknown control system? This work proposes to use a neural network to model a vehicle's trajectory. The neural network is first trained to learn the vehicle's responses at numerous conditions. Once being fully trained, given current vehicle states, winds, and desired future trajectory, the neural network should be able to predict the vehicle's future states at next time step. A complete 4-D trajectory are then generated step by step using the trained neural network. Experiments in this work show that the neural network can approximate the sUAV's model and predict the trajectory accurately.

Neural Networks↗
NASA NTRS1985

Solar photochemical process engineering for production of fuels and chemicals

The engineering costs and performance of a nominal 25,000 scmd (883,000 scfd) photochemical plant to produce dihydrogen from water were studied. Two systems were considered, one based on flat-plate collector/reactors and the other on linear parabolic troughs. Engineering subsystems were specified including the collector/reactor, support hardware, field transport piping, gas compression equipment, and balance-of-plant (BOP) items. Overall plant efficiencies of 10.3 and 11.6 percent are estimated for the flat-plate and trough systems, respectively, based on assumed solar photochemical efficiencies of 12.9 and 14.6 percent. Because of the opposing effects of concentration ratio and operating temperature on efficiency, it was concluded that reactor cooling would be necessary with the trough system. Both active and passive cooling methods were considered. Capital costs and energy costs, for both concentrating and non-concentrating systems, were determined and their sensitivity to efficiency and economic parameters were analyzed. The overall plant efficiency is the single most important factor in determining the cost of the fuel.

Biddle, J. R.↗
NASA NTRS1986

Ground-based time-guidance algorithm for control of airplanes in a time-metered air traffic control environment: A piloted simulation study

The rapidly increasing costs of flight operations and the requirement for increased fuel conservation have made it necessary to develop more efficient ways to operate airplanes and to control air traffic for arrivals and departures to the terminal area. One concept of controlling arrival traffic through time metering has been jointly studied and evaluated by NASA and ONERA/CERT in piloted simulation tests. From time errors attained at checkpoints, airspeed and heading commands issued by air traffic control were computed by a time-guidance algorithm for the pilot to follow that would cause the airplane to cross a metering fix at a preassigned time. These tests resulted in the simulated airplane crossing a metering fix with a mean time error of 1.0 sec and a standard deviation of 16.7 sec when the time-metering algorithm was used. With mismodeled winds representing the unknown in wind-aloft forecasts and modeling form, the mean time error attained when crossing the metering fix was increased and the standard deviation remained approximately the same. The subject pilots reported that the airspeed and heading commands computed in the guidance concept were easy to follow and did not increase their work load above normal levels.

Knox, C. E.↗
NASA NTRSDate not supplied

Tradeoffs When Considering Deep Reinforcement Learning for Contingency Management in Advanced Air Mobility

Air transportation is undergoing a rapid evolution globally with the introduction of Advanced Air Mobility (AAM) and with it comes novel challenges and opportunities for transforming aviation. As AAM operations introduce increasing heterogeneity in vehicle capabilities and density, increased levels of automation are likely necessary to achieve operational safety and efficiency goals. This paper focuses on one example where increased automation has been suggested. Autonomous operations will need contingency management systems that can monitor evolving risk across a span of interrelated (or interdependent) hazards and, if necessary, execute appropriate control interventions via supervised or automated decision making. Accommodating this complex environment may require automated functions (autonomy) that apply artificial intelligence (AI) techniques that can adapt and respond to a quickly changing environment. This paper explores the use of Deep Reinforcement Learning (DRL) which has shown promising performance in complex and high-dimensional environments where the objective can be constructed as a sequential decision-making problem. An extension of a prior formulation of the contingency management problem as a Markov Decision Process (MDP) is presented and uses a DRL framework to train agents that mitigate hazards present in the simulation environment. A comparison of these learning-based agents and classical techniques is presented in terms of their performance, verification difficulties, and development process.

machine learningautonomous systems; flight simulat↗