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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 181 records · Page 10
All sourcesNASA NTRSDOE OSTI
NASA NTRS2016

Physical Processes Controlling the Distribution of Relative Humidity in the Tropical Tropopause Layer over the Pacific

The distribution of relative humidity with respect to ice (RHI) in the Boreal wintertime Tropical Tropopause Layer (TTL - about 14-19 km) over the Pacific is examined with the extensive dataset of measurements from the NASA Airborne Tropical TRopopause EXperiment (ATTREX). Multiple deployments of the Global Hawk during ATTREX provided hundreds of vertical profiles spanning the Pacific with accurate measurements of temperature, pressure, water vapor concentration, ozone concentration, and cloud properties. We also compare the measured RHI distributions with results from a transport and microphysical model driven by meteorological analysis fields. Notable features in the distribution of RHI versus temperature and longitude include (1) the common occurrence of RHI values near ice saturation over the western Pacific in the lower TTL (temperatures greater than 200 K) and in airmasses with low ozone concentrations indicating recent detrainment from deep convection; (2) low RHI values in the lower TTL over the eastern Pacific where deep convection is infrequent; (3) RHI values following a constant H2O mixing ratio in the upper TTL (temperatures below about 195 degrees Kelvin), particularly for samples with ozone mixing ratios greater than about 50-100 parts-per-billion-volume indicating mixtures of tropospheric and stratospheric air, and (4) RHI values typically near ice saturation in the coldest airmasses sampled (temperatures less than about 190 degrees Kelvin). We find that the typically saturated air in the lower TTL over the western Pacific is largely driven by the frequent occurrence of deep convection in this region. The nearly-constant water vapor mixing ratios in the upper TTL result from the combination of slow ascent (resulting in long residence times) and wave-driven temperature variability on a range of time scales (resulting in most air parcels having experienced low temperature and dehydration).

ozone↗
NASA NTRS1998

The Application of Dedicated Controllers to Parallel/Serial Exhauster Trains

Twenty new dedicated controllers were interfaced to the Distributed Control System (DCS) at NASA Lewis Research Center. Better surge avoidance, improved energy savings, interface intricacies, and plant complexities are the subjects of this paper. Problems with the existing Programmable Logic Controller (PLC) control logic are described as well as the solutions implemented with the new controllers. The complexity of the new communication network between the dedicated controllers, Multipurpose Micro-Processor (MMPs), and the operator workstations are illustrated. The complicated nature of the plant and methods of controlling the associated processes are also discussed.

Sadhukhan, Debashis↗
NASA NTRS1994

Reliability-Based Design of a Safety-Critical Automation System: A Case Study

In 1986, NASA funded a project to modernize the NASA Ames Research Center Unitary Plan Wind Tunnels, including the replacement of obsolescent controls with a modern, automated distributed control system (DCS). The project effort on this system included an independent safety analysis (ISA) of the automation system. The purpose of the ISA was to evaluate the completeness of the hazard analyses which had already been performed on the Modernization Project. The ISA approach followed a tailoring of the risk assessment approach widely used on existing nuclear power plants. The tailoring of the nuclear industry oriented risk assessment approach to the automation system and its role in reliability-based design of the automation system is the subject of this paper.

Carroll, Carol W.↗
NASA NTRS2004

Sensor Needs for Control and Health Management of Intelligent Aircraft Engines

NASA and the U.S. Department of Defense are conducting programs which support the future vision of "intelligent" aircraft engines for enhancing the affordability, performance, operability, safety, and reliability of aircraft propulsion systems. Intelligent engines will have advanced control and health management capabilities enabling these engines to be self-diagnostic, self-prognostic, and adaptive to optimize performance based upon the current condition of the engine or the current mission of the vehicle. Sensors are a critical technology necessary to enable the intelligent engine vision as they are relied upon to accurately collect the data required for engine control and health management. This paper reviews the anticipated sensor requirements to support the future vision of intelligent engines from a control and health management perspective. Propulsion control and health management technologies are discussed in the broad areas of active component controls, propulsion health management and distributed controls. In each of these three areas individual technologies will be described, input parameters necessary for control feedback or health management will be discussed, and sensor performance specifications for measuring these parameters will be summarized.

Simon, Donald L.↗
NASA NTRS1993

Refurbishment of the Jet Propulsion Laboratory's Large Space Simulator

The JPL large space simulator has recently undergone a major refurbishment to restore and enhance its capabilities to provide high fidelity space simulation. The nearly completed refurbishment has included upgrading the vacuum pumping system by replacing old oil diffusion pumps with new cryogenic and turbomolecular pumps; modernizing the entire control system to utilize computerized, distributed control technology; replacing the Xenon arc lamp power supplies with new upgraded units; refinishing the primary collimating mirror; and replacing the existing integrating lens unit and the fused quartz penetration window.

JPL space simulator mirror cryopump solar simulati↗
NASA NTRS1996

Optimum Suction Distribution for Transition Control

The optimum suction distribution which gives the longest laminar region for a given total suction is computed. The goal here is to provide the designer with a method to find the best suction distribution subject to some overall constraint applied to the suction. We formulate the problem using the Lagrangian multiplier method with constraints. The resulting non-linear system of equations is solved using the Newton-Raphson technique. The computations are performed for a Blasius boundary layer on a flat-plate and crossflow cases. For the Blasius boundary layer, the optimum suction distribution peaks upstream of the maximum growth rate region and remains flat in the middle before it decreases to zero at the end of the transition point. For the stationary and travelling crossflow instability, the optimum suction peaks upstream of the maximum growth rate region and decreases gradually to zero.

Balakumar, P.↗
NASA NTRS2023

Reinforcement Learning Approach to Flight Control Allocation with Distributed Electric Propulsion

The flight control system of the SUSAN Electrofan concept aircraft achieves attitude control using both conventional flight control surfaces and differential thrust through distributed electric propulsion (DEP) from sixteen wing-mounted electric engines. The introduction of eight pairs of wing fans for attitude control creates a highly actuated system. Such a system requires more sophisticated control to operate, especially in the presence of wingfan failures where the loss of a single wingfan can result in a thrust imbalance. This paper investigates the use of deep reinforcement learning (RL) using proximal policy optimization (PPO) to achieve attitude control through a combination of DEP and control surface deflections. First, the paper examines the aircraft undergoing a coordinated turn. Then, it examines the aircraft experiencing a wingfan failure during cruise conditions. It is shown that deep reinforcement learning can be a potential avenue for nonlinear flight control design.

Distributed Electric Propulsion↗
NASA NTRS2017

Design and Development of a 200-kW Turbo-Electric Distributed Propulsion Testbed

There a few NASA funded electric and hybrid electric projects from different NASA Centers, including the NASA Armstrong Flight Research Center (AFRC) (Edwards, California). Each project identifies a specific technology gap that is currently inhibiting the growth and proliferation of relevant technologies in commercial aviation. This paper describes the design and development of a turbo-electric distributed propulsion (TeDP) hardware-in-the-loop (HIL) simulation bench, which is a test bed for discovering turbo-electric control, distributed electric control, power management control, and integration competencies while providing risk mitigation for future turbo-electric flying demonstrators.

propulsion↗
NASA NTRS1991

A distributed Petri Net controller for a dual arm testbed

This thesis describes the design and functionality of a Distributed Petri Net Controller (DPNC). The controller runs under X Windows to provide a graphical interface. The DPNC allows users to distribute a Petri Net across several host computers linked together via a TCP/IP interface. A sub-net executes on each host, interacting with the other sub-nets by passing a token vector from host to host. One host has a command window which monitors and controls the distributed controller. The input to the DPNC is a net definition file generated by Great SPN. Thus, a net may be designed, analyzed and verified using this package before implementation. The net is distributed to the hosts by tagging transitions that are host-critical with the appropriate host number. The controller will then distribute the remaining places and transitions to the hosts by generating the local nets, the local marking vectors and the global marking vector. Each transition can have one or more preconditions which must be fulfilled before the transition can fire, as well as one or more post-processes to be executed after the transition fires. These implement the actual input/output to the environment (machines, signals, etc.). The DPNC may also be used to simulate a Great SPN net since stochastic and deterministic firing rates are implemented in the controller for timed transitions.

Bjanes, Atle↗