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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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All sourcesNASA NTRSDOE OSTI
NASA NTRS1973

Life sciences payload definition and integration study, task C and D. Volume 2: Payload definition, integration, and planning studies

The Life Sciences Payload Definition and Integration Study was composed of four major tasks. Tasks A and B, the laboratory definition phase, were the subject of prior NASA study. The laboratory definition phase included the establishment of research functions, equipment definitions, and conceptual baseline laboratory designs. These baseline laboratories were designated as Maxi-Nom, Mini-30, and Mini-7. The outputs of Tasks A and B were used by the NASA Life Sciences Payload Integration Team to establish guidelines for Tasks C and D, the laboratory integration phase of the study. A brief review of Tasks A and B is presented provide background continuity. The tasks C and D effort is the subject of this report. The Task C effort stressed the integration of the NASA selected laboratory designs with the shuttle sortie module. The Task D effort updated and developed costs that could be used by NASA for preliminary program planning.

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NASA NTRS1973

Life science payload definition and integration study, task C and D. Volume 3: Appendices

Research equipment requirements were based on the Mini-7 and Mini-30 laboratory concepts defined in Tasks A and B of the intial LSPD contract. Modified versions of these laboratories and the research equipment within them were to be used in three missions of Shuttle/Sortie Module. These were designated (1) the shared 7-day laboratory (a mission with the life sciences laboratory sharing the sortie module with another scientific laboratory), (2) the dedicated 7-day laboratory (full use of the sortie module), and (3) the dedicated 30-day laboratory (full sortie module use with a 30-day mission duration). In defining the research equipment requirements of these laboratories, the equipment was grouped according to its function, and equipment unit data packages were prepared.

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NASA NTRS1973

Life sciences payload definition and integration study, task C and D. Volume 1: Management summary

The findings of a study to define the required payloads for conducting life science experiments in space are presented. The primary objectives of the study are: (1) identify research functions to be performed aboard life sciences spacecraft laboratories and necessary equipment, (2) develop conceptual designs of potential payloads, (3) integrate selected laboratory designs with space shuttle configurations, and (4) establish cost analysis of preliminary program planning.

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NASA NTRS1973

Life sciences payload definition and integration study, task C and D. Volume 4: Preliminary equipment item specification catalog

A specification catalog to define the equipment to be used for conducting life sciences experiments in a space laboratory is presented. The specification sheets list the purpose of the equipment item, and any specific technical requirements which can be identified. The status of similar hardware for ground use is stated with comments regarding modifications required to achieve spaceflight qualified hardware. Pertinent sketches, commercial catalog sheets, or drawings of the applicable equipment are included.

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NASA NTRS1976

STS payloads mission control study continuation phase A-1. Volume 2-C, task 3: Identification of joint activities and estimation of resources in preparation for joint flight operations

Payload mission control concepts are developed for real time flight operations of STS. Flight planning, training, simulations, and other flight preparations are included. Payload activities for the preflight phase, activity sequences and organizational allocations, and traffic and experience factors to establish composite man-loading for joint STS payload activities are identified for flight operations from 1980 to 1985.

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NASA NTRS1992

An expert system shell for inferring vegetation characteristics: The learning system (tasks C and D)

This report describes the implementation of a learning system that uses a data base of historical cover type reflectance data taken at different solar zenith angles and wavelengths to learn class descriptions of classes of cover types. It has been integrated with the VEG system and requires that the VEG system be loaded to operate. VEG is the NASA VEGetation workbench - an expert system for inferring vegetation characteristics from reflectance data. The learning system provides three basic options. Using option one, the system learns class descriptions of one or more classes. Using option two, the system learns class descriptions of one or more classes and then uses the learned classes to classify an unknown sample. Using option three, the user can test the system's classification performance. The learning system can also be run in an automatic mode. In this mode, options two and three are executed on each sample from an input file. The system was developed using KEE. It is menu driven and contains a sophisticated window and mouse driven interface which guides the user through various computations. Input and output file management and data formatting facilities are also provided.

Harrison, P. Ann↗
NASA NTRS1984

Research pressure instrumentation for NASA Space Shuttle main engine, modification no. 5

The purpose of Modification No. 5 of this contract is to expand the scope of work (Task C) of this research study effort to develop pressure instrumentation for the SSME. The objective of this contract (Task C) is to direct Honeywell's Solid State Electronics Division's (SSED) extensive experience and expertise in solid state sensor technology to develop prototype pressure transducers which are targeted to meet the SSME performance design goals and to fabricate, test and deliver a total of 10 prototype units. SSED's basic approach is to effectively utilize the many advantages of silicon piezoresistive strain sensing technology to achieve the objectives of advanced state-of-the-art pressure sensors in terms of reliability, accuracy and ease of manufacture. More specifically, integration of multiple functions on a single chip is the key attribute of this technology which will be exploited during this research study.

Anderson, P. J.↗
NASA NTRS2008

G-Guidance Interface Design for Small Body Mission Simulation

The G-Guidance software implements a guidance and control (G and C) algorithm for small-body, autonomous proximity operations, developed under the Small Body GN and C task at JPL. The software is written in Matlab and interfaces with G-OPT, a JPL-developed optimization package written in C that provides G-Guidance with guaranteed convergence to a solution in a finite computation time with a prescribed accuracy. The resulting program is computationally efficient and is a prototype of an onboard, real-time algorithm for autonomous guidance and control. Two thruster firing schemes are available in G-Guidance, allowing tailoring of the software for specific mission maneuvers. For example, descent, landing, or rendezvous benefit from a thruster firing at the maneuver termination to mitigate velocity errors. Conversely, ascent or separation maneuvers benefit from an immediate firing to avoid potential drift toward a second body. The guidance portion of this software explicitly enforces user-defined control constraints and thruster silence times while minimizing total fuel usage. This program is currently specialized to small-body proximity operations, but the underlying method can be generalized to other applications.

Acikmese, Behcet↗
NASA NTRS1993

Orbit transfer rocket engine technology program

An advanced near term (1990's) space-based Orbit Transfer Vehicle Engine (OTVE) system was designed, and the technologies applicable to its construction, maintenance, and operations were developed under Tasks A through F of the Orbit Transfer Rocket Engine Technology Program. Task A was a reporting task. In Task B, promising OTV turbomachinery technologies were explored: two stage partial admission turbines, high velocity ratio diffusing crossovers, soft wear ring seals, advanced bearing concepts, and a rotordynamic analysis. In Task C, a ribbed combustor design was developed. Possible rib and channel geometries were chosen analytically. Rib candidates were hot air tested and laser velocimeter boundary layer analyses were conducted. A channel geometry was also chosen on the basis of laser velocimeter data. To verify the predicted heat enhancement effects, a ribbed calorimeter spool was hot fire tested. Under Task D, the optimum expander cycle engine thrust, performance and envelope were established for a set of OTV missions. Optimal nozzle contours and quick disconnects for modularity were developed. Failure Modes and Effects Analyses, maintenance and reliability studies and component study results were incorporated into the engine system. Parametric trades on engine thrust, mixture ratio, and area ratio were also generated. A control system and the health monitoring and maintenance operations necessary for a space-based engine were outlined in Task E. In addition, combustor wall thickness measuring devices and a fiberoptic shaft monitor were developed. These monitoring devices were incorporated into preflight engine readiness checkout procedures. In Task F, the Integrated Component Evaluator (I.C.E.) was used to demonstrate performance and operational characteristics of an advanced expander cycle engine system and its component technologies. Sub-system checkouts and a system blowdown were performed. Short transitions were then made into main combustor ignition and main stage operation.

Gustafson, N. B.↗
NASA NTRS2006

Small Body GN&C Research Report: A G-Guidance Enhancement to Increase Mission Functionality

G-Guidance is a robust G&C (guidance and control) algorithm developed under the small-body GN&C task. The G-Guidance scheme utilizes a model predictive control approach, along with a convexification of the governing dynamics, control constraints, and trajectory/state constraints. This report details an enhancement to the FY2005 G-Guidance algorithm; the addition of a fire-second approach helps to nullify velocity errors and hit desired final velocities much more precisely than the original Fire-first scheme developed in FY2005. The enhancement preserves the primary benefit of the algorithm, which is to ensure required thruster silent times during trajectory maneuvers. The fire-second scheme increases the versatility of the algorithm for missions employing G-Guidance. For instance, a landing sequence could employ the fire-second scheme to ensure a null final velocity at landing, whereas an ascent sequence could utilize a fire-first scheme to ensure there is no drift of the spacecraft toward the ground at the beginning of the maneuver. Examples are provided within to demonstrate a fire-first versus fire-second guidance scheme. As in the existing G-Guidance algorithm, the examples and schemes incorporate gravity models and thruster firing times into discrete dynamics that are solved as a optimal control problem to minimize fuel consumption or thruster energy expenditure.

velocity errors↗
NASA NTRSDate not supplied

NASA’s Orbital Debris Optical Program: ES-MCAT Nearing Full Operational Capability (FOC)

The NASA JAO/ES-MCAT (Eugene Stansbery Meter Class Autonomous Telescope) Facility is nearing Full Operational Capability, or FOC. ES-MCAT is now fully capable of autonomously running all observations, including: (a) monitoring weather and closing when conditions are not safe, as well as halting observations when conditions are not suitable (e.g. too cloudy) for operations, (b) start-up/shut-down nightly tasking, (c) collecting calibration data and survey or TLE-tracked data, and (d) processing all collected data, including on-chip photometry and astrometry calibrations using the GAIA star catalogue. The processed data are then further analyzed at NASA Johnson Space Center to correlate detections with known objects in the Space Surveillance Network (SSN) catalogue. MCAT can collect data of specific objects with known orbits or can search for objects with orbits similar to those of spacecraft or rocket bodies that have recently broken up. However, the primary goal for ES-MCAT is to survey the geosynchronous (GEO) belt to provide a statistical sample of the GEO debris environment for both engineering models for spacecraft designers and long-term environment evolutionary purposes. The approach for sweeping the sky to statistically survey GEO has been investigated and updated from past surveys taken by NASA and will be reported, herein referred to as the Candy Cane method. ES-MCAT’s optical performance and the limiting magnitude for the full optical system will be discussed. An analysis used to determine which filter to use for GEO surveys (SDSS r′) includes combining the reflectivity of the primary and secondary mirrors, transmission of the field corrector and CCD window, and the quantum efficiency of the CCD detector, resulting in throughput of the full optical path. This throughput is then combined with the expected typical transparency of the atmosphere at ES-MCAT’s altitude/location for the Sloan Digital Sky Survey (SDSS) g′r′i′z′ and Johnson/Kron-Cousins BVRI filters to yield expected relative throughput.

S. M. Lederer↗
NASA NTRS1971

A control-theory model for human decision-making

A model for human decision making is an adaptation of an optimal control model for pilot/vehicle systems. The models for decision and control both contain concepts of time delay, observation noise, optimal prediction, and optimal estimation. The decision making model was intended for situations in which the human bases his decision on his estimate of the state of a linear plant. Experiments are described for the following task situations: (a) single decision tasks, (b) two-decision tasks, and (c) simultaneous manual control and decision making. Using fixed values for model parameters, single-task and two-task decision performance can be predicted to within an accuracy of 10 percent. Agreement is less good for the simultaneous decision and control situation.

Levison, W. H.↗