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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 NTRS2012

Usage of NASA's Near Real-Time Solar and Meteorological Data for Monitoring Building Energy Systems Using RETScreen International's Performance Analysis Module

This paper describes building energy system production and usage monitoring using examples from the new RETScreen Performance Analysis Module, called RETScreen Plus. The module uses daily meteorological (i.e., temperature, humidity, wind and solar, etc.) over a period of time to derive a building system function that is used to monitor building performance. The new module can also be used to target building systems with enhanced technologies. If daily ambient meteorological and solar information are not available, these are obtained over the internet from NASA's near-term data products that provide global meteorological and solar information within 3-6 days of real-time. The accuracy of the NASA data are shown to be excellent for this purpose enabling RETScreen Plus to easily detect changes in the system function and efficiency. This is shown by several examples, one of which is a new building at the NASA Langley Research Center that uses solar panels to provide electrical energy for building energy and excess energy for other uses. The system shows steady performance within the uncertainties of the input data. The other example involves assessing the reduction in energy usage by an apartment building in Sweden before and after an energy efficiency upgrade. In this case, savings up to 16% are shown.

Paul W Stackhouse, Jr.↗
NASA NTRS1977

Application of airborne infrared technology to monitor building heat loss

The application of airborne infrared technology to the requirements for energy conservation in buildings was studied. Quantitative airborne data of the City of Ypsilanti, Michigan, were collected and processed to identify roof temperatures. A thermal scanner was flown at an altitude of 1,200 feet with two thermal bands 8.2-9.3 micrometers and 10.4-12.5 micrometers recorded by an analog system. Calibration was achieved by standard hot and cold plates. Using a thermal model to interpret ceiling insulation status, environmental factors were found to influence the relation between roof temperature and insulation. These include interior and sky temperatures, roofing materials, and the pitch and orientation of the roof. A follow-up mail survey established the ability to identify insulated and uninsulated houses from the airborne infrared data.

Tanis, F. J.↗
NASA NTRS2018

NASA Advanced Explorations Systems: 2018 Advancements in Life Support Systems

The NASA Advanced Exploration Systems (AES) Life Support Systems (LSS) project strives to develop reliable, energy-efficient, and low-mass spacecraft systems to provide envi-ronmental control and life support systems (ECLSS) critical to enabling long duration human missions beyond low Earth orbit (LEO). Highly reliable, closed-loop life support systems are among the capabilities required for the longer duration human space exploration missions planned in the mid-2020s and beyond. The LSS Project is focused on three life support areas: air revitalization, wastewater processing/water management and environmental monitoring. Building upon the International Space Station (ISS) LSS systems (where applicable), the three-fold mission of the LSS Project is to address discrete LSS technology gaps, to improve the reliability of LSS systems, and to advance LSS systems toward integrated testing aboard the ISS. This paper is a follow on to the AES LSS development status reported in 2017 and provides additional details on the progress made since that publication with specific attention to the status of the Aerosol Sampler ISS Flight Experiment, the Spacecraft Atmosphere Monitor (SAM) Flight Experiment, the Brine Processor Assembly (BPA) Flight Experiment as well as the progress of the terrestrial development in air, water and environmental monitoring technologies.

trace contaminant control↗
NASA NTRS2025

Converter Compressor Building, SWMU 089 Monitoring Well 21 Area Implementation Work Plan Kennedy Space Center, Florida

This Implementation Work Plan (IWP) was prepared by Tetra Tech, Inc., for the National Aeronautics and Space Administration (NASA) under Indefinite Delivery Indefinite Quantity (IDIQ) Contract 80KSC019D0011/80KSC019F0070, for the implementation of a bioremediation Interim Measure (IM) at the Converter Compressor Building (CCB) site located at Kennedy Space Center (KSC), Florida. The location of CCB within KSC is shown on Figure A-1. CCB has been designated as Solid Waste Management Unit (SWMU) 089 under KSC’s Resource Conservation and Recovery Act (RCRA) Corrective Action Program. The overall Corrective Action Objective (CAO) for CCB is to reduce concentrations of trichloroethene (TCE), cis-1,2-dichloroethene (cDCE), trans-1,2-dichloroethene (tDCE), and vinyl chloride (VC) to less than State of Florida Groundwater Cleanup Target Levels (GCTLs). The objective for the IM associated with this IM is to reduce groundwater concentrations for the contaminants of concern (COCs) to less than their Natural Default Attenuation Concentrations (NADCs) within the Monitoring Well 21 Area (herein referred to as MW21 Area) via bioremediation injections to support transition to long term monitoring (LTM). This IWP describes the activities that will be performed for the treatment of the MW21 Area. The Revision 1 CCB MW21 Area Interim Measure Work Plan (IMWP; NASA, 2024), which was approved by the Florida Department of Environmental Protection (FDEP) on December 26, 2024, was used as the design basis for this IWP.

Mark Jonnet↗
NASA NTRS2024

2023 Annual Long-Term Monitoring Report Vehicle Assembly Building Area

This report presents a summary of the groundwater monitoring activities that occurred in February 2024 at two sites in the Vehicle Assembly Building (VAB) Area Long-Term Monitoring (LTM) program at the John F. Kennedy Space Center, Florida. The two sites are Fire Station 3 (formerly Fire Station 6) (FS3) – Solid Waste Management Unit (SWMU) 106 and Launch Complex 39 Observation Gantry Area (LC39OGA) – SWMU 107.

VAB Long-Term Groundwater Monitoring↗
NASA NTRS2014

RETScreen Plus Software Tutorial

Greater emphasis is being placed on reducing both the carbon footprint and energy cost of buildings. A building's energy usage depends upon many factors one of the most important is the local weather and climate conditions to which it's electrical, heating and air conditioning systems must respond. Incorporating renewable energy systems, including solar systems, to supplement energy supplies and increase energy efficiency is important to saving costs and reducing emissions. Also retrofitting technologies to buildings requires knowledge of building performance in its current state, potential future climate state, projection of potential savings with capital investment, and then monitoring the performance once the improvements are made. RETScreen Plus is a performance analysis software module that supplies the needed functions of monitoring current building performance, targeting projected energy efficiency improvements and verifying improvements once completed. This tutorial defines the functions of RETScreen Plus as well as outlines the general procedure for monitoring and reporting building energy performance.

Ganoe, Rene D.↗
NASA NTRSDate not supplied

Convolutional Neural Networks for Image Classification in Metal Selective Laser Meting Additive Manufacturing

Selective laser melting (SLM) is a metal additive manufacturing process that has several advantages such as the large range of metal materials that can be accommodated, 3D printing of complex shape components, the ability to adjust material properties, and cost reduction as expensive production equipment may not be required. Therefore, process monitoring is crucial in different stages of the component building. In this work, convolutional neural networks (CNNs) are investigated as a suitable technique for post-inspection of builds. The monitoring of manufactured parts was conducted by collecting computed tomography (CT) images and identifying defects. Five CNN models were implemented and tested for the classification of the CT images. The models were based on NASNetMobile and DenseNet121, and a custom built CNN model. The results of this work show that CNNs can be feasible and reliable for rapid monitoring and classification of defects in CT images from build fabrication using SLM.

Rodolfo Ledesma↗
NASA NTRS1995

Building an adaptive agent to monitor and repair the electrical power system of an orbital satellite

Over several years we have developed a multistrategy apprenticeship learning methodology for building knowledge-based systems. Recently we have developed and applied our methodology to building intelligent agents. This methodology allows a subject matter expert to build an agent in the same way in which the expert would teach a human apprentice. The expert will give the agent specific examples of problems and solutions, explanations of these solutions, or supervise the agent as it solves new problems. During such interactions, the agent learns general rules and concepts, continuously extending and improving its knowledge base. In this paper we present initial results on applying this methodology to build an intelligent adaptive agent for monitoring and repair of the electrical power system of an orbital satellite, stressing the interaction with the expert during apprenticeship learning.

Tecuci, Gheorghe↗
NASA NTRSDate not supplied

In-Situ Process Monitoring, Synchronization, and Mapping Laser Powder Bed Fusion Builds of Ti6Al4V

The use of in-situ process monitoring is of interest to lower the cost of inspection for the qualification of laser powder bed fusion (LPBF) parts. Precise monitoring of the LPBF-AM build process constitutes a multi-scale and multi-discipline task. There are several significant challenges to the in-situ approach: the synchronization of sensor signals to process steps, the physical interpretation and classification of sensor signals, managing very large datasets, and comparing the inputs with the observed monitoring signals. At NASA Langley Research Center, a configurable architecture additive testbed has been developed to monitor the build process with synchronized sensors. The philosophy and method adopted for the synchronization of the cameras with laser power & position Ti-6Al-4V LPBF are described. The synchronized in-situ monitoring signals are compared with ex-situ nondestructive inspection and optical microscopy observations. Such comparisons permit a better understanding of how the sequential process actions of LPBF-AM can affect build quality.

Laser Powder Bed Fusion↗
NASA NTRS2008

Inductive System Monitors Tasks

The Inductive Monitoring System (IMS) software developed at Ames Research Center uses artificial intelligence and data mining techniques to build system-monitoring knowledge bases from archived or simulated sensor data. This information is then used to detect unusual or anomalous behavior that may indicate an impending system failure. Currently helping analyze data from systems that help fly and maintain the space shuttle and the International Space Station (ISS), the IMS has also been employed by data classes are then used to build a monitoring knowledge base. In real time, IMS performs monitoring functions: determining and displaying the degree of deviation from nominal performance. IMS trend analyses can detect conditions that may indicate a failure or required system maintenance. The development of IMS was motivated by the difficulty of producing detailed diagnostic models of some system components due to complexity or unavailability of design information. Successful applications have ranged from real-time monitoring of aircraft engine and control systems to anomaly detection in space shuttle and ISS data. IMS was used on shuttle missions STS-121, STS-115, and STS-116 to search the Wing Leading Edge Impact Detection System (WLEIDS) data for signs of possible damaging impacts during launch. It independently verified findings of the WLEIDS Mission Evaluation Room (MER) analysts and indicated additional points of interest that were subsequently investigated by the MER team. In support of the Exploration Systems Mission Directorate, IMS is being deployed as an anomaly detection tool on ISS mission control consoles in the Johnson Space Center Mission Operations Directorate. IMS has been trained to detect faults in the ISS Control Moment Gyroscope (CMG) systems. In laboratory tests, it has already detected several minor anomalies in real-time CMG data. When tested on archived data, IMS was able to detect precursors of the CMG1 failure nearly 15 hours in advance of the actual failure event. In the Aeronautics Research Mission Directorate, IMS successfully performed real-time engine health analysis. IMS was able to detect simulated failures and actual engine anomalies in an F/A-18 aircraft during the course of 25 test flights. IMS is also being used in colla

Source record↗
NASA NTRS1990

The ISIS project: Fault-tolerance in large distributed systems

The semi-annual status report covers activities of the ISIS project during the second half of 1989. The project had several independent objectives: (1) At the level of the ISIS Toolkit, ISIS release V2.0 was completed, containing bypass communication protocols. Performance of the system is greatly enhanced by this change, but the initial software release is limited in some respects. (2) The Meta project focused on the definition of the Lomita programming language for specifying rules that monitor sensors for conditions of interest and triggering appropriate reactions. This design was completed, and implementation of Lomita is underway on the Meta 2.0 platform. (3) The Deceit file system effort completed a prototype. It is planned to make Deceit available for use in two hospital information systems. (4) A long-haul communication subsystem project was completed and can be used as part of ISIS. This effort resulted in tools for linking ISIS systems on different LANs together over long-haul communications lines. (5) Magic Lantern, a graphical tool for building application monitoring and control interfaces, is included as part of the general ISIS releases.

Birman, Kenneth P.↗
NASA NTRS2016

Establishing and Monitoring an Aseptic Workspace for Building the MOMA Mass Spectrometer

Mars Organic Molecule Analyzer (MOMA) is an instrument suite on the ESA ExoMars 2018 Rover, and the Mass Spectrometer (MOMA-MS) is being built at Goddard Space Flight Center (GSFC). As MOMA-MS is a life-detection instrument and it thus falls in the most stringent category of Planetary Protection (PP) biological cleanliness requirements. Less than 0.03 sporem2 is allowed in the instrument sample path. In order to meet these PP requirements, MOMA-MS must be built and maintained in a low bioburden environment. The MOMA-MS project at GSFC maintains three cleanrooms with varying levels of bioburden control. The Aseptic Assembly Cleanroom has the highest level of control, applying three different bioburden reducing methods: 70 IPA, 7.5 Hydrogen Peroxide, and Ultra-Violet C light. The three methods are used in rotation and each kills microbes by a different mechanism, reducing the likelihood of microorganisms developing resistance to all three. The Integration and Mars Chamber Cleanrooms use less biocidal cleaning, with the option to deploy extra techniques as necessary. To support the monitoring of cleanrooms and verification that MOMA-MS hardware meets PP requirements, a new Planetary Protection lab was established that currently has the capabilities of standard growth assays for spore or vegetative bacteria, rapid bioburden analysis that detects Adenosine Triphosphate (ATP), plus autoclave and DHMR verification. The cleanrooms are monitored both for vegetative microorganisms and by rapid ATP assay, and a clear difference in bioburden is observed between the aseptic the other cleanroom.

Contamination Control Engineering↗
NASA NTRS2016

Establishing and Monitoring an Aseptic Workspace for Building the MOMA Mass Spectrometer

Mars Organic Molecule Analyzer (MOMA) is an instrument suite on the European Space Agency (ESA) ExoMars 2020 Rover, and the Mass Spectrometer (MOMA-MS) is being built at Goddard Space Flight Center (GSFC). MOMA-MS is a life-detection instrument and thus falls in the most stringent category of Planetary Protection (PP) biological cleanliness requirements. Less than 0.03 spore/m2 are allowed in the instrument sample path. In order to meet these PP requirements, MOMA-MS must be built and maintained in a low bioburden environment. The MOMA-MS project at GSFC maintains three clean rooms with varying levels of bioburden control. The Aseptic Assembly Clean room has the highest level of control, applying three different bioburden reducing methods: 70% Isopropyl Alcohol (IPA), 7.5% Hydrogen Peroxide, and Ultra-Violet C (UVC) light. The three methods are used in rotation and each kills microorganisms by a different mechanism, reducing the likelihood of microorganisms developing resistance to all three. The Integration and Mars Chamber Clean rooms use less biocidal cleaning, with the option to deploy extra techniques as necessary. To support the monitoring of clean rooms and verification that MOMA-MS hardware meets PP requirements, a new Planetary Protection lab was established that currently has the capabilities of standard growth assays for spore or vegetative bacteria, rapid bioburden analysis that detects Adenosine Triphosphate (ATP), plus autoclave and Dry Heat microbial Reduction (DHMR) verification. The clean rooms are monitored for vegetative microorganisms and by rapid ATP assay, and a clear difference in bioburden is observed between the aseptic and other clean room.

Contamination Control Engineering↗
NASA NTRS1965

Miniature stress transducer has directional capability

Miniature stress transducer uses a semiconductive piezoresistive element to detect stress only on specific axes. Measurement of internal mass stress is based on the compressive deformation of the transducer. The device is applicable to constant stress monitoring in building and dam structural parts.

San Miguel, A.↗
NASA NTRS1982

Solar-energy-system performance evaluation: Honeywell OTS 44, Ocmulgee, Georgia

The operation and technical performance of the solar operational test site (OTS 44) are described, based on data collected between April, 1981 and August, 1981. The following topics are discussed: system description, performance assessment, operating energy, energy savings, system maintenance, and conclusions. The solar energy system at OTS 44 is a hydronic heating and cooling system consisting of 5040 square feet of liquid cooled flat plate collectors; a 4000 gallon thermal storage tank; one 25 ton capacity organic Rankine cycle engine assisted water chillers; a forced draft cooling tower; and associated piping, pumps, valves, controls and heat rejection equipment. The solar system has eight basic modes of operation and several combination modes for providing space conditioning and hot water to the building. Data monitored during the 4 months of the operational test period found that the solar system collected 285 MMBtu of thermal energy of the total incident solar energy of 1040 MMBtu and provided 210 MMBtu for cooling and 10 MMBtu for heating and hot water. The net electrical energy saving due to the solar system was approximately 2600 kWh(e), and fossil energy saving was about 20 million Btu (MMBtu).

Mathur, A. K.↗
NASA NTRS2008

Use of Spacecraft Command Language for Advanced Command and Control Applications

The purpose of this work is to evaluate the use of SCL in building and monitoring command and control applications in order to determine its fitness for space operations. Approximately 24,325 lines of PCG2 code was converted to SCL yielding a 90% reduction in the number of lines of code as many of the functions and scripts utilized in SCL could be ported and reused. Automated standalone testing, simulating the actual production environment, was performed in order to generalize and gauge the relative time it takes for SCL to update and write a given display. The use of SCL rules, functions, and scripts allowed the creation of several test cases permitting the detection of the amount of time it takes update a given set of measurements given the change in a globally existing CUI or CUI. It took the SCL system an average 926.09 ticks to update the entire display of 323 measurements.

Mims, Tikiela L.↗
NASA NTRS2004

Real-Time Adaptive Color Segmentation by Neural Networks

Artificial neural networks that would utilize the cascade error projection (CEP) algorithm have been proposed as means of autonomous, real-time, adaptive color segmentation of images that change with time. In the original intended application, such a neural network would be used to analyze digitized color video images of terrain on a remote planet as viewed from an uninhabited spacecraft approaching the planet. During descent toward the surface of the planet, information on the segmentation of the images into differently colored areas would be updated adaptively in real time to capture changes in contrast, brightness, and resolution, all in an effort to identify a safe and scientifically productive landing site and provide control feedback to steer the spacecraft toward that site. Potential terrestrial applications include monitoring images of crops to detect insect invasions and monitoring of buildings and other facilities to detect intruders. The CEP algorithm is reliable and is well suited to implementation in very-large-scale integrated (VLSI) circuitry. It was chosen over other neural-network learning algorithms because it is better suited to realtime learning: It provides a self-evolving neural-network structure, requires fewer iterations to converge and is more tolerant to low resolution (that is, fewer bits) in the quantization of neural-network synaptic weights. Consequently, a CEP neural network learns relatively quickly, and the circuitry needed to implement it is relatively simple. Like other neural networks, a CEP neural network includes an input layer, hidden units, and output units (see figure). As in other neural networks, a CEP network is presented with a succession of input training patterns, giving rise to a set of outputs that are compared with the desired outputs. Also as in other neural networks, the synaptic weights are updated iteratively in an effort to bring the outputs closer to target values. A distinctive feature of the CEP neural network and algorithm is that each update of synaptic weights takes place in conjunction with the addition of another hidden unit, which then remains in place as still other hidden units are added on subsequent iterations. For a given training pattern, the synaptic weight between (1) the inputs and the previously added hidden units and (2) the newly added hidden unit is updated by an amount proportional to the partial derivative of a quadratic error function with respect to the synaptic weight. The synaptic weight between the newly added hidden unit and each output unit is given by a more complex function that involves the errors between the outputs and their target values, the transfer functions (hyperbolic tangents) of the neural units, and the derivatives of the transfer functions.

Duong, Tuan A.↗