SEARCH · Engineering Papers
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.
Thermal analysis data plots for heat loads of electronic packages, environmental control systems, and vehicle thermal systems
The Central Atlantic Regional Ecological Test Site (CARETS) project was a demonstration project for introducing data from Landsat and high-altitude aircraft sensors into regional land planning and management. This report summarizes the CARETS project results and describes its output of maps, reports, computer tapes, and other products. CARETS used a geographic information system model under which land use maps were prepared from sensor data, then digitized, processed, and linked to other environmental and social data sets, and to environmental consequences such as air pollution, stream runoff, local climatic factors, and coastal erosion. Landsat data showed the test region in 1972 to be nine percent urban and built-up land, 38 percent agriculture, 50 percent forest, three percent nonforested wetlands, and less than one percent barren land, exclusive of water-covered areas. User surveys, conferences, and workshops involving 65 agencies facilitated widespread distribution of data products and produced evaluations concerning their usefulness. We found a heterogeneous user community with diverse information needs largely preferring aerial photographs rather than satellite multispectral scanner data. Among project recommendations are establishment of a network of regional land resource information centers, working toward improved compatibility of Federal, State and local information programs supportive of land use decisions.
Climate change is a significant risk for agricultural production. Even under optimistic scenarios for climate mitigation action, present-day agricultural areas are likely to face significant increases in temperatures in the coming decades, in addition to changes in precipitation, cloud cover, and the frequency and duration of extreme heat, drought, and flood events (IPCC, 2013). These factors will affect the agricultural system at the global scale by impacting cultivation regimes, prices, trade, and food security (Nelson et al., 2014a). Global-scale evaluation of crop productivity is a major challenge for climate impact and adaptation assessment. Rigorous global assessments that are able to inform planning and policy will benefit from consistent use of models, input data, and assumptions across regions and time that use mutually agreed protocols designed by the modeling community. To ensure this consistency, large-scale assessments are typically performed on uniform spatial grids, with spatial resolution of typically 10 to 50 km, over specified time-periods. Many distinct crop models and model types have been applied on the global scale to assess productivity and climate impacts, often with very different results (Rosenzweig et al., 2014). These models are based to a large extent on field-scale crop process or ecosystems models and they typically require resolved data on weather, environmental, and farm management conditions that are lacking in many regions (Bondeau et al., 2007; Drewniak et al., 2013; Elliott et al., 2014b; Gueneau et al., 2012; Jones et al., 2003; Liu et al., 2007; M¨uller and Robertson, 2014; Van den Hoof et al., 2011;Waha et al., 2012; Xiong et al., 2014). Due to data limitations, the requirements of consistency, and the computational and practical limitations of running models on a large scale, a variety of simplifying assumptions must generally be made regarding prevailing management strategies on the grid scale in both the baseline and future periods. Implementation differences in these and other modeling choices contribute to significant variation among global-scale crop model assessments in addition to differences in crop model implementations that also cause large differences in site-specific crop modeling (Asseng et al., 2013; Bassu et al., 2014).
The spatial sampling limitations of surface measurement systems necessitate the use of satellite data for the investigation of large-scale cloud processes. Understanding the information contained in the satellite-observed radiances, however, requires a connection between the remotely sensed cloud properties and those more directly observed within the troposphere. Surface measurements taken during the First ISCCP Regional Experiment (FIRE) Marine Stratocumulus Intensive Field Observations (IFO) are compared here to cloud properties determined from Geostationary Operational Environmental Satellite (GOES) data in order to determine how well the island measurements represent larger areas and to verify some of the satellite-measured parameters.
Milli-watt Radioisotope Power Systems (RPS) based on Radioisotope Heater Units (RHUs) could be an ideal power source for certain spacecraft that cannot use solar power due to large distances from the sun, or other environmental constraints, and where they enable or significantly enhance the ability of a mission to meet its scientific or operational goals. Various modular, compact RHU-based thermoelectric (TE) generator concepts developed or derived from current NASA Small Business Innovation Research (SBIR) projects satisfying this need have been investigated. These modular, compact and low mass power systems could support small, highly-mobile robotic exploration packages, and could be incorporated into different robotic package concepts, spacecraft or satellites. Current modular RPS design concepts with 40mW, 80mW and 120mW power levels use RHUs and Bi2Te3 TE converters. Skutterudites materials could be used in the future if new higher thermal energy output and higher temperature miniature heat sources were developed, for example, using technologies currently in the General Purpose Heat Source (GPHS) used in higher electric power output RTGs. Small (a.k.a., “mice-like”) robotic packages could effectively utilize these RHU-driven power levels to accommodate crawling, climbing, monitoring, taking measurements, and communicating during long-term planetary missions aimed at gathering environmental and geologic data (i.e., over multiple decades). Waste heat from the cold side of the TE converter could also be directed toward the electronics and / or energy storage (e.g. batteries) to keep them within design temperature ranges. In addition to power generation and electronics / battery heating, the RHU / TE configuration could be designed to survive an external 500°C bake out procedure for critical spacecraft sterilization, environmental certification and planetary protection. Analytical studies have been performed to optimize various design configurations for power, mass, volume and robotic mobility. Specific power (mW/kg) and volumetric specific power (mW/cm3) characteristics of various design configurations will be presented and key conceptual design tradeoffs will be discussed. Hot- and cold-side thermal interfaces required to meet power and mass goals and associated design sensitivities will also be discussed. RHU / TE systems must overcome critical design challenges to survive high-g loadings in some robotic applications and we will examine the mass impacts required to satisfy various dynamic loading environments up to 10,000 g’s. Power can be generated for a minimum of 30 years or more using plutonium-238 dioxide heat sources (given that Pu-238 has an 87.7 year half-life) with some reduction in power as the heat source naturally degrades.
A new rule-based, machine independent analytical tool for diagnosing spacecraft anomalies, the EnviroNET expert system, was developed. Expert systems provide an effective method for storing knowledge, allow computers to sift through large amounts of data pinpointing significant parts, and most importantly, use heuristics in addition to algorithms which allow approximate reasoning and inference, and the ability to attack problems not rigidly defines. The EviroNET expert system knowledge base currently contains over two hundred rules, and links to databases which include past environmental data, satellite data, and previous known anomalies. The environmental causes considered are bulk charging, single event upsets (SEU), surface charging, and total radiation dose.
Satellite gathered remote sensor data were used to update a basic environmental atlas of southern Louisiana to reflect recent dynamic geological changes, such as erosion by wave action along the coast and active delta building in the lower part of the Mississippi Basin. Standard pattern recognition programs were utilized to update LANDSAT pictures for three categories (generalized land use, ecological zones and vegetation) to obtain a simulated color photomap for LANDSAT frames for further rectification by a table lookup program.
The NPOESS Preparatory Project (NPP) provides remotely-sensed land, ocean, atmospheric, ozone, and sounder data that will serve the meteorological and global climate change scientific communities while also providing risk reduction for the National Polar-orbiting Operational Environmental Satellite System (NPOESS), the U.S. Government s future low-Earth orbiting satellite system monitoring global weather and environmental conditions. NPOESS and NPP are a new era, not only because the sensors will provide unprecedented quality and volume of data but also because it is a joint mission of three federal agencies, NASA, NOAA, and DoD. NASA's primary science role in NPP is to independently assess the quality of the NPP science and environmental data records. Such assessment is critical for making NPOESS products the best that they can be for operational use and ultimately for climate studies. The Science Data Segment (SDS) supports science assessment by assuring the timely provision of NPP data to NASA s science teams organized by climate measurement themes. The SDS breaks down into nine major elements, an input element that receives data from the operational agencies and acts as a buffer, a calibration analysis element, five elements devoted to measurement based quality assessment, an element used to test algorithmic improvements, and an element that provides overall science direction. This paper will describe how the NPP SDS will leverage on NASA experience to provide a mission-reliable research capability for science assessment of NPP derived measurements.
The DEVELOP Program, part of NASA’s Earth Applied Sciences’ Capacity Building Program, conducts 50-70 feasibility studies each year that utilize Earth observations to address local decision-making challenges and help inform action. DEVELOP uses these projects as the mechanism to build skills in its participants and partner organizations to assess and apply satellite data insights to environmental decision-making processes. While organized by thematic focus (ex. water resource management, ecological forecasting, disaster management), projects use a multidisciplinary team approach with teams of students, recent graduates, early career professionals, and transitioning career professionals, bringing different disciplines, life experience, and perspectives to execute projects that have been collaboratively designed with end-user partner organizations (federal agencies, state and local governments, non-profit and for-profit organizations, and international organizations). Projects are interdisciplinary in nature as they integrate methods from multiple disciplines, with a focus on the incorporation of satellite data with other data sources such as socioeconomic and demographic data, in situ measurements, model outputs, and partners’ knowledge, and take place under the guidance of science advisors from NASA, academia, and other partner organizations. The culmination of these multidisciplinary teams working on projects that draw from interdisciplinary methods and approaches, is a transdisciplinary solution for the partner organizations to explore further for potential adoption. This presentation will highlight the DEVELOP model of co-production and collaboration, lessons learned integrating people and project methodologies, and impact assessment activities surrounding the program’s efforts.
Real time airborne data acquisition, archiving and distribution on the NASA/Ames Research Center (ARC) C-130 has been improved over the past three years due to the implementation of the C-130 Automated Digital Data System (CADDS). CADDS is a real time, multitasking, multiprocessing ROM-based system. CADDS acquires data from both avionics and environmental sensors inflight for all C-130 data lines. The system also displays the data on video monitors throughout the aircraft.
The use of a fiber-optic-link CCD-detector Fabry-Perot interferometer (McMillan et al., 1985, 1986, and 1988) to obtain high-accuracy measurements of stellar Doppler shifts at KPNO is described in detail and illustrated with sample data. Particular attention is given to accuracy requirements and techniques for reducing errors, resolution (orders of 50 mA at wavelength 4300 A are separated by 640 mA), CCD sensitivity, observing and data-processing operations, and the control of environmental conditions. Standard-deviation data and statistics on seven solar-type stars are presented in tables, and the time evolution of the radial velocity of Beta Com is shown in a graph.
NASA's Office of Commercial Programs is funding a multispectral sensor system to be used in the development of remote sensing applications. The Airborne Terrestrial Applications Sensor (ATLAS) is designed to provide versatility in acquiring spectral and spatial information. The ATLAS system will be a test bed for the development of specifications for airborne and spaceborne remote sensing instrumentation for dedicated applications. This objective requires spectral coverage from the visible through thermal infrared wavelengths, variable spatial resolution from 2-25 meters; high geometric and geo-location accuracy; on-board radiometric calibration; digital recording; and optimized performance for minimized cost, size, and weight. ATLAS is scheduled to be available in 3rd quarter 1992 for acquisition of data for applications such as environmental monitoring, facilities management, geographic information systems data base development, and mineral exploration.
Understanding and monitoring the environmental impacts of global urbanization requires better urban datasets. Continuous field impervious surface change (ISC) mapping using Landsat data is an effective way to quantify spatiotemporal dynamics of urbanization. It is well acknowledged that Landsat-based estimation of impervious surface is subject to seasonal and phenological variations. The overall goal of this paper is to map 200-02010 ISC for India using Global Land Survey datasets and training data only available for 2010. To this end, a method was developed that could transfer the regression tree model developed for mapping 2010 impervious surface to 2000 using an iterative training and prediction (ITP) approach An independent validation dataset was also developed using Google Earth imagery. Based on the reference ISC from the validation dataset, the RMSE of predicted ISC was estimated to be 18.4%. At 95% confidence, the total estimated ISC for India between 2000 and 2010 is 2274.62 +/- 7.84 sq km.
ERTS Imagery has provided data relating to a number of environmental and limnological concerns such as water quality, lake flooding and lake ice formation. Pollution plume data provided by ERTS was recently used in the Supreme Court case involving the States of Vermont and New York and a paper company. Flooding of lowland tracts has been a major concern due to a repetitive pattern of high lake levels over the past three years, and ERTS imagery is being used to construct the first series of flood maps of the affected areas. Lake ice development and turbidity patterns have also been studied from ERTS, since these have significance for shore erosion studies.
In order to model passenger reaction to present and future aircraft environments, it is necessary to obtain data in several ways. First, of course, is the gathering of environmental and passenger reaction data on commercial aircraft flights. In addition, detailed analyses of particular aspects of human reaction to the environment are best studied in a controllable experimental situation. Thus the use of simulators, both flight and ground based, is suggested. It is shown that there is a reasonably high probability that the low frequency end of the spectrum will not be necessary for simulation purposes. That is, the fidelity of any simulation which omits the very low frequency content will not yield results which differ significantly from the real environment. In addition, there does not appear to be significant differences between the responses obtained in the airborne simulator environment versus those obtained on commercial flights.
Thermal Radiation Analyzer System (TRASYS) computer program is software program having generalized capability to solve equations of radiation-related aspects of thermal-analysis problems. Computes total thermal-radiation environment for spacecraft in orbit. Software calculates internode-radiation-interchange data as well as data on rates of incidence and absorption of heat originating from environmental radiant sources. Provides data of both types in format directly usable by such thermal-analyzer programs as SINDA '85/FLUINT (available from COSMIC, program number MSC-21528). CRAY version of TRASYS (P25) written in FORTRAN 77. Other versions available upon request.
Space biology and health data are critical for the success of deep space missions and sustainable human presence off-world. At the core of effectively managing biomedical risks is the commitment to open science principles, which ensure that data are findable, accessible, interoperable, reusable, reproducible and maximally open. The 2021 integration of the Ames Life Sciences Data Archive with GeneLab to establish the NASA Open Science Data Repository significantly enhanced access to a wide range of life sciences, biomedical-clinical, and mission telemetry data alongside existing ‘omics data from GeneLab. This paper describes the new database, its architecture, and new data streams supporting diverse data types and enhancing data submission, retrieval, and analysis. Features include the Biological Data Management Environment for improved data submission, a new user interface, controlled data access, an enhanced API, and comprehensive public visualization tools for environmental telemetry, radiation dosimetry data, and ‘omics analyses. By fostering global collaboration through its Analysis Working Groups and training programs, the Open Science Data Repository promotes widespread engagement in space biology, ensuring transparency and inclusivity in research. It supports the global scientific community in advancing our understanding of spaceflight's impact on biological systems, ensuring humans will thrive in future deep space missions.
This paper is aimed at atmospheric scientists without formal training in statistical theory. Its goal is to, 1) provide a critical review of the rationale for trend analysis of the time series typically encountered in the field of atmospheric chemistry; 2) describe a range of trend-detection methods; and 3) demonstrate effective means of conveying the results to a general audience. Trend detections in atmospheric chemical composition data are often challenged by a variety of sources of uncertainty, which often behave differently to other environmental phenomena such as temperature, precipitation rate, or stream flow, and may require specific methods depending on the science questions to be addressed. Some sources of uncertainty can be explicitly included in the model specification, such as autocorrelation and seasonality, but some inherent uncertainties are difficult to quantify, such as data heterogeneity and measurement uncertainty due to the combined effect of short- and long-term natural variability, instrumental stability, and aggregation of data from sparse sampling frequency. Failure to account for these uncertainties might result in an inappropriate inference of the trends and their estimation errors. On the other hand, the variation in extreme events might be interesting for different scientific questions, for example, the frequency of extremely high surface ozone events and their relevance to human health. In this study we aim to, 1) review trend detection methods for addressing different levels of data complexity in different chemical species; 2) demonstrate that the incorporation of scientifically interpretable covariates can outperform pure numerical curve fitting techniques in terms of uncertainty reduction and improved predictability; 3) illustrate the study of trends based on extreme quantiles that can provide insight beyond standard mean or median based trend estimates; and 4) present an advanced method of quantifying regional trends based on the inter-site correlations of multi-site data. All demonstrations are based on time series of observed trace gases relevant to atmospheric chemistry, but the methods can be applied to other environmental data sets.