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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 NTRSDate not supplied

Advanced Weather Hazard Perception Capabilities for Remotely Piloted Aircraft in Wildland Fire Operations

Weather, specifically winds, are the most variable and least predictable of the weather-related elements that affect wildfire behavior.1 This leads to aviation operations which must deal with increased risks due to dynamic weather factors. Remotely piloted aircraft provide the opportunity to remove human loss of life from these risk consequences and mitigate the overall risks for air operations for fire suppression and dropping of retardant. UAS also allows the opportunity to continue fire suppression with nighttime operations. However, with the use of new technologies in aviation operations comes additional risk factors and the need for remote sensing capabilities to identify and permit navigation around extreme weather conditions, with turbulence being the most unforeseeable. This paper presents advanced concepts for weather hazard perception capabilities for remote pilot operators to include data and display techniques intended for dynamic environments and low altitude operations. This work can support the potential of weather sensing capabilities to create hazard maps to track high-risk weather patterns in mountainous regions. Advanced sensing may provide opportunity to locate extreme turbulence hazards for uninhabited aerial systems (UAS) and decrease the likelihood of flying into unknown weather conditions to enable safe operations to assist in wildfire air support.

Tyler L Willhite↗
NASA NTRS2008

Extreme Space Weather Events and Charging Hazard Assessments in Lunar Environments

The sunlit lunar surface charges to positive potentials with mean values of a few tens of volts where photoelectron currents dominate the charging process. In contrast, surfaces in darkness may charge to negative potentials on the order of a few hundred volts when the charging process is dominated by hot electron populations in the absence of solar photons. Recently, observations of electron beams measured by instruments on spacecraft in low lunar orbit have been interpreted as evidence for extreme lunar surface potentials exceeding a few kilovolts suggesting that lunar orbital and surface plasma environments may contain charging risks similar to geostationary orbit during extreme space weather conditions. Space system design for successful operation in a wide range of lunar environments will therefore require evaluation of charging hazards during extreme space weather conditions. We present results from a study of space weather environments conducted to obtained credible extreme charging environments for use in charging hazard assessments for lunar missions including extreme conditions encountered when the Moon is in the solar wind, the magnetosheath, and the Earth's magnetotail.

Minow, Joseph I.↗
NASA NTRS1989

Aircraft and ground vehicle friction measurements obtained under winter runway conditions

Tests with specially instrumented NASA B-737 and B-727 aircraft together with several different ground friction measuring devices have been conducted for a variety of runway surface types and wetness conditions. This effort is part of the Joint FAA/NASA Aircraft/Ground Vehicle Runway Friction Program aimed at obtaining a better understanding of aircraft ground handling performance under adverse weather conditions, and defining relationships between aircraft and ground vehicle tire friction measurements. Aircraft braking performance on dry, wet, snow-, and ice-covered runway conditions is discussed together with ground vehicle friction data obtained under similar runway conditions. For the wet, compacted snow- and ice-covered runway conditions, the relationship between ground vehicles and aircraft friction data is identified. The influence of major test parameters on friction measurements such as speed, test tire characteristics, and surface contaminant-type are discussed. The test results indicate that use of properly maintained and calibrated ground vehicles for monitoring runway friction conditions should be encouraged particularly under adverse weather conditions.

Yager, Thomas J.↗
DOE OSTI2023

Wildland Fires in the Subtropical Hill Forests of Southeastern Bangladesh

The first ever comprehensive study on wildland fires in Bangladesh is carried out to develop a fire prevention and prediction method. The major causes of huge wildland fires (88%) in the subtropical Chittagong Hill forest (43% of total) of southern Bangladesh are reported as shifting cultivation, grazing and unauthorized settlement. We used satellite hotspot (HS) data from 2003 to 2021 (a total of 54,669 HSs) to clarify the spatio-temporal structure of wildland fires. Fire weather conditions were analyzed using various weather data and synoptic-scale weather maps at different air levels. Fires concentrated from March through April or a transitional period from the dry season, caused by the Asian winter monsoon, to the wet season, due to the Asian summer monsoon. Fire occurrence depended on dry conditions and pre-monsoon showers and their timing. The difference in 925 hPa heights of high and low pressure systems may be attributed to the different types of the Asian winter and summer monsoons. The average maximum air temperature and incident shortwave solar energy in April were the highest and strongest, contributing fire-prone weather conditions. Based on the analysis, a fire prevention and prediction method will be developed, and this report may also facilitate the establishment of future CO 2 reduction measures for Bangladesh.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS1989

Current status of Joint FAA/NASA Runway Friction Program

Tests with specially instrumented NASA B-737 and FAA B-727 aircraft together with several different ground friction measuring devices have been conducted for a variety of runway surface types and wetness conditions. This effort is part of the Joint FAA/NASA Aircraft Ground Vehicle Runway Friction Program aimed at obaining a better understanding of aircraft ground handling performance under adverse weather conditions and defining relationships between aircraft and ground vehicle tire friction measurements. Aircraft braking performance on dry, wet, snow-, and ice-covered runway conditions is discussed together with ground vehicle friction data obtained under similar runway conditions. For the wet, compacted snow- and ice-covered runway conditions, the relationship between ground vehicles and aircraft friction data is identified. The influence of major test parameters on friction measurements such as speed, test tire characteristics, and surface contaminant type are discussed. The test results indicate that use of properly maintained and calibrated ground vehicles for monitoring runway friction conditions should be encouraged particularly under adverse weather conditions. The current status of the runway friction program is summarized and future test plans are identified.

Yager, Thomas J.↗
NASA NTRS1994

Evaluation of Candidate Millimeter Wave Sensors for Synthetic Vision

The goal of the Synthetic Vision Technology Demonstration Program was to demonstrate and document the capabilities of current technologies to achieve safe aircraft landing, take off, and ground operation in very low visibility conditions. Two of the major thrusts of the program were (1) sensor evaluation in measured weather conditions on a tower overlooking an unused airfield and (2) flight testing of sensor and pilot performance via a prototype system. The presentation first briefly addresses the overall technology thrusts and goals of the program and provides a summary of MMW sensor tower-test and flight-test data collection efforts. Data analysis and calibration procedures for both the tower tests and flight tests are presented. The remainder of the presentation addresses the MMW sensor flight-test evaluation results, including the processing approach for determination of various performance metrics (e.g., contrast, sharpness, and variability). The variation of the very important contrast metric in adverse weather conditions is described. Design trade-off considerations for Synthetic Vision MMW sensors are presented.

Alexander, Neal T.↗
NASA NTRS2017

Determination of the Limiting Magnitude

The limiting magnitude of an optical camera system is an important property to understand since it is used to find the completeness limit of observations. Limiting magnitude depends on the hardware and software of the system, current weather conditions, and the angular speed of the objects observed. If an object exhibits a substantial angular rate during the exposure, its light spreads out over more pixels than the stationary stars. This spreading causes the limiting magnitude to be brighter when compared to the stellar limiting magnitude. The effect, which begins to become important when the object moves a full width at half max during a single exposure or video frame. For targets with high angular speeds or camera systems with narrow field of view or long exposures, this correction can be significant, up to several magnitudes. The stars in an image are often used to measure the limiting magnitude since they are stationary, have known brightness, and are present in large numbers, making the determination of the limiting magnitude fairly simple. In order to transform stellar limiting magnitude to object limiting magnitude, a correction must be applied accounting for the angular velocity. This technique is adopted in meteor and other fast-moving object observations, as the lack of a statistically significant sample of targets makes it virtually impossible to determine the limiting magnitude before the weather conditions change. While the weather is the dominant factor in observing satellites, the limiting magnitude for meteors also changes throughout the night due to the motion of a meteor shower or sporadic source radiant across the sky. This paper presents methods for determining the limiting stellar magnitude and the conversion to the target limiting magnitude.

Kingery, Aaron↗
DOE OSTI2025

Contrasting Trends in Colorado Fire Weather Index from Reanalysis and Observations

Recent wildfires in Colorado raise the question of whether rising global temperatures have increased fire weather occurrences in Colorado. The U.S. National Weather Service defines fire weather as when “forecast weather conditions will result in a significant threat for the ignition and/or spread of wildfires.” We use two datasets to address the question: “How has the occurrence of fire weather changed in Colorado?” Using 22 years of observed weather conditions from a meteorological tower at the National Renewable Energy Laboratory and 67 years of ERA5 reanalysis data, we assess changing trends in Colorado fire weather as defined by hot, dry, and windy conditions. Additionally, we explore if the difference in recorded wind speeds between observational data and reanalysis data can be explained by differences in spatial and temporal resolution and what are the implications in the context of quantifying fire weather occurrences. The observational data are limited in temporal extent and spatial representativeness, but they capture exact real-world conditions at a location in complex terrain. The reanalysis data are available for an extended period of time and for the entire state, but the data are of relatively coarse spatial and temporal resolution and may fail to capture extremes. To quantify fire risk, we calculate the hot–dry–windy index (HDWI), which relies on wind speed and vapor pressure deficit. No statistically significant trend in the HDWI appears in the observational dataset. However, according to the reanalysis data, strong increasing trends in HDWI values emerge across all of Colorado. This apparent conflict between observational and reanalysis data suggests that reanalysis data may not be representative. Further, more long-term observational datasets are required to assess fire risk.

17 WIND ENERGY↗
DOE OSTI2021

Forecasting Dynamic Line Rating with Spatial Variation Considerations

Dynamic line rating (DLR) is a technology that allows the ampacity of an electrical conductor to be calculated using real-time or forecasted weather conditions. Historically, the ampacity of a conductor has been determined using a static line rating method which assumes conservative weather assumptions. Therefore, not only can DLR give a more accurate measurement of the true ampacity of a conductor, but it can also increase its ampacity during weather conditions with greater thermal mitigations. The two primary cooling factors in the ampacity calculations are wind speed and direction. In complex terrain, wind speed and direction can have large variations over short distances. Therefore, accurately identifying the limiting span of a transmission line requires high spatial resolution of the wind along its path. One solution is to install dense weather stations along their path, though this can become costly over long distances. Therefore, researchers have investigated the use of Computation Fluid Dynamic (CFD) simulations to accurately compute the wind field along the path of a transmission line and use these results to identify the limiting section of the conductor. This work presents a case study that evaluates the coupling of CFD simulations and forecasted weather simulations using the High-Resolution Rapid Refresh (HRRR) model points over a 2-year span within a region in south eastern Idaho. The primary goal of the work is the evaluation of the number of HRRR model points used, i.e., weather stations, along the path of the line and the accuracy of the resulting DLR ampacity. This was done using 4, 10, 17, 26, and 35 HRRR model points along two transmission line paths. The results indicate that as the number of model points are increased, the DLR ampacity of the lines decrease, yet converge as more points are added and demonstrate little change with additional HRRR points. It is expected that these results can help transmission line operators identify the number of weather stations that must be installed when coupled with CFD simulations and DLR ampacity to ensure accurate ratings and safe operations.

17 WIND ENERGY↗
DOE OSTI2021

Forecasting Dynamic Line Rating with Spatial Variation Considerations

Dynamic line rating (DLR) is a technology that allows the ampacity of an electrical conductor to be calculated using real-time or forecasted weather conditions. Historically, the ampacity of a conductor has been determined using a static line rating method which assumes conservative weather assumptions. Therefore, not only can DLR give a more accurate measurement of the true ampacity of a conductor, but it can also increase its ampacity during weather conditions with greater thermal mitigations. The two primary cooling factors in the ampacity calculations are wind speed and direction. In complex terrain, wind speed and direction can have large variations over short distances. Therefore, accurately identifying the limiting span of a transmission line requires high spatial resolution of the wind along its path. One solution is to install dense weather stations along their path, though this can become costly over long distances. Therefore, researchers have investigated the use of Computation Fluid Dynamic (CFD) simulations to accurately compute the wind field along the path of a transmission line and use these results to identify the limiting section of the conductor. This work presents a case study that evaluates the coupling of CFD simulations and forecasted weather simulations using the High-Resolution Rapid Refresh (HRRR) model points over a 2-year span within a region in south eastern Idaho. The primary goal of the work is the evaluation of the number of HRRR model points used, i.e., weather stations, along the path of the line and the accuracy of the resulting ampacity. This was done using 4, 10, 17, 26, and 35 HRRR model points along two transmission line paths. The results indicate that as the number of model points are increased, the DLR ampacity of the lines decrease, yet converge as more points are added and demonstrate little change with additional HRRR points. It is expected that these results can help transmission line operators identify the number of weather stations that must be installed when coupled with CFD simulations and DLR ampacity to ensure accurate ratings and safe operations.

17 WIND ENERGY↗
NASA NTRS2024

Optimizing Air Traffic - Integrating Artificial Intelligence and Machine Learning in Flight Path Planning and 3D Airspace Visualization for Air Traffic Control

Air Traffic Control (ATC) systems are vital components of the National Airspace System (NAS). ATC, Airport Traffic Control Towers (ATCT), and Terminal Radar Approach Control (TRACON) are responsible for directing all flights departing from and arriving at airports, managing our nation’s airspace, preventing potential accidents, and ensuring that every flight is accounted for. However, these systems often face challenges in effectively monitoring the skies. Issues such as poor communication between operators, difficulty in performing operations, and the constant need for vigilance frequently burden ATC operators. Additionally, the projected increase in air traffic in the coming years will only exacerbate the stress associated with this role. To address these issues, we propose a system that assists ATC operators in situations such as handovers, emergencies, and routing aircraft to avoid weather hazards. Our solution includes an Artificial Intelligence (AI) and Machine Learning (ML)-based Flight Pathways Planning System (FPPS) designed to find the fastest and most optimal routes for aircraft, taking into account weather conditions, restricted terrain, and Extended-Range Twin-Engine Operational Performance Standards (ETOPS) ratings. The proposed Predictive Weather Planning Model, included in FPPS, adjusts routes based on real-time and forecasted weather conditions. Additionally, our NVIDIA Omniverse 3D Visualization System offers a highly interactive environment for better visualization and a clear view of the airspace. By incorporating these systems, the roles of ATC, ATCT, and TRACON operators will become more manageable and less stressful, equipping them to efficiently handle the growing density of airspace.

Regina Ayoubi↗
NASA NTRS2018

A Markov Decision Process Framework for Optimal Airport Reconfiguration

The airport runway configuration is defined as a combination set of runways for arrivals and departures used at a point during operation of the airport. An optimal configuration of these runways depends on a number of factors, including traffic demand, wind magnitude and direction, other adverse weather conditions, and noise restrictions, among others. Based on the current state of these factors and predictions of traffic demand and weather conditions, runway configuration changes are made and coordinated between tower controller, other air traffic control facilities, pilots, and ground personnel. Reconfigurations can be quite disruptive to airport operations; minimizing their frequency and scheduling them well in advance is essential for mitigating some of the added workload for controllers and pilots. Unfortunately, deciding on an appropriate time to change is challenging for human decision makers. Not only do multiple factors need to be evaluated, but the uncertainty in their forecasts must also be considered. Previous optimization methods, such as mixed linear integer programming, have been proposed. Although these methods can reason over a large set of variables, they do not systematically handle the uncertainty associated with weather movement, traffic demands, and other variables. In this work, we introduce a Markov Decision Process (MDP)-based decision making framework which can reason effectively over the inherent uncertainties and make optimal decisions on if/when to change the airport configuration. In a prototype implementation, we present a single runway with three aircraft and utilize knowledge of the forecasted wind speed and direction to determine whether to keep or change the current runway configuration. Our aim through this work is to present a framework for airport reconfiguration which can be scalable to additional aircraft, multiple runways, and various input parameters. This technique will optimize the airport reconfiguration procedure by providing a proactive approach, optimizing not just at the next optimal opportunity for a reconfiguration based on varying atmospheric and traffic conditions in the terminal airspace, but also anticipating future necessary reconfigurations. This will eliminate the inefficiencies of frequent changes currently associated with runway reconfiguration procedures.

runway reconfiguration↗
NASA NTRS1999

NASA Aircraft Vortex Spacing System Development Status

The National Aeronautics and Space Administration (NASA) is addressing airport capacity enhancements during instrument meteorological conditions through the Terminal Area Productivity (TAP) program. Within TAP, the Reduced Spacing Operations (RSO) subelement at the NASA Langley Research Center is developing an Aircraft VOrtex Spacing System (AVOSS). AVOSS will integrate the output of several systems to produce weather dependent, dynamic wake vortex spacing criteria. These systems provide current and predicted weather conditions, models of wake vortex transport and decay in these weather conditions, and real-time feedback of wake vortex behavior from sensors. The goal of the NASA program is to provide the research and development to demonstrate an engineering model AVOSS in real-time operation at a major airport. The demonstration is only of concept feasibility, and additional effort is required to deploy an operational system for actual aircraft spacing reduction. This paper describes the AVOSS system architecture, a wake vortex facility established at the Dallas-Fort Worth International Airport (DFW), initial operational experience with the AVOSS system, and emerging considerations for subsystem requirements. Results of the initial system operation suggest a significant potential for reduced spacing.

Hinton, David A.↗
NASA NTRS1996

NASA Wake Vortex Research for Aircraft Spacing

The National Aeronautics and Space Administration (NASA) is addressing airport capacity enhancements during instrument meteorological conditions through the Terminal Area Productivity (TAP) program. Within TAP, the Reduced Spacing Operations (RSO) subelement at the NASA Langley Research Center is developing an Aircraft Vortex Spacing System (AVOSS). AVOSS will integrate the output of several inter-related areas to produce weather dependent, dynamic wake vortex spacing criteria. These areas include current and predicted weather conditions, models of wake vortex transport and decay in these weather conditions, real-time feedback of wake vortex behavior from sensors, and operationally acceptable aircraft/wake interaction criteria. In today's ATC system, the AVOSS could inform ATC controllers when a fixed reduced separation becomes safe to apply to large and heavy aircraft categories. With appropriate integration into the Center/TRACON Automation System (CTAS), AVOSS dynamic spacing could be tailored to actual generator/follower aircraft pairs rather than a few broad aircraft categories.

Perry, R. Brad↗
DOE OSTI2023

Analyzing at-scale distribution grid response to extreme temperatures

Threats against power grids continue to increase, as extreme weather conditions and natural disasters (extreme events) become more frequent. Hence, there is a need for the simulation and modeling of power grids to reflect realistic conditions during extreme events conditions, especially distribution systems. Herein, this paper presents a modeling and simulation platform for electric distribution grids which can estimate overall power demand during extreme weather conditions. The presented platform's efficacy is shown by demonstrating estimation of electrical demand for 1) Electricity Reliability Council of Texas (ERCOT) during winter storm Uri in 2021, and 2) alternative hypothetical scenarios of integrating Distributed Energy Resources (DERs), weatherization, and load electrification. In comparing to the actual demand served by ERCOT during the winter storm Uri of 2021, the proposed platform estimates approximately 34 GW of peak capacity deficit. These numbers are consistent with state-of-the-art prediction results published in the literature. For the case of the future electrification of heating loads, peak capacity of 78 GW (124% increase) is estimated, which would be reduced to 47 GW (38% increase) with the adoption of efficient heating appliances and improved thermal insulation. Integrating distributed solar PV and storage into the grid causes improvement in the local energy utilization and hence reduces the potential unmet energy by 31% and 40%, respectively.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2022

Sizing ramping reserve using probabilistic solar forecasts: A data-driven method

Ramping products have been introduced or proposed in several U.S. power markets to mitigate the impact of load and renewable uncertainties on market efficiency and reliability. Current methods often rely on historical data to estimate the requirements of ramping products and fail to take into account the effects of the latest weather conditions and their uncertainties, which could lead to overly conservative or insufficient requirements. This study proposes a k-nearest-neighbor-based method to give weather-informed estimates of ramping needs based on short-term probabilistic solar irradiance forecasts. Forecasts from multiple sites are employed in conjunction with principal component analysis to derive numerical classifiers to characterize system-level weather conditions. In addition, we develop a data-driven method to optimize the model parameters in a rolling-forward manner. By using real-world data from the California Independent System Operator, we design two metrics to evaluate method performance: 1) frequency of shortage and 2) oversupply of ramping product. Our proposed method presents advantages in comparison with the baseline and a set of benchmark methods: without compromising system reliability, it reduces system ramping requirements by up to 25%, therefore improving both system reliability and economics.

14 SOLAR ENERGY↗
DOE OSTI2023

Understanding the Computing and Analysis Needs for Resiliency of Power Systems from Severe Weather Impacts

As the frequency and intensity of severe weather has increased, its effect on the electric grid has manifested in the form of significantly more and larger outages in the United States. This has become especially true for regions that were previously isolated from weather extremes. In this paper, we analyze the weather impacts on the electric power grid across a variety of weather conditions, draw correlations, and provide practical insights into the operational state of these systems. High resolution computational modeling of specific meteorological variables, computational approaches to solving power system models under these conditions, and the types of resiliency needs are highlighted as goal-oriented computing approaches are being built to address grid resiliency needs. An example analysis correlating outages to 1km day-ahead weather from two historical winter storms, calculated on a large cluster using a combination of interpolated and extrapolated inputs from multiple instrumented sites to workflows that produce primary meteorological outputs, is shown as initial proof of concept.

analysis↗
NASA NTRSDate not supplied

Modeling of Multiscale Solar Dynamics for Understanding Drivers of Space Weather

Understanding the solar dynamics is critical for improving our capabilities to forecast the evolution of space weather conditions. We take advantage of currently available computational capabilities to model solar dynamics from the deep interior to the corona and investigate mechanisms that may drive space weather conditions. The simulations are performed using the 3D radiative MHD code StellarBox. Comparison of synthetic spectroscopic observables obtained from numerical simulations and actual observations allows us to uncover physical processes associated with observed phenomena. To facilitate a transition from modeling short-term physical phenomena to developing a reliable forecast-oriented model, we suggest using the data assimilation approach. It allows us to cross-analyze dynamo model solutions and observations and to consider possible uncertainties and errors. In this presentation, we briefly summarize current multi-scale modeling capabilities and results and discuss ongoing developments to build a reliable physics-based forecast-oriented model of solar activity.

Irina N Kitiashvili↗