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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 NTRS2014

Visual Advantage of Enhanced Flight Vision System During NextGen Flight Test Evaluation

Synthetic Vision Systems and Enhanced Flight Vision System (SVS/EFVS) technologies have the potential to provide additional margins of safety for aircrew performance and enable operational improvements for low visibility operations in the terminal area environment. Simulation and flight tests were jointly sponsored by NASA's Aviation Safety Program, Vehicle Systems Safety Technology project and the Federal Aviation Administration (FAA) to evaluate potential safety and operational benefits of SVS/EFVS technologies in low visibility Next Generation Air Transportation System (NextGen) operations. The flight tests were conducted by a team of Honeywell, Gulfstream Aerospace Corporation and NASA personnel with the goal of obtaining pilot-in-the-loop test data for flight validation, verification, and demonstration of selected SVS/EFVS operational and system-level performance capabilities. Nine test flights were flown in Gulfstream's G450 flight test aircraft outfitted with the SVS/EFVS technologies under low visibility instrument meteorological conditions. Evaluation pilots flew 108 approaches in low visibility weather conditions (600 feet to 3600 feet reported visibility) under different obscurants (mist, fog, drizzle fog, frozen fog) and sky cover (broken, overcast). Flight test videos were evaluated at three different altitudes (decision altitude, 100 feet radar altitude, and touchdown) to determine the visual advantage afforded to the pilot using the EFVS/Forward-Looking InfraRed (FLIR) imagery compared to natural vision. Results indicate the EFVS provided a visual advantage of two to three times over that of the out-the-window (OTW) view. The EFVS allowed pilots to view the runway environment, specifically runway lights, before they would be able to OTW with natural vision.

Kramer, Lynda J.↗
NASA NTRS2016

Initial Analysis of and Predictive Model Development for Weather Reroute Advisory Use

In response to severe weather conditions, traffic management coordinators specify reroutes to route air traffic around affected regions of airspace. Providing analysis and recommendations of available reroute options would assist the traffic management coordinators in making more efficient rerouting decisions. These recommendations can be developed by examining historical data to determine which previous reroute options were used in similar weather and traffic conditions. Essentially, using previous information to inform future decisions. This paper describes the initial steps and methodology used towards this goal. A method to extract relevant features from the large volume of weather data to quantify the convective weather scenario during a particular time range is presented. Similar routes are clustered. A description of the algorithm to identify which cluster of reroute advisories were actually followed by pilots is described. Models built for fifteen of the top twenty most frequently used reroute clusters correctly predict the use of the cluster for over 60 of the test examples. Results are preliminary but indicate that the methodology is worth pursuing with modifications based on insight gained from this analysis.

machine learning↗
DOE OSTI2021

Increasing large wildfires over the western United States linked to diminishing sea ice in the Arctic

Abstract The compound nature of large wildfires in combination with complex physical and biophysical processes affecting variations in hydroclimate and fuel conditions makes it difficult to directly connect wildfire changes over fire-prone regions like the western United States (U.S.) with anthropogenic climate change. Here we show that increasing large wildfires during autumn over the western U.S. are fueled by more fire-favorable weather associated with declines in Arctic sea ice during preceding months on both interannual and interdecadal time scales. Our analysis (based on observations, climate model sensitivity experiments, and a multi-model ensemble of climate simulations) demonstrates and explains the Arctic-driven teleconnection through regional circulation changes with the poleward-shifted polar jet stream and enhanced fire-favorable surface weather conditions. The fire weather changes driven by declining Arctic sea ice during the past four decades are of similar magnitude to other leading modes of climate variability such as the El Niño-Southern Oscillation that also influence fire weather in the western U.S.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Parametric analysis on optimized design of hybrid solar power plants

There is increasing interest in utility-scale solar power plants with storage which can flexibly dispatch renewable energy to the grid. However, plant design possesses many degrees of freedom and non-obvious trade-offs in performance. Software tools can estimate or optimize the performance of a specific plant configuration under market and weather conditions of interest; the associated cost parameters and operating assumptions strongly influence estimates of plant performance and decisions regarding optimal sizing. We employ the National Renewable Energy Laboratory's Hybrid Optimization and Performance Platform, which incorporates optimal dispatch when evaluating plant performance, and investigate the sensitivity to weather and market conditions, operating limitations, and the presence of a capacity-based incentive. We demonstrate changes in plant performance and optimal sizing with respect to these inputs and discuss implications. Here results show that PV-with-battery designs are more profitable under our assumptions, but that designs including a concentrated solar power (CSP) system produce significantly greater annual energy; and that CSP-with-thermal energy storage designs maximizing the benefit-to-cost ratio have an input-dependent linear relationship between the CSP field solar multiple and the hours of storage as the project budget varies.

14 SOLAR ENERGY↗
DOE OSTI2022

Development of a Annual Air Handling Unit Fault Dataset for FDD Tools: Lessons Learned and Considerations for FDD Developers

As energy management and information systems (e.g., automated fault detection and diagnostics [AFDD] tools) become more prevalent in the commercial building stock, it is important to determine the effectiveness of these technologies by benchmarking their performance. The authors have been working to develop the largest publicly available dataset of HVAC fault data for performance benchmarking applications, covering the most common HVAC systems and designs including chiller plants, rooftop packaged units, dual duct air handling units and single duct air handling units. This study covers the development, modeling, and validation of a synthetic fault dataset for a single duct air handling unit (AHU), one of the most common HVAC configurations found in the commercial building stock. Despite this being a common system, real-world time series data are scarce and usually do not span a wide range of weather conditions. Due to this limitation, a detailed AHU model was employed to carry out annual simulations of numerous common sensor and mechanical faults, which were then validated by comparing their effects on system performance to expected symptoms. We summarize the nature of each fault and their impacts under different weather and operation conditions. Finally, we highlight considerations for FDD developers that may want to use this dataset to assess their algorithms’ performance and their improvement over time.

Casillas, Armando↗
NASA NTRS2004

Context-Aware Intelligent Assistant Approach to Improving Pilot's Situational Awareness

Faulty decision making due to inaccurate or incomplete awareness of the situation tends to be the prevailing cause of fatal general aviation accidents. Of these accidents, loss of weather situational awareness accounts for the largest number of fatalities. We describe a method for improving weather situational awareness through the support of a contextaware,domain and task knowledgeable, personalized and adaptive assistant. The assistant automatically monitors weather reports for the pilot's route of flight and warns her of detected anomalies. When and how warnings are issued is determined by phase of flight, the pilot s definition of acceptable weather conditions, and the pilot's preferences for automatic notification. In addition to automatic warnings, the pilot is able to verbally query for weather and airport information. By noting the requests she makes during the approach phase of flight, our system learns to provide the information without explicit requests on subsequent flights with similar conditions. We show that our weather assistant decreases the effort required to maintain situational awareness by more than 5.5 times when compared to the conventional method of in-flight weather briefings.

Spirkovska, Lilly↗
NASA NTRS1977

A review of current procedures for normalizing aircraft flyover noise data to reference meteorological conditions

Procedures that are currently used to normalize raw aircraft noise data to reference weather conditions are reviewed. These procedures sometimes result in calculated values of molecular absorption which differ from measured values, especially at higher frequencies. An explanation is offered for this discrepancy, and its effect on normalized sound levels and on calculations of effective perceived noise level is examined.

Deloach, R.↗
NASA NTRS2022

Climate Drivers of Global Wildfire Burned Area

Wildfire is an integral part of the Earth system, but at the same time it can pose serious threats to human society and to certain types of terrestrial ecosystems. Meteorological conditions are a key driver of wildfire activity and extent, which led to the emergence of the use of fire danger indices that depend solely on weather conditions. The Canadian Fire Weather Index (FWI) is a widely used fire danger index of this kind. Here, we evaluate how well the FWI, its components, and the climate variables from which it is derived, correlate with observation-based burned area (BA) for a variety of world regions. We use a novel technique, according to which monthly BA are grouped by size for each Global Fire Emissions Database (GFED) pyrographic region. We find strong correlations of BA anomalies with the FWI anomalies, as well as with the underlying deviations from their climatologies for the four climate variables from which FWI is estimated, namely, temperature, relative humidity, precipitation, and wind. We quantify the relative sensitivity of the observed BA to each of the four climate variables, finding that this relationship strongly depends on the pyrographic region and land type. Our results indicate that the BA anomalies strongly correlate with FWI anomalies at a GFED region scale, compared to the strength of the correlation with individual climate variables. Additionally, among the individual climate variables that comprise the FWI, relative humidity and temperature are the most influential factors that affect the observed BA. Our results support the use of the composite fire danger index FWI, as well as its sub-indices, the Build-Up Index (BUI) and the Initial Spread Index (ISI), comparing to single climate variables, since they are found to correlate better with the observed forest or non-forest BA, for the most regions across the globe.

Wildfire↗
NASA NTRS1977

Thunderstorm Research International Program (TRIP 77) report to management

A post analysis of the previous day's weather, followed by the day's forecast and an outlook on weather conditions for the following day is given. The normal NOAA weather charts were used, complemented by the latest GOES satellite pictures, the latest rawinsonde sounding, and the computer-derived thunderstorm probability forecasts associated with the sounding.

Taiani, A. J.↗
NASA NTRS2013

Automated Meteor Fluxes with a Wide-Field Meteor Camera Network

Within NASA, the Meteoroid Environment Office (MEO) is charged to monitor the meteoroid environment in near ]earth space for the protection of satellites and spacecraft. The MEO has recently established a two ]station system to calculate automated meteor fluxes in the millimeter ]size ]range. The cameras each consist of a 17 mm focal length Schneider lens on a Watec 902H2 Ultimate CCD video camera, producing a 21.7 x 16.3 degree field of view. This configuration has a red ]sensitive limiting meteor magnitude of about +5. The stations are located in the South Eastern USA, 31.8 kilometers apart, and are aimed at a location 90 km above a point 50 km equidistant from each station, which optimizes the common volume. Both single station and double station fluxes are found, each having benefits; more meteors will be detected in a single camera than will be seen in both cameras, producing a better determined flux, but double station detections allow for non ]ambiguous shower associations and permit speed/orbit determinations. Video from the cameras are fed into Linux computers running the ASGARD (All Sky and Guided Automatic Real ]time Detection) software, created by Rob Weryk of the University of Western Ontario Meteor Physics Group. ASGARD performs the meteor detection/photometry, and invokes the MILIG and MORB codes to determine the trajectory, speed, and orbit of the meteor. A subroutine in ASGARD allows for the approximate shower identification in single station meteors. The ASGARD output is used in routines to calculate the flux in units of #/sq km/hour. The flux algorithm employed here differs from others currently in use in that it does not assume a single height for all meteors observed in the common camera volume. In the MEO system, the volume is broken up into a set of height intervals, with the collecting areas determined by the radiant of active shower or sporadic source. The flux per height interval is summed to obtain the total meteor flux. As ASGARD also computes the meteor mass from the photometry, a mass flux can be also calculated. Weather conditions in the southeastern United States are seldom ideal, which introduces the difficulty of a variable sky background. First a weather algorithm indicates if sky conditions are clear enough to calculate fluxes, at which point a limiting magnitude algorithm is employed. The limiting magnitude algorithm performs a fit of stellar magnitudes vs camera intensities. The stellar limiting magnitude is derived from this and easily converted to a limiting meteor magnitude for the active shower or sporadic source.

Blaauw, R. C.↗
DOE OSTI2022

Modeling Air Handling Units to Create a Diverse Fault Dataset for FDD Innovation: Lessons Learned and Recommendations

As energy management and information systems (e.g., automated fault detection and diagnostics [AFDD] tools) become more prevalent in the commercial building stock, it is important to determine the effectiveness of these technologies by benchmarking their performance. The authors have been working to develop the largest publicly available dataset of HVAC fault datasets for performance benchmarking applications, covering the most common HVAC systems and designs including chiller plants, rooftop packaged units, dual duct air handling unit and single duct air handling units. This study covers the development, modeling, and validation of a synthetic fault dataset for the air handling unit (AHU), one of the most common HVAC configurations found in the commercial building stock. Despite this being a common system, real-world time series data are scarce and usually do not span a wide range of weather conditions. Due to this limitation, two detailed AHU models, which included the single duct AHU and dual duct AHU developed in the Modelica language and HVACSIM+ were employed to carry out annual simulations of numerous common sensor faults, mechanical faults, and control sequence faults. The fault inclusive data were then validated by comparing fault effects on system performance to expected symptoms. We summarize the nature of each fault and their impacts under different weather and operation conditions. We report some lessons learnt during the efforts of validating the high volumes of the FDD data sets. Finally, we highlight considerations for FDD developers that may want to use this dataset to assess their algorithms’ performance and their improvement over time.

Casillas, Armando↗
DOE OSTI2025

Influence of extreme temperature conditions on CO 2 direct air capture using amino-acid solutions

Geological features play a pivotal role in determining the feasibility of deploying CO₂ direct air capture (DAC) technologies, primarily because they influence the availability of cost-effective energy sources, such as natural gas and geothermal energy, and also due to the potential for CO₂ sequestration. Many regions face challenges due to variable weather conditions including seasonal temperature fluctuations, high or low humidity, and sub-ambient temperatures. These extremes can reduce DAC performance or even lead to catastrophic events. Aqueous solvents considered for DAC systems are particularly vulnerable to seasonal variations in colder climates, where the solvent may underperform or freeze. It is therefore essential to investigate the CO₂ capture efficiency of aqueous solvents across a broad range of environmental temperatures, spanning sub-zero to hot conditions (>30 °C). In this study, DAC operation is examined using a high-flux solvent–air crossflow contactor under two major weather scenarios: (i) cold conditions below 0 °C and (ii) hot conditions above 30 °C. A parametric study is conducted to investigate the contactor performance regarding CO₂ removal efficiency, uptake capacity, and reaction kinetics versus temperature when the air velocity through the contactor exceeds 1 m/s. The efficacy of the contactor is systematically investigated using various anti-freeze amino-acid solvent formulations. A mass-transfer mechanistic model is developed to assess the process performance over a wide temperature range and propose scalable design guidelines. Machine learning is also employed to identify key parameters affecting the CO₂ capture efficiency. It is shown that air velocity and temperature are the primary factors influencing CO₂ uptake. Based on performance data obtained under subfreezing temperatures, a technoeconomic analysis is conducted to evaluate the feasibility of using aqueous solvents in seasonal cold regions. In conclusion, the findings of this study provide valuable insights into siting considerations for deploying solvent-based DAC, thereby contributing to the advancement of sustainable carbon removal solutions.

Air–liquid contactor↗
DOE OSTI2020

A Comparison of PV Resource Modeling for Sizing Microgrid Components

Microgrid systems are being deployed with increased frequency to meet critical building loads during unplanned power outages. Determining the appropriate type and size of components that make up a microgrid can greatly affect its ability to meet this need. This paper presents a new tool to appropriately size PV, battery, and generator capacities to meet all site loads, given a resilience goal. It uses a statistical model to simulate microgrid behavior under a large range of solar resource conditions, providing resilience planners with confidence that critical loads will be met even under extreme weather conditions. We compare this model with one that uses typical weather data for sizing microgrids, and quantify the resilience effects of using these different methods. Our method produces more conservative generator capacities and fuel requirements in an outage, predicting up to 30% more fuel required in the most extreme case. Microgrid systems that are designed using more conservative estimates are more likely to continue to serve load during emergency outages under a large range of conditions.

Newman, Sarah F.↗
DOE OSTI2020

Q-Learning-Based Impact Assessment of Propagating Extreme Weather on Distribution Grids

The increasing number of power outage events due to extreme weather conditions is hampering us socioeconomically. Preparing in advance for the extreme weather event is critical. It can help utility operators to reduce grid damages, restore grid service quickly, allocate energy resources and repair crews strategically, and hence dramatically increase grid resilience. In this paper, we propose a method to identify the sequence of worst impact zones in the power grid caused by extreme weather events based on Q-learning (a reinforcement learning algorithm). To quantity the weather severity and its effect on the grid, we model the impact of extreme weather on the grid as a function of intensity, vulnerability, and exposure. A modified IEEE 123-node distribution feeder is presented in a mesh grid and experimented for sequences of zones identification. Finally, simulation results present the identified sequences and their associated impacts on the grid caused by extreme weather events.

distribution system↗
DOE OSTI2021

Q-Learning Based Impact Assessment of Propagating Extreme Weather on Distribution Grids: Preprint

Increasing number of power outage events due to extreme weather condition is hampering us socioeconomically. Preparing in advance for the extreme weather event is critical and can help utility operators to reduce grid damages, restore grid service quickly, allocate energy resources and repair crews strategically, and hence dramatically increase grid resilience. In this paper, we propose a method to identify the sequence of worst impact zones in the power grid caused by extreme weather events based on Q-learning (a reinforcement learning algorithm). To quantify weather severity and it’s effect on the grid, we model the impact of extreme weather on the grid as a function of intensity, vulnerability and exposure. A modified IEEE 123-node distribution feeder is presented in a mesh grid and experimented for sequences of zones identification. Finally, simulation results present the identified sequences and their associated impacts on the grid caused by the extreme weather events.

distribution system↗
DOE OSTI2023

A high‐order discontinuous Galerkin approach for physics‐based thermospheric modeling

Abstract The accurate prediction of aerodynamic drag on satellites orbiting in the upper atmosphere is critical to the operational success of modern space technologies, such as satellite‐based communication or navigation systems, which have become increasingly popular in the last few years due to the deployment of constellations of satellites in low‐Earth orbit. As a result, physics‐based models of the ionosphere and thermosphere have emerged as a necessary tool for the prediction of atmospheric outputs under highly variable space weather conditions. This paper proposes a high‐fidelity approach for physics‐based space weather modeling based on the solution of the Navier–Stokes equations using a high‐order discontinuous Galerkin method, combined with a matrix‐free strategy suitable for high‐performance computing on GPU architectures. The approach consists of a thermospheric model that describes a chemically frozen neutral atmosphere in nonhydrostatic equilibrium driven by the external excitation of the Sun. A novel set of variables is considered to treat the low densities present in the upper atmosphere and to accommodate the wide range of scales present in the problem. At the same time, and unlike most existing approaches, radial and angular directions are treated in a nonsegregated approach. The study presents a set of numerical examples that demonstrate the accuracy of the approximation and validate the current approach against observational data along a satellite orbit, including estimates of established empirical and physics‐based models of the ionosphere‐thermosphere system. Finally, a one‐dimensional radial derivation of the physics‐based model is presented and utilized for conducting a parametric study of the main thermal quantities under various solar conditions.

Engineering↗
DOE OSTI2024

Defining weather scenarios for simulation-based assessment of thermal resilience of buildings under current and future climates: A case study in Brazil

In response to increasingly severe weather conditions, optimization of building performance and investment provides an opportunity to consider co-benefits of thermal resilience during energy efficiency retrofits. This work aims to assess thermal resilience of buildings using building performance simulation to evaluate the indoor overheating risk under nine weather scenarios, considering historical (2010s), mid-term future (2050s), and long-term future (2090s) typical meteorological years, and heat wave years. Such an analysis is based on resilience profiles that combine six integrated indicators. A case study with a district of 92 buildings in Brazil was conducted, and a combination of strategies to improve thermal resilience was identified. Results reflect the necessity of planning for resilience in the context of climate change. This is because strategies recommended under current conditions might not be ideal in the future. Therefore, an adaptable design should be prioritized. Cooling energy consumption could increase by 48 % by the 2050s, while excessive overheating issues could reach 37 % of the buildings. Simple passive strategies can significantly reduce the heat stress. A comprehensive thermal resilience analysis should ultimately be accompanied by a thorough reflection on the stakeholders’ objectives, available resources, and planning horizon, as well as the risks assumed for not being resilient.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
NASA NTRS2019

Increasing Pilot’s Understanding of Future Automation State – an Evaluation of an Automation State and Trajectory Prediction System

A pilot in the loop flight simulation study was conducted at NASA Langley Research Center to evaluate a trajectory prediction system. The trajectory prediction system computes a five-minute prediction of the lateral and vertical path of the aircraft given the current and intent state of the automation. The prediction is shown as a graphical representation so the pilots can form an accurate mental model of the future state. Otherwise, many automation changes and triggers are hidden from the flight crew or need to be consolidated to understand if a change will occur and the exact timing of the change. Varying dynamic conditions like deceleration can obscure the future trajectory and the ability to meet constraints, especially in the vertical dimension. Current flight deck indications of flight path assume constant conditions and do not adequately support the flight crew to make correct judgments regarding constraints. The study was conducted using ten commercial airline crews from multiple airlines, paired by airline to minimize procedural effects. Scenarios spanned a range of conditions that provided evaluation in a realistic environment with complex traffic and weather conditions. In particular, scenarios probed automation state and loss of state awareness. The technology was evaluated and contrasted with current state-of-the-art flight deck capabilities modeled from the Boeing 787. Objective and subjective data were collected from aircraft parameters, questionnaires, audio/video recordings, head/eye tracking data, and observations. This paper details findings about the trajectory prediction system including recommendations about further study.

Etherington, Timothy J.↗