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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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The provided EAGLE-I historic dataset includes power outage information at the county level for 2025 at 15-minute intervals collected by the EAGLE-I program at ORNL. The data has been collected from utility's public outage maps using an ETL process. The dataset details FIPS code, county name, state name, total number of customers without power, and a date/timestamp. For detailed metadata, refer to the linked metadata DOI.
Program EAGLE (Eglin Arbitrary Geometry Implicit Euler) Numerical Grid Generation System is a composite (multi-block) algebraic or elliptic grid generation system designed to discretize the domain in and/or around any arbitrarily shaped three dimensional regions. This system combines a boundary conforming surface generation scheme and includes plotting routines designed to take full advantage of the DISSPLA Graphics Package (Version 9.0). Program EAGLE is written to compile and execute efficiently on any Cray machine with or without solid state disk (SSD) devices. Also, the code uses namelist inputs which are supported by all Cray machines using the FORTRAN compiler CFT77. The namelist inputs makes it easier for the user to understand the inputs and operation of Program EAGLE. EAGLE's numerical grid generator is constructed in the following form: main program, EGG (executive routine); subroutine SURFAC (surface generation routine); subroutine GRID (grid generation routine); and subroutine GRDPLOT (grid plotting routines). The EAGLE code was modified to use on the NASA-LaRC SNS computer (Cray 2S) system. During the modification a conversion program was developed for the output data of EAGLE's subroutine GRID to permit the data to be graphically displayed by IRIS workstations, using Plot3D. The code of program EAGLE was modified to make operational subroutine GRDPLOT (using DI-3000 Graphics Software Packages) on the NASA-LaRC SNS Computer System. How to implement graphically, the output data of subroutine GRID was determined on any NASA-LaRC graphics terminal that has access to the SNS Computer System DI-300 Graphics Software Packages. A Quick Reference User Guide was developed for the use of program EAGLE on the NASA-LaRC SNS Computer System. One or more application program(s) was illustrated using program EAGLE on the NASA LaRC SNS Computer System, with emphasis on graphics illustrations.
Program EAGLE (Eglin Arbitrary Geometry Implicit Euler) is a multiblock grid generation and steady-state flow solver system. This system combines a boundary conforming surface generation, a composite block structure grid generation scheme, and a multiblock implicit Euler flow solver algorithm. The three codes are intended to be used sequentially from the definition of the configuration under study to the flow solution about the configuration. EAGLE was specifically designed to aid in the analysis of both freestream and interference flow field configurations. These configurations can be comprised of single or multiple bodies ranging from simple axisymmetric airframes to complex aircraft shapes with external weapons. Each body can be arbitrarily shaped with or without multiple lifting surfaces. Program EAGLE is written to compile and execute efficiently on any CRAY machine with or without Solid State Disk (SSD) devices. Also, the code uses namelist inputs which are supported by all CRAY machines using the FORTRAN Compiler CF177. The use of namelist inputs makes it easier for the user to understand the inputs and to operate Program EAGLE. Recently, the Code was modified to operate on other computers, especially the Sun Spare4 Workstation. Several two-dimensional grid configurations were completely and successfully developed using EAGLE. Currently, EAGLE is being used for three-dimension grid applications.
This university design project concerns the Eagle RTS (Regional Transport System), a 66 passenger, twin turboprop aircraft with a range of 836 nautical miles. It will operate with a crew of two pilots and two flight attendents. This aircraft will employ the use of aluminum alloys and composite materials to reduce the aircraft weight and increase aerodynamic efficiency. The Eagle RTS will use narrow body aerodynamics with a canard configuration to improve performance. Leading edge technology will be used in the cockpit to improve flight handling and safety. The Eagle RTS propulsion system will consist of two turboprop engines with a total thrust of approximately 6300 pounds, 3150 pounds thrust per engine, for the cruise configuration. The engines will be mounted on the aft section of the aircraft to increase passenger safety in the event of a propeller failure. Aft mounted engines will also increase the overall efficiency of the aircraft by reducing the aircraft's drag. The Eagle RTS is projected to have a takeoff distance of approximately 4700 feet and a landing distance of 6100 feet. These distances will allow the Eagle RTS to land at the relatively short runways of regional airports.
The Eagle RTS (Regional Transport System) is a 66-passenger aircraft designed to satisfy the need for accessible and economical regional travel. The first design objective for the Eagle RTS is safety. Safety results primarily from avoidance of the hub airport air traffic, implementation of anti-stall characteristics by tailoring the canard, and proper positioning of the engines for blade shedding. To provide the most economical aircraft, the Eagle RTS will use existing technology to lower production and maintenance costs by decreasing the amount of new training required. In selecting the propulsion system, the effects on the environment were a main consideration. Two advantages of turbo-prop engines are the high fuel efficiency and low noise levels produced by this type of engine. This ensures the aircraft's usage during times of rising fuel costs and growing aircraft noise restrictions. The design of the Eagle RTS is for spoke-to-spoke transportation. It must be capable of landing on shorter runways and have speeds comparable to that of the larger aircraft to make its service beneficial to the airlines. With the use of turbo-prop engines and high lift devices, the Eagle RTS is highly adaptable to regional airports. The design topics discussed include: aerodynamics, stability, structures and materials, propulsion, and cost.
During a 22-month period from February 1991 to December 1993, a dedicated group of students, faculty, and staff at California State University, Los Angeles completed a project to design, build, and race their second world class solar-powered electric vehicle, the Solar Eagle 2. This is the final report of that project. As a continuation of the momentum created by the success of the GM-sponsored Sunrayce USA in 1990, the U.S. Department of Energy (DOE) picked up the banner from General Motors as sponsors of Sunrayce 93. In February 1991, the DOE sent a request for proposals to all universities in North America inviting them to submit a proposal outlining how they would design, build, and test a solar-powered electric vehicle for a seven-day race from Arlington, Texas to Minneapolis, Minnesota, to be held in June 1993. Some 70 universities responded. At the end of a proposal evaluation process, 36 universities including CSLA were chosen to compete. This report documents the Solar Eagle 2 project--the approaches take, what was learned, and how our experience from the first Solar Eagle was incorporated into Solar Eagle 2. The intent is to provide a document that would assist those who may wish to take up the challenge to build Solar Eagle 3.
During 2011 a series of progressively more challenging flight tests of the Mighty Eagle autonomous terrestrial lander testbed were conducted primarily to validate the GNC system for a proposed lunar lander. With the successful completion of this GNC validation objective the opportunity existed to utilize the Mighty Eagle as a flying testbed for a variety of technologies. In 2012 an Autonomous Rendezvous and Capture (AR&C) algorithm was implemented in flight software and demonstrated in a series of flight tests. In 2012 a hazard avoidance system was developed and flight tested on the Mighty Eagle. Additionally, GNC algorithms from Moon Express and a MEMs IMU were tested in 2012. All of the testing described herein was above and beyond the original charter for the Mighty Eagle. In addition to being an excellent testbed for a wide variety of systems the Mighty Eagle also provided a great learning opportunity for many engineers and technicians to work a flight program.
In the effort described here, the elliptic grid generation procedure in the EAGLE grid code was separated from the main code into a subroutine, and a new subroutine which evaluates several grid quality measures at each grid point was added. The elliptic grid routine can now be called, either by a computational fluid dynamics (CFD) code to generate a new adaptive grid based on flow variables and quality measures through multiple adaptation, or by the EAGLE main code to generate a grid based on quality measure variables through static adaptation. Arrays of flow variables can be read into the EAGLE grid code for use in static adaptation as well. These major changes in the EAGLE adaptive grid system make it easier to convert any CFD code that operates on a block-structured grid (or single-block grid) into a multiple adaptive code.
We present a rule-based framework, called EAGLE, that has been shown to be capable of defining and implementing a range of finite trace monitoring logics, including future and past time temporal logic, extended regular expressions, real-time and metric temporal logics, interval logics, forms of quantified temporal logics, and so on. A monitor for an EAGLE formula checks if a finite trace of states satisfies the given formula. We present, in details, an algorithm for the synthesis of monitors for EAGLE. The algorithm is implemented as a Java application and involves novel techniques for rule definition, manipulation and execution. Monitoring is achieved on a state-by-state basis avoiding any need to store the input trace of states. Our initial experiments have been successful as EAGLE detected a previously unknown bug while testing a planetary rover controller.
Power time series captured from all Eagle nodes using iLO (Integrated Lights Out) The Eagle HPC operated at NLR from 2019 through 2024. Eagle was a 2,000-node, 8-petaflop system. This dataset is a comprehensive time series of instantaneous snapshots of power usage at 1 minute intervals from all nodes at the node level. Data provided in compressed Hive dataset/Parquet format. iLO Power Time Series Fields ts: Timestamp dv: Device / Node - Rack and Unit - r103u17 == r(ack)103u(nit)17 vl: Value - Value in watts (instantaneous value at sampling time) day month year
Overview: Anonymized job-level records from the Eagle high-performance computing (HPC) system at the National Laboratory of the Rockies (NLR). Each record represents a Slurm batch job with scheduling metadata, resource requests, resource utilization, CPU/GPU energy consumption, and efficiency metrics. Sensitive fields (user, account, job name) are replaced with cryptographic hashes. System & Timeframe: Eagle was a 2,000-node, 8-petaflop system operated at NLR from 2019–2024. Data covers the full operational lifetime of the system. Slurm data was processed nightly; timestamps are in Mountain Time. Funding provided by the U.S. Department of Energy, EERE. Files: esif.hpc.eagle.job-anon.zip — Core anonymized job records (Hive-partitioned Parquet) esif.hpc.eagle.job-anon-energy-metrics.zip — Same records with additional iLO and Ganglia energy metrics datacard.md — Full dataset documentation ~13.8 million rows, 62 variables. Readable with PyArrow, pandas, DuckDB, Apache Spark, or any Parquet-compatible tool. Data Collection: Jobs collected via sacct through a pipeline: Eagle Jobs API → Redpanda → StreamSets → HPCMON API → PostgreSQL. Node-level power from iLO (HP Integrated Lights-Out); GPU power from Ganglia monitoring, joined to jobs via node lists and time ranges. Preprocessing: Anonymization of name, user, and account fields via cryptographic hashing Derived columns: queue_wait, cpu_eff, max_mem_eff Simplified job state mapping (e.g., "CANCELLED BY 12345" → "CANCELLED") QoS accounting rules (buy-in, standby, or Slurm QoS value) CPU energy estimated from TDP (200W, Intel Xeon Gold 6154, 18 cores) Timezone-aware columns (_tz) sourced from LEX accounting database to correctly handle DST transitions Key Variables: Scheduling: job_id, partition, state_simple, submit_time_tz, start_time_tz, end_time_tz, queue_waitResources: nodes_req/used, processors_req/used, memory_req, wallclock_req/used, gpus_requested Efficiency: cpu_eff, max_mem_eff Energy: cpu_energy_tdp_estimated_max/used_watt_hours, node_energy_total_watt_hours (iLO), gpu0/1_energy_total_watt_hours (Ganglia) Partitions: bigmem, bigmem-8600, bigscratch, csc, dav, ddn, debug, gpu, haswell, long, mono, short, standard Job States: CANCELLED, COMPLETED, FAILED, NODE_FAIL, OUT_OF_MEMORY, PENDING, RUNNING, TIMEOUT QoS Levels: Unknown, normal, buy-in, debug, penalty, high, standby Important Notes: Non-_tz timestamp columns may be off by one hour across DST boundaries; use _tz columns for time difference calculations Energy fields are null for jobs without monitoring coverage Job step records and raw Slurm JSONB fields are excluded from this extract Do not attempt to re-identify individuals from hashed fields
Ganglia node metrics and iLO (Integrated Lights Out) power data captured from six representative Eagle GPU nodes The Eagle HPC operated at NLR from 2019 through 2024. Eagle was a 2,000-node, 8-petaflop system. This dataset is a representative sample of metrics for 6 of the GPU nodes. Each GPU node contained 2 CPUs and 2 GPUs. Data provided in compressed CSV format. Ganglia and iLO Power Time Series Fields ts: Timestamp dv: Device / Node - Rack and Unit - r103u17 == r(ack)103u(nit)17 mt: Metric (only present for Ganglia) vl: Value - Value in watts for iLO power (instantaneous value at sampling time) or specified Ganglia metric below Ganglia Metrics Metric name -- Metric description -- Unit cpu_aidle -- Percent of time since boot idle CPU -- Percent cpu_idle -- Percent CPU idle -- Percent cpu_nice -- Percent CPU nice -- Percent cpu_speed -- Speed in MHz of CPU -- MHz cpu_user -- Percent CPU user -- Percent cpu_wio -- The percentage of CPU Wait I/O -- Percent gpu0_bar1_memory -- Used GPU bar1 memory -- MB gpu0_decoder_util -- GPU decoder utilization -- Percent gpu0_ecc_db_error -- Total ECC error counts for the GPU -- Number gpu0_encoder_util -- GPU encoder utilization -- Percent gpu0_fan -- Fan speed -- RPM gpu0_fb_memory -- Used GPU framebuffer memory -- MB gpu0_graphics_clock_report -- Current clock speeds for the device -- MHz gpu0_mem_total -- Memory total -- MB gpu0_mem_util -- Memory utilization -- Percent gpu0_power_usage_report -- Power usage report -- Watts gpu0_temp -- GPU 1 temperature -- Celsius gpu1_bar1_memory -- Used GPU bar1 memory -- MB gpu1_decoder_util -- GPU decoder utilization -- Percent gpu1_ecc_db_error -- Total ECC error counts for the GPU -- Number gpu1_encoder_util -- GPU encoder utilization -- Percent gpu1_fan -- Fan speed -- RPM gpu1_fb_memory -- Used GPU framebuffer memory -- MB gpu1_graphics_clock_report -- Current clock speeds for the GPU -- MHz gpu1_mem_total -- Memory total -- MB gpu1_mem_util -- Memory utilization -- MB gpu1_power_usage_report -- Power usage report -- Watts gpu1_temp -- GPU 1 temperature -- Celsius ipmi_cpu1_temp -- CPU 1 temperature -- Celsius ipmi_cpu2_temp -- CPU 2 temperature -- Celsius ipmi_inlet_ambient_temp -- Temperature measured at intake -- Celsius ipmi_vr_p1_temp -- CPU 1 voltage regulator temperature -- Celsius ipmi_vr_p2_temp -- CPU 2 voltage regulator temperature -- Celsius mem_buffers -- Amount of buffered memory -- Bytes mem_cached -- Amount of cached memory -- Bytes mem_free -- Amount of available memory -- Bytes mem_shared -- Amount of shared memory -- Bytes mem_total -- Amount of available memory -- Bytes
We briefly present a rule-based framework called EAGLE, shown to be capable of defining and implementing finite trace monitoring logics, including future and past time temporal logic, extended regular expressions, real-time and metric temporal logics (MTL), interval logics, forms of quantified temporal logics, and so on. In this paper we focus on a linear temporal logic (LTL) specialization of EAGLE. For an initial formula of size m, we establish upper bounds of O(m(sup 2)2(sup m)log m) and O(m(sup 4)2(sup 2m)log(sup 2) m) for the space and time complexity, respectively, of single step evaluation over an input trace. This bound is close to the lower bound O(2(sup square root m) for future-time LTL presented. EAGLE has been successfully used, in both LTL and metric LTL forms, to test a real-time controller of an experimental NASA planetary rover.
Last summer a trio of aeronauts made aviation history. Ben Abruzzo, Maxie Anderson and Larry Newman, all of Albuquerque, New Mexico, piloted their balloon Double Eagle I1 from Presque Isle, Maine to Miserey, France, some 50 miles from Paris. They were the first to negotiate a successful Atlantic crossing in a freeflying balloon after a score of attempts over a span of more than a century. A year earlier, Abruzzo and Anderson had made an unsuccessful try in their predecessor balloon Double Eagle. On that occasion, a NASA-developed satellite beacon helped save their lives. Carried aboard the balloon, the simple, seven-pound beacon continuously transmitted signals to NASA's Nimbus-6 satellite. Nimbus relayed the signals to monitors at Goddard Space Flight Center, enabling Goddard to compute the balloon's position. Position reports were then telephoned regularly to Double Eagle's control center at Bedford, Massachusetts. This monitoring system proved invaluable when the balloon encountered trouble several days after liftoff.
The provided EAGLE-I historic dataset includes power outage information at the county level for 2024 at 15-minute intervals collected by the EAGLE-I program at ORNL. The data has been collected from utility's public outage maps using an ETL process. The dataset details FIPS code, county name, state name, total number of customers without power, total customers per county, and a date/timestamp. For detailed metadata, refer to the linked metadata DOI.
During natural hazard events (hurricanes, wildfires, earthquakes, etc.) and recent man-made events (e.g., cyber attacks), the exchange of near real-time, spatially refined data within the response community is critical. The EAGLE-I$^{TM}$ platform is one tool that facilitates this data for decision makers within the energy sector. While much information can be collected and integrated into the system directly, other pertinent data must be augmented by other derived data products to enhance the information and allow for a consistent evaluation of on-the-ground conditions. One such data set that requires the addition of other derived data is the electric utility customer outage data that is aggregated to the county level within the EAGLE-I application. Without a county customer data set, outages can only be compared on total counts, which gives greater importance to higher population outages. Including an electric utility customer data set at the county level allows for these outage counts to be converted to percent outages and brings a consistent classification of outages and equal importance to all outages. To achieve this, several available data sets were combined and spatial disaggregation techniques were employed to model customer estimates at the county scale. This paper presents the approach to produce this data for the United States and lessons learned from working with these disparate data sets. Data validation is provided, where possible, and limitations of the model and possible improvements are discussed.
The results of the flight test program of the Daedalus and Light Eagle human powered aircraft in the winter of 1987/88 are given. The results from experiments exploring the Light Eagle's rigid body and structural dynamics are presented. The interactions of these dynamics with the autopilot design are investigated. Estimates of the power required to fly the Daedalus aircraft are detailed. The system of sensors, signal conditioning boards, and data acquisition equipment used to record the flight data is also described. In order to investigate the dynamics of the aircraft, flight test maneuvers were developed to yield maximum data quality from the point of view of estimating lateral and longitudinal stability derivatives. From this data, structural flexibility and unsteady aerodynamics have been modeled in an ad hoc manner and are used to augment the equations of motion with flexibility effects. Results of maneuvers that were flown are compared with the predictions from the flexibility model. To extend the ad hoc flexibility model, a fully flexible aeroelastic model has been developed. The model is unusual in the approximate equality of many structural natural frequencies and the importance of unsteady aerodynamic effects. the Gossamer Albatross. It is hypothesized that this inverse ground effect is caused by turbulence in the Earth's boundary layer. The diameters of the largest boundary layer eddies (which represent most of the turbulent kinetic energy) are proportional to altitude; thus, closer to the ground, the energy in the boundary layer becomes concentrated in eddies of smaller and smaller diameter. Eventually the eddies become sufficiently small (approximately 0.5 cm) that they trip the laminar boundary layer on the wing. As a result, a greater percentage of the wing area is covered with turbulent flow. Consequently the aircraft's drag and the pow er required both increase as the aircraft flies closer to the ground. The results of the flight test program are examined critically, especially with respect to future applications. Maneuvers that allow the observation of stability derivatives for flexible aircraft are detailed. Considerations for the design of autopilots for future human power aircraft and high-altitude RPV's are discussed. Techniques useful in estimating the power required to fly aircraft of very high lift to drag ratio are described.
The topics covered are presented in viewgraph form and include the following: Eagle-Picher heritage - single pressure vessel (SPV) technology; SPV units in process at Eagle-Pitcher; design features; basic battery design features; cell development considerations; SPV boiler plate cell; cell development activity; SPV cell capacity test - charge and discharge; and current plans.