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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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28 records · Page 2
All sourcesNASA NTRSDOE OSTI
NASA NTRS2009

Telemetry Boards Interpret Rocket, Airplane Engine Data

For all the data gathered by the space shuttle while in orbit, NASA engineers are just as concerned about the information it generates on the ground. From the moment the shuttle s wheels touch the runway to the break of its electrical umbilical cord at 0.4 seconds before its next launch, sensors feed streams of data about the status of the vehicle and its various systems to Kennedy Space Center s shuttle crews. Even while the shuttle orbiter is refitted in Kennedy s orbiter processing facility, engineers constantly monitor everything from power levels to the testing of the mechanical arm in the orbiter s payload bay. On the launch pad and up until liftoff, the Launch Control Center, attached to the large Vehicle Assembly Building, screens all of the shuttle s vital data. (Once the shuttle clears its launch tower, this responsibility shifts to Mission Control at Johnson Space Center, with Kennedy in a backup role.) Ground systems for satellite launches also generate significant amounts of data. At Cape Canaveral Air Force Station, across the Banana River from Kennedy s location on Merritt Island, Florida, NASA rockets carrying precious satellite payloads into space flood the Launch Vehicle Data Center with sensor information on temperature, speed, trajectory, and vibration. The remote measurement and transmission of systems data called telemetry is essential to ensuring the safe and successful launch of the Agency s space missions. When a launch is unsuccessful, as it was for this year s Orbiting Carbon Observatory satellite, telemetry data also provides valuable clues as to what went wrong and how to remedy any problems for future attempts. All of this information is streamed from sensors in the form of binary code: strings of ones and zeros. One small company has partnered with NASA to provide technology that renders raw telemetry data intelligible not only for Agency engineers, but also for those in the private sector.

Source record↗
NASA NTRS1988

Hot gas ingestion testing of an advanced STOVL concept in the NASA Lewis 9- by 15-foot low speed wind tunnel with flow visualization

Advanced Short Takeoff/Vertical Landing (STOVL) aircraft capable of operating from remote sites, damaged runways, and small air capable ships are being pursued for deployment around the turn of the century. To achieve this goal, it is important that the technologies critical to this unique class of aircraft be developed. Recognizing this need, NASA Lewis Research Center, McDonnell Douglas Aircraft, and DARPA defined a cooperative program for testing in the NASA Lewis 9- by 15-Foot Low Speed Wind Tunnel (LSWT) to establish a database for hot gas ingestion, one of the technologies critical to STOVL. Results from a test program are presented along with a discussion of the facility modifications allowing this type of testing at model scale. These modifications to the tunnel include a novel ground plane, an elaborate model support which included 4 degrees of freedom, heated high pressure air for nozzle flow, a suction system exhaust for inlet flow, and tunnel sidewall modifications. Several flow visualization techniques were employed including water mist in the nozzle flows and tufts on the ground plane. Headwind (free-stream) velocity was varied from 8 to 23 knots.

Johns, Albert L.↗
NASA NTRS1988

Hot gas ingestion testing of an advanced STOVL concept in the NASA Lewis 9- by 15-foot Low Speed Wind Tunnel with flow visualization

Advanced Short Takeoff/Vertical Landing (STOVL) aircraft capable of operating from remote sites, damaged runways, and small air capable ships are being pursued for deployment around the turn of the century. To achieve this goal, it is important that the technologies critical to this unique class of aircraft be developed. Recognizing this need, NASA Lewis Research Center, McDonnell Douglas Aircraft, and DARPA defined a cooperative program for testing in the NASA Lewis 9- by 15-foot Low Speed Wind Tunnel (LSWT) to establish a database for hot gas ingestion, one of the technologies critical to STOVL. Results from a test program are presented along with a discussion of the facility modifications allowing this type of testing at modal scale. These modifications to the tunnel include a novel ground plane, an elaborate model support which included 4 degrees of freedom, heated high pressure air for nozzle flow, a suction system exhaust for inlet flow, and tunnel sidewall modifications. Several flow visualization techniques were employed including water mist in the nozzle flows and tufts on the ground plane. Headwind (free-stream) velocity was varied from 8 to 23 knots.

Johns, Albert L.↗
DOE OSTI2022

A Semi-Empirical Correlation for the Onset of Density Wave Oscillations in a Helical Coil Steam Generator

NuScale Power has completed full length helical coil steam generator (HCSG) tests at SIET Laboratories in Piacenza, Italy. The test program included an investigation of the onset and evolution of density wave oscillations (DWO). This paper describes the mechanisms leading to the onset of DWO as inferred from tests performed in the full-height TF-2 counter-flow test facility. This research supports the concept that the onset of DWO is caused by liquid bridging inside the boiling length of the tubes. Using a separate cylinders approximation, a DWO onset correlation is obtained by performing a steady-state momentum balance on the density front created by liquid bridging. This yields a momentum balance equation in terms of a set of dimensionless superficial velocities, a corresponding dimensionless pressure drop term and an average void fraction. Envelope theory is then used to obtain a critical void fraction which is substituted into the dimensionless momentum balance to obtain a DWO onset correlation similar to the Wallis counter-current flooding correlation. A comparison of the DWO onset correlation to the TF-2 DWO experimental data is presented and shows excellent agreement when the average equilibrium vapor quality is greater than 0.2.

Density Wave Oscillations, Helical Coil Steam Gene↗
NASA NTRS1990

Hot gas ingestion characteristics and flow visualization of a vectored thrust STOVL concept

A 9.2 percent scale short takeoff and vertical landing (STOVL) hot gas ingestion model was designed and built by McDonnell Douglas Corporation (MCAIR) and tested in the NASA Lewis Research Center 9- by 15-Foot Low Speed Wind Tunnel (LSWT). Hot gas ingestion, the entrainment of heated engine exhaust into the inlet flow field, is a key development issue for advanced short takeoff and vertical landing aircraft. The Phase 1 test program, conducted by NASA Lewis and McDonnell Douglas Corporation, evaluated the hot ingestion phenomena and control techniques and Phase 2 test program which was conducted by NASA Lewis are both reported. The Phase 2 program was conducted at exhaust nozzles temperatures up to 1460 R and utilized a sheet laser system for flow visualization of the model flow field in and out of ground effects. Hot gas ingestion levels were measured for the several forward nozzle splay configurations and with flow control/lift improvement devices which reduced the hot gas ingestion. The model support system had four degrees of freedom, heated high pressure air for nozzle flow, and a suction system exhaust for inlet flow. The headwind (freestream) velocity for Phase 1 was varied from 8 to 90 kn, with primary data taken in the 8 to 23 kn headwind velocity range. Phase 2 headwind velocity varied from 10 to 23 kn. Results of both Phase 1 and 2 are presented. A description of the model, facility, a new model support system, and a sheet laser illumination system are also provided. Results are presented over a range of main landing gear height (model height) above the ground plane at a 10 kn headwind velocity. The results contain the compressor face pressure and temperature distortions, total pressure recovery, compressor face temperature rise, and the environmental effects of the hot gas. The environmental effects include the ground plane temperature and pressure distributions, model airframe heating, and the location of the ground flow separation. Results from the sheet laser flow visualization test are also shown.

Johns, Albert L.↗
NASA NTRS2008

Constitutive Soil Properties for Unwashed Sand and Kennedy Space Center

Accurate soil models are required for numerical simulations of land landings for the Orion Crew Exploration Vehicle. This report provides constitutive material models for one soil, unwashed sand, from NASA Langley's gantry drop test facility and three soils from Kennedy Space Center (KSC). The four soil models are based on mechanical and compressive behavior observed during geotechnical laboratory testing of remolded soil samples. The test specimens were reconstituted to measured in situ density and moisture content. Tests included: triaxial compression, hydrostatic compression, and uniaxial strain. A fit to the triaxial test results defines the strength envelope. Hydrostatic and uniaxial tests define the compressibility. The constitutive properties are presented in the format of LS-DYNA Material Model 5: Soil and Foam. However, the laboratory test data provided can be used to construct other material models. The four soil models are intended to be specific to the soil conditions discussed in the report. The unwashed sand model represents clayey sand at high density. The KSC models represent three distinct coastal sand conditions: low density dry sand, high density in-situ moisture sand, and high density flooded sand. It is possible to approximate other sands with these models, but the results would be unverified without geotechnical tests to confirm similar soil behavior.

Thomas, Michael A.↗
DOE OSTI2024

Innovating the next generation of commercial smart building software

Nearly 30% of commercial building energy use is wasted due to equipment faults and HVAC controls problems. The result is increased emissions, compromised comfort and productivity, and less reliable coordination of building power needs with a clean grid. The energy impact alone represents $17 billion in potential savings. Today’s smart building software provides a robust solution to address these operational deficiencies. Energy management and information systems (EMIS) are saving up to 9% on average, with two-year paybacks. They are being incorporated into energy management processes, commissioning services, and utility programs. As effective as they are, two barriers prevent even deeper benefits; limited personnel to fix problems once they are identified, and the expense and time to manually implement changes in control systems. In partnership with the research community, the EMIS industry is developing new capabilities to overcome these barriers. Moving beyond siloed products for either fault detection and diagnostics, or optimal control, these new capabilities empower users to not only automatically identify faults, but also to push corrective action, and control improvements to their buildings. In this paper, several areas for enhancements are documented: ‘one-time’ correction of faults such as setpoints, schedules, and economizer lockouts; short-term active testing for automated proportional integral derivative (PID) loop tuning and functional testing; and continuous supervisory control for demand flexibility and year-round efficiency. Results are presented from a pair of partner implementations out of a dozen providers integrating these enhancements into their products, including field tests from across the country, and insights into operator acceptance and integration into operations and maintenance practices.

Casillas, Armando↗
NASA NTRS1990

Hot gas ingestion test results of a two-poster vectored thrust concept with flow visualization in the NASA Lewis 9- x 15-foot low speed wind tunnel

A 9.2 percent scale Short Takeoff and Vertical Landing (STOVL) hot gas ingestion model was designed and built by McDonnell Douglas Corporation (MCAIR) and tested in the Lewis Research Center 9 x 15 foot Low Speed Wind Tunnel (LSWT). Hot gas ingestion, the entrainment of heated engine exhaust into the inlet flow field, is a key development issure for advanced short takeoff and vertical landing aircraft. Flow visualization from the Phase 1 test program, which evaluated the hot ingestion phenomena and control techniques, is covered. The Phase 2 test program evaluated the hot gas ingestion phenomena at higher temperatures and used a laser sheet to investigate the flow field. Hot gas ingestion levels were measured for the several forward nozzle splay configurations and with flow control/life improvement devices (LIDs) which reduced the hot gas ingestion. The model support system had four degrees of freedom - pitch, roll, yaw, and vertical height variation. The model support system also provided heated high-pressure air for nozzle flow and a suction system exhaust for inlet flow. The test was conducted at full scale nozzle pressure ratios and inlet Mach numbers. Test and data analysis results from Phase 2 and flow visualization from both Phase 1 and 2 are documented. A description of the model and facility modifications is also provided. Headwind velocity was varied from 10 to 23 kn. Results are presented over a range of nozzle pressure ratios at a 10 kn headwind velocity. The Phase 2 program was conducted at exhaust nozzle temperatures up to 1460 R and utilized a sheet laser system for flow visualization of the model flow field in and out of ground effects. The results reported are for nozzle exhaust temperatures up to 1160 R. These results will contain the compressor face pressure and temperature distortions, the total pressure recovery, the inlet temperature rise, and the environmental effects of the hot gas. The environmental effects include the ground plane contours, the model airframe heating, and the location of the ground flow separation.

Johns, Albert L.↗
DOE OSTI2022

Industry Level Feasibility of LiDAR Data into Fire Modeling Using Fire Risk Investigation in 3D (FRI3D)

Many evaluation, assessment, and modeling tasks at nuclear power plants require spatial information this often requires physical visits to locations within the facility because the 2D or 3D schematics and current models do not contain enough detail or do not capture as-built and real-world conditions. These visits require extensive manual labor for not only the requesting party, but support groups such as security. LIDAR mapping is trying to solve that problem by providing very detailed 3D models for low costs. However, the use of these models can be very limited because either component reference information is missing and too costly to add, or there is no way to extract specific spatial data needed for other tools. This report presents Idaho National Lab's work with Environmental Intellect (Ei) covering two main efforts. First, to reduce the effort of "tagging" data in large 3D models. By using both existing plant database information, and artificial intelligence (AI) to find and read equipment labels. This research explores the ability of to provide a simple way for the user to tag items and verify plant data, capturing both the speed of AI and human verification. The second part of the work is the development of an interface for importing pieces needed for Modeling & Simulation. Analysis work such as that for fire, flood, or physical security all require spatial or 3D models in various levels of detail. This interface will allow for the retrieval of item location or boundaries, enabling the auto generation of models for varying tools. The application program interface (API) of the fire risk investigation in 3D (FRI3D) was used to test feasibility of exporting the LiDAR tagged spatial information. Outcomes from this work provide preliminary data to determine if the tools and methods could provide substantial industry benefit if fully matured.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Forecasting Multi-Step-Ahead Street-Scale Nuisance Flooding using a seq2seq LSTM Surrogate Model for Real-Time Application in a Coastal-Urban City

In coastal-urban cities facing an elevated risk of nuisance flooding (by rain and tide) due to increased heavy rainfall, sea level rise, urbanization, and aging drainage systems, real-time flood forecasting at the street-scale can provide useful information to transportation decision-makers. Physics-Based Models (PBMs) that offer high accuracy come with high computational runtimes and costs that limit their application for real-time flood forecasting. To address this challenge, Machine Learning (ML) surrogate models trained from PBMs have been proposed to provide street-scale flood forecasts. Previous related studies have focused on using Long Short-Term Memory (LSTM) architectures to model hourly flood depth on streets. While LSTM models can capture input sequences effectively, they fall short in accurately preserving output sequences, limiting their suitability for multi-step-ahead forecasts. The seq2seq LSTM architecture offers a key advantage here by capturing the full sequence of input–output, making it potentially more suitable for multi-step-ahead flood forecasts compared to traditional LSTM models. However, seq2seq LSTM has not been tested for street-scale flood forecasting, particularly for rapidly fluctuating nuisance flooding events which require special attention to its temporal sequences. Hence, in this study, we applied the seq2seq LSTM model to explore multi-step-ahead street-scale nuisance flooding and compared its results to the traditional LSTM model as a benchmark model. LSTM and seq2seq LSTM surrogate models were applied to 22 flood-prone streets in Norfolk, Virginia, as a case study with a 4-hr (short-term) and 8-hr (long-term) lead time. The models were trained with environmental (rainfall and tide) and topographic (elevation, Topographic Wetness Index, and Depth-To-Water) features along with PBM-derived water depths for different storm events. The results demonstrated satisfactory performance of both LSTM and seq2seq LSTM surrogate models throughout the forecast period compared to the PBM. However, the seq2seq LSTM showed lower Mean Absolute Error (MAE)/ Root Mean Square Error (RMSE) and higher Nash–Sutcliffe Efficiency (NSE)/ correlation than the LSTM across most lead times, particularly for long-term forecasting due to its supremacy in handling both input–output sequences together, which is missing in the traditional LSTM. For example, in the long-term, the average RMSE ranges were 0.0268–0.0373 m for LSTM and 0.0226–0.0319 m for seq2seq LSTM, while in the short-term, they were 0.0263–0.0293 m and 0.0261–0.0283 m, respectively. Additionally, while both models exhibited similar performance in distinguishing flooded and non-flooded streets for flood depth ≥ 0.1 m, the seq2seq LSTM model demonstrated superior performance for higher flood depths (such as ≥ 0.2 m and ≥ 0.3 m). Once trained, inference took only 0.09 to 0.11 s (short-term) and 0.30 to 0.35 s (long-term) per storm event for the 22 streets, making the application highly suitable for real-time decision-making during nuisance flood events.

54 ENVIRONMENTAL SCIENCES↗