Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “MIXTURES”

Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 361 records · Page 20
All sourcesNASA NTRSDOE OSTI
NASA NTRSDate not supplied

Hierarchical Mixture of Experts for Advanced Air Mobility Flight Phase Classification

Advanced Air Mobility (AAM) and Urban Air Mobility (UAM) operations will have numerous vehicles and aircraft flying in the airspace, which poses safety and security concerns. Commercial airlines utilize Air Traffic Management (ATM) and Air Traffic Control (ATC) for real-time monitoring, surveillance, traffic coordination, and rerouting to maintain safe and efficient flight patterns. Transferring ATM and ATC architectures to AAM/UAM will be difficult to implement since AAM/UAM aircraft fly at lower altitudes, have more static and dynamic obstacles, operate in highly dense environments, and have several more aircraft to monitor for a given volume of the national airspace (NAS). Automatic flight phase classification will enhance efficiencies of ATM/ATC-like architectures for AAM/UAM. Classifying the main flight phases (takeoff, climb, cruise, descent, and landing) provides insight to ensure safe operations, provide situational awareness of the NAS, and monitor flights in case there are any emergencies. Typical flight phase classification methods are all-or-nothing, which will not capture or accurately classify the transitions between flight phases. Utilizing hierarchical mixture of experts (HME) provides a flight phase classification solution that includes transitions between the flight phases by assigning weights based on ground-based distributed sensor readings from cameras and radar. Adding the transitions between flight phases increases the fidelity of flight phase classification and provides deeper insight for flight phase classification by leveraging distributed sensing concepts.

distributed sensing↗
NASA NTRSDate not supplied

Hierarchical Mixture of Experts for Advanced Air Mobility Flight Phase Classification

Advanced Air Mobility (AAM) and Urban Air Mobility (UAM) operations will have numerous vehicles and aircraft flying in the airspace, which poses safety and security concerns. Commercial airlines utilize Air Traffic Management (ATM) and Air Traffic Control (ATC) for real-time monitoring, surveillance, traffic coordination, and rerouting to maintain safe and efficient flight patterns. Transferring ATM and ATC architectures to AAM/UAM will be challenging to implement since AAM/UAM aircraft fly at lower altitudes, have more static and dynamic obstacles, operate in highly dense environments, and have several more aircraft to monitor for a given volume of the national airspace (NAS). Aircraft typically have the following flight phases: takeoff, climb, cruise, descent, and landing. Classifying these flight phases provides insight into ensuring safe operations, providing situational awareness of the NAS, and monitoring flights in emergencies. Automatic flight phase classification will enhance the efficiencies of ATM/ATC-like architectures for AAM/UAM, especially since numerous aircraft will be flying in highly dense urban environments. Typical flight phase classification methods are all-or-nothing, which will not capture or accurately classify the transitions between flight phases. Utilizing hierarchical mixture of experts (HME) provides a flight phase classification solution that includes transitions between the flight phases by assigning weights based on ground-based distributed sensor readings from cameras and radar. Adding the transitions between flight phases increases the fidelity of flight phase classification and provides deeper insight into flight phase classification by leveraging distributed sensing concepts. Simulation results and post-processed flight test results demonstrate the utility of HME for automatic and robust flight phase classification for real-time AAM operations.

distributed sensing↗
NASA NTRS2024

Microgravity Flow Boiling Experiments With Liquid-Vapor Mixture Inlet Onboard the International Space Station

This article is part of a series of studies culminating from the multi-objective Flow Boiling and Condensation Experiment (FBCE) onboard the International Space Station, which utilized the Flow Boiling Module (FBM) for experiments between February and July 2022. This study investigates microgravity flow boiling of n-Perfluorohexane with liquid-vapor mixture (two-phase) inlet conditions to the FBM with either one or two opposite walls heated. The FBM’s channel has a rectangular cross-sectional area of 5.0 × 2.5 mm 2 and a heated length of 114.6 mm. Key parameters of interest include mass velocity (180 – 2400 kg/m 2 s), inlet quality (-0.01 – 0.87), inlet pressure (120 – 200 kPa), and heat flux (1.8 W/cm 2 to critical heat flux), and a large database is amassed. The flow is visualized via a high-speed video camera and photographs are recorded at each heating increment to assess the periodic flow patterns within the channel and the near-wall interfacial behavior. Flow patterns are complex and mainly characterized by high- and low-density fronts alternately traversing the channel to yield high- and low-density-dominant periods of boiling. At all operating conditions, high-density fronts are faster during high-density-dominant periods. At low inlet qualities, the flow is annular near the channel inlet with a central vapor core surrounded by an annular liquid layer. Each high-density front having a high liquid fraction leaves a thin liquid layer sheared onto the heated walls. Boiling occurs within the liquid layer and a vapor layer is formed next to the heated wall. Inlet quality and mass velocity most dictate the overall flow patterns followed by heating configuration, and to a much lesser extent, heat flux and inlet pressure. Heat transfer characteristics are assessed via averaged boiling curves, streamwise profiles of wall temperature and heat transfer coefficient, and parametric curves of local and averaged heat transfer coefficients. Inlet pressure has an insignificant effect on heat transfer. At similar operating conditions, both the heating configurations yield similar trends and values of heat transfer coefficient and critical heat flux (CHF, slightly higher for single-sided) even though double-sided heating adds twice the heat to the fluid and doubly raises local quality. The heat fluxes required for both onset of nucleate boiling degradation and CHF are larger at high mass velocities and low inlet qualities. For a fixed inlet quality, high mass velocities yield higher average heat transfer coefficients at both lower and higher heat fluxes, while the nucleate boiling regime at intermediate heat fluxes is unaffected. For a fixed mass velocity, higher inlet qualities yield higher and lower average heat transfer coefficients at lower and higher heat fluxes, respectively.

Microgravity↗