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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 847 records · Page 47

NASA 5.2%-Scale High Lift Common Research Model (CRM-HL) Test in the National Transonic Facility (NTF)

Problem: Predicting CL,max and assessing transition, turbulence models, and Reynolds number effect predictions in CFD requires wind tunnel data for comparison. Objective: Expand the CFD validation database by running multiple models designed using the same reference geometry in various wind tunnels around the world. The wind tunnel data and model geometry will be open source to allow for CFD validation use. Approach: Run the NASA 5.2%-scale High Lift Common Research Model (CRM-HL) semi-span model at the National Transonic Facility (NTF). Test the model at chord Reynolds numbers (Rec) between 1.6 million and 30 million and at varying Q/Es to assess Reynolds number effects and aeroelasticity affects on the model, while collecting force and moment, pressure and wing deformation data. Test the model with ice shapes installed on the leading edges. Results: Ran eight model configurations, including four landing, two landing with ice shapes installed, and two takeoff configurations. Ran in air (120°F) and nitrogen operations (-50°F, -180°F and -250°F) at 7 different chord Reynolds numbers. The data compare well with previous tests of the same model at DLR (The Germany Aerospace Center) and the 14- by 22-Foot Subsonic Tunnel (14x22). Significance: Provided valuable high Reynolds number data on a high lift configuration to the worldwide research community. Provide icing data at flight Reynolds number to the research community.

CRM-HL↗

Monitoring and Assessing Heat Vulnerability to Identify Locations for Heat Mitigation Efforts in Sarasota, Florida

Located in the coastal subtropical region, Florida’s Sarasota County receives plenty of sunlight for more than half of the year. However, the ongoing rise in summer temperatures attributed to climate change poses an increasing vulnerability to extreme heat for the residents. Moreover, the area's high relative humidity exacerbates the discomfort of high temperatures, thereby intensifying the severity of heat events and elevating the risks of heat-related illnesses. Our collaborations with Sarasota County Sustainability underscore shared concerns about the impact of urban heat island (UHI) on the community. We utilized Earth observation data from NASA Landsat 8 and Landsat 9’s Thermal Infrared Sensors (TIRS) and the International Space Station’s Ecosystem Spaceborne Thermal Radiometer Experiment on Space Station (ECOSTRESS) to model UHI effects within the county during the summer from 2019 to 2023. By implementing the open-source Integrated Valuation of Ecosystem Services and Tradeoffs (InVEST) Urban Cooling software model, ArcGIS ModelBuilder, and principal component analysis, we identified the land surface temperature variance within the county and areas that are least capable of mitigating the effects of UHI. In addition, we use socioeconomic and demographic data as indicators to quantify vulnerability at the census tract level. The results revealed that heat intensity varies significantly across Sarasota County, with the highest temperatures being in the more developed western part of the region. We pinpointed that at least three vulnerable communities reside in high-heat regions: North Sarasota, Venice, and North Port. These areas demonstrate a confluence between socioeconomic sensitivity and environmental hazard, indicating a high priority in future heat mitigation efforts.

Theresia Phoa↗

Impact of Anthropogenic Activity, Climate Change, and Urbanization on Wetland Habitat in the Platte River Basin​

The Platte River Basin (PRB) is a dynamic ecosystem where wetlands play a pivotal role as essential habitats for various flora and fauna, including local and migratory birds. It provides many crucial ecosystem services that benefit humans directly and indirectly. However, anthropogenic activity, climate change, and urbanization have resulted in decline in wildlife habitat, elevated flood risk, and wetland loss. To address this issue, NASA DEVELOP partnered with Audubon Great Plains (AGP) to address the vital habitats within urban areas to protect bird species, reduce flood hazards, and analyze the potential impact of future development on wetlands. We utilized remotely sensed data from Landsat 8 Operational Land Imager (OLI), Sentinel-2 Multispectral Instrument (MSI), and Sentinel-1 Synthetic Aperture Radar (SAR) to assess land use and land cover (LULC) change. Nighttime lights data from Suomi National Polar-orbiting Partnership (NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) as well as NASA Socioeconomic Data and Applications Center (SEDAC) population data were also used as inputs to simulate urban growth potential up to 2050 using the open-source FUTure Urban-Regional Environment Simulation (FUTURES) model. A broad scale analysis across 13 focal cities showed varied changes in land use patterns across the PRB, with the most notable being a decrease in agricultural land coverage and an increase in vegetation and grassland coverage. We overlaid a flood extent map with the LULC classifications in Grand Island to identify possible restoration sites under AGP’s Urban Woods and Prairies Initiative. The results for two proposed scenarios showed that at least 51 counties out of 81 in the PRB would experience growth by year 2050. The first scenario (all wetlands are protected) showed that there will be no loss of wetlands by 2050. However, the second scenario (no wetlands are protected) showed a decrease in wetland area and loss of habitat for bird conservation. The results will help AGP to lead awareness workshops for communities about wetland protection and to form impactful conservation strategies in the future.​

Nancee Uniyal↗

Natural Language Processing for Extracting Rich Disease Data Aligned To Satellite Meteorological Data

Global climate change is redefining our understanding of how diseases spread. In Sri Lanka, vector-borne diseases such as dengue fever, encephalitis, and leptospirosis historically surged during the monsoon seasons when temperatures were high enough for mosquito eggs to hatch. Unfortunately, due to rising temperatures and more erratic rainfall patterns, mosquito eggs can now hatch year-round and are increasingly unpredictable, leading to an alarmingly increasing number of hospitalizations and deaths. More data is needed to adapt our response to these diseases in an increasingly warmer world. In the contemporary landscape, a wealth of disease information is available, yet accessibility remains limited due to unstructured data formats such as PDFs. Therefore, converting unstructured disease reports into structured formats is necessary for effectively leveraging data. This paper introduces a comprehensive framework for collecting unstructured disease reports and transforming them into analyzable formats. By creating separate models tailored to each data format, we can ensure accuracy compared to general models. These straightforward models enhance accessibility and empower other researchers to use our tools. The returned structured data can then be harnessed for analysis, statistical purposes, and informing evidence-based public health interventions, thus facilitating more informed decision-making in healthcare. We deploy this framework to produce geospatial data for Sri Lanka and Brazil for many different conditions and align these data with satellite environmental data, providing for the first time a structured, aligned powerful dataset for disease modeling.

Open Source Open Science↗

Improving GES Disc Data Search and Discovery Through AI Metadata Augmentation

NASA’s Goddard Earth Science (GES) Data and Information Services Center (DISC) is one of twelve data centers in NASA's Science Mission Directorate (SMD), providing vital earth science data to a diverse user base. To enhance the discoverability of this data, GES DISC employs a keyword search system, which leverages scientific keywords embedded in dataset metadata. However, the evolving nature of scientific applications of our data necessitates regular review and augmentation of these keywords. To address this, we developed a service to automatically predict missing science keywords in the metadata. This service constructs a knowledge graph from the latest GES DISC metadata within NASA’s Common Metadata Repository (CMR). Using an open-source library, we trained a machine learning model to predict absent science keywords in the metadata. Our preliminary results indicate that the model has high levels of accuracy at predicting science keywords in the dataset metadata when exposed to data not included in its training. These predicted keywords were then evaluated by GES DISC data curation scientists and compared against other AI tools for metadata augmentation. We aim to enhance the overall usability and accessibility of NASA’s earth science data by implementing this tool in our data curation processes.

Kendall Gilbert↗

The Development and Deployment of Machine Learning Models for Aircraft Engine Concept Assessment

In today's competitive landscape, the effective development and utilization of machine-learning (ML) applications have become crucial across diverse economic sectors. This study presents an outline of the procedure involved in creating and implementing ML models for conceptualizing and evaluating aircraft engines. These models leverage supervised deep-learning algorithms to analyze patterns within an open-source repository containing data on both production and research conventional turbofan engines. The main areas of focus encompass crucial engine parameters like thrust-specific fuel consumption (TSFC), engine weight, engine diameter, and turbomachinery stage counts. While the creation of ML models is fundamental for their utilization, ensuring their seamless deployment holds equal significance. To address this aspect, a conversational AI chatbot that specifically focuses on propulsion has been developed. Leveraging natural language processing (NLP) techniques, this chatbot simplifies the deployment of machine learning (ML) models. The comprehensive workflow encompasses several key stages: gathering and enhancing engine data, training and cross validating the ML models, testing and evaluating their performance, and finally, deploying, monitoring, and updating the ML models. By following this systematic approach, the aim is to streamline the development and deployment process of ML models tailored for aircraft engine assessment.

AI Chatbot↗

Generic Urban Air Mobility Simulation

This research presents a simulation framework for autonomous research for a UAM vehicle using the NASA Revolutionary Vertical Lift Technology Lift+Cruise concept vehicle. Our research results were produced using the open-source, six degree of freedom, rigid-body, nonlinear generic urban air mobility (GUAM) simulation. The intent of this paper is to demonstrate the GUAM simulation and a series of Challenge Problems that our researchers have posed to the broader autonomous vehicle research community. Our team has developed the GUAM simulation for the express purpose of providing a high-fidelity transition vehicle dynamics model to foster collaboration and algorithm performance comparison across research teams. In this paper, we demonstrate some of the autonomous flight research challenges and some of our current approaches to tackling basic autonomous flight tasks (e.g., trajectory following, stationary and moving obstacle avoidance). Additionally, we propose some flight metrics to assess autonomous algorithm performance while accomplishing these basic autonomous tasks.

autonomous flight↗

A Workflow to Rapidly Interrogate Multiscale Model Simulation Results Across Multiple Length Scales

Many tools can be used to visualize field and state variables for a single scale analysis so that the influence of relevant mechanisms can be evaluated. Finite element software is often utilized to simulate a unit cell of a material and visualize results at that scale. Material properties can be homogenized from individual constituents and local deformation, damage, and failure mechanisms can be evaluated within the unit cell due to globally applied boundary conditions. Such solutions can produce satisfactory results if a user is only interested in analyzing a single scale. But materials in general contain features across multiple length scales, and assumptions must be made when attempting to account for lower length scale phenomena within a higher length scale model. Multiscale modeling is an attractive means to model materials because detailed material responses can be tracked across multiple disparate length scales while reducing the amount of required assumptions. However, as the complexity of these models increases, a large amount of data can be produced, and data traceability can become increasingly more difficult. Field and state variables, which are naturally dependent on spatial position, may themselves be calculated from one or more lower length scale unit cell models each with their own appropriate field and state variables. The NASA Multiscale Analysis Tool (NASMAT) is one software that can be used to perform a multiscale analysis efficiently and output requested data at all length scales in the analysis. A companion open-source Python software, NASMAT PrePost, can be used to visualize NASMAT model results and rapidly interrogate multiscale data across multiple length scales. This presentation will demonstrate some of the key features of NASMAT PrePost on two multiscale problems by quickly displaying and demonstrating connectivity among multiscale results from large datasets.

Python↗

Trajectory Engineering with Modular Patched Conics for Entry Systems and TPS (TEMPEST)

Brief Presenter Biography (35 word limit): Bohdan Wesely is an Aerospace Engineer in the Entry Systems and Technology Division at Ames. He has worked on a variety of projects for NASA including integrated TPS (thermal protection system) flight hardware deliveries and testing services for commercial partners. Introduction: TEMPEST is a new trajectory analysis framework that is designed to fill the gap between dedicated flight mechanics tools and aerothermal and TPS sizing tools. The project started as an SJSU master’s thesis and has since evolved into a general conceptual design tool capable of studying a wide variety of entry problems. Development is ongoing in the Entry Systems and Technology Division at NASA ARC. Why TEMPEST: Space missions involving entry into a planetary atmosphere involve a series of unique requirements across multiple disciplines. Whether it is traditional entry descent and landing (EDL), or aerocapture, the vehicle must navigate to its target landing location or orbit state, and the TPS must protect the payload during entry. The design process typically involves iterative handoffs between various flight mechanics, flow solver, and material response level tools. During the early conceptual phase, a wide variety of feasible trajectories are simulated in a Monte Carlo scenario which broadly satisfy the mission or landing requirements. Next, computational fluid dynamics (CFD), direct simulation Monte Carlo (DSMC), and other flow solver analyses are performed at various key trajectory points to generate an aero-database, heating and TPS design requirements also emerge at this stage. At this point, with updated aerodynamics from the various flow solvers, trajectories can be re-run, this in turn can change the required freestream conditions for the CFD tools, and as a project progresses, these analyses converge, and uncertainty is reduced. However, there is always a “hand-off” occurring between two inherently coupled phenomena. Analysis Description: One of the goals with TEMPEST is to use a variety of first principles estimation methods coupled with an atmosphere model to predict vehicle aerothermodynamics across the entire flight regime while propagating a 3 or 6 degree of freedom (DoF) trajectory. Aerodynamics methods include modified Newtonian, Maxwell and Cercignani- Lampis-Lord (CLL) for continuum, transitional, and free molecular flow regimes. Aerothermodynamics include boundary layer and reference enthalpy methods, and Mutation++ for non-equilibrium chemistry modeling. TEMPEST is also capable of stitching multiple trajectory segments together to study mission scenarios like multi-pass aerocapture and aero-gravity assists. Most of the program is implemented in MATLAB using modern system objects, it relies on several C++ shared libraries for supporting tools like Gmsh, the Global Reference Atmospheric Model (GRAM), and Mutation++. The various first principles aerothermal estimation methods are discretized across either a structured axisymmetric panel mesh or an unstructured tri-mesh generated from an open-source tool such as Gmsh, this allows solutions on the same mesh to be compared across tools such as CB-Aero. CFD Coupling. A physics-aware, gaussian process CFD anchoring scheme is proposed to adjust the various first principles methods as a CFD database is populated. One goal for this anchoring module is to inform the project where CFD should be run. Full knowledge of the entire trajectory, atmosphere, and aerothermodynamics allows for easier identification of high sensitivity areas and uncertainty quantification. While the first principles effects are well known and proven accurate in existing tools such as CB- Aero and Cart3D, a physics aware CFD anchoring scheme increases tool credibility across a project lifecycle. Material Response Modeling. Correct TPS sizing is critical for optimizing mass for science payloads and ensuring mission success. The process typically involves a thermal analysis along the trajectory with surface heating environments as a boundary condition. Several design constraints are maximum bondline temperature and maximum recession with various margining techniques. The material response tool FIAT, developed out of NASA Ames, is currently being integrated into the TEMPEST environment. TPS recession, shape change, mass loss, and mass property alteration are all factors that can perturb an entry trajectory. For missions like Mars 2020, recession was minimal and was safely handled separately as a post process. For missions such as Jupiter Galileo with a high TPS mass fraction or asteroid entries, recession plays a major role. The proposed fully coupled scheme is to use an epoch-based approach where the trajectory integration is halted after a recession threshold, the energy balance and FIAT are solved at each panel, the mesh, aerodynamics, and mass properties are updated, and the trajectory continues. Several computational tradeoffs have been made during the development of TEMPEST to limit the cost of a single trajectory and preserve its utility as a conceptual, rapid iteration tool. Conclusion: Development of TEMPEST is ongoing and the project is still in its infancy. This talk aims to showcase its unique capabilities to support future NASA entry systems missions.

Bohdan O Wesely↗

Thermochemically-Closed Sonic-Flow Inversion for Enthalpy and Temperature in Multispecies Arc-Jet Flows

A thermochemically-closed sonic-flow inversion framework (TSIF) is developed to infer bulk enthalpy and total temperature upstream of a choked nozzle in arc-jet flows. The formulation recasts a pressure-rise total enthalpy quantification technique as an inverse problem in characteristic-velocity c * space using measured mass flow rate, upstream total pressure, gas composition, and nozzle throat geometry as inputs. Unlike calorimetric energy-balance approaches or optical diagnostics, the method relies primarily on routinely measured facility quantities combined with explicit thermochemical closure. Thermochemical states are obtained using NASA’s open-source Chemical Equilibrium with Applications (CEA) code, enabling construction of a chemistry-consistent relation between characteristic velocity, total enthalpy, and total temperature under equilibrium or frozen assumptions. A discharge coefficient is self-calibrated using cold-flow (arc-off) operation data and applied to hot-flow (arc-on) measurements, enabling upstream losses to be accounted for without empirical correlations. The framework is applied to air, N 2 , and CO 2 –N 2 arc-jet flows and demonstrates expected trends for the inferred thermochemical states as function of arc power, specific energy input, mass-flow, heater configuration, and test gas. In the air limit, under equilibrium assumptions, the method recovers the classical high-enthalpy asymptotic correlation of Winovich with a mean residual of 4.4%, demonstrating compatibility with established sonic-flow scaling, while extending applicability to arbitrary multi-species mixtures and non-equilibrium chemistry. The framework provides a mixture-flexible methodology for determining bulk thermochemical states in modern arc-jet environments using routine facility pressure, mass-flow, gas-composition, and nozzle-geometry information together with a cold-flow calibration.

stagnation heat flux↗

Thermochemically-Closed Sonic-Flow Inversion for Enthalpy and Temperature in Multispecies Arc-Jet Flows

A thermochemically-closed sonic-flow inversion framework (TSIF) is developed to infer bulk enthalpy and total temperature upstream of a choked nozzle in arc-jet flows. The formulation recasts a pressure-rise total enthalpy quantification technique as an inverse problem in characteristic-velocity c * space using measured mass flow rate, upstream total pressure, gas composition, and nozzle throat geometry as inputs. Unlike calorimetric energy-balance approaches or optical diagnostics, the method relies primarily on routinely measured facility quantities combined with explicit thermochemical closure. Thermochemical states are obtained using NASA’s open-source Chemical Equilibrium with Applications (CEA) code, enabling construction of a chemistry-consistent relation between characteristic velocity, total enthalpy, and total temperature under equilibrium or frozen assumptions. A discharge coefficient is self-calibrated using cold-flow (arc-off) operation data and applied to hot-flow (arc-on) measurements, enabling upstream losses to be accounted for without empirical correlations. The framework is applied to air, N 2 , and CO 2 –N 2 arc-jet flows and demonstrates expected trends for the inferred thermochemical states as function of arc power, specific energy input, mass-flow, heater configuration, and test gas. In the air limit, under equilibrium assumptions, the method recovers the classical high-enthalpy asymptotic correlation of Winovich with a mean residual of 4.4%, demonstrating compatibility with established sonic-flow scaling, while extending applicability to arbitrary multi-species mixtures and non-equilibrium chemistry. The framework provides a mixture-flexible methodology for determining bulk thermochemical states in modern arc-jet environments using routine facility pressure, mass-flow, gas-composition, and nozzle-geometry information together with a cold-flow calibration.

inviscid theory↗

Advancing Wildfire Monitoring with TEMPO and ML tools: Hourly Smoke and Fire‑Front Mapping and Near‑Surface NO₂ Predictions

Wildfires impose substantial impacts on communities and regions downwind of wildfire smoke. We present a TEMPO‑enabled workflow that generates value‑added Level‑3 smoke‑plume masks and fire‑front maps for large wildfires, such as 2024 Park Fire, using the self‑supervised deep learning system SIT‑FUSE, along with near‑surface NO₂ predictions produced by a foundation model (Microsoft Aurora). We conclude by outlining a roadmap for expanding these capabilities to additional Western U.S. wildfire events and for delivering actionable tools to stakeholders. This open-source, reproducible workflow provides a scalable framework for cross-agency wildfire monitoring to overcome traditional limitations in smoke-cloud discrimination and air-quality forecasting by incorporating TEMPO data and beyond.

Xiaohua Pan↗

Subscale Hardware-In-The-Loop Results for Hybrid Electric Turbofan Controls Use Cases

NASA is investigating hybrid electric turbine engine systems for commercial transport aircraft due to the potentially significant improvements hybrid electric technology offers in performance, fuel consumption, and operational and design flexibility. Recently, the technology has been tested at full scale in partnership with industry and advanced to Technology Readiness Level 4. This presentation will focus on a recent subscale hardware-in-the-loop test of an open source turbofan engine model developed by NASA. The Advanced Geared Turbofan 30,000 lbf – electrified (AGTF30-e) engine is used as a reference model to demonstrate control system design and use cases for an example mild hybrid electric system with no large-scale energy storage. This model is run in real-time in NASA’s Hybrid Propulsion Emulation Rig (HyPER) and is used to drive an emulation of the turbomachinery system using subscale electric machines. This dynamic scaled shaft emulation interacts with a subscale (<100 kW) hybrid system consisting of electric machines, motor controllers, and a programmable electronic load. Specific use cases demonstrated include the use of Turbine Electrified Energy Management to improve operation during transients, megawatt-scale power extraction from the AGTF30-e, and power transfer between engine spools. Results related to the effectiveness of hybrid systems are qualitatively compared to results from industry testing.

Hybrid↗

Influence of Growth Parameters and Annealing on Properties of MBE Grown GaAsSbN SQWs

In this paper we report the growth of GaAsSbN/GaAs single quantum well (SQW) heterostructures by molecular beam epitaxy (MBE) and their properties. A systematic study has been carried out to determine the effect of growth conditions, such as the source shutter opening sequence and substrate temperature, on the structural and optical properties of the layers. The substrate temperatures in the range of 450-470 C were found to be optimal. Simultaneous opening of the source shutters (SS) resulted in N incorporation almost independent of substrate temperature and Sb incorporation higher at lower substrate temperatures. The effects of ex-situ annealing in nitrogen ambient and in-situ annealing under As ovepressure on the optical properties of the layers have also been investigated. A significant increase in photoluminescence (PL) intensity with reduced full width at half maxima (FWHM) in conjunction with a blue shift in the emission energy was observed on 10 annealing the samples. In in-situ annealed samples, the PL line shapes were more symmetric and the temperature dependence of the PL peak energy indicated significant decrease in the exciton localization energy as exhibited by a less pronounced S-shaped curve. The inverted S-shaped curve observed in the temperature dependence of PL FWHM is also discussed. 1.61 micrometer emission with FWHM of 25 meV at 20K has been obtained in in-situ annealed GaAsSbN/GaAs SQW grown at 470 C by SS.

Wu, Liangjin↗

Growth and Properties of Lattice Matched GaAsSbN Epilayer on GaAs for Solar Cell Applications

The growth and properties of GaAsSbN single quantum wells (SQWs) are investigated in this work. The heterostructures were grown on GaAs substrates in an elemental solid source molecular beam epitaxy (MBE) system assisted with a RF plasma nitrogen source. A systematic study has been carried out to determine the influence of various growth conditions, such as the growth temperature and the source shutter-opening sequence, on the quality of the grown layers and the incorporation of N and Sb. The effects of ex situ and in situ annealing under As overpressure on the optical properties of the layers have also been investigated. Substrate temperature in the range of 450-470 C was found to be optimum. Simultaneous opening of the source shutters was found to yield sharper QW interfaces. N and Sb incorporations were found to depend strongly upon substrate temperatures and source shutter opening sequences. A significant increase in PL intensity with a narrowing of PL line shape and blue shift in emission energy were observed on annealing the GaAsSbN/GaAs SQW, with in situ annealing under As overpressure providing better results, compared to ex situ annealing.

Bharatan, Sudhakar↗

NASA’s Satellite Needs Working Group Management Office: Developing Solutions in an Agile, Open Science Environment

Every two years, the National Aeronautics and Space Administration (NASA) leads an assessment of U.S. Federal civilian agency Earth observation needs submitted through the Satellite Needs Working Group (SNWG) survey. In four survey cycles beginning in 2016, nearly 400 high-priority satellite needs have been identified, spanning Earth Science and representing a wide variety of potential applications for Earth observation data. During each assessment cycle, new data products and services (i.e., solutions) that meet the needs of multiple agencies are identified and proposed for funding. The majority of solutions being developed or currently operational are global in scope, including harmonized land surface reflectance data from Landsat and Sentinel-2; composites of cloud properties derived from MODIS, VIIRS, and five geostationary satellites; dynamic surface water extent and land surface disturbance products derived from multiple optical and radar missions; a suite of low-latency products from the ICESat-2 mission; and a soil moisture product derived from the upcoming NISAR mission. The SNWG Management Office, within the Earth Action element of NASA’s Earth Science Division, manages both the biennial SNWG survey assessment and the development of solutions starting at full capacity with the 2020 cycle. Each solution project is required to align with NASA’s open science policy, including developing source code in an open code repository, having an open-source software license, and making all data freely available via NASA’s Earthdata website. The presentation will include an overview of the SNWG process, its emphasis on open science, and highlight several operational solutions freely available to the global research and applications communities.

Katrina Virts↗

Tip Vortex and Wake Characteristics of a Counterrotating Open Rotor

One of the primary noise sources for Open Rotor systems is the interaction of the forward rotor tip vortex and blade wake with the aft rotor. NASA has collaborated with General Electric on the testing of a new generation of low noise, counterrotating Open Rotor systems. Three-dimensional particle image velocimetry measurements were acquired in the intra-rotor gap of the Historical Baseline blade set. The velocity measurements are of sufficient resolution to characterize the tip vortex size and trajectory as well as the rotor wake decay and turbulence character. The tip clearance vortex trajectory is compared to results from previously developed models. Forward rotor wake velocity profiles are shown. Results are presented in a form as to assist numerical modeling of Open Rotor system aerodynamics and acoustics.

VanZante, Dale E.↗

Tip Vortex and Wake Characteristics of a Counterrotating Open Rotor

One of the primary noise sources for Open Rotor systems is the interaction of the forward rotor tip vortex and blade wake with the aft rotor. NASA has collaborated with General Electric on the testing of a new generation of low noise, counterrotating Open Rotor systems. Three-dimensional particle image velocimetry measurements were acquired in the intra-rotor gap of the Historical Baseline blade set. The velocity measurements are of sufficient resolution to characterize the tip vortex size and trajectory as well as the rotor wake decay and turbulence character. The tip clearance vortex trajectory is compared to results from previously developed models. Forward rotor wake velocity profiles are shown. Results are presented in a form as to assist numerical modeling of Open Rotor system aerodynamics and acoustics.

Van Zante, Dale E.↗