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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 739 records · Page 41
All sourcesNASA NTRSDOE OSTI
NASA NTRS2006

Phase Calibration of Microphones by Measurement in the Free-field

Over the past several years, significant effort has been expended at NASA Langley developing new Micro-Electro-Mechanical System (MEMS)-based microphone directional array instrumentation for high-frequency aeroacoustic measurements in wind tunnels. This new type of array construction solves two challenges which have limited the widespread use of large channel-count arrays, namely by providing a lower cost-per-channel and a simpler method for mounting microphones in wind tunnels and in field-deployable arrays. The current generation of array instrumentation is capable of extracting accurate noise source location and directivity on a variety of airframe components using sophisticated data reduction algorithms [1-2]. Commercially-available MEMS microphones are condenser-type devices and have some desirable characteristics when compared with conventional condenser-type microphones. The most important advantages of MEMS microphones are their size, price, and power consumption. However, the commercially-available units suffer from certain important shortcomings. Based on experiments with array prototypes, it was found that both the bandwidth and the sound pressure limit of the microphones should be increased significantly to improve the performance and flexibility of the microphone array [3]. It was also desired to modify the packaging to eliminate unwanted Helmholtz resonance s exhibited by the commercial devices. Thus, new requirements were defined as follows: Frequency response: 100 Hz to 100 KHz (+/-3dB) Upper sound pressure limit: Design 1: 130 dB SPL (THD less than 5%) Design 2: 150-160 dB SPL (THD less than 5%) Packaging: 3.73 x 6.13 x 1.3 mm can with laser-etched lid. In collaboration with Novusonic Acoustic Innovation, NASA modified a Knowles SiSonic MEMS design to meet these new requirements. Coupled with the design of the enhanced MEMS microphones was the development of a new calibration method for simultaneously obtaining the sensitivity and phase response of the devices over their entire broadband frequency range. Traditionally, electrostatic actuators (EA) have been used to characterize air-condenser microphones; however, MEMS microphones are not adaptable to the EA method due to their construction and very small diaphragm size [4]. Hence a substitution based, free-field method was developed to calibrate these microphones at frequencies up to 80 kHz. The technique relied on the use of a random, ultrasonic broadband centrifugal sound source located in a small anechoic chamber. The free-field sensitivity (voltage per unit sound pressure) was obtained using the procedure outlined in reference 4. Phase calibrations of the MEMS microphones were derived from cross spectral phase comparisons between the reference and test substitution microphones and an adjacent and invariant grazing-incidence 1/8-inch standard microphone. The free-field calibration procedure along with representative sensitivity and phase responses for the new high-frequency MEMS microphones are presented here.

Shams, Qamar A.↗
DOE OSTI2025

Toward Drilling the Perfect Geothermal Well: An International Research Coordination Network for Geothermal Drilling Optimization Supported by Deep Machine Learning and Cloud Based Data Aggregation

The EDGE project, supported by the U.S. Department of Energy Geothermal Technologies Office under award DE-EE0008793, established a data-driven framework for improving the efficiency, cost-effectiveness, and reliability of geothermal well drilling. The project focused on developing scalable data infrastructure, advanced machine learning and probabilistic models, and integrated analytics tools to support continuous drilling optimization. A central objective was to reduce geothermal drilling costs by up to seventy percent while minimizing the risk of well failure through predictive diagnostics and adaptive planning. Over the project period, a comprehensive data repository was designed and deployed, incorporating records from over one hundred geothermal wells across varied geological settings. This repository supported both structured and unstructured data and adhered to FAIR data principles, enabling provenance tracking, quality control, and standardized metadata. The project introduced automated ingestion pipelines and a cloud-hosted platform that facilitated access to raw, processed, and derived datasets. This infrastructure served as the foundation for model development and analysis. Machine learning workflows were developed to predict key drilling metrics including rate of penetration, non-productive time, and total drilling costs. Self-organizing maps and dimensionality reduction methods were used to uncover operational patterns and outliers, while supervised learning algorithms such as random forests and deep neural networks were applied to forecast performance outcomes. The models were validated on heterogeneous datasets from both U.S. and Icelandic fields, demonstrating variable but significant predictive accuracy. The results indicated that finer temporal resolution, inclusion of lithological data, and consistency in operational annotations could substantially improve model performance. The project also implemented process mining techniques to reconstruct state-transition models from drilling event logs. These models enabled the identification of deviations from optimal workflows and provided insights into recurring failure modes. Analysis of non-productive time highlighted the impact of equipment failures, geological challenges, and human factors, offering opportunities for targeted mitigation strategies. The EDGE Dashboard was developed as a web-based expert system integrating data visualization, model outputs, and user-driven queries. It provided an accessible interface for operators to explore historical data, evaluate predicted outcomes, and compare drilling scenarios. Initial feedback from project partners suggested that the dashboard could serve as a foundation for more advanced advisory and optimization tools. Overall, the EDGE project demonstrated the feasibility and value of applying modern data science techniques to geothermal drilling. It delivered a set of interoperable tools and models that can support more efficient, lower-risk well development. The findings point toward a viable path for transitioning from advisory analytics to semi-autonomous drilling systems, contingent on continued collaboration, expanded datasets, and field validation. The project results have immediate relevance for drilling operations, data management practices, and future geothermal R&D efforts aimed at achieving reliable, cost-competitive geothermal energy at scale.

15 GEOTHERMAL ENERGY↗
NASA NTRS2022

NASA Engineering and Safety Center Technical Bulletin No. 22-06: Treatment of Slosh Stability Margin Reductions for Human-Rated Launch Vehicles

Slosh dynamics pose a stability concern for human-rated launch vehicles during ascent. Historical perspectives on the treatment of slosh dynamics, newly developed rules of thumb, the utility of flight data, and methods for analyzing and dispositioning slosh instability risks should be considered when linear stability margins are lower than typically accepted for human-rated systems.

Fuel Slosh↗
NASA NTRS1978

Measurement of flow angle and mass flow rate in an unknown flow field using hot film and hot wire probes

A data reduction technique applicable to constant temperature hot film and hot wire probes is presented which is used to determine flow angle and mass flow rate in an unknown flow field over a wide range of flow conditions at supersonic and hypersonic velocities. The technique virtually eliminates the effect of Reynolds number on a probe's flow angle sensitivity. Methods for extrapolating a limited amount of mass flow rate calibration data to include the range of mass flow rate encountered in an experiment are also given. This technique has been applied to data obtained using a hot film probe during surveys in the leeside flow field of a space shuttle orbiter configuration.

Helms, V. T., III↗
DOE OSTI2024

Tracking the topology of neural manifolds across populations

Neural manifolds summarize the intrinsic structure of the information encoded by a population of neurons. Advances in experimental techniques have made simultaneous recordings from multiple brain regions increasingly commonplace, raising the possibility of studying how these manifolds relate across populations. However, when the manifolds are nonlinear and possibly code for multiple unknown variables, it is challenging to extract robust and falsifiable information about their relationships. We introduce a framework, called the method of analogous cycles, for matching topological features of neural manifolds using only observed dissimilarity matrices within and between neural populations. We demonstrate via analysis of simulations and in vivo experimental data that this method can be used to correctly identify multiple shared circular coordinate systems across both stimuli and inferred neural manifolds. Conversely, the method rejects matching features that are not intrinsic to one of the systems. Further, as this method is deterministic and does not rely on dimensionality reduction or optimization methods, it is amenable to direct mathematical investigation and interpretation in terms of the underlying neural activity. We thus propose the method of analogous cycles as a suitable foundation for a theory of cross-population analysis via neural manifolds.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

X-ray Absorption Spectroscopy Studies of a Molecular CO 2 -Reduction Catalyst Deposited on Graphitic Carbon Nitride

Metal-ligand complexes have been extensively explored as well-defined molecular catalysts in small molecule activation reactions such as carbon dioxide (CO 2 ) reduction. Many hybrid photocatalysts have been prepared by coupling such complexes with photoactive surfaces for use in solar CO 2 reduction. In this work, we employ X-ray absorption near edge structure (XANES) and extended X-ray absorption fine structure (EXAFS) spectroscopies, density functional theory (DFT) and computational XANES modeling to interrogate the structure of a hybrid photocatalyst consisting of a macrocyclic cobalt complex deposited on graphitic carbon nitride (C 3 N 4 ). Results show that the cobalt complex binds on C 3 N 4 through surface OH or NH 2 groups. By refining the local geometry and binding sites of this well-defined molecular cobalt complex on C 3 N 4 , here we established an important benchmark for modeling a large class of molecular catalysts that can be adapted to in situ/operando studies and further enhanced by applying chemometrics-based approaches and machine learning methods of XANES data analysis.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Accelerating Ultrafast Spectroscopy with Compressive Sensing

We report ultrafast spectroscopy is an important tool for studying photoinduced dynamical processes in atoms, molecules, and nanostructures. Typically, the time to perform these experiments ranges from several minutes to hours depending on the choice of spectroscopic method. It is desirable to reduce this time overhead not only to shorten time and laboratory resources, but also to make it possible to examine fragile specimens that quickly degrade during long experiments. In this article, we motivate using compressive sensing to significantly shorten data acquisition time by reducing the total number of measurements in ultrafast spectroscopy. We apply this technique to experimental data from ultrafast transient absorption spectroscopy and ultrafast terahertz spectroscopy and show that good estimates can be obtained with as low as 15% of the total measurements, implying a sixfold reduction in the data acquisition time.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2025

Cluster spin glass correlations and dynamics in Zn 0.5⁢ Mn 0.5⁢ Te

Here, we present a combined magnetometry, muon spin-relaxation (𝜇⁢SR), and neutron-scattering study of the insulating spin glass Zn 0.5 ⁢Mn 0.5 ⁢Te, for which magnetic Mn 2+ and nonmagnetic Zn 2+ ions are randomly distributed on a face-centered cubic lattice. The magnetometry and 𝜇⁢SR results confirm a spin freezing transition around 𝑇 𝑓 ≈ 23 K, with the spin-fluctuation rate decreasing gradually and somewhat inhomogeneously through the sample volume as the temperature decreases toward 𝑇 𝑓 . Characteristic spin-correlation times well above 𝑇 𝑓 are on the order of 10 −10 s, much slower than typically observed in canonical spin glasses but in line with expectations for a cluster spin glass. Using magnetic pair distribution function (mPDF) analysis and reverse Monte Carlo (RMC) modeling of the magnetic diffuse neutron-scattering data, we show that the spin-glass ground state consists of clusters of spins exhibiting short-range-ordered type-III antiferromagnetic correlations with a locally ordered moment of 3.1⁢(1)⁢𝜇 B between nearest-neighbor spins. The type-III correlations decay exponentially as a function of spin separation distance with a correlation length of approximately 5 Å. The diffuse magnetic scattering and corresponding mPDF show no significant changes across 𝑇 𝑓 , indicating that the dynamically fluctuating short-range spin correlations in the paramagnetic state retain the same basic type-III configuration that characterizes the spin-glass state; the only change apparent from the neutron-scattering data is a gradual reduction of the correlation length and locally ordered moment with increasing temperature. Taken together, these results paint a unique and detailed picture of the local magnetic structure and dynamics in Zn 0.5 ⁢Mn 0.5⁢ Te and provide strong evidence that this material is best described as a cluster spin glass. In addition, this work showcases a statistical method for extracting diffuse scattering signals from neutron powder diffraction data, which we developed to facilitate the mPDF and RMC analysis of the neutron data. This method has the potential to be broadly useful for neutron powder diffraction experiments on a variety of materials with short-range atomic or magnetic order.

magnetism↗
DOE OSTI2026

Integration of GOES Data for Solar Resource Assessment of the Contiguous United States

The National Solar Radiation Database (NSRDB), produced by the National Laboratory of the Rockies (NLR), provides high-resolution solar resource data for the contiguous United States (CONUS) using Geostationary Operational Environmental Satellite (GOES) East and West observations. This study evaluates the integration of multi-satellite data within the GOES-East/West overlap regions, where conventional longitude-based selection methods often produce an artificial boundary seam. Our results demonstrate that an advanced blending algorithm, which incorporates sun-satellite scattering angles and satellite viewing zenith angles, improves NSRDB accuracy and creates a spatially continuous dataset. Validation against ground-based irradiance measurements reveals reductions in both percentage error (PE) and normalized Root Mean Square Error (nRMSE), particularly in the central United States. The dynamical integration of multi-satellite data provides a robust foundation for more precise modeling of solar resource and improved spatiotemporal analysis of solar ramp across the CONUS.

14 SOLAR ENERGY↗
NASA NTRS2013

Semi-Empirical Validation of the Cross-Band Relative Absorption Technique for the Measurement of Molecular Mixing Ratios

Studies were performed to carry out semi-empirical validation of a new measurement approach we propose for molecular mixing ratios determination. The approach is based on relative measurements in bands of O2 and other molecules and as such may be best described as cross band relative absorption (CoBRA). . The current validation studies rely upon well verified and established theoretical and experimental databases, satellite data assimilations and modeling codes such as HITRAN, line-by-line radiative transfer model (LBLRTM), and the modern-era retrospective analysis for research and applications (MERRA). The approach holds promise for atmospheric mixing ratio measurements of CO2 and a variety of other molecules currently under investigation for several future satellite lidar missions. One of the advantages of the method is a significant reduction of the temperature sensitivity uncertainties which is illustrated with application to the ASCENDS mission for the measurement of CO2 mixing ratios (XCO2). Additional advantages of the method include the possibility to closely match cross-band weighting function combinations which is harder to achieve using conventional differential absorption techniques and the potential for additional corrections for water vapor and other interferences without using the data from numerical weather prediction (NWP) models.

Pliutau, Denis↗
DOE OSTI2020

A comparison of cost and quality of three methods for estimating density for wild pig (Sus scrofa)

A critical element in efective wildlife management is monitoring the status of wildlife populations; however, resources to monitor wildlife populations are typically limited. We compared cost efectiveness of three common population estimation methods (i.e. non-invasive DNA sampling, camera sampling, and sampling from trapping) by applying them to wild pigs (Sus scrofa) across three habitats in South Carolina, U.S.A where they are invasive. We used mark-recapture analyses for fecal DNA sampling data, spatially-explicit capture-recapture analyses for camera sampling data, and a removal analysis for removal sampling from trap data. Density estimates were similar across methods. Camera sampling was the least expensive, but had large variances. Fecal DNA sampling was the most expensive, although this technique generally performed well. We examined how reductions in efort by method related to increases in relative bias or imprecision. For removal sampling, the largest cost savings while maintaining unbiased density estimates was from reducing the number of traps. For fecal DNA sampling, a reduction in efort only minimally reduced costs due to the need for increased lab replicates while maintaining high quality estimates. For camera sampling, efort could only be marginally reduced before inducing bias. We provide a decision tree for researchers to help make monitoring decisions.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Unlocking the potential: machine learning applications in electrocatalyst design for electrochemical hydrogen energy transformation

Machine learning (ML) is rapidly emerging as a pivotal tool in the hydrogen energy industry for the creation and optimization of electrocatalysts, which enhance key electrochemical reactions like the hydrogen evolution reaction (HER), the oxygen evolution reaction (OER), the hydrogen oxidation reaction (HOR), and the oxygen reduction reaction (ORR). This comprehensive review demonstrates how cutting-edge ML techniques are being leveraged in electrocatalyst design to overcome the time-consuming limitations of traditional approaches. ML methods, using experimental data from high-throughput experiments and computational data from simulations such as density functional theory (DFT), readily identify complex correlations between electrocatalyst performance and key material descriptors. Leveraging its unparalleled speed and accuracy, ML has facilitated the discovery of novel candidates and the improvement of known products through its pattern recognition capabilities. This review aims to provide a tailored breakdown of ML applications in a format that is readily accessible to materials scientists. Hence, we comprehensively organize ML-driven research by commonly studied material types for different electrochemical reactions to illustrate how ML adeptly navigates the complex landscape of descriptors for these scenarios. We further highlight ML's critical role in the future discovery and development of electrocatalysts for hydrogen energy transformation. Potential challenges and gaps to fill within this focused domain are also discussed. As a practical guide, we hope this work will bridge the gap between communities and encourage novel paradigms in electrocatalysis research, aiming for more effective and sustainable energy solutions.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
NASA NTRS1980

Modal cost analysis for linear matrix-second-order systems

Reduced models and reduced controllers for systems governed by matrix-second-order differential equations are obtained by retaining those modes which make the largest contributions to quadratic control objectives. Such contributions, expressed in terms of modal data, used as mode truncation criteria, allow the statement of the specific control objectives to influence the early model reduction from very high order models which are available, for example, from finite element methods. The relative importance of damping, frequency, and eigenvector in the mode truncation decisions are made explicit for each of these control objectives: attitude control, vibration suppression and figure control. The paper also shows that using modal cost analysis (MCA) on the closed loop modes of the optimally controlled system allows the construction of reduced control policies which feedback only those closed loop modal coordinates which are most critical to the quadratic control performance criterion. In this way, the modes which should be controlled (and hence the modes which must be observable by choice of measurements), are deduced from truncations of the optimal controller.

Skelton, R. E.↗
NASA NTRS2024

Urban Air Quality Management at Low-Cost Using Micro Air Sensors: A Case Study From Accra, Ghana

Urban air quality management is dependent on the availability of local air pollution data. In many major urban centers of Africa, there is limited to non-existent information on air quality. This is gradually changing in part due to the increasing use of micro air sensors which have the potential to enable the generation of ground-based air quality data at fine scales for understanding local emission trends. Regional literature on the application of the high-resolution data for emission source identification in this region is limited. In this study a micro air sensor was co-located at the Physics Department, University of Ghana with a reference grade instrument to evaluate its performance for estimating PM2.5 pollution accurately at fine scales and the value of this data in identification of local sources and their behavior over time. For this study 15 weeks of data at hourly resolution with approximately 2500 data pairs are generated and analyzed (June 01, 2023, to September 15, 2023). For this time period a coefficient of determination (r 2 ) of 0.83 was generated with a mean absolute error (MAE) of 5.44 μgm -3 between the pre local calibration micro air sensor (i.e. out of box) and the reference-grade instrument. Following currently accepted best practice methods (see e.g., PAS4023) a domain specific (i.e. local) calibration factor was generated using a multi-linear regression model and when this factor is applied to the micro air sensor data, a reduction i.e., improvement in MAE to 1.43 μgm -3 was found. Daily variation was calculated, a receptor model was applied, and time series plots as a function of wind direction were generated, including PM2.5/PM10 ratio scatter and count plots to explore the utility of this observational approach for local source identification. The 3 data sets were compared (out of box, domain calibrated and reference-grade) and it was found that although there were variations in the data reported, source areas highlighted based on these data were similar, with input from local sources such as traffic emissions and biomass burning. As the temporal resolution of observational data associated with these micro air sensors is higher than for reference grade instruments (primarily due to costs and logistics limitations), they have the potential to provide insight into the complex, often hyper localized sources associated with urban areas, such as those found in major African cities.

Source apportionment↗
NASA NTRS2015

Measurements and Predictions of the Noise from Three-Stream Jets

An experimental and numerical investigation of the noise produced by high-subsonic and supersonic three-stream jets was conducted. The exhaust system consisted of externally-mixed-convergent nozzles and an external plug. Bypass- and tertiary-to-core area ratios between 1.0 and 2.5, and 0.4 and 1.0, respectively, were studied. Axisymmetric and offset tertiary nozzles were investigated for heated and unheated conditions. For axisymmetric configurations, the addition of the third stream was found to reduce peak- and high-frequency acoustic levels in the peak-jet-noise direction, with greater reductions at the lower bypass-to-core area ratios. For the offset configurations, an offset duct was found to decrease acoustic levels on the thick side of the tertiary nozzle relative to those produced by the simulated two-stream jet with up to 8 dB mid-frequency noise reduction at large angles to the jet inlet axis. Noise reduction in the peak-jet-noise direction was greater for supersonic core speeds than for subsonic core speeds. The addition of a tertiary nozzle insert used to divert the third-stream jet to one side of the nozzle system provided no noise reduction. Noise predictions are presented for selected cases using a method based on an acoustic analogy with mean flow interaction effects accounted for using a Green's function, computed in terms of its coupled azimuthal modes for the offset cases, and a source model previously used for round and rectangular jets. Comparisons of the prediction results with data show that the noise model predicts the observed increase in low-frequency noise with the introduction of a third, axisymmetric stream, but not the high-frequency reduction. For an offset third stream, the model predicts the observed trend of decreased sound levels on the thick side of the jet compared with the thin side, but the predicted azimuthal variations are much less than those seen in the data. Also, the shift of the spectral peak to lower frequencies with increasing polar angle is over-predicted. For an offset third stream with a heated core, it is shown that including the enthalpy-flux source terms in the acoustic analogy model improves predictions compared with those obtained using only the momentum flux.

Three-Stream Jets↗
NASA NTRS2015

Measurements and Predictions of the Noise from Three-Stream Jets

An experimental and numerical investigation of the noise produced by high-subsonic and supersonic three-stream jets was conducted. The exhaust system consisted of externally-mixed-convergent nozzles and an external plug. Bypass- and tertiary- to-core area ratios between 1.0 and 2.5, and 0.4 and 1.0, respectively, were studied. Axisymmetric and offset tertiary nozzles were investigated for heated and unheated conditions. For axisymmetric configurations, the addition of the third stream was found to reduce peak- and high-frequency acoustic levels in the peak-jet-noise direction, with greater reductions at the lower bypass-to-core area ratios. For the offset configurations, an offset duct was found to decrease acoustic levels on the thick side of the tertiary nozzle relative to those produced by the simulated two-stream jet with up to 8 dB mid-frequency noise reduction at large angles to the jet inlet axis. Noise reduction in the peak-jet-noise direction was greater for supersonic core speeds than for subsonic core speeds. The addition of a tertiary nozzle insert used to divert the third-stream jet to one side of the nozzle system provided no noise reduction. Noise predictions are presented for selected cases using a method based on an acoustic analogy with mean flow interaction effects accounted for using a Green's function, computed in terms of its coupled azimuthal modes for the offset cases, and a source model previously used for round and rectangular jets. Comparisons of the prediction results with data show that the noise model predicts the observed increase in low-frequency noise with the introduction of a third, axisymmetric stream, but not the high-frequency reduction. For an offset third stream, the model predicts the observed trend of decreased sound levels on the thick side of the jet compared with the thin side, but the predicted azimuthal variations are much less than those seen in the data. Also, the shift of the spectral peak to lower frequencies with increasing polar angle is over-predicted. For an offset third stream with a heated core, it is shown that including the enthalpy-flux source terms in the acoustic analogy model improves predictions compared with those obtained using only the momentum- flux.

Three-Stream Jets↗
NASA NTRSDate not supplied

More Data Needed for Failure Rate Estimation, Validation, and Uncertainty Reduction

Current Environmental Control and Life Support System (ECLSS) development and test activities are not generating data fast enough to provide statistically-supportable precise Orbital Replacement Unit (ORU) failure rate estimates for future missions. Accurate and precise failure rate estimates are critical for missions beyond Low Earth Orbit (LEO) because current risk mitigation approaches – namely regular resupply and rapid abort capabilities – will not be available. Safe operations will depend on mission planners’ ability to accurately forecast spares demand and efficiently provide the necessary resources. However, even after more than a decade of operations on board the International Space Station (ISS), a significant amount of uncertainty remains in failure rate estimates. Uncertain or inaccurate failure rates result in increased risk and spares mass for future missions. A Bayesian failure rate estimation approach, such as the one currently implemented by the ISS Program, can help reduce uncertainty by incorporating engineering judgement into failure rate estimates. However, experience on the ISS and with other complex systems shows that these prior failure rate estimates are often inaccurate. In addition, prior failure rate estimates are typically point values; some level of uncertainty must be added to convert these into probability distributions for Bayesian updating, and there are several potential methods for doing so. Due to the low rate of data collection, these subjective (and often inaccurate) prior estimates currently have a strong influence on the end result. This paper examines the challenges associated with failure rate estimation, validation, and uncertainty reduction in the context of ECLSS development for beyond-LEO missions. A variety of techniques for generating and updating Bayesian priors are discussed and evaluated using both real-world and simulated data. Potential solutions for improving failure rate estimation, including testing additional units, are analyzed and discussed, and a set of recommendations are provided for next-generation system development activities.

Supportability↗
NASA NTRS1985

Preliminary measurement of the noise from the 2/9 scale model of the Large-scale Advanced Propfan (LAP) propeller, SR-7A

Noise data on the Large-scale Advanced Propfan (LAP) propeller model SR-7A were taken into the NASA Lewis 8- by 6-Foot Wind Tunnel. The maximum blade passing tone decreases from the peak level when going to higher helical tip Mach numbers. This noise reduction points to the use of higher propeller speeds as a possible method to reduce airplane cabin noise while maintaining high flight speed and efficiency. Comparison of the SR-7A blade passing noise with the noise of the similarly designed SR-3 propeller shows good agreement as expected. The SR-7A propeller is slightly noisier than the SR-3 model in the plane of rotation at the cruise condition. Projections of the tunnel model data are made to the full-scale LAP propeller mounted on the test bed aircraft and compared with design predictions. The prediction method is conservative in the sense that it overpredicts the projected model data.

Dittmar, J. H.↗