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NASA NTRSDate not supplied

Improving Radiative Fluxes for the CERES FluxByCldTyp Data Product Using Deep Neural Network

The NASA Clouds and the Earth's Radiant Energy System (CERES) product provides over 20 years of accurately observed top-of-the-atmosphere (TOA) and surface flux data record for climate monitoring and diagnostic studies. The interaction between clouds and radiation interaction is a key factor that dominate climate feedbacks but is not well understood. To further advance our understanding of the cloud-radiation interaction, a new CERES FluxByCldTyp (FBCT) product has been developed that contains radiative fluxes by cloud-type, which can provide more stringent constraints when validating models and reveal more insight into the interactions between clouds and climate. For CERES partly cloudy and multiple cloud-type footprints, the FBCT product utilizes Moderate Resolution Imaging Spectroradiometer (MODIS) narrow-band (NB) imager channel radiances partitioned by cloud-type within a CERES footprint to estimate the cloud-type broadband fluxes. The MODIS multi-channel derived broadband fluxes were compared with the CERES observed footprint fluxes and were found to be within 1% and 2.5% for LW and SW, respectively, as well as being mostly free of cloud property dependencies. The FBCT all-sky and clear-sky monthly averaged fluxes were found to be consistent with the CERES SSF1deg product. This study takes advantage of recent progress in machine learning (ML) field by applying deep neural network algorithm to improve fluxes based on MODIS NB radiances. The preliminary study shows ML produce are an improvement over the current FBCT Edition 4 NB2BB algorithm. Furthermore, unlike Ed4 NB2BB, the new ML method convert NB radiances directly to broadband fluxes. For future Ed5, new NB radiances are proposed and used by ML to improve fluxes calculation. Preliminary results show significant LW improvement.

Sun, Moguo↗

NASA NTRSDate not supplied

Continuing Long-term Global SO 2 Data Record with JPSS OMPS Instruments

NASA’s long-term Earth Observing System (EOS) SO 2 climate data record (CDR) started with Aura/Ozone Monitoring Instrument (OMI, launched in 2004) and is now being continued with the SNPP/Ozone Mapping and Profiler Suite (OMPS, launched in 2011). Both OMI and SNPP/OMPS SO 2 CDRs are produced with the Goddard principal component analysis (PCA) spectral fitting algorithm. By inherently accounting for various instrumental factors, the PCA technique enables highly consistent retrievals between different instruments. In this presentation, we will provide an overview on our effort to further extend the EOS SO 2 CDR, by implementing the PCA SO 2 algorithm with multiple OMPS instruments flying on the Joint Polar Satellite System (JPSS) constellation, including NOAA-20 (launched in 2017) and NOAA-21 (launched in 2022). We will present results analyzing our new NOAA-20/OMPS PCA SO 2 EOS continuity product, to be publicly released in fall of 2023. We will show statistical analyses on the quality of NOAA-20 PCA SO 2 product, such as retrieval noise, biases over background areas, and long-term stability. We will employ a previously established top-down method to estimate SO2 emissions from selected large point sources, using NOAA-20 SO 2 retrievals and assimilated wind fields as input. The SO 2 emission estimates derived from NOAA-20 retrievals will be compared with those from OMI, SNPP/OMPS, and S5P/TROPOMI (TROPOspheric Monitoring Instrument). We will also demonstrate the application of a new machine learning technique that further reduces the noise of NOAA-20 SO 2 retrievals. Finally, we will present preliminary PCA SO 2 retrievals from recently launched satellite sensors, including NOAA-21/OMPS and NASA’s geostationary TEMPO (Tropospheric Emissions: Monitoring of Pollution) instrument.

SO2↗

NASA NTRSDate not supplied

System-Theoretic Analysis of Unsafe Collaborative Control in Teaming Systems

The interactions that occur in human-teaming are inspiring novel aerospace designs aimed at improving how humans and machines, or multiple machines, work together. Unfortunately, current Systems Engineering processes are ill-equipped to handle these complex relationships and are unable to design and assure the safety for these systems. To close part of this gap, this paper introduces a novel system-theoretic analytical process to identify unsafe collaborative control actions. It is part of a broader set of techniques that extend the state-of-the-art in hazard analysis, System Theoretic Process Analysis (STPA), to systematically address collaboration. The method rigorously expresses the different ways multiple commands may be unsafe together. Using Systems Theory, it employs abstraction to manage the combinatorial complexity in enumerating control contributions from multiple collaborating components. An algorithm integrates these concepts into an end-to-end process and is supported by automation to enumerate, refine, prune, and prioritize unsafe combinations of control actions. The output of the method feeds the specification of system requirements to implement safety-guided design starting early in concept development. The process is demonstrated on a manned-unmanned aircraft teaming case study and finds new causal factors that were not previously found in a past hazard analysis of the same system.

System Safety↗

NASA NTRS2012

Performance of Simple Gas Foil Thrust Bearings in Air

Foil bearings are self-acting hydrodynamics devices used to support high speed rotating machinery. The advantages that they offer to process fluid lubricated machines include: high rotational speed capability, no auxiliary lubrication system, non-contacting high speed operation, and improved damping as compared to rigid hydrodynamic bearings. NASA has had a sporadic research program in this technology for almost 6 decades. Advances in the technology and understanding of foil journal bearings have enabled several new commercial products in recent years. These products include oil-free turbochargers for both heavy trucks and automobiles, high speed electric motors, microturbines for distributed power generation, and turbojet engines. However, the foil thrust bearing has not received a complimentary level of research and therefore has become the weak link of oil-free turbomachinery. In an effort to both provide machine designers with basic performance parameters and to elucidate the underlying physics of foil thrust bearings, NASA Glenn Research Center has completed an effort to experimentally measure the performance of simple gas foil thrust bearing in air. The database includes simple bump foil supported thrust bearings with full geometry and manufacturing techniques available to the user. Test conditions consist of air at ambient pressure and temperatures up to 500 C and rotational speeds to 55,000 rpm. A complete set of axial load, frictional torque, and rotational speed is presented for two different compliant sub-structures and inter-pad gaps. Data obtained from commercially available foil thrust bearings both with and without active cooling is presented for comparison. A significant observation made possible by this data set is the speed-load capacity characteristic of foil thrust bearings. Whereas for the foil journal bearing the load capacity increases linearly with rotational speed, the foil thrust bearing operates in the hydrodynamic high speed limit. In this case, the load capacity is constant and in fact often decreases with speed if other factors such as thermal conditions and runner distortions are permitted to dominate the bearing performance.

Bruckner, Robert J.↗

NASA NTRS1998

A Cognitive-System Model for En Route Air Traffic Management

NASA Ames Research Center has been engaged in the development of advanced air traffic management technologies whose basic form is cognitive aiding systems for air traffic controller and flight deck operations. In the design and evaluation of such systems the dynamic interaction between the airborne aiding system and the ground-based aiding systems forms a critical coupling for control. The human operator is an integral control element in the system and the optimal integration of human decision and performance parameters with those of the automation aiding systems offers a significant challenge to cognitive engineering. This paper presents a study in full mission simulation and the development of a predictive computational model of human performance. We have found that this combination of methodologies provide a powerful design-aiding process. We have extended the computational model Man Machine Integrated Design and Analysis System (N13DAS) to include representation of multiple cognitive agents (both human operators and intelligent aiding systems), operating aircraft airline operations centers and air traffic control centers in the evolving airspace. The demands of this application require the representation of many intelligent agents sharing world-models, and coordinating action/intention with cooperative scheduling of goals and actions in a potentially unpredictable world of operations. The operator's activity structures have been developed to include prioritization and interruption of multiple parallel activities among multiple operators, to provide for anticipation (knowledge of the intention and action of remote operators), and to respond to failures of the system and other operators in the system in situation-specific paradigms. We have exercised this model in a multi-air traffic sector scenario with potential conflict among aircraft at and across sector boundaries. We have modeled the control situation as a multiple closed loop system. The inner and outer loop alerting structure of air traffic management has many implications that need to be investigated to assure adequate design. First, there are control and stability factors implicit in the design. As the inner loop response time approaches that of the outer loop, system stability may be compromised in that controllers may be solving a problem the nature of which has already been changed by pilot action. Second, information exchange and information presentation for both air and ground must be designed to complement as opposed to compete with each other. Third, the level of individual and shared awareness in trajectory modification and flight conformance needs to be defined. Fourth, the level of required awareness and performance impact of mixed fleet operations and failed-mode recovery must be explored.

Corker, Kevin M.↗

NASA NTRS2014

The Vibration Ring

The vibration ring was conceived as a driveline damping device to prevent structure-borne noise in machines. It has the appearance of a metal ring, and can be installed between any two driveline components like an ordinary mechanical spacer. Damping is achieved using a ring-shaped piezoelectric stack that is poled in the axial direction and connected to an electrical shunt circuit. Surrounding the stack is a metal structure, called the compression cage, which squeezes the stack along its poled axis when excited by radial driveline forces. The stack in turn generates electrical energy, which is either dissipated or harvested using the shunt circuit. Removing energy from the system creates a net damping effect. The vibration ring is much stiffer than traditional damping devices, which allows it to be used in a driveline without disrupting normal operation. In phase 1 of this NASA Seedling Fund project, a combination of design and analysis was used to examine the feasibility of this concept. Several designs were evaluated using solid modeling, finite element analysis, and by creating prototype hardware. Then an analytical model representing the coupled electromechanical response was formulated in closed form. The model was exercised parametrically to examine the stiffness and loss factor spectra of the vibration ring, as well as simulate its damping effect in the context of a simplified driveline model. The results of this work showed that this is a viable mechanism for driveline damping, and provided several lessons for continued development.

Asnani, Vivake M.↗

NASA NTRS2020

Amino Acid Encoding for Deep Learning Applications

Background: The number of applications of deep learning algorithms in bioinformatics is increasing as they usually achieve superior performance over classical approaches, especially, when bigger training datasets are available. In deep learning applications, discrete data, e.g. words or n-grams in language, or amino acids or nucleotides in bioinformatics, are generally represented as a continuous vector through an embedding matrix. Recently, learning this embedding matrix directly from the data as part of the continuous iteration of the model to optimize the target prediction – a process called ‘end-to-end learning’ – has led to state-of-the-art results in many fields. Although usage of embeddings is well described in the bioinformatics literature, the potential of end-to-end learning for single amino acids, as compared to more classical manually-curated encoding strategies, has not been systematically addressed. To this end, we compared classical encoding matrices, namely one-hot, VHSE8 and BLOSUM62, to end-to-end learning of amino acid embeddings for two different prediction tasks using three widely used architectures, namely recurrent neural networks (RNN), convolutional neural networks (CNN), and the hybrid CNN-RNN. Results: By using different deep learning architectures, we show that end-to-end learning is on par with classical encodings for embeddings of the same dimension even when limited training data is available, and might allow for a reduction in the embedding dimension without performance loss, which is critical when deploying the models to devices with limited computational capacities. We found that the embedding dimension is a major factor in controlling the model performance. Surprisingly, we observed that deep learning models are capable of learning from random vectors of appropriate dimension. Conclusion: Our study shows that end-to-end learning is a flexible and powerful method for amino acid encoding. Further, due to the flexibility of deep learning systems, amino acid encoding schemes should be benchmarked against random vectors of the same dimension to disentangle the information content provided by the encoding scheme from the distinguishability effect provided by the scheme.

Deep-learning↗

NASA NTRS2012

Design and Characterization of Thin Stainless Steel Burst Disks for Increasing Two-Stage Light Gas Launcher Efficiency

Laser etched 300 series Stainless Steel Burst Disks (SSBD) ranging between 0.178 mm (0.007-in.) and 0.508mm (0.020-in.) thick were designed for use in a 17-caliber two-stage light gas launcher. First, a disk manufacturing method was selected using a combination of wire electrical discharge machining (EDM) to form the blank disks and laser etching to define the pedaling fracture pattern. Second, a replaceable insert was designed to go between the SSDB and the barrel. This insert reduced the stress concentration between the SSBD and the barrel, providing a place for the petals of the SSDB to open, and protecting the rifling on the inside of the barrel. Thereafter, a design of experiments was implemented to test and characterize the burst characteristics of SSBDs. Extensive hydrostatic burst testing of the SSBDs was performed to complete the design of experiments study with one-hundred and seven burst tests. The experiment simultaneously tested the effects of the following: two SSBD material states (full hard, annealed); five SSBD thicknesses 0.178, 0.254, 0.305, 0.381 mm (0.007, 0.010, 0.012, 0.015, 0.020-in.); two grain directions relative); number of times the laser etch pattern was repeated (varies between 5-200 times); two heat sink configurations (with and without heat sink); and, two barrel configurations (with and without insert). These tests resulted in the quantification of the relationship between SSBD thickness, laser etch parameters, and desired burst pressure. Of the factors investigated only thickness and number of laser etches were needed to develop a mathematical relationship predicting hydrostatic burst pressure of disks using the same barrel configuration. The fracture surfaces of two representative SSBD bursts were then investigated with a scanning electron microscope, one burst hydrostatically in a fixture and another dynamically in the launcher. The fracture analysis verified that both burst conditions resulted in a ductile overload failure indicated by transgranular microvoid coalescence, non-fragmenting rupture and mixed tensile and shear failure modes, regardless of the material states tested. More testing is underway to determine the relationship between SSBD burst pressure and projectile velocity.

Tylka, Jonathan M.↗

NASA NTRS2000

Autonomous Performance Monitoring System: Monitoring and Self-Tuning (MAST)

Maintaining the long-term performance of software onboard a spacecraft can be a major factor in the cost of operations. In particular, the task of controlling and maintaining a future mission of distributed spacecraft will undoubtedly pose a great challenge, since the complexity of multiple spacecraft flying in formation grows rapidly as the number of spacecraft in the formation increases. Eventually, new approaches will be required in developing viable control systems that can handle the complexity of the data and that are flexible, reliable and efficient. In this paper we propose a methodology that aims to maintain the accuracy of flight software, while reducing the computational complexity of software tuning tasks. The proposed Monitoring and Self-Tuning (MAST) method consists of two parts: a flight software monitoring algorithm and a tuning algorithm. The dependency on the software being monitored is mostly contained in the monitoring process, while the tuning process is a generic algorithm independent of the detailed knowledge on the software. This architecture will enable MAST to be applicable to different onboard software controlling various dynamics of the spacecraft, such as attitude self-calibration, and formation control. An advantage of MAST over conventional techniques such as filter or batch least square is that the tuning algorithm uses machine learning approach to handle uncertainty in the problem domain, resulting in reducing over all computational complexity. The underlying concept of this technique is a reinforcement learning scheme based on cumulative probability generated by the historical performance of the system. The success of MAST will depend heavily on the reinforcement scheme used in the tuning algorithm, which guarantees the tuning solutions exist.

Peterson, Chariya↗

NASA NTRS2008

Single-Mode WGM Resonators Fabricated by Diamond Turning

A diamond turning process has made possible a significant advance in the art of whispering-gallery-mode (WGM) optical resonators. By use of this process, it is possible to fashion crystalline materials into WGM resonators that have ultrahigh resonance quality factors (high Q values), are compact (ranging in size from millimeters down to tens of microns), and support single electromagnetic modes. This development combines and extends the developments reported in "Few- Mode Whispering-Gallery-Mode Resonators" (NPO-41256), NASA Tech Briefs, Vol. 30, No. 1 (January 2006), page 16a and "Fabrication of Submillimeter Axisymmetric Optical Components" (NPO-42056), NASA Tech Briefs, Vol. 31, No. 5 (May 2007), page 10a. To recapitulate from the first cited prior article: A WGM resonator of this special type consists of a rod, made of a suitable transparent material, from which protrudes a thin circumferential belt of the same material. The belt is integral with the rest of the rod and acts as a circumferential waveguide. If the depth and width of the belt are made appropriately small, then the belt acts as though it were the core of a single-mode optical fiber: the belt and the rod material adjacent to it support a single, circumferentially propagating mode or family of modes. To recapitulate from the second cited prior article: A major step in the fabrication of a WGM resonator of this special type is diamond turning or computer numerically controlled machining of a rod of a suitable transparent crystalline material on an ultrahigh-precision lathe. During the rotation of a spindle in which the rod is mounted, a diamond tool is used to cut the rod. A computer program is used to control stepping motors that move the diamond tool, thereby controlling the shape cut by the tool. Because the shape can be controlled via software, it is possible to choose a shape designed to optimize a resonator spectrum, including, if desired, to limit the resonator to supporting a single mode. After diamond turning, a resonator can be polished to increase its Q. By virtue of its largely automated, computer-controlled nature, the process is suitable for mass production of nominally identical single-mode WGM resonators. In a demonstration of the capabilities afforded by this development, a number of WGM resonators of various designs were fabricated side by side on the surface of a single CaF2 rod (see figure).

Grudinin, Ivan↗

NASA NTRSDate not supplied

A Neural Network Parametrization of Volumetric Cloud Fraction Profiles Using Satellite Observations and MERRA-2 Reanalysis Meteorological Data

Clouds play a crucial role in regulating the hydrologic cycle and Earth's radiative energy budget, yet they are often poorly represented in global climate models (GCMs). This study applies deep machine learning techniques to develop a physical parameterization of volumetric cloud fraction (VCF), the fraction of a 3-D grid volume occupied by clouds using satellite lidar-radar measurements. The neural network (NN) captures the complicated relationships between observed VCF profiles and collocated meteorological variables from MERRA-2 reanalysis data. Our results show that the NN model, particularly a sequence-to-sequence long short-term memory (LSTM) network with a sixfactor loss function, effectively learns the underlying cloud physical processes. The NN model outperforms MERRA-2 reanalysis in representing low-level clouds in tropical and subtropical regions and low- and middle-level clouds over midlatitude storm-track regions, and also improves VCF histograms. These improvements are reflected in the vertical distributions of zonally, meridionally, and globally averaged VCFs, geographic distributions of low-, middle-, and high-level clouds, and seasonal variations in monthly-mean VCF. Furthermore, the NN predictions effectively capture the El Niño-Southern Oscillation (ENSO) effects and other interannual variations. The NN parameterization is further evaluated through a sensitivity analysis, in which a single predictor is perturbed at a time. This reveals that relative humidity (RH) is the dominant factor influencing variations in globally averaged VCF at low and middle altitudes, followed by temperature. At higher altitudes, temperature becomes the primary driver of VCF through its effect on RH. Changes in wind components had minimal impact on globally averaged VCF.

Shan Zeng↗

NASA NTRS2003

Lifetime Predictions of a Titanium Silicate Glass with Machined Flaws

A dynamic fatigue study was performed on a Titanium Silicate glass to assess its susceptibility to delayed failure and to compare the results with those of a previous study. Fracture mechanics techniques were used to analyze the results for the purpose of making lifetime predictions. The material strength and lifetime was seen to increase due to the removal of residual stress through grinding and polishing. Influence on time-to-failure is addressed for the case with and without residual stress present. Titanium silicate glass otherwise known as ultra-low expansion (ULE)* glass is a candidate for use in applications requiring low thermal expansion characteristics such as telescope mirrors. The Hubble Space Telescope s primary mirror was manufactured from ULE glass. ULE contains 7.5% titanium dioxide which in combination with silica results in a homogenous glass with a linear expansion coefficient near zero. delayed failure . This previous study was based on a 230/270 grit surface. The grinding and polishing process reduces the surface flaw size and subsurface damage, and relieves residual stress by removing the material with successively smaller grinding media. This results in an increase in strength of the optic during the grinding and polishing sequence. Thus, a second study was undertaken using samples with a surface finish typically achieved for mirror elements, to observe the effects of surface finishing on the time-to-failure predictions. An allowable stress can be calculated for this material based upon modulus of rupture data; however, this does not take into account the problem of delayed failure, most likely due to stress corrosion, which can significantly shorten lifetime. Fortunately, a theory based on fracture mechanics has been developed enabling lifetime predictions to be made for brittle materials susceptible to delayed failure. Knowledge of the factors governing the rate of subcritical flaw growth in a given environment enables the development of relations between lifetime, applied stress and failure probability for the material under study. Dynamic fatigue is one method of obtaining the necessary information to develop these relationships. In this study, the dynamic fatigue method was used to construct a time-to-failure diagram for polished ULE glass.

Tucker, Dennis S.↗

NASA NTRS2022

Cloud Mask Intercomparison eXercise (CMIX): An evaluation of cloud masking algorithms for Landsat 8 and Sentinel-2

Cloud cover is a major limiting factor in exploiting time-series data acquired by optical spaceborne remote sensing sensors. Multiple methods have been developed to address the problem of cloud detection in satellite imagery and a number of cloud masking algorithms have been developed for optical sensors but very few studies have carried out quantitative intercomparison of state-of-the-art methods in this domain. This paper summarizes results of the first Cloud Masking Intercomparison eXercise (CMIX) conducted within the Committee Earth Observation Satellites (CEOS) Working Group on Calibration & Validation (WGCV). CEOS is the forum for space agency coordination and cooperation on Earth observations, with activities organized under working groups. CMIX, as one such activity, is an international collaborative effort aimed at intercomparing cloud detection algorithms for moderate-spatial resolution (10–30 m) spaceborne optical sensors. The focus of CMIX is on open and free imagery acquired by the Landsat 8 (NASA/USGS) and Sentinel-2 (ESA) missions. Ten algorithms developed by nine teams from fourteen different organizations representing universities, research centers and industry, as well as space agencies (CNES, ESA, DLR, and NASA), are evaluated within the CMIX. Those algorithms vary in their approach and concepts utilized which were based on various spectral properties, spatial and temporal features, as well as machine learning methods. Algorithm outputs are evaluated against existing reference cloud mask datasets. Those datasets vary in sampling methods, geographical distribution, sample unit (points, polygons, full image labels), and generation approaches (experts, machine learning, sky images). Overall, the performance of algorithms varied depending on the reference dataset, which can be attributed to differences in how the reference datasets were produced. The algorithms were in good agreement for thick cloud detection, which were opaque and had lower uncertainties in their identification, in contrast to thin/semi-transparent clouds detection. Not only did CMIX allow identification of strengths and weaknesses of existing algorithms and potential areas of improvements, but also the problems associated with the existing reference datasets. The paper concludes with recommendations on generating new reference datasets, metrics, and an analysis framework to be further exploited and additional input datasets to be considered by future CMIX activities.

Sergii Skakun↗

NASA NTRSDate not supplied

Expanding Biological Repository Data Available for Sharing and Knowledge Discovery

Biology has developed next-generation data science and alternative analytical approaches with methodologies which require principal investigator (PI) experimental assay data be re-used. This new approach involves mining multiple datasets at once from various hierarchical organizations of biological complexity, while concurrently evaluating how experimental factors affect endpoints of standard assays. The purpose of the NASA Ames Life Sciences Data Archive (ALSDA) is to collect, curate, and make findable, accessible, interoperable, and reusable (FAIR) all non-human space-relevant biological data. These data include mission metadata, subject metadata, assay metadata (parameters), raw and processed assay data, assay imagery, and subject-experienced telemetry (radiation, temperature, humidity, acoustics, vibrations). ALSDA has transformed to bring current biological repository data and all future collected data into this new scientific data mining reality. It has integrated into the ‘NASA Open Science’ group of projects to facilitate a suite of new tools and workflows to improve data accessibility and reusability by implementing data management plans, automating data submission agreements, and adopting the single-point-of-entry data submission portal, originally developed by NASA GeneLab. These systems required ALSDA to develop science assay configurations for the submission portal, capturing essential assay parameters according to established norms in each sub-field within biology. The submission portal expedites data collection by enhancing ease of PI data submission, providing a user interface and specificity for which data is to be submitted. ALSDA datasets are curated to maintain rich metadata, accuracy of datasets, data transparency, provenance, and additionally ensure data are machine-readable (e.g., R and Python languages). ALSDA integration with GeneLab and its analysis portals enable higher-order physiological-level datasets be mined in conjunction with -omics datasets. As ALSDA physiological-level datasets are published (micro-computed tomography, histology, intraocular pressure, hormonal assays, immunostaining, ultrasonography), the merging of hierarchical organizations of biological complexity from spaceflight will enable new knowledge discovery approaches.

Ryan T Scott↗

NASA NTRSDate not supplied

A Deep Neural Network for Achieving Spectrally Consistent and Seamless Infrared Radiance Measurements Across Geostationary Satellite Domains

The NASA Clouds and the Earth's Radiant Energy System (CERES) project provides the scientific community with observed top-of-atmosphere (TOA) shortwave and longwave fluxes for climate monitoring and climate model validation. To achieve this goal, CERES relies on TOA broadband fluxes derived from geostationary satellite (GEO) imagery to account for the diurnal flux variations between the CERES observation intervals. Consistent global flux derivation depends on accurate and consistent cloud retrievals. Scene-dependent spectral measurement inconsistency of the instruments that make up the contiguous ring of GEO observations (GEO-Ring), as well as limb darkening effects, can cause discontinuities in derived cloud properties and radiative fluxes at the boundaries of adjacent imager domains. Although the algorithms utilize radiative transfer models to account for instrument-band-dependent atmospheric correction and viewing zenith angle (VZA) dependency, small discontinuities may persist due to uncertainties inherent to the multiple imager-specific algorithms. Furthermore, while hyperspectral-instrument-based spectral band adjustment factors may effectively account for spectrally induced bias, they are less effective at reducing variance owed to the specific composition of the viewed scene, which is challenging to robustly characterize. As such, this article highlights the use of a deep neural network (DNN) to resolve spectral-and VZA-induced biases between GEO-Ring imagers. The DNN uses available infrared (IR) channels from the GEO instruments, along with viewing and solar illumination geometry, to estimate homogenized, VIIRS-like IR radiances for use in the GEO cloud algorithm. This approach is effective at mitigating scene-dependent spectral variance and VZA dependency, resulting in consistent radiance measurements across the GEO-Ring, thereby leading toward a more seamless global cloud assessment.

deep learning↗

NASA NTRS2003

Development of Novel Methods for the Reduction of Noise and Weight in Helicopter Transmissions

Over the 70-year evolution of the helicopter, man's understanding of vibration control has greatly increased. However, in spite of the increased performance, the extent of helicopter vibration problems has not significantly diminished. Crew vibration and noise remains important factors in the design of all current helicopters. With more complex and critical demands being placed on aircrews, it is essential that vibration and noise not impair their performance. A major source of helicopter cabin noise (which has been measured at a sound pressure level of over 100 dB) is the gearbox. Reduction of this noise has been a goal of NASA and the U.S. Army. Gear mesh noise is typically in the frequency range of 1000 to 3000 Hz, a range important for speech. A requirement for U.S. Army/NASA Advanced Rotorcraft Transmission project has been a 10-dB reduction compared to current designs. A combined analytical/experimental effort has been underway, since the end of the 80's, to study effects of design parameters on noise production. The noise generated by the gear mesh can be transmitted to the surrounding media through the bearings that support the gear shaft. Therefore, the use of fluid film bearings instead of rolling element bearings could reduce the transmission noise by 10 dB. In addition, the fluid film bearings that support the gear shaft can change the dynamics of the gear assembly by providing damping to the system and by being softer than rolling element bearings. Wave bearings can attenuate, and filter, the noise generated by a machine component due to the dynamic stiffness and damping coefficients. The attenuation ratio could be as large as 35-40 dB. The noise components at higher frequencies than a synchronous frequency can be almost eliminated.

Dimofte, Florin↗

NASA NTRSDate not supplied

Predicting Airport Runway Configuration for Decision-Support Using Supervised Learning

One of the most challenging tasks for air traffic controllers is runway configuration management (RCM). It deals with the optimal selection of runways to operate on (for arrivals and departures) based on current and forecast of traffic, surface wind speed, wind direction, other environmental variables, noise constraints, and several other airport-specific factors. In this paper, a methodology using supervised learning is developed to build a predictive model for RCM decision-support from large volumes of historical data. Data from two full years (2018 and 2019) related to current and forecast weather, demand/capacity, etc. is collected, analyzed, and fused together. A variety of supervised learning algorithms are tested for predicting runway configuration and hyperparameter tuning is carried out to select the best performing model. The validation process involves two airports of low (Charlotte Douglas International Airport, CLT) and high (Denver International Airport, DEN) complexity of configuration decision-making. The results show significant promise for the two airports with test accuracy of 93% (CLT) and 73% (DEN). The methodology is scalable and generalizable to other airports across the U.S. National Airspace System.

air traffic management↗

NASA NTRSDate not supplied

Predicting Airport Runway Configurations for Decision-Support Using Supervised Learning

air traffic management↗