SEARCH · Engineering Papers

Results for “Architecture patterns”

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.

SourceResearch connectionFrom yearThrough year

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

At least 559 records · Page 31

All sources NASA NTRS DOE OSTI

DOE OSTI2022

Cabana: A Performance Portable Library for Particle-Based Simulations

Particle-based simulations are ubiquitous throughout many fields of computational science and engineering, spanning the atomistic level with molecular dynamics (MD), to mesoscale particle-in-cell (PIC) simulations for solid mechanics, device-scale modeling with PIC methods for plasma physics, and massive N-body cosmology simulations of galaxy structures, with many other methods in between (Hockney & Eastwood, 1989). While these methods use particles to represent significantly different entities with completely different physical models, many low-level details are shared including performant algorithms for short- and/or long-range particle interactions, multi-node particle communication patterns, and other data management tasks such as particle sorting and neighbor list construction. Cabana is a performance portable library for particle-based simulations, developed as part of the Co-Design Center for Particle Applications (CoPA) within the Exascale Computing Project (ECP) (Alexander et al., 2020). The CoPA project and its full development scope, including ECP partner applications, algorithm development, and similar software libraries for quantum MD, is described in (Mniszewski et al., 2021). Cabana uses the Kokkos library for on-node parallelism (Edwards et al., 2014; Trott et al., 2022), enabling simulation on multi-core CPU and GPU architectures, and MPI for GPU-aware, multi-node communication. Cabana provides particle simulation capabilities on almost all current Kokkos backends, including serial execution, OpenMP (including OpenMP-Target for GPUs), CUDA (NVIDIA GPUs), HIP (AMD GPUs), and SYCL (Intel GPUs), providing a clear path for the coming generation of accelerator-based exascale hardware. Cabana builds on Kokkos by providing new particle data structures and particle algorithms resulting in a similar execution policy-based, node-level programming model that is intended to be used in addition to the core Kokkos library within an application. Cabana is designed as an application and physics agnostic, but particle-specific toolkit which can either be used to generate a new application, or to be used as needed in existing applications at various levels of invasiveness including through interfaces that wrap user memory in existing data structures.

97 MATHEMATICS AND COMPUTING↗

NASA NTRS2009

Digital Beamforming Scatterometer

This paper discusses scatterometer measurements collected with multi-mode Digital Beamforming Synthetic Aperture Radar (DBSAR) during the SMAP-VEX 2008 campaign. The 2008 SMAP Validation Experiment was conducted to address a number of specific questions related to the soil moisture retrieval algorithms. SMAP-VEX 2008 consisted on a series of aircraft-based.flights conducted on the Eastern Shore of Maryland and Delaware in the fall of 2008. Several other instruments participated in the campaign including the Passive Active L-Band System (PALS), the Marshall Airborne Polarimetric Imaging Radiometer (MAPIR), and the Global Positioning System Reflectometer (GPSR). This campaign was the first SMAP Validation Experiment. DBSAR is a multimode radar system developed at NASA/Goddard Space Flight Center that combines state-of-the-art radar technologies, on-board processing, and advances in signal processing techniques in order to enable new remote sensing capabilities applicable to Earth science and planetary applications [l]. The instrument can be configured to operate in scatterometer, Synthetic Aperture Radar (SAR), or altimeter mode. The system builds upon the L-band Imaging Scatterometer (LIS) developed as part of the RadSTAR program. The radar is a phased array system designed to fly on the NASA P3 aircraft. The instrument consists of a programmable waveform generator, eight transmit/receive (T/R) channels, a microstrip antenna, and a reconfigurable data acquisition and processor system. Each transmit channel incorporates a digital attenuator, and digital phase shifter that enables amplitude and phase modulation on transmit. The attenuators, phase shifters, and calibration switches are digitally controlled by the radar control card (RCC) on a pulse by pulse basis. The antenna is a corporate fed microstrip patch-array centered at 1.26 GHz with a 20 MHz bandwidth. Although only one feed is used with the present configuration, a provision was made for separate corporate feeds for vertical and horizontal polarization. System upgrades to dual polarization are currently under way. The DBSAR processor is a reconfigurable data acquisition and processor system capable of real-time, high-speed data processing. DBSAR uses an FPGA-based architecture to implement digitally down-conversion, in-phase and quadrature (I/Q) demodulation, and subsequent radar specific algorithms. The core of the processor board consists of an analog-to-digital (AID) section, three Altera Stratix field programmable gate arrays (FPGAs), an ARM microcontroller, several memory devices, and an Ethernet interface. The processor also interfaces with a navigation board consisting of a GPS and a MEMS gyro. The processor has been configured to operate in scatterometer, Synthetic Aperture Radar (SAR), and altimeter modes. All the modes are based on digital beamforming which is a digital process that generates the far-field beam patterns at various scan angles from voltages sampled in the antenna array. This technique allows steering the received beam and controlling its beam-width and side-lobe. Several beamforming techniques can be implemented each characterized by unique strengths and weaknesses, and each applicable to different measurement scenarios. In Scatterometer mode, the radar is capable to.generate a wide beam or scan a narrow beam on transmit, and to steer the received beam on processing while controlling its beamwidth and side-lobe level. Table I lists some important radar characteristics

Rincon, Rafael F.↗

DOE OSTI2021

Designing Anion-Exchange Ionomers with Oriented Nanoscale Phase Separation at a Silver Interface

We report performance of polymer electrolyte-based energy systems is significantly impacted by transport within the electrode catalyst layer, where ionomer thin films coat catalyst particles. Proton exchange ionomer thin films have been thoroughly characterized, but few studies have critically examined anion-exchange ionomer (AEI) thin films. Further, none have reported nanoscale phase separation for hydrocarbon AEIs, which is critical to mitigate transport resistances. In this work, a set of hydrocarbon-based AEIs with nanoscale phase separation are developed from tunable block copolymer systems composed of polyisoprene (PIp) and polychloromethylstyrene (PCMS). The effect of the PIp/PCMS ratio, architecture, and thickness on the thin-film morphology of the neutral block copolymer precursors on silicon and silver substrates is investigated using grazing-incidence small-angle X-ray scattering (GISAXS) and atomic force microscopy (AFM). AEIs are prepared by quaternizing with trimethylamine or methylpiperidine and their cation-dependent morphology is characterized at 60 °C and 95% RH. A perpendicularly aligned morphology is observed on silver, while no phase separation is observed on silicon, indicating that silver-polymer interfacial interactions drive phase separation. After quaternization, dipole-dipole interactions induce some disorder, but nanoscale phase separation is still maintained. GISAXS patterns are modeled using a Unified Fit approach to understand water uptake and swelling, and recommendations for AEI design are presented.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2023

Integrating LEO and GEO Observations: Toward Optimal Summertime Satellite Precipitation Retrieval

Abstract Reliable quantitative precipitation estimation with a rich spatiotemporal resolution is vital for understanding the Earth’s hydrological cycle. Precipitation estimation over land and coastal regions is necessary for addressing the high degree of spatial heterogeneity of water availability and demand, and for resolving the extremes that modulate and amplify hazards such as flooding and landslides. Advancements in computation power along with unique high spatiotemporal and spectral resolution data streams from passive meteorological sensors aboard geosynchronous Earth-orbiting (GEO) and low Earth-orbiting (LEO) satellites offer exciting opportunities to retrieve information about surface precipitation phenomena using data-driven machine learning techniques. In this study, the capabilities of U-Net–like architecture are investigated to map instantaneous, summertime surface precipitation intensity at the spatial resolution of 2 km. The calibrated brightness temperature products from the Global Precipitation Measurement (GPM) Microwave Imager (GMI) radiometer are combined with multispectral images (visible, near-infrared, and infrared bands) from the Advanced Baseline Imager (ABI) aboard the GOES-R satellites as main inputs to the U-Net–like precipitation algorithm. Total precipitable water and 2-m temperature from the Global Forecast System (GFS) model are also used as auxiliary inputs to the model. The results show that the U-Net–like algorithm can capture fine-scale patterns and intensity of surface precipitation at high spatial resolution over stratiform and convective precipitation regimes. The evaluations reveal the potential of extracting relevant, high spatial features over complex surface types such as mountainous regions and coastlines. The algorithm allows users to interpret the inputs’ importance and can serve as a starting point for further exploration of precipitation systems within the field of hydrometeorology.

Meteorology & Atmospheric Sciences↗

NASA NTRS1994

A neural network with modular hierarchical learning

This invention provides a new hierarchical approach for supervised neural learning of time dependent trajectories. The modular hierarchical methodology leads to architectures which are more structured than fully interconnected networks. The networks utilize a general feedforward flow of information and sparse recurrent connections to achieve dynamic effects. The advantages include the sparsity of units and connections, the modular organization. A further advantage is that the learning is much more circumscribed learning than in fully interconnected systems. The present invention is embodied by a neural network including a plurality of neural modules each having a pre-established performance capability wherein each neural module has an output outputting present results of the performance capability and an input for changing the present results of the performance capabilitiy. For pattern recognition applications, the performance capability may be an oscillation capability producing a repeating wave pattern as the present results. In the preferred embodiment, each of the plurality of neural modules includes a pre-established capability portion and a performance adjustment portion connected to control the pre-established capability portion.

Baldi, Pierre F.↗

DOE OSTI2023

Enhancing photonic systems using topology and non-Hermiticity

The broad goal of this project was to develop new analytical and numerical insights for how important photonic processes can be improved using recently discovered principles in topological and non-Hermitian physics. In particular, there are two recent discoveries that we aimed to harness to achieve this goal. First, it was discovered in condensed matter physics that crystalline symmetries can protect low-dimensional topologically protected states in lattices without the need for breaking time-reversal symmetry. These so-called ‘higher-order’ topological systems represent an important development for photonic systems, where it is very difficult to break time-reversal symmetry, and which had been previously thought necessary to realize topological phenomena. Second, the last decade has seen a significant amount of interest in phenomena which are unique to non-Hermitian systems, i.e., systems which do not conserve energy. For example, spatially patterned gain and loss can be used to realize exceptional points, which are degeneracies in a system’s spectrum where the system becomes defective, while the existence of radiative losses also enables a new route to confinement through bound states in the continuum. For such non-Hermitian phenomena, photonics again represents a critical platform, as photonic systems naturally lose light to their radiative environments, making them generally non-Hermitian, and it is also possible to incorporate additional gain or loss. Based on these broad principles, we pursued a range of projects to harness bound states in the continuum in a variety of different systems and architectures, develop real-space methods for classifying topological systems to yield better photonic design principles, and a novel Brillouin-based fiber laser for sensing strain.

77 NANOSCIENCE AND NANOTECHNOLOGY↗

DOE OSTI2021

Programmable Site‐Specific Functionalization of DNA Origami with Polynucleotide Brushes

Abstract Combining surface‐initiated, TdT (terminal deoxynucleotidyl transferase) catalyzed enzymatic polymerization (SI‐TcEP) with precisely engineered DNA origami nanostructures (DONs) presents an innovative pathway for the generation of stable, polynucleotide brush‐functionalized DNA nanostructures. We demonstrate that SI‐TcEP can site‐specifically pattern DONs with brushes containing both natural and non‐natural nucleotides. The brush functionalization can be precisely controlled in terms of the location of initiation sites on the origami core and the brush height and composition. Coarse‐grained simulations predict the conformation of the brush‐functionalized DONs that agree well with the experimentally observed morphologies. We find that polynucleotide brush‐functionalization increases the nuclease resistance of DONs significantly, and that this stability can be spatially programmed through the site‐specific growth of polynucleotide brushes. The ability to site‐specifically decorate DONs with brushes of natural and non‐natural nucleotides provides access to a large range of functionalized DON architectures that would allow for further supramolecular assembly, and for potential applications in smart nanoscale delivery systems.

Yang, Yunqi↗

DOE OSTI2023

Microscale spatiotemporal patterns of water, soil organic carbon, and enzymes in plant litter detritusphere

Understanding biophysical and biochemical processes in detritusphere is critical to quantifying and modeling plant residue decomposition dynamics and subsequent soil organic carbon (C) accrual. The objectives of the study were to explore (i) micro-environmental conditions within the detritusphere formed around decomposing corn and soybean leaves, and (ii) the relationships between moisture distribution, enzyme activity, and C dynamics during decomposition. We assessed spatial and temporal dynamics of moisture distribution using X-ray and neutron computed tomography, and activities of β-glucosidase and chitinase, two enzymes involved in soil C and N processing, using zymography. We used 13 C labeled residue to track residue contribution to atmospheric CO 2 and soil organic C. Moisture redistribution pattern varied depending on the residue type. While the water was immediately absorbed by the corn leaves and maintained afterward, switchgrass leaves absorbed water more slowly and created water-deficient zones within ~1mm from the residue. This initial moisture depletion led to lower chitinase activity and residue-derived CO 2 emissions in switchgrass. In contrast to chitinase, β-glucosidase activity was influenced by a combination of vegetation history and residue type, and it was higher when the origin of the residue matched the vegetation history of the soil. Pore size had an opposite impact on the studied enzymes, supporting the notion that contrasting soil pore architecture can stimulate activities of different enzymes through a selection of dominant enzyme producers. We concluded that the decomposition dynamics of plant residues is not only a simple function of residue chemistry, but rather a combined effect of the vegetation history, in part through its effect on microbial community composition, the plant residue chemical and likely physical characteristics, and the soil pore structure in the detritusphere. Together, they create temporally dynamic micro-environmental conditions influencing decomposition. Specifically, our study demonstrated that the initial micro-environment formulated in detritusphere can play an important role in enzyme activities and consequent C dynamics.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2026

CAHS: Context-Aware Homology Search

Protein homology search is foundational to bioinformatics: it supports annotation transfer, structure/function inference, and evolutionary analysis over rapidly expanding sequence repositories (e.g., UniProtKB). Profile hidden Markov models (pHMMs), as implemented in HMMER, remain the most widely trusted approach because they provide statistically calibrated E-values; however, their gap behavior is fixed once a profile is trained, despite biological evidence that insertion/deletion tolerance varies across flexible loops and intrinsically disordered regions. We present CAHS (Context-Aware Homology Search), a lightweight query-time adapter for pHMM search that incorporates learned and biologically motivated signals without changing HMMER's downstream search pipeline or its calibrated E-value reporting. Given a query sequence, CAHS computes per-residue representations from a protein language model and a disorder predictor, maps these to profile coordinates, and modulates only match-state transition rows (gap-open and gap-extension probabilities) while preserving Plan7 constraints. We comprehensively evaluate CAHS across six structurally diverse protein families and multi-domain architectures against a 570k-sequence target corpus. CAHS expands detection capability, retrieving thousands of additional remote homologs at relaxed thresholds by maintaining alignment quality through flexible regions. For multi-domain proteins, context-aware modulation resolves 94% of fragmented alignments. Crucially, CAHS preserves hit-set invariance at stringent operating points (E<10-10), demonstrating increased statistical confidence without inflating false positives. Furthermore, sharper statistical distinction between homologs and background noise during early filter stages yields up to a 3.87× acceleration in end-to-end wall-clock time on high-performance computing clusters. Overall, CAHS illustrates a practical AI-for-science design pattern: augmenting a trusted probabilistic model with query-specific learned signals to improve interpretable, reproducible inference in data-rich biology.

Bhattaram, Swethasree [Georgia Institute of Techno↗

DOE OSTI2020

Data-Driven Template Discovery Using Graph Convolutional Neural Networks

Modeling adversarial activities is a critical component of developing high-con?dence indicators of efforts to acquire, fabricate, proliferate, and/or deploy weapons of mass terror (WMTs). Current approaches to generating representative patterns of interest (a.k.a templates) from the real-world domains involve a Subject Matter Expert (SME)-guided manual process. The goal of Data-Driven Template Discovery (DDTD) is to use a (potentially small) set of SME generated templates to discover other previously unknown and interesting templates in an attributed graph. A template is an activity pattern describing a set of interactions among a group of nodes in the graph. The motivation behind DDTD is to expand the original set of templates, without having SMEs craft all the templates by hand. DDTD also provides seed templates to SMEs, to help them construct larger, high-?delity, and scenario-oriented templates. In these cases, obtaining a larger set of templates that are related (contain similar signals) to the original set is of great value. In this work, we propose to use Graph Convolutional Neural Networks (GCNs) to discover new templates that are heavily related to the original set. GCNs are a family of Neural Network (NN) architectures especially designed to work directly on graphs. In contrast to the traditional NNs, that require considerable amounts of labeled data, GCNs do not require a big labeled training set because they can directly leverage the graph structure instead. This property makes GCNs the perfect tool for creating activity templates.

Joaristi, Mikel↗

DOE OSTI2025

Electrically Reconfigurable Liquid Metal Nanophotonic Platform for Color Display and Imaging

Dynamically tunable optical materials and device architectures are essential for future photonic technologies, yet conventional solid metals lack the intrinsic tunability required for advanced functionalities. Gallium-based liquid metals (LMs) present an appealing alternative thanks to their distinctive mechanical and optical properties; however, their practical integration in tunable photonic elements remains largely unexplored. Here, in this study, an electrochemically controlled nanophotonic platform is demonstrated that integrates a dynamically reconfigurable LM ground plane with gold nanoantenna arrays within a microfluidic system, enabling precise and reversible modulation of optical resonances across the visible to mid-infrared spectral ranges. By employing moderate operational voltages (1.5–3.0 V), real-time tuning of high-resolution structural color patterns is achieved through nanoscale control of the interfacial gap between the LM and Au nanoantennas. This innovative platform facilitates electrically programmable, high-contrast color patterns suitable for dynamic optical displays, secure anti-counterfeiting labels, and imaging applications. Additionally, this platform enables tunable mid-infrared spectral responses, which may be utilized for chemical and biological sensing applications. This versatile integrated LM-based nanophotonic platform opens new paths toward multifunctional, actively tunable/reconfigurable photonic device and system technologies.

Imaging↗

NASA NTRSDate not supplied

Space Launch System Liftoff and Separation Dynamics Analysis Tool Chain

A flexible, hierarchical tool chain that is being applied to NASA’s Space Launch System (SLS) for critical dynamics phenomena is described. This tool chain, called CLVTOPS, is used to investigate lateral liftoff movement of the vehicle as it departs and clears the mobile launch tower and separation of the two solid rocket boosters without collision with the core stage and payload. The toolset’s architecture was configured to take advantage of a modern software engineering approach for maximum flexibility and utilization of open-source simulations and associated tools. As opposed to a “monolithic” approach, scripting languages were used to “bind” together a tool chain to configure and organize input data, execute and produce analysis results, and post-process these results to facilitate a rapid, iterative analysis process to quickly address issues and pursue alternatives with emphasis on analysis automation. Key capabilities in the tool chain include processing and mining of very large data sets, a wide range of graphical depictions, and high-fidelity, physics-based simulations. The paper begins with a problem description and the motivation for liftoff and separation dynamics analysis followed by a historical survey of dynamics analyses for previous NASA human-rated launch vehicles. Details of the tool chain and its components are then introduced and divided, first, into description of the scripting language architecture used to “bind” the simulation tools, programs, and scripts together and, second, the physics models and simulations. Representative analyses and data products for liftoff and booster separation dynamics are shown in order to provide in-depth insight into the tool chain’s capabilities. Supporting activities such as simulation tool chain verification, version archiving and data management, and training are addressed. The paper concludes with case examples on how the tool chain can be tailored to related aerospace dynamics analyses, both large and small. The flexibility and versatility of this tool chain in supporting analyses of such a diverse range of aerospace applications demonstrates the feasibility of applying these patterns and techniques for tool construction to other aerospace simulations.

6DOF↗

DOE OSTI2024

Hierarchical Phased-Array Antennas Coupled to Al KIDs: A Scalable Architecture for Multi-band Millimeter/Submillimeter Focal Planes

We present the optical characterization of two-scale hierarchical phased-array antenna kinetic inductance detectors (KIDs) for millimeter/submillimeter wavelengths. Our KIDs have a lumped-element architecture with parallel plate capacitors and aluminum inductors. The incoming light is received with a hierarchical phased array of slot dipole antennas, split into 4 frequency bands (between 125 GHz and 365 GHz) with on-chip lumped-element band-pass filters, and routed to different KIDs using microstriplines. Individual pixels detect light for the 3 higher-frequency bands (190–365 GHz), and the signals from four individual pixels are coherently summed to create a larger pixel detecting light for the lowest frequency band (125–175 GHz). The spectral response of the band-pass filters was measured using Fourier transform spectroscopy (FTS), the far-field beam pattern of the phased-array antennas was obtained using an infrared source mounted on a 2-axis translating stage, and the optical efficiency of the KIDs was characterized by observing loads at 294 K and 77 K. We report on the results of these three measurements.

47 OTHER INSTRUMENTATION↗

DOE OSTI2020

Predicting the Activity and Selectivity of Bimetallic Metal Catalysts for Ethanol Reforming using Machine Learning

Machine learning is ideally suited for the pattern detection in large uniform datasets, but consistent experimental datasets on catalyst studies are often small. Here we demonstrate how a combination of machine learning and first-principles calculations can be used to extract knowledge from a relatively small set of experimental data. The approach is based on combining a complex machine-learning model trained on an extensive computational library of transition-state energies with simple linear regression models of experimental catalytic activities and selectivities from the literature. Using the combined model, we identify the key C–C bond scission reactions involved in ethanol reforming and perform a computational screening for ethanol reforming on monolayer bimetallic catalysts with architectures TM-Pt-Pt(111) and Pt-TM-Pt(111) (TM = 3d transition metals). The model also predicts four promising catalyst compositions for future experimental studies. In conclusion, the approach is not limited to ethanol reforming but is of general use for the interpretation of experimental observations as well as for the computational discovery of novel catalytic materials.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2021

PLANC: Parallel Low-rank Approximation with Nonnegativity Constraints

In this work, we consider the problem of low-rank approximation of massive dense nonnegative tensor data, for example, to discover latent patterns in video and imaging applications. As the size of data sets grows, single workstations are hitting bottlenecks in both computation time and available memory. We propose a distributed-memory parallel computing solution to handle massive data sets, loading the input data across the memories of multiple nodes, and performing efficient and scalable parallel algorithms to compute the low-rank approximation. We present a software package called Parallel Low-rank Approximation with Nonnegativity Constraints, which implements our solution and allows for extension in terms of data (dense or sparse, matrices or tensors of any order), algorithm (e.g., from multiplicative updating techniques to alternating direction method of multipliers), and architecture (we exploit GPUs to accelerate the computation in this work). We describe our parallel distributions and algorithms, which are careful to avoid unnecessary communication and computation, show how to extend the software to include new algorithms and/or constraints, and report efficiency and scalability results for both synthetic and real-world data sets.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2025

Glancing Angle Deposition in Gas Sensing: Bridging Morphological Innovations and Sensor Performances

Glancing Angle Deposition (GLAD) has emerged as a versatile and powerful nanofabrication technique for developing next-generation gas sensors by enabling precise control over nanostructure geometry, porosity, and material composition. Through dynamic substrate tilting and rotation, GLAD facilitates the fabrication of highly porous, anisotropic nanostructures, such as aligned, tilted, zigzag, helical, and multilayered nanorods, with tunable surface area and diffusion pathways optimized for gas detection. This review provides a comprehensive synthesis of recent advances in GLAD-based gas sensor design, focusing on how structural engineering and material integration converge to enhance sensor performance. Key materials strategies include the construction of heterojunctions and core–shell architectures, controlled doping, and nanoparticle decoration using noble metals or metal oxides to amplify charge transfer, catalytic activity, and redox responsiveness. GLAD-fabricated nanostructures have been effectively deployed across multiple gas sensing modalities, including resistive, capacitive, piezoelectric, and optical platforms, where their high aspect ratios, tailored porosity, and defect-rich surfaces facilitate enhanced gas adsorption kinetics and efficient signal transduction. These devices exhibit high sensitivity and selectivity toward a range of analytes, including NO2, CO, H2S, and volatile organic compounds (VOCs), with detection limits often reaching the parts-per-billion level. Emerging innovations, such as photo-assisted sensing and integration with artificial intelligence for data analysis and pattern recognition, further extend the capabilities of GLAD-based systems for multifunctional, real-time, and adaptive sensing. Finally, current challenges and future research directions are discussed, emphasizing the promise of GLAD as a scalable platform for next-generation gas sensing technologies.

Chemistry↗

NASA NTRS1978

Modular on-board adaptive imaging

Feature extraction involves the transformation of a raw video image to a more compact representation of the scene in which relevant information about objects of interest is retained. The task of the low-level processor is to extract object outlines and pass the data to the high-level process in a format that facilitates pattern recognition tasks. Due to the immense computational load caused by processing a 256x256 image, even a fast minicomputer requires a few seconds to complete this low-level processing. It is, therefore, necessary to consider hardware implementation of these low-level functions to achieve real-time processing speeds. The considered project had the objective to implement a system in which the continuous feature extraction process is not affected by the dynamic changes in the scene, varying lighting conditions, or object motion relative to the cameras. Due to the high bandwidth (3.5 MHz) and serial nature of the TV data, a pipeline processing scheme was adopted as the overall architecture of this system. Modularity in the system is achieved by designing circuits that are generic within the overall system.

Eskenazi, R.↗

NASA NTRS2005

Data Fusion for Enhanced Aircraft Engine Prognostics and Health Management

Aircraft gas-turbine engine data is available from a variety of sources, including on-board sensor measurements, maintenance histories, and component models. An ultimate goal of Propulsion Health Management (PHM) is to maximize the amount of meaningful information that can be extracted from disparate data sources to obtain comprehensive diagnostic and prognostic knowledge regarding the health of the engine. Data fusion is the integration of data or information from multiple sources for the achievement of improved accuracy and more specific inferences than can be obtained from the use of a single sensor alone. The basic tenet underlying the data/ information fusion concept is to leverage all available information to enhance diagnostic visibility, increase diagnostic reliability and reduce the number of diagnostic false alarms. This report describes a basic PHM data fusion architecture being developed in alignment with the NASA C-17 PHM Flight Test program. The challenge of how to maximize the meaningful information extracted from disparate data sources to obtain enhanced diagnostic and prognostic information regarding the health and condition of the engine is the primary goal of this endeavor. To address this challenge, NASA Glenn Research Center, NASA Dryden Flight Research Center, and Pratt & Whitney have formed a team with several small innovative technology companies to plan and conduct a research project in the area of data fusion, as it applies to PHM. Methodologies being developed and evaluated have been drawn from a wide range of areas including artificial intelligence, pattern recognition, statistical estimation, and fuzzy logic. This report will provide a chronology and summary of the work accomplished under this research contract.

Volponi, Al↗