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Grid Architecture Mapping to Understand Transformation (GAMUT): Methods and Framework Architecture

Grid architecture (GA) is a concept that was developed to address the need for a comprehensive view of power grid challenges. GA can be viewed as a relatively consistent and fixed high-level approach; however, for any instantiation of grid structures, a combinatorial explosion results from each lower-layer expansion. This constitutes the main challenge with GA—it is a grid architect’s view of the system, which might not be very informative at the implementation level. Grid Architecture Mapping to Understand Transformation (GAMUT project) seeks to bridge that gap by integrating subject matter expertise across GA structures, providing users who lack expertise in GA approaches with valuable insights and informational materials. GAMUT seeks to answer feasibility questions for the approach. System-level expectations are that a GA baseline needs to be established in order for GA to be the common framework to which any lower layer approach is tied. This report explores a potential information ingestion and documentation framework to support GAMUT. The main concepts that enable the solution domain of GAMUT are discussed, and examples are provided. The solution domain leverages already-existing technology and concepts related to GA, knowledge management, and other relevant areas. To assess GAMUT building blocks and the overall approach, a feasibility assessment is proposed, rooted in systems engineering and GA architecture evaluation concepts.

24 POWER TRANSMISSION AND DISTRIBUTION

Macromolecular differentiation of Golgi stacks in root tips of Arabidopsis and Nicotiana seedlings as visualized in high pressure frozen and freeze-substituted samples

The plant root tip represents a fascinating model system for studying changes in Golgi stack architecture associated with the developmental progression of meristematic cells to gravity sensing columella cells, and finally to "young" and "old", polysaccharide-slime secreting peripheral cells. To this end we have used high pressure freezing in conjunction with freeze-substitution techniques to follow developmental changes in the macromolecular organization of Golgi stacks in root tips of Arabidopsis and Nicotiana. Due to the much improved structural preservation of all cells under investigation, our electron micrographs reveal both several novel structural features common to all Golgi stacks, as well as characteristic differences in morphology between Golgi stacks of different cell types. Common to all Golgi stacks are clear and discrete differences in staining patterns and width of cis, medial and trans cisternae. Cis cisternae have the widest lumina (approximately 30 nm) and are the least stained. Medial cisternae are narrower (approximately 20 nm) and filled with more darkly staining products. Most trans cisternae possess a completely collapsed lumen in their central domain, giving rise to a 4-6 nm wide dark line in cross-sectional views. Numerous vesicles associated with the cisternal margins carry a non-clathrin type of coat. A trans Golgi network with clathrin coated vesicles is associated with all Golgi stacks except those of old peripheral cells. It is easily distinguished from trans cisternae by its blebbing morphology and staining pattern. The zone of ribosome exclusion includes both the Golgi stack and the trans Golgi network. Intercisternal elements are located exclusively between trans cisternae of columella and peripheral cells, but not meristematic cells. In older peripheral cells only trans cisternae exhibit slime-related staining. Golgi stacks possessing intercisternal elements also contain parallel rows of freeze-fracture particles in their trans cisternal membranes. We propose that intercisternal elements serve as anchors of enzyme complexes involved in the synthesis of polysaccharide slime molecules to prevent the complexes from being dragged into the forming secretory vesicles by the very large slime molecules. In addition, we draw attention to the similarities in composition and apparent site of synthesis of xyloglucans and slime molecules.

NASA Program Space Biology

Intercomparison of Deep Learning Model Architectures for Atmospheric River Prediction

With a rapid surge in the application of machine learning (ML) for a diverse range of tasks in climate science, the present study addresses a challenge for climate scientists when selecting the optimal ML or deep learning (DL) architecture for a given application. In particular, a DL intercomparison study was performed with a focus on forecasting the position of atmospheric rivers (ARs) on short-range time scales (up to 5-day lead times). AR predictions from multiple DL architectures, including various types of convolutional autoencoders and a vision transformer (ViT), were compared against ECMWF ERA5 reanalysis and hindcasts from a global climate model. DL models with similar trainable parameters were trained on ERA5 reanalysis data and AR positions derived from a thresholding algorithm to ensure a fair comparison among the DL models. Each model’s performance and accuracy in forecasting AR location and key input fields within a 5-day window were assessed using metrics of root-mean-square error, anomaly correlation, and mean intersection over union. The ViT architecture outperformed other autoencoder models in most of the metrics. Incorporating additional meteorological fields only yielded slight improvements in forecasting certain fields at longer lead times. The results also suggest that a smaller number of input time steps or smaller number of autoregressive steps can achieve better prediction skills, while also improving the overall computational efficiency. This research offers valuable insights into the strengths and weaknesses of different DL techniques for AR forecasting, hopefully guiding the development of improved models for forecasting this phenomenon.

54 ENVIRONMENTAL SCIENCES

Pattern Identification - A Foundation for Research in the Emphasis of Design Patterns in Systems Engineering and Knowledge Capture

Pattern Language describes the morphology and functionality of a system in the absence of design particulars. Harnessing this capability will provide the Systems Engineering discipline a means of managing the development of increasingly complex systems with increasingly distributed design teams while capturing and retaining knowledge for future generations. Pattern Language is a syntax for describing, and structurally relating, design patterns. Design patterns contextually describe the application of domain knowledge in the engineered solution to the force balance problem. The parallels between pattern recognition and application, as a fundamental stage of human learning, and pattern observation within a complex system, suggests pattern language may be a valuable tool in the capture and dissemination of knowledge. Pattern application has enjoyed considerable study over the last several decades, however much of this work has focused on the replication of design particulars. This work returns to the roots of Pattern Language and explores the utility of patterns as an architectural description and guide, and knowledge capture method, for complex system development beginning with the identification of a time proven design pattern.

Russell, Samuel P.

Parallel-Processing CMOS Circuitry for M-QAM and 8PSK TCM

There has been some additional development of parts reported in "Multi-Modulator for Bandwidth-Efficient Communication" (NPO-40807), NASA Tech Briefs, Vol. 32, No. 6 (June 2009), page 34. The focus was on 1) The generation of M-order quadrature amplitude modulation (M-QAM) and octonary-phase-shift-keying, trellis-coded modulation (8PSK TCM), 2) The use of square-root raised-cosine pulse-shaping filters, 3) A parallel-processing architecture that enables low-speed [complementary metal oxide/semiconductor (CMOS)] circuitry to perform the coding, modulation, and pulse-shaping computations at a high rate; and 4) Implementation of the architecture in a CMOS field-programmable gate array.

Gray, Andrew

NASA Tech Briefs, October 2008

Topics covered include: Control Architecture for Robotic Agent Command and Sensing; Algorithm for Wavefront Sensing Using an Extended Scene; CO2 Sensors Based on Nanocrystalline SnO2 Doped with CuO; Improved Airborne System for Sensing Wildfires; VHF Wide-Band, Dual-Polarization Microstrip-Patch Antenna; Onboard Data Processor for Change-Detection Radar Imaging; Using LDPC Code Constraints to Aid Recovery of Symbol Timing; System for Measuring Flexing of a Large Spaceborne Structure; Integrated Formation Optical Communication and Estimation System; Making Superconducting Welds between Superconducting Wires; Method for Thermal Spraying of Coatings Using Resonant-Pulsed Combustion; Coating Reduces Ice Adhesion; Hybrid Multifoil Aerogel Thermal Insulation; SHINE Virtual Machine Model for In-flight Updates of Critical Mission Software; Mars Image Collection Mosaic Builder; Providing Internet Access to High-Resolution Mars Images; Providing Internet Access to High-Resolution Lunar Images; Expressions Module for the Satellite Orbit Analysis Program Virtual Satellite; Small-Body Extensions for the Satellite Orbit Analysis Program (SOAP); Scripting Module for the Satellite Orbit Analysis Program (SOAP); XML-Based SHINE Knowledge Base Interchange Language; Core Technical Capability Laboratory Management System; MRO SOW Daily Script; Tool for Inspecting Alignment of Twinaxial Connectors; An ATP System for Deep-Space Optical Communication; Polar Traverse Rover Instrument; Expert System Control of Plant Growth in an Enclosed Space; Detecting Phycocyanin-Pigmented Microbes in Reflected Light; DMAC and NMP as Electrolyte Additives for Li-Ion Cells; Mass Spectrometer Containing Multiple Fixed Collectors; Waveguide Harmonic Generator for the SIM; Whispering Gallery Mode Resonator with Orthogonally Reconfigurable Filter Function; Stable Calibration of Raman Lidar Water-Vapor Measurements; Bimaterial Thermal Compensators for WGM Resonators; Root Source Analysis/ValuStream[Trade Mark] - A Methodology for Identifying and Managing Risks; Ensemble: an Architecture for Mission-Operations Software; Object Recognition Using Feature-and Color-Based Methods; On-Orbit Multi-Field Wavefront Control with a Kalman Filter; and The Interplanetary Overlay Networking Protocol Accelerator.

Source record

Intelligent Elements for ISHM

There are a number of architecture models for implementing Integrated Systems Health Management (ISHM) capabilities. For example, approaches based on the OSA-CBM and OSA-EAI models, or specific architectures developed in response to local needs. NASA s John C. Stennis Space Center (SSC) has developed one such version of an extensible architecture in support of rocket engine testing that integrates a palette of functions in order to achieve an ISHM capability. Among the functional capabilities that are supported by the framework are: prognostic models, anomaly detection, a data base of supporting health information, root cause analysis, intelligent elements, and integrated awareness. This paper focuses on the role that intelligent elements can play in ISHM architectures. We define an intelligent element as a smart element with sufficient computing capacity to support anomaly detection or other algorithms in support of ISHM functions. A smart element has the capabilities of supporting networked implementations of IEEE 1451.x smart sensor and actuator protocols. The ISHM group at SSC has been actively developing intelligent elements in conjunction with several partners at other Centers, universities, and companies as part of our ISHM approach for better supporting rocket engine testing. We have developed several implementations. Among the key features for these intelligent sensors is support for IEEE 1451.1 and incorporation of a suite of algorithms for determination of sensor health. Regardless of the potential advantages that can be achieved using intelligent sensors, existing large-scale systems are still based on conventional sensors and data acquisition systems. In order to bring the benefits of intelligent sensors to these environments, we have also developed virtual implementations of intelligent sensors.

Schmalzel, John L.

Model-Based Fault Diagnosis: Performing Root Cause and Impact Analyses in Real Time

Generic, object-oriented fault models, built according to causal-directed graph theory, have been integrated into an overall software architecture dedicated to monitoring and predicting the health of mission- critical systems. Processing over the generic fault models is triggered by event detection logic that is defined according to the specific functional requirements of the system and its components. Once triggered, the fault models provide an automated way for performing both upstream root cause analysis (RCA), and for predicting downstream effects or impact analysis. The methodology has been applied to integrated system health management (ISHM) implementations at NASA SSC's Rocket Engine Test Stands (RETS).

Figueroa, Jorge F.

Flexible All-Digital Receiver for Bandwidth Efficient Modulations

An all-digital high data rate parallel receiver architecture developed jointly by Goddard Space Flight Center and the Jet Propulsion Laboratory is presented. This receiver utilizes only a small number of high speed components along with a majority of lower speed components operating in a parallel frequency domain structure implementable in CMOS, and can currently process up to 600 Mbps with standard QPSK modulation. Performance results for this receiver for bandwidth efficient QPSK modulation schemes such as square-root raised cosine pulse shaped QPSK and Feher's patented QPSK are presented, demonstrating the flexibility of the receiver architecture.

Gray, Andrew

A 90GHz Bolometer Camera Detector System for the Green Bank Telescope

We describe a close-packed, two-dimensional imaging detector system for operation at 90GHz (3.3mm) for the 100 m Green Bank Telescope (GBT) This system will provide high sensitivity (<1mjy in 1s rapid imaging (15'x15' to 250 microJy in 1 hr) at the world's largest steerable aperture. The heart of this camera is an 8x8 close packed, Nyquist-sampled array of superconducting transition edge sensor bolometers. We have designed and are producing a functional superconducting bolometer array system using a monolithic planar architecture and high-speed multiplexed readout electronics. With an NEP of approx. 2.10(exp 17) W/square root Hz, the TES bolometers will provide fast linear sensitive response for high performance imaging. The detectors are read out by and 8x8 time domain SQUID multiplexer. A digital/analog electronics system has been designed to enable read out by SQUID multiplexers. First light for this instrument on the GBT is expected within a year.

Benford, Dominic J.

Modeling Spacecraft Safe Mode Events

Spacecraft enter a ‘safe mode’ to protect the vehicle when a potentially harmful anomaly occurs. This minimally functioning state isolates faults, establishes contact with Earth, and orients the vehicle into a power positive attitude until operators intervene. Though ‘safings’ are inherently unpredictable, mission teams build in time margin during operations to determine root causes and restore functionality. Planning and managing this margin is both critical and enabling on mission architectures dependent on near-continuous operability – such as a low-thrust electric propulsion mission. To better quantify the occurrences and severity of safe mode anomalies, the Jet Propulsion Laboratory (JPL) has assembled a database of safings from past and active missions. Currently nearly 240 records are captured from 21 beyond-Earth missions, stemming from a collaboration between teams at JPL, Ames Research Center, Goddard Space Flight Center, and the Johns Hopkins University Applied Physics Laboratory. This paper discusses the event database, explores a statistical approach in modeling the occurrences and severity of safing events, presents a simulation technique, and details recommendations and future work to benefit future concepts.

Nicholas, Austin

Crowdsourcing the Frontier: Advancing Hybrid Physics‐ML Climate Simulation via a $\$$50,000 Kaggle Competition

Subgrid machine-learning (machine learning [ML]) parameterizations have the potential to introduce a new generation of climate models that incorporate the effects of higher-resolution physics without incurring the prohibitive computational cost associated with more explicit physics-based simulations. However, important issues, ranging from online instability to inconsistent online performance, have limited their operational use for long-term climate projections. To more rapidly drive progress in solving these issues, domain scientists and ML researchers opened up the offline aspect of this problem to the broader ML and data science community with the release of ClimSim, a NeurIPS Data sets and Benchmarks publication, and an associated Kaggle competition. This paper reports on the downstream results of the Kaggle competition by coupling emulators inspired by the winning teams' architectures to an interactive climate model (including full cloud microphysics, a regime historically prone to online instability) and systematically evaluating their online performance. Our results demonstrate that online stability in the low-resolution real-geography setting is reproducible across multiple diverse architectures, which we consider a key milestone. All tested architectures exhibit strikingly similar offline and online biases, though their responses to architecture-agnostic design choices (e.g., expanding the list of input variables) can differ significantly. Multiple Kaggle-inspired architectures achieve state-of-the-art results on certain metrics such as zonal mean bias patterns and global Root Mean Squared Error, indicating that crowdsourcing the essence of the offline problem is one path to improving online performance in hybrid physics-AI climate simulation.

Environmental sciences

A 90GHz Bolometer Camera Detector System for the Green

We describe a close-packed, two-dimensional imaging detector system for operation at 90GHz (3.3 mm) for the 100m Green Bank Telescope (GBT). This system will provide high sensitivity (less than 1mJy in 1s) rapid imaging (15'x15' to 150 micron Jy in 1 hr) at the world's largest steerable aperture. The heart of this camera is an 8x8 close-packed, Nyquist-sampled array of superconducting transition edge sensor (TES) bolometers. We have designed and are producing a functional superconducting bolometer array system using a monolithic planar architecture and high-speed multiplexed readout electronics. With an NEP of approximately 2 x 10(exp -17) W/square root of Hz, the TES bolometers will provide fast, linear, sensitive response for high performance imaging. The detectors are read out by an 8x8 time domain SQUID multiplexer. A digital/analog electronics system has been designed to enable read out by SQUID multiplexers. First light for this instrument on the GBT is expected within a year.

Benford, Dominic J.

GISMO

The 2 mm (1 50 GHz) atmospheric window enables unparalleled ground-based observations of the earliest active dusty galaxies in the universe. We have undertaken the development of a bolometer camera, GISMO (the Goddard-IRAM Superconducting 2-Millimeter Observer), which will obtain large and sensitive sky maps at this wavelength. The instrument will be used at the IRAM 30 m telescope, where we hope to have a trial observing run in Spring of 2007. The innovative element in this camera is its 8x1 6 fully sampled planar array of multiplexed superconducting transition edge sensor bolometers. This array is based on our recently demonstrated Backshort Under Grid architecture, designed to be scaled to kilopixel arrays with high sensitivity (of around 4 * 10 (exp -17)/ W* square root of Hz at T(sub c) approximates 450mK). A compact cryogenic optical system provides a wide field of view, (almost 2'x4') enabling GISMO to be very efficient at detecting sources serendipitously in large sky surveys, while retaining diffraction-limited imaging performance. GISMO will provide significantly greater imaging sensitivity and mapping speed at this wavelength than has previously been possible. The major scientific driver for the instrument is to detect dust emission from high-z Ultraluminous Infrared Galaxies (ULIRGs) and quasars. The instrument provides an important portion of the spectrum of high redshift galaxies at the Rayleigh-Jeans part of the dust emission spectrum, even at the highest redshifts. Models of galaxy evolution predict that GISMO will serendipitously detect one galaxy in every four hours of observing blank sky, and that one quarter of these galaxies will be at a redshift of z > 6.5.

Benford, Dominic

Component-Level Electronic-Assembly Repair (CLEAR) Synthetic Instrument Capabilities Assessment and Test Report

The role of synthetic instruments (SIs) for Component-Level Electronic-Assembly Repair (CLEAR) is to provide an external lower-level diagnostic and functional test capability beyond the built-in-test capabilities of spacecraft electronics. Built-in diagnostics can report faults and symptoms, but isolating the root cause and performing corrective action requires specialized instruments. Often a fault can be revealed by emulating the operation of external hardware. This implies complex hardware that is too massive to be accommodated in spacecraft. The SI strategy is aimed at minimizing complexity and mass by employing highly reconfigurable instruments that perform diagnostics and emulate external functions. In effect, SI can synthesize an instrument on demand. The SI architecture section of this document summarizes the result of a recent program diagnostic and test needs assessment based on the International Space Station. The SI architecture addresses operational issues such as minimizing crew time and crew skill level, and the SI data transactions between the crew and supporting ground engineering searching for the root cause and formulating corrective actions. SI technology is described within a teleoperations framework. The remaining sections describe a lab demonstration intended to show that a single SI circuit could synthesize an instrument in hardware and subsequently clear the hardware and synthesize a completely different instrument on demand. An analysis of the capabilities and limitations of commercially available SI hardware and programming tools is included. Future work in SI technology is also described.

Oeftering, Richard C.

Stable Machine‐Learning Parameterization of Subgrid Processes in a Comprehensive Atmospheric Model Learned From Embedded Convection‐Permitting Simulations

Modern climate projections often suffer from inadequate spatial and temporal resolution due to computational limitations, resulting in inaccurate representations of sub-grid processes. A promising technique to address this is the multiscale modeling framework (MMF), which embeds a kilometer-resolution cloud-resolving model (CRM) within each atmospheric column of a host climate model to replace traditional convection and cloud parameterizations. Machine learning offers a unique opportunity to make MMF more accessible by emulating the embedded CRM and reducing its substantial computational cost. Although many studies have demonstrated proof-of-concept success of achieving stable hybrid simulations, it remains a challenge to achieve near operational-level success with real geography and comprehensive variable emulation that includes, for example, explicit cloud condensate coupling. In this study, we present a stable hybrid model capable of integrating for at least 5 years with near operational-level complexity, including coarse-grid geography, seasonality, explicit cloud condensate and wind predictions, and land coupling. Our model demonstrates skillful online performance, achieving a 5-year zonal mean tropospheric temperature bias within 2 K, water vapor bias within 1 g/kg, and a precipitation root mean square error of 0.96 mm/day. Key factors contributing to our online performance include an expressive U-Net architecture and physical thermodynamic constraints for microphysics. With microphysical constraints mitigating unrealistic cloud formation, our work is the first to demonstrate realistic multi-year cloud condensate climatology under the MMF framework. Despite these advances, online diagnostics reveal persistent biases in certain regions, highlighting the need for innovative strategies to further optimize online performance.

Hu, Zeyuan [NVIDIA Corporation, Santa Clara, CA (U

Three approaches to reliability analysis

It is noted that current reliability analysis tools differ not only in their solution techniques, but also in their approach to model abstraction. The analyst must be satisfied with the constraints that are intrinsic to any combination of solution technique and model abstraction. To get a better idea of the nature of these constraints, three reliability analysis tools (HARP, ASSIST/SURE, and CAME) were used to model portions of the Integrated Airframe/Propulsion Control System architecture. When presented with the example problem, all three tools failed to produce correct results. In all cases, either the tool or the model had to be modified. It is suggested that most of the difficulty is rooted in the large model size and long computational times which are characteristic of Markov model solutions.

Mesh networks

On recursive least-squares filtering algorithms and implementations

In many real-time signal processing applications, fast and numerically stable algorithms for solving least-squares problems are necessary and important. In particular, under non-stationary conditions, these algorithms must be able to adapt themselves to reflect the changes in the system and take appropriate adjustments to achieve optimum performances. Among existing algorithms, the QR-decomposition (QRD)-based recursive least-squares (RLS) methods have been shown to be useful and effective for adaptive signal processing. In order to increase the speed of processing and achieve high throughput rate, many algorithms are being vectorized and/or pipelined to facilitate high degrees of parallelism. A time-recursive formulation of RLS filtering employing block QRD will be considered first. Several methods, including a new non-continuous windowing scheme based on selectively rejecting contaminated data, were investigated for adaptive processing. Based on systolic triarrays, many other forms of systolic arrays are shown to be capable of implementing different algorithms. Various updating and downdating systolic algorithms and architectures for RLS filtering are examined and compared in details, which include Householder reflector, Gram-Schmidt procedure, and Givens rotation. A unified approach encompassing existing square-root-free algorithms is also proposed. For the sinusoidal spectrum estimation problem, a judicious method of separating the noise from the signal is of great interest. Various truncated QR methods are proposed for this purpose and compared to the truncated SVD method. Computer simulations provided for detailed comparisons show the effectiveness of these methods. This thesis deals with fundamental issues of numerical stability, computational efficiency, adaptivity, and VLSI implementation for the RLS filtering problems. In all, various new and modified algorithms and architectures are proposed and analyzed; the significance of any of the new method depends crucially on specific application.

Hsieh, Shih-Fu