High-speed civil transport flight- and propulsion-control technological issues

Technology advances required in the flight and propulsion control system disciplines to develop a high speed civil transport (HSCT) are identified. The mission and requirements of the transport and major flight and propulsion control technology issues are discussed. Each issue is ranked and, for each issue, a plan for technology readiness is given. Certain features are unique and dominate control system design. These features include the high temperature environment, large flexible aircraft, control-configured empennage, minimizing control margins, and high availability and excellent maintainability. The failure to resolve most high-priority issues can prevent the transport from achieving its goals. The flow-time for hardware may require stimulus, since market forces may be insufficient to ensure timely production. Flight and propulsion control technology will contribute to takeoff gross weight reduction. Similar technology advances are necessary also to ensure flight safety for the transport. The certification basis of the HSCT must be negotiated between airplane manufacturers and government regulators. Efficient, quality design of the transport will require an integrated set of design tools that support the entire engineering design team

Aeronautical Engineering: A special bibliography with indexes, supplement 51

This bibliography lists 206 reports, articles, and other documents introduced into the NASA Scientific and Technical Information System in November 1974

Applying Machine Learning Techniques to Improve Safety and Mobility of Urban Transportation Systems Using Infrastructure- and Vehicle-Based Sensors

The importance of sensing technologies in the field of transportation is ever increasing. Rapid improvements of cloud computing, Internet of Vehicles (IoV), and intelligent transport system (ITS) enables fast acquisition of sensor data with immediate processing. Machine learning algorithms provide a way to classify or predict outcomes in a selective and timely fashion. High accuracy and increased volatility are the main features of various learning algorithms. In this dissertation, we aim to use infrastructure- and vehicle-based sensors to improve safety and mobility of urban transportation systems. Smartphone sensors were used in the first study to estimate vehicle trajectory using lane change classification. It addresses the research gap in trajectory estimation since all previous studies focused on estimating trajectories at roadway segments only. Being a mobile application-based system, it can readily be used as on-board unit emulators in vehicles that have little or no connectivity. Secondly, smartphone sensors were also used to identify several transportation modes. While this has been studied extensively in the last decade, our method integrates a data augmentation method to overcome the class imbalance problem. Results show that using a balanced dataset improves the classification accuracy of transportation modes. Thirdly, infrastructure-based sensors like the loop detectors and video detectors were used to predict traffic signal states. This system can aid in resolving the complex signal retiming steps that is conventionally used to improve the performance of an intersection. The methodology was transferred to a different intersection where excellent results were achieved. Fourthly, magnetic vehicle detection system (MVDS) was used to generate traffic patterns in crash and non-crash events. Variational Autoencoder was used for the first time in this study as a data generation tool. The results related to sensitivity and specificity were improved by up to 8% as compared to other state-of-the-art data augmentation methods

A Digital Triplet for Utilizing Offline Environments to Train Condition Monitoring Systems for Rolling Element Bearings

Manufacturing competitiveness is related to making a quality product while incurring the lowest costs. Unexpected downtime caused by equipment failure negatively impacts manufacturing competitiveness due to the ensuing defects and delays caused by the downtime. Manufacturers have adopted condition monitoring (CM) techniques to reduce unexpected downtime to augment maintenance strategies. The CM adoption has transitioned maintenance from Breakdown Maintenance (BM) to Condition-Based Maintenance (CbM) to anticipate impending failures and provide maintenance actions before equipment failure. CbM is the umbrella term for maintenance strategies that use condition monitoring techniques such as Preventive Maintenance (PM) and Predictive Maintenance (PdM). Preventive Maintenance involves providing periodic checks based on either time or sensory input. Predictive Maintenance utilizes continuous or periodic sensory inputs to determine the machine health state to predict the equipment failure. The overall goal of the work is to improve bearing diagnostic and prognostic predictions for equipment health by utilizing surrogate systems to generate failure data that represents production equipment failure, thereby providing training data for condition monitoring solutions without waiting for real world failure data. This research seeks to address the challenges of obtaining failure data for CM systems by incorporating a third system into monitoring strategies to create a Digital Triplet (DTr) for condition monitoring to increase the amount of possible data for condition monitoring. Bearings are a critical component in rotational manufacturing systems with wide application to other industries outside of manufacturing, such as energy and defense. The reinvented DTr system considers three components: the physical, surrogate, and digital systems. The physical system represents the real-world application in production that cannot fail. The surrogate system represents a physical component in a test system in an offline environment where data is generated to fill in gaps from data unavailable in the real-world system. The digital system is the CM system, which provides maintenance recommendations based on the ingested data from the real world and surrogate systems. In pursuing the research goal, a comprehensive bearing dataset detailing these four failure modes over different collection operating parameters was created. Subsequently, the collections occurred under different operating conditions, such as speed-varying, load-varying, and steadystate. Different frequency and time measures were used to analyze and identify differentiating criteria between the different failure classes over the differing operating conditions. These empirical observations were recreated using simulations to filter out potential outliers. The outputs of the physical model were combined with knowledge from the empirical observations to create ”spectral deltas” to augment existing bearing data and create new failure data that resemble similar frequency criteria to the original data. The primary verification occurred on a laboratory-bearing test stand. A conjecture is provided on how to scale to a larger system by analyzing a larger system from a local manufacturer. From the subsequent analysis of machine learning diagnosis and prognosis models, the original and augmented bearing data can complement each other during model training. The subsequent data substitution verifies that bearing data collected under different operating conditions and sizes can be substituted between different systems. Ostensibly, the full formulation of the digital triplet system is that bearing data generated at a smaller size can be scaled to train predictive failure models for larger bearing sizes. Future work should consider implementing this method for other systems outside of bearings, such as gears, non-rotational equipment, such as pumps, or even larger complex systems, such as computer numerically controlled machine tools or car engines. In addition, the method and process should not be restricted to only mechanical systems and could be applied to electrical systems, such as batteries. Furthermore, an investigation should consider further data-driven approximations to specific bearing characteristics related to the stiffness and damping parameters needed in modeling. A final consideration is for further investigation into the scalability quantities within the data and how to track these changes through different system levels

NASA patent abstracts bibliography: A continuing bibliography. Section 1: Abstracts (supplement 27)

Abstracts are provided for 92 patents and patent applications entered into the NASA scientific and technical information system during the period January 1985 through June 1985. Each entry consist of a citation, and abstract, and in most cases, a key illustration selected from the patent or patent application

Application of Agisoft Photoscan and sediment transport modeling for the analysis of sediment wave propagation succeeding gravel augmentation, Oak Grove Fork of the Clackamas River, Oregon

Application of agisoft photoscan and sediment transport modeling for the analysis of sediment wave propagation succeeding gravel augmentation, oak grove fork of the clackamas river, oregon Mindi Lea Curran Physical features in alluvial rivers such as riffles, gravel bars, pools, and side channels provide refugia, nutrients, and spawning and rearing habitat for anadromous fish and other aquatic organisms. The downstream transport of gravels that continuously replenish these features is prevented by dams, and often leads to a coarsened channel bed condition and other geomorphic changes that have negative impacts on aquatic organisms. Geomorphic change in rivers can be challenging to capture in high resolution, making the propagation and distribution of sediment difficult to quantify, especially if the deposition occurs in small quantities or thin layers. One solution for replenishing physical features that have been cut off from gravel supply downstream of dams is gravel augmentation. This thesis uses two independent methods to investigate the transport and storage of augmented gravels as they route downstream: 1) topographic change detection using photogrammetry and differencing of Digital Terrain Models (DTMs), and 2) a 1D sediment transport model created in HEC-RAS (Hydrologic Engineering Centers River Analysis System) to model flow and sediment scenarios. Together, these methods are used to investigate sediment wave propagation and channel response to augmented gravels. The location of study is the Oak Grove Fork (OGF), one of the largest tributaries of the Clackamas River, located in northwestern Oregon. The Lake Harriet Dam and diversion were built on the OGF in 1924 as part of a hydroelectric development project by Portland General Electric. Decreased flow and sediment supply downstream of Lake Harriet Dam has resulted in geomorphic and biological changes (including reduced salmonid habitat), leading to a mandated gravel augmentation program that began in September of 2016, which introduced 250 tons of gravel into the river. High resolution DTMs, generated using photogrammetry, captured topographic change at sites on the order of tenths of feet, with vertical accuracy also on the order of tenths of feet. All change detected at photogrammetry sites within one year of augmentation was determined to be a record of typical, natural year-to-year change and is not attributed to transport and deposition of augmented gravels. The 1D sediment transport model suggests that peak flows, exceeding 1,200 cfs, are the primary driving factors of sediment transport, and that higher peak flows exceeding those seen in 2016 and 2017 will be required to transport the augmented gravels downstream 0.81 miles, past a naturally occurring fish barrier waterfall to where anadromous fish habitat begins. A storage capacity estimate calculation suggests that up to 600 tons of gravel could fill interstitial spaces between existing boulders and cobbles as gravel routes downstream, past Barrier Falls, and into accessible habitat

SSTAC/ARTS review of the draft Integrated Technology Plan (ITP). Volume 8: Aerothermodynamics Automation and Robotics (A/R) systems sensors, high-temperature superconductivity

Viewgraphs of briefings presented at the SSTAC/ARTS review of the draft Integrated Technology Plan (ITP) on aerothermodynamics, automation and robotics systems, sensors, and high-temperature superconductivity are included. Topics covered include: aerothermodynamics; aerobraking; aeroassist flight experiment; entry technology for probes and penetrators; automation and robotics; artificial intelligence; NASA telerobotics program; planetary rover program; science sensor technology; direct detector; submillimeter sensors; laser sensors; passive microwave sensing; active microwave sensing; sensor electronics; sensor optics; coolers and cryogenics; and high temperature superconductivity

Aeronautical engineering: A continuing bibliography (supplement 230)

This bibliography lists 563 reports, articles and other documents introduced into the NASA scientific and technical information system in August, 1988