Urban Air Mobility-defined as safe and efficient air traffic operations in a metropolitan area for manned aircraft and unmanned aircraft systems-is being researched and developed by industry, academia, and government. Significant resources have been invested toward cultivating an ecosystem for Urban Air Mobility that includes manufacturers of electric vertical takeoff and landing aircraft, builders of takeoff and landing areas, and researchers of the airspace integration concepts, technologies, and procedures needed to conduct Urban Air Mobility operations safely and efficiently alongside other airspace users. This paper provides high-level descriptions of both emergent and early expanded operational concepts for Urban Air Mobility that NASA is developing. The scope of this work is defined in terms of missions, aircraft, airspace, and hazards. Past and current Urban Air Mobility operations are also reviewed, and the considerations for the data exchange architecture and communication, navigation, and surveillance requirements are also discussed. This paper will serve as a starting point to develop a framework for NASA's Urban Air Mobility airspace integration research and development efforts with partners and stakeholders that could include fast-time simulations, human-in-the-loop simulations, and flight demonstrations.A https://ntrs.nasa.gov/search.jsp?R=20180005218 2020-07-07T23:11:55+00:00Z of ODM that is focused on air traffic operations in metropolitan areas with aircraft capable of seating a small number of passengers or equivalent volume of goods flying trips of about 100 nautical miles (nmi) or less.The technologies and procedures required for ODM were investigated in a NASA study [14] that covered the range of the airspace integration problem, including mission planning, separation from hazards (e.g., terrain, obstacles, other aircraft), contingency management, demand-capacity balancing, traffic flow management, as well as sequencing, scheduling, and spacing. A similar spectrum of topics will be covered in this complementary paper on UAM airspace integration. This paper also describes at a high level NASA's initial airspace integration concepts for both emergent and early expanded UAM operations. It also serves as a framework for NASA's UAM airspace integration research and development efforts with partners and stakeholders.The remainder of this paper is organized as follows. Section II reviews past and current UAM operations. Section III presents an overview of UAM, including the goals, principles, barriers, and benefits. This section also discusses the competing considerations that need to be taken into account and balanced for UAM operations, as well as the requirements for communication, navigation, and surveillance. Section IV defines the scope of the concepts with regard to missions, aircraft, airspace, and hazards. Section V describes at a high level NASA's initial airspace integration concepts for both emergent and early expanded UAM operations. Section VI discusses NASA's plan to develop and refi...

Enterprise-scale storage systems, which can contain hundreds of host computers and storage devices and up to tens of thousands of disks and logical volumes, are difficult to design. The volume of choices that need to be made is massive, and many choices have unforeseen interactions. Storage system design is tedious and complicated to do by hand, usually leading to solutions that are grossly overprovisioned, substantially under-performing or, in the worst case, both.To solve the configuration nightmare, we present MINERVA: a suite of tools for designing storage systems automatically. MINERVA uses declarative specifications of application requirements and device capabilities; constraint-based formulations of the various subproblems; and optimization techniques to explore the search space of possible solutions. This paper also explores and evaluates the design decisions that went into MINERVA, using specialized micro and macro-benchmarks. We show that MINERVA can successfully handle a workload with substantial complexity (a decision-support database benchmark). MINERVA created a 16-disk design in only a few minutes that achieved the same performance as a 30-disk system manually designed by human experts. Of equal importance, MINERVA was able to predict the r esulting system's performance before it was built. AbstractEnterprise-scale storage systems, which can contain hundreds of host computers and storage devices and up to tens of thousands of disks and logical volumes, are difficult to design. The volume of choices that need to be made is massive, and many choices have unforeseen interactions. Storage system design is tedious and complicated to do by hand, usually leading to solutions that are grossly over-provisioned, substantially under-performing or, in the worst case, both.To solve the configuration nightmare, we present MIN-ERVA: a suite of tools for designing storage systems automatically. MINERVA uses declarative specifications of application requirements and device capabilities; constraintbased formulations of the various sub-problems; and optimization techniques to explore the search space of possible solutions. This paper also explores and evaluates the design decisions that went into MINERVA, using specialized microand macro-benchmarks. We show that MINERVA can successfully handle a workload with substantial complexity (a decision-support database benchmark). MIN-ERVA created a 16-disk design in only a few minutes that achieved the same performance as a 30-disk system manually designed by human experts. Of equal importance, MINERVA was able to predict the resulting system's performance before it was built.

Pratt & Whitney's RL10 engine line has a long and rich history, beginning in 1958 and continuing today. This paper provides a historical summary of launch vehicles using RL10 engine derivatives dating from 1962-2005. The historical launch data is used to derive baseline launch success rates and growth curves for vehicles configured with RL10 engines in the upper stage. Because it was the first liquid hydrogen fueled rocket engine, the RL10 engine launch history provides a unique opportunity to investigate the maturity trends for revolutionary new complex systems. All of the data used in this survey was acquired through publicly-available sources [1-21]. In all, 190 vehicles configured with RL10 upper stage engines were launched between 1962 and 2005. There were 12 upper stage failures that either failed to reach orbit, or reached a lower, unintended orbit. The early failures were dominated by knowledge gaps in system interactions and operational flight conditions. There is a clear trend of early development growth with an eventual plateau as system knowledge improved as a result of flight experience and more thorough test programs. Failures due to process-based issues (fabrication techniques, quality control, etc.), however, do not appear to exhibit maturity growth. Eventually, as the knowledge-based failures are removed, these process-based failures become the dominant risk driver. Vehicles that use mature, highly-reliable components are still vulnerable to process or functional changes, and failures of this type occur fairly uniformly with flight experience. In order to improve future reliability estimates for such systems, it is important to understand the trends and relationship between the knowledge-based and process-based issues, and determine which class of issues currently dominates. It should be noted that of the 12 upper stage failures, only one was caused by a defective part.

A simulation-based risk assessment approach is presented and is applied to the analysis of abort during the ascent phase of a space exploration mission. The approach utilizes groupings of launch vehicle failures, referred to as failure bins, which are mapped to corresponding failure environments. Physical models are used to characterize the failure environments in terms of the risk due to blast overpressure, resulting debris field, and the thermal radiation due to a fireball. The resulting risk to the crew is dynamically modeled by combining the likelihood of each failure, the severity of the failure environments as a function of initiator and time of the failure, the robustness of the crew module, and the warning time available due to early detection. The approach is shown to support the launch vehicle design process by characterizing the risk drivers and identifying regions where failure detection would significantly reduce the risk to the crew.

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