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Capristan, Francisco M.

Publications and source records attributed to Capristan, Francisco M..

Aircraft Analysis Using the Layered and Extensible Aircraft Performance System (LEAPS)

The Layered and Extensible Aircraft Performance System (LEAPS) is a new air- craft analysis tool being developed by members of the Aeronautics Systems Analysis Branch (ASAB) and the Vehicle Analysis Branch (VAB) at NASA Langley Research Center. LEAPS will enable the analysis of advanced aircraft concepts and architec- tures that include electric and hybrid-electric propulsion systems. The development of LEAPS is motivated by the analysis gaps found in traditional aircraft analysis tools such as the Flight Optimization System (FLOPS). FLOPS has been the tool of choice of the ASAB for over 30 years and has proven to be a reliable analysis tool for conventional aircraft. However, FLOPS is not suitable to analyze the cur- rent unconventional vehicles that are of interest to industry, government agencies, and academia. In contrast, LEAPS is being developed with a flexible architecture that leverages new analysis methodologies that will enable the analysis of unconven- tional aircraft. This paper presents the first complete working version of LEAPS by showing the analysi at include fuel-based and hybrid-electric conceptual aircraft.

Capristan, Francisco M.↗

Analysis of the Parallel Electric-Gas Architecture with Synergistic Utilization Scheme (PEGASUS) Concept

The Parallel Electric-Gas Architecture with Synergistic Utilization Scheme (PE- GASUS) vehicle is a regional aircraft concept that uses electric and hybrid-electric propulsors located strategically to obtain aerodynamic and mission benefits. Traditional aircraft analysis tools are not well suited to analyze the PEGASUS aircraft due to the different propulsor types used. This report summarizes a methodology that addresses some of the mission analysis challenges expected in modeling this vehicle concept. An initial baseline design is selected and sensitivity studies are performed to further understand the potential benefits of the concept.

Capristan, Francisco M.↗

In-Time Non-Participant Casualty Risk Assessment to Support Onboard Decision Making for Autonomous Unmanned Aircraft

Numerous operational paradigms, technologies, and missions are emerging as newcomers to the National Airspace System (NAS) develop small Unmanned Aircraft Systems (sUAS), personal air vehicles and other Urban Air Mobility (UAM) concepts. As the list of applications expands, maintaining the safety of the current airspace system remains one of the core concerns preventing widespread commercial implementation of these concepts. Further, the risks associated with unmanned aircraft operations themselves have to be recognized and mitigated in a timely manner. Safety-critical risks include, but are not limited to, flight outside of approved airspace, unsafe proximity to people or property, critical system failures, loss-of control, and cyber-security related risks. Instead of reacting to accidents, a set of predictive and data-driven risk monitoring, assessment, and mitigation capabilities are envisioned to help capture and eliminate hazards as these systems become operational. NASA’s System-wide Safety project is performing R&D on such a safety assurance concept. As part of this concept, this paper describes an architecture that continuously monitors a diverse set of onboard and ground-based sources to estimate and predict non-participant casualty risk during flight. Timely identification of the changing nature of this risk can inform decision making processes to mitigate current and impending situations.

Ancel, Ersin↗

An Energy-Based Low-Order Approach for Mission Analysis of Air Vehicles in LEAPS

Mission analysis methods that allow rapid analysis of conceptual aircraft provide great benefits to designers because a large number of feasibility and optimization studies can be performed quickly. This paper presents a novel low-order mission analysis approach that will be included in a new aircraft analysis tool called the Layered and Extensible Aircraft Performance System, also known as LEAPS. This approach is based on the mission analysis method used in the Flight Optimization System, also known as FLOPS. FLOPS has been developed at NASA Langley Research Center for over 30 years, and it is mainly used to analyze conventional aircraft (e.g., gas turbine-powered aircraft). One of the main features of FLOPS is its ability to perform rapid mission analysis. This quick analysis turnaround makes FLOPS a suitable tool to perform a large number of trade studies and optimizations. For this reason, the main concepts applied in FLOPS will be used and expanded in LEAPS to allow the analysis of unconventional aircraft that use novel propulsion systems. This expanded approach includes the capability to handle electric propulsion systems as well as multiple propulsor classes at the same time. A novel hybrid-electric aircraft configuration is used to investigate the potential benefits of the proposed mission analysis approach. The results show that the design space increases which can potentially lead to better designs.

Capristan, Francisco M.↗

Real-Time Risk Assessment Framework for Unmanned Aircraft System (UAS) Traffic Management (UTM)

The new Federal Aviation Administration (FAA) Small Unmanned Aircraft rule (Part 107) marks the first national regulations for commercial operation of small unmanned aircraft systems (sUAS) under 55 pounds within the National Airspace System (NAS). Although sUAS flights may not be performed beyond visual line-of-sight or over non- participant structures and people, safety of sUAS operations must still be maintained and tracked at all times. Moreover, future safety-critical operation of sUAS (e.g., for package delivery) are already being conceived and tested. NASA's Unmanned Aircraft System Trac Management (UTM) concept aims to facilitate the safe use of low-altitude airspace for sUAS operations. This paper introduces the UTM Risk Assessment Framework (URAF) which was developed to provide real-time safety evaluation and tracking capability within the UTM concept. The URAF uses Bayesian Belief Networks (BBNs) to propagate off -nominal condition probabilities based on real-time component failure indicators. This information is then used to assess the risk to people on the ground by calculating the potential impact area and the effects of the impact. The visual representation of the expected area of impact and the nominal risk level can assist operators and controllers with dynamic trajectory planning and execution. The URAF was applied to a case study to illustrate the concept.

Ancel, Ersin↗