Results for “UAM” — Page 14

NASA NTRSDate not supplied

Quantifying Effects of Departure and Flight Time Uncertainty on Urban Air Mobility Operations

Demand capacity balancing is a key mechanism for maintaining safe and efficient Urban Air Mobility (UAM) operations. However, uncertainties such as departure delays and flight time variation may reduce the effectiveness of algorithms used for balancing and detrimentally impact the safety and efficiency of UAM operations. In this paper, the effects of these uncertainties on UAM operations are quantified by modeling a distribution of departure and flight time errors. A route network in the Dallas/Fort Worth metropolitan area was used to simulate traffic demand with and without uncertainty. Simulations were conducted with three main models of uncertainty – first uncertainty in departure time delay resulting in late takeoffs, second with uncertainty in flight times in addition to departure delays, and finally, uncertainty in departure times that cause either late or early takeoffs. Each of these simulations were performed using varied standard deviations to fully understand the effects of uncertainties. Results from these simulations were compared to a baseline simulation using the same parameters, but without any uncertainty. The results suggest that both safety and efficiency are significantly impacted by uncertainty even with relatively low uncertainty introduced. These results work towards quantifying the effects of uncertainty in flight scheduling for UAM. They will also aid in the further development of the demand capacity balancing algorithms for UAM operations and associated air traffic management.

Urban Air Mobility↗

NASA NTRSDate not supplied

Quantifying the Effects of Departure and Flight Time Uncertainty on Urban Air Mobility Operations

Demand capacity balancing is a key mechanism for maintaining safe and efficient Urban Air Mobility (UAM) operations. However, uncertainties such as departure delays and flight time variation may reduce the effectiveness of algorithms used for balancing and detrimentally impact the safety and efficiency of UAM operations. In this paper, the effects of these uncertainties on UAM operations are quantified by modeling a distribution of departure and flight time errors. A route network in the Dallas/Fort Worth metropolitan area was used to simulate traffic demand with and without uncertainty. Simulations were conducted with three main models of uncertainty – first uncertainty in departure time delay resulting in late takeoffs, second with uncertainty in flight times in addition to departure delays, and finally, uncertainty in departure times that cause either late or early takeoffs. Each of these simulations were performed using varied standard deviations to fully understand the effects of uncertainties. Results from these simulations were compared to a baseline simulation using the same parameters, but without any uncertainty. The results suggest that both safety and efficiency are significantly impacted by uncertainty even with relatively low uncertainty introduced. These results work towards quantifying the effects of uncertainty in flight scheduling for UAM. They will also aid in the further development of the demand capacity balancing algorithms for UAM operations and associated air traffic management.

Urban Air Mobility↗

DOE OSTI2021

Ultrafast charging of energy-dense lithium-ion batteries for urban air mobility

Urban air mobility (UAM) demands batteries with high energy density, long cycle life, and fast rechargeability. In this paper, we demonstrate an energy-dense lithium-ion battery (LiB) with ultralong cycle life under ultrafast charging. By using the asymmetric temperature modulation (ATM) method, i.e., charging at an elevated temperature and discharging around the ambient temperature, it is experimentally shown that the 209 Wh/kg LiB is charged to 88% state of charge (SOC) in ~5 min under UAM cycling while retaining 97.7% capacity after 1,000 cycles. Moreover, an experimentally validated electrochemical-thermal (ECT) model is developed to elucidate the fast charging process and the degradation mode of UAM batteries, quantitatively capturing lithium plating during fast charging. We find that the LiBs for UAM applications are most prone to lithium plating due to their higher initial SOC required as the reserve for safety; nevertheless, the ATM method is effective in minimizing or preventing lithium plating in the high SOC range of 30-90%. In addition to slowing down capacity fade, the ATM method also raises the usable capacity by 10%, which boosts the battery energy density and ensures the battery to perform full UAM cycles even at the end of life.

25 ENERGY STORAGE↗

NASA NTRS2020

Electrical Cable Design for Urban Air Mobility Aircraft

Urban Air Mobility (UAM) describes a new type of aviation focused on efficient flight within urban areas for moving people and goods. There are many different configurations of UAM vehicles, but they generally use an electric motor driving a propeller or ducted fan powered by batteries or a hybrid electric power generation system. Transmission cables are used to move energy from the storage or generation system to the electric motors. Though terrestrial power transmission cables are well established technology, aviation applications bring a whole host of new design challenges that are not typical considerations in terrestrial applications. Aircraft power transmission cable designs must compromise between resistance-per-length, weight-per-length, volume constraints, and other essential qualities. In this paper we use a multidisciplinary design optimization to explore the sensitivity of these qualities to a representative tiltwing turboelectric UAM aircraft concept. This is performed by coupling propulsion and thermal models for a given mission criteria. Results presented indicate that decreasing cable weight at the expense of increasing cable volume or cooling demand is effective at minimizing maximum takeoff weight (MTO). These findings indicate that subsystem designers should update their modeling approach in order to contribute to system-level optimality for highly-coupled novel aircraft. Mobility (UAM) vehicles have the potential to change urban and intra-urban transport in new and interesting ways. In a series of two papers Johnson et al.1 and Silva et al.2 presented four reference vehicle configurations that could service different niches in the UAM aviation category. Of those, this paper focuses on the Vertical Take-off and Landing (VTOL) tiltwing configuration shown in Figure 1. This configuration uses a turboelectric power system, feeding power from a turbo-generator through a system of transmission cables to four motors spinning large propellers on the wings. Previous work on electric cable subsystems leaves much yet to be explored, especially in the realm of subsystem coupling. Several aircraft optimization studies1, 3, 4 only considered aircraft electrical cable weight and ignored thermal effects. Electric and hybrid-electric aircraft studies by Mueller et al.5 and Hoelzen et al.6 selected a cable material but did not investigate alternative materials. Advanced cable materials have been examined by a number of authors: Alvarenga7 examined carbon nanotube (CNT) conductors for low-power applications. De Groh8, 9 examined CNT conductors for motor winding applications. Behabtu et al.,10 and Zhao et al.11 examined CNT conductors for a general applications. There were some studies that examined the thermal effects of cables but they did not allow the cable material to change; El-Kady12 optimized ground-cable insulation and cooling subject constraints. Vratny13 selected cable material based on vehicle power demand, and required resulting cable heat to be dissipated by the Thermal Management System (TMS). None of these previous studies allowed for the selection of the cable material based on a system level optimization goal. Instead, they focused on sub-system optimality such as minimum weight, which comes at the expense of incurring additional costs for other subsystems. Dama14 selected overhead transmission line materials using a weighting function and thermal constraints. However, that work was not coupled with any aircraft subsystems like a TMS. The traditional aircraft design approach, which relies on assembling groups of optimal subsystems, breaks down when considering novel aircraft concepts like the tiltwing vehicle. In a large part, this is because novel concepts have a much higher degree of interaction or coupling between subsystems. For example, when a cable creates heat, this heat needs to be dissipated by the TMS, which needs power supplied by the turbine, and delivering the power creates more heat. The cable, the TMS, and the turbine are all coupled. A change to one subsystem will affect all the other subsystems, much to the consternation of subsystem design experts. Multidisciplinary optimization is the design approach that can address these challenges. However, to fully take advantage of this, we must change the way we think about subsystem design. Specifically, we must move away from point design, and focus on creating solution spaces. The work presented in this paper uses the multidisciplinary optimization approach with aircraft level models to study the system-level sensitivity of cable traits: weight-per-length and resistance-per-length. Additionally, we examined the effects of vehicle imposed volume constraints on these traits. This is useful for three purposes: (1) to demonstrate a framework that can perform a coupled analysis between the aircraft thermal and propulsion systems, (2) to provide a method by which future cable designs can be evaluated against each other given a system-level design goal, (3) to provide insight into what cable properties may be promising for future research. This last element is explored given the caveat that the models contained in this analysis do not represent high-fidelity systems. Thus, while we can demonstrate coupling in between systems, the exact system-level sensitivity to a given parameter may change if a subsystem model or the assumptions governing that model change. The organization of this paper is as follows, in Sec II we outline a method to combine the VTOL vehicle design and cable information in order to produce cables sensitivity studies. Results analysis and discussion are contained in Sec III. Conclusions are presented in Sec IV.

Aretskin-Hariton, Eliot D.↗

NASA NTRSDate not supplied

Strategic Planning with Unscented Optimal Guidance for Urban Air Mobility

This study proposes a strategic trajectory planning framework to support development of Urban Air Mobility (UAM) traffic networks and optimization of UAM aircraft trajectories that are robust to uncertain wind fields. The development of UAM traffic networks considers static aviation constraints and dynamic weather constraints in urban airspace and the connections to ground transportation networks for preliminary selection of feasible time-optimal routes. The trajectory optimization of UAM aircraft utilizes an unscented optimal guidance approach to generate cost-optimal trajectories constrained on the selected sigma values of probability distribution of uncertain wind fields while ensuring that the end-point constraints are met for reducing wind-induced trajectory uncertainty. Unscented guidance commands are assessed based on trajectory perturbations at subsequent end-points in various wind fields by conducting Monte Carlo simulations.An example of wind-optimal UAM corridor and the associated trajectory-based operation volume is created utilizing the perturbations of the unscented trajectories for preliminary assessment of required aircraft separation minima without knowledge of aircraft navigation performance.

Strategic Planning↗

NASA NTRSDate not supplied

Thinking outside the box: The human role in increasingly automated aviation systems

Rapid advances in artificial intelligence are enabling automated systems to operate in an increasingly autonomous manner in domains that previously required the involvement of human operators. Examples are rail transport systems, self-driving cars, and warehouse delivery systems. From time to time, such automation encounters operational conditions that fall outside a “competency box” within which the system has been designed to operate. Human operators add resilience because they can see and act outside the competency box of scenarios and environments for which the system was designed. The system’s competencies can be expanded over time with modifications to software, sensors, etc.; however, it is unclear at what point the competency box becomes large enough to safely eliminate the role of the human operator. One area where advanced automation may be applied is Urban Air Mobility (UAM). Current UAM concepts envision fleets of highly automated air vehicles providing on-demand transport for people and goods. A phased development of UAM has been proposed, beginning with on-board pilots and transitioning to a future state where automated vehicles operate with minimal human involvement. Proponents of UAM note that this final state reduces cost as well as eliminating pilot error, identified as a contributing factor in many aircraft accidents. However, eliminating human involvement also risks eliminating their positive contributions to system resilience. Here we examine Concepts of Operation proposed for future UAM systems and explore how humans can best be incorporated to maintain resilience while minimizing cost and risk. A human-autonomy teaming approach is suggested.

Advanced Air Mobility↗

NASA NTRSDate not supplied

SIRIUS: Simulation Infrastructure for Research on Interoperating Unmanned Systems

This paper presents the Simulation Infrastructure for Research on Interoperating Unmanned Systems (SIRIUS), a research framework for simulation and analysis of future conceptual Urban Air Mobility (UAM) operations. SIRIUS is being developed under the auspices of the NASA Air Traffic Management eXploration project, UAM subproject (ATM-X UAM). SIRIUS provides an intuitive, highly configurable graphical user interface to design complex traffic scenarios and airspace configurations representative of conceptual UAM operations. Aircraft simulated with SIRIUS can be equipped with flight-tested capabilities for detect and avoid (DAA), geofencing, distributed merging and spacing, path conformance, and path planning while executing time-constrained, 4D trajectories generated by a UAM ground operations system. Central to the design of the SIRIUS simulation framework is the capability to evaluate the integration and interoperability of ground-based separation services (e.g., strategic separation) with extended DAA functionality (e.g., path monitoring, separation provision, merging and spacing, etc.) The simulation environment also supports modelling of wind, navigation, and sensor uncertainties, as well as communication delays. SIRIUS enables distributed simulation of large-scale scenarios. An interactive graphical analysis capability helps isolate, visualize, and compare relevant vehicle state data and widely used measures of performance metrics across multiple scenarios.

Andrew Peters↗

NASA NTRS2021

Development of Near-term Urban Air Mobility Routes and Airspace Integration

Growing interest in Urban Air Mobility (UAM) has been demonstrated by a great deal of investment in related research made by industry, government, and academia. Based on this research effort along with considerations of the maturing concept for UAM airspace integration, a cognitive walkthrough exercise and a human-in-the-loop simulation were conducted with controller subject matter experts (SMEs) to evaluate Dallas-Fort Worth (DFW) current day helicopter routes for near-term use as UAM routes. One of the outcomes of this work is a set of heuristics that should be applied when designing UAM routes in the future. The following heuristics were identified as critical to UAM route design: the proximity of routes to surrounding airports (including approach and departure paths of traditional commercial traffic), the configuration of surrounding airports, avoiding congested or heavily populated areas, avoiding route segments that would go through several sectors, avoiding route segments that would go in and out of Class B airspace, creating routes outside of Class B airspace when able, using routes with two-way, altitude-separated traffic when able, minimizing the length of the route, avoiding commonly placed Temporary Flight Restrictions, and creating Non-Movement Areas or UNICOM Areas. This paper describes each of these identified heuristics and relevant examples based on a human-in-the-loop study conducted using the DFW area airspace.

Urban Air Mobility↗

NASA NTRSDate not supplied

Feasibility Study of Distributed Decision-Making on the Edge for Urban Air Mobility

The Concept of Operations for Urban Air Mobility (UAM) put forward by FAA, NASA, and several industry stakeholders acknowledges the diversity and complexity in UAM operations and, thereby, envisions a federated architecture for UAM management. In this architecture, the decision-making is distributed to a set of service providers who collectively manage the shared airspace usage by different stakeholders. This notionally brings autonomy closer to the UAM businesses and encourages to explore the feasibility of decision making on the very edge, which is the topic of the presented research. This paper reports research conducted on the hypothesis based on which the residual compute capability onboard smart unmanned aerial systems (UASs) is utilized to build situational awareness and resolve conflicts by passive and active coordination among multiple UASs, thereby implementing a layer of distributed autonomy in UAM. Key features of the edge-computing approach involve inter-UAS information exchange, independent assessment of own flight and environmental conditions, and estimation of other UASs’ flight preferences, incorporating machine learning techniques in the last two. Parallel computing on portable graphics processing unit (GPU) enables the machine learning workflow on the edge. A custom-built 3D simulator is used to evaluate the efficacy of the distributed decision-making on the edge. Each edge node, representing a smart UAS, connects to the simulator from a remote location and independently controls the behavior of the corresponding virtual asset in the simulator, analogous to participants in an online multi-player game. The presented edge-computing-based distributed decision-making framework is envisioned to pave the way for collective mobility of autonomous air vehicles in the future shared airspace, while allowing the inclusion of the business preferences of the UAS operators within allowed regulatory limits.

Edge computing↗

NASA NTRSDate not supplied

Feasibility Study of Distributed Decision-Making on the Edge for Urban Air Mobility

The Concept of Operations for Urban Air Mobility (UAM) put forward by FAA, NASA, and several industry stakeholders acknowledges the diversity and complexity in UAM operations and, thereby, envisions a federated architecture for UAM management. In this architecture, the decision-making is distributed to a set of service providers who collectively manage the shared airspace usage by different stakeholders. This notionally brings autonomy closer to the UAM businesses and encourages to explore the feasibility of decision making on the very edge, which is the topic of the presented research. This paper reports research conducted on the hypothesis based on which the residual compute capability onboard smart unmanned aerial systems (UASs) is utilized to build situational awareness and resolve conflicts by passive and active coordination among multiple UASs, thereby implementing a layer of distributed autonomy in UAM. Key features of the edge-computing approach involve inter-UAS information exchange, independent assessment of own flight and environmental conditions, and estimation of other UASs’ flight preferences, incorporating machine learning techniques in the last two. Parallel computing on portable graphics processing unit (GPU) enables the machine learning workflow on the edge. A custom-built 3D simulator is used to evaluate the efficacy of the distributed decision-making on the edge. Each edge node, representing a smart UAS, connects to the simulator from a remote location and independently controls the behavior of the corresponding virtual asset in the simulator, analogous to participants in an online multi-player game. The presented edge-computing-based distributed decision-making framework is envisioned to pave the way for collective mobility of autonomous air vehicles in the future shared airspace, while allowing the inclusion of the business preferences of the UAS operators within allowed regulatory limits.

Edge computing↗

NASA NTRSDate not supplied

Human Factors Research Considerations for Terminal Area Urban Air Mobility Operations

In this presentation, we discuss the human factors research challenges from introducing greater levels of automation in a future air transportation concept called Urban Air Mobility (UAM). UAM is an air transportation concept that aims to provide air transportation services to the daily commuter, as well as emergency response and package delivery. The principal innovation over current day large air transport system is the greater distribution of important safety functions to automated and human agents; these functions include air traffic management, traditionally an air traffic controller responsibility. A central aspect of UAM is the development of an automated air traffic manager, whose primary responsibility is to approve airspace access for vehicle operators. Vehicle operator roles may include onboard and remote pilots, as well as a human manager who will supervise an entire fleet. Alternatively, both fleet manager and vehicle operators can be merged into a single role – a feasible option if UAM aircraft are autonomous. In lieu of tower controllers, vertiport managers, with the assistance of automation, will manage arrival and departure schedules between vertiports, as well as supervise surface operations. Our approach here will be to introduce use cases currently being developed by NASA, and then provide preliminary definitions for each of the roles introduced above and how coordination between them can be configured to support the operations within the use cases described. Subsequently, we review the tools and interfaces being developed to support the various roles. To conclude, we present current human factors work related to defining the roles above and suggest future work to advance the UAM concept.

trial planning↗

NASA NTRS2024

An Annoyance Model for Urban Air Mobility Vehicle Noise in the Presence of a Masker

Proposed Urban Air Mobility (UAM) operations offer an alternative to road and rail traffic for local and regional movement of people and goods. To allow for large-scale adoption of UAM vertical takeoff and landing (VTOL) aircraft, it is critical to predict human annoyance response to the acoustic noise generated by these vehicles. We propose a model that predicts an individual’s perceived level of annoyance when presented with UAM VTOL aircraft noise in the context of a representative masking noise. The annoyance model is based on the psychoacoustic annoyance model of Fastl and Zwicker (Zwicker and Fastl, 1999), with an additional tonality term based on subjective testing of UAM sound quality (Boucher, et al., 2023). The model also predicts changes in annoyance when UAM noise is masked by a background sound, based on subjective evaluation of detection, noticeability, and annoyance of noise in the presence of a masker.

Noise↗

NASA NTRSDate not supplied

On the Modeling of Urban Air Mobility Vehicle Takeoff and Landing Operations in the FAA Aviation Environmental Design Tool

Urban air mobility (UAM) vehicles are anticipated to operate in close proximity to the public. A possible barrier to the introduction of UAM vehicles as a transit solution is their community noise impact, particularly around vertiports. The Federal Aviation Administration Aviation Environmental Design Tool (AEDT) is the mandated tool to assess aircraft noise and other environmental impacts due to federal actions at civilian airports, vertiports, or in U.S. airspace for commercial flight operations. However, AEDT was designed to model fixed-wing aircraft and conventional helicopter operations, not UAM vehicles. Prior work by the authors showed significant differences in noise contours of UAM departures and approaches when modeling operations as fixed-wing and helicopter types in AEDT. This paper identifies the sources of those differences. Additionally, a NASA time-marching simulation tool is used to generate noise exposure predictions for equivalent operations to help identify differences associated with the noise model implemented in AEDT and offer possible changes to assess UAM community noise impact more consistently with simulation-based modeling.

aeroacoustics↗

NASA NTRSDate not supplied

On the Modeling of Urban Air Mobility Vehicle Takeoff and Landing Operations in the FAA Aviation Environmental Design Tool

Urban air mobility (UAM) vehicles are anticipated to operate in close proximity to the public. A possible barrier to the introduction of UAM vehicles as a transit solution is their community noise impact, particularly around vertiports. The Federal Aviation Administration Aviation Environmental Design Tool (AEDT) is the mandated tool to assess aircraft noise and other environmental impacts due to federal actions at civilian airports, vertiports, or in U.S. airspace for commercial flight operations. However, AEDT was designed to model fixed-wing aircraft and conventional helicopter operations, not UAM vehicles. Prior work by the authors showed significant differences in noise contours of UAM departures and approaches when modeling operations as fixed-wing and helicopter types in AEDT. This paper identifies the sources of those differences. Additionally, a NASA time-marching simulation tool is used to generate noise exposure predictions for equivalent operations to help identify differences associated with the noise model implemented in AEDT and offer possible changes to assess UAM community noise impact more consistently with simulation-based modeling.

aeroacoustics↗

NASA NTRSDate not supplied

Overview of an Exploratory, Real-Time, Multi-Pilot Simulation Study of Early eVTOL Operations at Non-Towered Vertiports

This paper provides a report out on an exploratory, multi-aircraft/multi-pilot, real-time simulation study conducted by NASA of early commercial powered-lift, Urban Air Mobility (UAM) operations at a non-towered vertiport. As used in this paper, vertiport refers to the primary ground and airspace elements facilitating the takeoff and landing of electric vertical takeoff and landing (eVTOL) aircraft with central emphasis on a vertipad, i.e. the physical touch-down and lift-off area and surrounding approach , departure, local pattern procedures. The study, known as the Piloted UML-2 ConOps Study (PUCS), had two high-level goals. The first goal was providing preliminary insights and observations relevant to the piloting and flight operations of early, commercial UAM operations aligned with the initial stage of the FAA’s Advanced Air Mobility (AAM) Implementation Plan and the second level NASA’s UAM Maturity Level (UML) scale. The second goal was evaluating a novel, medium-fidelity, extensible, many-pilot, real-time simulation capability known as the UAM Flyers developed by NASA. The Flyers are intended to allow rapid development, screening, evaluation, and demonstrations of potential Concepts of Operation (ConOps) for UAM flight operations and airspace management in a modular and low-cost, real-time, human-in-the-loop rapid simulation prototyping environment. For this study, ten Flyer cockpits were configured to evaluate flight operations through a non-towered vertiport with pilot interfaces and displays (external and in-cockpit) appropriate for operations under visual flight rules (VFR) and employing flight and communication procedures representative of current operations at non-towered airports. The presented results include an achieved operational tempo; durations of individual flight tasks for approaches and departures; off-nominal events and triggers; and pilot comments regarding potential procedural and technology improvements.

Urban Air Mobility↗

NASA NTRSDate not supplied

Overview of an Exploratory, Multi-Pilot Simulation Study of Early eVTOL Operations at Non-Towered Vertiports

This paper provides a report out on an exploratory, multi-aircraft/multi-pilot, real-time simulation study conducted by NASA of early commercial powered-lift, Urban Air Mobility (UAM) operations at a non-towered vertiport. As used in this paper, vertiport refers to the primary ground and airspace elements facilitating the takeoff and landing of electric vertical takeoff and landing (eVTOL) aircraft with central emphasis on a vertipad, i.e. the physical touch-down and lift-off area and surrounding approach , departure, local pattern procedures. The study, known as the Piloted UML-2 ConOps Study (PUCS), had two high-level goals. The first goal was providing preliminary insights and observations relevant to the piloting and flight operations of early, commercial UAM operations aligned with the initial stage of the FAA’s Advanced Air Mobility (AAM) Implementation Plan and the second level NASA’s UAM Maturity Level (UML) scale. The second goal was evaluating a novel, medium-fidelity, extensible, many-pilot, real-time simulation capability known as the UAM Flyers developed by NASA. The Flyers are intended to allow rapid development, screening, evaluation, and demonstrations of potential Concepts of Operation (ConOps) for UAM flight operations and airspace management in a modular and low-cost, real-time, human-in-the-loop rapid simulation prototyping environment. For this study, ten Flyer cockpits were configured to evaluate flight operations through a non-towered vertiport with pilot interfaces and displays (external and in-cockpit) appropriate for operations under visual flight rules (VFR) and employing flight and communication procedures representative of current operations at non-towered airports. The presented results include an achieved operational tempo; durations of individual flight tasks for approaches and departures; off-nominal events and triggers; and pilot comments regarding potential procedural and technology improvements.

Urban Air Mobility↗

NASA NTRSDate not supplied

Developing Concepts of Operations Using Multi-Step Tool Techniques With Large Language Models

The National Aeronautics and Space Administration (NASA) Air Mobility Pathfinders (AMP) project is developing and evaluating concepts of operations (ConOps) for safe, secure, and scalable Urban Air Mobility (UAM) operations. The AMP project’s Operational Concepts, Architecture, and Requirements Integration (OCARI) Team is using a Model Based System Engineering (MBSE) approach for integration, interoperability, and traceability of Advanced Air Mobility (AAM) ecosystems centered around urban air taxi services. The team’s goal is to define structures and behaviors needed for system feasibility, readiness, and interoperability, establish a UAM knowledge base, and trace and validate assumptions and requirements relevant to AAM. NASA Langley Research Center (LaRC) is spearheading an innovative digital engineering approach to integrate, communicate, and facilitate the research of multi-modal transportation systems. The Knowledge-based Digital Platform (KbDP) is a concept being developed that ties the workflows of Project Managers (PM), Principal Investigators (PI), and System Engineers together across organizational boundaries. It does so through the management of an information database defined by mathematical, data science, and system engineering principles. Machine Learning (ML) algorithms play a key role in this concept by extracting meaningful knowledge from relational and graph databases, document repositories, and system artifacts, which the human user leverages to greatly improve the efficiency and effectiveness of their research. Recent advancements in the field of Large Language Models (LLMs), specifically models trained for tool use, such as Command-R , now allow for the reliable implementation of single-step and multi-step tool-centric systems. These techniques provide the LLM with a set of tools, in our case Python functions, that can be called on to answer a much wider range of questions compared to LLMs implemented using a traditional single-source or Retrieval Augmented Generation (RAG) approach. Through this method, the LLM can pull information from multiple data sources, such as relational or graph databases, document repositories, application programming interfaces (APIs), and SysML artifacts depending on the user’s question. The LLM can also output the information in a variety of different formats, using output generation tools, such as CSV, UML, or SysML artifacts. Additionally, tools can be assigned roles and can work together to provide answers to queries in an “agent” like approach, similar to that implemented by Microsoft’s AutoGen framework where different agents can converse with each other to accomplish tasks. Previously, our team developed a chatbot system with “agent like” functionality in the form of different “modes” the user could select from a user interface (UI), this architecture can be seen on the left in figure 1. Three different modes were implemented, the first mode allowed the LLM to utilize the structures and algorithms within a graph database to trace UAM requirements. The second mode gave the LLM access to a vector search capable of providing relevant information from thousands of document pages related to UAM ConOps and requirements. The third mode served as a general assistant where users could enter open-ended questions and custom prompts to utilize the LLM for different use-cases. This system improved the process surrounding generating and analyzing information related to UAM requirements, however, the implementation provided a clunky user experience. Users were required to know what mode to select within the UI in advance before entering their question to the selected tool. Moreover, the different tools were isolated from each other, they lacked bidirectional links that would allow for tools to collaborate to generate better responses. Our team is working on a new architecture, seen on the right in the below figure, with the goal to address many of the UX shortcomings of our original system while improving the accuracy and depth of responses from the LLM. This new system will automatically select the appropriate tool to use based off the user’s question. Each tool will be capable of calling on any of the other tools available to the LLM, resulting in a collaborative pipeline where tools can pass data between other tools until enough data is received to generate an answer to the user’s question. Using a locally deployed, open-source, LLM, the NASA OCARI team, in collaboration with Collins Aerospace, will implement a prototype application that will bridge knowledge across multiple sources to assist System Engineers (SEs) with requirements discovery and tracing, research question and use case identification, and assumption validation. Such a system will also allow SEs to more easily, and intuitively, explore the AAM ecosystem, ultimately improving the efficiency and effectiveness of the SE's research and decision-making processes surrounding ConOps development and validation. In this session, our team will provide a video demonstration of our new prototype architecture in action. We will also present an overview of our prototype system architecture and talk about its advantages over traditional LLM deployments along with how those advantages can provide additional value to the field of System Engineering.

systems engineering↗

NASA NTRSDate not supplied

Urban Air Mobility: A Control-Centric Approach to Addressing Technical Challenges

Urban Air Mobility (UAM) is an emerging aviation sector and is playing an integral part in the on-demand mobility revolution. UAM is powered by the convergence of advances in distributed electrical propulsion (DEP) and vehicle autonomy. The complexity of operations in the urban environment and the unconventional vehicle configurations designed to take advantage of new propulsion technologies, result in numerous challenges that benefit from a control-centric approach. In this talk we outline some of these challenges and present our current approach to addressing them. For example, in order to achieve full market potential and access to UAM, vehicle autonomous flight is required. A key barrier to autonomous flight in a large multi-agent system is dealing with off-nominal situations and contingencies in a safe and predictable manner. We present our approach to intelligent contingency management, and share recent results and open problems. Additionally, we discuss another major barrier to ubiquitous UAM – the noise signature produced by vehicles with multiple rotors. We present our approach to minimizing such noise within the framework of the acoustically-aware vehicle.

UAM↗