Information Storage and Retrieval

Sengupta A., Guha A.

Natural laminar flow airfoils are essential technologies designed to reduce drag and significantly enhance aerodynamic performance. A notable example is the SHM1 airfoil, created to meet the requirements of the small-business jet by Honda R&D. This airfoil has undergone extensive testing across various operational conditions, including low-speed wind tunnel tests and flight tests across a range of Reynolds numbers and free-stream Mach numbers. Additionally, investigations into drag-divergence behavior have been conducted using a transonic wind tunnel, with subsequent studies focusing on transonic shock boundary layer interactions through both experimental and numerical approaches. This study employs a series of numerical simulations to analyze the flow physics and aerodynamic performance across different free-stream Mach numbers in the subsonic and transonic regimes. The analysis offers a comprehensive overview of the aerodynamic performance by making use of instantaneous and time-averaged load and pressure distributions, highlighting the different flow structures (trailing edge vortices, Kutta waves, shock waves - both normal and oblique) and associated time scales in the unsteady flow field and how these impact the performance and extent of separated flow on the SHM1 airfoil. This is achieved by examining computed instantaneous numerical Schlieren for various design conditions (such as low speed, climb, and cruise) and off-design scenarios (including transonic shock emergence, drag-divergence, and shock-induced separation). The dominant time scales, the time-averaged load distributions and boundary layer parameters are compared to provide a comprehensive overview of the SHM1’s aerodynamics, establishing benchmark results for optimization of various flow separation and shock control techniques.

Conlon N., Acharya A., McGinley J., Slack T., Hirst C.A., D’Alonzo M., Hebert M.R., Reale C., Frew E.W., Russell R., Ahmed N.R.

IntroductionFuture concepts for airborne autonomy point toward human operators moving out of the cockpit and into supervisory roles. Urban air mobility, airborne package delivery, and military intelligence, surveillance, and reconnaissance (ISR) are all actively exploring such concepts or currently undergoing this transition. Supervisors of these systems will be faced with many challenges, including platforms that operate outside of visual range and the need to decipher complex sensor or telemetry data in order to make informed and safe decisions with respect to the platforms and their mission. A central challenge to this new paradigm of non-co-located mission supervision is developing systems which have explainable and trustworthy autonomy and internal decision-making processes.MethodsCompetency self-assessments are methods that use introspection to quantify and communicate important information pertaining to autonomous system capabilities and limitations to human supervisors. We first discuss a computational framework for competency self-assessment: factorized machine self-confidence (FaMSeC). Within this framework, we then define the generalized outcome assessment (GOA) factor, which quantifies an autonomous system’s ability to meet or exceed user-specified mission outcomes. As a relevant example, we develop a competency-aware learning-based autonomous uncrewed aircraft system (UAS) and evaluate it within a multi-target ISR mission.ResultsWe present an analysis of the computational cost and performance of GOA-based competency reporting. Our results show that our competency self-assessment method can capture changes in the ability of the UAS to achieve mission critical outcomes, and we discuss how this information can be easily communicated to human partners to inform decision-making.DiscussionWe argue that competency self-assessment can enable AI/ML transparency and provide assurances that calibrate human operators with their autonomous teammate’s ability to meet mission goals. This in turn can lead to informed decision-making, appropriate trust in autonomy, and overall improvements to mission performance.

Luettig B., Akhiat Y., Daw Z.

Artificial Intelligence (AI) technologies can potentially revolutionize the aerospace industry with applications such as remote sensing data refinement, autonomous landing, and drone-based agriculture. However, safety concerns have prevented the widespread adoption of AI in commercial aviation. Currently, commercial aircraft do not incorporate AI components, even in entertainment or ground systems. This paper explores the intersection of AI and aerospace, focusing on the challenges of certifying AI for airborne use, which may require a new certification approach. We conducted a comprehensive literature review to identify common AI-enabled aerospace applications, classifying them by the criticality of the application and the complexity of the AI method. An applicability analysis was conducted to assess how existing aerospace standards - for system safety, software, and hardware - apply to machine learning technologies. In addition, we conducted a gap analysis of machine learning development methodologies to meet the stringent aspects of aviation certification. We evaluate current efforts in AI certification by applying the EASA concept paper and Overarching Properties (OPs) to a case study of an automated peripheral detection system (ADIMA). Aerospace applications are expected to use a range of methods tailored to different levels of criticality. Current aerospace standards are not directly applicable due to the manner in which the behavior is specified by the data, the uncertainty of the models, and the limitations of white box verification. From a machine learning perspective, open research questions were identified that address validation of intent and data-driven requirements, sufficiency of verification, uncertainty quantification, generalization, and mitigation of unintended behavior. For the ADIMA system, we demonstrated compliance with EASA development processes and achieved key certification objectives. However, many of the objectives are not applicable due to the human-centric design. OPs helped us to identify and uncover several defeaters in the applied ML technology. The results highlight the need for updated certification standards that take into account the unique nature of AI and its failure types. Furthermore, certification processes need to support the continuous evolution of AI technologies. Key challenges remain in ensuring the safety and reliability of AI systems, which calls for new methodologies in the machine learning community.

Andreeva-Mori A.

The advancement in uncrewed aircraft systems such as small drones and advanced air mobility vehicles such as Electric Vertical Take Off and Landing aircraft (eVTOL) has called for airspace integration at low altitudes of both traditional aircraft, such as helicopters and new entrants, such as drones and eVTOL. Currently, the trajectories and necessary buffers around them of flights operating under visual flight rules are not possible to predict. This research proposes the use of flight mission characteristics to model the trajectory and evaluates temporal, lateral and vertical deviations to define the safety buffers which can be used to generate operation volumes, geo-fencing such low-altitude flights and separating them from other traffic in a safe and efficient manner. Real flight test data obtained for the purposes of this study and pilots’ interviews assure high fidelity and practicability of the proposal.

Agarwal R.K.

Shayak B., Girdhar S., Malviya S.

We propose a closed-form system of nonlinear equations for the pitch plane or longitudinal motions of a fixed-wing aircraft and use it to demonstrate a possible path to the unification of theoretical flight dynamics and practical analysis of aircraft manoeuvres. The derivation of an explicit model free of data tables and interpolated functions is enabled by our use of empirical formulae for lift and drag which agree with experiments. We validate the model by recovering the well-known short period and phugoid modes, and the regions of normal and reversed command. We then use the model to present detailed simulations of two acrobatic manoeuvres, an Immelmann turn and a vertical dive. Providing new quantitative insights into the dynamics of aviation, our model-based manoeuvre analysis has the potential to impact both the academic flight dynamics curriculum and the ground training program for pilots of manned and unmanned aircraft. Possible consequences of future model-centric pilot training may include improved safety standards in general and commercial aviation as well as expedited theoretical course completion in air transport.

Andreeva-Mori A.

Air traffic inefficiencies lead to excess fuel burn, emissions and air traffic controller (ATCo) workload. Various stakeholders have developed metrics to assess the operation performance. Most metrics compare the actual trajectories to some benchmark ones to calculate excess time or distance. This research is inspired by cellular automata (CA) and develops a combined time-distance lateral inefficiency and predictability metric using discrete space and time mapping on published flight routes. The analysis is focused on Tokyo International Airport, but uses only track data and published routes, which makes it easily applicable to any other hub airport worldwide. The mapping and velocity analyses are used to investigate when and where ATCos are most likely to intervene to provide save separation. A metric which can be adjusted to evaluate both traffic flow predictability and efficiency is proposed. This metric can be applied to better understand current traffic and enable future improvements towards seamless air traffic flow management.

Van Beck C., Raman V.

High-fidelity simulations of RDEs with H2-Air-NOx chemistry are employed to study NOx emissions in such devices. Discrete injection of gaseous hydrogen fuel and continuous injection of air oxidizer is used at various mass flow rate conditions in several 3D RDE simulations to understand resulting NOx production behaviors. Simulations are also performed for two different injector configurations, one in which air is injected axially into the detonation chamber [Axial Air Inlet (AAI)] and one in which air is injected radially [Radial Air Inlet (RAI)]. It is seen that the AAI RDE produces much less NOx than the RAI RDE, mainly due to the weaker waves seen in this system as a result of parasitic combustion losses from product gas recirculation. Parasitic combustion does lead to NOx formation in its own right, but the emissions levels from this process are negligible compared to emissions stemming directly from detonation processes. In regards to detonation strength in particular, it is generally seen that detonation strength increases with increasing mass flow rate, in turn increasing peak pressure, peak heat release and NOx emissions levels. Nevertheless, even the highest recorded NOx levels at the combustor exit in this study remain on the same order of magnitude as compared to gas turbine exhaust emissions levels, supporting the claim that significant differences between detonative and deflagrative combustion do not necessarily lead to significant differences in NOx levels. Overall, this study provides greater understanding into the behaviors of NOx formation in RDEs and how these behaviors are affected by changes in operating parameters.

Hawes N.C., Wilhelm J.P.

Autonomous uncrewed aircraft will require collision avoidance systems (CASs) designed with autonomy in mind as they integrate into the increasingly crowded national airspace system. Current uncrewed aircraft CASs typically require a remote pilot to execute avoidance or to provide poorly defined guidance that does not benefit autonomous systems. The Path Recovery Automated Collision Avoidance System (PRACAS) re-plans flight paths to autonomously adjust for collisions using path planners and keep-out zones (KOZs), but it does not currently detect or mitigate overtaking collisions. This work investigates the effect of geometric KOZs on overtaking scenarios for autonomous uncrewed aircraft. KOZ shapes were developed by relating relative velocities and turn rates of aircraft in overtaking scenarios and were tested using PRACAS. The operational ranges for approach heading, relative velocity, and look-ahead time were then determined. The set of KOZs that were developed prevented intruder aircraft from entering the minimum separation distance of one wingspan from the mission aircraft in overtaking scenarios with look-ahead times between 5 and 12 s, relative velocities of 2–20, and approach angles between 110° and −110° measured from the heading of the main UAS. Minimum separation was maintained for low-speed encounters with relative velocities between 1.1 and 2.0 for look-ahead times between 2 and 8 s for all approach angles. With look-ahead times ranging from 5 to 8 s, overtaking collisions of all tested approach angles and relative speeds are handled with more than twice the separation required for success, showing that the KOZs developed are feasible in possible autonomous CASs.

Paielli R.A.

A method of generating trajectories based on power is proposed for Urban Air Taxis. The method is simpler and more direct than traditional methods because it does not require a detailed aircraft model or a flight control model. Instead, it allows the user to specify the route, the static longitudinal profile (altitude as a function of distance), and a power model to determine the progress in time along that profile. The power model can be determined from a recorded or simulated trajectory of the same aircraft type. This capability allows a trajectory to be generated or reshaped to avoid conflicts while preserving the basic performance characteristics. Net or excess power is defined as the rate of change of mechanical (kinetic and potential) energy, and it is modeled as a function of airspeed. The time steps between discrete points in space along the trajectory are used to yield a specified power as a function of airspeed, and they are determined by solving a cubic polynomial at each point. An elliptical profile is used to generate an example trajectory. The dependence of trip flight time on various parameters is analyzed and plotted.

Cohen K.

Tokadlı G., Dorneich M.C.

This paper developed human-autonomy teaming (HAT) characteristics and requirements by comparing and synthesizing two aerospace case studies (Single Pilot Operations/Reduced Crew Operations and Long-Distance Human Space Operations) and the related recent HAT empirical studies. Advances in sensors, machine learning, and machine reasoning have enabled increasingly autonomous system technology to work more closely with human(s), often with decreasing human direction. As increasingly autonomous systems become more capable, their interactions with humans may evolve into a teaming relationship. However, humans and autonomous systems have asymmetric teaming capabilities, which introduces challenges when designing a teaming interaction paradigm in HAT. Additionally, developing requirements for HAT can be challenging for future operations concepts, which are not yet well-defined. Two case studies conducted previously document analysis of past literature and interviews with subject matter experts to develop domain knowledge models and requirements for future operations. Prototype delegation interfaces were developed to perform summative evaluation studies for the case studies. In this paper, a review of recent literature on HAT empirical studies was conducted to augment the document analysis for the case studies. The results of the two case studies and the literature review were compared and synthesized to suggest the common characteristics and requirements for HAT in future aerospace operations. The requirements and characteristics were grouped into categories of team roles, autonomous teammate types, interaction paradigms, and training. For example, human teammates preferred the autonomous teammate to have human-like characteristics (e.g., dialog-based conversation, social skills, and body gestures to provide cue-based information). Even though more work is necessary to verify and validate the requirements for HAT development, the case studies and recent empirical literature enumerate the types of functions and capabilities needed for increasingly autonomous systems to act as a teammate to support future operations.

Mayle M.N., Sharma R.

A novel control technique for the platooning of aerial vehicles is here introduced, and its stability is analyzed. The controller applies a missile guidance law that was initially adapted for path-following and subsequently extended to platooning. The positions of all agents within a platoon employing this controller are estimated by exploiting cooperative localization, and these estimated positions are fed back into the controller. Using simulation, the agents within a platoon are demonstrated to follow their desired path and avoid collision, even in environments with intermittent Global Positioning System signals and limited sensing ranges.

Chin C., Qin V., Gopalakrishnan K., Balakrishnan H.

The demand for advanced air mobility (AAM) operations is expected to be at a much larger scale than conventional aviation. Additionally, AAM flight operators are likely to compete in providing a range of on-demand services in congested airspaces, with varying operational costs. These characteristics motivate the need for the development of new traffic management algorithms for advanced air mobility. In this paper, we explore the use of traffic management protocols (“rules-of-the-road” for airspace access) to enable efficient and fair operations. First, we show that it is possible to avoid gridlock and improve efficiency by leveraging the concepts of cycle detection and backpressure. We then develop a cost-aware traffic management protocol based on the second-price auction. Using simulations of representative advanced air mobility scenarios, we demonstrate that our traffic management protocols can help balance efficiency and fairness, in both the operational and the economic contexts.

Krokavec D., Filasová A.

This paper attempts to resolve the problem concerning the interval observers design for linear systems with ostensible Metzler system matrices. Because system dynamics matrices are partially different from strictly Metzler structures, a solution is achieved by constructing a composed system matrix representation, which combines pre-compensated interval matrix structures fixed with a prescribed region of D-stability and the reconstructed strictly Metzler matrix structure, related to the original interval system matrix parameter definition. A novel design procedure is presented, which results in a strictly positive observer gain matrix and guarantees that the lower estimates of the positive state variables are non-negative when considering the given system structure and the non-negative system state initial values. The design is computationally simple since it is reduced to the feasibility of the set of linear matrix inequalities.