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This paper describes a psychoacoustic test in the Exterior Effects Room (EER) at the NASA Langley Research Center. The test investigated the degree to which sound quality metrics (sharpness, tonality, etc.) are predictive of annoyance to notional sounds of Urban Air Mobility (UAM) vehicles (e.g., air taxis). A suite of 136 unique (4.6 second duration) UAM rotor noise stimuli was generated. These stimuli were based on aeroacoustic predictions of a NASA reference UAM quadrotor aircraft under two flight conditions. The synthesizer changed rotor noise parameters such as the blade passage frequency, the relative level of broadband self-noise, and the relative level of tonal motor noise. With loudness constant, the synthesis parameters impacted sound quality in a way that created a spread of predictors both in synthesizer parameters and in sound quality metrics.

The National Aeronautics and Space Administration (NASA) remotely administered a psychoacoustic test in fall of 2022 as the first of two phases of a cooperative Urban Air Mobility (UAM) vehicle noise human response study. This first phase, described here, was a Feasibility Test to compare human subject responses with a previous in-person psychoacoustic test that found an annoyance response difference between small Uncrewed Aerial System (sUAS) noise and ground vehicle noise. This paper discusses the Feasibility Test online layout, sound calibration method, software development, stimuli selection, test subject recruitment, and test administration. Test performance is measured through comparison of annoyance response data with the previous in-person test. The test also investigated whether a contextual cue to test subjects influenced their annoyance response. Response differences between test subjects in geographically distinct areas are analyzed.

NASA and the FAA conducted two flight campaigns to quantify onboard weather radar measurements with in-situ measurements of high concentrations of ice crystals found in deep convective storms. The ultimate goal of this research was to improve the understanding of high ice water content (HIWC) and develop onboard weather radar processing techniques to detect regions of HIWC ahead of an aircraft to enable tactical avoidance of the potentially hazardous conditions. Both HIWC RADAR campaigns utilized the NASA DC-8 Airborne Science Laboratory equipped with a Honeywell RDR-4000 weather radar and in-situ microphysical instruments to characterize the ice crystal clouds. The purpose of this paper is to summarize how these campaigns were conducted and highlight key results. The first campaign was conducted in August 2015 with a base of operations in Ft. Lauderdale, Florida.

High Ice Water Content (HIWC) has been identified as a primary causal factor in numerous engine events over the past two decades. Previous attempts to develop a remote detection process utilizing modern commercial radars have failed to produce reliable results. This paper discusses the reasons for previous failures and describes a new technique that has shown very encouraging accuracy and range performance without the need for any modifications to industry’s current radar design(s). The performance of this new process was evaluated during the joint NASA/FAA HIWC RADAR II Flight Campaign in August of 2018. Results from that evaluation are discussed, along with the potential for commercial application, and development of minimum operational performance standards for future radar products.

Recent studies have found that high mass concentrations of ice particles in regions of deep convective storms can adversely impact aircraft engine and air probe (e.g. pitot tube and air temperature) performance. Radar reflectivity in these regions suggests that they are safe for aircraft penetration, yet high ice water content (HIWC) is still encountered. The aviation weather community seeks additional remote sensing methods for delineating where ice particle (or crystal) icing conditions are likely to occur, including products derived from geostationary (GEO) satellite imagery that is now available in near-real time at increasingly high spatio-temporal detail from the global GEO satellite constellation.

This paper introduces a data driven model for predicting airport arrival capacity with 2-8 hour look-ahead forecast data. The model is suitable for air traffic flow management by explicitly investigating the impact of convective weather on airport arrival meter fix throughput. Estimation of the arrival airport capacity under arrival meter fix flow constraints due to severe weather is an important part of Air Traffic Management (ATM). Airport arrival capacity can be reduced if one or more airport arrival meter fixes are partially or completely blocked by convective weather. When the predicted airport arrival demands exceed the predicted available airport’s arrival capacity for a sustained period, Ground Delay Program (GDP) operations will be triggered by ATM system.

This article presents strategies for evaluating the mean, variance, and failure probability of a response variable given measurements subject to both epistemic and aleatory uncertainties. We focus on a case in which standard sensor calibration techniques cannot be used to eliminate measurement error since the uncertainties affecting the metrology system depend upon the measurement itself (e.g., the sensor bias is not constant and the measurement noise is colored). To this end, we first characterize all possible realizations of the true response that might have led to each of such measurements. This process yields a surrogate of the data for the unobservable true response taking the form of a random variable. Each of these variables, called a Random Datum Model (RDM), is manufactured according to a measurement and to the underlying structure of the uncertainty.

The blade crossing event of a coaxial counter-rotating rotor is a potential source of noise and impulsive blade loads. Blade crossings occur many times during each rotor revolution. In previous research by the authors, this phenomenon was analyzed by simulating two airfoils passing each other at specified speeds and vertical separation distances, using the compressible Navier-Stokes solver OVERFLOW. The simulations explored mutual aerodynamic interactions associated with thickness, circulation, and compressibility effects. Results revealed the complex nature of the aerodynamic impulses generated by upper/lower airfoil interactions. In this paper, the coaxial rotor system is simulated using two trains of airfoils, vertically offset, and traveling in opposite directions. The simulation represents multiple blade crossings in a rotor revolution by specifying horizontal distances between each airfoil in the train based on the circumferential distance between blade tips.

Corrugated-core sandwich structures with integrated acoustic resonator arrays have been of recent interest for launch vehicle noise control applications. Previous tests and analyses have demonstrated the ability of this concept to increase sound absorption and reduce sound transmission at low frequencies. However, commercial aircraft manufacturers often require fibrous or foam blanket treatments for broadband noise control and thermal insulation. Consequently, it is of interest to further explore the noise control benefit and trade-offs of structurally integrated resonators when combined with various degrees of blanket noise treatment in an aircraft-representative cylindrical fuselage system. In this study, numerical models were developed to predict the effect of broadband and multi-tone structurally integrated resonator arrays on the interior noise level of cylindrical vibroacoustic systems.

The Elytron 2S is a prototype aircraft concept to allow VTOL capabilities together with fixed wing aircraft performance. It has a box wing design with a centrally mounted tilt-wing supporting two rotors. This paper explores the aerodynamic characteristics of the aircraft using computational fluid dynamics in hover and low speed forward flight, as well as analyzing the unique control system in place for hover. The results are then used to build an input set for NASA Design and Analysis if Rotorcraft software allowing trim and flight stability and control estimations to be made with SIMPLI-FLYD.

Coaxial rotors are finding use in advanced rotorcraft concepts. Combined with lift offset rotor technology, they offer a solution to the problems of dynamic stall and reverse flow that often limit single rotor forward flight speeds. In addition, coaxial rotorcraft systems do not need a tail rotor, a major boon during operation in confined areas. However, the operation of two counter-rotating rotors in close proximity generates many possible aerodynamic interactions between rotor blades, blades and vortices, and between vortices. With two rotors, the parameter design space is very large, and requires efficient computations as well as basic experiments to explore aerodynamics of a coaxial rotor and the effects on performance, loads, and acoustics.

This paper summarizes the recent development of an adaptive aeroelastic wing shaping control technology called variable camber continuous trailing edge flap (VCCTEF). As wing flexibility increases, aeroelastic interactions with aerodynamic forces and moments become an increasingly important consideration in aircraft design and aerodynamic performance. Furthermore, aeroelastic interactions with flight dynamics can result in issues with vehicle stability and control. The initial VCCTEF concept was developed in 2010 by NASA under a NASA Innovation Fund study entitled “Elastically Shaped Future Air Vehicle Concept,” which showed that highly flexible wing aerodynamic surfaces can be elastically shaped in-flight by active control of wing twist and bending deflection in order to optimize the spanwise lift distribution for drag reduction. A collaboration between NASA and Boeing Research & Technology was subsequently funded by NASA from 2012 to 2014 to further develop the VCCTEF concept.

The National Aeronautics and Space Administration (NASA) is interested in characterizing the responses of THOR (test device for human occupant restraint) anthropometric test device (ATD) to representative loading acceleration pulse s. Test conditions were selected both for their applicability to anticipated NASA landing scenarios, and for comparison to human volunteer data previously collected by the United States Air Force (USAF). THOR impact testing was conducted in the fore-to-aft frontal (-x) and in the upward spinal (-z) directions with peak sled accelerations ranging from 8 to 12 G and rise times of 40, 70, and 100ms. Each test condition was paired with historical huma n data sets under similar test conditions that were also conducted on the Horizontal Impulse Accelerator (HIA). A correlation score was calculated for each THOR to human comparison using CORA (CORrelation and Analysis) software.

When the demand for either a region of airspace or an airport approaches or exceeds the available capacity, miles-in-trail (MIT) restrictions are the most frequently issued traffic management initiatives (TMIs) that are used to mitigate these imbalances. Miles-in-trail operations require aircraft in a traffic stream to meet a specific inter-aircraft separation in exchange for maintaining a safe and orderly flow within the stream. This stream of aircraft can be departing an airport, over a common fix, through a sector, on a specific route or arriving at an airport. This study begins by providing a high-level overview of the distribution and causes of arrival MIT restrictions for the top ten airports in the United States. This is followed by an in-depth analysis of the frequency, duration and cause of MIT restrictions impacting the Hartsfield-Jackson Atlanta International Airport (ATL) from 2009 through 2011.

This paper features a set of advanced technologies for autonomy and intelligence in advanced inspection systems of facility operations. These technologies offer a significant contribution to set a path to establish a system and an operating environment with autonomy and intelligence for inspection, monitoring and safety via gas and ambient sensors, video mining and speech recognition commands on unmanned ground vehicles and other platforms to support operational activities in the Cryogenics Test bed and other facilities and vehicles. These advanced technologies are in current development and progress and their functions and operations require guidance and formulation in conjunction with the development team(s) toward the system architecture.

This paper presents a novel health monitoring and fault adaptive control architecture for an unmanned hexrotor helicopter. The technologies developed to achieve the described level of robust fault contingency management include; 1.) A Particle Swarm Optimization (PSO) routine for maximizing the “built-in” fault tolerance that the closed loop flight control system affords, 2.) A two-stage Kalman filter scheme for real-time identification of faults that are masked by control system compensation, and 3.) A reconfigurable control allocation method which compensates for large degradations of the six main motor/rotor assemblies. The fault adaptive control system presented herein has strong robustness against small faults without the need for controller reconfiguration, and strong tolerance of large faults through adaptive accommodation of the fault source and severity.

The term Integrated Vehicle Health Management (IVHM) describes a set of capabilities that enable sustainable and safe operation of components and subsystems within aerospace platforms. However, very little guidance exists for the systems engineering aspects of design with IVHM in mind. It is probably because of this that designers have to use knowledge picked up exclusively by experience rather than by established process. This motivated a group of leading IVHM practitioners within the aerospace industry under the aegis of SAE's HM-1 technical committee to author a document that hopes to give working engineers and program managers clear guidance on all the elements of IVHM that they need to consider before designing a system. This proposed recommended practice (ARP6883 [1]) will describe all the steps of requirements generation and management as it applies to IVHM systems, and demonstrate these with a “real-world” example related to designing a landing gear system.

This paper discusses the ground and range operations for a Shuttle derived Heavy-Lift Vehicle being launched from the Kennedy Space Center on the Eastern range. Comparisons will be made between the Shuttle and a heavy lift configuration (SLS-ETF MPCV - April 2011) by contrasting their subsystems. The analysis will also describe a simulation configuration with the potential to be utilized for heavy lift vehicle processing/range simulation modeling and the development of decision-making systems utilized by the range. In addition, a simple simulation model is used to provide the required critical thinking foundations for this preliminary analysis.

A review of the research accomplished in 2009 in the System-Level Design, Analysis and Simulation Tools (SLDAST) of the NASA's Airspace Systems Program is presented. This research thrust focuses on the integrated system-level assessment of component level innovations, concepts and technologies of the Next Generation Air Traffic System (NextGen) under research in the ASP program to enable the development of revolutionary improvements and modernization of the National Airspace System. The review includes the accomplishments on baseline research and the advancements on design studies and system-level assessment, including the cluster analysis as an annualization standard of the air traffic in the U.S. National Airspace, and the ACES-Air MIDAS integration for human-in-the-loop analyzes within the NAS air traffic simulation.

Scheduled arriving aircraft demand may exceed airport arrival capacity when there is abnormal weather at an airport. In such situations, Federal Aviation Administration (FAA) institutes ground-delay programs (GDP) to delay flights before they depart from their originating airports. Efficient GDP planning depends on the accuracy of prediction of airport capacity and demand in the presence of uncertainties in weather forecast. This paper presents a study of the impact of dynamic airport surface weather on GDPs. Using the National Traffic Management Log, effect of weather conditions on the characteristics of GDP events at selected busy airports is investigated. Two machine learning methods are used to generate models that map the airport operational conditions and weather information to issued GDP parameters and results of validation tests are described.