Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “UAM”

Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 559 records · Page 31
All sourcesNASA NTRSDOE OSTI
NASA NTRSDate not supplied

Application of Low & Mid Fidelity Tools to the Moog SureFly Vehicle

The results from a comprehensive acoustic analysis using low- and mid-fidelity tools to predict the acoustic signature of the Moog SureFly® UAM vehicle are presented and compared to experimental ground test data taken in 2019. The data presented is taken from a ground plate microphone at a distance of just under 100 feet from the propeller source. Simulations were performed for a single propeller and a coaxial pair. Results indicate that the first few blade passing frequency (BPF) harmonics match the experimental data reasonably well using both low- and mid-fidelity prediction toolchains.

Acoustics↗
NASA NTRSDate not supplied

Adaptive Multi-Sensor Fusion Based Object Tracking for Autonomous Urban Air Mobility Operations

Autonomous operations are a crucial aspect in the context of Urban Air Mobility and other emerging aviation markets. In order to enable this autonomy, systems must be able to build independently an accurate and detailed understanding of the own vehicle state as well as the surrounding environment, this includes detecting and avoiding moving objects in the sky, which can be cooperative (aircraft, UAM vehicles, etc.) as well as noncooperative (smaller drones, birds, ...). This paper focuses on the object tracking part that relies on adaptive multi-sensor fusion, taking into account specific properties and limitations of different sensor types. Results show the impact of dropouts of individual sensors on the accuracy of the tracking results for this adaptive sensor fusion approach.

sensor fusion↗
NASA NTRSDate not supplied

Adaptive Multi-Sensor Fusion Based Object Tracking for Autonomous Urban Air Mobility Operations

Autonomous operations are a crucial aspect in the context of Urban Air Mobility and other emerging aviation markets. In order to enable this autonomy, systems must be able to build independently an accurate and detailed understanding of the own vehicle state as well as the surrounding environment, this includes detecting and avoiding moving objects in the sky, which can be cooperative (aircraft, UAM vehicles, etc.) as well as noncooperative (smaller drones, birds, ...). This paper focuses on the object tracking part that relies on adaptive multi-sensor fusion, taking into account specific properties and limitations of different sensor types. Results show the impact of dropouts of individual sensors on the accuracy of the tracking results for this adaptive sensor fusion approach.

object tracking↗
NASA NTRSDate not supplied

A Data-Driven Reduced Order Model of an Isolated Rotor

There are numerous conceptual design stage rotorcraft analysis tasks which demand a high-fidelity and low cost method for rotor load distribution predictions. Considering Urban Air Mobility (UAM) vehicles aim to operate in close proximity to buildings and with unique rotor configurations, there is a significant challenge in quickly and accurately modeling rotors operating in complex, turbulent flow fields. One potential path for deriving a high-fidelity, low cost rotor model is with data-driven surrogate modeling. In this study, an initial investigation is taken to apply a proper orthogonal decomposition (POD) based reduced order model (ROM) for the purpose of pressure distribution prediction. In this study, a POD ROM was derived to produce distributed pressure predictions on rotor blades subjected to topology change due to variation in twist and taper ratio. Rotor twist was varied between 0◦, 10◦, 20◦, and 30◦ while taper ratio was varied between 1.0, 0.9, 0.8, and 0.7. All rotors consisted of a single blade. The POD ROM was validated for three demonstration cases; a high thrust rotor in hover, a low thrust rotor in hover, and a rotor in forward flight with a flight speed of M = 0.1. Results showed highly accurate distributed load predictions could be achieved at minimal computational cost. Computational cost for hovering blade surface pressure modeling was reduced from 12 hours on 440 cores to 10−5 seconds on a single core. For blade in forward flight cost was reduced from 20 hours on 440 cores to 0.6 seconds on a single core. For cases of high thrust and low thrust rotors, POD ROM was used to undergo a design optimization of the rotor such that figure of merit was maximized. Total optimization time for each case was 1 minute.

Data-Driven↗
NASA NTRSDate not supplied

Comparison of the CHARM Predictions of the Multirotor Test Bed with Wind Tunnel Experimental Results

Urban air mobility as a fast transportation solution has captured the attention of private companies and government aviation departments in the 21st century. New designs of aerial vehicles are being developed to meet industry needs but often neglect the aerodynamic characteristics and the effects of interacting rotors. This work focuses on predicting rotor behavior and understanding the importance of the rotor wake interaction for future urban air mobility (UAM) designs. The Multirotor Test Bed (MTB) project was initiated at NASA Ames Research center to support the NASA Revolutionary Vertical Lift Technology Project to study rotorcraft performance specifically for multirotor aircraft. The MTB is a modular multirotor test stand that makes testing feasible for up to six rotors at different angles and rotor arrangements, including Tall and Short configurations at different horizontal and vertical rotor separation distances. The MTB was tested in the U.S Army’s 7-by 10-Foot Wind Tunnel at NASA Ames Research Center in late 2019. This work focuses on exploring the impact of aerodynamic interactions between the MTB rotors using the Comprehensive Hierarchical Aeromechanics Rotorcraft Model (CHARM) software. The CHARM software is capable of modeling Vertical Take Off and Landing aircraft aerodynamics in maneuvering and steady flight conditions. CHARM allows the user to define flow and body characteristics, including the rotor geometry, aerodynamic conditions, wind tunnel speed, and airfoil tables as inputs. As the first step, a single MTB rotor was simulated in hover, and the results were compared with wind tunnel test data to confirm the CHARM parameters. Once the accurate performance was verified, the MTB rotors were simulated in forward flight, and each rotor was trimmed to the measured thrust. The simulation variables include one, two, four, and six rotors at the Short and Tall configurations, with MTB pitch angles of 0, -5, and -10 degrees. The MTB was simulated with and without wind tunnel walls. These results demonstrate the rotor wake interaction and its impact on rotor performance. This information also helps determine which configurations should be explored for future wind tunnel tests.

CHARM Predictions↗
NASA NTRS2022

Run Time Assurance for Electric Vertical Takeoff and Landing Aircraft

NASA is conducting research to demonstrate and evaluate the application of Run Time Assurance (RTA) as a means to assure safety in Electric Vertical Takeoff and Landing (eVTOL) aircraft with highly automated or autonomous flight capability supervised by a single onboard pilot. The work described in this report demonstrates an application of RTA and examines the implications for design and analysis of aircraft functions and systems; aircraft safety hazards; safety assurance; development assurance; and pilot tasks and performance. This research effort also seeks to assess the efficacy of the combined application of traditional Functional Hazard Analysis (FHA) and the more modern System Theoretic Process Analysis (STPA) techniques to perform hazard analyses on aircraft with complex automated and autonomous systems and an onboard pilot. During the research effort we developed architectural designs of two alternate eVTOL aircraft, generally following the process characterized in the SAE standards ARP4754 and ARP4761. The design has focused on the control architectures of these aircraft, which are identical except that one incorporates RTA techniques to reduce the criticality of some key software components. Artifacts of this process include a taxonomy of aircraft-level functions, aircraft-level architecture diagrams, aircraft-level functional hazard assessments (AFHA), function allocations onto aircraft systems and subsystems, functional block diagrams for a select set of control-related functions, and system-level functional hazard assessments (SFHA) for those functions. This project has highlighted the notion that DAL D is something of a sweet spot for low-confidence controllers in an RTA-based design. Among the many activities described in DO-178C, the activities related to requirement verifiability, algorithmic accuracy, and test coverage can be the most challenging for the kinds of advanced control techniques that may be desirable in novel UAM designs, such as adaptive control, machine-learning, artificial intelligence, numerical search, and Monte Carlo based algorithms. Moreover, the standard requires that development teams demonstrate that errors leading to unacceptable failure conditions have been removed from the software. The RTA architecture, which cordons off the low-confidence function, makes it much easier to show this for these kinds of algorithms. With regard to the use of STPA and FHA as complementary hazard analysis techniques, our research effort led us to the conclusion that STPA should be used to derive requirements for hardware and software systems and/or components. Also, STPA is a natural complement to other processes in ARP4754A involving design studies and iteration.

Run-time assurance↗
NASA NTRSDate not supplied

Exploring Airfoil Table Generation using XFOIL and OVERFLOW

The rotorcraft design process is a continuously evolving field of research that incorporates a number of software programs. An accurate airfoil table is critical in the design and testing process for rotorcraft. With multiple flow solvers available and flow conditions of multirotor UAM vehicles potentially covering a wide range of Reynolds and Mach numbers, a documented approach for developing airfoil tables is needed. Using benchmark data from legacy airfoil tables and wind tunnel tests for comparison, simulations for a comprehensive test matrix could guide rotorcraft design engineers in generating their own airfoil tables using the XFOIL and OVERFLOW solvers. The motivation for this study is to investigate flow solver features to develop a best practices document for airfoil table generation. The study uses the OVERFLOW and XFOIL flow solvers, coupled with the airfoil table generator AFTGen, to analyze three airfoils for a specific Reynolds numbers flow regime and provide details on how well each flow solver performs within a specific angle of attack range, Mach number range, Reynolds number range, and in different flow conditions, such as turbulent and transitional flow. OVERFLOW analyses in AFTGen for fully turbulent and transition flow are compared with XFOIL results and experimental test data for the section lift, section drag, and pressure coefficients. XFOIL ultimately yields results that are accurate within the linear angle of attack range and below a Mach number of 0.4 but tends to overpredict lift and underpredict drag unless the flow is in the compressible regime. XFOIL cannot accurately model stall and post-stall conditions due to the nature of the solver. This is evident in nearly every case run with XFOIL, where the linear range is usually predicted acceptably and the lift coefficient is overpredicted as the stall angle of attack is approached (with the exception being the generally poor correlation with most of the SSC-A09 cases). OVERFLOW is limited at low Mach numbers, and appears to perform best at Mach numbers of 0.4 and above. The exploration of airfoil table generation using XFOIL and OVERFLOW yielded moderately successful results for the NACA 0012 airfoil, reasonably good results for the RC(4)-10 airfoil, and less accurate results for the SSC-A09 airfoil.

Airfoil Table Generation↗
NASA NTRSDate not supplied

Communication Demand in the National Airspace - A Federated Learning Approach

Within the national airspace system (NAS), efficient use of spectrum remains a challenge; as UAS and UAM missions evolve, the amount of mission-critical aircraft communications are expected to significantly grow. To accommodate the increased demand, NASA Glenn Research Center is investigating artificial intelligence approaches that could dynamically allocate spectrum; however, these solutions are driven by communication and aviation data items, many of which are not directly available. One such cornerstone data item is communication demand, parameterizing the needs within a sector in terms that may directly inform spectrum allocation, such as channel access duration, bandwidth, and modulation type. This paper considers the complexity of predicting communication demand as a function of NAS behaviors. Unlike prior prediction work in communications, this research must consider how the NAS may be impacted by external factors - such as convective weather and closures - rather than estimating demand from time-series forecasting alone. Much of this research considers a federated learning design to predict communication events in terms of the type of event occurring (sector coordination, conflict resolution, etc.). To do so, an investigation of products from Sherlock Data Warehouse is conducted, identifying the trends, sufficiency, and correlations of each product to identified events. Additionally, a preliminary discussion for inferring associations between these event types and their communication parameters (duration, bandwidth, modulation) is presented. By utilizing federated learning, imbalances in the types of events and data present throughout the NAS can inform local models without impairing global training. Furthermore, the two-stage approach proposed allows for robust and speculative communication modelling, as communication techniques continue to evolve. As a result, this model enables a generalized approach to understanding NAS communications which is able to inform long-term changes to aviation spectrum management.

Nathan Schimpf↗
NASA NTRSDate not supplied

A Federated Learning Approach to Predicting Communication Demand in the National Airspace

Within the national airspace system (NAS), efficient use of spectrum remains a challenge; as UAS and UAM missions evolve, the amount of mission-critical aircraft communications are expected to significantly grow. To accommodate the increased demand, NASA Glenn Research Center is investigating artificial intelligence approaches that could dynamically allocate spectrum; however, these solutions are driven by communication and aviation data items, many of which are not directly available. One such cornerstone data item is communication demand, parameterizing the needs within a sector in terms that may directly inform spectrum allocation, such as channel access duration, bandwidth, and modulation type. This paper considers the complexity of predicting communication demand as a function of NAS behaviors. Unlike prior prediction work in communications, this research must consider how the NAS may be impacted by external factors - such as convective weather and closures - rather than estimating demand from time-series forecasting alone. Much of this research considers a federated learning design to predict communication events in terms of the type of event occurring (sector coordination, conflict resolution, etc). To do so, an investigation of products from Sherlock Data Warehouse is conducted, identifying the trends, sufficiency, and correlations of each product to identified events. Additionally, a preliminary discussion for inferring associations between these event types and their communication parameters (duration, bandwidth, modulation) is presented. By utilizing federated learning, imbalances in the types of events and data present throughout the NAS can inform local models without impairing global training. Furthermore, the two-stage approach proposed allows for robust and speculative communication modelling, as communication techniques continue to evolve. As a result, this model enables a generalized approach to understanding NAS communications which is able to inform long-term changes to aviation spectrum management.

Nathan Schimpf↗
NASA NTRSDate not supplied

xTM - ATM Coordination - Knowledge Elicitation Results: xTM Operational Domain

Crown Consulting Inc. has been tasked by NASA to elicit information from operators, vehicle manufacturers, and service suppliers on the expected xTM-ATC coordination. For this presentation, first, an introduction for the task and the process is discussed. The second part of the presentation will go through the results which are broken up into the participant company types that we interviewed to elicit feedback on the issues / barriers associated with the introduction of new vehicles and their system (called xTM) into the National Airspace System. The company types are HAPS (Balloons/HALE), sUAS, Large UAS, UAM, Supersonics, and Supporting Technology Providers.

xTM-ATM coordination↗
NASA NTRS2022

Optimal Locations for Air Mobility Vertiports

The purpose of this study was to effectively solicit the public to obtain inputs on potential Advanced Air Mobility (AAM) vertiport locations for UAM and RAM operations that transport people, specifically: where they would like to see vertiports located, and how they would like to use this new mode of transport (e.g. for “local” trips across town, for more “regional” trips between cities/towns, for business-related purposes, for leisure/personal purposes, routinely or only for “special” occasions, etc.) Maven conducted an electronic survey of individuals residing in specific geographies in order to obtain a broadly representative sample of the citizenry across all demographics and population density designations (e.g. rural, exurban, suburban, and urban). For the purposes of this survey, Maven received input from over 1,500 individuals divided between the Los Angeles Metropolitan area (as a general proxy for urban residents), and the state of Ohio (as a general proxy for suburban, exurban, and rural residents).

Advanced air mobility↗
NASA NTRSDate not supplied

Computationally Guided Development of Components for High Energy Density Solid-State Lithium-Sulfur Batteries

All electric vertical take-off and landing vehicles (eVTOL) for urban air mobility (UAM) concepts face numerous challenging technical barriers before their introduction into the consumer marketplace. The most challenging of these technical barriers to overcome is developing an energy storage system capable of meeting the rigorous aerospace safety and performance criteria1. The performance metrics for eVTOL craft, such as specific energy, specific power, and safety, exceed those of electric automobiles by a factor of two to four. Current state-of-the-art (SOA) lithium-ion batteries are incapable of meeting the key performance criteria of energy and safety for eVTOL. Therefore, next generation advanced chemistries and designs must be developed to meet required performance metrics for electric aviation2. Beyond lithium-ion chemistries, such as lithium-sulfur, show promise in their high energy, while limitations exist in their power and cyclability due to low electrical conductivity and high intermediate solubility in organic liquid electrolytes. Several strategies to overcome the low electrical conductivity involve the use of selenium as a dopant in the active sulfur material, along with the incorporation of 2-dimensional electron-conducting holey-graphene to improve the composite cathodes electronic conductivity. Furthermore, combining this chemistry with a solid-electrolyte avoids the components’ dissolution issues3. However, the development of composite solid-state cathodes is non-trivial as several components must be intimately mixed so that the active component has sufficient access to both electrons and lithium ions to undergo full electrochemical conversion. Mathematical modeling of battery components can assist experimental design through a robust and rigorous combination of computational modeling techniques covering multiple length scales. The objective is to leverage modern computational materials methods combined with battery multiphysics tools to develop radically advanced compatible cathode and electrolyte materials, build and test solid state lithium-sulfur cells and packs. A NASA-based cross-organizational team of high-powered experts combined integrated computational predictive modeling, fundamental chemistry analysis, advanced material science, and battery cell development to tackle this very challenging, multidisciplinary problem. This presentation will show a multiscale computational modeling approach that has produced a novel particle dynamics method called Solid Electrolyte Sphere Approximation Model (SESAM). SESAM modeling targets the 1-10 µm scale structures and provides electromechanical and grain interactions for predictive design guidelines for the manufacturing of solid-state components. Parameters such as particle size and volume fraction of the constituent materials were modeled and experimentally fabricated to optimize electrochemical performance through improved microstructure design. Experimental feedback was provided through ionic and electronic conductivity assessment and structural analysis of developed materials and cell components.

battery↗
NASA NTRSDate not supplied

Usability Evaluation of Fleet Management Interface for High Density Vertiplex Environments

To meet the rising demand for an Advanced Air Mobility (i.e. urban and rural unmanned aircraft systems) ecosystem, NASA Aeronautics Research Mission Directorate (ARMD) is hosting a series of simulations and flight tests under the High Density Vertiplex sub-project (HDV). HDV aims to develop an integrated automation architecture to support terminal area flight operations. The HDV simulations and flight tests address safety, integration, and operational challenges, while integrated systems and software demonstrate design readiness, robustness, and interoperability. During the initial HDV simulation in 2021, a prototype traffic management tool developed by NASA called Fleet Management Interface (FMI) was tested. FMI was designed to introduce an advanced level of human-automation interaction to aid both Ground Control Station Operators (GCSOs) and Fleet Managers (FMs) in remotely managing flights under their ownership. In a human-in-the-loop simulation, a usability study was conducted with FMI to identify optimal approaches for displaying information for human operators using subjective measures of usability, workload, situation awareness, risk, and trust, along with qualitative feedback. This study consisted of task analysis in which GCSO and FM subjects used an Urban Air Mobility (UAM) environment to develop and execute a plan for two different traffic scenarios of remotely controlled vehicles. In each scenario, a controlled vehicle completed a takeoff, active flight, and landing sequence while automated traffic flew in the background at a rate of 20 operations per hour. In the first scenario, the controlled vehicle flew a nominal route with takeoff and landing at the same vertiport. In the second scenario, the controlled vehicle started on the nominal route, then diverted to an unplanned location mid-flight. Results showed that self-reported performance, usability, trust, and situation awareness ratings of FMI were moderately to strongly high. There were small differences between scenarios, with Scenario 2 being perceived as more unstable, complex, variable, risky, and potentially harmful than Scenario 1. Furthermore, participants described improvements that could be made to create a better user experience. For example, users would like greater configurability of the interface based on their personal information requirements, and they would like the opportunity to review routes before assigning them. The results from this study will inform future development of the FMI with the end goal of creating a reference automation tool for airspace management procedures in AAM. The FMI could be introduced as a potential way to reduce dependency on traditional air navigation services through increased automation in high density vertiplex environments.

vertiplex↗
NASA NTRSDate not supplied

Usability Evaluation of Fleet Management Interface for High Density Vertiplex Environments

To meet the rising demand for an Advanced Air Mobility (AAM) (i.e. urban and rural unmanned aircraft systems) ecosystem, the NASA Aeronautics Research Mission Directorate (ARMD) is hosting a series of simulations and flight tests under the High Density Vertiplex sub-project (HDV) to prototype and study the effectiveness AAM capabilities under various operational contexts. HDV aims to develop an integrated automation architecture to support terminal area flight operations. The HDV simulations and flight tests address safety, integration, and operational challenges, while integrated systems and software demonstrate design readiness, robustness, and interoperability. During the initial HDV simulation in 2021, a prototype traffic management tool developed by NASA called the Fleet Management Interface (FMI) was tested. FMI was designed to introduce an advanced level of human-automation interaction to aid both Ground Control Station Operators (GCSOs) and Fleet Managers (FMs) in remotely managing flights under their ownership. In a human-in-the-loop simulation, a usability study was conducted with the FMI to identify optimal approaches for displaying information to human operators using subjective measures of usability, workload, situation awareness, risk, and trust, along with qualitative feedback. This study consisted of task analysis in which GCSO and FM subjects used an Urban Air Mobility (UAM) environment to develop and execute a plan for two different traffic scenarios of remotely controlled vehicles. In each scenario, a remotely controlled vehicle completed a takeoff, active flight, and landing sequence while simulated traffic flew in the background at a rate of 20 operations per hour. In the first scenario, the controlled vehicle flew a nominal route with takeoff and landing at the same vertiport. In the second scenario, the controlled vehicle started on the nominal route, then diverted to an unplanned location mid-flight. Results showed that self- reported performance, usability, trust, and situation awareness ratings of FMI were moderately to strongly high. There were small differences between scenarios, with Scenario 2 being perceived as more unstable, complex, variable, risky, and potentially harmful than Scenario 1. Furthermore, participants described improvements that could be made to create a better user experience. For example, users suggested customizable interfaces to accommodate information display preferences, and the ability to review routes before assigning them. The results from this study will inform future development of the FMI with the end goal of creating a reference automation tool for airspace management procedures in AAM. The FMI could serve to reduce dependency on traditional air navigation services through increased automation in high density vertiplex environments.

Fleet manager↗
NASA NTRSDate not supplied

PVT+AUDIO. Online Psychoacoustic Testing iOS App for Evaluating AAM/UAM Noise Resonse

PVT+AUDIO is an extension of the existing Psychomoter Vigilance Task application that NASA released over 2 years ago for evaluating pilot fatigue. PVT+AUDIO is designed to present audio stimuli using a method of limits to determine the relationship between objective acoustic parameters and psychacoustic response, for application to Urban Air Mobility (UAM) and Advanced Air Mobility (AAM). Responses include annoyance, acceptance, blend with ambient sound, and detection. The app runs within Apple's iOS ecosystem and utilizes binaural audio simulations of vehicles and ambient sound, and is designed to compensate for headphone response. A prototype experiment is described, along with issues related to calibration and subject engagement.

aircraft noise disturbance↗
NASA NTRSDate not supplied

HAT m:N Activity Overview

Since 2020, researchers from the Human Autonomy Teaming (HAT) Laboratory at NASA Ames Research Center have conducted human-in-the-loop (HITL) simulation research to study a new control paradigm for operations involving multiple remotely piloted aircraft systems (RPAS). Colloquially referred to as "m:N," this paradigm is characterized by multiple operators collaboratively controlling multiple vehicles between them. The m:N name expresses a ratio whereby m is the number of operators and N is the number of vehicles shared between them. In this presentation, HAT Lab researchers provide a high-level overview of the m:N studies that have been performed to-date. These include a study of the m:N concept of operations (CONOPS) and the attending roles and responsibilities ("ConOps/R&R Sim"), a study focused on contingency management involving dynamic, inter-operator transfers of vehicles ("Handoff Sim"), and a study examining the effects of pilot-ATC communication systems on workload ("UAM Comms Sim"). A selection of key results are provided. The presentation concludes with a brief discussion of planned research into m:N operations.

m:N↗
NASA NTRSDate not supplied

RApid Blade and Blade-Vortex InTeraction (RABBIT) GUI implementation via MATLAB Application Designer

Rotorcraft noise source identification and reduction is crucial to the emergence of the Urban Air Mobility (UAM) market. One key rotor noise source is Blade Vortex Interaction (BVI), caused by the rotor wake interacting with the rotor blades. The low-fidelity RApid Blade and Blade-Vortex InTeraction (RABBIT) tool was created to predict the location and characteristics of Blade-Vortex Interaction (BVI) noise. RABBIT enables engineers to quickly locate and understand designs or configurations that cause significant BVI noise. Furthermore, RABBIT can identify the time and location of blade overlap for coaxial rotors. RABBIT is not a general acoustic prediction tool, but instead utilizes vortex and wake parameters to visualize and predict only BVI noise. To date, RABBIT has been validated against CAMRAD II and ANOPP2/AARON for three NASA Revolutionary Vertical Lift Technology (RVLT) concept vehicles: the Quiet Single Main Rotor (QSMR), Side-by-Side, and Quadrotor. RABBIT has been shown to be a useful BVI prediction tool capable of visualizing BVI as a function of various parameters. Impulse factor and time rate of change of loading allows for a unique BVI prediction technique that provides information on impulse strength, location, and frequency. This presentation will highlight how MATLAB’s App Designer was utilized to enable users to navigate RABBIT easily and efficiently without prior experience with MATLAB or rotorcraft acoustics. RABBIT users can build and view rotors in real time with options to control blade chord, radius, phasing, and airfoil geometry. Furthermore, multiple rotors can be combined to create state-of-the-art multi-rotor vehicles or fleets. Editable tables, sliding bars, and push buttons were implemented within MATLAB’s App Designer for RABBIT’s GUI to allow engineers and designers to predict low order acoustic impact of BVI occurrences.

Natasha Lydia Schatzman↗