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Predicting the aerodynamic performance of an aircraft in icing conditions is critical as failures in an aircraft’s ice protection system can compromise flight safety. Aerodynamic effects of icing have typically relied on RANS modeling, which usually struggles to predict stall behavior, including those induced by surface roughness. Encouraged by recent studies using LES that demonstrate the ability to predict stall characteristics on full aircraft with smooth wings at an affordable cost [1], this study seeks to apply this methodology to icing conditions. Measurements of lift, drag, and pitching moments of a NACA23012 airfoil under clean and iced conditions are collected at Re = 1.8M. Using laser scanned, detailed representations of the icing geometries, LES calculations are conducted to compare integrated loads against experimental measurements in both clean and iced conditions at various angles of attack through the onset of stall [2].

Modeling of icing is important for the design of aircraft lifting surfaces and for the design of efficient propulsion systems. The computational modeling of ice accretion prediction is important to replace the expensive experimental techniques for calculating the ice shapes in Icing tunnels, and the first step toward modeling ice accretion is to accurately compute the droplet collection efficiency which acts as the input to the accretion model. In this work, we perform large-eddy simulations of supercooled droplet transport and impingement onto complex aircraft geometries using a Lagrangian particle approach. We assess the improvement in modeling droplet impingement by computing the droplet collection efficiency and by comparing with the existing experimental data.

Lithium-ion batteries (LIBs) repurposed from retired electric vehicles (EVs) for grid-scale energy storage systems (ESSs) have the potential to contribute to a sustainable, low-carbon-emissions energy future. The economic and technological value of these “second-life” LIB ESSs must be evaluated based on their operation on the electric grid, which determines their aging trajectories. The battery research community needs experimental data to understand the operation of these batteries using laboratory experiments, yet there is a lack of work on experimental evaluation of second-life batteries. Previous studies in the literature use overly-simplistic duty cycling in order to age second-life batteries, which may not produce aging trajectories that are representative of grid-scale ESS operation. This mismatch may lead to inaccurate valuation of retired EV LIBs as a grid resource.

Gasoline direct-injection (GDI) engine technology improves vehicle fuel economy while decreasing CO2 emissions. The main drawback of GDI technology is the increase in particulate emissions compared to the commonly used port fuel injection technologies. Today’s adopted strategy to limit such emissions relies upon the use of aftertreatment gasoline particulate filters (GPFs). GPFs reduce particulates resulting from fuel combustion. Soot oxidation (also known as regeneration) is required at regular intervals to clean the filter, maintain a consistent soot trapping efficiency, and avoid the formation of soot plugs in the GPF channels. In this paper, starting from a multiphysics GPF model accounting for mass, momentum, and energy transport, a sensitivity analysis is carried out to choose the best mesh refinement, time step, and relative tolerance to ensure a stable numerical solution of the transport equations during regeneration while maintaining low computational time.

In this work, we conduct a sensitivity analysis of the mean-value internal combustion engine exergy-based model, recently developed by the authors, with respect to different driving cycles, ambient temperatures, and exhaust gas recirculation rates. Such an analysis allows to assess how driving conditions and environment affect the exergetic behavior of the engine, providing insights on the system’s inefficiency. Specifically, the work is carried out for a military series hybrid electric vehicle.

This study investigates the use of machine learning methods for the selection of energy storage devices in military electrified vehicles. Powertrain electrification relies on proper selection of energy storage devices, in terms of chemistry, size, energy density, and power density, etc. Military vehicles largely vary in terms of weight, acceleration requirements, operating road environment, mission, etc. This study aims to assist the energy storage device selection for military vehicles using the data-drive approach. We use Machine Learning models to extract relationships between vehicle characteristics and requirements and the corresponding energy storage devices. After the training, the machine learning models can predict the ideal energy storage devices given the target vehicles design parameters as inputs. The predicted ideal energy storage devices can be treated as the initial design and modifications to that are made based on the validation results.

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.

Gasoline particulate filters (GPFs) are devices used to filter soot emitted by gasoline direct injection (GDI) engines. A numerical model for a ceria-coated GPF presented in a previous paper by H. Arunachalam et al. in 2017 was developed to predict internal temperature and soot amount combusted during regeneration events. Being that both the internal temperature and the accumulated soot cannot be directly measured during real-time operation and owing to their critical importance for GPF health monitoring as well as regeneration scheduling, the above model turns out to be a valuable tool for OBD applications. In this paper, we first conduct a stochastic analysis to understand the relation between the model parameters and the initial value of the ceria (IV) oxide volume fraction, as a deterministic value for such a state is not known.

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.

For high-load applications, natural gas represents a clean burning, readily available, and relatively inexpensive alternative to number 2 Diesel fuel. However, the fuel’s poor ignitability has previously limited implementation to spark ignited and dual-fueled engines. These approaches suffer from reduced peak load and high engine-out particulate emissions, respectively, requiring lean operation and expensive aftertreatment to meet regulatory standards. A high-temperature combustion strategy can overcome the difficult ignitibility, allowing for true Diesel-style combustion of pure methane-the least ignitable and least sooting component of natural gas. In order to achieve this result, a compression system was designed to supply fuel at pressures suitably high to achieve good mixing and short injection durations, and a solenoid-actuated Diesel fuel injector was modified to function at these pressures with a gaseous fuel.

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.

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 study investigates using driver prediction to anticipate energy usage over a 160-meter look-ahead distance for a series, plug-in, hybrid-electric vehicle to improve conventional thermostatic powertrain control. Driver prediction algorithms utilize a hidden Markov model to predict route and a regression tree to predict speed over the route. Anticipated energy consumption is calculated by integrating force vectors over the look-ahead distance using the predicted incline slope and vehicle speed. Thermostatic powertrain control is improved by supplementing energy produced by the series generator with regenerative braking during events where anticipated energy consumption is negative, typically associated with declines or decelerations.

Autonomous vehicles represent a new class of transportation that may be qualitatively different from existing cars. Two online experiments assessed lay perceptions of moral norms and responsibility for traffic accidents involving autonomous vehicles. In Experiment 1, 120 US adults read a narrative describing a traffic incident between a pedestrian and a motorist. In different experimental conditions, the pedestrian, the motorist, or both parties were at fault. Participants assigned less responsibility to a self-driving car that was at fault than to a human driver who was at fault. Participants confronted with a self-driving car at fault allocated greater responsibility to the manufacturer and the government than participants who were confronted with a human driver at fault did.

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.

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.

To enable the modeling of modern diesel engines, this work furthers the development of multi-dimensional flamelet models for application to designs that employ multiple injection strategies. First, the flamelet equations are extended to two dimensions following the work of Hasse and Peters [1] and Doran et al. [2] and a method of coupling the resulting equations interactively to a turbulent flow simulation for use in unsteady calculations is described. The external parameters required to solve the flamelet equations are the scalar dissipation rates. In previous studies, the dissipation rates of each mixture fraction have been scaled according to their realizable bounds and the cross-dissipation rate between mixture fractions has been neglected.