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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].

This paper details results of a numerical and experimental investigation into the relative performance of vaned and vaneless mixed flow turbines for application to medium and heavy-duty diesel engines utilizing pulse exhaust systems. Previous investigations into the impact of nozzle vanes on turbine performance considered only open turbine housings, whereas a majority of medium and heavy-duty diesel engine applications are six-cylinder engines using pulse exhaust systems with divided turbines. The two turbine stages for this investigation were carefully designed to meet the constraints of engines with pulse exhaust systems and to control confounding factors that would undermine the vaned vs vaneless performance comparison. Detailed CFD analysis and turbine dynamometer test results confirm a significant efficiency advantage for the vaned turbine stage under both full and partial admission conditions.

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.

The Society of Automotive Engineers Fatigue Design & Evaluation Committee [SAE FD&E] is actively working on a total life project for weldments, in which the welding residual stress is a key contributor to an accurate assessment of fatigue life. Physics-based welding process simulation and various types of residual stress measurements were pursued to provide a representation of the residual stress field at the failure location in the fatigue samples. A well-controlled and documented robotic welding process was used for all sample fabrications to provide accurate inputs for the welding simulations. One destructive (contour method) residual stress measurement and several non-destructive residual stress measurements-surface X-ray diffraction (XRD), energy dispersive X-ray diffraction (EDXRD), and neutron diffraction (ND)-were performed on the same or similarly welded samples.

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.

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 automotive industry is dramatically changing. Many automotive Original Equipment Manufacturers (OEMs) proposed new prototype models or concept vehicles to promote a green vehicle image. Non-traditional players bring many latest technologies in the Information Technology (IT) industry to the automotive industry. Typical vehicle’s characteristics became wider compared to those of vehicles a decade ago, and they include not only a driving range, mileage per gallon and acceleration rating, but also many features adopted in the IT industry, such as usability, connectivity, vehicle software upgrade capability and backward compatibility. Consumers expect the latest technology features in vehicles as they enjoy in using digital applications in laptops and mobile phones. These features create a huge challenge for a design of a new vehicle, especially for a human-machine-interface (HMI) system.

Today’s business climate and economy demand new, innovative strategies from the initial kickoff of research and development - to the mining of ore from the earth - to the final inspection of a finished product in a mid-western factory. From startup companies with two employees to the largest companies, the world faces new and challenging requirements every day. The demands from companies, customers, executives, and shareholders continue to drive for higher outputs with more efficient use of personnel and investments. Fortunately, the rate of technology continues to exponentially accelerate, which allows those at the cutting edge of technology to capitalize. Caterpillar has been a pioneer in advanced technology since its inception and has been developing the foundation for autonomy over the past four decades.

China has become the world’s largest vehicle market in terms of sales volume. Automobiles sales keep growing in recent years despite the declining economic growth rate. Due to the increasing attention given to the environmental impact, more stringent emission regulations are being drafted to control traditional internal combustion engine emissions. In order to reduce vehicle emissions, environmentally-friendly new-energy vehicles, such as electric vehicles and plug-in hybrid vehicles, are being promoted by government policies. The Chinese government plans to boost sales of new-energy cars to account for about five percent of China’s total vehicle sales. It is well known that more electric and electronic components will be integrated into a vehicle platform during vehicle electrification.

Age and experience influence driver ability to cope with transitions between automated and manual driving, especially when drivers are engaged in media use. This study evaluated three age cohorts (young/new drivers, adults, and seniors) on their performance in transitions from automated driving to manual vehicle control in a laboratory driving simulator. Drivers were given three tasks to perform during the automated driving segments: to watch a movie on a tablet, to read a story on a tablet, or to supervise the car's driving. We did not find significant differences in people's driving performance following the different tasks. We also did not find significant differences in driving performance between the people in each age group who successfully completed the study; however, the rejection rate of the senior age group was over 30% because many of the people in this age group had difficulty hearing instructions, understanding tasks, or remembering what to do.

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.

All around the world, steps are being taken to improve the quality of our environment. Prominent among these are the definition, implementation, and attainment of increasingly stringent emissions regulations for all types of engines, including off-highway diesels. These rigorous regulations have driven use of technologies like after-treatment, advanced air systems, and advanced fuel systems. Fuel dispensed off-highway is routinely and significantly dirtier than fuel from on-highway outlets. Furthermore, fuels used in developing countries can be up to 30 times dirtier than the average fuels in North America. Poor fuel cleanliness, coupled with the higher pressures and performance demands of modern fuel systems, create life challenges greater than encountered with cleaner fuels. This can result in costly disruption of operations, loss of productivity, and customer dissatisfaction in the off-highway market.

The application of Artificial Intelligence (AI) in the automotive industry can dramatically reshape the industry. In past decades, many Original Equipment Manufacturers (OEMs) applied neural network and pattern recognition technologies to powertrain calibration, emission prediction and virtual sensor development. The AI application is mostly focused on reducing product development and validation cost. AI technologies in these applications demonstrate certain cost-saving benefits, but are far from disruptive. A disruptive impact can be realized when AI applications finally bring cost-saving benefits directly to end users (e.g., automation of a vehicle or machine operation could dramatically improve the efficiency). However, there is still a gap between current technologies and those that can fully give a vehicle or machine intelligence, including reasoning, knowledge, planning and self-learning.

Despite numerous research efforts, there is no reliable and widely accepted tool for the prediction of erosion prone material surfaces due to collapse of cavitation bubbles. In the present paper an Erosion Aggressiveness Index (EAI) is proposed, based on the pressure loads which develop on the material surface and the material yield stress. EAI depends on parameters of the liquid quality and includes the fourth power of the maximum bubble radius and the bubble size number density distribution. Both the newly proposed EAI and the Cavitation Aggressiveness Index (CAI), which has been previously proposed by the authors based on the total derivative of pressure at locations of bubble collapse (DP/Dt>0, Dα/Dt<0), are computed for a cavitating flow orifice, for which experimental and numerical results on material erosion have been published. The predicted surface area prone to cavitation damage, as shown by the CAI and EAI indexes, is correlated with the experiments.

Off-highway machine mounting system isolation, especially the cab mounting system, significantly affects the operator comfort by providing damping to the harsh inputs and isolating the structure-borne energy from traveling into the cab. Mounting system isolation performance is decided not only by the isolation component, but also the mounting bracket structure, and should be treated as a system. This paper gives a review of how the mounting system isolates structural energy and the effect of the bracket structure stiffness to the mounting system isolation performance.

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.

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.

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.

Faced with competitive environments, pressure to lower development costs and aggressive timelines engineers are not only increasingly adopting numerical simulation techniques but are also embracing design optimization schemes to augment their efforts. These techniques not only provide more understanding of the trade-offs but are also capable of proactively guiding the decision making process. However, design optimization and exploration tools have struggled to find complete acceptance and are typically underutilized in many applications; especially in situations where the algorithms have to compete with existing swift decision making processes. In this paper we demonstrate how the type of setup and algorithmic choice can have an influence and make optimization more lucrative in a new product development atmosphere. We also present some results from a design exploration activity, involving linkage and structural development, of an earth moving machine application.

The Mission Operations Directorate (MOD) of the Johnson Space Center is responsible for providing continuous operations support for the International Space Station (ISS). Operations support requires flight controllers who are skilled in team performance as well as the technical operations of the ISS. ISS flight controller certification has evolved to include a balanced focus on the development of team performance and technical expertise. The latest challenge the ISS team faces is how to certify an ISS flight controller to the required level of effectiveness in one year. Space Flight Resource Management (SFRM) training, a NASA adapted variant of Crew Resource Management (CRM), is expanding the role of senior flight controllers as mentors to help meet that challenge. This paper explains our mentoring approach and discusses its effectiveness and future applicability in promoting SFRM/CRM skills.