Search Results

In 2021 the Federal Aviation Administration in collaboration with the National Research Council of Canada performed research on altitude ice crystal icing of aircraft engines using the modular compressor rig, ICE-MACR, in an altitude wind tunnel. The aim of the research campaign was to address research needs related to ice crystal icing of aircraft engines outlined in FAA publication Engine Ice Crystal Icing Technology Plan with Research Needs. This paper reports the findings on ice accretion from a configuration of ICE-MACR with two compression stages. Inherent in two-stage operation is not just additional fracturing and heating by the second stage but also higher axial velocity and potentially greater centrifuging of particles. These factors influence the accretion behavior in the test article compared to single stage accretion.

The performance of low-adhesion surfaces in a realistic, in-flight icing environment with supercooled liquid droplets is evaluated using a NACA 0018 airfoil in the National Research Council of Canada Altitude Icing Wind Tunnel. This project was completed in collaboration with McGill University, the University of Toronto and the NRC Aerospace Manufacturing Technologies Centre in March 2022. Each collaborator used significantly different methods to produce low-adhesion surface treatments. The goal of the research program was to demonstrate if the low-adhesion surfaces reduced the energy required to de-ice or anti-ice an airfoil in an in-flight icing environment. Each collaborator had been developing their own low-adhesion surfaces, using bench tests in cold rooms and a spin rig in the wind tunnel to evaluate their performance. The most promising surface treatments were selected for testing on the airfoil.

Under contract to Transport Canada (TC) and with joint funding support from the Federal Aviation Administration (FAA), a vertical stabilizer common research model (VS-CRM) has been designed and built by the National Research Council of Canada (NRC). This model is a realistic, scaled representation of modern vertical stabilizer designs without being specific to a particular aircraft. The model was installed and tested in the NRC 3 m × 6 m Icing Wind Tunnel in late 2021/early 2022. Testing was led by APS Aviation Inc., with support from NRC and NASA, in order to observe the anti-icing fluids flow-off behavior with and without freezing or frozen precipitation during simulated take-off velocity profiles. The model dry-air aerodynamic properties were characterized using flow visualization tufts and boundary layer rakes. Using this data, a target baseline configuration was selected with a yaw angle equal to 0° and rudder deflection angle equal to -10°.

Since the introduction of ice crystal icing certification requirements [1], icing facilities have played an important role in demonstrating compliance of aircraft air data probes, engine probes, and increasingly, of turbine engines. Most sea level engine icing facilities use the freezing-out of a water spray to simulate ice crystal icing conditions encountered at altitude by an aircraft in flight. However, there are notable differences in the ice particles created by freeze-out versus those observed at altitude [2, 3, 4]. Freeze-out crystals are generally spherical as compared to altitude crystals which have variable crystalline shapes. Additionally, freeze-out particles may not completely freeze in their centres, creating a combination of super-cooled liquid and ice impacting engine hardware. An alternative method for generating ice crystals in a test facility is the grinding of ice blocks or cubes to create irregular shaped crystals.

This article presents an autonomous steering control scheme for articulated heavy vehicles (AHVs). Despite economic and environmental benefits in freight transportation, lateral stability is always a concern for AHVs in high-speed highway operations due to their multi-unit vehicle structures, and high centers of gravity (CGs). In addition, North American harsh winter weather makes the lateral stability even more challenging. AHVs often experience amplified lateral motions of trailing vehicle units in high-speed evasive maneuvers. AHVs represent a 7.5 times higher risk than passenger cars in highway operation. Human driver errors cause about 94% of traffic collisions. However, little attention has been paid to autonomous steering control of AHVs.

Road-vehicle platooning is known to reduced aerodynamic drag. Recent aerodynamic-platooning investigations have suggested that follower-vehicle drag-reduction benefits persist to large, safe inter-vehicle driving distances experienced in everyday traffic. To investigate these traffic-wake effects, a wind-tunnel wake-generator system was designed and used for aerodynamic-performance testing with light-duty-vehicle (LDV) and heavy-duty-vehicle (HDV) models. This paper summarizes the development of this Road Traffic and Turbulence System (RT2S), including the identification of typical traffic-spacing conditions, and documents initial results from its use with road-vehicle models. Analysis of highway-traffic-volume data revealed that, in an uncongested urban-highway environment, the most-likely condition is a speed of 105 km/h with an inter-vehicle spacing of about 50 m.

The aerodynamic forces experienced by vehicles depend on a variety of factors including wind direction, traffic, and roadside vegetation. Such complex boundary conditions often result in unsteady flow separation and the formation of large-scale coherent structures, which, in turn, significantly influence the aerodynamics of following vehicles. To gain a deeper understanding of the unsteady behaviour of such vehicle wakes under large-scale conditions, a time-resolved field measurement technique is required. Existing methods, such as tomographic particle image velocimetry and three-dimensional particle tracking velocimetry are unfortunately quite limited at these scales. Furthermore, such techniques require complex multi-camera calibrations, hazardous lasers, and optical access from many vantage points.

Autonomous vehicles are currently a subject of great interest and there is heavy research on creating and improving algorithms for detecting objects in their vicinity. A ROS-based deep learning approach has been developed to detect objects using point cloud data. With encoded raw light detection and ranging (LiDAR) and camera data, several basic statistics such as elevation and density are generated. The system leverages a simple and fast convolutional neural network (CNN) solution for object identification and localization classification and generation of a bounding box to detect vehicles, pedestrians and cyclists was developed. The system is implemented on an Nvidia Jetson TX2 embedded computing platform, the classification and location of the objects are determined by the neural network. Coordinates and other properties of the object are published on to various ROS topics which are then serviced by visualization and data handling routines.

People through many years of experience, have developed a great intuitive sense for navigation and spatial awareness. With this intuition people are able to apply a near rules based approach to their driving. With a transition to autonomous driving, these intuitive skills need to be taught to the system which makes perception is the most fundamental and critical task. One of the major challenges for autonomous vehicles is accurately knowing the position of the vehicle relative to the world frame. Currently, this is achieved by utilizing expensive sensors such as a differential GPS which provides centimeter accuracy, or by using computationally taxing algorithms to attempt to match live input data from LiDARs or cameras to previously recorded data or maps. Within this paper an algorithm and accompanying hardware stack is proposed to reduce the computational load on the localization of the robot relative to a prior map.

The SAE G-27 committee was tasked by ICAO to develop a performance-based packaging standard for lithium batteries transported as cargo on aircraft. The standard details test criteria to qualify packages of lithium batteries & cells for transportation as cargo on-board passenger aircraft. Lithium batteries and cells have been prohibited from shipment as cargo on passenger aircraft since 2016. This paper summarizes the results of the tests conducted by Transport Canada and National Research Council Canada to support the development of this standard with evidence-based recommendations. It includes a description of the test specimens, the test set up, instrumentation used, and test procedures following the standard as drafted to date. The study considered several lithium-ion battery and cell chemistries that were tested under various proposed testing scenarios in the draft standard.

Under contract to Airlines for America (A4A), APS Aviation Inc. (APS), in collaboration with the National Research Council of Canada (NRC), completed an aircraft ground icing exploratory research project at the NRC 3 m × 6 m Wind Tunnel in Ottawa in January 2019. The purpose of this project was to investigate the feasibility of using aerodynamic data to evaluate the performance of contaminated anti-icing fluid, rather than the traditional visual fluid failure indicators that are used to develop Holdover Times (HOTs). The aerodynamic performance of a supercritical airfoil model with anti-icing fluids and snow contamination was evaluated against the clean, dry performance of the airfoil in order to calculate the associated aerodynamic penalty. The visual failure of the fluid was also evaluated for each run, and the visual and aerodynamic results were compared against each other for each contamination exposure time.

The National Research Council’s Gas Turbine Laboratory provides industry leading icing facilities that allow manufacturers to develop, validate and certify new products for flight in adverse conditions. This paper shows how NRC measurement techniques are used across the facilities, and presents a literature-review of recently developed capabilities. The overview includes new details on some facilities, and future capabilities that are in development or planned for the near future. Methods developed at the NRC for characterizing inclement conditions are discussed and include the Isokinetic Probe, Particle Shadow Velocimetry, the Particle Detection Probe, and a size-binned real-time thermodynamic evaporation model.

Recent research to investigate the aerodynamic-drag reduction associated with truck platooning systems has begun to reveal that surrounding traffic has a measurable impact on the aerodynamic performance of heavy trucks. A 1/15-scale wind-tunnel study was undertaken to measure changes to the aerodynamic drag experienced by heavy trucks in the presence of upstream traffic. The results, which are based on traffic conditions with up to 5 surrounding vehicles in a 2-lane configuration and consisting of 3 vehicle shapes (compact sedans, SUVs, and a medium-duty truck), show drag reductions of 1% to 16% for the heavy truck model, with the largest reductions of the same order as those experienced in a truck-platooning scenario. The data also reveal that the performance of drag-reduction technologies applied to the heavy-truck model (trailer side-skirts and a boat-tail) demonstrate different performance when applied to an isolated vehicle than to conditions with surrounding traffic.

With increasing use of boat-tails on Canadian roads, a concern had been raised regarding the possibility for ice and snow to accumulate and shed from the cavity of a boat-tail affixed to a dry-van trailer, posing a hazard for other road users. This paper describes a preliminary evaluation of the potential for ice and snow accumulation in the cavity of a boat-tail-equipped heavy-duty vehicle. A transient CFD approach was used and combined with a quasi-static particle-tracking simulation to evaluate, firstly, the tendency of various representative ice or snow particles to be entrained in the vehicle wake, and secondly, the potential of such particles to accumulate on the aft end of a dry-van trailer with and without various boat-tail configurations. Results of the particle tracking analyses showed that the greatest numbers of particles impinge on the base of the trailer for the no-boat-tail case, concentrated on the upper surface of the back face of the trailer.

The noise generated by the flow of air past a transport truck is a key design factor for the manufacturers of these vehicles as the sound levels in the cabin are a significant component of driver comfort. This paper describes a collaboration between Volvo GTT and the National Research Council Canada to measure the in-cabin aeroacoustics of a full-scale cab-over tractor in the NRC 9 m Wind Tunnel. Acoustic instrumentation was installed inside the tractor to record cabin noise levels and externally to acquire tunnel background noise data. Using a microphone mounted on the driver’s-side tunnel wall as a reference to remove variations in background noise levels between data points, differences in cabin noise levels were able to be detected when comparing the tractor with different configurations. The good repeatability of the data allowed for differences of as little as 0.5 dB to be measured.

A novel method was developed to predict the free-stream velocity experienced by a traveling vehicle based on track-side anemometric measurements. The end objective of this research was to enhance the reliability of the prediction of free-stream conditions in order to improve the accuracy of aerodynamic drag coefficient (CD) assessments from track tests of surface vehicles. Although the technique was applied to heavy-duty vehicles in the present work, it is equally applicable to any vehicle type. The proposed method is based on Taylor’s hypothesis, a principle applied in fluid mechanics to convert temporal signals into the spatial domain. It considers that the turbulent wind velocity fluctuations measured at one point are due to the "passage of an unchanging pattern of turbulent motion over the point". The method is applied to predict the wind velocity that the vehicle will experience as it encounters a wind pattern detected earlier by an anemometer located upwind.

Turbulence is known to influence the aerodynamic and aeroacoustic performance of ground vehicles. What is not thoroughly understood are the characteristics of turbulence that influence this performance and how they can be applied in a consistent manner for aerodynamic design and evaluation purposes. Through collaboration between Transport Canada and the National Research Council Canada (NRC), a project was undertaken to develop a system for generating road-representative turbulence in the NRC 9 m Wind Tunnel, named the Road Turbulence System (RTS). This endeavour was undertaken in support of a larger project to evaluate new and emerging drag reduction technologies for heavy-duty vehicles. A multi-stage design process was used to develop the RTS for use with a 30% scale model of a heavy-duty vehicle in the NRC 9m Wind Tunnel.

This paper describes the equipment, analysis methods and results obtained for particle size measurements based on a particle imaging velocimetry (PIV) system in which a short duration laser pulse is used to backlight airborne particles. This produces high quality and high resolution images of fast moving airborne particles in a non-intrusive manner. This imaging technique is also used to examine particle morphology and 2D particle trajectory and velocity. The image analysis methods are outlined and validation test results discussed which show the measurement of reference glass beads between 20 and 400 microns were generally to within their stated size. As well, validation testing using known icing wind tunnel droplet distributions were compared with Spraytek 2000 Malvern droplet size measurements and showed agreement of the MVD's to be within ±5% for distributions having nominally 20, 40 and 80 micron MVD's.

Simulations of supercooled large droplet (SLD) icing environments within the NRC's Altitude Icing Wind Tunnel (AIWT) have been performed in which broad band mass distribution spectra are achieved that include a distinct pattern of liquid water content (LWC) over a range of droplet sizes (i.e., bi-modal distribution). The mass distribution is achieved through modification of the existing spray system of the AIWT to allow two spray profiles with differing LWC and median volumetric diameter (MVD) to be simultaneously injected into the flow. Results of spray profile distributions measured in the test section have demonstrated that freezing drizzle conditions, where MVD is either less than or greater than 40 μm, can be achieved.

Transport Canada, through its ecoTECHNOLOGY for Vehicles program, retained the services of the National Research Council Canada to undertake a test program to examine the operational and human factors considerations concerning the removal of the side mirrors on a Class 8 tractor equipped with a 53 foot dry van semi-trailer. Full scale aerodynamic testing was performed in a 2 m by 3 m wind tunnel on a system component basis to quantify the possible fuel savings associated with the removal of the side mirrors. The mirrors on a Volvo VN780 tractor were removed and replaced with a prototype camera-based indirect vision system consisting of four cameras mounted in the front fender location; two cameras on either side of the vehicle. Four monitors mounted in the vehicle - two mounted on the right A-pillar and two mounted on the left A-pillar - provided indirect vision information to the vehicle operator.