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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 Flight Research Laboratory (FRL), National Research Council (NRC) of Canada is currently developing an in-flight aircraft aerodynamic model identification technique that determines the small perturbation model at a given test condition. Initial demonstrations have been carried out using the NRC Falcon 20 research aircraft. An efficient system architecture, in terms of both software algorithms and hardware processing, has been designed to meet the stringent near real-time requirements of an in-flight system. As well, novel hardware and software techniques are being applied to the calibration and measurement of the fundamental in-flight parameters, such as air data. The small perturbation models are then combined to develop a global model of the aircraft that is validated by comparing the model response to flight data. The maneuvers were performed according to the FAA Acceptance Test Guide (ATG).

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.

Data is presented from a number of flight research aircraft, which have been involved in the research of the effects of inflight icing, in a variety of atmospheric supercooled droplet and mixed-phase icing environmental conditions. The aircraft Types considered cover both Pneumatic and Thermal Ice Protection Systems (IPS). Icing includes supercooled droplet impact icing upon airframe and propeller blades and cold-soaked frost icing. The drag effects of inflight icing, from mixed-phase small and large droplets encountered during the course of SALPEX cloud physics research operations, upon a Fokker F-27 turboprop transport aircraft, have been analyzed. Furthermore, during the course of AIRS 1.5 and AIRS II inflight icing flight research operations, the NRC Convair conducted aerodynamic characterization maneuvers, following and during icing accretion in a wide range of environmental conditions of altitude, air temperature, LWC and droplet spectra.

This paper describes the commissioning of a linear compressor cascade rig for ice crystal research. The rig is located in an altitude chamber so the test section stagnation pressure, temperature and Mach number can be varied independently. The facility is open-circuit which eliminates the possibility of recirculating ice crystals reentering the test section and modifying the median mass diameter and total water content in time. As this is an innovative facility, the operating procedures and instrumentation used are discussed. Sample flow quality data are presented showing the distribution of velocity, temperature, turbulence intensity and ice water concentration in the test section. The control and repeatability of experimental parameters is also discussed.

Pyrolysis, the chemical cracking of organic materials such as polymeric materials represents an innovative technology to recover resources contained in automobile shredder residues (ASR). In this study the technical capabilities, economic viability and environmental impact of pyrolysis as applied to ASR has been investigated. Based upon data provided by pyrolysis equipment suppliers, the pyrolysis of ASR appears to be a viable option to deal with the growing quantities of this material currently being produced. However, the selection of the most appropriate pyrolysis technology is dependant upon local needs and requirements.

Strain controlled thermo-mechanical fatigue (TMF) tests were conducted on a high Silicon ductile cast iron (SiMo) as the baseline material and a similar SiMo cast iron with aluminum addition (SiMoAl). The much improved fatigue life with aluminum addition is analyzed using the integrated creep-fatigue theory (ICFT) in combination with the metallurgical analysis on the tested coupons. Addition of about 3 wt.% Aluminum significantly improved TMF life of the SiMo cast iron. The results are explained by elimination of brittleness at middle temperature range, the higher flow stress, lower creep rate and higher oxidation resistance from Al addition.

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.

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.

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.

Road vehicles in the real world experience aerodynamic conditions that might be unappreciated and omitted in wind-tunnel experiments or in numerical simulations. Precipitation can potentially have an impact on the aerodynamics of road vehicles. An experimental study was devised to measure, in a wind tunnel, the impact of rain on the aerodynamic forces of the DrivAer research model. In this study, a rain system was commissioned to simulate natural rain in a wind-tunnel environment for full-scale rain rates between about 8 and 250 mm/hr. A 30%-scale DrivAer model was tested with and without precipitation for two primary configurations: the notch-back and estate-back variants. In addition, mirror-removal and covered-wheel-well configurations were investigated. The results demonstrate a distinct relationship between increasing rain intensities and increased drag of the model, providing evidence that road vehicles experience higher drag when travelling in precipitation conditions.