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The design of vehicles, particularly hybrid and other advanced technology vehicles, is typically complex and benefits from systems engineering processes. Vehicle modeling and simulation have become increasingly important system design tools to improve the accuracy, repeatability, and flexibility of the design process. In developing vehicle computational models and simulation, there is an inevitable compromise between the level of detail and the development/computational cost. The tradeoff is specific to the requirements of each vehicle design effort. The assumptions and detail limitations used for vehicle simulations lead to a varying degree of result uncertainty for each design effort. This paper provides a literature review to investigate the state of the art vehicle simulation methods, and quantifies the uncertainty associated with components that are commonly allocated uncertainty.

When a two-element airfoil for the wing of a racecar has to be inside a rectangle space dictated by regulations or dictated by the available space, the ratio of the flap chord length to the main element chord length, the overlap and gap sizes between the main element and the flap are design parameters, besides the element shapes. To find the configuration for the high downforce-to-drag ratio, CFD simulations were performed in 2D using the FX63-137 airfoil for both the main element and the flap. Some important findings are that the flap chord length should be 50 to 70% of the main element chord length to achieve the high lift-to-drag ratio. This finding will help design better two-element wings.

Motorsport Engineering is developing a foothold, around the World, as a field of academic preparation at the post-graduate level. To gain the appropriate practical skills to augment classroom education, and thus, for the graduates to successfully compete for employment in the Motorsport Industry, it is critical that the degree program has a strong experiential component. This paper describes the need to take an engineering approach to motorsport education by combining a discovery-based education with the traditional lecture format to realize synergistic results. The idea is that to effectively “engineer” the graduate, the student must have a strong skill set or a strong grasp of the fundamentals. The growth of the current educational program at Colorado State University and the effectiveness of merging the “inside-out” process, typical of the research mission, with the instructional practices of the University and with the needs of the Motorsport Industry are discussed.

A measurement system based on photogrammetry has been developed and used to measure the “as-assembled” geometry of a variety of racecar suspensions. A standard methodology for photographing a suspension, and special targets have been developed to use with commercially available photogrammetry software. Several types of targets are discussed; these included targets to identify the center of rotation of the linkages and the orientation of the wheel mounting surface. The system is used with a 5.1 mega-pixel camera to measure the 3D geometry of a suspension in space. Physical camber and toe variation in bump is then measured and correlated with the numerical computation of camber and toe variation using a suspension kinematics package and the geometry generated using the technique.

It is not uncommon in motorsport for a team to chase a chassis setup throughout a race weekend, changing many different suspension settings, yet not getting consistent response from the chassis. In at least some of these cases it has been later determined that these inconsistencies stemmed from either chassis damage or fastener loosening, leading to a decrease in chassis stiffness. The current research investigates a method for quickly and accurately measuring the torsional stiffness, or static compliance, of a racecar chassis and suspension at various stations along the length, which can be utilized in the paddock area. When compared to baseline measurements of a newly assembled racecar, the post-race static compliance of the vehicle can be used to reveal the length-wise region of new damage or softening of components.

Achieving maximum downforce during cornering is critical in the aerodynamic design of a race car. During cornering, all race cars will be at some angle of yaw relative to the vehicle velocity vector, and in certain forms of racing the yaw angle can be large. For this reason it is important to take into consideration the effect of yaw on the aerodynamic characteristics of the vehicle. Most aerodynamic elements on vehicles have been examined in some detail in straight ahead motion. However, an element such as a wing/end plate combination optimized for straight forward motion may not perform well under yaw conditions. This effect may substantially diminish the aerodynamic advantage for race cars in high yaw, such as those raced at the Pike's Peak Hill Climb. As a part of a comprehensive research program relating to optimized aerodynamics at high yaw angles, the effect of end plate design on lift and drag of a rear wing in free stream is being considered.

In racing, understanding how the setup and setup changes affect the racecar's ability to produce optimum longitudinal and lateral acceleration is vital to producing a winning team. To better understand and characterize how setup and setup changes affect the racecar, the use of state transition diagrams to model the vehicle's transient roll and pitch while negotiating the track is being investigated. State transition diagrams are made up of statistically significant patterns or events, and show the interconnection or transition from one state to another.1 The basic application of a state transition diagram to the phenomena of a racecar's roll and pitch is to identify the locations on the race course of the major events that make up the vehicle braking into, maneuvering through, and accelerating out of a corner. Major events that are examined include the maximum roll and pitch displacements, velocities, and accelerations.

Fiber reinforced, shape memory, polymer matrix, composites have recently been demonstrated in a variety of applications. Once cured, these composites, based on thermoset shape memory resins, have the ability to be semi-permanently deformed from the cured shape at elevated temperatures and then subsequently returned to the original shape. However, the vast majority of the applications demonstrated have made use of very thin composite laminates. The current research considers composite sandwich panel structures formed from shape memory composite facesheets and a rigid foam core created from shape memory resin. The goal is to investigate the potential deformability in these much more rigid geometries to assess the potential for use in conformable, structural applications.

A current general need within Six Sigma methodologies is to utilize statistical methods including experimental design in the confirmation of new processes and their parameters. This is typically done in the improve step of the DMAIC process. This need is even more evident in microjoining (small scale resistance welding) due to the number and complexity of the process variables. This paper outlines the improve step of a Six Sigma project in which statistical methods are applied to a microjoining process. These statistical methods include linear experimental design, regression analysis with linear transformation and mathematical modeling. The paper documents the methodology used to establish process parameters in microjoining of an electrical lead frame design.

The EcoCAR3 competition challenges student teams to redesign a 2016 Chevrolet Camaro to reduce environmental impacts and increase energy efficiency while maintaining performance and safety that consumers expect from a Camaro. Energy management of the new hybrid powertrain is an integral component of the overall efficiency of the car and is a prime focus of Colorado State University’s (CSU) Vehicle Innovation Team. Previous research has shown that error-less predictions about future driving characteristics can be used to more efficiently manage hybrid powertrains. In this study, a novel, real-world implementable energy management strategy is investigated for use in the EcoCAR3 Hybrid Camaro. This strategy uses a Nonlinear Autoregressive Artificial Neural Network with Exogenous inputs (NARX Artificial Neural Network) trained with real-world driving data from a selected drive cycle to predict future vehicle speeds along that drive cycle.

From November 2010 until May of 2011, NASA's Icing Remote Sensing System was positioned at Platteville, Colorado between the National Science Foundation's S-Pol radar and Colorado State University's CHILL radar (collectively known as FRONT, or ‘Front Range Observational Network Testbed’). This location was also underneath the flight-path of aircraft arriving and departing from Denver's International Airport, which allowed for comparison to pilot reports of in-flight icing. This work outlines how the NASA Icing Remote Sensing System's derived liquid water content and in-flight icing hazard profiles can be used to provide in-flight icing verification and validation during icing and non-icing scenarios with the purpose of comparing these times to profiles of polarized moment data from the two nearby research radars.

Autonomous vehicle development has benefited from sanctioned competitions dating back to the original 2004 DARPA Grand Challenge. Since these competitions, fully autonomous vehicles have become much closer to significant real-world use with the majority of research focused on reliability, safety and cost reduction. Our research details the recent challenges experienced at the 2017 Self Racing Cars event where a team of international Udacity students worked together over a 6 week period, from team selection to race day. The team’s goal was to provide real-time vehicle control of steering, braking, and throttle through an end-to-end deep neural network. Multiple architectures were tested and used including convolutional neural networks (CNN) and recurrent neural networks (RNN). We began our work by modifying a Udacity driving simulator to collect data and develop training models which we implemented and trained on a laptop GPU.

Standard operation of autonomous vehicles on public roads results in significant exposure to high levels of risk. There is a significant need to develop metrics that evaluate safety of an automated system without reliance on the rate of vehicle accidents and fatalities compared to the number of miles driven; a proactive rather than a reactive metric is needed. Resilience engineering is a new paradigm for safety management that focuses on evaluating complex systems and their interaction with the environment. This paper presents the overall methodology of resilience engineering and the resilience assessment grid (RAG) as an evaluation tool to measure autonomous systems' resilience. This assessment tool was used to evaluate the ability to respond to the system. A Pure Pursuit controller was developed and utilized as the path tracking control algorithm, and the Carla simulator was used to implement the algorithm and develop the testing environment for this methodology.

Practical applications of recently developed sensor fusion algorithms perform poorly in the real world due to a lack of proper evaluation during development. Existing evaluation metrics do not properly address a wide variety of testing scenarios. This issue can be addressed using proactive performance measurements such as the tools of resilience engineering theory rather than reactive performance measurements such as root mean square error. Resilience engineering is an established discipline for evaluating proactive performance on complex socio-technical systems which has been underutilized for automated vehicle development and evaluation. In this study, we use resilience engineering metrics to assess the performance of a sensor fusion algorithm for vehicle localization. A Kalman Filter is used to fuse GPS, IMU and LiDAR data for vehicle localization in the CARLA simulator.

High accuracy hybrid vehicle fuel consumption (FC) and emissions models used in practice today are the product of years of research, are physics based, and bear a large computational cost. However, it may be possible to replace these models with a non-physics based, higher accuracy, and computationally efficient versions. In this research, an alternative method is developed by training and testing a time series artificial neural network (ANN) using real world, on-road data for a hydraulic hybrid truck to predict instantaneous FC and emissions. Parameters affecting model fidelity were investigated including the number of neurons in the hidden layer, specific training inputs, dataset length, and hybrid system status. The results show that the ANN model was computationally faster and predicted FC within a mean absolute error of 0-0.1%. For emissions prediction the ANN model had a mean absolute error of 0-3% across CO2, CO, and NOx aggregate predicted concentrations.

Recent developments in materials science related to nano-technology have led to the creation and investigation of a new group of potential coolants known as nanofluids. These suspensions have been shown to have improved heat transfer capabilities over fluids without nanoparticles. The application of such fluids in motor racing shows potential for improvements in engine thermal management and aerodynamics. This study investigates the effect of additions of nanoparticles on the heat capacity of water using Differential Scanning Calorimetry (DSC). Fluids incorporating a variety of nanoparticles, of varying size and volume fraction, are investigated. It is shown that the volumetric heat capacity of water is only slightly affected by the addition of nanoparticles. The similarity in volumetric heat capacities, coupled with proven increased thermal conductivity of nanofluids, yields fluids with high thermal diffusivities that respond more quickly to changes in thermal environment.