Search Results

The research described examines the relationship between damage/repair scenarios and the resulting effect on the coupled response of the structure. The monocoque is simplified as a box beam and damage is simulated by introducing a hole in one side of the tube. Repair is simulated by adding plies to an undamaged area of the beam side. Composite beam samples were manufactured and tested using a 3-axis coordinate measurement machine (CMM), to experimentally verify the computer numerical predictions of the deformed shape. The beams were loaded with a combined torsion-bending load using an eccentric tip load and rigidly fixing the opposite end. Structural coupling was observed by computing the distortion center of the beam profile at cross-sections along its length and comparing the results to the undamaged/unrepaired beam. The experimental results correlate well with the finite element simulations and generally follow the predictions of the analytical model.

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.

Prediction of vehicle velocity is important since it can realize improvements in the fuel economy/energy efficiency, drivability, and safety. Velocity prediction has been addressed in many publications. Several references considered deterministic and stochastic approaches such as Markov chain, autoregressive models, and artificial neural networks. There are numerous new sensor and signal technologies like vehicle-to-vehicle and vehicle-to-infrastructure communication that can be used to obtain inclusive datasets. Using these inclusive datasets of sensors in deep neural networks, high accuracy velocity predictions can be achieved. This research builds upon previous findings that Long Short-Term Memory (LSTM) deep neural networks provide low error velocity prediction. We developed an LSTM deep neural network that uses different groups of datasets collected in Fort Collins, Colorado.

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.

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 student design team at Colorado State University (CSU) has developed an innovative snowmobile to compete in the Clean Snowmobile Challenge 2003 competition. This engine concept was originally developed for the CSC 2002 competition and demonstrated the lowest emissions of any engine that competed that year. The team utilized a 3-cylinder, 594cc, loop-scavenged, two-stroke cycle engine (Arctic Cat ZRT600) and then modified the engine to operate with direct in-cylinder fuel injection using the Orbital OCP air-assisted fuel injection system. This conversion required that the team design and cast new heads for the engine. The direct-injection approach reduced carbon monoxide (CO) emissions by 70% and total hydrocarbon (THC) emissions by 90% from a representative stock snowmobile. An oxidation catalyst was then used to oxidize the remaining CO and THC.

A number of racing series have seen an influx of motorcycle engines as basic powerplants which incorporate a performance oriented sequential shift transmission. However, due to common placement of the engine behind the driver, shift actuation can often become a difficult design issue. Further, the time of one up-shift can be 500 ms or more when the clutch is used, and manually unloading the transmission to allow shifting does not substantially reduce the time lost. A lightweight, low cost electronic liftless shift system has been designed to overcome the problems of packaging and improve shift speed. The system uses a small 12v DC gearmotor, cam and follower to execute the up-shift and downshift, and a current sensor and programmable IC's are used to automatically unload the drivetrain for liftless up-shifts.

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.

Quality issues in magnet wire stripping and soldering have led to continuous improvement efforts in ignition coil manufacturing using Six Sigma methodologies. This effort has resulted in the investigation of an alternative product and process design, microjoining. This paper describes the continuation of development occurring during the improvement phase of a Six Sigma project. The confirmation of the results is accomplished through the use of experimental design, response surface methodologies, mathematical modeling and optimization of the process. Nonlinear design of experiments have been used to confirm a breakthrough microjoining process developed that is an alternative to soldering. The statistical methods used to develop the process build on the current documented research efforts.

Concerns with stripping and soldering copper magnet wire in ignition coils and other related products have led to the investigation of an alternative product and process design, microjoining. This paper describes the initial development occurring during the improvement phase of a Six Sigma project. The use of microjoining with a folded over welding tab terminal design coupled with a parallel gap welding process is developed as a suitable method for joining a tin plated brass terminals to the 0.65mm magnet wire without prior removal of the polyesterimide over-coated polyamideimide insulation.

Optimal energy management of hybrid electric vehicles has previously been shown to increase fuel economy (FE) by approximately 20% thus reducing dependence on foreign oil, reducing greenhouse gas (GHG) emissions, and reducing Carbon Monoxide (CO) and Mono Nitrogen Oxide (NOx) emissions. This demonstrated FE increase is a critical technology to be implemented in the real world as Hybrid Electric Vehicles (HEVs) rise in production and consumer popularity. This review identifies two research gaps preventing optimal energy management of hybrid electric vehicles from being implemented in the real world: sensor and signal technology and prediction scope and error impacts. Sensor and signal technology is required for the vehicle to understand and respond to its environment; information such as chosen route, speed limit, stop light locations, traffic, and weather needs to be communicated to the vehicle.

Previous research has demonstrated an increase in Fuel Economy (FE) using an optimal controller based on limited foreknowledge using methods such as Engine Equivalent Minimization Strategy (ECMS) and Stochastic Dynamic Programming (SDP) with stochastic error in the prediction signal considerations. This study seeks to quantify the sensitivity of prediction-derived vehicle FE improvements to prediction signal quality assuming optimal control. In this research, a hill pattern and route type identification scenario control subjected to varying prediction signal quality is selected for in depth study. This paper describes the development of a baseline Toyota Prius Hybrid Vehicle (HV) simulation models, real world drive cycles and real-world disturbances, and an optimal controller incorporating a prediction of vehicle power requirements.

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.

Transportation vehicle and network system efficiency can be defined in two ways: 1) reduction of travel times across all the vehicles in the system, and 2) reduction in total energy consumed by all the vehicles in the system. The mechanisms to realize these efficiencies are treated as independent (i.e., vehicle and network domains) and, when combined, they have not been adequately studied to date. This research aims to integrate previously developed and published research on Predictive Optimal Energy Management Strategies (POEMS) and Intelligent Traffic Systems (ITS), to address the need for quantifying improvement in system efficiency resulting from simultaneous vehicle and network optimization. POEMS and ITS are partially independent methods which do not require each other to function but whose individual effectiveness may be affected by the presence of the other. In order to evaluate the system level efficiency improvements, the Mobility Energy Productivity (MEP) metric is used.

Studies have shown that obtaining and utilizing information about the future state of vehicles can improve vehicle fuel economy (FE). However, there has been a lack of research into whether near-term technologies can be utilized to improve FE and the impact of real-world prediction error on potential FE improvements. In this study, a speed prediction method utilizing simulated vehicle-to-vehicle (V2V) communication with real-world driving data and a drive cycle database was developed to understand if incorporating near-term technologies could be utilized in a predictive energy management strategy to improve vehicle FE. This speed prediction method informs a predictive powertrain controller to determine the optimal engine operation for various prediction durations. The optimal engine operation is input into a validated high-fidelity fuel economy model of a Toyota Prius.

Specification (spec) race engines are intended to reduce costs and increase the competitiveness in many racing classes. However, engines prepared by the best race engine builders routinely outperform truly ‘standard’ engines or engines prepared by less experienced tuners. This paper describes how engines can be modified to increase their power output and discusses various spec engine preparation techniques. Experimental and computational evidence is used to quantify the potential benefits that can be expected from each of the modifications discussed. By combining several relatively small improvements, a well prepared engine may be expected to enjoy a 5-8 % power benefit over an ‘average’ race engine, and perhaps as much as a 14-17 % benefit in power versus a truly standard production engine off the assembly line. This analysis also reveals the claims of much larger power improvements by some high-performance engine tuners can not be substantiated unless further modifications are made.