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The Urban Vehicle Design Competition was inspired by the success of the Clean Air Car Race and the Great Electric Car Race. The academic community recognized the tremendous educational value of these events, and encouraged development of UVDC from its inception. The project was designed by engineering students to benefit students throughout North America. The rules of the competition include technical paper requirements that make the competition extremely attractive to professors who wish to build a course around this theme. The response of more than 2000 engineering students at 80 universities throughout the United States and Canada has indicated the success of the structure of the competition. The first major objective of the UVDC project has been met. Ninety-three teams throughout the country entered the UVDC design portion of the contest. The second portion of the project is the prototype contest of August 1972.

This paper describes a three component strain gage balance designed to measure aerodynamic forces exerted on small automobile models when subjected to turbulence in an experimental wind tunnel. The instrument is described and the details of obtaining values with it are fully explained. Although tests were conducted on these models at quarter-scale Reynolds number, results agree closely with similar tests on larger models. The balance makes practical some unusual preliminary investigations before developing full-scale prototypes.

A linearized lumped parameter heat balance model was developed and is discussed for the general case of resistance welding to see the effects of each parameter on the lobe shape. The parameters include material properties, geometry of electrodes and work piece, weld time and current, and electrical and thermal contact characteristics. These are then related to heat dissipation in the electrodes and the work piece. The results indicate that the ratio of thermal conductivity and heat capacity to electrical resistivity is a characteristic number which is representative of the ease of spot weldability of a given material. The increases in thermal conductivity and heat capacity of the sheet metal increase the lobe width while increases in electrical resistivity decrease the lobe width. Inconsistencies in the weldability of thin sheets and the wider lobe width at long welding times can both be explained by the heat dissipation characteristics.

A human-centered systems analysis was applied to the adverse aircraft weather encounter problem in order to identify desirable functions of weather and icing information. The importance of contingency planning was identified as emerging from a system safety design methodology as well as from results of other aviation decision-making studies. The relationship between contingency planning support and information on regions clear of adverse weather was investigated in a scenario-based analysis. A rapid prototype example of the key elements in the depiction of icing conditions was developed in a case study, and the implications for the components of the icing information system were articulated.

Future planetary extravehicular activities (EVAs) will go beyond what was experienced during Apollo. As mission duration becomes longer, inevitably, the astronauts on the surface of the Moon and Mars will actively plan and re-plan their own sorties. To design robust decision support aids for these activities, we have to first characterize all the different types of excursions that are possible. This paper describes a framework that organizes parameters and constraints that define a single planetary EVA. We arrived at this framework through case studies: by reviewing the EVA lessons learned during Apollo, conducting an observational study of excursions in a Mars-analog environment, and applying part of the framework to a prototype path planner for human planetary exploration.

Future human space exploration includes returning to the Moon and continuing to Mars. Essential to these missions is each planetary extravehicular activity, or EVA, where astronauts and robotic agents will explore lunar and planetary surfaces. Real-time decision support systems will help these explorers in efficiently planning and re-planning under time pressure sorties. Information and functional requirements for such a system are recommended and are based on on-going human-computer collaboration research.

Many vehicle-dynamics models exist to study the motion of a vehicle. Most of these models fall into one of two categories: very simple models for basic analyses and high-order models consisting of many degrees-of-freedom. For many scenarios, the simple models are not adequate. At the same time, for many vehicle handling and braking studies, the high-order models are more complex than necessary. This paper presents a model that includes the dynamics that are relevant to studying vehicle handling and braking, but is still simple enough to run in near real-time. The model was implemented in such a way that it is easily customized for a particular study. Predictions from this simplified model were compared against a high-order model and against actual vehicle test data. The simulations indicate a close agreement in the results.

A one-dimensional model for crevice HC post-flame oxidation is used to calculate and understand the effect of operating parameters and fuel type (propane and isooctane) on the extent of crevice hydrocarbon and the product distribution in the post flame environment. The calculations show that the main parameters controlling oxidation are: bulk burned gas temperatures, wall temperatures, turbulent diffusivity, and fuel oxidation rates. Calculated extents of oxidation agree well with experimental values, and the sensitivities to operating conditions (wall temperatures, equivalence ratio, fuel type) are reasonably well captured. Whereas the bulk gas temperatures largely determine the extent of oxidation, the hydrocarbon product distribution is not very much affected by the burned gas temperatures, but mostly by diffusion rates. Uncertainties in both turbulent diffusion rates as well as in mechanisms are an important factor limiting the predictive capabilities of the model.

The linked cell system gives both reduced cost and volume flexibility. The characteristics of the linked cell system are a consequence of decoupling the operators from the machines, using standard work in process between the cells and by integrating the information system with the cell and system design. By decoupling the operators from the machines the capacity can be increased/decreased in small increments by using more or fewer operators in the cell. The information system is integrated with the linked cell design by the use of a Heijunka box. The Heijunka is used to level production and to initiate the pace of production as a result of pulling withdrawal kanban at a standard time interval. This standard time interval is called the pitch of production. The kanban cards give information about what to produce, when to produce, when to make changeovers but they also give information to control the material replenishment.

With the recent advancement in sensing and controller technologies architecture design of an autonomous driving system becomes an important issue. Researchers have been developing different sensors and data processing technologies to solve the issues associated with fast processing, diverse weather, reliability, long distance recognition performance, etc. Necessary considerations of diverse traffic situations and safety factors of autonomous driving have also increased the complexity of embedded software as well as architecture of autonomous driving. In these circumstances, there are almost countless numbers of possible architecture designs. However, these design considerations have significant impacts on cost, controllability, and system reliability. Thus, it is crucial for the designers to make a challenging and critical design decision under several uncertainties during the conceptual design phase.

This paper describes a post-race analysis of team KOMMIT EVT’s electric motorcycle data collected during the 2016 Pikes Peak International Hill Climb (PPIHC). The motorcycle consumed approximately 4 kWh of battery energy with an average and maximum speed of 107 km/h and 149 km/h, respectively. It was the second fastest electric motorcycle with a finishing time of 11:10.480. Data was logged of the motorcycle’s speed, acceleration, motor speed, power, currents, voltages, temperatures, throttle position, GPS position, rider’s heart rate and the ambient environment (air temperature, pressure and humidity). The data was used to understand the following factors that may have prevented a faster time: physical fitness of the rider, thermal limits of the motor and controller, available battery energy and the sprocket ratio between the motor and rear wheel.

MIT Lincoln Laboratory is tasked by the U.S. Federal Aviation Administration to investigate the use of the NEXRAD polarimetric radars* for the remote sensing of icing conditions hazardous to aircraft. A critical aspect of the investigation concerns validation that has relied upon commercial airline icing pilot reports and a dedicated campaign of in situ flights in winter storms. During the month of February in 2012 and 2013, the Convair-580 aircraft operated by the National Research Council of Canada was used for in situ validation of snowstorm characteristics under simultaneous observation by NEXRAD radars in Cleveland, Ohio and Buffalo, New York. The most anisotropic and easily distinguished winter targets to dual pol radar are ice crystals.

We report on a feasibility study of an optical micro-electro-mechanical systems (MEMS) flow sensor to measure flow rate using Moiré fringe displacement of a floating element. Due to constraints on weight, power, and size for space environmental systems, the development of sensor components that minimize the equivalent systems mass (ESM) while maintaining or exceeding required specifications is highly desirable. A feature of the optical detection method is a physical separation of electrical components from the flow stream. The geometric Moiré fringe shift optically amplifies small displacements by the ratio of the fringe pitch to the movable grating pitch that is detected using an external CCD imager, providing an electrically isolated, robust, direct scheme for detecting flow from shear stress induced displacement.

Concept of a new decoupled cylinder liner design for internal combustion (IC) engines is presented from the framework of axiomatic design to improve friction and wear characteristics. In the current design, the piston rings fail to satisfy their functional requirements at the two dead centers of the piston stroke where lubrication is poor. It is proposed that by using undulated cylindrical surfaces selectively along the cylinder liner, much of the existing friction and wear problems of IC engines may be solved. The main idea behind undulated surface is to trap wear particles at the piston-cylinder interface in order to minimize plowing, and thus maintain low friction even in areas where lubrication fails to be hydrodynamic. In dry sliding tests using a modified engine motored at low speeds, undulated cylinders operated for significantly longer time than smooth cylinders without catastrophic increase in friction.

The NOx emission and knock characteristics of a PFI engine operating on ethanol/gasoline mixtures were assessed at 1500 and 2000 rpm with λ =1 under Wide-Open-Throttle condition. There was no significant charge cooling due to fuel evaporation. The decrease in NOx emission and exhaust temperature could be explained by the change in adiabatic flame temperature of the mixture. The fuel knock resistance improved significantly with the gasohol so that ignition could be timed at a value much closer or at MBT timing. Changing from 0% to 100% ethanol in the fuel, this combustion phasing improvement led to a 20% increase in NIMEP and 8 percentage points in fuel conversion efficiency at 1500 rpm. At 2000 rpm, where knocking was less severe, the improvement was about half (10% increase in NIMEP and 4 percentage points in fuel conversion efficiency).

In an engine system, the piston pin is subjected to high loading and severe lubrication conditions, and pin seizures still occur during new engine development. A better understanding of the lubricating oil behavior and the dynamics of the piston pin could lead to cost- effective solutions to mitigate these problems. However, research in this area is still limited due to the complexity of the lubrication and the pin dynamics. In this work, a numerical model that considers structure deformation and oil cavitation was developed to investigate the lubrication and dynamics of the piston pin. The model combines multi-body dynamics and elasto-hydrodynamic lubrication. A routine was established for generating and processing compliance matrices and further optimized to reduce computation time and improve the convergence of the equations. A simple built-in wear model was used to modify the pin bore and small end profiles based on the asperity contact pressures.

Ground impact caused by road debris can result in very severe fire accident of Electric Vehicles (EV). In order to study the ground impact accidents, a Finite Element model of the battery pack structure is carefully set up according to the practical designs of EVs. Based on this model, the sequence of the deformation process is studied, and the contribution of each component is clarified. Subsequently, four designs, including three enhanced shield plates and one enhanced housing box, are investigated. Results show that the BRAS (Blast Resistant Adaptive Sandwich) shield plate is the most effective structure to decrease the deformation of the battery cells. Compared with the baseline case, which adopts a 6.35-mm-thick aluminum sheet as the shield plate, the BRAS can reduce the shortening of cells by more than 50%. Another type of sandwich structure, the NavTruss, can also improve the safety of battery pack, but not as effectively as the BRAS.

Accurately predicting real-world vehicle energy consumption is essential for optimizing vehicle designs, enhancing energy efficiency, and developing effective energy management strategies. This paper presents a data-driven approach that utilizes machine learning techniques and a comprehensive dataset of vehicle parameters and environmental factors to create precise energy consumption prediction models. The methodology involves recording real-world vehicle data using data loggers to extract information from the CAN bus systems for ICE and hybrid electric, as well as hydrogen and battery fuel cell vehicles. Data cleaning and cycle-based analysis are employed to process the dataset for accurate energy consumption prediction. This includes cycle detection and analysis using methods from statistics and signal processing, and then pattern recognition based on these metrics.