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Aerodynamic performance testing of heavy duty fans involve complicated test setups with specialized equipment and measurement systems as summarized in ANSI 210-07 standard. This paper describes virtual testing and simulation methods to obtain the fan aerodynamic performance data using commercial Computational Fluid Dynamics (CFD) software. Two different virtual test environments were used during the analysis. The first one is a virtual test chamber which is constructed based on the actual fan system installation. The second one is a virtual flow tube which approximates a fan flow test set-up as outlined in ANSI 210-07. The virtual fan is created from (i) the laser scan of the actual fan and (ii) the design specifications of the fan. The virtual test conditions simulate the actual test arrangement by imposing free boundary at flow inlet/outlet and proper fan rotation. The aerodynamic flow rate is controlled by a variable orifice located at the virtual test chamber outlet.

An optimal energy management strategy (Optimal EMS) can yield significant fuel economy (FE) improvements without vehicle velocity modifications. Thus it has been the subject of numerous research studies spanning decades. One of the most challenging aspects of an Optimal EMS is that FE gains are typically directly related to high fidelity predictions of future vehicle operation. In this research, a comprehensive dataset is exploited which includes internal data (CAN bus) and external data (radar information and V2V) gathered over numerous instances of two highway drive cycles and one urban/highway mixed drive cycle. This dataset is used to derive a prediction model for vehicle velocity for the next 10 seconds, which is a range which has a significant FE improvement potential. This achieved 10 second vehicle velocity prediction is then compared to perfect full drive cycle prediction, perfect 10 second prediction.

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.

Accurate perception of the driving environment and a highly accurate position of the vehicle are paramount to safe Autonomous Vehicle (AV) operation. AVs gather data about the environment using various sensors. For a robust perception and localization system, incoming data from multiple sensors is usually fused together using advanced computational algorithms, which historically requires a high-compute load. To reduce AV compute load and its negative effects on vehicle energy efficiency, we propose a new infrastructure information source (IIS) to provide environmental data to the AV. The new energy–efficient IIS, chip–enabled raised pavement markers are mounted along road lane lines and are able to communicate a unique identifier and their global navigation satellite system position to the AV. This new IIS is incorporated into an energy efficient sensor fusion strategy that combines its information with that from traditional sensor.

A range of cycle characteristics have been used to estimate the hybrid potential for vehicle duty cycles including characteristic acceleration, aerodynamic velocity, kinetic intensity, stop time, etc. These parameters give an indication of overall hybrid potential benefits, but do not contain information on the distribution of the available braking energy and the hybrid system power required to capture the braking energy. In this paper, the authors propose two new cycle characteristics to help evaluate overall hybrid potential of vehicle cycles: P50 and P90, which are non-dimensional power limits at 50% and 90% of available braking energy. These characteristics are independent of vehicle type, and help illustrate the potential hybridization benefit of different drive cycles. First, the distribution of available braking energy as a function of brake power for different vehicle cycles and vehicle classes is analyzed.

While machine learning in autonomous vehicles development has increased significantly in the past few years, the use of reinforcement learning (RL) methods has only recently been applied. Convolutional Neural Networks (CNNs) became common for their powerful object detection and identification and even provided end-to-end control of an autonomous vehicle. However, one of the requirements of a CNN is a large amount of labeled data to inform and train the neural network. While data is becoming more accessible, these networks are still sensitive to the format and collection environment which makes the use of others’ data more difficult. In contrast, RL develops solutions in a simulation environment through trial and error without labeled data. Our research expands upon previous research in RL and Proximal Policy Optimization (PPO) and the application of these algorithms to 1/18th scale cars by expanding the application of this control strategy to a full-sized passenger vehicle.

Developing a complete understanding of the structure and behavior of the near-wall region (NWR) in reciprocating, internal combustion (IC) engines and of its interaction with the core flow is needed to support the implementation of advanced combustion and engine operation strategies, as well as predictive computational models. The NWR in IC engines is fundamentally different from the canonical steady-state turbulent boundary layers (BL), whose structure, similarity and dynamics have been thoroughly documented in the technical literature. Motivated by this need, this paper presents results from the analysis of two-component velocity data measured with particle image velocimetry near the head of a single-cylinder, optical engine. The interaction between the NWR and the core flow was quantified via statistical moments and two-point velocity correlations, determined at multiple distances from the wall and piston positions.

Today’s Advanced Driver Assistance Systems (ADAS) predominantly utilize cameras to increase driver and passenger safety. Computer vision, as the enabler of this technology, extracts two key environmental features: the drivable region and surrounding objects (e.g., vehicles, pedestrians, bicycles). Lane lines are the most common characteristic extracted for drivable region detection, which is the core perception task enabling ADAS features such as lane departure warnings, lane-keeping assistance, and lane-centering. However, when subject to adverse weather conditions (e.g., occluded lane lines) the lane line detection algorithms are no longer operational. This prevents the ADAS feature from providing the benefit of increased safety to the driver. The performance of one of the leading computer vision system providers was tested in conditions of variable snow coverage and lane line occlusion during the 2020-2021 winter in Kalamazoo, Michigan.

Light duty vehicle emission standards are getting more stringent than ever before as stipulated by US EPA Tier 2 Standards and LEV III regulations proposed by CARB. The research in this paper sponsored by US DoE is focused towards developing a Tier 2 Bin 2 Emissions compliant light duty pickup truck with class leading fuel economy targets of 22.4 mpg “City” / 34.3 mpg “Highway”. Many advanced technologies comprising both engine and after-treatment systems are essential towards accomplishing this goal. The objective of this paper would be to discuss key engine technology enablers that will help in achieving the target emission levels and fuel economy. Several enabling technologies comprising air-handling, fuel system and base engine design requirements will be discussed in this paper highlighting both experimental and analytical evaluations.

Current production heavy duty diesel engines have a brake thermal efficiency (BTE) between 43-46% [1]. In partnership with the United States Department of Energy (DOE) as part of the Supertruck 2 program, Cummins has undertaken a research program to develop a new heavy-duty diesel engine designed to deliver greater than 50% BTE without the use of waste heat recovery. A system level optimization focused on: increased compression ratio, higher injection rate, carefully matched highly efficient turbocharging, variable lube oil pump, variable cooling components, and low restriction after treatment designed to deliver 50% BTE at a target development point. This work will also illustrate the system level planning and understanding of interactions required to allow that same 50% BTE heavy duty diesel engine to be integrated with a waste heat recovery (WHR) system to deliver system level efficiency of 55% BTE at a single point.

The influence of big-end bore fretting on connecting rod fatigue fracture is investigated. A finite element model, including rod-bearing contact interaction, is developed to simulate a fatigue test rig where the connecting rod is subjected to an alternating uniaxial load. Comparison of the model results with a rod fracture from the fatigue rig shows good correlation between the fracture location and the peak ‘Ruiz’ criterion, rather than the peak tensile stress location, indicating the potential of fretting to initiate a fatigue fracture and the usefulness of the ‘Ruiz’ criterion as a measure of location and severity. The model is extended to simulate a full engine cycle using pressure loads from a bearing EHL analysis. A fretting map and a ‘Ruiz’ criterion map are developed for the full engine cycle, giving an indication of a safe ‘Ruiz’ level from an existing engine which has been in service for more than 5 years.

The recent interest to use Alternative Fuels for power generation and or mobile power is mainly driven by the serious environmental concerns and energy security of the nation. In this context, the utilization of the landfill gases which are generally low heating valve (Btu) content fuels and normally produced from municipal and city solid waste such as in landfills or garbage dump sites has been increasingly used due to the two fold benefits; firstly, the proper utilization of the gas will avoid the green house gas effect and other environmental concerns and secondly it can produce revenue for the local governments or industries at the same time. The paper describes the results of the investigation of the utilization of landfill gases in a spark ignition (SI) engine. Methane and landfill gas compositions were tested and their performance and combustion characteristics are studied and compared.

In modern engine design, downsizing and reducing weight while still providing an increased amount of power has been a general trend in recent decades. Traditionally, an engine design with superior NVH performance usually comes with a heavier, thus sturdier structure. Therefore, modern engine design requires that NVH be considered in the very early design stage to avoid modifications of engine structure at the last minute, when very few changes can be made. NVH design optimization of engine components has become more practical due to the development of computer software and hardware. However, there is still a need for smarter algorithms to draw a direct relationship between the design and the radiated sound power. At the moment, techniques based on modal acoustic transfer vectors (MATVs) have gained popularity in design optimization for their good performance in sound pressure prediction.

This paper takes a realistic approach to develop a techno-economic analysis for fixed-route autonomous shuttles. To develop a model for analysis, the current state of technology was used to approximate three timelines for achieving SAE level 5 capabilities: progressive, realistic, and conservative. Within these timelines, there are four different increments for advancements in the technology laid out as follows: SAE Level 0 - human driver, SAE Level 4 - in-vehicle safety operator, SAE Level 4 - remote safety operator, and SAE Level 5 - no safety operator. These increments in the changes of the technology were chosen based on the trends in the industry. Various shuttle models were used based on different rider quantities and drive-train requirements (electric vs gas) in this analysis. This allows for further understanding of how these deployment plans will vary the cost for shuttles operating in high, mid, and low ridership demand environments.

With increasing energy prices and concerns about the environmental impact of greenhouse gas (GHG) emissions, a growing number of national governments are putting emphasis on improving the energy efficiency of the equipment employed throughout their transportation systems. Within the U.S. transportation sector, energy use in commercial vehicles has been increasing at a faster rate than that of automobiles. A 23% increase in fuel consumption for the U.S. heavy duty truck segment is expected from 2009 to 2020. The heavy duty vehicle oil consumption is projected to grow while light duty vehicle (LDV) fuel consumption will eventually experience a decrease. By 2050, the oil consumption rate by LDVs is anticipated to decrease below 2009 levels due to CAFE standards and biofuel use. In contrast, the heavy duty oil consumption rate is anticipated to double. The increasing trend in oil consumption for heavy trucks is linked to the vitality, security, and growth of the U.S. and global economies.

Prediction based optimal energy management systems are a topic of high interest in the automotive industry as an effective, low-cost option for improving vehicle fuel efficiency. With the continuing development of connected and autonomous vehicle (CAV) technology there are many data streams which may be leveraged by transportation stakeholders. The Suite of CAVs-derived data streams includes advanced driver-assistance (ADAS) derived information about surrounding vehicles, vehicle-to-vehicle (V2V) communications for real time and historical data, and vehicle-to-infrastructure (V2I) communications. The suite of CAVs-derived data streams have been demonstrated to enable improvements in system-level safety, emissions and fuel economy.

Fatigue damage sensing and monitoring of any structure is a prerequisite for reliable and effective structural health diagnosis. The designed sensor has alternate slots and strips with different strain magnification factor with respect to the nominal strain at its location. The strips experience the strains which closely resemble the actual strain distribution in the critical area of the component. One of the major advantages of this sensor is that it can be placed at any convenient location, still experiencing the same fatigue damage as a critical location. It can be used on various structures from ground civilian and military vehicles to steel bridges. This can predict the remaining useful life of a component or the number of miles (for any automobile) left for the component before it needed replacement. This paper mainly describes the design aspects of this sensor following analytical and finite element analysis (FEA) approaches.

Pressure vessels are being widely employed worldwide as a means to carry, store or receive fluids. The pressure differential is dangerous and many fatal accidents have occurred in the history of their development and operation. Therefore, it is imperative to understand the behavioral effect of cylindrical pressure vessel with torispherical endclosure subjected to an internal pressure. In this paper, two dimensional static stress analyses are performed using the finite element method for different vessel thicknesses in order to understand the stresses and deflections in the vessel walls due to internal pressure. From the analysis, it is observed that the stress variation over the section of the geometry and thickness of the vessel play an important role in withstanding the applied internal pressure.

Changing diesel engine emission requirements for 2002 have led many diesel engine manufacturers to incorporate cooled Exhaust Gas Recirculation, EGR, as a means of reducing NOx. This has resulted in higher levels of soot being present in used oils. This paper builds on earlier work with fresh oils and describes a study of the effect of highly sooted oils on the low temperature pumpability in diesel engines. Four experimental diesel engine oils, of varying MRV TP-1 viscosities, were run in a Mack T-8 engine to obtain a soot level ranging between 6.1 and 6.6%. These sooted oils were then run in a Cummins M11 engine installed in a low temperature cell. Times to lubricate critical engine components were measured at temperatures ranging between -10 °C and -25 °C. A clear correlation was established between the MRV TP-1 viscosity of a sooted oil and the time needed to lubricate critical engine components at a given test temperature.

Production NO sensors have a strong cross-sensitivity to ammonia which limits their use for closed-loop SCR control and diagnostics since increases in sensor output can be caused by either gas component. Recently, Ammonia/NO Ratio (ANR) perturbation methods have been proposed for determining the dominant component in the post-SCR exhaust as part of the overall SCR control strategy, but these methods or the issue of sensor cross-sensitivity have not been critically evaluated or studied in their own right. In this paper the dynamic sensor direct- and cross-sensitivities are estimated from experimental FTIR data (after compensating for the dynamics of the gas sampling system) and compared to nominal values provided by the manufacturer. The ANR perturbation method and the use of different input excitations are then discussed within an analytical framework, and applied to experimental data from a large diesel engine.