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The focus of this paper is to discuss the modeling and control of intake plenum pressure on the Powertrain Control Research Laboratory's (PCRL) Single-Cylinder Engine (SCE) transient test system using a patented device known as the Intake Air Simulator (IAS), which dynamically controls the intake plenum pressure, and, subsequently, the instantaneous airflow into the cylinder. The IAS exists as just one of many devices that the PCRL uses to control the dynamic boundary conditions of its SCE transient test system to make it “think” and operate as though it were part of a Multi-Cylinder Engine (MCE) test system. The model described in this paper will be used to design a second generation of this device that utilizes both continuously and discretely actuating valves working in parallel.

A direct calibration has been performed on laser-induced fluorescence measurements of the oil film in a single cylinder air-cooled research engine by simultaneously measuring the minimum oil film thickness by the capacitance technique. At the minimum oil film thickness the capacitance technique provides an accurate measure of the ring-wall distance, and this value is used as a reference for the photomultiplier voltage, giving a calibration coefficient. This calibration coefficient directly accounts for the effect of temperature on the fluorescent properties of the constituents of the oil which are photoactive. The inability to accurately know the temperature of the oil has limited the utility of off-engine calibration techniques. Data are presented for the engine under motoring conditions at speeds from 800 - 2400 rpm and under varying throttle positions.

A global optimization method has been developed for an engine simulation code and utilized in the search of optimal fuel injection strategies. This method uses a Lagrange interpolation function which interpolates engine output data generated at the vertices and the intermediate points of the input parameters. This interpolation function is then used to find a global minimum over the entire parameter set, which in turn becomes the starting point of a CFD-based optimization. The CFD optimization is based on a steepest descent method with an adaptive cost function, where the line searches are performed with a fast-converging backtracking algorithm. The adaptive cost function is based on the penalty method, where the penalty coefficient is increased after every line search. The parameter space is normalized and, thus, the optimization occurs over the unit cube in higher-dimensional space.

The demand for highly flexible manipulation of plant growth generations, modification of specific plant processes, and genetically engineered crop varieties in a controlled environment has led to the development of a Commercial Plant Biotechnology Facility (CPBF). The CPBF is a quad-middeck locker playload to be mounted in the EXPRESS Rack that will be installed in the International Space Station (ISS). The CPBF integrates proven ASTROCULTURE” technologies, state-of-the-art control software, and fault tolerance and recovery technologies together to increase overall system efficiency, reliability, robustness, flexibility, and user friendliness. The CPBF provides a large plant growing volume for the support of commercial plant biotechnology studies and/or applications for long time plant research in a reduced gravity environment.

Optimal fuel injection strategies are obtained with a micro-genetic algorithm and an adaptive gradient method for a nonroad, medium-speed DI diesel engine equipped with a multi-orifice, asynchronous fuel injection system. The gradient optimization utilizes a fast-converging backtracking algorithm and an adaptive cost function which is based on the penalty method, where the penalty coefficient is increased after every line search. The micro-genetic algorithm uses parameter combinations of the best two individuals in each generation until a local convergence is achieved, and then generates a random population to continue the global search. The optimizations have been performed for a two pulse fuel injection strategy where the optimization parameters are the injection timings and the nozzle orifice diameters.

Neural networks are useful tools for optimization studies since they are very fast, so that while capturing the accuracy of multi-dimensional CFD calculations or experimental data, they can be run numerous times as required by many optimization techniques. This paper describes how a set of neural networks trained on a multi-dimensional CFD code to predict pressure, temperature, heat flux, torque and emissions, have been used by a genetic algorithm in combination with a hill-climbing type algorithm to optimize operating parameters of a diesel engine over the entire speed-torque map of the engine. The optimized parameters are mass of fuel injected per cycle, shape of the injection profile for dual split injection, start of injection, EGR level and boost pressure. These have been optimized for minimum emissions. Another set of neural networks have been trained to predict the optimized parameters, based on the speed-torque point of the engine.

Neural networks have been used for engine computations in the recent past. One reason for using neural networks is to capture the accuracy of multi-dimensional CFD calculations or experimental data while saving computational time, so that system simulations can be performed within a reasonable time frame. This paper describes three methods to improve upon neural network predictions. Improvement is demonstrated for in-cylinder pressure predictions in particular. The first method incorporates a physical combustion model within the transfer function of the neural network, so that the network predictions incorporate physical relationships as well as mathematical models to fit the data. The second method shows how partitioning the data into different regimes based on different physical processes, and training different networks for different regimes, improves the accuracy of predictions.

A hydrostatic dynamometer capable of accurately controlling the speed and torque of an engine has been designed and constructed. The thrust of this work is not only to build a better dynamometer, it is the first step in creating a system for laboratory simulation of the actual load environment of engines and powertrains. This paper presents the design, construction, and evaluation of a hydrostatic dynamometer. The evaluation includes speed and torque limits, and bandwidth of the dynamometer. Also, the dynamometer is compared with those in common use, and the feasibility of accurately reproducing the engine or powertrain load environments are assessed. This is the first phase of a development program; future research is discussed.

Combustion under stratified operating conditions in a direct-injection spark-ignition engine was investigated using simultaneous planar laser-induced fluorescence imaging of the fuel distribution (via 3-pentanone doped into the fuel) and the combustion products (via OH, which occurs naturally). The simultaneous images allow direct determination of the flame front location under highly stratified conditions where the flame, or product, location is not uniquely identified by the absence of fuel. The 3-pentanone images were quantified, and an edge detection algorithm was developed and applied to the OH data to identify the flame front position. The result was the compilation of local flame-front equivalence ratio probability density functions (PDFs) for engine operating conditions at 600 and 1200 rpm and engine loads varying from equivalence ratios of 0.89 to 0.32 with an unthrottled intake. Homogeneous conditions were used to verify the integrity of the method.

The ASTROCULTURE™ (ASC) middeck flight experiment series was developed to test subsystems required to grow plants in reduced gravity, with the goal of developing a plant growth unit suitable for conducting quality biological research in microgravity. Previous Space Shuttle flights (STS-50 and STS-57) have successfully demonstrated the ability to control water movement through a particulate rooting matrix in microgravity and the ability of LED lighting systems to provide high levels of irradiance without excessive heat build-up in microgravity. The humidity and temperature control system used in the middeck flight unit is described in this paper. The system controls air flow and provides dehumidification, humidification, and condensate recovery for a plant growth chamber volume of 1450 cm3.

Automatic manufacturability analysis of injection moldings, sheet metal castings, stampings, forgings, etc., using knowledge-based heuristics depends on shape features, which are abstractions of the three dimensional (3D) geometric model of the parts. Conventional CAD systems do not explicitly contain shape feature information, therefore such information needs to be extracted from them. So far, extraction of shape features has been restricted to models with simple geometric shapes such as planar, cylindrical or conical shapes. Extending shape feature extraction to non-linear geometric models will allow Design For Manufacturability (DFM) analysis of non-linear models. This paper presents an approach to extract features from non-linear geometric models. The approach is based on abstract geometric entities called C-loops. The formation of a C-loop depends on a geometric entity called a silhouette. The C-loops are derived from the silhouette boundaries of an object.

Transient operation of automobile engines is known to contribute significantly to regulated exhaust emissions, and is also an area of drivability concerns. Furthermore, many on-board diagnostic algorithms do not perform well during transient operation and are often temporarily disabled to avoid problems. The inability to quickly and repeatedly test engines during transient conditions in a laboratory setting limits researchers and development engineers ability to produce more effective and robust algorithms to lower vehicle emissions. To meet this need, members of the Powertrain Control Research Laboratory (PCRL) at the University of Wisconsin-Madison have developed a high-bandwidth, hydrostatic dynamometer system that will enable researchers to explore transient characteristics of engines and powertrains in the laboratory.

Traffic state identification is a key problem in intelligent transportation system. As a new technology, connected and automated vehicle can play a role of identifying traffic state with the installation of onboard sensors. However, research of lane level traffic state identification is relatively lacked. Identifying lane level traffic state is helpful to lane selection in the process of driving and trajectory planning. In addition, traffic state identification precision with low penetration of connected and automated vehicles is relatively low. To fill this gap, this paper proposes a novel method of identifying traffic state in the presence of connected and automated vehicles with low penetration rate. Assuming connected and automated vehicles can obtain information of surrounding vehicles’, we use the perceptible information to estimate imperceptible information, then traffic state of road section can be inferred.

Forage harvesting, processing and handling equipment research is currently underway which will improve commodity quality, produce “value -added” products from forages, reduce energy and labor requirements of the equipment and improve forage marketability. Technologies are described which could increase forage quality and value by removing it from the field sooner after it is mowed to minimize the risk of weather damage. Mechanisms and management strategies for reducing the labor and energy required for field processing and for improving the marketability of forages are also described.

Identifying edge cases for designed algorithms is critical for functional safety in autonomous driving deployment. In order to find the feasible boundary of designed algorithms, simulations are heavily used. However, simulations for autonomous driving validation are expensive due to the requirement of visual rendering, physical simulation, and AI agents. In this case, common sampling techniques, such as Monte Carlo Sampling, become computationally expensive due to their sample inefficiency. To improve sample efficiency and minimize the number of simulations, we propose a tailored active learning approach combining the Support Vector Machine (SVM) and the Gaussian Process Regressor (GPR). The SVM learns the feasible boundary iteratively with a new sampling point via active learning. Active Learning is achieved by using the information of the decision boundary of the current SVM and the uncertainty metric calculated by the GPR.

Lithium-ion and Lithium polymer batteries are fast becoming ubiquitous in high-discharge rate applications for military and non-military systems. Applications such as small aerial vehicles and energy transfer systems can often function at C-rates greater than 1. To maximize system endurance and battery health, there is a need for models capable of precisely estimating the battery state-of-charge (SoC) under all temperature and loading conditions. However, the ability to perform state estimation consistently and accurately to within 1% error has remained unsolved. Doing so can offer enhanced endurance, safety, reliability, and planning, and additionally, simplify energy management. Therefore, the work presented in this paper aims to study and develop experimentally validated mathematical models capable of high-accuracy battery SoC estimation.

This paper reviews the engine modeling program in the Powertrain Control Research Laboratory at the University of Wisconsin-Madison, focuses on simulation results obtained from a complete modular turbocharged diesel engine dynamic model developed in this lab, and suggests ways that dynamic engine system models can be used in the design process. It examines the dynamic responses and interactions between various components in the engine system, looks at how these components affect the overall performance of the system in transient and steady state operation.

We present an approach in which an open-source software infrastructure is used for testing the behavior of autonomous vehicles through computer simulation. This software infrastructure is called CAVE, from Connected Autonomous Vehicle Emulator. As a software platform that allows rapid, low-cost and risk-free testing of novel designs, methods and software components, CAVE accelerates and democratizes research and development activities in the field of autonomous navigation.

Appropriate spray modeling in multidimensional simulations of diesel engines is well known to affect the overall accuracy of the results. More and more accurate models are being developed to deal with drop dynamics, breakup, collisions, and vaporization/multiphase processes; the latter ones being the most computationally demanding. In fact, in parallel calculations, the droplets occupy a physical region of the in-cylinder domain, which is generally very different than the topology-driven finite-volume mesh decomposition. This makes the CPU decomposition of the spray cloud severely uneven when many CPUs are employed, yielding poor parallel performance of the spray computation. Furthermore, mesh-independent models such as collision calculations require checking of each possible droplet pair, which leads to a practically intractable O(np2/2) computational cost, np being the total number of droplets in the spray cloud, and additional overhead for parallel communications.

The present work is aimed at developing a computational model of the interelectrode phenomena in thermionic energy converters which will be accurate over a very wide range of plasma conditions and operating modes. Previous models have achieved only moderate degrees of accuracy and, in a limited range, of validity. This limited range excludes a number of advanced thermionic devices, such as barium-cesium converters. The model under development promises improved accuracy in prediction of conventional devices and extension of predictive capability to advanced devices. The approach is to adapt the “Converted Scheme”, or CS method, to the cesium vapor plasma diode. This method, developed at the University of Wisconsin- Madison, is an extremely efficient algorithm for the solution of charged-particle kinetic equations and has been successfully used to simulate helium RF glow discharges.