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The identification of the dominant noise sources in diesel engines and the assessment of their contribution to far-field noise is a process that can involve both fired and motored testing. In the present work, the cross-spectral densities of signals from cylinder pressure transducers, accelerometers mounted on the engine surface, and microphones (in the near and far fields), were used to identify dominant noise sources and estimate the transfer paths from the various “inputs” (i.e., the cylinder pressures, the accelerometers and the near field microphones) to the far field microphones. The method is based on singular value decomposition of the input cross-spectral matrix to relate the input measurements to independent virtual sources. The frequencies at which a particular input is strongly affected by an independent source are highlighted, and with knowledge of transducer locations, inferences can be drawn as to possible noise source mechanisms.

The most common and lethal weapons against military vehicles are the improvised explosive devices (IEDs). In an explosion, critical cabin’s penetrations and high accelerations can cause serious injuries and death of military personnel. This investigation uses single and multi-fidelity Bayesian optimization (BO) to design sandwich composite armors for blast mitigation. BO is an efficient methodology to solve optimization problems that involve black-box functions. The black-box function of this work is the finite element (FE) simulation of the armor subjected to blast. The main two components of BO are the surrogate model of the black-box function and the acquisition function that guides the optimization. In this investigation, the surrogate models are Gaussian Process (GP) regression models and the acquisition function is the multi-objective expected improvement (MEI) function. Information from low and high fidelity FE models is used to train the GP surrogates.

Excessive vibration and poor controllability occur in many mobile fluid power applications, with negative consequences as concerns operators' health and comfort as well as machine safety and productivity. This paper addresses the problem of reducing oscillations in fluid power machines presenting a novel control technique of general applicability. Strong nonlinearities of hydraulic systems and the unpredictable operating conditions of the specific application (e.g. uneven ground, varying loads, etc.) are the main challenges to the development of satisfactory general vibration damping methods. The state of the art methods are typically designed as a function of the specific application, and in many cases they introduce energy dissipation and/or system slowdown. This paper contributes to this research by introducing an energy efficient active damping method based on feedback signals from pressure sensors mounted on the flow control valve block.

The optimal design of hybrid-electric vehicle power systems poses a challenge to the system analyst, who is presented with a host of parameters to fine-tune, along with stringent performance criteria and multiple design objectives to meet. Herein, a methodology is presented to transform such a design task into a constrained multi-objective optimization problem, which is solved using a distributed evolutionary algorithm. A power system model representative of a series hybrid-electric vehicle is considered as a paradigm to support the illustration of the proposed methodology, with particular emphasis on the power system's time-domain performance.

An experimental apparatus and a numerical model have been designed and developed to examine the lubrication condition and frictional losses at the piston and cylinder interface. The experimental apparatus utilizes components from a single cylinder, ten horsepower engine in a novel suspended liner arrangement. The test rig has been specifically designed to reduce the number of operating variables while utilizing actual components and geometry. A mixed lubrication model for the complete ring-pack and piston skirt was developed to correlate with experimental measurements and provide further insight into the sources of frictional losses. The results demonstrate the effects of speed and viscosity on the overall friction losses at the piston and cylinder liner interface. Comparisons between the experimental and analytical results show good agreement.

The need for greater capacity in automotive transportation (in the midst of constrained resources) and the convergence of key technologies from multiple domains may eventually produce the emergence of a “swarm” concept of operations. The swarm, a collection of vehicles traveling at high speeds and in close proximity, will require management techniques to ensure safe, efficient, and reliable vehicle interactions. We propose a shared-autonomy approach in which the strengths of both human drivers and machines are employed in concert for this management. A fuzzy logic-based control implementation is combined with a genetic algorithm to select the shared-autonomy architecture and sensor capabilities that optimize swarm operations.

This paper describes the numerical modeling of the hydraulic circuit of a self-moving boom lift. Boom lifts consist of several hydraulic actuators, each of them performs a specific movement. Hydraulic systems for lifting applications must ensure consistent performance no matter what the load and how many users are in operation at the same time. Common solutions comprise a fixed or a variable displacement pump with load-sensing control strategy. Instead, the hydraulic circuit studied in this paper includes a fixed displacement pump and an innovative (patented) proportional valve assembly. Each proportional valve (one for each user) permits a flow regulation for all typical load conditions and movement simultaneously. The study of the hydraulic system required a detailed modeling of some components such as: the overcenter valves, for the control of the assistive loads; the proportional valve, which keeps a constant flow independently of pressure drop across itself.

A methodology for modeling elastomeric mounts as nonlinear lumped parameter models is discussed. A key feature of this methodology is that it integrates dynamic test results under different conditions into the model. The first step is to model the mount as a linear model that is simple but reproduces accurately results from dynamic tests under small excitations. Frequency Response Functions (FRF) enables systematic calculation of the parameters for the model. Under more realistic excitation, the mount exhibits non-linearity, which is investigated in the next step. For nonlinear structures, a simple and intuitive method is to use time-domain force-displacement (F-x) curves. Experiments to obtain the F-x curves involve controlling the displacement excitation and measuring the induced forces. From the F-x curves, stiffness and damping parameters are obtained with an optimization technique.

This paper presents the continuation of the modeling work described in a companion paper “Modeling of Nonlinear Elastomeric Mounts. Part 1: Dynamic Testing and Parameter Identification” by the same authors. That paper discussed a dynamic test procedure and an optimization methodology to identify and model an elastomeric mount as a non-linear lumped parameter structure. This paper discusses a numerical modeling methodology to confirm or improve the agreement between the dynamic test results and the input-output relationship of the analytical model generated in the companion paper. In this paper, the model developed in the companion paper and the model parameters are input into a dynamic simulation model using a commercial simulation package. The model is then run to produce the numerical force-versus-displacement (F-x) curves of the mount. The numerical F-x curves are compared with the F-x curves obtained from the experiments.

Design optimization for functionality, and manufacturability was virtually impossible in the past. However, recent standardization of file storing formats resulted in seamless data transfer from one software package to another; thus, allowing integration of all facets of product design optimization. This paper describes a metalcasting design optimization process. It focuses on the design of cast parts according to functional requirements while optimizing shape with respect to structural integrity, while ascertaining that the part can be manufactured (cast) without defects.

Mobile Earth Moving Machinery like Skid-steer loaders have tight turning radius in limited spaces due to a short wheelbase which prevents the use of suspensions in these vehicles. The absence of a suspension system exposes the vehicle to ground vibrations of high magnitude and low frequency. Vibrations reduce operator comfort, productivity and life of components. Along with vibrations, the machine productivity is also hampered by material spillage which is caused by the tilting of the bucket due to the extension of the boom. The first part of the paper focuses on vibration damping. The chassis’ vibrations are reduced by the use of an active suspension element which is the hydraulic boom cylinder which is equivalent to a spring-damper. With this objective, a linear model for the skid steer loader is developed and a state feedback control law is implemented.

This work proposes a new method to design crashworthiness structures that made of functionally graded cellular (porous) material. The proposed method consists of three stages: The first stage is to generate a conceptual design using a topology optimization algorithm so that a variable density is distributed within the structure minimizing its compliance. The second stage is to cluster the variable density using a machine-learning algorithm to reduce the dimension of the design space. The third stage is to maximize structural crashworthiness indicators (e.g., internal energy absorption) and minimize mass using a metamodel-based multi-objective genetic algorithm. The final structure is synthesized by optimally selecting cellular material phases from a predefined material library. In this work, the Hashin-Shtrikman bounds are derived for the two-phase cellular material, and the structure performances are compared to the optimized structures derived by our proposed framework.

Diesel engine cylinder deactivation (CDA) can be used to reduce petroleum consumption and greenhouse gas (GHG) emissions of the global freight transportation system. Heavy duty trucks require complex exhaust aftertreatment (A/T) in order to meet stringent emission regulations. Efficient reduction of engine-out emissions require a certain A/T system temperature range, which is achieved by thermal management via control of engine exhaust flow and temperature. Fuel efficient thermal management is a significant challenge, particularly during cold start, extended idle, urban driving, and vehicle operation in cold ambient conditions. CDA results in airflow reductions at low loads. Airflow reductions generally result in higher exhaust gas temperatures and lower exhaust flow rates, which are beneficial for maintaining already elevated component temperatures. Airflow reductions also reduce pumping work, which improves fuel efficiency.

This work presents the implementation of the Efficient Global Optimization (EGO) approach for the design of composite materials under dynamic loading conditions. The optimization algorithm is based on design and analysis of computer experiments (DACE) in which smart sampling and continuous metamodel enhancement drive the design towards a global optimum. An expected improvement function is maximized during each iteration to locate the designs that update the metamodel until convergence. The algorithm solves single and multi-objective optimization problems. In the first case, the penetration of an armor plate is minimized by finding the optimal fiber orientations. Multi-objective formulation is used to minimize the intrusion and impact acceleration of a composite tube. The design variables include the fiber orientations and the size of zones that control the tube collapse.

In this paper, computations of pulsating flows in a duct with multiple inlets using the lattice Boltzmann method (LBM) are reported. As future emissions standards present a significant challenge for Diesel engine manufacturers, several options are being investigated to identify strategies to meet such regulations. Exhaust gas aftertreatment is one of the most important among them. As the performance of the various aftertreatment devices is sensitive to the flow conditions in the exhaust, a greater understanding of the flows under pulsating conditions in the presence of multiple cylinders is needed. The Lattice Boltzmann Method (LBM) is a relatively new and promising computational approach for applications to fluid dynamics problems. Two advantages of the method relative to traditional methods are ease of implementation and ease of parallelization and performance on parallel computers.

The application of biological treatment to solid waste is very promising to facilitate recycling of water, carbon, and nutrients and to reduce the resupply needs of long-term crewed space missions. Degradation of biodegradable solid wastes generated during such a mission is under investigation as part of the NASA Center of Research and Training (NSCORT) at Purdue University. Processing in the solids thermophilic aerobic reactor (STAR) involves the use of high temperature micro-aerobic slurry conditions to degrade solid wastes, enabling the recycling of water, carbon, and nutrients for further downstream uses. Related research presently underway includes technical development and optimization of STAR operations as well as a complementary evaluation of post-STAR processing for gas-stream purification, water recovery by condensate purification, and residuals utilization for both mushroom growth media and nutritional support for fish growth.

Current gasoline-gas vapor recovery system is incomplete, for it cannot adjust the vapor-liquid ratio automatically due to the change of working temperature. To solve this problem, this paper intends to design a new system and optimize its parameters. In this research, variables control method is used for tests while linear regression is used for data processing. This new system moves proportion valve away and adds a DSP control module, a frequency conversion device, and a temperature sensor. With this research, it is clearly reviewed that the vapor-liquid ratio should remains 1.0 from 0 °C to 20 °C as its working temperature, be changed into 1.1 from 20 °C to 25 °C, be changed into 1.2 from 25 °C to 30 °C, and be changed into 1.3 when the working temperature is above 30 °C.

An experimental-analytic method of determining the stress distribution in narrow faced spur gear teeth is presented. The successful application of photostress to this contact problem is reported. It utilizes a digital computer routine developed for separating stresses in any general two-dimensional region. Results for two pairs of gears are presented. Comparison is made with values predicted by the modified Lewis formula, the Kelley and Pedersen equation, and by the Belajef solution of the Hertz contact problem for two cylinders.

In the past decades, automotive structure design has sought to minimize its mass while maintaining or improving structural performance. As such, topology optimization (TO) has become an increasingly popular tool during the conceptual design stage. While the designs produced by TO methods provide significant performance-to-mass ratio improvements, they require considerable computational resources when solving large-scale problems. An alternative for large-scale problems is to decompose the design domain into multiple scales that are coupled with homogenization. The problem can then be solved with hierarchical multiscale topology optimization (MSTO). The resulting optimal, homogenized macroscales are de-homogenized to obtain a high-fidelity, physically-realizable design. Even so MSTO methods are still computationally expensive due to the combined costs of solving nested optimization problems and performing de-homogenization.

The design optimization of thin-walled tubular structures is of relevance in the automotive industry due to their low cost, ease of manufacturing and installation, and high-energy absorption efficiency. This study presents a methodology to design thin-walled tubular structures for crashworthiness applications. During an impact, thin-walled tubular structures may exhibit progressive collapse/buckling, global collapse/buckling, or mixed collapse/buckling. From a crashworthiness standpoint, the most desirable collapse mode is progressive collapse due to its high-energy absorption efficiency, stable deformation, and low peak crush force (PCF). In the automotive industry, thin-walled components have complex structural geometries. These complexities and the several loading conditions present in a crash reduce the possibility of progressive collapse. The Hybrid Cellular Automata (HCA) method has shown to be an efficient continuum-based approach in crashworthiness design.