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Component sound quality is an important factor in the design of competitive diesel engines. One component noise that causes complaints is the gear rattle that originates in the front-of-engine gear train which drives the fuel pump and other accessories. The rattle is caused by repeated tooth impacts resulting from fluctuations in differential torsional acceleration of the driving gears. These impacts generate a broadband, impulsive noise that is often perceived as annoying. In most previous work, the overall sound quality of diesel engines has been considered without specifically focusing on predicting the perception of gear rattle. Gear rattle level has been quantified based on angular acceleration measurements, but those measurements can be difficult to perform. Here, the emphasis was on developing a metric based on subjective testing of the perception of gear rattle.

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.

Helmholtz resonator has been used in industry for a long time to reduce the noise from exhaust system in vehicle or machinery. Numerous investigations have been done in the past to study the effect of a Helmholtz resonator connected to a pipeline. A general procedure for the analysis of curved or flat, thin two dimensional gas cavities such as thin compressor or engine manifolds or so-called thin shell type muffler elements, which can efficiently utilize the limited space of hermetically sealed compressors or small engine compartments, has been developed by the authors, as long as the thickness of the cavities is substantially small compared to the shortest wavelength of interest. However, to the authors' knowledge, a Helmholtz resonator attached to a rectangular thin muffler element, which is similar to a refrigeration compressor muffler, has not been analyzed.

The firing frequency components of the dynamic diesel particulate filter pressure signals carry significant information about the particulate load. Specifically, the normalized magnitude and relative phase of the firing frequency components exhibit clear dependence on the particulate load in a filter. Further, the test-to-test variation and back-to-back repeatability in this work was better for the dynamic pressure signal features than for the mean value pressure drop. This work provides a promising extension or alternative to the mean value pressure drop correlation to particulate load through Darcy's Law. The results may be particularly useful for filter monitoring and control.

A fast time-scale 1-D dynamic diesel particulate filter model capable of resolving the pressure pulsations due to individual cylinder firing events is presented. The purpose of this model is to investigate changes in the firing frequency component of the pulsating exhaust flow at different particulate loadings. Experimental validation data and simulation results clearly show that the magnitude and phase of the firing frequency components are directly correlated to the mass of particulate stored in a diesel particulate filter. This dynamic pressure signal information may prove particularly useful for monitoring particulate load during vehicle operation.

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.

Experimental modal analysis (EMA) provides many parameters that are required in numerical modeling of dynamic and vibratory behavior of structures. This paper discusses EMA on an exhaust system of an off-road car. The exhaust structure is tested under three boundary conditions: free-free, supported with two elastomeric mounts, and mounted to the car. The free-free modal parameters are compared to finite element results. The two-mount tests are done with the mounts fixed to a rigid and heavy frame. The rigidity of the frame is verified experimentally. The on-car test is done with realistic boundary conditions, where the exhaust structure is fixed to the engine manifold as well as the two elastomeric mounts. The two-mount and the on-car tests result in highly complex mode shapes.

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.

In recent years, there has been an interest in early-injection Diesel engines as it has the potential of achieving a more homogeneous and leaner mixture close to top-dead-center (TDC) compared to standard Diesel engines. The more homogeneous mixture may result in reduced NOx and soot emissions and higher efficiency. Diesel engines in which a homogeneous mixture is achieved close to TDC are known as Homogenous Charge Compression Ignition (HCCI) engines. PREmixed lean DIesel Combustion (PREDIC) engines in which the start of fuel injection is considerably advanced in comparison with that of the standard Diesel engine is an attempt to achieve a mode of operation close to HCCI. Earlier studies have shown that in a PREDIC engine, the fuel injection timing affects the mixture formation and hence influences combustion and pollutant formation.

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.

The latest US emission regulations require dramatic reductions in Nitrogen Oxide (NOx) emissions from vehicular diesel engines. Selective Catalytic Reduction (SCR) is the current technology that achieves NOx reductions of up to 90%. It is typically mounted downstream of the existing after-treatment system, i.e., after the Diesel Oxidation Catalyst (DOC) and Diesel Particulate Filter (DPF). Accurate prediction of input NO₂:NO ratio is useful for control of SCR urea injection to reduce NOx output and NH₃ slippage downstream of the SCR catalyst. Most oxidation of NO to NO₂ occurs in the DOC since its main function is to oxidize emission constituents. The DOC thus determines the NO₂:NO ratio as feedgas to the SCR catalyst. The prediction of NO₂:NO ratio varies as the catalyst in the DOC ages or deteriorates due to poisoning. Thus, the DOC prediction model has to take into account the correlation of DOC conversion effectiveness and the aging of the catalyst.

A gravimetric particulate measurement system, which extracts samples isokinetically from raw exhaust, is presented to quantify the particulate mass stored in diesel particulate filters. The purpose of this measurement system is to facilitate the study of wall-flow filter behavior at different particulate load levels. Within this paper, the design considerations for the particulate measurement system are detailed and its operation is described. The accuracy of the measurement is examined through a theoretical error analysis and direct experimental comparison to the differential weight of a diesel particulate filter. Experimental results are also presented to validate the ability of the system to maintain the isokinetic sampling condition.

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.