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In computational fluid dynamic (CFD) and computational aeroacoustics (CAA) simulations, the wall surface is normally treated as a purely reflective wall. However, some surface treatments are usually applied in experiments. Thus, the acoustic simulations cannot be validated by experimental results. One of the major challenges is how to define acoustically boundary conditions in a well-posed way. In aeroacoustics analysis, impedance is a quantity to characterize reflectivity and absorption of an acoustically treated surface, which may be introduced into the numerical models as a frequency-domain boundary condition. However, CFD and CAA simulations are time-domain computations, meaning the frequency-domain impedance boundary condition cannot be adopted directly. Several methods, including the three-parameter model, the z-transform method and the reflection coefficient model, were developed.

Over the last decade, a number of hybrid architectures have been proposed with the main goal of minimizing energy consumption of off-highway vehicles. One of the architecture subsets which has progressively gained attention is hydraulic hybrids for earth-moving equipment. Among these architectures, hydraulic hybrids with secondary-controlled drives have proven to be a reliable, implementable, and highly efficient alternative with the potential for up to 50% engine downsizing when applied to excavator truck-loading cycles. Multi-input multi-output (MIMO) robust linear control strategies have been developed by the authors' group with notable improvements on the control of the state of charge of the high pressure accumulator. Nonetheless, the challenge remains to improve the actuator position and velocity tracking.

A temperature estimation algorithm (thermal observer) that provides accurate estimates of the thermal states of an electric machine in real time is presented. The thermal observer is designed to be a Kalman filter that combines thermal state predictions from a lumped-parameter thermal model of the electric machine with temperature measurements from a single external temperature sensor. An analysis based on the error covariance matrix of the Kalman filter is presented to guide the selection of the best sensor location. The thermal observer performance is demonstrated using a 3.8 kW permanent-magnet machine. Comparison of the thermal observer estimates and the actual temperatures demonstrate that this approach can provide accurate knowledge of the machine's thermal states despite modeling uncertainty and unknown initial machine thermal states.

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.

In this paper, a recently-developed algorithmic method of deriving the state equations of power systems containing power electronic components is described. Therein the system is described by the pertinent branch parameters and the circuit topology; however, unlike circuit-based algorithms, the difference equations are not implemented at the branch level. Instead, the composite system state equations are established. A demonstration of the computer implementation of this algorithm to model a variable-speed, constant-frequency aircraft generation system is described. Because of the large number of states and complexity of the system, particular attention is placed on the development of a model structure which provides optimal simulation efficiency.

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.

A mathematical model of a gas-charged mono-tube racing damper is presented. The model includes bleed orifice, piston leakage, and shim stack flows. It also includes models of the floating piston and the stiffness characteristics of the shim stacks. The model is validated with experimental tests on an Ohlins WCJ 22/6 damper and shown to be accurate. The model is exercised to show the effects of tuning on damper performance. The important results of the exercise are 1) the pressure variation on the compression side of the piston is insignificant relative to that on the rebound side because of the gas charge, 2) valve shim stiffness can be successfully modeled using stacked thin circular plates, 3) bleed orifice settings dominate the low speed regime, and 4) shim stack stiffness dominates the high speed regime.

Today, there are several hundreds of manufacturing processes available to the designer to choose from, and the number is constantly increasing. The ability to choose a manufacturing process for a particular user need set in the early stage of the design process is necessary. In metalcasting alone, there are over forty different processes with different capabilities. A designer can benefit from knowing the manufacturing process alternatives available to him. Inaccurate process selection can lead to financial losses and market share erosion. This paper discusses a methodology for selection of a metalcasting process based on a number of user specified attributes or requirements. A model of user requirements was developed and these requirements were matched with the capabilities of each metalcasting process. The metalcasting process which best meets these needs is suggested.

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.

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.

“Truck Ride” in this study refers to some vehicle ride parameters involved in tractor-trailer combinations. For the study, a mathematical model of a tractor-trailer vehicle as a vibrating system was developed. Principles of vibration theory were applied to the model while a digital computer was employed to investigate the complex system. To parallel the analytical investigation of the tractor-trailer vehicle, vehicle studies were conducted using a magnetic tape recorder and associated instrumentation installed in the tractor. Parameters studied included coupler position on the tractor, laden weight of trailer, spring rates of the different axles of the combination, damping capacity associated with each spring rate, vehicle speed, and “tar strip” spacing of the highway and cab mountings. The mathematical results were used as a basis for empirical study. A comparison of calculated and empirical data are reported.

Several available mathematical models for vibration dampers were compared to dynamic test results. The comparison results in a simple model that agrees well with both the magnitude and phase characteristics of experimentally obtained frequency response functions. The resulting model can be used as a correct boundary condition for finite element models of the structure to which the dampers are attached.

The kinematic flow ripple for gerotor pumps is often used as a metric for comparison among different gearsets. However, compressibility, internal leakages, and throttling effects have an impact on the performance of the pump and cause the real flow ripple to deviate from the kinematic flow ripple. To counter this phenomenon, the ports can be designed to account for fluid effects to reduce the outlet flow ripple, internal pressure peaks, and localized cavitation due to throttling while simultaneously improving the volumetric efficiency. The design of the ports is typically heuristic, but a more advanced approach can be to use a numerical fluid model for virtual prototyping. In this work, a multi-objective optimization by genetic algorithm using an experimentally validated, lumped parameter, fluid-dynamic model is used to design the port geometry.

A novel Blended Hydraulic Hybrid transmission architecture is presented in this paper with benefits over conventional designs. This novel configuration combines elements of a hydrostatic transmission, a parallel hybrid, and a selectively connectable high pressure accumulator using passive and actively controlled logic elements. Losses are reduced compared to existing series hybrid transmissions by enabling the units to operate efficiently at pressures below the current high pressure accumulator's pressure. A selective connection to the high pressure accumulator also allows for higher system precharge which increases regenerative braking torque and energy capture with little determent to system efficiency. Finally operating as a hydrostatic transmission increases transmission stiffness (i.e. driver response) and may improve driver feel in certain situations when compared to a conventional series hybrid transmission.

This work contributes to the overall goal of identifying and reducing noise sources and propagation in hydraulic systems. This is a general problem and a primary design concern for all fluid power applications. The need for new methods for identification of noise sources and transmission is evident in order to direct future modeling and experimental efforts aimed at reducing noise emissions of current fluid power machines. In this paper, this goal is accomplished through the formulation of noise functions used to identify contributions and transfer paths from different components of the system. An experimental method for noise transfer path analysis was developed and tested on a simple hydraulic system composed of a reference external gear pump, attached lines, and loading valve. Pressure oscillations in the working fluid are measured at the outlet of the pump. Surface vibrations are measured at multiple locations on the pump and connected system.

This work introduces a new design algorithm to optimize progressively folding thin-walled structures and in order to improve automotive crashworthiness. The proposed design algorithm is composed of three stages: conceptual thickness distribution, design parameterization, and multi-objective design optimization. The conceptual thickness distribution stage generates an innovative design using a novel one-iteration compliant mechanism approach that triggers progressive folding even on irregular structures under oblique impact. The design parameterization stage optimally segments the conceptual design into a reduced number of clusters using a machine learning K-means algorithm. Finally, the multi-objective design optimization stage finds non-dominated designs of maximum specific energy absorption and minimum peak crushing force.

Near-Field Acoustical Holography (NAH) is an inverse process in which sound pressure measurements made in the near-field of an unknown sound source are used to reconstruct the sound field so that source distributions can be clearly identified. NAH was originally based on performing spatial transforms of arrays of measured pressures and then processing the data in the wavenumber domain, a procedure that entailed the use of very large microphone arrays to avoid spatial truncation effects. Over the last twenty years, a number of different NAH methods have been proposed that can reduce or avoid spatial truncation issues: for example, Statistically Optimized Near-Field Acoustical Holography (SONAH), various Equivalent Source Methods (ESM), etc.

High surface area particles have drawn attention in the context of noise control due to their good sound absorption performance at low frequencies, which is an advantage compared with more traditional materials. That observation suggests that there is a good potential to use these particles in various scenarios, especially where low frequency noise is the main concern. To facilitate their application, composite materials are formed by dispersing particles within a fiber matrix, thus giving more flexibility in positioning those particles. In this work, a hybrid model that combines a model for limp porous materials and a model of high surface area particles is proposed to describe the acoustic performance of such composites. Two-microphone standing wave tube test results for several types of composites with different thickness, basis weight, and particle concentration are provided.

Nested planetary gear trains, which consist of two integrated co-axial single-stage planetary gearsets, have recently been widely implemented in automobile transmissions and various other applications. In the current study, a non-destructive vibrational and acoustical monitoring technique is developed to detect a common type of gear grinding defect for a complex nested gear train structure. A nested gear train which has an unground pinion with unpolished teeth profile is used to exemplify the developed methodology. An experimental test stand with an open and vertical mounting configuration has been designed to acquire both vibrational and acoustical data. The measured data are investigated using several signal processing techniques to identify unground pinions in the gear system. A general frequency spectrum analysis is performed initially, which is then followed by a peak finding algorithm to identify the peaks in the spectrum.