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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.

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.

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.

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.

Pumped two-phase systems using mini or microchannel heat sink evaporators are prime candidates for high heat flux applications due to relatively low pumping power requirements and efficient heat removal in compact designs. A number of challenges exist in the implementation of these systems including: ensuring subcooled liquid to the pump to avoid cavitation, avoiding dry out conditions in heat exchangers that can lead to failures of the components under cooling, and avoiding flow instabilities that can damage components in an integrated system. To reduce risk and cost, modeling and simulation can be employed in the design and development of these complex systems, but such modeling must include the relevant behavior necessary to capture the above dynamic effects.

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.

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.

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.

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.

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 study was conducted to quantify intracranial biomechanical responses and external blast overpressures using physical head model to understand the biomechanics of blast traumatic brain injury and to provide experimental data for computer simulation of blast-induced brain trauma. Ellipsoidal-shaped physical head models, made from 3-mm polycarbonate shell filled with Sylgard 527 silicon gel, were used. Six blast tests were conducted in frontal, side, and 45° oblique orientations. External blast overpressures and internal pressures were quantified with ballistic pressure sensors. Blast overpressures, ranging from 129.5 kPa to 769.3 kPa, were generated using a rigid cannon and 1.3 to 3.0 grams of pentaerythritol tetranitrate (PETN) plastic sheet explosive (explosive yield of 13.24 kJ and TNT equivalent mass of 2.87 grams for 3 grams of material).

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 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.

Three replicate aerobic-heterotrophic biotrickling filters were designed to promote the simultaneous biodegradation of graywater and a waste gas containing NH3, H2S and CO2. Upon visual observation of discolored solids, it was originally hypothesized that gas-phase CO2 concentrations were excessive, causing regions of anoxic zones to form within the biotrickling filters. Observed discolored (black) biofilm of this nature is typically assumed to be either lysed bacterial cells or anaerobic regions, implying alteration of operational conditions. Solid (biofilm) samples were collected in the presence and absence of gas-phase wastestream(s) to determine if the gas-phase contaminants were contributing to the solid-phase discoloration. Two sets of experiments (shaker flask and solids characterization) were conduced to determine the nature of the discolored solids. Results indicated that the discolored solids were neither anaerobic bacteria nor lysed cells.

Strawberry is a life-support-system candidate crop species that is long-lived, asexually propagated, and can bear large quantities of fruit high in sugar and antioxidant content. Strawberries of four day-neutral cultivars (‘Tribute’, ‘Tristar’, ‘Seascape’, and ‘Fern’) and one ever-bearing cultivar (‘Cavendish’) were grown under greenhouse conditions or varying temperature regimes in three growth chambers. Flowers in growth chambers were hand pollinated three-times weekly with stored pollen, and ripe berries were harvested, counted, weighed, and tested organolepticly. In the greenhouse, two different pollination treatments were compared, while another group of plants was left unpollinated, receiving only occasional mechanical stimulation from normal greenhouse airflow, berry harvest, and plant maintenance. A second group was pollinated with a vibrating wand, and a third group was hand pollinated with stored pollen.

Most of the gaseous contaminants generated inside ALS (Advanced Life Support) cabins can be degraded to some degree by microbial degradation in a biofilter. The entry of biofiltration techniques into ALS will most likely involve integration with existing physico-chemical methods. However, in this study, cabin air quality treated by only biofiltration was predicted using the one-box and biofiltration models. Based on BVAD (Baseline Values and Assumptions Document) and SMAC (Spacecraft Maximum Allowable Concentrations), ammonia and carbon monoxide will be the critical compounds for biofilter design and control. Experimentation is needed to identify the pertinent microbial parameters and removal efficiency of carbon monoxide and to validate the results of this preliminary investigation.

The Bioregenerative Air Treatment for Health system has been proposed for Advanced Life Support (ALS) planetary base applications. The system will be operated as a biotrickling filter to simultaneously treat graywater and waste gas. Preliminary experiments have focused on carbon removal from a graywater simulant. Six bench scale biotrickling filter reactors were constructed and monitored continuously. After a reactor startup phase of 40 days, the average total organic carbon (TOC) removal for reactors packed with Tri-packs® packing material was 62%. A second set of experiments was designed to evaluate TOC removal using different packing materials (Bee-cell and Biobale). It was hypothesized that the alternative packing materials would reduce the effects of channeling in the reactors, thus improving TOC removal. However, TOC removal did not significantly improve during the second set of experiments.