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Since transient vehicle HVAC computational fluids (CFD) simulations take too long to solve in a production environment, the goal of this project is to automatically create a lumped-parameter flow network from a steady-state CFD that solves nearly instantaneously. The data mining algorithm k-means is implemented to automatically discover flow features and form the network (a reduced order model). The lumped-parameter network is implemented in the commercial thermal solver MuSES to then run as a fully transient simulation. Using this network a “localized heat transfer coefficient” is shown to be an improvement over existing techniques. Also, it was found that the use of the clustering created a new flow visualization technique. Finally, fixing clusters near equipment newly demonstrates a capability to track localized temperatures near specific objects (such as equipment in vehicles).

The Time Variant Discrete Fourier Transform was implemented as a real-time order tracking method using developed software and commercially available hardware. The time variant discrete Fourier transform (TVDFT) with the application of the orthogonality compensation matrix allows multiple tachometers to be tracked with close and/or crossing orders to be separated in real-time. Signal generators were used to create controlled experimental data sets to simulate tachometers and response channels. Computation timing was evaluated for the data collection procedure and each of the data processing steps to determine how each part of the process affects overall performance. Many difficulties are associated with a real-time data collection and analysis tool and it becomes apparent that an understanding of each component in the system is required to determine where time consuming computation is located.

In long-duration, closed human habitats in space that include crop growth, one challenge that is faced while designing a candidate waste processor is the composition of solid-waste loads, which include human waste, packaging and food-processing materials, crop spoilage, and plant residues. In this work, a new modeling tool is developed to characterize crop residues and food wastes based on diet in order to support the design of solid-waste technologies for closed systems. The model predicts amounts of crop residues and food wastes due to food processing, crop harvests, and edible spoilage. To support the design of solid-waste technologies, the generation of crop residues and food wastes was characterized for a 600-day mission to Mars using integrated menu, crop, and waste models. The three sources of plant residues and food waste are identified to be food processors, crop harvests, and edible spoilage.

Dynamics of machine tool components play a critical role in the outcome of machining processes. This paper addresses several important issues on machine tool and machining dynamics. It illustrates the dynamic behavior of structural components under operating conditions and presents an improved technique for modeling structural non-linearity. It also describes spindle modeling capability that has been developed to predict dynamic and thermal characteristics of spindle systems. Finally, the paper discusses the impact of non-linear dynamics on machining stability.

This paper presents in-process monitoring and control based on a novel ultrasonic sensing technique. The developed ultrasonic system provides non-contact measurement of surface roughness, which is applicable to wet machining environments. The utility and robustness of the technique are demonstrated through applications to different processes and materials. In-process surface roughness monitoring capability of the system is also shown along with its potential to monitor flank wear conditions. The result of in-process surface roughness control implementation based on the developed technique shows the control scheme is able to maintain consistent surface roughness values regardless of the tool wear state.

The On-line Project Information System (OPIS) is a LAMP-based (Linux, Apache, MySQL, PHP) system being developed at NASA Ames Research Center to improve Agency information transfer and data availability, largely for improvement of system analysis and engineering. The tool will enable users to investigate NASA technology development efforts, connect with experts, and access technology development data. OPIS is currently being developed for NASA's Exploration Life Support (ELS) Project. Within OPIS, NASA ELS Managers assign projects to Principal Investigators (PI), track responsible individuals and institutions, and designate reporting assignments. Each PI populates a “Project Page” with a project overview, team member information, files, citations, and images. PI's may also delegate on-line report viewing and editing privileges to specific team members. Users can browse or search for project and member information.

Wheat is a candidate crop for the Advanced Life Support (ALS) system, and cereal grains and their products will be included on long-term space missions beyond low earth orbit. While the exact supply scenario has yet to be determined, some type of post-processing of these grains must occur if they are shipped as bulk ingredients or grown on site for use in foods. Understanding the requirements for processing grains in space is essential for incorporating the process into the ALS food system. The ESM metric developed by NASA describes and compares individual system impact on a closed system in terms of a single parameter, mass. The objective of this study was to compare the impact of grain mill type on the ESM of producing yeast and flat breads. Hard red spring wheat berries were ground using a Brabender Quadrumat Jr. or the Kitchen-Aid grain mill attachment (both are proposed post-harvest technologies for the ALS system) to produce white and whole wheat flour, respectively.

One of the challenges NASA faces today is developing an Advanced Life Support (ALS) system that will enable long duration space missions beyond low earth orbit (LEO). This ALS system must include a food processing subsystem capable of producing a variety of nutritious, acceptable, and safe edible ingredients and food products from pre-packaged and re-supply foods as well as salad crops grown on the transit vehicle or other crops grown on planetary surfaces. However, designing, building, developing, and maintaining such a subsystem is bound to many constraints and restrictions. The limited power supply, storage locations, variety of crops, crew time, need to minimize waste, and other ESM parameters influence the selection of processing equipment and techniques.

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.

Recent trends in signal processing have led to the discovery and implementation of wavelets as tools of many different applications. This paper focuses on their use as a tool for transient extraction. From the Discrete Wavelet Transform (DWT), specific coefficients are picked using a coherence-based criterion. These coefficients are then taken back to the time domain as the extracted transient. If the extracted transient is a response from a measured input, then a frequency response function can be formulated.

A practical approach for evaluating and validating global system designs for Squeak and Rattle performance is proposed. Using simple slip and rattle models, actual sound and vibration data, and the fundamentals of audiological perception, analysis tools adapted from Chaos Theory are used to establish threshold levels of performance and identify system characteristics which are significant contributors to Squeak and Rattle. Focus on system design is maintained by using a simple rattle noise indicator and relating rattle events to levels of dynamic motion (acceleration, velocity, etc.). The threshold level is defined as the level of acceleration at which the system moves from a non-rattling state to a rattling state. The approach is demonstrated with a simple analytical model applied to an experimental structure under dynamic load.

Noise Path Analysis techniques (NPA) have been developed and refined by the automotive industry for structure-borne noise and vibration evaluation of their products. Off-highway vehicles, particularly those with enclosed cabs, are excellent candidates for the application of these techniques. Like automobiles, many off-highway machines are typically driven by a rotating power source, have a well-defined acoustic receiver space, and use some form of isolation between source and receiver sub-systems. These structural characteristics make NPA a useful tool for identifying dominant sources and energy transfer paths. The objectives of this paper are to revisit the fundamental theory of matrix inversion as it applies to NPA techniques, and to address the common setup and measurement issues encountered when acquiring noise path data on off-highway machines. A general overview of the procedures involved in applying NPA to an off-highway machine will be presented.

Several very different order tracking and analysis techniques for rotating equipment have been developed recently that are available in commercial noise and vibrations software packages. Each of these order tracking methods has distinct trade-offs for many common applications and very specific advantages for special applications in sound quality or noise and vibrations troubleshooting. The Kalman, Vold-Kalman, Computed Order Tracking, and the Time Variant Discrete Fourier Transform as well as common FFT based order analysis methods will all be presented. The strengths and weaknesses of each of the methods will be presented as well as the highlights of their mathematical properties. This paper is intended to be an overview of currently available technology with all methods presented in a common format that allows easy comparison of their properties. Several analytical examples will be presented to thoroughly document each methods' behavior with different types of data.

Sound intensity measurement techniques that used a two-microphone configuration, were first developed in the late 1970s. Originally, the focus was on improving precision during testing or post-processing. However, with the advent of modern, sophisticated equipment, the focus has shifted to the apparatus. Availability of phase-matched microphones has made post-test correction obsolete as the microphones eliminate a majority of the errors before the data is even collected. This accuracy, however, comes at a cost, as phase-matched microphones are highly priced. This paper discusses employing the method of improving post-processing precision, using inexpensive, current equipment. The phase error of the system is corrected using a simple calibration technique and a handheld phase calibrator that is similar to the one used for amplitude calibration of microphones.

Noise generation in axial piston machines can be attributed to two main sources; fluid borne and structure borne. Any attempt towards noise reduction in axial piston machines should focus on simultaneous reduction of these two sources. A multi-parameter multi-objective optimization approach to design valve plates to reduce both sources of noise for pumps which operate in a wide range of operating conditions has been detailed in a previous work (Seeniraj and Ivantysynova, 2008). The focus of this paper is to explain the background and to demonstrate the functionality and usefulness of the methodology for pump design.

The AutoDrive Challenge competition sponsored by General Motors and SAE gives undergraduate and graduate students an opportunity to get hands-on experience with autonomous vehicle technology and development as they work towards their degree. Michigan Technological University has participated in the AutoDrive Challenge since its inception in 2017 with students participating through MTU’s Robotic System Enterprise. The MathWorks Simulation Challenge has been a component of the competition since its second year, tasking students with the development of perception, control and testing algorithms using MathWorks software products. This paper presents the pedagogical approach graduate student mentors used to enable students to build their understanding of autonomous vehicle concepts using familiar tools. This approach gives undergraduate students a productive experience with these systems that they may not have encountered in coursework within their academic program.