Engineering Commons^™

A Bi-Invariant Approach To Approximate Motion Synthesis Of Planar Four-Bar Linkage, Tianze Xu, David H. Myszka, Andrew P. Murray

Mechanical and Aerospace Engineering Faculty Publications

This paper presents a planar four-bar approximate motion synthesis technique that uses only pole locations. Synthesis for rigid-body guidance determines the linkage dimensions that guide a body in a desired manner. The desired motion is specified with task positions including a location and orientation angle. Approximation motion synthesis is necessary when an exact match to the task positions cannot be obtained. A linkage that achieves the task positions as closely as possible becomes desired. Structural error refers to the deviations between the task positions and the linkage's generated positions. A challenge in approximate motion synthesis is that structural error involves …

Characterization Of Upper Extremity Kinematics Using Virtual Reality Movement Tasks And Wearable Imu Technology, Skyler A. Barclay, Lanna N. Klausing, Tessa M. Hill, Allison L. Kinney, Timothy Reissman, Megan E. Reissman

Mechanical and Aerospace Engineering Faculty Publications

Task-specific training has been shown to be an effective neuromotor rehabilitation intervention, however, this repetitive approach is not always very engaging. Virtual reality (VR) systems are becoming increasingly popular in therapy due to their ability to encourage movement through customizable and immersive environments. Additionally, VR can allow for a standardization of tasks that is often lacking in upper extremity research. Here, 16 healthy participants performed upper extremity movement tasks synced to music, using a commercially available VR game known as Beat Saber. VR tasks were customized to characterize participants' joint angles with respect to each task's specified cardinal direction (inward, …

Utilizing Machine Learning Models To Estimate Energy Savings From An Industrial Energy System, Eva Mclaughlin, Jun-Ki Choi

Mechanical and Aerospace Engineering Faculty Publications

Energy audits are an important part of reducing energy usage, costs, and carbon emissions, but there have been discrepancies in the quality of audits depending upon the auditor, which can negatively affect the impacts and credibility of the energy assessment. In this paper, historical energy auditing data from a U.S. Department of Energy sponsored research program was gathered and analyzed with a machine-learning algorithm to predict demand savings from a compressed air system assessment recommendation in industrial manufacturing facilities. Different energy auditors calculate savings for repairing leaks in compressed air systems in various ways, so the energy demand savings have …

Smart Wifi Thermostat-Enabled Thermal Comfort Control In Residences, Robert Lou, Kevin P. Hallinan, Kefan Huang, Timothy Reissman

Mechanical and Aerospace Engineering Faculty Publications

The present research leverages prior works to automatically estimate wall and ceiling R-values using a combination of a smart WiFi thermostat, building geometry, and historical energy consumption data to improve the calculation of the mean radiant temperature (MRT), which is integral to the determination of thermal comfort in buildings. To assess the potential of this approach for realizing energy savings in any residence, machine learning predictive models of indoor temperature and humidity, based upon a nonlinear autoregressive exogenous model (NARX), were developed. The developed models were used to calculate the temperature and humidity set-points needed to achieve minimum thermal comfort …

Predicting Industrial Building Energy Consumption With Statistical And Machine-Learning Models Informed By Physical System Parameters, Sean Kapp, Jun-Ki Choi, Taehoon Hong

Mechanical and Aerospace Engineering Faculty Publications

The industrial sector consumes about one-third of global energy, making them a frequent target for energy use reduction. Variation in energy usage is observed with weather conditions, as space conditioning needs to change seasonally, and with production, energy-using equipment is directly tied to production rate. Previous models were based on engineering analyses of equipment and relied on site-specific details. Others consisted of single -variable regressors that did not capture all contributions to energy consumption. New modeling techniques could be applied to rectify these weaknesses. Applying data from 45 different manufacturing plants obtained from industrial energy audits, a supervised machine-learning model …

Predicting The Impact Of Utility Lighting Rebate Programs On Promoting Industrial Energy Efficiency: A Machine Learning Approach, Phillip Shook, Jun-Ki Choi

Mechanical and Aerospace Engineering Faculty Publications

Implementation costs are a major factor in manufacturers' decisions to invest in energy-efficient technologies. Emerging technologies in lighting systems, however, typically require small investment costs and offer short, simple payback periods, due, in part, to federal, state, and utility incentive programs. Recently, however, certain state and federal mandates have reduced the support for and efficacy of electricity utility incentivizing programs. To determine the impact of such support programs, this study examined historical data regarding lighting retrofit savings, implementation costs, and utility rebates gathered from 13 years of industrial energy audits by a U.S. Department of Energy Industrial Assessment Center in …

An Improved Method To Estimate Savings From Thermal Comfort Control In Residences From Smart Wi-Fi Thermostat Data, Abdulelah D. Alhamayani, Qiancheng Sun, Kevin P. Hallinan

Mechanical and Aerospace Engineering Faculty Publications

The net-zero global carbon target for 2050 needs both expansion of renewable energy and substantive energy consumption reduction. Many of the solutions needed are expensive. Controlling HVAC systems in buildings based upon thermal comfort, not just temperature, uniquely offers a means for deep savings at virtually no cost. In this study, a more accurate means to quantify the savings potential in any building in which smart WiFi thermostats are present is developed. Prior research by Alhamayani et al. leveraging such data for individual residences predicted cooling energy savings in the range from 33 to 47%, but this research was based …

Six-Bar Linkage Models Of A Recumbent Tricycle Mechanism To Increase Power Throughput In Fes Cycling, Nicholas A. Lanese, David H. Myszka, Anthony L. Bazler, Andrew P. Murray

Mechanical and Aerospace Engineering Faculty Publications

This paper presents the kinematic and static analysis of two mechanisms to improve power throughput for persons with tetra- or paraplegia pedaling a performance tricycle via FES. FES, or functional electrical stimulation, activates muscles by passing small electrical currents through the muscle creating a contraction. The use of FES can build muscle in patients, relieve soreness, and promote cardiovascular health. Compared to an able-bodied rider, a cyclist stimulated via FES produces an order of magnitude less power creating some notable pedaling difficulties especially pertaining to inactive zones. An inactive zone occurs when the leg position is unable to produce enough …

Estimating Smart Wi-Fi Thermostat-Enabled Thermal Comfort Control Savings For Any Residence, Abdulelah D. Alhamayani, Qiancheng Sun, Kevin Hallinan

Mechanical and Aerospace Engineering Faculty Publications

Nowadays, most indoor cooling control strategies are based solely on the dry-bulb temperature, which is not close to a guarantee of thermal comfort of occupants. Prior research has shown cooling energy savings from use of a thermal comfort control methodology ranging from 10 to 85%. The present research advances prior research to enable thermal comfort control in residential buildings using a smart Wi-Fi thermostat. "Fanger's Predicted Mean Vote model" is used to define thermal comfort. A machine learning model leveraging historical smart Wi-Fi thermostat data and outdoor temperature is trained to predict indoor temperature. A Long Short-Term-Memory neural network algorithm …

Toward Cost-Effective Residential Energy Reduction And Community Impacts: A Data-Based Machine Learning Approach, Adel Naji, Badr Al Tarhuni, Jun-Ki Choi, Salahaldin Alshatshati, Seraj Ajena

Mechanical and Aerospace Engineering Faculty Publications

Many U.S. utilities incentivize residential energy reduction through rebates, often in response to state mandates for energy reduction or from a desire to reduce demand to mitigate the need to grow generating assets. The assumption built into incentive programs is that the least efficient residences will be more likely take advantage of the rebates. This, however, is not always the case. The main goal of this study was to determine the potential for prioritized incentivization, i.e., prioritizing incentives that deliver the greatest energy savings per investment through an entire community. It uses a data mining approach that leverages known building …

Automated Residential Energy Audits Using A Smart Wifi Thermostat-Enabled Data Mining Approach, Abdulrahman Alanezi, Kevin Hallinan, Kefan Huang

Mechanical and Aerospace Engineering Faculty Publications

Smart WiFi thermostats, when they first reached the market, were touted as a means for achieving substantial heating and cooling energy cost savings. These savings did not materialize until additional features, such as geofencing, were added. Today, average savings from these thermostats of 10–12% in heating and 15% in cooling for a single-family residence have been reported. This research aims to demonstrate additional potential benefit of these thermostats, namely as a potential instrument for conducting virtual energy audits on residences. In this study, archived smart WiFi thermostat measured temperature data in the form of a power spectrum, corresponding historical weather …

Using Smart-Wifi Thermostat Data To Improve Prediction Of Residential Energy Consumption And Estimation Of Savings, Abdulrahman Alanezi, Kevin P. Hallinan, Rodwan Elhashmi

Mechanical and Aerospace Engineering Faculty Publications

Energy savings based upon use of smart WiFi thermostats ranging from 10 to 15% have been documented, as new features such as geofencing have been added. Here, a new benefit of smart WiFi thermostats is identified and investigated; namely, as a tool to improve the estimation accuracy of residential energy consumption and, as a result, estimation of energy savings from energy system upgrades, when only monthly energy consumption is metered. This is made possible from the higher sampling frequency of smart WiFi thermostats. In this study, collected smart WiFi data are combined with outdoor temperature data and known residential geometrical …

Self-Learning Algorithm To Predict Indoor Temperature And Cooling Demand From Smart Wifi Thermostat In A Residential Building, Kefan Huang, Kevin Hallinan, Robert Lou, Abdulrahman Alanezi, Salahaldin Alshatshati, Qiancheng Sun

Mechanical and Aerospace Engineering Faculty Publications

Smart WiFi thermostats have moved well beyond the function they were originally designed for; namely, controlling heating and cooling comfort in buildings. They are now also learning from occupant behaviors and permit occupants to control their comfort remotely. This research seeks to go beyond this state of the art by utilizing smart WiFi thermostat data in residences to develop dynamic predictive models for room temperature and cooling/heating demand. These models can then be used to estimate the energy savings from new thermostat temperature schedules and estimate peak load reduction achievable from maintaining a residence in a minimum thermal comfort condition. …

A Machine Learning Framework For Drop-In Volume Swell Characteristics Of Sustainable Aviation Fuel, Shane Kosir, Joshua Heyne, John Graham

Mechanical and Aerospace Engineering Faculty Publications

A machine learning framework has been developed to predict volume swell for 10 non-metallic materials submerged in neat compounds. The non-metallic materials included nitrile rubber, extracted nitrile rubber, fluorosilicone, low temp fluorocarbon, lightweight polysulfide, polythioether, epoxy (0.2 mm), epoxy (0.04 mm), nylon, and Kapton. Volume swell, a material compatibility concern, serves as a significant impediment for the minimization of the greenhouse gas emissions of aviation. Sustainable aviation fuels, the only near and mid-term solution to mitigating greenhouse gas emissions, are limited to low blend limits with conventional fuel due to material compatibility issues (i.e. O-ring swell). A neural network was …

Machine Learning Modeling Of Horizontal Photovoltaics Using Weather And Location Data, Christil Pasion, Torrey Wagner, Clay Koschnick, Steven Schuldt, Jada Williams, Kevin Hallinan

Mechanical and Aerospace Engineering Faculty Publications

Solar energy is a key renewable energy source; however, its intermittent nature and potential for use in distributed systems make power prediction an important aspect of grid integration. This research analyzed a variety of machine learning techniques to predict power output for horizontal solar panels using 14 months of data collected from 12 northern-hemisphere locations. We performed our data collection and analysis in the absence of irradiation data-an approach not commonly found in prior literature. Using latitude, month, hour, ambient temperature, pressure, humidity, wind speed, and cloud ceiling as independent variables, a distributed random forest regression algorithm modeled the combined …

Sustainable Aviation Fuels Approval Streamlining: Auxiliary Power Unit Lean Blowout Testing, Erin E. Peiffer, Joshua S. Heyne, Meredith Colket

Mechanical and Aerospace Engineering Faculty Publications

An underpinning hindrance in the market penetration of sustainable aviation fuel is the approval process for alternative jet fuels. One solution to this is to develop low-cost screening tools that can be implemented earlier in the approval process. Auxiliary power unit combustors historically show the most sensitivity to physical and volatile fuel properties, making it a useful tool in assessing potential alternative jet fuel effects at test conditions representative of operability stability limits. It is hypothesized that these observations can be explained via timescale analysis considering fuel droplet breakup and evaporation, combustor mixing, and chemical reactivity timescales on the progression …

Special Issue: Selected Papers From Idetc 2017, Andreas Mueller, Andrew Murray, Venkat N. Krovi

Mechanical and Aerospace Engineering Faculty Publications

The Mechanisms and Robotics Conference has traditionally provided a vigorous and stimulating international forum for the exchange of technical and scientific information on the theory and practice of mechanical systems. The topical coverage has span areas central to mechanical systems including design (novel mechanisms and robots, synthesis), analysis (kinematics, dynamics, computational approaches, and software systems), applications (from micro-air vehicles, modular robotics, origami applications, medical robotics, to exoskeleton-assistive systems), and educational practices.

Special Issue: Selected Papers From Idetc 2015, Venkat N. Krovi, Andrew P. Murray, James Schmiedeler

Mechanical and Aerospace Engineering Faculty Publications

This second IDETC Special Issue, containing 19 papers from researchers in seven countries on three continents, seeks to cap- ture the current interest topics and latest results from the 39th ASME Mechanisms and Robotics (M&R) conference. The topics span the synthesis and analysis of novel mechanisms and robots as well as their validation in a variety of applications. The papers are organized with contributions to the core theoretical methodol- ogies of M&R (five papers) appearing first. The application areas that follow are micro air vehicles (MAVs) (two papers), modular robotics (three papers), origami applications (three papers), medi- cal robotics (three …

Simulation Model Of An Automatic Commercial Ice Machine, Haithem Murgham, David Myszka, Vijay Bahel, Rajan Rajendran, Kurt Knapke, Suresh Shivashankar, Kyaw Wynn

Mechanical and Aerospace Engineering Faculty Publications

Automatic commercial ice-making machines that produce a batch of cube ice at regular intervals are known as “cubers." Such machines are commonly used in food service, food preservation, hotel, and health service industries. The machines are typically rated for the weight of ice produced over a 24-hour period at ambient air temperatures of 90°F and water inlet temperature of 70°F.

These cubers typically utilize an air-cooled, vapor-compression cycle to freeze circulating water flowing over an evaporator grid. Once a sufficient amount ice is formed, a valve switches to enable a harvest mode, where the compressor’s discharge gas is routed into …

Evaluation Of Different Optimal Control Problem Formulations For Solving The Muscle Redundancy Problem, Friedl De Groote, Allison Kinney, Anil Rao, Benjamin J. Fregly

Mechanical and Aerospace Engineering Faculty Publications

This study evaluates several possible optimal control problem formulations for solving the muscle redundancy problem with the goal of identifying the most efficient and robust formulation. One novel formulation involves the introduction of additional controls that equal the time derivative of the states, resulting in very simple dynamic equations. The nonlinear equations describing muscle dynamics are then imposed as algebraic constraints in their implicit form, simplifying their evaluation. By comparing different problem formulations for computing muscle controls that can reproduce inverse dynamic joint torques during gait, we demonstrate the efficiency and robustness of the proposed novel formulation.

Wing Tip Vortices From An Exergy-Based Perspective, Muhammad Omar Memon, Kevin Wabick, Aaron Altman, Rainer M. Buffo

Mechanical and Aerospace Engineering Faculty Publications

The lens of exergy is used to investigate a wingtip vortex in the near wake over a range of angles of attack. Exergy is the measure of thermodynamically “available” energy as determined through the more discriminating second law of thermodynamics. Experiments were conducted in a water tunnel at Institute of Aerospace Systems at Aachen.

The data were taken three chord lengths downstream in the Trefftz plane of an aspect ratio 5 Clark-Y wing with a square-edged wing tip using particle image velocimetry. Intuitively, the minimum available energy state is expected to correspond to the maximum lift-to-drag ratio angle of attack. …

Development Of A Muscle Model Parameter Calibration Method Via Passive Muscle Force Minimization, Allison Kinney, Benjamin J. Fregly

Mechanical and Aerospace Engineering Faculty Publications

Computational predictions of subject-specific muscle and knee joint contact forces during walking may improve individual rehabilitation treatment design. Such predictions depend directly on specified model parameter values. However, model parameters are difficult to measure non-invasively. Methods for muscle model parameter calibration have been developed previously. However, it is currently unknown how the musculoskeletal system chooses muscle model parameter values. Previous studies have hypothesized that muscles avoid injury during walking by generating little passive force and operating in the ascending region of the force-length curve. This hypothesis suggests that muscle model parameter values may be selected by the body to minimize …

Synergy-Based Two-Level Optimization For Predicting Knee Contact Forces During Walking, Gil Serrancolí, Allison Kinney, Josep M. Font-Llagunes, Benjamin J. Fregly

Mechanical and Aerospace Engineering Faculty Publications

Musculoskeletal models and optimization methods are combined to calculate muscle forces. Some model parameters cannot be experimentally measured due to the invasiveness, such as the muscle moment arms or the muscle and tendon lengths. Moreover, other parameters used in the optimization, such as the muscle synergy components, can be also unknown. The estimation of all these parameters needs to be validated to obtain physiologically consistent results. In this study, a two-step optimization problem was formulated to predict both muscle and knee contact forces of a subject wearing an instrumented knee prosthesis. In the outer level, muscle parameters were calibrated, whereas …

Hydrogen And Syngas Production From Gasification Of Lignocellulosic Biomass In Supercritical Water Media, Jun-Ki Choi, Abtin Ataei, Ahmad Tavasoli, Farid Safari

Mechanical and Aerospace Engineering Faculty Publications

Novel biomass-processing technologies have been recently used for conversion of organic wastes into valuable biofuels like bio-hydrogen. Agricultural wastes are available and renewable energy resources to supply energy demand of the future. The purpose of this study is to investigate the production of hydrogen-rich syngas from wheat straw, walnut shell, and almond shell.

The Influence Of Neuromusculoskeletal Model Calibration Method On Predicted Knee Contact Forces During Walking, Gil Serrancolí, Allison Kinney, Benjamin J. Fregly, Josep M. Font-Llagunes

Mechanical and Aerospace Engineering Faculty Publications

This study explored the influence of three model calibration methods on predicted knee contact and leg muscle forces during walking. Static optimization was used to calculate muscle activations for all three methods. Approach A used muscle-tendon model parameter values (i.e., optimal muscle fiber lengths and tendon slack lengths) taken directly from literature. Approach B used a simple algorithm to calibrate muscle-tendon model parameter values such that each muscle operated within the ascending region of its normalized force-length curve. Approach C used a novel two-level optimization procedure to calibrate muscle-tendon, moment arm, and neural control model parameter values while simultaneously predicting …

A Mechanical Regenerative Brake And Launch Assist Using An Open Differential And Elastic Energy Storage, David H. Myszka, Andrew P. Murray, Kevin Giaier, Vijay Krishna Jayaprakash, Christoph Gillum

Mechanical and Aerospace Engineering Faculty Publications

Regenerative brake and launch assist (RBLA) systems are used to capture kinetic energy while a vehicle decelerates and subsequently use that stored energy to assist propulsion. Commercially available hybrid vehicles use generators, batteries and motors to electrically implement RBLA systems. Substantial increases in vehicle efficiency have been widely cited.

This paper presents the development of a mechanical RBLA that stores energy in an elastic medium. An open differential is coupled with a variable transmission to store and release energy to an axle that principally rotates in a single direction. The concept applies regenerative braking technology to conventional automobiles equipped with …

Simulating Energy Efficient Control Of Multiple-Compressor Compressed Air Systems, Sean Murphy, J. Kelly Kissock

Mechanical and Aerospace Engineering Faculty Publications

In many industrial facilities it is common for more than one air compressor to be operating simultaneously to meet the compressed air demand. The individual compressor set-points and how these compressors interact and respond to the facility demand have a significant impact on the compressed air system total power consumption and efficiency. In the past, compressors were staged by cascading the pressure band of each compressor in the system. Modern automatic sequencers now allow more intelligent and efficient staging of air compressors.

AirSim, a compressed air simulation tool, is now able to simulate multiple-compressor systems with pressure band and automatic …

A Multi-Directional Treadmill Training Program For Improving Gait, Balance, And Mobility In Individuals With Parkinson’S Disease: A Case Series, Kimberly Smith, Kurt Jackson, Kimberly Edginton Bigelow, Lloyd L. Laubach

Mechanical and Aerospace Engineering Faculty Publications

Treadmill training is a commonly used intervention for improving gait in people with Parkinson’s disease (PD). However, little is known about how treadmill training may also influence balance and other aspects of mobility.

The purpose of this case series was to explore the feasibility and possible benefits of multi-directional treadmill training for individuals with PD. Four participants (62.3 ± 6.5 yrs, Hoehn & Yahr 2-4) performed 8 weeks of treadmill training 3 times per week. Weeks 1-4 included forward walking only, while weeks 5-8 included forward and multi-directional walking. Participants were tested every 4 weeks on 4 separate occasions. Outcome …

Leveraging Students’ Passion And Creativity: Ethos At The University Of Dayton, Margaret Pinnell, Malcolm Daniels, Kevin P. Hallinan, Gretchen Berkemeier

Mechanical and Aerospace Engineering Faculty Publications

The Engineers in Technical Humanitarian Opportunities of Service-learning (ETHOS) program was developed in the spring of 2001 by an interdisciplinary group (electrical, chemical, civil and mechanical) of undergraduate engineering students at the University of Dayton (UD). ETHOS was founded on the belief that engineers are more apt and capable to appropriately serve our world if they have an understanding of technology’s global linkage with values, culture, society, politics, and the economy. Since 2001, the ETHOS program at UD has grown and changed.

From conceptualization, to implementation, to maturation and national recognition, the program has addressed challenges of academic acceptance, programmatic …

Deltoid Moment Arms During Abduction: A Subject-Specific Musculoskeletal Modeling Study In Healthy Shoulders And Shoulders With Rtsa, David Walker, Allison Kinney, Aimee Struk, Benjamin J. Fregly, Thomas Wright, Scott Banks

Mechanical and Aerospace Engineering Faculty Publications

Reverse total shoulder arthroplasty (RTSA) is increasingly used in the United States since approval by the FDA in 2003. RTSA relieves pain and restores mobility in arthritic rotator cuff deficient shoulders. Though many advantages of RTSA have been demonstrated, there still are a variety of complications (implant loosening, shoulder impingement, infection, frozen shoulder) making apparent much still is to be learned how RTSA modifies normal shoulder function. The goal of this study was to assess how RTSA affects deltoid muscle moment generating capacity post-surgery using a subjectspecific computational model driven by in vivo kinematic data.