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, …

Experimental And Modelling Of Lightning Damage To Carbon Fibre-Reinforced Composites Under Swept Stroke, Chengzhao Kuang, Kunkun Fu, Juhyeong Lee, Huixin Zhu, Qizhen Shi, Xiaoyu Cui

Mechanical and Aerospace Engineering Faculty Publications

Lightning swept stroke creates multiple lightning attachments along an aircraft in flight. This introduces distinct structural damage compared to that from a single-point lightning current injection test in laboratory. This study presents both experimental and numerical studies on lightning damage in carbon fibre-reinforced polymer (CFRP) composites under swept stroke. Coupled electrical–thermal finite element (FE) models were proposed to predict lightning damage to CFRP composites under single-point current injection and swept stroke, respectively. A lightning swept stroke testing method was proposed by embedding a copper wire inside the composites to simulate multiple lightning attachments on the composites. The FE-predicted damage from …

Dynamics And Scaling Of Particle Streaks In High-Reynolds-Number Turbulent Boundary Layers, Tim Berk, Filippo Coletti

Mechanical and Aerospace Engineering Faculty Publications

Inertial particles in wall-bounded turbulence are known to form streaks, but experimental evidence and predictive understanding of this phenomenon is lacking, especially in regimes relevant to atmospheric flows. We carry out wind tunnel measurements to investigate this process, characterizing the transport of microscopic particles suspended in turbulent boundary layers. The friction Reynolds number Re_𝜏 = O(10⁴) allows for significant scale separation and the emergence of large-scale motions, while the range of viscous Stokes number St⁺ = 18–870 is relevant to the transport of dust and fine sand in the atmospheric surface layer. We …

Towards A Virtual Test Framework To Predict Residual Compressive Strength After Lightning Strikes, Scott L.J. Millen, Xiaodong Xu, Juhyeong Lee, Suparno Mukhopadhyay, Michael R. Wisnom, Adrian Murphy

Mechanical and Aerospace Engineering Faculty Publications

A novel integrated modelling framework is proposed as a set of coupled virtual tests to predict the residual compressive strength of carbon/epoxy composites after a lightning strike. Sequentially-coupled thermal-electric and thermo-mechanical models were combined with Compression After Lightning Strike (CAL) analyses, considering both thermal and mechanical lightning strike damage. The predicted lightning damage was validated using experimental images and X-ray Computed Tomography. Delamination and ply degradation information were mapped to a compression model, with a maximum stress criterion, using python scripts. Experimental data, in which artificial lightning strike and compression testing were performed, was used to assess the predictive capabilities …

On Quantifying Uncertainty In Lightning Strike Damage Of Composite Laminates: A Hybrid Stochastic Framework Of Coupled Transient Thermal-Electrical Simulations, R. S. Chahar, J. Lee, T. Mukhopadhyay

Mechanical and Aerospace Engineering Faculty Publications

Lightning strike damage can severely affect the thermo-mechanical performance of composite laminates. It is essential to quantify the effect of lightning strikes considering the inevitable influence of material and geometric uncertainties for ensuring the operational safety of aircraft. This paper presents an efficient support vector machine (SVM)-based surrogate approach coupled with computationally intensive transient thermal-electrical finite element simulations to quantify the uncertainty in lightning strike damage. The uncertainty in epoxy matrix thermal damage and electrical responses of unprotected carbon/epoxy composite laminates is probabilistically quantified considering the stochasticity in temperature-dependent multi-physical material properties and ply orientations. Further, the SVM models are …

Bending Performance And Failure Mechanisms Of Hybrid And Regular Sandwich Composite Structures With 3d Printed Corrugated Cores, S.Z.H. Shah, Khurram Altaf, Juhyeong Lee, Tahir Sharif, Rizwan Saeed Choudhry, S. M. Hussain

Mechanical and Aerospace Engineering Faculty Publications

The effect of core geometry and hybridization on the bending performance and failure mechanisms of carbon fibre-reinforced polymer (CFRP) and glass fibre-reinforced polymer (GFRP) corrugated sandwich composite structures (SCS) were experimentally investigated using a three-point bend test. The CFRP and GFRP corrugated cores and facesheets were produced using Fused Filament Fabrication (FFF) and vacuum-assisted infusion processes, respectively. Three types of corrugated SCSs were built: SCSs with different core geometries (circular, square, trapezoidal, sinusoidal, and triangular), hybrid SCSs with different CFRP and GFRP cores and facesheets, and fully 3D-printed CFRP and GFRP SCSs. The corrugated SCS with square core geometry outperformed …

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 …

Multiscale Damage Modelling Of Notched And Un-Notched 3d Woven Composites With Randomly Distributed Manufacturing Defects, S.Z.H. Shah, Juhyeong Lee, P.S.M. Megat-Yusoff, Syed Zahid Hussain, T. Sharif, R.S. Choudhry

Mechanical and Aerospace Engineering Faculty Publications

This work proposes a stochastic multiscale computational framework for damage modelling in 3D woven composite laminates, by considering the random distribution of manufacturing-induced imperfections. The proposed method is demonstrated to be accurate, while being simple to implement and requiring modest computational resources. In this approach, a limited number of cross-sectional views obtained from micro-computed tomography (µCT) are used to obtain the stochastic distribution of two key manufacturing-induced defects, namely waviness and voids. This distribution is fed into a multiscale progressive damage model to predict the damage response of three-dimensional (3D) orthogonal woven composites. The accuracy of the proposed model was …

Microscale Modelling Of Lightning Damage In Fibre-Reinforced Composites, Scott L. J. Millen, Juhyeong Lee

Mechanical and Aerospace Engineering Faculty Publications

In this work, three-dimensional (3D) finite element simulations were undertaken to study the effects of lightning strikes on the microscale behaviour of continuous fibre-reinforced composite materials and to predict and understand complex lightning damage mechanisms. This approach is different from the conventional mesoscale or macroscale level of analysis, that predicts the overall lightning damage in composite laminates, thus providing better understanding of lightning-induced thermo-mechanical damage at a fundamental level. Micromechanical representative volume element (RVE) models of a UD composite laminate were created with circular carbon fibres randomly distributed in an epoxy matrix. The effects of various grounding conditions (one-, two-, …

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 …

Developing Test Methods For Compression After Lightning Strikes, Xiaodong Xu, Scott L. J. Millen, Juhyeong Lee, Gasser Abdelal, Daniel Mitchard, Michael R. Wisnom, Adrian Murphy

Mechanical and Aerospace Engineering Faculty Publications

Research into residual strength after lightning strike is increasing within the literature. However, standard test methods for measuring residual compressive strength after lightning strikes do not exist. For the first time, a systematic experimental study is undertaken to evaluate modifications necessary to standard Compression After Impact (CAI) specimen geometry and test jig design to induce specimen failure at the lightning damage region. Four laboratory generated lightning strike currents with peak amplitudes ranging from 25 to 100 kA have been studied. Test set-up modifications were made considering the scale of the lightning damage and its potential proximity to specimen edges. Specimen …

Optimal Spacecraft Guidance, Matthew W. Harris, M. Benjamin Rose

Mechanical and Aerospace Engineering Faculty Publications

This book is designed for a one-semester course at Utah State University titled MAE 6570 Optimal Spacecraft Guidance. The class meets for 75 minutes, twice per week, for 14 weeks. There are no prerequisites other than graduate standing in engineering. Proficiency in calculus, differential equations, linear algebra, and computer programming is required. Students find that previous experience in space dynamics, linear multivariable control, or optimal control is helpful.

The goal of the book and course is for students to develop fundamental skills needed to do professional work in the area of spacecraft guidance. After working through the book, students should …

Design Of Composite Double-Slab Radar Absorbing Structures Using Forward, Inverse, And Tandem Neural Networks, Devin Nielsen, Juhyeong Lee, Young-Woo Nam

Mechanical and Aerospace Engineering Faculty Publications

The survivability and mission of a military aircraft is often designed with minimum radar cross section (RCS) to ensure its long-term operation and maintainability. To reduce aircraft’s RCS, a specially formulated Radar Absorbing Structures (RAS) is primarily applied to its external skins. A Ni-coated glass/epoxy composite is a recent RAS material system designed for decreasing the RCS for the X-band (8.2 – 12.4 GHz), while maintaining efficient and reliable structural performance to function as the skin of an aircraft. Experimentally measured and computationally predicted radar responses (i.e., return loss responses in specific frequency ranges) of multi-layered RASs are expensive and …

Hyper-Velocity Impact Performance Of Foldcore Sandwich Composites, Nathan Hoch, Chase Mortensen, Juhyeong Lee, Khari Harrison, Kalyan Raj Kota, Thomas Lacy

Mechanical and Aerospace Engineering Faculty Publications

A foldcore is a novel core made from a flat sheet of any material folded into a desired pattern. A foldcore sandwich composite (FSC) provides highly tailorable structural performance over conventional sandwich composites made with honeycomb or synthetic polymer foam cores. Foldcore design can be optimized to accommodate complex shapes and unit cell geometries suitable for protective shielding structures

This work aims to characterize hypervelocity impact (> 2000 m/s, HVI) response and corresponding damage morphologies of carbon fiber reinforced polymer (CFRP) FSCs. A series of normal (0° impact angle) and oblique (45° impact angle) HVI (~3km/s nominal projectile velocity) impact …

Predicting Stochastic Lightning Mechanical Damage Effects On Carbon Fiber Reinforced Polymer Matrix Composites, Juhyeong Lee, Syed Zulfiqar Hussain Shah

Mechanical and Aerospace Engineering Faculty Publications

Three stochastic air blast models are developed with spatially varying elastic properties and failure strengths for predicting lightning mechanical damage to AS4/3506 carbon/epoxy composites subjected to < 100 kA peak currents: (1) the conventional weapon effects program (CWP) model, (2) the coupled eulerianlagrangian (CEL) model, and (3) the smoothed-particle hydrodynamics (SPH) model. This work is an extension of our previous studies [1–4] that used deterministic air blast models for lightning mechanical damage prediction. Stochastic variations in composite material properties were generated using the Box-Muller transformation algorithm with the mean (i.e., room temperature experimental data) and their standard deviations (i.e., 10% of the mean herein as reference). The predicted dynamic responses and corresponding damage initiation prediction for composites under equivalent air blast loading were comparable for the deterministic and stochastic models. Overall, the domains with displacement, von-Mises stress, and damage initiation contours predicted in the stochastic models were somewhat sporadic and asymmetric along the fiber’s local orientation and varied intermittently. This suggests the significance of local property variations in lightning mechanical damage prediction. Thus, stochastic air blast models may provide a more accurate lightning mechanical damage approximation than traditional (deterministic) air blast models. All stochastic models proposed in this work demonstrated satisfactory accuracy compared to the baseline models, but required substantial computational time due to the random material model generation/assignment process, which needs to be optimized in future work.

Identifying Fibre Orientations For Fracture Process Zone Characterization In Scaled Centre-Notched Quasi-Isotropic Carbon/Epoxy Laminates With A Convolutional Neural Network, Xiaodong Xu, Aser Abbas, Juhyeong Lee

Mechanical and Aerospace Engineering Faculty Publications

This paper presents a novel X-ray Computed Tomography (CT) image analysis method to characterize the Fracture Process Zone (FPZ) in scaled centre-notched quasi-isotropic carbon/epoxy laminates. A total of 61 CT images of a small specimen were used to fine-tune a pre-trained Convolutional Neural Network (CNN) (i.e., VGG16) to classify fibre orientations. The proposed CNN model achieves a 100% accuracy when tested on the CT images of the same scale as the training set. However, the accuracy drops to a maximum of 84% when tested on unlabelled images of the specimens having larger scales potentially due to their lower resolutions. Another …

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 …

A Review Of Avian-Inspired Morphing For Uav Flight Control, Christina Harvey, Lawren L. Gamble, Christian R. Bolander, Douglas F. Hunsaker, James J. Joo, Daniel J. Inman

Mechanical and Aerospace Engineering Faculty Publications

The impressive maneuverability demonstrated by birds has so far eluded comparably sized uncrewed aerial vehicles (UAVs). Modern studies have shown that birds’ ability to change the shape of their wings and tail in flight, known as morphing, allows birds to actively control their longitudinal and lateral flight characteristics. These advances in our understanding of avian flight paired with advances in UAV manufacturing capabilities and applications has, in part, led to a growing field of researchers studying and developing avian-inspired morphing aircraft. Because avian-inspired morphing bridges at least two distinct fields (biology and engineering), it becomes challenging to compare and contrast …

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 …

Thermal Barrier Coating For Carbon Fiber-Reinforced Composite Materials, Heejin Kim, Jungwon Kim, Juhyeong Lee, Min Wook Lee

Mechanical and Aerospace Engineering Faculty Publications

Carbon fiber-reinforced plastic (CFRP) composites are widely employed in lightweight and high performance applications including supercars, aero-vehicles, and space components. However, although carbon fibers are thermally stable, the low thermal endurance of the matrix materials remains a critical problem in terms of the performance of the material. In this study, we proposed a new, Al₂O₃-based thermal barrier coating (TBC) for the CFRP composites. The TBC comprised α-phase Al₂O₃ particles with a mean diameter of 9.27 μm. The strong adhesion between the TBC and the CFRP substrate was evaluated using a three point bending …

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 …

Modeling And Simulation Of A Supercritical Co2-Liquid Sodium Compact Heat Exchanger For Sodium Fast Reactors, Hailei Wang, Sean M. Kissick

Mechanical and Aerospace Engineering Faculty Publications

The study focuses on modeling and simulations of sodium-sCO2 intermediary compact heat exchangers for sodium-cooled fast reactors (SFR). A simplified 1-D analytical model was developed in companion with a 3-D CFD model. Using classic heat transfer correlations for Nusselt number, some simulation results using the 1-D model have achieved reasonable match with the CFD simulation results for longer channels (i.e., 40 cm and 80 cm). However, for short channel (10 cm) when axial conduction within the sodium fluid is significant, the 1-D model significantly over-predicted the heat transfer effectiveness. By incorporating the temperature-jump model, the 1-D model can extend its …