Mechanical Engineering Research Works

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2021 Annual Report - Center for Engineering Concepts Development
(2021-10-01) Anand, Davinder; Hazelwood, Dylan
CECD is twenty-one years old and continues to be a platform for experimenting with new ideas in engineering research and education with special attention to the impact of engineering on society. I’m pleased to report that we continue to be supported by ARL, NSWC-IHEODTD, the State of Maryland, and the Neilom Foundation. One hundred guests helped us celebrate our twenty years of activities highlighting innovative activities of contemporary interest that benefit the economic welfare of the State of Maryland and the Nation. This report provides a brief overview of those accomplishments as well as ongoing activities that bring great credit to our faculty and students that comprise CECD.
3D-Printed Microinjection Needle Arrays via a Hybrid DLP-Direct Laser Writing Strategy
(Wiley, 2023-02-05) Sarker, Sunandita; Colton, Adira; Wen, Ziteng; Xu, Xin; Erdi, Metecan; Jones, Anthony; Kofinas, Peter; Tubaldi, Eleonora; Walczak, Piotr; Janowski, Miroslaw; Liang, Yajie; Sochol, Ryan D.
Microinjection protocols are ubiquitous throughout biomedical fields, with hollow microneedle arrays (MNAs) offering distinctive benefits in both research and clinical settings. Unfortunately, manufacturing-associated barriers remain a critical impediment to emerging applications that demand high-density arrays of hollow, high-aspect-ratio microneedles. To address such challenges, here, a hybrid additive manufacturing approach that combines digital light processing (DLP) 3D printing with “ex situ direct laser writing (esDLW)” is presented to enable new classes of MNAs for fluidic microinjections. Experimental results for esDLW-based 3D printing of arrays of high-aspect-ratio microneedles—with 30 µm inner diameters, 50 µm outer diameters, and 550 µm heights, and arrayed with 100 µm needle-to-needle spacing—directly onto DLP-printed capillaries reveal uncompromised fluidic integrity at the MNA-capillary interface during microfluidic cyclic burst-pressure testing for input pressures in excess of 250 kPa (n = 100 cycles). Ex vivo experiments perform using excised mouse brains reveal that the MNAs not only physically withstand penetration into and retraction from brain tissue but also yield effective and distributed microinjection of surrogate fluids and nanoparticle suspensions directly into the brains. In combination, the results suggest that the presented strategy for fabricating high-aspect-ratio, high-density, hollow MNAs could hold unique promise for biomedical microinjection applications.
A 1D Reduced-Order Model (ROM) for a Novel Latent Thermal Energy Storage System
(MDPI, 2022-07-14) Kailkhura, Gargi; Mandel, Raphael Kahat; Shooshtari, Amir; Ohadi, Michael
Phase change material (PCM)-based thermal energy storage (TES) systems are widely used for repeated intermittent heating and cooling applications. However, such systems typically face some challenges due to the low thermal conductivity and expensive encapsulation process of PCMs. The present study overcomes these challenges by proposing a lightweight, low-cost, and low thermal resistance TES system that realizes a fluid-to-PCM additively manufactured metal-polymer composite heat exchanger (HX), based on our previously developed cross-media approach. A robust and simplified, analytical-based, 1D reduced-order model (ROM) was developed to compute the TES system performance, saving computational time compared to modeling the entire TES system using PCM-related transient CFD modeling. The TES model was reduced to a segment-level model comprising a single PCM-wire cylindrical domain based on the tube-bank geometry formed by the metal fin-wires. A detailed study on the geometric behavior of the cylindrical domain and the effect of overlapped areas, where the overlapped areas represent a deviation from 1D assumption on the TES performance, was conducted. An optimum geometric range of wire-spacings and size was identified. The 1D ROM assumes 1D radial conduction inside the PCM and analytically computes latent energy stored in the single PCM-wire cylindrical domain using thermal resistance and energy conservation principles. The latent energy is then time-integrated for the entire TES, making the 1D ROM computationally efficient. The 1D ROM neglects sensible thermal capacity and is thus applicable for the low Stefan number applications in the present study. The performance parameters of the 1D ROM were then validated with a 2D axisymmetric model, typically used in the literature, using commercially available CFD tools. For validation, a parametric study of a wide range of non-dimensionalized parameters, depending on applications ranging from pulsed-power cooling to peak-load shifting for building cooling application, is included in this paper. The 1D ROM appears to correlate well with the 2D axisymmetric model to within 10%, except at some extreme ranges of a few of the non-dimensional parameters, which lead to the condition of axial conduction inside the PCM, deviating from the 1D ROM.
A Deep Adversarial Approach Based on Multi-Sensor Fusion for Semi-Supervised Remaining Useful Life Prognostics
(MDPI, 2019-12-27) Verstraete, David; Droguett, Enrique; Modarres, Mohammad
Multi-sensor systems are proliferating in the asset management industry. Industry 4.0, combined with the Internet of Things (IoT), has ushered in the requirements of prognostics and health management systems to predict the system’s reliability and assess maintenance decisions. State of the art systems now generate big machinery data and require multi-sensor fusion for integrated remaining useful life prognostic capabilities. When dealing with these data sets, traditional prediction methods are not equipped to handle the multiple sensor signals in unison. To address this challenge, this paper proposes a new, deep, adversarial approach to any remaining useful life prediction in which a novel, non-Markovian, variational, inference-based model, incorporating an adversarial methodology, is derived. To evaluate the proposed approach, two public multi-sensor data sets are used for the remaining useful life prediction applications: (1) CMAPSS turbofan engine dataset, and (2) FEMTO Pronostia rolling element bearing data set. The proposed approach obtains favorable results when against similar deep learning models.
A Thermodynamic Entropy Approach to Reliability Assessment with Applications to Corrosion Fatigue
(MDPI, 2015-10-16) Imanian, Anahita; Modarres, Mohammad
This paper outlines a science-based explanation of damage and reliability of critical components and structures within the second law of thermodynamics. The approach relies on the fundamentals of irreversible thermodynamics, specifically the concept of entropy generation as an index of degradation and damage in materials. All damage mechanisms share a common feature, namely energy dissipation. Dissipation, a fundamental measure for irreversibility in a thermodynamic treatment of non-equilibrium processes, is quantified by entropy generation. An entropic-based damage approach to reliability and integrity characterization is presented and supported by experimental validation. Using this theorem, which relates entropy generation to dissipative phenomena, the corrosion fatigue entropy generation function is derived, evaluated, and employed for structural integrity and reliability assessment of aluminum 7075-T651 specimens.
A Unique Failure Mechanism in the Nexus 6P Lithium-Ion Battery
(MDPI, 2018-04-04) Saxena, Saurabh; Xing, Yinjiao; Pecht, Michael
Nexus 6P smartphones have been beset by battery drain issues, which have been causing premature shutdown of the phone even when the charge indicator displays a significant remaining runtime. To investigate the premature battery drain issue, two Nexus 6P smartphones (one new and one used) were disassembled and their batteries were evaluated using computerized tomography (CT) scan analysis, electrical performance (capacity, resistance, and impedance) tests, and cycle life capacity fade tests. The “used” smartphone battery delivered only 20% of the rated capacity when tested in a first capacity cycle and then 15% of the rated capacity in a second cycle. The new smartphone battery exceeded the rated capacity when first taken out of the box, but exhibited an accelerated capacity fade under C/2 rate cycling and decreased to 10% of its initial capacity in just 50 cycles. The CT scan results revealed the presence of contaminant materials inside the used battery, raising questions about the quality of the manufacturing process.
Algorithm to Determine the Knee Point on Capacity Fade Curves of Lithium-Ion Cells
(MDPI, 2019-07-29) Diao, Weiping; Saxena, Saurabh; Han, Bongtae; Pecht, Michael
Lithium-ion batteries typically exhibit a transition to a more rapid capacity fade trend when subjected to extended charge–discharge cycles and storage conditions. The identification of the knee point can be valuable to identify the more severe degradation trend, and to provide guidance when scheduling battery replacements and planning secondary uses of the battery. However, a concise and repeatable determination of a knee point has not been documented. This paper provides a definition of the knee point which can be used as a degradation metric, and develops an algorithm to identify it. The algorithm is implemented on various data cases, and the results indicate that the approach provides repeatable knee point identification.
An Energy Consumption Intensity Ranking System for Rapid Energy Efficiency Evaluation of a Cluster of Commercial Buildings
(ASHRAE Transactions, 2023-06) Aditya Ramnarayan, Andres Sarmiento, Armin Gerami, Michael Ohadi; Professor Michael Ohadi
Buildings in the U.S. account for roughly 74% of electricity usage and about 40% of all primary energy use associated with greenhouse gas (GHG) emissions. The Nationally Determined Contribution (NDC) for the U.S., as determined in the Paris Agreement, sets a goal of reducing GHG emissions by ~50% compared to 2005 levels by 2030 while working towards achieving net-zero emissions by 2050. To meet these carbon reduction targets, the U.S. must substantially reduce of energy consumption and improve buildings' energy efficiency. To this end, this study introduces an energy consumption ranking tool that can be used to analyze the energy consumption profile of a cluster of buildings/campuses and provide an efficient tool to measure, monitor, and reduce end-use energy and CO2 emissions. The tool bases its rankings on a standard benchmark or a targeted energy efficiency goal. The tool generates a band of ranking, from the best to the worst energy efficiency performance, which directs the attention of building designers, operators, and government regulation/enforcement agencies to buildings having subpar energy efficiency performance. The proposed methodology is extrapolated to encompass a broad range of energy and CO2 consumption metrics in various building types and climate zones, thus having local, regional, and international applications. Using end-use energy utility data from the relevant database for the selected cluster of buildings and campuses, a total square footage area of ~26 million square feet of buildings and campuses was taken as the sample set for performing virtual audits using the custom-developed software. Using dynamic scatter plots and several ranking metrics, buildings with an underwhelming energy performance are identified for detailed energy audits. Once the outliers are spotted, energy modeling is performed to identify and delineate the root cause for the high energy use pattern for the facility. A breakdown of utilities and their corresponding energy analytics are visualized, thus highlighting the range of energy efficiency improvements and the potential for electrification. For the case example studied, the virtual audits are projected to result in minimum annual energy savings of 1,280,461 MMBtu and a corresponding minimum annual GHG reduction of 91,309 metric tons of CO2.
An Entropy-Based Damage Characterization
(MDPI, 2014-12-05) Amiri, Mehdi; Modarres, Mohammad
This paper presents a scientific basis for the description of the causes of damage within an irreversible thermodynamic framework and the effects of damage as observable variables that signify degradation of structural integrity. The approach relies on the fundamentals of irreversible thermodynamics and specifically the notion of entropy generation as a measure of degradation and damage. We first review the state-of-the-art advances in entropic treatment of damage followed by a discussion on generalization of the entropic concept to damage characterization that may offers a better definition of damage metric commonly used for structural integrity assessment. In general, this approach provides the opportunity to described reliability and risk of structures in terms of fundamental science concepts. Over the years, many studies have focused on materials damage assessment by determining physics-based cause and affect relationships, the goal of this paper is to put this work in perspective and encourage future work of materials damage based on the entropy concept.
Analysis of Manufacturing-Induced Defects and Structural Deformations in Lithium-Ion Batteries Using Computed Tomography
(MDPI, 2018-04-13) Wu, Yi; Saxena, Saurabh; Xing, Yinjiao; Wang, Youren; Li, Chuan; Yung, Winco K. C.; Pecht, Michael
Premature battery drain, swelling and fires/explosions in lithium-ion batteries have caused wide-scale customer concerns, product recalls, and huge financial losses in a wide range of products including smartphones, laptops, e-cigarettes, hoverboards, cars, and commercial aircraft. Most of these problems are caused by defects which are difficult to detect using conventional nondestructive electrical methods and disassembly-based destructive analysis. This paper develops an effective computed tomography (CT)-based nondestructive approach to assess battery quality and identify manufacturing-induced defects and structural deformations in batteries. Several unique case studies from commercial e-cigarette and smartphone applications are presented to show where CT analysis methods work.
Applying Six Sigma Quality Methods to Improve Customer Service Satisfaction
(2017-05-14) Carr, Charles
A panel of thought leading global experts on manufacturing was convened by the National Academy of Engineering in 1992, and among other major findings in their report “Manufacturing Systems” [1] that panel shared their curiosity about there being “Laws of Manufacturing” and how, if found, would extend industrial engineering methods of that time. Since that time, acceptance of lean six sigma thinking and culture has profoundly and irreversibly changed our management methods. Likewise in that time frame the short comings of MRP systems of the long past, have evolved to ERP systems of today that are viewed at a minimum as beneficial to the end user. In the net-centric, ERP driven supply chains of today, we find the loudest “voice of the customer” in their sales data, and it needs to be given considerable weight in our planning. Presented here is the generalization of “Little’s Law” to a much broader utilization in operations management. That generalization is facilitated by six sigma statistical methods applied to data typically available in any contemporary ERP system, to innovate and improve operational planning to drive gains to greater customer satisfaction with the “When” of their orders.
Artificial Intelligence-Related Research Funding by the U.S. National Science Foundation and the National Natural Science Foundation of China
(IEEE, 2020-10-06) Abadi, Hamidreza Habibollahi Najaf; He, Zhou; Pecht, Michael
For the United States and China, artificial intelligence (AI) algorithms, methods, and applications are considered key to a nation's economic competitiveness and security. This paper investigates funding by the U.S. National Science Foundation and National Natural Science Foundation of China from 2010 to 2019, including the key institutions and universities that received AI awards, and the key AI disciplines and applications of focus in the research. Comparisons between the U.S. National Science Foundation and the National Natural Science Foundation of China, including the number of published papers as a result of the awards, are also presented.
Battery Stress Factor Ranking for Accelerated Degradation Test Planning Using Machine Learning
(MDPI, 2021-01-30) Saxena, Saurabh; Roman, Darius; Robu, Valentin; Flynn, David; Pecht, Michael
Lithium-ion batteries power numerous systems from consumer electronics to electric vehicles, and thus undergo qualification testing for degradation assessment prior to deployment. Qualification testing involves repeated charge–discharge operation of the batteries, which can take more than three months if subjected to 500 cycles at a C-rate of 0.5C. Accelerated degradation testing can be used to reduce extensive test time, but its application requires a careful selection of stress factors. To address this challenge, this study identifies and ranks stress factors in terms of their effects on battery degradation (capacity fade) using half-fractional design of experiments and machine learning. Two case studies are presented involving 96 lithium-ion batteries from two different manufacturers, tested under five different stress factors. Results show that neither the individual (main) effects nor the two-way interaction effects of charge C-rate and depth of discharge rank in the top three significant stress factors for the capacity fade in lithium-ion batteries, while temperature in the form of either individual or interaction effect provides the maximum acceleration.
Big Machinery Data Preprocessing Methodology for Data-Driven Models in Prognostics and Health Management
(MDPI, 2021-10-14) Cofre-Martel, Sergio; Lopez Droguett, Enrique; Modarres, Mohammad
Sensor monitoring networks and advances in big data analytics have guided the reliability engineering landscape to a new era of big machinery data. Low-cost sensors, along with the evolution of the internet of things and industry 4.0, have resulted in rich databases that can be analyzed through prognostics and health management (PHM) frameworks. Several data-driven models (DDMs) have been proposed and applied for diagnostics and prognostics purposes in complex systems. However, many of these models are developed using simulated or experimental data sets, and there is still a knowledge gap for applications in real operating systems. Furthermore, little attention has been given to the required data preprocessing steps compared to the training processes of these DDMs. Up to date, research works do not follow a formal and consistent data preprocessing guideline for PHM applications. This paper presents a comprehensive step-by-step pipeline for the preprocessing of monitoring data from complex systems aimed for DDMs. The importance of expert knowledge is discussed in the context of data selection and label generation. Two case studies are presented for validation, with the end goal of creating clean data sets with healthy and unhealthy labels that are then used to train machinery health state classifiers.
Can You Do That Again? Time Series Consolidation as a Robust Method of Tailoring Gesture Recognition to Individual Users
(MDPI, 2022-10-03) Dankovich, Louis J. IV; Vaughn-Cooke, Monifa; Bergbreiter, Sarah
Robust inter-session modeling of gestures is still an open learning challenge. A sleeve equipped with capacitive strap sensors was used to capture two gesture data sets from a convenience sample of eight subjects. Two pipelines were explored. In FILT a novel two-stage algorithm was introduced which uses an unsupervised learning algorithm to find samples representing gesture transitions and discards them prior to training and validating conventional models. In TSC a confusion matrix was used to automatically consolidate commonly confused class labels, resulting in a set of gestures tailored to an individual subject’s abilities. The inter-session testing accuracy using the Time Series Consolidation (TSC) method increased from a baseline inter-session average of 42.47 ± 3.83% to 93.02% ± 4.97% while retaining an average of 5.29 ± 0.46 out of the 11 possible gesture categories. These pipelines used classic machine learning algorithms which require relatively small amounts of data and computational power compared to deep learning solutions. These methods may also offer more flexibility in interface design for users suffering from handicaps limiting their manual dexterity or ability to reliably make gestures, and be possible to implement on edge devices with low computational power.
Causal Pathways Leading to Human Failure Events in Information-Gathering System Response Activities
(2023-07) Levine, Camille S; Al-Douri, Ahmad; Groth, Katrina M; Groth, Katrina M
Human Failure Events (HFEs) are complex, multi layer events culminating with a human machine team’s failure to complete a plant objective. HFEs can be further described by Crew Failure Modes CFMs which document specific ways the objective tasks may be un successfully performed. In turn, these CFMs are affected by Performance Influencing Factors (PIFs ), some of which exert a more direct influence than others. However, in current Human Reliability Analysis HRA methods, the multitude s of causal relationshi ps between PIFs, CFMs, and HFEs are not explicitly modeled. This work seeks to fill that gap by developing structured causal models that document direct and indirect pathways from PIFs, through CFMs, and into HFEs. This work is intended to expand the curre nt application of causal based HRA modeling beyond control room environments to external environments under natural hazard scenarios. A Bayesian network of information gathering operator activities in response to a system demand is developed by following the causal mapping methodology defined in Zwirglmaier et al. 2017 )). The relationships in this structure are substantiated with existing psychological and organizational literature, thereby allowing for the identification of the main causal pathways leadin g to a particular CFM, and therefore an HFE. The work draws upon proximate causes of failure from the NRC’s NUREG 2114 , CFMs in the Phoenix HRA method, and PIFs from Groth’s 2012 hierarchy. Capturing these causal pathways provides the foundation for an imp roved causal basis of HRA, which represents a promising strategy for enhancing the accuracy and technical basis of HRA. Future efforts will include validation of the structures, constructing similar models for decision
Center for Engineering Concepts Development (CECD) 20th Anniversary Celebration and Middleton Luncheon
(2019-04-17) Anand, Davinder; Hazelwood, Dylan
The Center for Engineering Concepts Development (CECD) celebrated its distinguished history and two decades of accomplishments with the 20th Anniversary and Middleton Luncheon on April 17, 2019. The event opened with welcoming remarks from CECD director, Dr. Davinder Anand, and hosted guest speakers U.S. Senator Chris Van Hollen (D-Md.) and Maryland State Senator Thomas “Mac” Middleton (D-Charles Co.), who were early supporters of the research center. Included are remarks, presentation slides, program and CECD overview, and pictures from the April 17th 2019 CECD 20th Anniversary Celebration and Middleton Luncheon.
Characterization and control of plastic deformation in premolded components in in-mold assembled mesoscale revolute joints using bi-directional filling strategy
(2008-12) Ananthanarayanan, Arvind; Gupta, Satyandra K.; Bruck, Hugh
Characterization of aerosol plumes from singing and playing wind instruments associated with the risk of airborne virus transmission
(Wiley, 2022-06-13) Wang, Lingzhe; Lin, Tong; Da Costa, Hevander; Zhu, Shengwei; Stockman, Tehya; Kumar, Abhishek; Weaver, James; Spede, Mark; Milton, Donald K.; Hertzberg, Jean; Toohey, Darin W.; Vance, Marina E.; Miller, Shelly L.; Srebric, Jelena
The exhalation of aerosols during musical performances or rehearsals posed a risk of airborne virus transmission in the COVID-19 pandemic. Previous research studied aerosol plumes by only focusing on one risk factor, either the source strength or convective transport capability. Furthermore, the source strength was characterized by the aerosol concentration and ignored the airflow rate needed for risk analysis in actual musical performances. This study characterizes aerosol plumes that account for both the source strength and convective transport capability by conducting experiments with 18 human subjects. The source strength was characterized by the source aerosol emission rate, defined as the source aerosol concentration multiplied by the source airflow rate (brass 383 particle/s, singing 408 particle/s, and woodwind 480 particle/s). The convective transport capability was characterized by the plume influence distance, defined as the sum of the horizontal jet length and horizontal instrument length (brass 0.6 m, singing 0.6 m and woodwind 0.8 m). Results indicate that woodwind instruments produced the highest risk with approximately 20% higher source aerosol emission rates and 30% higher plume influence distances compared with the average of the same risk indicators for singing and brass instruments. Interestingly, the clarinet performance produced moderate source aerosol concentrations at the instrument’s bell, but had the highest source aerosol emission rates due to high source airflow rates. Flute performance generated plumes with the lowest source aerosol emission rates but the highest plume influence distances due to the highest source airflow rate. Notably, these comprehensive results show that the source airflow is a critical component of the risk of airborne disease transmission. The effectiveness of masking and bell covering in reducing aerosol transmission is due to the mitigation of both source aerosol concentrations and plume influence distances. This study also found a musician who generated approximately five times more source aerosol concentrations than those of the other musicians who played the same instrument. Despite voice and brass instruments producing measurably lower average risk, it is possible to have an individual musician produce aerosol plumes with high source strength, resulting in enhanced transmission risk; however, our sample size was too small to make generalizable conclusions regarding the broad musician population.
Characterizing and modeling the enhancement of lift and payload capacity resulting from thrust augmentation in a propeller-assisted flapping wing air vehicle
(SAGE Publications, Inc., 2017-08-16) Holness, Alex E; Bruck, Hugh A; Gupta, Satyandra K
Biologically-inspired flapping wing flight is attractive at low Reynolds numbers and at high angles of attack, where fixed wing flight performance declines precipitously. While the merits of flapping propulsion have been intensely investigated, enhancing flapping propulsion has proven challenging because of hardware constraints and the complexity of the design space. For example, increasing the size of wings generates aerodynamic forces that exceed the limits of actuators used to drive the wings, reducing flapping amplitude at higher frequencies and causing thrust to taper off. Therefore, augmentation of aerodynamic force production from alternative propulsion modes can potentially enhance biologicallyinspired flight. In this paper, we explore the use of auxiliary propellers on Robo Raven, an existing flapping wing air vehicle (FWAV), to augment thrust without altering wing design or flapping mechanics. Designing such a platform poses two major challenges. First, potential for negative interaction between the flapping and propeller airflow reducing thrust generation. Second, adding propellers to an existing platform increases platform weight and requires additional power from heavier energy sources for comparable flight time. In this paper, three major findings are reported addressing these challenges. First, locating the propellers behind the flapping wings (i.e. in the wake) exhibits minimal coupling without positional sensitivity for the propeller placement at or below the platform centerline. Second, the additional thrust generated by the platform does increase aerodynamic lift. Third, the increase in aerodynamic lift offsets the higher weight of the platform, significantly improving payload capacity. The effect of varying operational payload and flight time for different mixed mode operating conditions was predicted, and the trade-off between the operational payload and operating conditions for mixed mode propulsion was characterized. Flight tests revealed the improved agility of the platform when used with static placement of the wings for various aerobatic maneuvers, such as gliding, diving, or loops.