UMBC Mechanical Engineering Department

Permanent URI for this collectionhttp://hdl.handle.net/11603/52

Mechanical Engineering is one of the oldest and broadest engineering disciplines. In the more traditional sense, Mechanical Engineering employs principles of physics and chemistry and knowledge from the fields of mathematics, mechanics and materials science for the analysis, design, manufacturing and maintenance of mechanical systems. Again, in the traditional sense, a Mechanical Engineer would be the expert in the production and usage of heat and mechanical power which are critical in the design, production and operation of tools, components and machines used in manufacturing durable goods and in using advanced technologies for the benefit of society.

In recent years, the discipline of Mechanical Engineering has taken on an expansive, and critical role in the advancement of transformative new technologies such as the information-, bio-, nano-technologies and MEMS (Micro-Electro-Mechanical Systems). It is clearly a new and exciting world out there. We are delighted to be at the forefront of acquiring new knowledge through cutting edge research in this new and expansive field of Mechanical Engineering. As reflected in their posted profiles and accomplishments, our faculty are recognized nationally and internationally for their research and discoveries. Many of them are Fellows of engineering societies and recipients of prestigious research awards granted for excellence by agencies such as the National Science Foundation and the National Institutes of Health.

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Investigation of Solid Fuel Ramjets Using Analytical Theory and Computational Fluid Dynamics
(2025-01-03) Khokhar, Gohar T.; McBeth, Joshua; Hanquist, Kyle M.; Oveissi, Parham; Goel, Ankit
This paper investigates the modeling and analysis of a solid-fuel ramjet (SFRJ) using analytical theory and computational fluid dynamics (CFD). The primary objectives of this study are to first apply analytical theories of an SFRJ in combination with combustion physics from NASA CEA software to establish a foundation for analytically modeling the thrust of an SFRJ with a specified bypass ratio. Secondly, this study aims to model the thrust output of an SFRJ using a simplified backward-facing step computational model consisting of a single inlet and outlet through computational analysis. In the computational model, combustion is modeled as wall heat addition, and thermal choking effects leading to engine unstart conditions are predicted. While there are more complex SFRJ modeling approaches, consideration of computational cost is an important aspect of this work, since these models will be coupled with a control system. This research serves as a foundational step in a broader investigation aimed at coupling SFRJ thrust dynamics with control systems for regulating thrust under uncertain operating conditions.
Learning-Based Thrust Regulation of Solid-Fuel Ramjet in Flight Conditions
(2025-01-03) Oveissi, Parham; Dorsey, Alex; McBeth, Joshua; Hanquist, Kyle M.; Goel, Ankit
This paper investigates the performance of a learning-based control system for regulating the thrust generated by a solid fuel ramjet engine in realistic flight scenarios. An integrated simulation framework is developed that combines a longitudinal missile dynamics model, a missile autopilot, a quasi-static engine dynamics model, and a learning controller for thrust regulation. The missile autopilot is based on the classical three-loop topology. The learning controller is an adaptive PID controller whose gains are recursively optimized using the retrospective cost adaptive control algorithm. First, harmonic acceleration commands are used to simulate variable flight conditions that affect the thrust generated by the engine model. Next, an interception scenario is simulated by integrating a guidance law in the loop. Numerical results indicate that the learning controller can regulate the generated thrust despite wide variations in operating conditions.
Adaptive Numerical Differentiation for Extremum Seeking with Sensor Noise
(2025-01-08) Verma, Shashank; Salazar, Juan Augusto Paredes; Delgado, Jhon Manuel Portella; Goel, Ankit; Bernstein, Dennis S.
Extremum-seeking control (ESC) is widely used to optimize performance when the system dynamics are uncertain. However, sensitivity to sensor noise is an important issue in ESC implementation due to the use of high-pass filters or gradient estimators. To reduce the sensitivity of ESC to noise, this paper investigates the use of adaptive input and state estimation (AISE) for numerical differentiation. In particular, this paper develops extremum-seeking control with adaptive input and state estimation (ESC/AISE), where the high-pass filter of ESC is replaced by AISE to improve performance under sensor noise. The effectiveness of ESC/AISE is illustrated via numerical examples.
Adaptive Combustion Regulation in High-Fidelity Computational Model of Solid Fuel Ramjet
(2025-01-03) Oveissi, Parham; Dorsey, Alex; Khokhar, Gohar T.; Hanquist, Kyle M.; Goel, Ankit
Controlling the combustion process under hypersonic conditions remains a significant challenge. This paper uses a data-driven, learning-based control technique to regulate the combustion process within a solid fuel ramjet, aiming to regulate the generated thrust under uncertain operating conditions. A high-fidelity computational model combining compressible flow theory with equilibrium chemistry is developed to simulate combustion dynamics. This model evaluates the stability of the combustion dynamics and defines the engine’s operational envelope. An online learning controller based on retrospective cost optimization is integrated with the computational model to regulate the thrust. Numerical simulations indicate that the learning control system can regulate the thrust generated by an SFRJ without requiring any modeling information.
Comparison of Several Neural Network-Enhanced Sub-Grid Scale Stress Models for Meso-Scale Hurricane Boundary Layer Flow Simulation
(AIAA, 2025-01-03) Hasan, MD Badrul; Yu, Meilin; Oates, Tim
The complicated energy cascade and backscatter dynamics present a challenge when studying turbulent flows in storms at the meso-scale. When performing standard large-eddy simulations (LES), sub-grid scale (SGS) stress models usually fail to consider energy backscatter. These models assume that kinetic energy only moves continuously from larger to smaller scales. However, coherent energy backscatter structures exist when analyzing hurricane boundary layer flows at the meso-scale. Our recent research has shown that machine-learning SGS models trained with high-resolution data can effectively forecast forward and backward energy transfers in meso-scale hurricane-like vortex flows. Therein, physical and geometrical invariances were introduced to better represent flow physics. This further improved the predictability and generalizability of machine-learning-enhanced SGS models. In this study, we compare the performance of several machine-learning-enhanced SGS models, especially those based on neural networks (NNs), with varying physical and geometrical invariance embedding levels for SGS stress modeling in an a priori sense, which sets the cornerstone for ongoing a posteriori tests of NN models.
FINITE ELEMENT ANALYSIS OF A THREE-STAGE 3D PRINTED TENSEGRITY ICOSAHEDRON STRUCTURE
(2024-01-01) Bharata, Abhinav; Zhu, Weidong; Mechanical Engineering; Engineering, Mechanical
Tensegrity is a crucial factor in the progress of humanity in various domains, including architecture and biology. This work is centered on the tensegrity structure of an icosahedron configuration, which is also referred to as a diamond tensegrity. This structure consists of six bars in each stage, with four strings linked to each end of the bar, forming a diamond shape. A three-stage tensegrity-icosahedron (T-Icosahedron) structure, composed of 3D printed components, was studied. The entire structure contains a total of 72 strings and 18 bars manufactured from Ninja Flex and Polylactic acid material. A novel vibration-based method was developed to measure tensions in the strings in the structure. A Polytec laser vibrometer was utilized to accurately measure the tensions and calibrate the nodal coordinates of the structure. The study subsequently focuses on a finite element analysis technique to examine the natural frequencies of the structure and generate its mode shapes using MATLAB. The obtained results are then compared with those obtained from the commercial finite element program ANSYS. This work provides a thorough examination of the T-Icosahedron structure, as well as potential opportunities for additional investigation involving alterations in geometry and material composition of the structure.
Development of a Novel General-purpose Three-dimensional Continuously Scanning Laser Doppler Vibrometer System
(2024-01-01) Yuan, Ke; Zhu, Weidong; Mechanical Engineering; Engineering, Mechanical
The objective of this work is to develop a novel general-purpose three-dimensional (3D) continuously scanning laser Doppler vibrometer (CSLDV) system for 3D vibration measurement of structures with arbitrarily curved surfaces. This work is motivated by the fact that 3D full-field vibration measurement is significant to structures, especially those with curved and complex surfaces such as turbine blades, vehicle bodies, and aircraft wings. Modal tests that obtain vibration components along three axes of a coordinate system can provide more information and locate defects on more complex structures than those that only obtain single-axis vibration, and can improve the accuracy of their structural health monitoring. 3D full-field vibration can also be used to identify dynamic characteristics of a complex structure and update its finite element model during structural analysis and product design where vibration must be determined in all its components. A triaxial accelerometer can be attached on a structure to measure its 3D vibrations, which can lead to mass loading, especially when multiple triaxial accelerometers are needed in modal tests of light-weight structures. A 3D scanning laser Doppler vibrometer (SLDV) system can be used to measure 3D vibration of a structure in a non-contact. However, it usually takes the 3D SLDV system a long time to obtain high spatial resolution, especially for structures with large surfaces, because laser spots must stay at one measurement point for enough time before they are moved to the next one to conduct more averages of measurement data when high frequency resolution is needed. The 3D CSLDV system proposed in this work can rapidly obtain 3D full-field vibration shapes, such as mode shapes and operating deflection shapes (ODSs) of a structure, in a non-contact way by sweeping three laser spots over its surface in a continuous and synchronous mode. The universality of the system lies in its capability to measure vibration of structures with various shapes, including flat, curved, and difficult-to-access areas, as well as structures under various excitation, including sinusoidal and random excitation. The major contributions of this work include: 1) developing a calibration method to achieve synchronous scanning of three laser spots, and improving it to fit measurement on curved surfaces and virtual surfaces behind a mirror; and 2) developing a signal processing method to identify modal parameters of structures under random excitation.
Broadband Energy Harvester for Tram Vibration Utilizing a 2-DOF Mass-Spring-Damper System
(2024-01-01) Umar, Hamza; Lee, Soobum; Mechanical Engineering; Engineering, Mechanical
This thesis presents a broadband vibrational energy harvesting device that utilizes a varied frequency from a tram using a 2-degree-of-freedom vibrational system combined with electromagnetic energy conversion. This paper will compare the power output for two design configurations of 2-DOF systems: open-end and closed-end designs. A stepwise optimization process is applied to determine (1) mechanical parameters for frequency tuning to adjust to the trams’ operational conditions, and (2) electromagnetic parameters for the whole system design to maximize power output. The 1st step will determine mechanical design parameters for frequency tuning: masses (mi) and spring constants (ki). The 2nd step will use these parameters as initial guesses and present electrical parameters and damping coefficients (ci) to maximize the power output in the frequency band of interest. The design results indicate that the closed-end design has a preferable performance, compared to the open-end design, in terms of broadband vibration energy harvesting with higher average power as well as manufacturability within design constraints.
Novel Image-based Tracking Continuously Scanning Laser Doppler Vibrometer Systems for Vibration Measurement of Rotating Wind Turbine Blades
(2024-01-01) Lyu, Linfeng; Zhu, Weidong; Mechanical Engineering; Engineering, Mechanical
The objective of the proposed research is to develop efficient vibration monitoring and structural health monitoring (SHM) methods for a horizontal-axis rotating wind turbine blade using a novel image-based tracking continuous scanning laser Doppler vibrometer (CSLDV) and new signal processing methods associated with tracking continuous scanning laser vibrometry. The image-based tracking CSLDV consists of a camera, a scanner with a set of orthogonal mirrors, and a single-point laser head. Currently, there are no suitable non-contact in-situ vibration monitoring and SHM methods for rotating wind turbine blades, and vibration monitoring and SHM of wind turbine blades are mainly accomplished by visual inspection of stationary blades or by using a limited number of embedded sensors. The image-based tracking CSLDV is capable of rapidly obtaining spatially dense vibration shapes (VSs), such as mode shapes and operating deflection shapes (ODSs) of a rotating structure under random excitation, by continuously sweeping its laser spot over the structure surface. VSs measured by the image-based tracking CSLDV can have more measurement points than those by a commercial scanning laser Doppler vibrometer in a point-by-point manner, and the former needs much less measurement time. Different image processing methods are developed for the image-based tracking CSLDV to extract the rotating structure from its background in captured images of the camera. Estimated real-time positions of the rotating structure can be used for estimating its rotation speed. Once the position of the rotating structure is determined in captured images, a scan path can be generated on it and rotation angles of mirrors of the scanner can be controlled so that the laser spot of the image-based tracking CSLDV can be swept along the path. New operational modal analysis methods are developed based on rigorous rotating beam and plate theories, which can estimate modal parameters, such as damped natural frequencies, modal damping ratios, undamped mode shapes, and ODSs, of the rotating structure under random excitation. A novel demodulation method with a reference signal is developed to identify positions of damages in a beam without its base-line information. Prototypes of an image-based short-range tracking CSLDV and an image-based long-range tracking CSLDV are developed for experimental validation of the proposed methods. Rotation speeds and modal parameters of rotating blades with different speeds that are excited by air flow that is considered as random excitation are estimated. Both undamped mode shapes of rotating blades on a straight scan path and their full-field undamped mode shapes are estimated and compared with each other. The proposed methods can address major challenges to monitor the vibration of a rotating horizontal-axis wind turbine blade and detect its potential damage in operational conditions.
High Performance Flexible and Scalable Thermoelectric Device and its Application as a Self-Sufficient Power Supply for Wearable Electronic Devices
(2024-01-01) Huang, Jiyuan; Madan, Deepa; Mechanical Engineering; Engineering, Mechanical
The rise of wearable electronics has emphasized the need for alternative power sources, as traditional batteries have limitations in terms of portability and cost. One promising solution is thermoelectric power generation, which can harvest waste heat or body heat to power these devices using thermoelectric (TE) materials. To address the global demand for low-cost, flexible thermoelectric generators (TEGs), this work describes a novel, energy-efficient TEG fabrication method of controlling the composite microstructure using 4 synergetic factors. The combination of (1) a small amount of chitosan binder (0.05 wt%), (2) heterogeneous (mixed nanoscale and microscale) TE particles, (3) applied mechanical pressure of 20 MPa combined with curing at a low temperature of 120°C for 30 mins and (4) thickness variation (170, 240, 300 ?m), results in an enhanced TE property of TEGs. The combination of these four factors controls the micro and nanostructure of the films to decouple their electrical and thermal conductivity effectively by achieving a improved electrical conductivity and a reduced thermal conductivity. This resulted in figures of merit (ZTs) of 0.89 and 0.5 for p-BST and n-BTS thinner (170 µm) films, respectively, comparable to other additive manufacturing methods despite eliminating the high-temperature, long-duration curing process. The process was also used to fabricate a 6-couple TEG device, which could generate 357.6 µW with a power density of 5.0 mW/cm2 at a ?T of 40 K. The device demonstrated air stability and flexibility for 1000 cycles of bending. Finally, the device was integrated with a voltage step-up converter to power an LED and charge and discharge capacitor at a ?T of 17 K, demonstrating its applicability as a self-sufficient power source. This work also further explored possibilities and approaches to integrate the TEGs to real world applications. Although the TE results are promising, there are still 2 factors to concern, (1) chalcogenides materials like p-BST and n-BTS are toxic and earth-rare, (2) The temperature difference will not be ideal as it in the research labs. Therefore, it is important to find new materials and methods to address these concerns. Tetrahedrites are promising thermoelectric materials in high-temperature applications because they are non-toxic and earth-abundant. MXene (Ti3C2) is a novel 2D material which has ultra-high electrical conductivity. Herein, this work demonstrates the fabrication of scalable and sustainable Cu12Sb4S13 (CAS) based composite films and flexible TEG devices (f-TEGs) with 2D MXene nanosheets using the previously developed energy efficient method for room temperature applications. 2D MXene nanosheets introduced energy-barrier scattering and nanoscale features to effectively increase the room-temperature ZT to 0.22, 10% higher than bulk CAS, by decoupling electrical conductivity, Seebeck coefficient, and thermal conductivity. CAS and 2D MXenes were found to be environmentally safe through a bacterial viability study. The process is used to create a 5-leg f-TEG device producing a power of 5.3 µW and a power density of 140 at a ?T of 25 K. Therefore, this work demonstrates that combining scalable and sustainable materials and methods is an effective strategy for high-performance room-temperature f-TEGs that could potentially harvest the low waste heat energy of the human body.
EVALUATING MICROCRACKS IN ENHANCING NANOFLUID FLOW FROM CAPILLARIES TO THE INTERSTITIAL SPACE IN TUMOR CORE REGIONS – THEORETICAL SIMULATIONS
(2024-01-01) Bireshetty, Veda Venkata Ramana; Zhu, Liang; Mechanical Engineering; Engineering, Mechanical
Limited drug delivery outcomes using systemic delivery are attributed to tumor microenvironment. The objective of this study is to investigate the effect of introduction of microcracks in tumors on tumoral IFP reduction to facilitate therapeutic agent delivery to tumors. We developed a theoretical model for fluid transport in a spherical tumor incorporating Starling’s law and Darcy’s law. Results have shown that introduction of 4 microcracks in tumors is effective to lower the maximal IFP in the tumor from 1941 Pa to 1722.1 Pa, by up to 11.3%. Although the reduction of the average IFP in the entire tumor is limited, it is evident that this approach is effective to decrease the average IFP in the tumor core regions with up to 16.8% reduction from the baseline case without microcrack. We conclude that introducing several microcracks in tumors is an effective method to facilitate transcapillary fluid/drug flow to tumor interstitial fluid space.
Custom multi-component force transducer design using topology optimization
(IOP, 2025-01-07) Kyun Sung, Myung; Lee, Soobum; Burns, Devin Edward; Persia, Jude Thaddeus
This paper proposes an innovative design framework for a multi-component force transducer subject to reversible load direction using topology optimization. Multi-component force transducers are used widely in industries ranging from robotics to healthcare. In this work, the proposed design framework is applied to a specific force transducer, a wind tunnel balance used within aeronautic ground testing. The axial component is one of the six components of the wind tunnel balance, and this component is difficult to design because the axial force is typically much smaller than other force components. This paper uses topology optimization to obtain a non-intuitive axial component design. To realize the design requirements, a new design formulation is suggested to amplify the gauge reading under a small axial loading and to suppress the gauge reading under nonaxial loadings. Prototypes are manufactured and their performances are experimentally verified. The proposed framework can be applied to any type of force transducer that needs to amplify a response from a certain force and to suppress the other responses.
Reversible symmetry breaking of BIC graphene plasmons for tunable mid-infrared absorption
(Optica, 2024-12-02) Guadagnini, Silvia; Ghanekar, Alok; Shrewsbury, Bo; Povinelli, Michelle L.
We use symmetry breaking to create switchable absorption peaks in mid-infrared (MIR) graphene metamaterials. We consider a structure consisting of graphene nanoribbons in contact with a gold grating. The unit cell of the untuned device is symmetric and supports both a bright and dark plasmon; the latter is also known as a BIC (bound state in the continuum) mode. We break symmetry by dynamically tuning the chemical potential of one of two graphene nanoribbons per unit cell. We show numerically that the BIC mode couples to the continuum, turning on an absorption peak. As the tuning increases, the two modes spatially flip and concentrate in a single nanoribbon. By controlling the relative chemical potential of two ribbons, we can control the wavelength of the switchable absorption peak.
RETHINKING DATA SCIENCE PEDAGOGY WITH EMBEDDED ETHICAL CONSIDERATIONS
(INTED, 2022-07) Janeja, Vandana; Sanchez, Maria
The focus of this paper is to present a tool to meet the need of developing ethical critical thinking in data science curricula for undergraduate students. New data science methods impact societies, communities directly or indirectly when dealing with open and other real-world datasets. In particular, for data science there is a need to develop ethical critical thinking while analyzing the data. In the knowledge discovery process there are many opportunities for ethical decision making that a data scientist can evaluate throughout the entire lifecycle of the data to do no harm. To address these concerns within a learning environment focused on skilled workforce development we first introduce a novel ethical data lifecycle framework and then propose a vehicle for implementation through a short term module that can be embedded into a fast paced data science course. The objective of the module is to increase the ethical thinking of students when analyzing data. Pre and post surveys were conducted across each of the two semesters to evaluate students’ attitudes towards ethical thinking. The analysis of the survey results suggests that the objective was achieved based on a positive shift toward agreement with statements related to the importance of ethical thinking.
Full-field Modal Analysis of a Tensegrity Column Using a Three-dimensional Scanning Laser Doppler Vibrometer with a Mirror
(ASME, 2024-11-05) Yuan, Ke; Yuan, Sichen; Zhu, Weidong
Tensegrity structures become important components of various engineering structures due to their high stiffness, light weight, and deployable capability. Existing studies on their dynamic analyses mainly focus on responses of their nodal points while overlook deformations of their cable and strut members. This study proposes a non-contact approach for experimental modal analysis of a tensegrity structure to identify its three-dimensional (3D) natural frequencies and full-field mode shapes, which include modes with deformations of its cable and strut members. A 3D scanning laser Doppler vibrometer is used with a mirror for extending its field of view to measure full-field vibration of a novel three-strut metal tensegrity column with free boundaries. Tensions and axial stiffnesses of its cable members are determined using natural frequencies of their transverse and longitudinal modes, respectively, to build its theoretical model for dynamic analysis and model validation purposes. Modal assurance criterion (MAC) values between experimental and theoretical mode shapes are used to identify their paired modes. Modal parameters of the first 15 elastic modes of the tensegrity column identified from the experiment, including those of the overall structure and its cable members, can be classified into five mode groups depending on their types. Modes paired between experimental and theoretical results have MAC values larger than 78%. Differences between natural frequencies of paired modes of the tensegrity column are less than 15%. The proposed non-contact 3D vibration measurement approach allows accurate estimation of 3D full-field modal parameters of the tensegrity column.
Reinforcement Learning Based Delay Line Design for Crosstalk Minimization
(IEEE, 2024-10-31) Jung, Jaeho; Yu, Younggyun; Lee, Soobum
Reinforcement learning (RL) is one of the artificial intelligence techniques that build an artificial neural network to achieve the most optimum decision. In this study, Deep Q-Network (DQN) RL is applied to the design of delay lines in electrical circuits for signal synchronization. The delay line is usually designed densely in a confined space that results in electrical noise named crosstalk. The challenges of delay line design root from the fact that the line should connect the start and end point using a given length, not to be entangled in a predefined two-dimensional space. The genetic algorithms (GA) or the random exploration method can be used, but their learning efficiency is very low and time-consuming. We propose and implement a novel connected exploration method to significantly expedites the design process. In each state, the direction of the line rendering (left, straight, or right) is considered as an action, and the artificial intelligence agent learns how to design a delay line of the desired length. As a result, we are able to obtain the optimal designs 3,000 times faster than the case of using the GA from our previous study. The proposed method can be applied to various routing design problems such as circuit routing or flow path configuration with seriously reduced design time, and can potentially lead to the discovery of new designs not relied on human intuition.
Performance enhancement of magnetoactive elastomer soft robots through a coupled magnetoelastic topology optimization scheme
(IOP, 2024-11-08) Bergen, Christian Joseph; Ounaies, Zoubeida; von Lockette, Paris
We have found that considering local magnetic fields, large deformations, and magnetoelastic coupling simultaneously significantly affect the resulting shape in magnetoelastic topology optimization in a uniaxial actuator case. In contrast to the work presented here, other works incorporate magnetoelastic formulations that include simplifying assumptions on the local field, and subsequent effects on the magnetization response of the material, or the absence of large deformation mechanics, or both. These assumptions were shown to produce solutions that differ substantively from cases where local fields and large deformations are addressed concurrently. Magnetoelastic topology optimization schemes are needed to optimize magnetoactive elastomer (MAE) devices. MAE devices are magnetic particle-filled polymer matrices designed for specific actuations and controlled remotely by an external magnetic field. They garner considerable research interest as an emerging technology for actuators in soft robots or in applications requiring untethered actuation. The material properties of MAEs are dependent on the volume fraction of particles in the elastomer matrix, where a high-volume fraction increases relative permeability (for soft magnetic particles) but also increases elastic modulus. For optimal actuation, a tradeoff between low stiffness and high magnetic response must be made by adjusting volume fraction and controlling material placement. Using a topology optimization scheme that considers both the magnetic and mechanical properties of the material, the shape and material composition of the device can be tuned to best achieve the desired actuation displacement. In this work, a density-based magnetoelastic multimaterial topology optimization scheme for soft magnetic material is developed in COMSOL Multiphysics. The optimization scheme uses multiphysics coupling that considers local magnetic fields and large deformations at each iteration through a Maxwell stress tensor formulation. A simulated example is then considered to demonstrate the effectiveness and necessity of a coupled optimization. The effect of considering large deformations during optimization is also investigated. It was found that a coupled topology optimization scheme with large deformations produced shapes with modes of actuation not captured by schemes with simplifying assumptions, leading to better performance at lower material cost. Considering large deformations in the coupled scheme offered significantly better performance, with an increase of 81.3% in a side-by-side performance simulation when compared to uncoupled cases.
RETHINKING DATA SCIENCE PEDAGOGY WITH EMBEDDED ETHICAL CONSIDERATIONS
(IATED, 2022) Janeja, Vandana; Sanchez, Maria
The focus of this paper is to present a tool to meet the need of developing ethical critical thinking in data science curricula for undergraduate students. New data science methods impact societies, communities directly or indirectly when dealing with open and other real-world datasets. In particular, for data science there is a need to develop ethical critical thinking while analyzing the data. In the knowledge discovery process there are many opportunities for ethical decision making that a data scientist can evaluate throughout the entire lifecycle of the data to do no harm. To address these concerns within a learning environment focused on skilled workforce development we first introduce a novel ethical data lifecycle framework and then propose a vehicle for implementation through a short term module that can be embedded into a fast paced data science course. The objective of the module is to increase the ethical thinking of students when analyzing data. Pre and post surveys were conducted across each of the two semesters to evaluate students’ attitudes towards ethical thinking. The analysis of the survey results suggests that the objective was achieved based on a positive shift toward agreement with statements related to the importance of ethical thinking.
MPC-guided, Data-driven Fuzzy Controller Synthesis
(2024-10-09) Salazar, Juan Augusto Paredes; Goel, Ankit
Model predictive control (MPC) is a powerful control technique for online optimization using system model-based predictions over a finite time horizon. However, the computational cost MPC requires can be prohibitive in resource-constrained computer systems. This paper presents a fuzzy controller synthesis framework guided by MPC. In the proposed framework, training data is obtained from MPC closed-loop simulations and is used to optimize a low computational complexity controller to emulate the response of MPC. In particular, autoregressive moving average (ARMA) controllers are trained using data obtained from MPC closed-loop simulations, such that each ARMA controller emulates the response of the MPC controller under particular desired conditions. Using a Takagi-Sugeno (T-S) fuzzy system, the responses of all the trained ARMA controllers are then weighted depending on the measured system conditions, resulting in the Fuzzy-Autoregressive Moving Average (F-ARMA) controller. The effectiveness of the trained F-ARMA controllers is illustrated via numerical examples.
Numerical Study of Heat Transfer Enhancement Using Nano-Encapsulated Phase Change (NPC) Slurries in Wavy Microchannels
(MDPI, 2024-10-09) Zaw, Myo Min; Zhu, Liang; Ma, Ronghui
Researchers have attempted to improve heat transfer in mini/microchannel heat sinks by dispersing nano-encapsulated phase change (NPC) materials in base coolants. While NPC slurries have demonstrated improved heat transfer performance, their applications are limited by decreasing enhancement at increased flow rates. To address this challenge, the present study numerically investigates the effects of wavy channels on the performance of NPC slurries. Simulation results reveal that a wavy channel induces Dean vortices that intensify the mixing of the working fluid and enlarge the melting fractions of the NPC material, thus offering a significantly higher heat transfer efficiency than a straight channel. Moreover, heat transfer enhancement by NPC slurries varies with the imposed heat flux and flow rate. Interestingly, the maximum heat transfer enhancement obtained with the wavy channel not only exceeds the straight one, but also occurs at a higher heat flux and faster flow rate. This finding demonstrates the advantage of wavy channels in management of intensive heat fluxes with NPC slurries. The study also investigates wavy channels with varying amplitude and wavelength. Increasing the wave aspect ratio from 0.2 to 0.588 strengthens Dean vortices and consequently increases the Nusselt number, optimal heat flux, and overall thermal performance factor.

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