bluuebunny/crossref_metadata_2025_split huggingface dataset:
A Comparison of Probabilistic-Monte Carlo and Fuzzy-Monte Carlo Simulation Approaches for Dynamic Fault Tree: Case of Renewable Power Systems
Authors: Asghari Jafar , Pourgol Mohammad Mohammad 
Published: February 2021

Abstract
Conventional Fault Tree Analysis (FTA) suffers from a variety of shortcomings which must be considered in critical analysis. The main disadvantage of FTA is its inability to model the dynamic failure behaviors of the components. Accordingly, Dynamic Fault Tree Analysis (DFTA) has been proposed to overcome such limitation. In this study, first, Monte Carlo simulation (MSC) approach is used to handle internal calculations of dynamic gates. The second main weakness of common FTA is about handling the different types of uncertainties. Hence, in the second step of this study, a combined of fuzzy numbers and MCS has been proposed to overcome the limitation of FTA in dealing with uncertainties. The main purpose of this paper is a comparative study on differences between MCS and Fuzzy-Monte Carlo simulation (FMCS) approaches for solving DFTA of a typical mechanical system. From available literatures, our proposed approaches have been demonstrated on different renewable energy systems, as case studies, and results are discussed. A comparison between MCS and FMCS shows that the results of FMCS method are reasonable and more realistic. Finally, in the last section, conclusions and some of the future wok are proposed.
Crashworthiness Optimization Based on the Probability of Traumatic Brain Injury Accounting for Simulation Noise and Impact Conditions
Authors: Ahmadisoleymani Seyed Saeed , Missoum Samy 
Published: February 2021

Abstract
Finite element-based crashworthiness optimization is nowadays extensively used to improve the safety of vehicles. However, the responses of a crash simulation are notoriously noisy. In addition, the actual or simulated responses during a crash can be highly sensitive to uncertainties. These uncertainties appear in various forms such as uncontrollable random parameters (e.g., impact conditions).
To address these challenges, an optimization algorithm based on a Stochastic Kriging (SK) and an Augmented Expected Improvement (AEI) infill criterion is proposed. A SK enables the approximation of a response while accounting for the noise-induced aleatory variance. In addition, SK has the advantage of reducing the dimensionality of the problem by implicitly accounting for the influence of random parameters and their contribution to the overall aleatory variance. In the proposed algorithm, the aleatory variance is initially estimated through direct sampling and subsequently approximated by a regression kriging. This aleatory variance approximation, which is refined adaptively, is used for the computation of the infill criterion and probabilistic constraints.
The algorithm is implemented on a crashworthiness optimization problem that involves a sled and dummy models subjected to an acceleration pulse. The sled model includes components of a vehicle occupant restraint system such as an airbag, seatbelt, and steering column. In all problems considered, the objective function is the probability of traumatic brain injury, which is computed through the Brain Injury Criterion (BrIC) and a logistic injury risk model. In some cases, probabilistic constraints corresponding to other types of bodily injuries such as thoracic injury are added to the optimization problem. The design variables correspond to the properties of the occupant restraint system (e.g., loading curve that dictates the airbag vent area versus pressure). In addition to the inherent simulation noise, uncertainties in the loading conditions are introduced in the form of a random scaling factor of the acceleration pulse.
Developing an Efficient Approach for Unmanned Aerial Vehicle Reliability Analysis
Authors: Khayyati Ahmad , Pourgol-Mohammad Mohammad 
Published: February 2021

Abstract
Unmanned Aerial Vehicles (UAV) are increasingly get popularity in many applications. Their operation requires high level of safety and reliability to accomplish successful missions. In this study, the reliability was comparatively analyzed by different available approaches to select the efficient method. First, failure model of the system is developed. Then, three different scenarios are considered to study the effect of redundancies on the system reliability results. In the first scenario, there is no redundancy where in the second scenario there is only one redundant component and in the third scenario, there are three redundant components. Static reliability analysis such as Fault Tree Analysis (FTA), Reliability Block Diagram (RBD), Markov Chain (MC), and Bayesian Networks (BN) are applied on proposed scenarios and results are obtained. Regarding to time dependencies between redundant components, a dynamic-based methodology is also developed in this study through applying Dynamic Fault Tree (DFT) analysis. Proposed static and dynamic approaches are applied on an UAV as a case study and results are discussed. Finally, characteristics of each methodology and related conditions are clarified for selecting the efficient reliability analysis approach.
Numerical Study of the Percussive Riveting Process: Simulation Results
Authors: Krovvidi Sai C. , Ramulu M. , Reinhall Per G. 
Published: February 2021

Abstract
Percussive riveting is a widely used assembly method in the aerospace industry. The joints produced using this technique have consistently high fatigue strength. It is a manual assembly technique but automation has been introduced in certain instances on the work floor to assist assembly workers. In this paper, study was set up to analyze the effect of important geometric parameters on the residual stress and strain distributions within the riveting stackup.
In the current paper, a realistic set of boundary conditions have been adopted with both movable riveting die and movable bucking bar in an axisymmetric thermomechanical model that has a countersunk rivet.
The knowledge of this evolution is important to gain understanding of the differences between quasi-static squeeze riveting process and the percussive riveting process. The distribution of residual strains and stresses play an important role in influencing the fatigue strength of the assembled joined. Most if not all of the percussive research till date is focused on the process automation advances but enough work has not been done to understand the properties of the assembled joint using the percussive technique.
The percussive results will be compared with quasi-static squeeze process results. Strain rate effects and thermal effects are negligible in the quasi-static process while these effects are present in the percussive process. So, the results and observations from quasi-static DOE will be used as a benchmark against which the percussive DOE results will be compared. The Johnson-Cook material model has been used for describing the flow stress of the alloys used in the percussive process.
A Machine Learning Based Approach for Energy Consumption Forecasting in K-12 Schools
Authors: Doiphode Ganesh , Najafi Hamidreza 
Published: February 2021

Abstract
Energy costs are the second highest operational expense for K-12 schools in the United States. Improving energy efficiency and moving towards sustainable school buildings not only result in substantial cost savings and reduction of environmental emissions, but also provides an opportunity to enhance students’ awareness regarding energy, environment, and sustainability. Effective tools and techniques that provide thorough understanding of energy consumption in school buildings are valuable to school districts by helping them with prioritizing energy efficiency projects. In the present paper, a multi-layer perceptron (MLP) neural network model is developed for estimating monthly energy consumption of K-12 schools in Brevard County, Florida. The inputs to the network are considered as number of occupants, days of operation per months, building’s area, average monthly outdoor dry-bulb temperature and relative humidity, as well as the month’s number and the output from the network is monthly energy consumption. Various network topologies are considered and tested to achieve the optimal configuration for the network. The selected network is successfully trained using three years of energy consumption data for 25 schools in Brevard County, FL (high schools, middle schools, and elementary schools). The results showed that the developed neural network model is capable of accurate estimation of monthly energy consumption of schools. The network tested and validated using the data from schools which were not included in the training dataset and the errors between the known values and estimated values for monthly energy consumptions are evaluated and discussed. Although the current study covers one particular school district (Brevard county) in a given climate zone (2a-hot and humid), the developed approach can be extended to incorporate various climate zones and serve as an effective tool for school energy conservation managers. The end user may obtain a clear idea of the energy consumption of the school building and how it compares against other buildings within the same category and climate zone, with minimum input data required.
A Multi-Fidelity Approach for the Reliability Assessment of Shell and Tube Heat Exchangers
Authors: Pidaparthi Bharath , Missoum Samy , Li Peiwen 
Published: February 2021

Abstract
The objective of this paper is to efficiently perform the reliability assessment of shell and tube heat exchangers. Although inexpensive empirical/analytical heat exchanger models exist and could be used for brute force Monte-Carlo simulations (MCS) based reliability analysis, they typically do not characterize the shell side flow accurately. Hence, higher fidelity models are often needed for predicting the shell and tube heat exchanger performance with a desired level of accuracy. These higher fidelity models are generally associated with higher computational costs, making them impractical for MCS-based reliability analysis. To circumvent this problem, a multi-fidelity technique leveraging the lower and higher fidelity models is proposed to locally construct the failure boundaries using support vector machines (SVM). For this purpose, an adaptive sampling scheme, which explores the regions of inconsistency between failure boundaries from lower and higher fidelity models, is developed.
Reverse Electrowetting-on-Dielectric Energy Harvesting Integrated With Charge Amplifier and Rectifier for Self-Powered Motion Sensors
Authors: Adhikari Pashupati R. , Tasneem Nishat T. , Biswas Dipon K. , Reid Russell C. , Mahbub Ifana 
Published: February 2021

Abstract
This paper presents a reverse electrowetting-on-dielectric (REWOD) energy harvester integrated with rectifier, boost converter, and charge amplifier that is, without bias voltage, capable of powering wearable sensors for monitoring human health in real-time. REWOD has been demonstrated to effectively generate electrical current at a low frequency range (&lt; 3 Hz), which is the frequency range for various human activities such as walking, running, etc. However, the current generated from the REWOD without external bias source is insufficient to power such motion sensors. In this work, to eventually implement a fully self-powered motion sensor, we demonstrate a novel bias-free REWOD AC generation and then rectify, boost, and amplify the signal using commercial components. The unconditioned REWOD output of 95–240 mV AC is generated using a 50 μL droplet of 0.5M NaCl electrolyte and 2.5 mm of electrode displacement from an oscillation frequency range of 1–3 Hz. A seven-stage rectifier using Schottky diodes having a forward voltage drop of 135–240 mV and a forward current of 1 mA converts the generated AC signal to DC voltage. ∼3 V DC is measured at the boost converter output, proving the system could function as a self-powered motion sensor. Additionally, a linear relationship of output DC voltage with respect to frequency and displacement demonstrates the potential of this REWOD energy harvester to function as a self-powered wearable motion sensor.
Optimization of Cu(In,Ga)Se2 Solar Cell and its Comparative Performance Analysis for Employing Different Buffer and Window Layers Through Numerical Simulation and Analysis Using SCAPS-1D
Authors: Zulkarnain Meah Imtiaz , Islam Nazmul 
Published: February 2021

Abstract
In this research, a numerical simulation and analysis of the second generation thin film solar cell Copper Indium Gallium diselenide, Cu(In,Ga)Se2 or, CIGS, is conducted in order to optimize its performance and compare among the cells using different materials for buffer and window layers. The one-dimensional solar cell simulation program SCAPS-1D (Solar Cell Capacitance Simulator) is used for the simulation and analysis purpose. The effects of variation of bandgap, concentration and thickness of the p-type CIGS absorber layer on the efficiency of CIGS solar cell are investigated. The change in CIGS solar cell efficiency with change in temperature is studied, too. Two different buffer layers namely CdS and In2S3 are considered for the simulation of the CIGS solar cell. The thickness of the buffer layer, its bandgap and concentration are taken into consideration for optimization. As for the window layer, ZnO and SnO2 are employed for the numerical simulation. The thickness of the window layer is varied and its effect on the efficiency of the solar cell is investigated. The open-circuit voltage, short-circuit current density, fill factor and quantum efficiency of the CIGS solar cell are observed from the SCAPS simulation besides the solar cell efficiency. A comparison among the different CIGS cell structures employing different buffer layers and window layers is performed in terms of efficiency and other essential parameters as mentioned above. The solar cell performances of the structures explored in this work were also put in comparison against some laboratory research cell output. The simulation result shows a possible better performance for all the simulated CIGS cell structures compared to the experimental results. In2S3 appears to increase efficiency and thus poses a great potential for non-toxic CIGS solar cell. Though CIGS absorber layer requires more thickness for desired output, successful application of much thinner SnO2 replacing ZnO buffer layer paves the way to less thicker CIGS thin film solar cell.
Shape Memory Polymer Foam With Tunable Properties for Treatment of Intracranial Aneurysm
Authors: Pineda-Castillo Sergio A. , Luo Jishan , Bohnstedt Bradley N. , Lee Chung-Hao , Liu Yingtao 
Published: February 2021

Abstract
Intracranial aneurysms have the potential to be fatal; when detected, they must be treated promptly by surgical clipping or by endovascular methods. The latter, while having better long-term overall survival than the former, fail to provide complete occlusion of the aneurysm lumen, creating risks for therapy-related adverse events, such as embolic device migration or recanalization. Polyurethane shape memory polymers (SMPs) have the potential to provide patient-specific treatment to reduce rates of incomplete occlusion and mass effect. In this study, SMP matrices are infiltrated with carbon nanotubes (CNTs) to induce electrical conductivity and provide a precise triggering method for deployment of the embolic device. Through thermomechanical characterization of the composite, it was determined that CNTs play a significant role in resistivity of the SMP foam and its ultimate shape recovery properties. Cyclic mechanical testing allowed to determine that CNTs might induce polymeric matrix damage, creating the need for new approaches to CNT infiltration. The studied composite foams were able to occlude an in vitro idealized aneurysm phantom model, which allowed to conclude that the proposed CNT-infiltrated SMP foams exhibit potential as biomedical devices for endovascular therapy of intracranial aneurysms.
Defect Detection in Additively Manufactured Metal Parts Using In-Situ Steady-State Ultrasonic Response Data
Authors: Jacobson Erica M. , Cummings Ian T. , Fickenwirth Peter H. , Flynn Eric B. , Wachtor Adam J. 
Published: February 2021

Abstract
The increasing implementation of additively manufactured parts into safety-critical applications is accelerating the demand for non-destructive evaluation as a means of quality control for defect detection during the build process. Identification of a critical defect in the part before its completion can enable early termination of the build, resulting in both reduced material costs and machine time. Additionally, current post-build inspection techniques have limited resolution capability as the size of the manufactured part increases.
An adaptation of Acoustic Wavenumber Spectroscopy technology was implemented into a laser powder bed fusion machine to perform in-situ direct-part measurements and provide a three-dimensional inspection volume of the entire build. The processing of data for each layer is quick enough to be performed during the lasing of the subsequent layer, enabling the potential for early termination of the build when critical defects are identified.
Various processing techniques were used to detect changes in the steady-state ultrasonic response that indicate different defect types. The data analyses used to identify regions of defects based on the response data are presented. This work ultimately demonstrates a practical means for in-situ monitoring of additive manufacturing parts to ensure quality control for safety-critical applications.
Design of Robotic Manipulator to Hollow Out Zucchini
Authors: Alkhatib Rami , El Bobo Tarek , Swaidan Afif , Al Soussi Jad , Diab Mohamad O. , Khaled Nassim 
Published: February 2021

Abstract
Robotics entered the food industry starting from packaging to cooking. Zucchini is an important dish in the Middle Eastern kitchen. The eventual challenge of hollowing out Zucchini is to avoid poking its bottom with the corer. This paper introduces a novel robotic mechanism for hollowing out zucchini tasks precisely and efficiently. The mobility of the robot arm ensured a smooth hollowing while the corer is put in motion. Moreover, frames are assigned to the tip of the corer, holder, zucchini and camera to avoid stabbing the zucchini bottom. Accordingly, the special Euclidean group of the homogenous transformation matrices derived between different links are discussed in the context of their properties. The kinematic analysis is based on Mozzi-Chasles’ theorem rather than using the traditional Denavit Hartenberg convention for better task-oriented planning of hollowing out zucchini mechanism. Results indicate a promising mechanism that is well designed, simple and easy to build, maintained and most importantly to perform the required task. This paper forms an important block in building the whole automated system to hollow out Zucchini.
Advanced Control Techniques for Semi-Active Suspension Systems
Authors: Maradey Lazaro Jessica Gissella , Cáceres Mojica Kevin Sebastián , Navarro Quintero Silvia Juliana 
Published: February 2021

Abstract
Semiactive suspension system provides comfort and precise support for any type of driving in vehicles. Their main feature consists in the modification of the damping coefficient by applying an external control. Commonly, these suspensions work with non-linear dampers, such as magnetorheological, electrorheological, pneumatic, dry friction, among others; which generate a discontinuous behavior of force, causing an annoying noise known as “chattering”; however, this can be deleted by the correct application of the control technique. So, control strategy selection is a key task in the modeling of dynamic behavior and to describe the variation of characteristics, as well as to achieve the best vehicle’s driving experience in terms of comfort, performance, reliability, stability, and safety.
This article shows three advanced control techniques used to design a semi-active vehicle suspension taking the quarter car as the model. From the review of the state of the art, relevant works and authors on the subject are reported. After, the application of the control techniques is shown together with the results obtained, specially, the performance of the system is carried out by means of computer simulations in the Matlab/Simulink virtual environment, accompanied by near-reality disturbances to verify the effectiveness of this study.
Ethanol Mechanism Reduction Based on DRGEPSA Method at Various Operating Ranges
Authors: Roy Shrabanti , Askari Omid 
Published: February 2021

Abstract
Reducing the size of a detail chemical kinetic is necessary in the prospect of numerical computation. In this work a skeleton reduction is done on a detail mechanism of ethanol. The detailed ethanol mechanism used here is developed through reaction mechanism generator (RMG). The generated mechanism is validated at wide range of engine relevant operating conditions using laminar burning speed (LBS), ignition delay time (IDT) and species mole fraction calculation at different reactor conditions. This detail mechanism consists of 67 species and 1031 reactions. Though the mechanism is in a very good agreement at various operating ranges with experimental data, it is costly to use a detail mechanism for 3D computational fluid dynamics (CFD) analysis. To make the mechanism applicable for CFD simulation further reduction of species and reactions is essential. In this work a skeleton mechanism is generated using directed relation graph technique with error propagation and sensitivity analysis (DRGEPSA). The DRGEPSA method, works based on error calculation at user defined condition. This technique is a combination of two methods, directed relation graph with error propagation (DRGEP) and directed relation graph with sensitivity analysis (DRGASA). To ensure the wide range of applicability of the skeleton mechanism, IDT is calculated at temperature, pressure, and equivalence ratio ranges from 700–2000 K, 1–40 atm and 0.6–1.4 respectively. A 10% error estimation is considered during the process. Initially DRGEP is applied on the detail mechanism to eliminate unimportant species. Further, sensitivity analysis helps to identify and reduce more unimportant species from the mechanism. Reactions related to the deleted species are automatically removed from the mechanism in each step. The final skeleton mechanism has 42 species and 464 reactions. This skeleton mechanism is validated and compared with different IDT data for the conditions not used in reduction technique. Results of LBS and different species concentration from reactor conditions is considered for validation. The skeleton mechanism can reduce computational time by 35% for LBS and 25% for IDT calculation. For future work, this skeleton mechanism will be considered in optimum reduction process.
On the Modulation of Origami Phononic Structure for Adaptive Wave Transmission in Brillouin Zone
Authors: Hathcock Megan , Popa Bogdan-Ioan , Wang K. W. 
Published: February 2021

Abstract
Recently the presence of a Dirac cone within the band structure of graphene has inspired research on phononic crystals with Dirac-like behaviors — including structures mimicking zero refractive index materials. The interesting phenomena produced by these structures occur at fixed frequencies and cannot be adaptive to needs and environmental changes. To address this constraint, researchers have designed tunable phononic structures; however, the tunable frequency ranges from the studies reported to date are limited by geometric constraints. Using a reconfigurable origami structure to modulate between different classes of phononic Bravais lattices, this research numerically investigates the effects of phononic lattice perturbation to produce drastic changes in the frequency of useful accidental degeneracies.