bluuebunny/crossref_metadata_2025_split huggingface dataset:
The Use of Blogs in a Project-Based Learning Context for First-Year Engineering Students’ Teams
Authors: Alves Anabela C. , Pereira Ana C. , Leão Celina P. , Fernandes Sandra , Uebe-Mansur Andre F. 
Published: February 2021

Abstract
The Integrated Project of Industrial Engineering and Management 1 (IPIEM1) is a curricular unit of the first year developed in the first semester of the Integrated Masters degree of Industrial Engineering and Management (IEM11) – University of Minho, Portugal. In the 2019’s 1st semester and in the IPIEM1 previous versions, Project-Based Learning (PBL) was usually adopted as a learning methodology. In this pedagogical development context, freshman students develop a project integrating all five courses related to this current semester. To undertake this project, the students work in large teams that comprise nine to ten members. Throughout the semester, each team must accomplish the project phases and tasks. To communicate their progress and results developed during the semester, each team designed a blog. In the IPIEM1 previous editions of PBL of this year in this program, the weblog (blog) digital technology was also adopted, but it was never assessed. Thus, this paper has two main objectives: 1) to evaluate the importance of the blog for the teams; 2) to discuss teams’ engagement during its development, knowing that it would be a part of the assessment method. The survey results revealed that the teams considered the blog useful to keep an update record of the project progress and to stimulate the writing and reflection about project contents. Furthermore, some of teachers’ and students’ considerations showed the need for providing more training and experience in the development of blogs.
Investigating Acoustic Excitation and Reburn Interaction on NOx Concentration in Bio-Fuel Engines Exhaust
Authors: Abdul Shukoor Nellimala , Ali Mohamed I. Hassan , Thomas Rijo Jacob 
Published: February 2021

Abstract
Although gasoline and diesel fuels are likely to continue fuelling the internal combustion engines for the near future, there is an increasing concern over the emission of greenhouse gases and other environmental pollutants. Alternative fuels such as biofuels are promising as they are renewable, sustainable, and reduce engine emissions. However, nitric oxides (NO) and nitrogen dioxides (NO2) are typically released in greater proportion, generally known as NOx. Applying acoustic excitation on a biofuel operated engine exhaust gases is investigated with and without reburn. Four biofuels, namely Jatropha, Karanja, Palm and neem oils, are burned in a single-cylinder Internal Combustion Engine (ICE). The exhaust gases is carried out through a vertical tube where a trace of liquid petroleum gas (LPG) is injected before it passes an acoustic cross tube. Variable frequency acoustic waves were passed through the setup using a speaker and amplifier. NOx is sampled from the top of the vertical tube. Acoustic wave frequency, LPG reburn, and combination of both acoustic waves and reburn influence on NOx mitigation are investigated. The integration of acoustic wave excitation and reburn showed the most significant reduction in the NOx. The second-largest NOx reduction was achieved with the sole use of reburn technology. Integrating the acoustic excitation with reburning was found to produce about 80% NOx reduction at a range of 100–150 Hz for all tested biodiesel fuels. Out of the fuels under consideration, Neem Oil generated the lowest amount of NOx and showed 87% reduction in NOx.
Impact of the Gas Phase on the Properties of Foams
Authors: Pruvot Antoine Jean-Claude Jacques , Zhang Zhifeng , Conner Cathryn , McAndrew James 
Published: February 2021

Abstract
A better understanding of the impact of gas on foam can enable designing a foam system with desirable properties for applications ranging from enhanced oil recovery to preparation of food and beverages. In this work we have explored the effect of different gases on foam generation, coalescence and drainage using different pore-size spargers to vary bubble size. We have also demonstrated new measurements of foam drainage as a function of bubble size, and shown that they agree with a simple theoretical model. The results have potential benefits process improvement.
ROM of Superharmonic Resonance of Fourth Order of Electrostatically Actuated Clamped MEMS Circular Plates: Voltage Response
Authors: Caruntu Dumitru I. , Beatriz Julio 
Published: February 2021

Abstract
This paper investigates the voltage-amplitude response of superharmonic resonance of fourth order of electrostatically actuated clamped MEMS circular plates. The system consists of flexible MEMS circular plate parallel to a ground plate. Hard excitations (voltage large enough) and AC voltage of frequency near one eight of the natural frequency of the MEMS plate resonator lead it into a superharmonic resonance. Hard excitations produce actuation forces large enough to produce resonance away from the primary resonance zone. There is no DC component in the voltage applied. The partial differential equation of motion describing the behavior of the system is solved using two modes of vibration reduced order model (ROM). This model is solved through a continuation and bifurcation analysis using the software package AUTO 07P which produces the voltage-amplitude response (bifurcation diagram of the system, and a numerical integration of the system of differential equations using Matlab that produces time responses of the system. Numerical simulations are conducted for a typical MEMS silicon circular plate resonator. For this resonator the quantum dynamics effects such as Casimir effect or Van der Waals effect are negligible. Both methods show agreement for the entire range of voltage values and amplitudes. The response consists of an increase of the amplitude with the increase of voltage, except around the value of 4 of the dimensionless voltage where the resonance shows two saddle-node bifurcations and a peak amplitude about ten times larger than the amplitudes before and after the dimensionless voltage of 4. The softening effect is present. The pull-in voltage is reached at large values of the dimensionless voltage, namely about 14. The effects of damping and frequency on the voltage response are reported. As the damping increases, the peak amplitude decreases for the resonance. However, the pull-in voltage is not affected. As the frequency increases, the resonance zone is shifted to lower voltage values and lower peak amplitudes. However, the pull-in voltage and the behavior for large voltage values are not affected.
Computational Modeling of Combat Helmet Performance Analysis Integrating Blunt and Blast Loadings
Authors: Tan X. Gary , Bagchi Amit 
Published: February 2021

Abstract
Combat helmets have gone through many changes, from shells made of metal to advanced composites using Kevlar and Dyneema, along with introduction of pad suspensions to provide comfort and protection. Helmets have been designed to perform against ballistic and blunt impact threats. But, in today’s warfare, combat helmets are expected to protect against all three threats, blunt, ballistic impacts and blast effects to minimize traumatic brain injury (TBI) and provide a better thermal comfort. We are developing a helmet system analysis methodology integrating the effect of multiple threats, i.e., blast and blunt impacts, to achieve an optimal helmet system design, by utilizing multi-physics computational tools. We used a validated human head model to represent the warfighter’s head. The helmet composite shell was represented by an orthotropic elasto-plastic material model. A strain rate dependent model was employed for pad suspension material. Available dynamic loading data was used to calibrate the material parameters. Multiple helmet system configurations subjected to blast and blunt loadings were considered to quantify their influence on brain biomechanical response. Parametric studies were carried out to assess energy absorption for different suspension geometry and material morphology for different loadings. The resulting brain responses were used with published injury criteria to characterize the helmet system performance through a single metric for each threat type. Approaches to combine single-threat metrics to allow aggregating performance against multiple threats were discussed.
Dehumidification Strategies to Improve Energy Use at Retailers: A Case Study of a Supermarket Located in Guayaquil, Ecuador
Authors: Litardo Jaqueline , Hidalgo-León Rubén , Coronel Paul , Damian Andrea , Macías José , Soriano Guillermo 
Published: February 2021

Abstract
This study aims to model and evaluate the energy performance of a supermarket located in Guayaquil, Ecuador. For this purpose, we modeled the baseline of the supermarket using EnergyPlus software. All input parameters used in the simulations were provided by the supermarket staff. Air-conditioners and refrigerated cases were modeled using the default templates in EnergyPlus but replacing the specifications of the site systems. The baseline model predicted, within ±10%, the energy consumption of the case study when compared to the real electricity bills of the building. Then, we incorporated a desiccant wheel for each of the air-conditioner units that supply the frozen food area. Results showed relevant reductions of about 52% in cooling energy by decreasing the humidity level from 70% to 40% with the use of desiccant systems. However, the introduction of these systems increased the total building energy consumption by about 3% due to the regeneration process.
Initial Study Towards the Integrated Design of Bone Scaffolds Based on Cell Diffusion, Growth Factor Release and Tissue Regeneration
Authors: Sahin Mervenaz , Tabak Ahmet Fatih , Kiziltas Sendur Gullu 
Published: February 2021

Abstract
Three-dimensional (3D) porous tissue scaffolds combined with bioactive molecules and cells offer key advantages for bone repair mechanisms. A functional bone tissue scaffold should provide mechanical support with an adequate combination of porosity and permeability for nutrients, oxygen supply, waste removal, and growth factors release as well as controlled degradation. Although a vast amount of work exist to address these challenges, to the best of our knowledge, a design framework taking tissue differentiation, diffusion, and growth factor (GF) release into account in time-domain simultaneously does not exist. In this paper, we provide an initial effort to address such a need by laying down the foundations for a simulation framework incorporating these effects within a Finite Element Analysis (FEA) model in COMSOL Multiphysics® software. The effectiveness of the numerical model is demonstrated via preliminary mechano-biology analyses on a simulated 3D poroelastic bone scaffold. Initial time-dependent results demonstrate the suitability of this model for an optimization framework. More specifically, it is demonstrated that coupled Multiphysics equations of diffusion, GF release, and differentiation could provide valuable inputs for ideal bone scaffold systems for effective bone repair tasks in the future.
A Dynamic Battery State-of-Health Forecasting Model for Electric Trucks: Li-Ion Batteries Case-Study
Authors: Huotari Matti , Arora Shashank , Malhi Avleen , Främling Kary 
Published: February 2021

Abstract
It is of extreme importance to monitor and manage the battery health to enhance the performance and decrease the maintenance cost of operating electric vehicles. This paper concerns the machine-learning-enabled state-of-health (SoH) prognosis for Li-ion batteries in electric trucks, where they are used as energy sources. The paper proposes methods to calculate SoH and cycle life for the battery packs. We propose autoregressive integrated modeling average (ARIMA) and supervised learning (bagging with decision tree as the base estimator; BAG) for forecasting the battery SoH in order to maximize the battery availability for forklift operations. As the use of data-driven methods for battery prognostics is increasing, we demonstrate the capabilities of ARIMA and under circumstances when there is little prior information available about the batteries. For this work, we had a unique data set of 31 lithium-ion battery packs from forklifts in commercial operations. On the one hand, results indicate that the developed ARIMA model provided relevant tools to analyze the data from several batteries. On the other hand, BAG model results suggest that the developed supervised learning model using decision trees as base estimator yields better forecast accuracy in the presence of large variation in data for one battery.
Thermal and Electrical Performance Analysis of a Novel Medium Concentration Dual Photovoltaic System for Different Phase Change Materials
Authors: Hasnain Arshmah , Sarwar Jawad , Abbas Qamar , Younas Muhammad Azeem , Kakosimos Konstantinos E. 
Published: February 2021

Abstract
In this work, performance analysis of a medium concentrated photovoltaic system employing two mono-facial cells is carried out using a validated finite element based coupled optical, electrical, and thermal model. The environmental conditions of Lahore, Pakistan are considered, and the system is thermally regulated with a phase change material. Nine commercially available phase change materials (PCM) having melting temperature ranges between 41–65°C are selected. These PCMs include LA, RT47, S-series salt, ClimSel™ C48, STL47, RT54, RT60, RT62, and RT64. Temperature regulation, melt fraction, thermal and electrical efficiency are determined for each material for four months of January, March, July, and September representing four seasons of a year. The comparison of the materials has shown that S-series salt and C48 melt completely during the day and regenerate to solid-phase during night in the whole year except January. But S-series salt is found to be most suitable in current configuration due to its higher overall efficiency over the whole year.
Application of Random Forest Machine Learning Models to Forecast Combustion Profile Parameters of a Natural Gas Spark Ignition Engine
Authors: Liu Jinlong , Ulishney Christopher , Dumitrescu Cosmin E. 
Published: February 2021

Abstract
Predicting internal combustion (IC) engine variables such as the combustion phasing and duration are essential to zero-dimensional (0D) single-zone engine simulations (e.g., for the Wiebe function combustion model). This paper investigated the use of random forest machine learning models to predict these engine combustion parameters as a modality to reduce expensive engine dynamometer tests. A single-cylinder four-stroke heavy-duty spark-ignition engine fueled with methane was operated at different engine speeds and loads to provide the data for training, validation, and testing the proposed correlated model. Key engine operating variables such as spark timing, mixture equivalence ratio, and engine speed were the model inputs. The performance of the models was validated by comparing the prediction dataset with the experimentally measured results. Results showed that the prediction error of the random forest machine learning algorithm was acceptable, suggesting that it can be used to predict the combustion parameters of interest with acceptable accuracy.
Prediction of Efficient Operating Conditions Inside a Heavy-Duty Natural Gas Spark Ignition Engine Using Artificial Neural Networks
Authors: Liu Jinlong , Ulishney Christopher , Dumitrescu Cosmin E. 
Published: February 2021

Abstract
Research engines with optical access can assist traditional engine development and optimization by providing first-hand information of in-cylinder combustion process. However, the fragility of the optical engine components (e.g., the see-thru windows are usually made from fused silica) limit the engine operating conditions such as the maximum in-cylinder pressure and pressure rise rate. To make it easier to determine if a particular engine operating condition can be used for optical investigations, a back-propagation artificial neural network model was built to provide the values of pressure-based parameters of interest for engine safety. The training data came from steady-state engine experiments that changed spark timing, mixture equivalence ratio, and engine speed, but using the non-optical configuration of the engine to widen the testing conditions. The comparison between model predictions and experimental data indicated that the well-trained artificial neural network model can provide fast and consistent results, making it an easy-to-use tool for designing future optical engine investigations.
Predicting Nonlinear Seismic Response of Structural Braces Using Machine Learning
Authors: Bas Elif Ecem , Aslangil Denis , Moustafa Mohamed Aly 
Published: February 2021

Abstract
Numerical modeling of different structural materials that have highly nonlinear behaviors has always been a challenging problem in engineering disciplines. Experimental data is commonly used to characterize this behavior. This study aims to improve the modeling capabilities by using state of the art Machine Learning techniques, and attempts to answer several scientific questions: (i) Which ML algorithm is capable and is more efficient to learn such a complex and nonlinear problem? (ii) Is it possible to artificially reproduce structural brace seismic behavior that can represent real physics? (iii) How can our findings be extended to the different engineering problems that are driven by similar nonlinear dynamics? To answer these questions, the presented methods are validated by using experimental brace data. The paper shows that after proper data preparation, the long-short term memory (LSTM) method is highly capable of capturing the nonlinear behavior of braces. Additionally, the effects of tuning the hyperparameters on the models, such as layer numbers, neuron numbers, and the activation functions, are presented. Finally, the ability to learn nonlinear dynamics by using deep neural network algorithms and their advantages are briefly discussed.
Artificial Neural Network Based on Ensemble Empirical Mode Decomposition Features for Human Gait Diagnosis and Classification
Authors: Alkhatib Rami , Diab Mohamad O. , Corbier Christophe , El Badaoui Mohamed 
Published: February 2021

Abstract
Human gait analysis has been widely used to assess the stage of disease affecting the walking ability. The gait signals, namely vertical ground reaction force signals, become more unsteady and non-linear with the progress of the disease. This paper makes use of ground reaction force signals measured from both normal and subjects diagnosed with Parkinson. New features are then extracted from different intrinsic mode functions as a result of the ensemble mode decomposition. The extracted features are divided randomly into a training set of 60%, a validation set of 15% and testing set of 25%. The neural network is then employed which yield an interesting overall classification accuracy of 95.7 %. This paper will pave the way for better rehabilitative programs, understanding of gait biomechanics and fall prevention among the elderly.
Hydrogen Embrittlement Characteristics in Irradiated Stainless Steel
Authors: Khalil Sarah , Hatem Tarek M. 
Published: February 2021

Abstract
Hydrogen is produced in nuclear reactors as a by-product of the corrosion reaction between the pressure vessel and the cooling water, where hydrogen produced may enter the metal in atomic form. During operation a reactor vessel is exposed to avalanche of neutron irradiation fluxes, in addition to corrosion from cooling water.
A novel cluster dynamics model that accounts for off-stoichiometry of clusters and matrix was developed and applied to investigate the clustering behavior of Hydrogen-vacancy and Hydrogen-interstitial clusters in proton irradiated stainless steel has been developed. The differences in point defect migration energies and binding energy of H to lattice defects, makes it possible to have vacancy and interstitial clusters having compositions different from those of pure iron. The model predicts populations of Defect-Hydrogen complexes in iron.
The model is applied to the early stage formation of voids and dislocation loops in stainless steel in the presence of atomic hydrogen. This study investigates the effect of irradiation dose and temperature on the concentration of vacancy-Hydrogen (VmHn) and Intersitial Fe-H (FemHn) complexes on bulk α-Iron. The re
Intrinsic Mechanisms of Self-Healing in Metallic Structures
Authors: Ibrahim Mohamed A. , El-Mahallawi Iman S. , Hatem Tarek M. , Khalil Sarah 
Published: February 2021

Abstract
Self-healing is the ability of a material to repair damages automatically. Approaches to self-healing are separated into two major categories, those are: 1) autonomous healing methods that depend on intrinsic mechanisms, and 2) assisted healing methods that need an external intervention. Recently, computational methods have gained a wide application to study self-healing in metals using molecular dynamics (MD) and finite element (FE) methods. These methods can be used to demonstrate and optimize different metallic alloys potential to self-heal, and to further tailor these metallic structures toward improving their mechanical and fracture properties through self-healing. Computational studies of self-healing phenomenon in metals have been small in number and scope till recently. Therefore, the current paper starts with a general introduction of different mechanisms of intrinsic self-healing in metallic structures. The paper highlights previous studies using different experimental and computational approaches to explore self-healing in metallic systems, while focusing on Iron/Steel alloys. Furthermore, the paper present authors work to study self-healing and its impact on mechanical properties of Iron. Simulations are carried on bi-crystalline iron sample to investigate the effect of alloying elements diffusion on the fracture / healing properties of iron alloys and their impact on its mechanical properties. Then the effect of the alloying elements diffusion on healing is studied upon stress application after annealing. Different samples been compared to healthy samples and cracked samples without self-healing to demonstrate the effectiveness of self-healing in Iron alloys.
Blue Shift in Ultraviolet Absorption Spectra of Oxygen Doped Titanium Nitride Thin Films
Authors: Roy Manosi , Kumar Dhananjay 
Published: February 2021

Abstract
The objective of this study is to investigate the effect of film thickness on the bandgap of oxygen (O2)-doped titanium nitride (TiN) thin films. To accomplish this, high-quality two-dimensional O2-doped TiN films have been prepared on single-crystal sapphire substrates using a pulsed laser deposition method. The film thicknesses were varied from 3 to 100 nm by varying the number of laser pulses, while other deposition parameters are kept constant. X-ray diffraction (XRD) patterns have shown that the films grow in (111) orientation on the sapphire substrate. The increase in the intensity of the XRD (111) peak also demonstrates a better orientational alignment of the TiN films with substrate as the film thickness increases. The x-ray rocking curve has been used to measure the full width half maxima (FWHM) for each film. The FWHM values has been found to vary from 0.07 to 0.2° as the film thickness decreases. This is taken to indicate that the grain size decreases with a decrease in film thickness. Ultraviolet visible spectroscopy measurements in the wavelength range (200–800 nm) have been performed as well, which indicates an increase in the bandgap of O2-doped TiN films with a decrease in film thickness. The decrease in the film thickness leads to a blue shift of the peak in the ultraviolet-visible absorption (UV-A) region; this blueshift is accompanied by an increase in the bandgap of O2-doped TiN from 3.2 to 3.8 eV. The change in the bandgap due to a change in film thickness has been explained using the quantum confinement effect.