bluuebunny/crossref_metadata_2025_split huggingface dataset:
Highly Efficient Mid-Wavelength Infrared (MWIR) Polarizer by ORMOCHALC Composite With Improved Thermomechanical Stability and Spectral Selectivity
Authors: Islam Md Didarul , Liu Sipan , Derov John , Urbas Augustine M. , Ku Zahyun , Sihn Amy , Smith Evan M. , Boyd Darryl A. , Kim Woohong , Sanghera Jasbinder S. , Nguyen Vinh Q. , Myers Jason D. , Baker Colin C. , Ryu Jong Eun 
Published: January 2022

Abstract
Mid-wavelength infrared (MWIR, λ = 3–5 μm) materials are of great importance due to their applications in optical sensors and devices for military, industry, and non-invasive medical diagnostics. Specifically, MWIR polarimetry has significantly improved biometric recognition and camouflaged detection. Most commercial polarizers are based on expensive inorganic materials that are heavy, fragile, and brittle. Thus a suitable polymeric material for MWIR optics is highly desired. Herein, sulfur-based organically modified chalcogenides (ORMOCHALC) polymers have been utilized to fabricate MWIR polarizers by a simple thermal imprinting method followed by Ay deposition. A parametric study to choose suitable geometry for the polarizer was conducted, and highly efficient devices were designed that possess competitive extinction coefficients to the commercial polarizers. However, a significant limitation of the ORMOCHALC polymer is that to increase the refractive index of the polymer, the chalcogenide (i.e., S) content needs to be increased, which results in reduced Young’s modulus and lower glass transition temperature. This decayed thermomechanical stability compromises the structural integrity of ORMOCHALC optical devices. In addition to polymeric MWIR polarizer fabrication, composite materials were also synthesized and characterized for future MWIR device fabrications. Poly(S-r-DIB) was reinforced with zinc sulfide nanoparticles to simultaneously improve the refractive index and the thermomechanical properties. The addition of ZnS nanoparticles significantly improved the glass transition temperature (Tg) of the ORMOCHALC (9.6 °C to 31.4 °C), and the refractive index (Δn = 6.6 %). Then, a figure of merit subwavelength wire-grid polarizers was also analyzed based on the optically and mechanically reinforced composites. If fabricated, nanoparticles reinforced polarizers will possess superior structural integrity due to higher glass transition temperature. Moreover, the polarizers show a spectral selectivity as the resonance wavelength of the transmitted-reflected curve was redshifted to larger wavelengths for ZnS reinforced ORMOCHALC composite. These polarizers with superior extinction coefficient, spectral selectivity, and improved thermomechanical stability demonstrate a border implementation opportunity in the MWIR optics.
Study on Convective Heat Transfer Characteristics of Supercritical CO2 in Printed Circuit Heat Exchanger Under Ocean Condition
Authors: Li Shulei , Liu Dechao , Qin Lei , Xie Gongnan 
Published: January 2022

Abstract
The supercritical carbon dioxide (sCO2) Brayton cycle has a wide application prospect in the field of the waste-heat utilization of the marine gas turbine due to its characteristics of high efficiency, compact, safety and good economy. Inspired by this, the convective heat transfer characteristics of sCO2 in PCHE under static and ocean conditions are studied in this paper. Firstly, a numerical model is established to explore the flow and heat transfer behaviors of sCO2. Then, the heat transfer enhance (HTE) phenomenon of sCO2 flowing in the PCHE channel with airfoil fin under static condition is discussed. Finally, the effects of ocean oscillation on heat transfer performance and frictional pressure drop of sCO2 were investigated under different rolling parameters. Under static state, it can be found that the secondary flow induced by the buoyancy effect obviously increases turbulent dissipation on the upper surface of the channel when mass flow of hot side is lower, which finally results in significant heat transfer difference between the upper and lower walls. However, buoyancy effect has little effect on heat transfer performance at high mass flow rate of hot side. Under rolling condition, the time-average frictional pressure drop of the cold side always increases, but the increment is small with maximum increment of 4.2%. The rolling motion has drag reduction effect on the fluids of the hot side, and the time-average pressure drop at the hot side can be reduced by 20.3% at the high mass flow rate. The time-average heat transfer performance under rolling motion tends to be strengthened, which becomes more significant at low mass flow rate of hot side. Compared with the static state, the time-average heat transfer coefficient can be increased by up to 4.0%. The results have a helpful guiding significance for the design of marine waste heat recovery system.
Tolerance Specification Model for Systematic Application of GD&T in Product Design
Authors: Petruccioli Andrea , Pini Fabio , Leali Francesco 
Published: January 2022

Abstract
The Dimensional Management (DM) is well known as the reference methodology for the management of dimensional and geometric variations of industrial products. Over the years, it has assumed a central role, thanks to the development of a specific design approach, known as Design for Tolerancing (DfT). Based on the Geometric Dimensioning and Tolerancing (GD&T) symbolic language, DfT allows to check and verify functional and qualitative requirements from the early design phases. Although its strength and potential to improve design optimization, DfT industrial application is still limited. Consolidated design practices, complexity of tolerance specification process, lack of support from Computer-Aided tools still limit the tolerance specification to final validation of product design. The paper aims to define a tolerance specification model for systematic application of GD&T specification. The model formalizes the identification and translation of product requirements on the components geometry, through the definition of the main step of tolerance design. Based on the integration between the GD&T-based approach and parametric three-dimensional CAD modelling, the model has been applied to validate the GD&T and the tolerance specification of two mechanical assemblies with common features. The methodology proves its general effectiveness to support engineers in tolerance design and selection of the most suitable GD&T schemes.
Parkinsons Disease: Tremor Suppression With Wearable Device
Authors: Winston Sam E. , Dehmer Riley C. , Doughty Timothy A. 
Published: January 2022

Abstract
Parkinson’s Disease (PD) is a neurodegenerative disorder that affects nearly a million people in the United States. Hand tremors are a well-known symptom associated with PD and result in anxiety, frustration, and malnutrition. While there is no cure, several approaches attempt to treat the symptoms. Many are invasive, including the use of pharmaceuticals and surgery [1]. Noninvasive technologies are often cumbersome and do not address the conspicuous nature experiencing tremors in public. This study is motivated by design criteria established from previous research [2], with a goal of an affordable, purely mechanical solution. In both cases, human subject testing echoed lab tests in effective tremor reduction. The extension to a wearable device gives the user the ability to hold or handle any object, or no object, with a significant reduction in tremor.
Two separate wearable devices were tested for effectiveness while the simulated user ‘held’ two different objects to simulate different applications. Biomechanical modeling of the human hand informed the development of an adjustable mechanical hand-tremor system for testing. Models of the devices and the hand-device interface were used to simulate the dynamic response of the coupled systems. Each device was secured to the mechanical hand-tremor system and harmonic stimulus and response data was collected over the range of typical tremor frequencies. The results demonstrate the versatility of both designs for reducing the oscillations associated with tremors. The Ratio of Reduction (RoR) was defined to compare the tremor amplitude of the hand-tremor system with and without the device. Both designs were considered effective for each object with a max RoR of 28.09 for Device A and a max RoR of 99.32 for Device B.
A Prediction Software to Evaluate Frisbee Movement
Authors: Yang Haowen 
Published: January 2022

Abstract
Frisbee is a popular item for both entertainment and professional sport. The aerodynamics study of the frisbee is crucial to understand its movement and optimize its design. In this paper, I used basic fluid analysis to investigate the effects of parameters on the flying trajectory, as well as maximum distance and maximum height of a frisbee. First, the aerodynamic forces on a moving frisbee, including lift force, drag force, and gravity, were analyzed. Second, a set of governing equations describing the movement of the frisbee was derived. Third, a Matlab-based program to evaluate the trajectory of a flying frisbee was developed. Finally, I designed the user graphic interface and published the software for public use. By inputting the settings of the frisbee movement, such as frisbee diameter, initial velocity, attack angle, wind speed, etc., any user, without knowing the aerodynamic theories, can use this software to quickly determine the trajectory and the maximum distance of a moving frisbee. With the developed software, I investigated how the parameters influence the aerodynamic characteristics and a frisbee’s flying performance.
Effect of Tensile Strain on Electron Transport Properties of Dumbbell-Shape Graphene Nanoribbons With Metallic-Semiconducting Interfaces
Authors: Suzuki Ken , Zhang Qinqiang , Qiao Xiangyu 
Published: January 2022

Abstract
In this study, first-principles calculations based on density functional theory (DFT) were performed to analyze in detail the electronic transport properties of dumbbell-shape graphene nanoribbons (DS-GNRs) and their strain dependence as a strong function of the combination of the basic structure of GNRs in the narrow segment and the structures at both ends of DS-GNRs. Then, the current-voltage characteristics (I-V characteristics) and orbital distributions of DS-GNRs were investigated to develop a highly sensitive DS-GNR-based strain sensor. By combining two GNRs with metallic and semiconducting electronic properties, a non-negligible transition layer (gradient Schottky barrier) was formed near the junction. The length of the transition layer was about five six-membered rings of carbon atoms. The formation of this transition layer is considered to be due to the exudation of the wave function from the wide segment to the narrow segment in DS-GNR. In the DS-GNR with metal-semiconductor interfaces, the strain dependence of the electronic transport properties was very complicated due to the presence of the transient Schottky barrier. On the other hand, in a DS-GNR consisting of two metallic GNRs, the Schottky barrier and the transition layer disappeared, and stable current-voltage and piezoresistive characteristics close to those of a single GNR were observed. The predicted gauge factor of DS-GNRs was larger than that of conventional metal foils (gauge factor 2–5) and close to that of conventional polysilicon (gauge factor ±30). These results indicate that DS-GNRs have the potential to produce highly sensitive and reliable strain sensors.
Optimizing Power Output of a Wave Energy Converter by Employing Superposed Hydrodynamic Model
Authors: Kathyayani Nandakumar , Samad Abdus 
Published: January 2022

Abstract
Optimization of wave energy converters (WECs) through robust controls is mandatory to maximize the energy absorption in the face of sea waves’ stochastic nature. Single optimization criterion predicted by linear models is rendered ambiguous when modulated by viscous forces, dominant at the controlled conditions. An efficient hydrodynamic model capable of identifying suitable parameters for nonlinear controller design is desirable but missing. Hence, this paper proposes a superposed hydrodynamic model (SPHM) to optimize the power output of a scaled-down point absorber WEC. Two variants of SPHM are considered to evaluate the differences between linear and nonlinear viscous models. Optimization is guided by a non-predictive latch control strategy. The model is numerically solved using the fourth Runge-Kutta method to obtain the time domain response of buoy. The nonlinear SPHM reveals a new optimization parameter based on the maximum velocity criterion. At off-resonant states, the controller enhances the system power by eleven times.
An Ultra-Compliant and Flexible Structural Sensing Neural System for Damage Detection in Wind Turbine Blades
Authors: Wang Junzhen , Shen Yanfeng , Peng Xuezhi , Han Zhengyang , Jiang Shaopeng 
Published: January 2022

Abstract
This paper presents a new ultra-compliant and flexible structural sensing neural system for damage detection in wind turbine blades. The entire sensing cluster is integrated inside a flexible printed circuit (FPC), which complies the structural geometric features as a neural skin planted on the structural surfaces. It is worth of noting that the proposed sensing system can be mounted on arbitrary locations of a wind turbine blade, mimicking the neurons of the human biological system to detect and monitor the damage in the blade. Besides, all the sensing elements are interfaced with a laptop-controlled data acquisition board to receive the structural dynamic responses. In addition, a miniature electromagnetic actuator is utilized to excite the blade. Additional mass is employed to simulate the damage situation. Several signal processing techniques are implemented to analyze the oscillatory responses, such as wavelet analysis, fast Fourier transform (FFT), and short time Fourier transform (STFT). Furthermore, the damage indices (DIs) which correlate the structural spectral power density of both pristine and damaged cases can accurately identify the location of damage. Such a sensing neural system possesses the tremendous potential in future Structural Health Monitoring (SHM) and Nondestructive Evaluation (NDE) applications. This paper finishes with discussion, concluding remarks, and suggestions for future work.
A Numerical Study Into the Importance of Equivalence Ratio Measurement Accuracy for Spark Ignition Engines
Authors: Yang Ruomiao , Sun Xiaoxia , Liu Zhentao , Zhang Yu , Fu Jiahong 
Published: January 2022

Abstract
The internal combustion engines will continue to be the major power source for transportation. Spark ignition (SI) engines are still widely used for mobility due to their wide range of operating conditions. The engine key operating variables are primarily controlled by an already calibrated engine control unit (ECU). However, a less precise sensor may lead to larger variations in engine performance and emissions. The goal of this study was to investigate the importance of an air-fuel ratio sensor sensitivity during various engine operations. A one-dimensional (1D) computational fluid dynamics (CFD) model was utilized in this study to analyze the engine responses caused by a slight change of equivalence ratio at various engine speeds and loads. The results indicated that variations in the fuel-air ratio had a significant effect on emissions. Carbon monoxide was most sensitive to the air-fuel ratio, followed by nitrogen oxides, while unburned hydrocarbons were not sensitive to it. For engine performance, it varied slightly with small changes in the fuel-to-air ratio. The results also demonstrated that at higher engine speeds, slight changes in the air-fuel ratio resulted in smaller changes in emissions. Overall, for precise combustion control, it is recommended that the accuracy of the sensor is higher than 1% at least for the engine investigated in this study.
Development of a Robust and Real-Time Thermal Deformation Prediction System for Machine Tool by Multi-Point Temperature Measurement
Authors: Tanaka Shun , Marukawa Yuya , Kizaki Toru , Tomita Kenichi , Tsujimura Shinji , Noda Daisuke , Kobayashi Hisashi , Sugita Naohiko 
Published: January 2022

Abstract
More than 70% of machining errors in machine tools are caused by thermal displacement. In this paper, we proposed a method of estimating for thermal displacement by measuring the temperature at a large number of points to achieve high-precision machining under disturbances such as cutting fluid. The sensor locations were determined from the results of the thermal simulation in which the heat generation at various heat sources, such as bearings and ball screws, were determined according to the results of the operational tests. The tool center point (TCP) error was estimated from the temperature distribution measured by 284 temperature sensors. The proposed method reduced the estimation error by 50% compared to the conventional method with 17 temperature measuring locations, and the accuracy was confirmed to be unchanged even when cutting fluid was supplied. In addition, a real-time method to estimate TCP relative displacement was developed to implement thermal deformation compensation in a machine tool. Considering actual machining, it is necessary to reduce the computational load of the finite element method (FEM) or to consider a faster displacement estimation model to output the TCP relative displacements at multiple points. By implementing this thermal displacement estimation system, it may be possible to achieve high-precision machining even with machines that do not have high precision.
Evaluation of Normalization Methods in a Cerebral Artery Atlas for Automatic Labeling
Authors: Jin Kazuyoshi , Kitamura Ko , Mugikura Shunji , Mori Naoko , Ohta Makoto , Anzai Hitomi 
Published: January 2022

Abstract
An existence probability atlas has been used for automatic labeling of cerebral arteries. However, the number of arteries varies frequently because of image quality and individual variation of the artery structure. To moderate the influence of number imbalance on labeling accuracy, we propose a new normalized atlas for automatic labeling of cerebral artery centerlines.
The number of arteries, which was obtained from magnetic resonance angiography, varies from 11 to 46 among the artery sites. Based on the centerline and diameter, the arterial volume was reconstructed into a voxel space for each subject. After superimposing arteries from 46 subjects, three normalization methods were compared: dividing by the number of subjects (N), by N and the arterial length (L), and by N and the arterial volume (V). To compare the labeling accuracy and precision, the summation of probability and labeling method was also used.
The accuracy of all normalization methods was &gt; 85% in all arteries. The precision improved in some parts, with the atlas normalized by N-L and by N-V. The use of N-L and N-V changed the relative value of the existence probability among the parts. Consequently, some normalization methods changed the tendency toward misclassification, which changed the precision.
Stochastic Predictive Control for Crash Avoidance in Autonomous Vehicles Based on Stochastic Reachable Set Threat Assessment
Authors: Khattar Vanshaj , Eskandarian Azim 
Published: January 2022

Abstract
Unexpected hazardous road situations frequently arise on the roads, which lead to crashes. Detecting a threat in advance and generating a fallback trajectory are some of the major challenges faced by autonomous vehicles. Stochastic Model Predictive Control (SMPC) approaches have recently proven to be highly effective in controlling systems in highly uncertain environments. However, the chance-constrained formulation of the SMPC makes it computationally expensive for fast real-time implementation. This paper presents a fast, proactive decision-making approach for crash avoidance in autonomous vehicles based on SMPC. Two problems are addressed in this study: 1) Assessing threat to the autonomous vehicle from the surrounding vehicles and 2) Fast safe trajectory generation for the autonomous vehicle in case of a future predicted crash. Threat assessment is addressed using stochastic reachability analysis for the surrounding vehicles, which accounts for surrounding drivers likely future motion and intents. We have proposed a fast, proactive decision-making algorithm to generate crash avoidance trajectories based on Stochastic Model Predictive Control (SMPC). We reformulate the SMPC probabilistic constraints as deterministic constraints using convex hull formulation, allowing for faster real-time implementation. This deterministic SMPC implementation ensures in real-time that the vehicle maintains a minimum probabilistic safety. It is shown that the proposed decision-making approach is successful in avoiding crash scenarios with a medium risk factor range.
Methodical Modeling of Product and Process Data of Design Methods Using the Example of Modular Lightweight Design
Authors: Hanna Michael , Schwede Lea-Nadine , Schwenke Johann , Laukotka Fabian , Krause Dieter 
Published: January 2022

Abstract
This paper shows how the methodically modeling of product and process data of design methods using the example of modular lightweight design to improve data traceability and continuity can be implemented. Therefore, the underlying methods from the areas of modular lightweight design, Model-Based Systems Engineering and methodical process steps are analyzed and their challenges collected. As basis a data-driven linkage of product and process models for methods is used. To establish this within the method of the modular lightweight design, the six main steps were analyzed with regards to possible inconsistencies and links. In this context, it is particularly important which input and which output data are required or generated in each of these steps. Using this information, the link between the process and product data models can be established. With this link, the consistency and continuity of the data can be improved, as uses and changes in one step can be traced to other relevant steps. Because the data itself now is more consistent, the steps and the method itself provide a better consistency and continuity. A SysML-model is presented, with which the product data model can be consistently linked with the process data model, based on a data tree. This integrated process and product data model is applied to aircraft cabin development to develop a continuous, consistent models in a methodical manner.