bluuebunny/crossref_metadata_2025_split huggingface dataset:
Experimental Research on Photo-Induced PLZT-Based Electrostatic Micro Gripper
Authors: Lv Zhen , Liu Zhicheng , Tang Yujuan , Wang Xinjie 
Published: February 2024

Abstract
Photo-induced actuators have arisen as a particularly attractive alternative among different actuators in these days. In contrast to traditional actuation mechanism (e.g., piezoelectric, electromagnetic, electrostatic effects), photo-induced actuators provide a clean, contactless actuation mechanism and require no external electrical power supply. In this paper, we report a strategy to realize rapid photo-induced actuation using a composite actuation mechanism, which combines photoelectric effect and electrostatic actuation mechanism. Photo-induced PLZT-based electrostatic actuator is then proposed towards micro gripper. Due to the nonlinear variation of electrostatic force, there is the electrostatic pull-in phenomenon enabling the gripper with rapid grabbing action. Micro grippers with sandwich-like multilayer structure arms are fabricated using a simplified photolithography process. A series of experiments are conducted to characterize the fundamental operation and performance of our gripper. The experimental results show that a higher light intensity leads to a more efficient grabbing action. For the light intensity of 300 mW/cm2, the grabbing action can be achieved about 1.5 s, while 10 s for the light intensity of 100 mW/cm2. The ability for grabbing objects of different materials is further investigated to demonstrate the versatility of the gripper. In summary, the developed gripper is rapidly and reliably driven by UV light, easy to fabricate, providing a great potential to facilitate the current research in photo-induced actuation applications.
Data-Informed Design Process for SME: A Streamlined Validation and Prediction Approach for Customer Perception of Innovative Material Prototypes
Authors: Quattelbaum Bastian , Steinem Christine , Neumann Marc 
Published: February 2024

Abstract
Designing innovative materials can be difficult and time-consuming. Small and medium-sized companies in particular struggle to validate their designs against customer opinions.
This paper presents an approach to validate customer perception with human senses of textile material prototypes and to predict this perception. The two most important factors for the customer’s product experience are the visual and the tactile sense. With haptics, a conscious sensation is realized when a customer strikes or touches the product. In times of short time-to-market goals and tight budgets, it is very difficult to realize this interaction with enough people.
A methodology is presented that displays the surface properties of designed materials only via a digital channel and is purely visually perceptible. The first step to product success is to gain insights into how customers perceive the newly designed products through digital channels. Furthermore, predicting the customer’s judgment on surface attributes as early as the design-prototype phase would further help to overcome the given limitations. Therefore, this paper examines whether and how specific surface measurements correlate with customer perceptions of different surface attributes. This offers the possibility to control customer-oriented design activities.
Finally, all collected data should be integrated into a data-informed design process to make informed product design decisions based on collected data. This goal is achieved as the methodology introduced makes the required data easily accessible and analyzable.
Global Modeling: Scaling Up Machine Learning Models for Predictive Maintenance
Authors: Morgenthal Allyson , Jain Akhilesh , Aman Michael , Gullikson Kevin , Egboga Nkem , Horton Marcus 
Published: February 2024

Abstract
Machine learning models based on Normal Behavior Modeling (NBM) have demonstrated effective anomaly detection for various equipment on Oil & Gas platforms such as compressors, pumps, separators, and level control valves. The NBM modeling approach uses autoencoder deep neural network architecture to learn the behavior of the equipment during normal operating periods and generates alerts when it encounters new or abnormal behavior. The autoencoder architecture outputs a risk score aggregated over the residuals from all input features. A Sequential Probability Ratio Test (SPRT) is performed on the risk score to determine abnormal regimes during operation to raise alerts. This approach has been very successful in identifying abnormal behavior with good performance on recall, precision, and lead time. However, scaling up these models to large volumes remains a challenge as an individual model is required for each piece of equipment of an asset type and requires years of data from normal operation for training. Our objective is to modify the original NBM workflow to create global models that generalize for all equipment of the same asset type and can be deployed across sites and platforms. A global modeling approach is applicable for equipment of the same asset type, having the same sensors, a consistent physical correlation between these sensors, and sufficient data during normal operation. Once these criteria are established for a set of equipment, a global model is created by training the model on a combined training set from the set of equipment. The combined training data is created by removing data with any downtime, failure events, and operational issues. A portion of this combined data is set aside for validation. Once the model is trained, new data is used for making predictions. We applied this workflow successfully on Uninterrupted Power Supply (UPS) units and Dry Gas Seals (DGS) for gas turbine compressors. This work is currently being expanded to Subsea Power and Communication Units (SPCUs) and Annulus Wells. The proposed approach allows for the quick deployment of a single global anomaly detection model to the entire asset type instead of having to build equipment-specific models. Global models are exposed to a more varied training dataset allowing for less retraining during operational shifts and can be deployed for equipment with no historical data.
Investigation of Annular Gas Seal Performance With Combined Cavity Patterns Using Computational Fluid Dynamics
Authors: Yildiz Saltuk , Untaroiu Alexandrina 
Published: February 2024

Abstract
Annular gas seals are crucial components in turbomachinery applications since they reduce leakage flow from high-pressure to low-pressure sections. However, the fluid-induced rotor instabilities that may be governed by these components result in reduced efficiency and even failure in rotating machines. Thus, the proper selection and design of annular seals are critical to ensuring the stability and reliability of rotating machinery. Minimization of leakage rate while improving rotordynamic performance can be achieved by using proper cavity geometries. In this aspect, this work investigates leakage and rotordynamic characteristics of combined hole-pattern annular gas seals composed of circular and hexagonal patterns using 3D computational fluid dynamics (CFD). In literature, annular gas seals with circular and hexagonal cavities are investigated depending on shape of the patterns. According to previous research, the rotordynamic and leakage performance can be improved with the parameterization-based design optimization of the cavity shapes such as hole depth, diameter, distancing etc. Most of the existing studies investigate the performance of annular gas seals using bulk-flow analysis. Although bulk-flow model is more efficient in terms of computation time, CFD is a more proper tool to analyze patterned seals which have geometric complexity in terms of accuracy. The main reason is that the 3D CFD model enables to perform more realistic assumptions which results in more accurate outputs especially for complex designs in comparison to bulk-flow analysis. The performance of proposed combined-pattern seals is investigated comparing with the performance of existing industrial regular circular and hexagonal (i.e., honeycomb) patterned annular gas seals. Computational models are verified through existing experimental data for the circular hole-pattern seal which is selected as the baseline model. This investigation performs optimal selection of combined-pattern cavity geometry selected from the pool of configurations considered and provides an insight for further optimization efforts on patterned annular gas seals.
Automatic Evaluation of Inline Surface Topology Measurement Data of Material Jetted Metal Parts for Closed Loop Control
Authors: Rehekampff Christoph , Huber Markus , Kirchebner Benedikt , Irlinger Franz , Lueth Tim C. 
Published: February 2024

Abstract
Additive Manufacturing is becoming increasingly important, which is demonstrated by its rapidly growing market volume. For processing metals, mainly powder based methods such as Powder Bed Fusion (e.g. Selective Laser Melting, Electron Beam Melting) or Direct Energy Deposition (e.g. Laser Metal Deposition) are used. The drawback of these methods is the raw material: Its price per weight is much higher than the raw material price, since powders with a narrow particle size distribution and spherical shape are required for these processes. Furthermore, the handling of the metal powder requires specific safety precautions due to the high risk of fire and negative health implications.
Material Jetting is an additive manufacturing method where the build material is deposited onto a platform as single droplets. The raw material can have almost any shape, as it is melted in a crucible. For printing a part, either the build platform or the printhead are moved along a predefined path while droplets are ejected to generate the desired part geometry. For polymers, there are multiple commercially available systems that operate this way. For metal printing however, the systems are still in an early stage of development due to the challenges of the process. Especially generating droplets of a certain size and depositing them at a precise position next to each other is challenging.
In this work, a method for a closed-loop layer height control based on inline surface measurement of the printed layers is described. For this, the determined measurement data needs to be automatically processed and compared to the desired layer height. An approach to compensate deviations by individually varying the size of single droplets is described.
Studying Reultrasonication Effects on the Suspension Stability of Stored Nanofuels Based on Optical Measurements
Authors: Mollick Rahat , Nagarkar Nitin , Loskill Ford , Ratner Albert 
Published: February 2024

Abstract
Suspensions of pure energetic nanomaterials in a liquid fuel, commonly termed as nanofuels, are desirable for their vastly different combustion properties compared to base fuels. Large scale, as well as lab scale, storage of such nanofuels is very challenging due to settling that occurs in these fuels over time. Various effects including high specific surface area (SSA) and high surface energy of nanoparticles facilitate nanomaterial agglomeration. The gradual agglomeration of the initially dispersed nanoparticles promotes their separation from the base fuel, followed by gravitational settling to the bottom due to the agglomerates growing in size. Reultrasonication of these stored colloidal suspensions has been taken to be the effective, and thus primarily employed, method to redisperse the settled nanomaterials in the base fuel. However, the settling behavior of these reultrasonicated samples can be significantly different from that of the initially prepared samples. This effect can introduce errors in determining important parameters such as burning rate, flame standoff ratio (FSR), and thus contributes to overall uncertainty in experimental results. To address this particular concern, the present research deploys a non-contact, non-invasive, quantitative, and simple experimental configuration to analyze suspension behavior. Soy Biodiesel, a promising renewable fuel was chosen as the base fuel and Acetylene Black was selected as the nanomaterial for the suspensions in this experimental study. Acetylene Black (AB) is both inexpensive and environmentally benign and has been previously employed as a carbon-based nanomaterial fuel additive. Surfactants are commonly used to help disperse nanomaterials, and they are included in this test protocol as well. To investigate the effect of such surfactants on the settling behavior, Span 80 (SP 80), a non-ionic surfactant with a Hydrophilic-lipophilic balance (HLB) value of 4.3, is tested and the effect of its concentration on the settling time of the samples is also recorded.
Hydrocarbon Condensation Heat Transfer Enhancement Using Silica Nanosprings and Slippery Liquid-Infused Porous Surfaces (SLIPS)
Authors: Vuth Adam Y. , Dillman Andrew R. , Conway Daniel F. , Chamberlin Joseph P. , Corti Giancarlo , Sommers Andrew D. 
Published: February 2024

Abstract
In this work, slippery liquid-infused porous surfaces (SLIPS) and fluorosilane coatings are applied to amorphous silica nanospring (SN) structures grown on the surface of aluminum tubes to promote dropwise condensation and to enhance heat transfer performance during methanol condensation. Initially, the fluorosilane coating (i.e. heptadecafluoro-1,1,2,2-tetrahydrodecyltrimeth oxysilane) and SLIPS were applied to flat aluminum samples containing SNs to measure coating performance. After determining the optimal coating parameters, round aluminum tubes were prepared using the same coating parameters. These tubes were then placed in a custom-built environmental chamber and subjected to saturated methanol vapor where Tsat = 21–22°C. The performance of the coating was quantified using static and advancing contact angle (CA) measurements and scanning electron microscope (SEM) imaging. Heat transfer data were collected at different subcooling degrees and cooling water volumetric flow rates to see how the condensation behavior changed with different operating conditions. Preliminary data with methanol have shown that SN mats functionalized with the fluorosilane and SLIPS coating have CAH values as low as 5.71°. Similarly, the CAH of SNs functionalized with the fluorosilane alone (no SLIPS) was shown to decrease from 50.5° to 28.0° following three repeated liquid immersion-based coatings in the 17F silane. Current work is focused on measuring the heat transfer performance of these SN coated aluminum tubes functionalized with the SLIPS and fluorosilane coatings to see what combination leads to the best heat transfer performance. This analysis should provide additional insight into how to improve condensation heat transfer performance of low-surface tension fluids like methanol and also highlight the use of SLIPS and fluorosilane coatings in these types of applications.
Determining the Physical Components of Resistance Acting on a Hydrofoil
Authors: Chernyshev Lev , Kabaliuk Natalia , Jermy Mark , Corkery Simon , Bernasconi Daniel 
Published: February 2024

Abstract
Hydrofoils have become a popular design addition to different kinds of waterborne vessels, particularly yachts and race boats. A prime example of this is the Americas Cup yacht regatta, where hydrofoils are now a mainstay of boat architecture. Hydrofoils permit vessels to lift their hulls above the water surface through the generation of lift. When in this “foiling” regime, the drag forces on the hull are drastically lower, permitting greater sailing speed. Predicting hydrodynamic forces produced from hydrofoils on race yachts remains challenging, due to a wide operating regime and complex hydrodynamical phenomena. The purpose of this study was to investigate methods of decomposing the total drag force of a hydrofoil from computational fluid dynamics (CFD) simulations into its physical components. A rectangular NACA4412 was simulated fully submerged at a depth of one chord. Viscous flow was simulated with the k-ω SST turbulence model in steady state; and the volume-of-fluid (VOF) model was used to capture free surface effects. Chord Froude numbers of 0.5, 1, 2 and 4 were investigated, corresponding to Reynolds numbers of 2.57 × 105, 5.15 × 105, 1.03 × 106 and 2.06 × 106. Force decomposition was performed using wave pattern analysis, Trefftz plane analysis and viscous wake surveys. These yielded the wave-pattern, induced, and viscous drag components respectively. Wave drag was seen to decrease with Froude number, while the induced drag increased. The sum of viscous and wave drag closely approximated the total drag computed using surface integration, supporting the Froude decomposition of drag force. Induced drag was seen to interact with the other drag components and became the dominant component at Froude numbers above unity.
Hydro-Structural Design Exploration of Floating Platform for Offshore Energy Systems
Authors: Cain Chandler S. , Lee Yong Hoon 
Published: February 2024

Abstract
This paper presents a comprehensive study on the hydro-structural design exploration of floating platforms for offshore energy systems. The study aims to develop a novel design method that optimizes the structure of the platform for stable dynamic responses to ocean waves, ensuring that the motion of the platform remains within acceptable acceleration ranges in all six degrees of freedom, while ensuring satisfactory of geometrical constraints. The study delves into the free-form design of the outer columns of the floating platform beyond conventional pre-defined shapes to enhance the overall performance of the system. The design utilizes a parameterization based on free-form spline interpolation for the outer shape of the hull and fixed-shaped pontoons to connect to the central structure where the energy-generating device (e.g. wind turbine) is installed. The study employs hydrostatic, hydrodynamic, and time-domain structural dynamic simulations within a monolithic multidisciplinary design optimization formulation to evaluate the overall dynamic responses of floating platforms. Overall, this study provides valuable insights into the hydro-structural design of the floating platforms for offshore energy systems. The optimal shape of the outer column suggests that the concave design enhances dynamic performance by effectively reducing the span-wise footprint of the platform. The results offer design considerations for floating platform hull developers to create robust designs that can withstand harsh metocean conditions. The findings obtained from the optimization solutions suggest the need for advanced design exploration and shape optimization of the floating platform hull, including the pontoons and the central structure for optimal performance. The study also suggests employing manufacturability constraints and wave loadings in all possible directions to reflect real-world operating conditions of floating platforms.
A Comparison of Motion Planning Methods for Autonomous Ground Vehicle Exploration and Search
Authors: Khairnar Apoorva , Sivashangaran Shathushan , Eskandarian Azim 
Published: February 2024

Abstract
Navigation of an Autonomous Ground Vehicle (AGV) is a major challenge for complex applications such as exploration of uncharted terrain, search and rescue (SAR) missions, and military assignments. An AGV is subjected to numerous uncertainties such as unexpected obstacles, uneven terrain, and hazardous regions. Motion planning algorithms are responsible for the safe, quick, and energy-efficient autonomous navigation of an AGV in the surrounding environment. In this paper, we compare the performances of conventional motion planning algorithms that include A*, RRT, RRT*, pRRT, and APF for exploration and search tasks. The comparison is carried out in a realistic environment simulated in AutoVRL, a high-fidelity simulator built using open-source tools. This study assumes the presence of a high-level planner that provides the algorithms with environmental information, and application-specific goal positions. The algorithms are compared on the basis of computation time, and efficiency of the generated trajectories, to navigate to goal positions determined by the high-level planner. The results indicate that A* and pRRT perform significantly better than the other tested algorithms, yielding the most efficient trajectories with the least computation time.
Higher Order Sensitivity Analysis for Elastic Problems Using the Multidual Finite Element Method
Authors: Avila David , Montoya Arturo , Millwater Harry 
Published: February 2024

Abstract
In this research study, the multidual finite element method (ZFEM) was assessed for its accuracy and efficiency in computing second-order derivatives of output variables with respect to input variables in elastic problems. The accuracy of multidual ZFEM in elastic problems was examined by comparing the multi-order derivatives of displacement and stress fields with respect to material, geometric, and loading parameters in a problem involving a thick-walled cylinder subjected to internal pressure. The findings revealed that higher-order derivatives with respect to material parameters exhibited greater accuracy compared to those associated with geometry, as measured by the L2 norm. This trend was also observed for mixed derivatives involving various types of input parameters, such as shape and material parameters. Overall, the multidual ZFEM demonstrated the ability to accurately compute higher order derivatives in elastic problems, regardless of the type of input parameter, within a single analysis. This stands in contrast to conventional numerical techniques like Finite Difference, which necessitate multiple analyses to determine the appropriate step size for perturbing the input variable. The higher-order derivatives provided by multidual ZFEM hold significant utility across different engineering domains, including uncertainty quantification.
A Reduced Order Model for Estimation of Fractional Flow Reserve (FFR) in Coronary Artery Disease: Assessing the Impact of Side Branches
Authors: Vila Arber , Ahmed Mohammad , Taebi Amirtaha , Dong Pengfei , Gu Linxia , Thibbotuwawa Gamage Peshala 
Published: February 2024

Abstract
Millions of people worldwide are affected by coronary artery disease (CAD), which is a major cause of morbidity and mortality. To plan effective treatment and stratify risk, it is crucial to evaluate the severity of CAD. Fractional flow reserve (FFR) is a vital parameter used in clinical practice to evaluate CAD severity, which measures the ratio of mean distal coronary pressure to mean aortic pressure during hyperemic conditions. Non-invasive methods for estimating FFR have gained popularity, but computational fluid dynamics (CFD), which is often used, is impractical for routine clinical use due to the time and resources required. To address this issue, a reduced order model is proposed that effectively captures hyperemic conditions and considers the impact of side branch flow on FFR. The model approximates artery sections and branches as Windkessel models and simulates the hyperemic condition by varying microvascular resistance. The study’s preliminary results reveal that the proposed model accurately captures hyperemic conditions and side branch flow’s impact on FFR, providing crucial insights for clinical decision-making. This approach presents a promising way to evaluate CAD severity more efficiently and accurately using non-invasive methods, paving the way for non-invasive assessment of CAD severity. Further studies are necessary to validate the model’s accuracy and potential for clinical translation.