Multiple Configurations Research Articles

Numerical Study on Hydrodynamic Performance and Vortex Dynamics of Multiple Cylinders Under Forced Vibration at Low Reynolds Number

Flow around clustered cylinders is widely encountered in engineering applications such as wind energy systems, pipeline transport, and marine engineering. To investigate the hydrodynamic performance and vortex dynamics of multiple cylinders under forced vibration at low Reynolds numbers, with a focus on understanding the interference characteristics in various configurations, this study is based on a self-developed radial basis function iso-surface ghost cell computing platform, which improves the implicit iso-surface interface representation method to track the moving boundaries of multiple cylinders, and employs a self-constructed CPU/GPU heterogeneous parallel acceleration technique for efficient numerical simulations. This study systematically investigates the interference characteristics of multiple cylinder configurations across various parameter domains, including spacing ratios, geometric arrangements, and oscillation modes. A quantitative analysis of key parameters, such as aerodynamic coefficients, dimensionless frequency characteristics, and vorticity field evolution, is performed. This study reveals that, for a dual-cylinder system, there exists a critical gap ratio between X/D = 2.5 and 3, which leads to an increase in the lift and drag coefficients of both cylinders, a reduction in the vortex shedding periodicity, and a disruption of the wake structure. For a three-cylinder system, the lift and drag coefficients of the two upstream cylinders decrease with increasing spacing. On the other hand, this increased spacing results in a rise in the drag of the downstream cylinder. In the case of a four-cylinder system, the drag coefficients of the cylinders located on either side of the flow direction are relatively high. A significant increase in the lift coefficient occurs when the spacing ratio is less than 2.0, while the drag coefficient of the downstream cylinder is minimized. The findings establish a comprehensive theoretical framework for the optimal configuration design and structural optimization of multicylinder systems, while also providing practical guidelines for engineering applications.

Bargaining powers in cooperative Carbon Dioxide Removal deployment

ABSTRACT International cooperation has the potential to significantly reduce the costs of implementing Carbon Dioxide Removal (CDR) in line with the Paris Agreement. However, the success of interregional cooperation depends on whether a satisfying agreement can be reached. Regional bargaining powers may heavily influence the outcome of such an agreement. This paper uses cooperative game theory to assess bargaining powers in the cooperative deployment of CDR between the United States (US), the European Union (EU), Brazil, and China. We compute least-costly CDR pathways under multiple cooperative configurations using the Modelling and Optimisation of Negative Emissions Technologies (MONET) framework, assuming regional CDR targets that are proportional to greenhouse gas emissions. Then, we apply cooperative game theory to derive relative bargaining powers from the cost evaluations in MONET. We find that cooperation can lead to substantial CDR cost reductions, ranging from 11% to 43%. Furthermore, we identify two distinct types of regions that possess considerable bargaining power: (1) regions with minimal historical responsibility towards climate change but abundant resources for CDR implementation (exemplified by Brazil in this study); and (2) regions with limited domestic resources amidst large CDR targets (represented by either the USA or China, here). These findings illustrate the potential leverage certain regions in the Global South could wield in the collaborative deployment of CDR under Article 6 of the Paris Agreement.

Ultra-miniaturized Bloch mode metasplitters for one-dimensional grating waveguides.

We present, for the first time, to our knowledge, power splitters with multiple channel configurations in one-dimensional grating waveguides (1DGWs) that maintain crystal lattice-sensitive Bloch mode profiles without perturbation across all output channels, all within an ultra-miniaturized footprint of just 2.1 × 2.2 μm2. This novel capability reduces the need for transition regions, simplifies multi-channel configurations of 1DGWs, and maximizes the effective use of chip area. The pixelated metamaterial approach, integrated with a time-domain heuristic algorithm, is utilized to concurrently achieve broadband operation, optimized dispersion control, and minimal loss. We experimentally demonstrate that the 1 × 2 and 1 × 3 metasplitters achieve average minimum losses per channel of 3.80 dB and 5.36 dB, respectively, which are just 0.80 dB and 0.59 dB above ideal splitting. The measurements for both designs demonstrate a 1 dB bandwidth of 15 nm, with excellent uniformity across all output channels. These versatile metasplitter designs can serve as fundamental building blocks for ultrahigh-bandwidth, densely integrated photonic circuits and in scenarios where slow light is essential.

Determination of Mechanical Properties of Blind Rivet Joints Using Numerical Simulations and Experimental Testing.

This study explores the tensile performance of blind rivet joints in galvanized steel sheets, focusing on their behavior under shear and normal load conditions. Blind rivets are frequently used in structural applications due to their ease of installation and ability to be applied from one side, making them highly effective in industries like aerospace and automotive. Two types of DIN 7337-4.8 × 8 blind rivets-galvanized steel St/St and stainless steel A2/A2-paired with galvanized steel sheets DX51D + Z275, were experimentally tested to assess how their material properties affect their joint strength, deformation patterns, and failure modes. Single-lap shear, double-lap shear, and pure normal load tests were conducted in multiple configurations to evaluate joint performance under varying loading conditions, simulating real-world stresses. Using custom-built equipment, controlled forces were applied perpendicular to the rivet joints to replicate practical loading conditions. The results revealed distinct differences in the load-bearing capacities of the two materials, offering valuable insights for applications where corrosion resistance and structural integrity are critical. Finite element analysis (FEA) was then used to simulate the behavior of the joints, with the results validated against experimental data. To enhance the reliability of numerical simulations in optimizing the design of rivet joints, a methodology was proposed to calibrate non-linear FEA models to experimental results, and a substantial agreement of 92.53% was achieved via optimization in ANSYS OptiSLang. This research contributes to our broader understanding of riveted connections, providing practical recommendations for assessing the performance of such joints in various engineering fields.

Tunable magnetic and electronic properties of CrS2/VS2 lateral superlattices.

Two-dimensional (2D) lateral heterostructures and superlattices, especially those based on transition metal dichalcogenides, boast exceptional properties for electronics, optoelectronics, and photovoltaics. Our study delves into the intricate superlattice architecture of CrS2/VS2, as well as its magnetic and electronic attributes, utilizing the framework of density functional theory. The CrS2/VS2 superlattice, crafted by seamlessly stitching together CrS2 and VS2 monolayers along their armchair interfaces, demonstrates remarkable stability and magnetism. Notably, the magnetic phase transitions exhibited by this superlattice are intricately linked to its overall size and sublattice width. Furthermore, the electronic structures of these CrS2/VS2 superlattices exhibit a strong dependence on the widths of the CrS2 and VS2 ribbons. In smaller superlattices, spin-down electrons establish semiconductor-semiconductor contacts with a distinct type II band alignment. Conversely, spin-up electrons forge metal-metal contacts, facilitating spin-dependent 2D electron segregation. However, in larger superlattices, the electronic states are more constrained, leading to metal-semiconductor contacts that exhibit ohmic conductivity within a single spin channel. This versatility in integrating various magnetic and contact modes fosters multiple structural configurations, ushering in an exciting new paradigm characterized by significant tunability. This advancement holds immense promise for the development and application of multifunctional spintronic devices, offering a wide range of possibilities for future technological innovations.

Impact of Room Colors and Construction Materials on Temperature Distribution in Tropical Climate: An Experimental Study in Nonthaburi, Thailand

This experimental research investigates the thermal performance of different room colors and construction materials under Thailand's tropical climate conditions. The study was conducted over a six-month period in Nonthaburi, Thailand, examining temperature variations across multiple room configurations. Using precision monitoring equipment, we analyzed how traditional Thai building materials and various wall colors affect indoor temperature distribution. The research encompassed both modern and traditional Thai construction materials, including concrete, brick, traditional teak wood, and bamboo, combined with different wall colors (white, cream, terra cotta, and dark blue). Results demonstrated that rooms with traditional materials like teak wood maintained lower average temperatures (by 3.2°C) compared to modern concrete structures, while light-colored walls showed up to 4.1°C temperature difference compared to darker colors during peak sunshine hours.

How Users' Personality Traits Predict Sentiment Tendencies of User-Generated Content in Social Media: A Mixed Method of Configuration Analysis and Machine Learning.

Social media content created by users with different personality traits presents various sentiment tendencies, easily leading to irrational public opinion. This study aims to explore the relationships between users' personality traits and sentiment tendencies of user-generated content (UGC). We crawled 18,686 tweets of 1, 215 users from Twitter to figure out the relationships between personality traits and sentiment tendencies. This study utilizes Essays and Sentiment datasets to train machine learning models for the identification of personality traits and sentiment tendencies and then explores the configuration effect of personality traits on sentiment tendency via crisp-set Qualitative Comparative Analysis (csQCA). The findings suggest that (1) one-dimensional personality trait is not a necessary condition for the sentiment tendencies of UGC. (2) There are multiple equivalent configurations that lead to the sentiment tendencies of UGC. The study suggests that the sentiment tendencies pattern of UGC can be discovered via the configurations of various dimensions of personality traits.

Monitoring conformational changes in the human neurotransmitter transporter homologue LeuT with 19F-NMR spectroscopy.

Neurotransmitter:sodium symporters (NSS) reuptake neurotransmitter molecules from the synaptic space through Na+-coupled transport. They are thought to work via the alternating access mechanism, exploring multiple configurations dictated by the binding of substrates and ions. Much of the current knowledge about these transporters has been derived from examining the structure of the Leucine Transporter (LeuT), a bacterial counterpart to human NSSs. Multiple crystal structures of LeuT provided valuable information regarding the steps involved in this mechanism. Dynamical data connecting the crystal structure to the transport cycle are critical to understanding how ligands are translated through the membrane. In the present study, we applied 19F-based nuclear magnetic resonance (NMR) spectroscopy to 19F labelled LeuT to monitor how substrates and ions binding affect the conformations of the transporter. By selecting mutations and ligands known to affect the conformational equilibrium of LeuT, we identified and assigned four NMR resonances to specific conformational states of LeuT. We observe that Na+ ions produce closure of the extracellular vestibule to a state similarly induced by Na+ and substrates. Conversely, K+ ions seem to shift the conformational equilibrium toward inward-facing intermediates, arguably by competing with Na+. The present study assembles a framework for NMR-based dynamical studies of NSS transporters and demonstrates its feasibility for tackling large membrane LeuT-fold transporters.

Design of Multi-Wing Deployment Mechanism Based on Multiple Performance Indicators and Topology: Theoretical and Experimental Study

The folding wing deployment mechanisms of aircraft are constrained by weight and space limitations, necessitating fewer actuators, multiple outputs, compact designs, and high efficiency. However, existing folding wing mechanisms often overlook the impact of performance in configuration design. Additionally, they struggle to achieve the synchronous unfolding of multiple wings using a single drive. This paper presents a design methodology for multi-wing deployment mechanisms based on configuration topology and multiple performance indicators. Specifically, seventeen configurations of multi-wing deployment mechanisms are developed based on functional requirements, along with expression methods for performance indicators, including compactness, mechanism stiffness, dynamic sensitivity, motion transmission, and joint transmission efficiency. The optimal configuration for the multi-wing deployment mechanism is identified utilizing the fuzzy comprehensive evaluation. Moreover, the transmission efficiency of multiple configurations is calculated across different scale parameters. Simulation analyses and prototype experiments are conducted to validate the design. The results indicate that the transmission efficiency of the optimized configuration consistently exceeds that of the other configurations. Maximum efficiencies surpass those of the other configurations by 2.4% and 5.7%. Importantly, this study introduces a quantitative expression method for multiple performance indicators at the configuration design stage. This approach enables the integration of various performance metrics with configuration designs. Overall, this research provides a novel approach for the innovative design of multi-wing deployment mechanisms in aircraft. Additionally, considering performance in the selection of mechanism configurations during engineering design offers valuable insights and potential applications.

Development and validation of a generalized, AI-based inline void defect detection solution for FSW based on force feedback

Friction stir welding is a solid-state joining process that operates below the material’s melting point commonly used to join aluminum parts, avoiding the drawbacks of fusion-based methods. These resulting advantages have accelerated growth and are increasing the number of applications across a range of industrial sectors, many of which are safety–critical. Along with the increase in applications and rise in productivity the need for reliable and cost-effective, non-destructive inline quality monitoring is rapidly growing. This publication is based on the research group’s ongoing efforts to develop a capable generalized inline-monitoring solution. To detect and classify FSW defects, convolutional neural networks (CNNs) based on the DenseNet architecture are used to evaluate recorded process data. The CNNs are modified to include weld and workpiece-specific metadata in the classification. These networks are then trained to classify transient weld data over a wide range of welding parameters, three different Al alloys, and two sheet thicknesses. The hyperparameters are incrementally tuned to increase weld defect detection. The defect detection threshold is tuned to prevent false negative classifications by adjusting the cost function to fit the needs of a force-based detection system. Classification accuracies > 99% are achieved with multiple neural network configurations. System validation is provided utilizing a newly recorded weld dataset from a different welding machine with previously used parameter/workpiece combinations as well as parameter combinations and alloys as well as sheet thicknesses outside the training parameter range. The generalization capabilities are demonstrated by the detection of > 99.9% of weld defects in the validation data.

Light Weight Deflectometer Configurations for Testing Thin Asphalt Pavements

The light weight deflectometer (LWD) is used to determine fundamental soil properties such as deflection and modulus. Recently, the LWD has been investigated as a potential structural evaluation tool for thin flexible pavements to generate input data for structural performance prediction models of thin flexible pavements. The falling weight deflectometer (FWD) can be used for the evaluation of thin flexible pavements; however, the relatively high cost and logistics burden, which also may render FWD unsuitable for many local agencies, are disadvantageous for the rapid assessment of dispersed rural roadways. The LWD is a potential alternative; however, since various LWD models are available and multiple testing configurations are possible, this study focused on determining which LWD configuration(s) produce structural responses that most closely correspond to FWD. Ten different LWD combinations of base plate diameter, drop weight, and drop height were tested on two full-scale thin asphalt test sections under closely comparable temperature and moisture conditions. The LWD’s ability to indicate structural conditions was compared with that of the FWD through impulse stiffness modulus. The distribution of stress within the pavements was measured during testing. In addition, the LWD’s repeatability was assessed and found to be similar to that of the FWD. As a result, the small 5.91-in. plate diameter, 22-in. drop height, and largest available hammer weights for both LWD models obtained good repeatability and the closest correspondence to FWD when tested on thin flexible pavements.

On the Local Formation of the TRAPPIST-1 Exoplanets

The discovery of seven approximately Earth-mass planets orbiting the 0.09 M ⊙ M dwarf TRAPPIST-1 captivated the public and sparked a proliferation of investigations into the system’s origins. Among other properties, the resonant architecture of the planets has been interpreted to imply that orbital migration played a dominant role in the system’s early formation. If correct, this hypothesis could imply that all of the seven worlds formed far from the star, and might harbor enhanced inventories of volatile elements. However, multiple factors also contradict this interpretation. In particular, the planets’ apparent rocky compositions and nonhierarchical mass distribution might be evidence that they formed closer to their current orbital locations. In this paper, we investigate the latter possibility with over 600 accretion simulations that model the effects of collisional fragmentation. In addition to producing multiple TRAPPIST-like configurations, we experiment with a number of different models for tracking the evolution of the planets’ volatile contents and bulk iron-to-silicate ratios. We conclude that a trend in bulk iron contents is the more likely explanation for the observed radial trend of decreasing uncompressed densities in the real system. Given the degree of radial mixing that occurs in our simulations, in most cases we find that all seven planets finish with similar volatile contents. Another confounding quality of the TRAPPIST-1 system is the fact that the innermost planets are not in first-order resonances with one another. By applying a tidal migration model to our most promising accretion model results, we demonstrate cases where higher-order resonances are populated.

Methane emission reduction through hydrogen blending in a large bore 2-stroke lean-burn natural gas compressor engine

Impending and increasingly stringent emissions regulations regarding natural gas compressor engines drive the research behind blending hydrogen with natural gas to make these internal combustion engines and their combustion process more efficient. This investigation seeks to answer two fundamental questions: will blending hydrogen with natural gas reduce overall engine fuel consumption, and can greenhouse gas emissions be reduced by blending hydrogen with natural gas? A 4-cylinder Cooper–Bessemer GMV engine, housed at Colorado State University’s Powerhouse facility, was investigated for hydrogen–natural gas blending using multiple engine configurations. A lean-burn engine uses an active pre-combustion chamber as its ignition source, along with electronically activated high pressure fuel injection in the main combustion chamber. One configuration tested utilized high-pressure fuel injection and blending in hydrogen, up to 40% by volume, in both the main chamber and pre-combustion chamber fuel supplies. A second configuration, where the main combustion chamber fuel was solely natural gas and only the pre-combustion chamber received hydrogen-blended natural gas, was also tested. The final configuration to be tested used low pressure fuel injection with mechanically actuated valves in the main chamber with a traditional spark plug ignition source. All engine configurations saw reductions in methane emissions of up to 30% using blended natural gas and hydrogen. Carbon dioxide emissions were also shown to be reduced for the two configurations. A reduction in brake-specific fuel consumption of up to 2% was also seen for two configurations. These results support the hypothesis that blending hydrogen into natural gas can reduce engine total fuel consumption and reduce greenhouse gas emissions.

A Fluorinated Ether Co-solvent Enables High Operating Temperature Li-ion Batteries

Abstract Li-ion batteries are commonly used as electrochemical energy storage systems due to their high energy density. However, few Li-ion batteries can reliably function at elevated temperatures, which is necessary for space and defense applications. In this study, Li-ion electrolytes were prepared and investigated for use at 100°C. The previously developed baseline electrolyte, 1.0 M lithium hexafluorophosphate (LiPF6) in 1:1 ethylene carbonate (EC):ethyl-methyl carbonate (v/v) with 2 wt. % vinylene carbonate (VC), was altered to observe the effects of the lithium salts lithium difluoro(oxalato)borate (LiDFOB) and lithium difluorophosphate (LiDFP), and the fluorinated co-solvent 1,1,2,2-tetrafluoroethyl-2,2,3,3-tetrafluoropropyl ether (TTE). The resulting formulations showed significantly improved capacity retention at 100°C in multiple cell configurations. X-ray photoelectron spectroscopy characterization of the electrodes following cycling at high temperatures with the improved electrolyte revealed the cathode-electrolyte-interface to be boron-rich, while the graphite anodes were found to have little boron but were more fluorine rich compared to the baseline anodes. Raman spectroscopy determined notable changes in solvation structure upon addition of the TTE diluent. Overall, the use of various Li salts as well as the TTE co-solvent improved specific capacity retention at 100°C in Li-ion cells.

Binding of Sulfates and Water to Monovalent Cations.

The binding of the sulfate ligand group to monovalent cations in the presence of water is important for many systems. To understand the structure and energetics of sulfate complexes, we use density functional theory to study ethyl sulfate binding to the monovalent cations Li+, Na+, and K+, and to water. The free energies of binding and optimal structures are calculated for a range of the number of ethyl sulfates and waters. Without water, the most optimal structure for all the cations is bidentate binding by two ethyl sulfates, yielding a 4-fold coordination. With water, the lowest free energy structures also have two ethyl sulfates, but the coordination varies with cations. For complexes with water, the four oxygen atoms in the sulfate group enable multiple binding geometries for the cations and for hydrogen bonding with water. Many of these geometries differ in free energy by only a small amount (1-2 kcal/mol), meaning there will be multiple binding configurations in bulk solution. In comparison to the optimal structures for binding to the carboxylate group, there is more variation for binding to the sulfate group as a function of cation type and the number of waters. The polarization of the atoms is significant and varies among the sulfate oxygen atoms. The water oxygen charge is often larger than that of sulfate oxygen, which plays a role in the preference for monodentate ligand binding to cations in the presence of water.

Identifying multiple configurations in global innovation system (GIS): lessons from the salmon farming industry

ABSTRACT The global innovation system (GIS) approach has until now focused on defining industries by a single GIS configuration at a given time. However, by analysing 739 innovation projects in the Norwegian global salmon farming industry, we find that the four ideal GIS configurations can be identified simultaneously. We argue that this is because we refer to an industry with a large diversity of innovation projects generating different resources along varied scales of interaction. Our analysis is based on text analysis of descriptive documents and interviews about innovation projects. With this information, we built four GIS configurations that illustrate how actors, networks and institutions form and are connected through different types of subsystems. The purpose is to understand why some actors have diverse priorities when participating in different types of innovation projects within the same industry and suggest relevant policy ideas.

Two-dimensional optical binding based on graphene surface plasmon excitation.

In this work, we present a detailed analysis of the optical binding force resulting from the near field scattering between dielectric nanoparticles and a graphene substrate. We pay special attention to the two-dimensional array formation for which the stability is raised owing to the multiple surface plasmon (SP) scattering. Because of the small values of the SP wavelength on graphene in comparison with those of the photon, the size of these particle arrangements falls below the diffraction limit. By using a rigorous formalism based on the electromagnetic Green theory, we calculate the binding energy potential whose depth quantifies the stability of such configurations. We find multiple configurations for a given number of nanoparticles, from which only a subset of them remains stable under thermal fluctuations. Our contribution can be valuable for understanding the formation of new optically reconfigurable polaritonic materials.

PETAT — an ASIC for simple and efficient readout of large PET scanners

Modern PET scanners based on scintillating crystals use solid state photo detectors for light readout. The small area of these devices is beneficial for spatial resolution, but also leads to a large number of electronic channels to be read out, mostly by application specific integrated circuits (ASICs) containing amplification, noise reduction, hit finding, time stamping and amplitude measurement. Although each ASIC provides up to ≈ 64 channels, a large number of chips is required with the need for auxiliary electronic components like voltage regulators or FPGAs for control and data readout. The FPGAs in turn often require multiple supply voltages and configuration infrastructure, so that PCBs get complicated, cumbersome and power-hungry, in addition to the significant power requirement of the front-end ASICs. We address this issue in the latest generation of our PETA readout ASIC for SiPMs by a simplified control scheme and, in particular, by a hierarchical serial data readout which does not require any additional FPGA. In addition, it provides a time-sorted stream of hit data, allowing early on-detector data reduction and hit pre-processing like the removal of hits with no coincident partner. The simplicity of this readout facilitates a supply scheme where power/ground of multiple ASICs are connected in series instead of the standard parallel connection. This `serial-powering' approach can reduce supply current (while increasing overall supply voltage) so that voltage drop issues in the supply are alleviated.

Classifying School Scope Using Deep Neural Networks Based on Students' Surrounding Living Environments

This research investigates school scope classification using Deep Neural Networks (DNN), focusing on students living environments and educational opportunities. By addressing the interplay of socioeconomic and educational factors, the study aims to develop an analytical framework for understanding how environmental contexts shape academic trajectories. The research provides a nuanced understanding of the importance of features in educational classification by developing DNN models based on Spearman's Rank Correlation Coefficient (SRCC). The methodology employs machine learning techniques, integrating data wrangling, exploratory analysis, and multiple DNN models with K-fold cross-validation. The study analyzes 677 student records from two schools. The research examined multiple model configurations. Results show that the 'All Data' model achieved 83.08% accuracy, the 'Top 5' model 81.54%, and the 'Non-Top 5' model 79.23%. The SRCC-based approach revealed that while top correlated features are important, additional variables significantly contribute to model performance. The study highlights the profound impact of family background, social environment, and educational contexts on school selection. Furthermore, it demonstrates DNN's capability to uncover intricate, non-linear relationships, offering actionable insights for policymakers to leverage machine learning's potential in developing targeted educational strategies.

Design evolution of indirect evaporative air-cooling system through multiple configurations for the enhancement of heat and mass transfer mechanism

Cooling demand is escalating because of the climate change and population growth in emerging nations. One of the today's concerns is meeting exponentially raising cooling requirements. Additionally, sustainable development goals (SDGs) such as SDG 03, SDG 07, and SDG 13 emphasize the need of environmentally friendly cooling techniques for human thermal comfort. Therefore, it is essential to develop novel approaches for cooling indoor spaces. The current study presents the design evolution of a Maisotsenko cycle-based indirect evaporative air-cooling system (IEC), with a focus on air-water flow patterns, structural design, and the improved energy efficiency by utilizing locally accessible low-cost polymeric materials. This study also comprises a thorough experimental investigation of an indirect evaporative cooling system by developing multiple configurations (Config) of heat and mass exchangers at the stack level. The experimental findings demonstrate that thermal efficiency of the IECs enhances by increasing the ambient air temperature and wetted area in wet channels. Overall, the resultant wetbulb and dewpoint effectiveness of Config 1-Config 6 vary from 0.29 to 1.12 and 0.22 to 0.86, respectively. Moreover, the configuration 6 has the maximum cooling capacity, coefficient of performance (COP), and energy efficiency ratio (EER) of 2.07 kW, 6.91, and 23.59, respectively under control conditions by utilizing the air conditioning laboratory unit. The results clearly indicate that proposed design configurations incorporating polymeric materials, having high CC and EER, are more effective for air-cooling in hot and dry climate conditions.