Small-scale Model Research Articles

On V-Geometric Ergodicity Markov Chains of the Two-Inertia Systems

This study employs the diffusion process to construct Markov chains for analyzing the common two-inertia systems used in industry. Two-inertia systems are prevalent in commonly used equipment, where the load is influenced by the coupling of external force and the drive shaft, leading to variations in the associated output states. Traditionally, the control of such systems is often guided by empirical rules. This paper examines the equilibrium distribution and convergence rate of the two-inertia system and develops a predictive model for its long-term operation. We explore the qualitative behavior of the load end at discrete time intervals. Our findings are applicable not only in control engineering, but also provide insights for small-scale models incorporating dual-system variables.

Large-Scale Laboratory Experiments On The Wave Generation Due To The Collapse Of Partially And Fully Submerged Granular Columns

Landslides that occur in coastal environments can drive cascading consequences such as wave forces, flooding, and infrastructure damage to coastal communities. It can be difficult to classify these slides as subaerial or submarine, and the mechanics of wave generation associated with partially submerged failures are not well understood. Limited physical modelling has been conducted that encompasses both the triggering of granular landslides and subsequent waves associated with partially and fully submerged mass movements. To date, laboratory work investigating tsunamis generated by submarine landslides has focused on the wave formed in the direction of the mass movement (seaward direction) for rigid block experiments (eg. Rzadkiewicz, 1997) and deformable slide masses (e.g. Grilli et al., 2017, Takabatake, 2020, Bullard et al., 2023). From these experimental data sets, predictive relationships connecting slide acceleration, mass, and initial submergence depth to the amplitude of the wave formed have been presented for the seaward wave. Such relationships have not been presented for the landward directed wave, which propagates in the opposite direction of the submarine landslide motion. Further, not all landslides are easily classified as either subaerial or submarine. Consider the 2018 Anak Krakatoa landslide in which the sliding surface was estimated to be 100 m below sea level (Pakoksung et al., 2020), resulting in one third of the total collapse being submerged. In comparison to the end-member conditions of subaerial and submarine failures, the mechanics of wave generation associated with partially submerged failures is much less clear. Granular column collapse experiments provide an idealized experimental framework to explore momentum transfer processes and the resulting waves generated in partially submerged and fully submerged conditions. Work by Cabrera et al., (2020) made use of granular collapse experiments of partially to fully submerged columns to derive a continuous momentum-based function to estimate the maximum seaward wave amplitude based on the initial column submergence ratio (Hw/Ho). However, these experiments were conducted at a small-scale (Ho = 0.15 m) with a width of one particle (2.4 mm diameter). To address this research gap, a series of 22 large-scale granular collapse experiments were conducted by releasing columns of river stone (0.75 m and 0.50 m high) into a laboratory flume reservoir with water depths ranging up to 1.10 m to explore the wave generation and runup processes in both seaward and landward directions. The columns were released by a rapid pneumatically actuated vertical rising gate designed to enable the near instantaneous loss of support of the source volumes resulting in granular collapse. The failure mechanics were captured with high-speed cameras (Figure 1a,b) and wave amplitudes were measured using wave capacitance gauges (Figure 1c,d). This work also provides the first experimental data set of the landward propagating wave and runup associated with submerged granular collapse experiments. Overall, the seaward wave amplitudes measured in these highly-instrumented, large-scale physical models agree with empirical relationships developed in a previous study using smaller-scale models.

Methodology for Evaluating the Generalization of ResNet

Convolutional neural networks (CNNs) have achieved promising results in many tasks, and evaluating the model’s generalization ability based on the trained model and training data is paramount for practical applications. Although many measures for evaluating the generalization of CNN models have been proposed, the existing works are limited to small-scale or simplified model sets, which would result in poor accuracy and applicability of the derived methods. This study addresses these limitations by leveraging ResNet models as a case study to evaluate the model’s generalization ability. We utilized Intersection over Union (IoU) as a method to quantify the ratio of task-relevant features to assess model generalization. Class activation maps (CAMs) were used as a representation of the distribution of features learned by the model. To systematically investigate the generalization ability, we constructed a diverse model set based on the ResNet architecture. A total of 2000 CNN models were trained on the ImageNet subset by systematically changing commonly used hyperparameters. The results of our experiments revealed a strong correlation between the IoU-based evaluation method and the model’s generalization performance (Pearson correlation coefficient more than 0.8). We also performed extensive experiments to demonstrate the feasibility and robustness of the evaluation methods.

Investigation on similitude method for dynamic response of high-speed train body considering thickness distortion

Small-scale model test is an economical and efficient method to study the collision process analysis and crashworthiness design of full-scale high-speed trains. For high-speed trains, the vehicle body thickness of scaled train models is too small to be processed, resulting in thickness distortion. Although the distorted model can predict dynamic responses of the full-scale model, there are some errors in predicting the dynamic response parameters. This article proposes a new similitude distortion method to improve the prediction accuracy of the high-speed train body distorted model. First, the complete similitude relationship for train collisions was derived using dimensional analysis. According to the Buckingham π theory, the theoretical expression between prediction coefficient and distortion coefficient was obtained when the high-speed train thickness distortion occurred. Then, based on a full-scale high-speed train body prototype, the benchmark model and distorted model were established. Numerical simulation was used to explore the relationship between prediction coefficient and distortion coefficient. Finally, the distorted similar model was established using the similitude distortion method. The accuracy and feasibility of this approach were verified by comparing and analysing the dynamic response curves obtained by numerical simulations between the distorted similar model and prototype. The errors of the maximum displacement and deceleration were 2.70% and 8.26%. The results show that the similitude distortion method is reliable to relate dynamic responses of scaled models to the prototype when the vehicle body thickness distorts.

A two-scale method to include essential behavior of bolted connections in structures including elevated temperatures

A two-scale method is proposed to simulate the essential behavior of bolted connections in structures including elevated temperatures. It is presented, verified, and validated for the structural behavior of two plates, connected by a bolt, under a variety of loads and elevated temperatures. The method consists of a global-scale model that simulates the structure (here the two plates) by volume finite elements, and in which the bolt is modelled by a spring. The spring properties are provided by a small-scale model, in which the bolt is modelled by volume elements, and for which the boundary conditions are retrieved from the global-scale model. To ensure the small-scale model to be as computationally efficient as possible, simplifications are discussed regarding the material model and the modelling of the threads. For the latter, this leads to the experimentally validated application of a non-threaded shank with its stress area. It is shown that a non-linear elastic spring is needed for the bolt in the global-scale model, so the post-peak behavior of the structure can be described efficiently. All types of bolted connection failure as given by design standards are simulated by the two-scale method, which is successfully validated (except for net section failure) by experiments, and verified by a detailed system model, which models the structure in full detail. The sensitivity to the size of the part of the plate used in the small-scale model is also studied. Finally, multi-directional load cases, also for elevated temperatures, are studied with the two-scale method and verified with the detailed system model. As a result, a computationally efficient finite element modelling approach is provided for all possible combined load actions (except for nut thread failure and net section failure) and temperatures. The two-scale method is shown to be insightful, for it contains a functional separation of scales, revealing their relationships, and consequently, local small-scale non-convergence can be handled. Not presented in this paper, but the two-scale method can be used in e.g. computationally expensive two-way coupled fire-structure simulations, where it is beneficial for distributed computing and densely packed bolt configurations with stiff plates, for which a single small-scale model may be representative of several connections.

Measurement-based V2V propagation modeling in highway, suburban, and urban environments

Vehicle-to-vehicle (V2V) communication is one of the promising communication applications designed to optimize traffic conditions and has played a crucial role in the improvement of intelligent transportation technologies. Since there is still some uncertainty regarding generalized models that provide a more accurate representation of propagation environments, the existing literature emphasizes the need for additional experimental studies in various countries and propagation environments. This study aims to investigate the low-density and high-density characteristics of V2V channels for highway, suburban, and urban propagation environments in Türkiye. Thus, first, channel measurements were conducted for all propagation scenarios. Then, after the estimation of path loss parameters, the best-fitted path loss model was determined for each propagation scenario by comparing log-distance, two-ray, and log-ray models. It was observed that the log-ray model offered remarkably better performance than the two-ray model, especially in the majority of scenarios with two-ray characteristics. In addition, small-scale modeling and shadowing were also examined, and the outcomes were compared to relevant literature. Last, generalized path loss models were developed for six propagation scenarios and compared with previous studies. Providing additional experimental data on the impact of traffic and road environments that vary across countries on the V2V channel, this study not only validated and compared existing propagation models but also improved the representing accuracy and generalizability of the newly proposed propagation models. Here, all findings were presented in detail to support the motivation of the research.

Flow Exchange In Vegetated Environments: Integrating Experimental Insights Into Practical Engineering

Vegetation provides practical protective tools for estuarine and coastal regions. The roots, stems, and canopy systems of mangroves can divert and retard the flow field within and surrounding vegetation regions (Truong et al., 2019) and also absorb external forces from waves (Phan et al., 2015). The area within the vegetation is usually calmer compared to the unprotected region outside. Consequently, sediment tends to be deposited inside the vegetation region (Vargas Luna et al., 2015). The sediment deposited then may have feedback on the wave and flow field and the growth conditions of the vegetation (Truong et al., 2017). These mutual interactions between ecological area (vegetation), hydrodynamic conditsions (wave and flow field), and morphological conditions (sediment transport) are the crux of any proposed nature-based solutions (NbS). From a hydraulic engineering perspective, these dynamic interactions can translate into the momentum and mass exchange processes between vegetation and nearby areas. By observing the evolution of mangrove forests and associated with the rate of erosion/accretion of the shoreline, Phan, 2015 and Truong., 2017 proposed a hypothesis of “squeezed mangrove forest”, in which “the mangrove width” is considered a crucial length-scales that is related to the sustainable development of the mangroves. This length scale was physically interpreted and connected to the penetration of the mixing layer into the vegetation region (Truong., 2017; Truong et al., 2019). It is noted that whereas the characteristic of the incoming waves mainly controls the penetration of the mixing layer into the coastal mangroves, that of the estuarine mangroves is mainly governed by the characteristic of lateral flow. The latter is the primary focus of this study. Large vortex structures caused by the Kelvin-Helmholtz instability at the vegetation’s edge play an essential role in the transverse exchange of mass and momentum (White & Nepf, 2007; Truong et al., 2019). These structures are usually large compared to the water depths and are termed large horizontal coherent structures (LHCSs). The Reynolds Shear stresses (RSs) induced by LHCSs contribute more than 90% to the total turbulent shear stress at the edge of the floodplain vegetated region (Truong & Uijttewaal 2019). Nevertheless, our understanding of this topic often stems from small-scale laboratory experiments. Whether the presence and characteristic of vortex structures at the interface of the low flow and fast flow region obtained from small-scale physical models remain true for estuaries and coasts has not yet been determined. In order to obtain more insight into the physics of the exchange processes occurring at the vegetation interface at different scales, two unique physical models of vegetated channels have been conducted. One small-scale and another large-scale experiment, both with and without vegetation, were conducted at TU Delft Water Lab and the Korea Institute of Civil Engineering and Building Technology - River Experiment Center (KICT-REC), respectively. Two digital twin models of this flume were subsequently constructed using Delft3D, which were calibrated and validated using the collected datasets. In this study, recent findings pertaining to these experiments are presented

Snow depth in high-resolution regional climate model simulations over southern Germany – suitable for extremes and impact-related research?

Abstract. Snow dynamics play a critical role in the climate system, as they affect the water cycle, ecosystems, and society. In climate modelling, the representation of the amount and extent of snow on the land surface is crucial for simulating the mass and energy balance of the climate system. Here, we evaluate simulations of daily snow depths against 83 station observations in southern Germany in an elevation range of 150 to 1000 m over the time period 1987–2018. Two simulations stem from high-resolution regional climate models – the Weather Research & Forecasting (WRF) model at 1.5 km resolution and the COnsortium for Small scale MOdelling model in CLimate Mode (COSMO-CLM; abbreviated to CCLM hereafter) at 3 km resolution. Additionally, the hydrometeorological snow model Alpine MUltiscale Numerical Distributed Simulation ENgine (AMUNDSEN) is run at point scale at the locations of the climate stations, based on the atmospheric output of CCLM. To complement the comparison, the ERA5-Land dataset (9 km), a state-of-the-art reanalysis land-surface product, is also compared. All four simulations are driven by the atmospheric boundary conditions of ERA5. Due to an overestimation of the snow albedo, the WRF simulation features a cold bias of 1.2 °C, leading to the slight overestimation of the snow depth in low-lying areas, whereas the snow depth is underestimated at snow-rich stations. The number of snow days (days with a snow depth above 1 cm) is reproduced well. The WRF simulation can recreate extreme snow depths, i.e. annual maxima of the snow depth, their timings, and inter-station differences, and thereby shows the best performance of all models. The CCLM reproduces the climatic conditions with very low bias and error metrics. However, all snow-related assessments show a strong systematic underestimation, which we relate to deficiencies in the snow module of the land-surface model. When driving AMUNDSEN with the atmospheric output of the CCLM, the results show a slight tendency to overestimate snow depth and number of snow days, especially in the northern parts of the study area. Snow depth extremes are reproduced well. For ERA5-Land (ERA5L), the coarser spatial resolution leads to larger differences between the model elevation and the station elevation, which contributes to a significant correlation of climatic biases with the elevation bias. In addition, the mean snow depth and number of snow days are strongly overestimated, with conditions that are too snowy in the late winter. Extreme snow depth conditions are reproduced well in the low-lying areas, whereas strong deviations occur with more complex topography. In sum, due to the high spatial resolution of convection-permitting climate models, they show the potential to reproduce the winter climate (temperature and precipitation) in southern Germany. However, different sources of uncertainties, i.e. the spatial resolution, the snow albedo parametrisation, and other parametrisations within the snow model, prevent their further use in a straightforward manner for impact research. Hence, careful evaluation is needed before any impact-related interpretation of the simulations, such as in the context of climate change research.

YOLO-based Small-scaled Model for On-Shelf Availability in Retail

On-shelf availability (OSA) in the retail industry plays a very crucial role in continuous sales. Product unavailability may lead to a bad impression on customers and reduce sales. The retail industry may continue to develop through the rapidly advancing technology era to thrive in a market where competition is increasingly tough. Along with technological advancements in recent decades, Artificial Intelligence has begun to be applied to support OSA, particularly using object detection technology. In this research, we develop a small-scaled object detection model based on four versions of You Only Look Once (YOLO) algorithm, namely YOLOv5-nano, YOLOv6-nano, YOLOv7-tiny, and YOLOv8-nano. The developed model can be used to support automatic detection of OSA. A small-scale model has developed in the sense of post-practical implementation through low-cost mobile applications. We also use the quantization method to reduce the model size, INT8 and FP16. This small-scaled model implementation also offers implementation flexibility. With a total of 7697 milk-based retail product images and 125 different product classes, the experiment results show that the developed YOLOv8-nano model, with a mAP50 score of 0.933 and an inference time of 13.4 ms, achieved the best performance.

AGENT-BASED MODELING OF UP DILIMAN INTRACAMPUS PEDESTRIAN MOBILITY USING NETLOGO AND GIS

Abstract. In the Philippines, campus mobility studies remain underexplored. Since small-scale models help in policy making that may also be applied in a large spatiotemporal context, intracampus mobility modeling and simulation offer support for future mobility research. In this study, we designed a NetLogo model to apply ABM and GIS in understanding intracampus student pedestrian mobility. We created a deterministic model using discrete student schedule and a probabilistic model using the multinomial logistic (MNL) regression model derived from the student demographics data to analyze student movement. To provide a realistic representation of student pedestrians and campus environment, GIS spatiotemporal data was proven crucial to the pedestrian mobility models. The generated pedestrian count heatmaps show a significant increase in student count from online learning switching to face–to–face instruction. The overall campus building occupancy served as the key factor influencing the changes in agent behaviors such as walking speed and student tardiness. Moreover, the probabilistic model showed erratic movements of students. The model evaluation depends on the heavily imbalanced datasets and its multiclass nature with relatively higher model performance (F1 score > 0.8) is observed during morning, noon, and end schedule times, whereas suboptimal performance (F1 score < 0.6) where the dataset has a higher number of classes. The simulations demonstrate that ABM is a useful tool for examining pedestrian and transport patterns and can be enhanced further to allow multimodal and multiagent systems.

BiGNN: Bipartite graph neural network with attention mechanism for solving multiple traveling salesman problems in urban logistics

The multiple traveling salesman problems (MTSP), which arise from real world problems, are essential in urban logistics. Variations such as MinMax-MTSP and Bounded-MTSP aim to distribute workload evenly among salesmen and impose constraints on visited cities, respectively. Branch-and-bound (B&B) provides an exact algorithm solution for these problems. The Learn to Branch (L2B) approach guides branch node selection through deep learning. In this study, we utilize mathematical modeling of Bipartite Graph Neural Network (BiGNN) and an attention mechanism to support B&B in exploring solution spaces through imitation learning. The problems are framed to formulate mixed integer linear programming, which is different from conventional algorithms. It is proposed that a bipartite graph network approach makes a feature representation by setting a structure of constraints and variables. Experimental results showed that our model can generate more accurate solutions than three benchmark models. The BiGNN model can effectively learn the strong branch strategy, which reduces solution time by replacing complex calculations with fast approximations. Additionally, the small-scale instances model can be applied to larger-scale ones.

Evaluation of COSMO-CLM Model Parameter Sensitivity in the Study of Extreme Events across the Eastern Region of India

The present study aims to identify the parameters from the Consortium for Small-scale Modelling in CLimate Mode (COSMO-CLM) regional climate model that strongly controls the prediction of extreme events over West Bengal and the adjoining areas observed between 2013 to 2018. Metrics, namely Performance Score (PS) screen out the most persuasive parameter on model output. Additionally, the Performance Index (PI) measure the reliability of the model and Skill Score (SS) establishes the model performance against the reference simulation leading to the optimization of the model for a given variable. In this study, parameter screening for four output variables such as 2m-temperature, surface latent heat flux, precipitation and cloud cover of COSMO-CLM is accomplished. For heat wave simulations, 2m-temperature and surface latent heat flux are explored whereas cloud cover and precipitation are examined for extreme rainfall events. A total of 25 adjustable parameters representing the following parameterization schemes: turbulence, land surface process, microphysics, convection, radiation and soil. Out of the six parameterization schemes, the scaling factor of the laminar boundary layer for heat (rlam_heat) and the ratio of laminar scaling factors for heat over sea and land (rat_sea) from the land surface process is sensitive to SLH, TP. The exponent to get the effective surface area (e_surf) from the land surface has a large impact on 2m-temperature. A few parameters from microphysics (cloud ice threshold for auto conversion), convection (mean entrainment rate for shallow convection) and radiation (parameter for computing the amount of cloud cover in saturated conditions) play a significant role in producing TP, and TCC fields. It is evident from the results that the parameter sensitivities on model performance depend on the choice of the meteorological field. Furthermore, in almost all input model parameters, the model performance reveals the opposite character in different domains for a given meteorological field.

Optimization of unsteady jet control flow method for aerodynamic drag reduction of heavy truck model

The aerodynamic drag reduction of ground vehicles is crucial for meeting the increasingly strict emission standards. In this context, a potential approach to achieve this objective is to use active flow control techniques. Many active flow control techniques have been applied for ground vehicles, and the unsteady jet device has been proven to have better control energy efficiency than some other flow actuators. Therefore, this study focuses on applying this unsteady jet technique to a realistic truck model. The main parameters of the control flow device, including jet amplitude, jet angle, and control frequency were optimized to reduce the aerodynamic drag of the truck model and achieve the maximum control energy efficiency. Numerical studies were performed based on the CFD method with the k-ω SST turbulence model. A 25-level optimal Latin Hypercube design experimental study was conducted. The optimization method based on the Kriging response surface model and genetic algorithm was chosen to determine the optimal solution. The results show that the maximum energy efficiency is 9.174 % using the unsteady jet devices with the optimal parameters on the small-scale truck model.

Physical-Model-Based Assessment of Reduction of Hydraulic Forces Acting on Channel Bed through Advanced Energy Dissipator Design

The hydraulic engineering designers focus on shaping up the flow regime so that the greatest energy dissipation is ensured. On the other hand, the structural designers focus on load bearing capacity and durability of the structural components which may contradict the focus of the hydraulic experts. Such a case may occur when the spillway chute floor slab must be thin due to the limited spaces. Then, the dynamic load acting on the chute floor slab may compromise its long-term operation. The dynamic forces are created by the violent turbulences which may even coincide with the natural frequency of the reinforced concrete floor slab. Therefore, the objective of this paper is a complex approach to small-scale physical model testing which makes use of the latest rapid-prototyping techniques and the particle image velocimetry, and which in the end allows to estimate the magnitude of the dynamic hydraulic forces acting on the channel bed. The newly developed organic-shape energy dissipators play a key role in reducing the kinetic energy of water through its enhances aeration. The proposed method on a small scale can assist favorably the design process of real-scale hydraulic reinforced concrete structures.

Remove legacy perfluoroalkyl acids and emerging per- and polyfluoroalkyl ether acids by single-use and regenerable anion exchange resins: Rapid small-scale column tests and model fits

Rapid small-scale column tests (RSSCT) are used to study the removal of per- and polyfluoroalkyl substances (PFAS) for drinking water treatment by ion exchange. Breakthroughs of 15 emerging per- and perfluoroalkyl ether acids and six legacy perfluoroalkyl acid analogs are studied using a single-use PFAS-selective anion exchange resin (AER1) and a regenerable, generic anion exchange resin (AER2). The Bohart-Adams model was used to describe and predict breakthrough, with the modeled results reasonably aligned with RSSCT results in most cases, enabling shorter RSSCT duration for future applications. AER1 exhibited high uptake capacity with no breakthrough for 11 of the 21 tested PFAS during the 144,175 BV continuous operation, allowing compliance with the new National Primary Drinking Water Regulation in many application scenarios. AER2 exhibited much faster breakthroughs for most PFAS and is not a promising option for drinking water treatment. However, the summed PFAS capacity via model fit and total PFAS adsorbed via measurement were only <0.01 % of both resin capacities at full breakthrough, suggesting PFAS could only occupy a tiny portion of the ion exchange sites even for the PFAS-selective AER1. Ether group insertion in the PFAS group leads to later breakthrough, and linear isomers were better captured by the resins than the branched isomers. Overall, PFAS uptake capacity increases and kinetics decrease when the PFAS molecular volume increases. Regeneration using 10 % NaCl solutions partially released PFAS from AER2 but not from AER1, with more short-chain PFAS released than long-chain ones. Ether group insertion decreased the PFAS recoveries during the regeneration of AER2. The regenerated resins showed much faster breakthroughs than the pristine resins, making them unfavorable for drinking water treatment applications. Adsorption displacement of short-chain PFAS by long-chain PFAS was observed in pristine AER1, and post-regeneration leaching occurred for both resins, both phenomena making the resins a possible PFAS source in long-term use.

Persona-Identified Chatbot through Small-Scale Modeling and Data Transformation

Research on chatbots aimed at facilitating more natural and engaging conversations is actively underway. With the growing recognition of the significance of personas in this context, persona-based conversational research is gaining prominence. Despite the abundance of publicly available chit-chat datasets, persona-based chat datasets remain scarce, primarily due to the higher associated costs. Consequently, we propose a methodology for transforming extensive chit-chat datasets into persona-based chat datasets. Simultaneously, we propose a model adept at effectively incorporating personas into responses, even with a constrained number of parameters. This model can discern the most relevant information from persona memory without resorting to a retrieval model. Furthermore, it makes decisions regarding whether to reference the memory, thereby enhancing the interpretability of the model’s judgments. Our CC2PC framework demonstrates superior performance in both automatic and LLM evaluations when compared to high-cost persona-based chat dataset. Additionally, experimental results on the proposed model indicate the improved persona-based response capabilities.

Modular production of small ship models using 3D printing for model tests

This paper presents a cost-effective approach for the production of small-scale ship models, particularly suitable for model testing of high-speed vessels characterized by a high Froude number. In contrast to traditional approach employed in ship model production, the proposed approach uses 3D printing technology integrated with modularity. This strategy enables the mitigation of expenses associated with procuring 3D printers, facilitates dynamic design changes, and accommodates parallel printing using multiple printers, thereby resulting in a reduction in production time. The manufacturing process involves modularization of the original CAD model, build scheduling, interface design, assembly, fabrication, and painting. A case study is presented, which demonstrates the proposed process in ship model production using 3D printing. The geometric similarity between the built model and the original CAD model was assessed by generating a point cloud using a 3D scanner. To evaluate the resistance performance of the 3D printed ship model, experiment and numerical simulation were conducted. These results indicate a high level of similarity and reliability.

Ground Effect on the Thrust Performance of Staggered Rotor System

In this study, the thrust performance of a staggered rotor system in-ground effect (IGE) and out-of-ground effect (OGE) while considering the interaction on wake characteristics were investigated experimentally. A thorough comprehension of their performance holds significant importance for trajectory planning, aircraft design, landing safety, and energy-efficient landings. The complex interactions within staggered rotor systems and the impact of ground effects make rotor distance and ground interactions critical factors influencing near-ground flight performance. The study investigated the influence and enhancements of rotor thrust performance through an examination of rotor speed, lateral distance, and altitude. Experimental tests were conducted on two small-scale rotor models to assess the effects of these parameters. These experiments compared the performance of staggered rotor systems with isolated rotors, analyzing the competition mechanism between the thrust loss caused by interference and the thrust gain of rotors IGE. Furthermore, emphasis was placed on analyzing the thrust gain issues exhibited by staggered rotor systems under the condition of H = 2R. Additionally, the analysis was focused on identifying prominent relative positions for thrust performance and parameters for improving thrust performance in ground effects in staggered rotor systems.

Toward Atomistic Understanding of Iron Phosphate Glass: A First-Principles-Based Density Functional Theory Modeling and Study of Its Physical Properties.

Iron phosphate glasses (IPGs) have been proposed as futuristic materials for nuclear waste immobilization and anode materials for lithium batteries. Recently, many attempts have been made to propose atomistic models of IPGs to explain their properties from an atomistic viewpoint. In this paper, we seek to produce small scale models of IPG that can be handled within the scheme of density functional theory (DFT) to study its electronic structure. The starting models, generated using the Monte Carlo (MC) method, were subsequently annealed using ab initio molecular dynamics (AIMD) to minimize the coordination defects. The geometry of the equilibrated structure was then optimized at 0 K. This hybrid approach (MC + AIMD + DFT optimization) produced good atomistic models of IPG, which can reproduce the experimentally observed band gap, vibrational density of states, magnetic moment of Fe, and mechanical and optical properties. Computationally expensive melt-quench simulation can be avoided using the present approach, allowing the use of DFT for accurate calculations of properties.

Effects of Different CaO/Al2O3 Ratios on the Phase Composition and Desulfurization Ability of CaO-Based Desulfurizers in Hot Metal

In response to the development of low-carbon smelting technology, reducing the use of fluor-containing materials in desulfurizers is an important research topic. The development of new-generation KR (Kambara Reactor) desulfurizers is shifting towards a higher Al2O3 content rather than CaF2, yet there is currently an absence of thorough and comprehensive mechanisms for desulfurization. Consequently, this research provides an extensive comparison using a specially constructed small-scale KR desulfurization hot model test, alongside FactSage simulation and SEM analysis (of desulfurization process). The findings indicate that at 1400 °C, for the desulfurization of molten iron, the capacity for desulfurization initially increases and then diminishes as the Al2O3 content in the KR desulfurizer rises. With Al2O3 content in the desulfurizer below 22 wt.%, the phase composition predominantly consists of C3A, employing a solid(slag)–liquid(metal) diffusion method for desulfurization. The optimal desulfurization capacity (Ls: 64.1) is observed when the Al2O3 content is 15 wt.%, attributed to the simultaneous presence of CaO particle precipitation and C3A. However, as the Al2O3 content reaches 20 wt.%, all the oversaturated CaO integrates into C3A, leading to a reduction in Ls from 64.1 to 10.7, thereby diminishing the desulfurization capacity by approximately sixfold. When Al2O3 exceeds 22 wt.%, the phase composition transitions from the C3A to C12A7 phase, and the desulfurization approach shifts from solid(slag)–liquid(metal) to liquid(slag)–liquid(metal) diffusion, with Ls decreasing to 23.4. This reduction is due to C12A7’s lower sulfur capacity compared to C3A and the absence of saturated CaO particle precipitation. Therefore, for Al2O3 to effectively replace fluorite in KR desulfurizers, a higher presence of C3A phases and CaO particle precipitation are essential. The desulfurizer must contain over 65 wt.% CaO and maintain Al2O3 levels at 10~16.2 wt.%.