Simulation System Research Articles

Insights into core-mantle differentiation from bulk Earth melt simulations

The earth is thought to have gone through complex physicochemical changes during the accretion and magma ocean stages. To better understand this evolution process at the fundamental level, we investigate the behavior of a bulk earth melt system by simulating the composition Fe35.7Mg19.0Si15.2O30.2 (in wt%) at high pressure. A deep neural network potential trained by first-principles data can enable accurate molecular dynamics simulation of large supercells that greatly enhances sampling for reliable evaluation of elemental partitioning. Our simulated system undergoes a phase separation in which the four elements clump together to different extents into two major domains. Based on the coordination and space-decomposition analyses, the inferred composition at 3000 K and 29.1 GPa contains 96.2, 0.1, 1.9 and 1.7 wt% of Fe, Mg, Si, and O, respectively, for the one domain and the corresponding elemental proportions are 3.0, 29.7, 22.0, and 45.3 wt% for the other domain. The predicted segregation thus leads to the formation of an iron-rich phase which corresponds to the metallic core and a magma ocean phase which corresponds to the silicate mantle. The metallic domain incorporates more silicon and more oxygen whereas the magma ocean domain gains more iron oxides at higher temperatures. Our predicted compositions compare favorably with those derived from experimental work for the equilibrium state metal and silicate reacting under high-pressure conditions.

A compound planning algorithm considering both collision detection and obstacle avoidance for intelligent demolition robots

This paper presents a compound planning algorithm considering both collision detection and obstacle avoidance for intelligent demolition robots working safely in high-radiation environments. Firstly, configurations and kinematics of the intelligent demolition robot are detailed to detect the possible obstacles in its workspace. A collision detection function based on the improved dual vector method is proposed to detect the different distances between the robot and obstacles in three cases: a point and a line segment, two line segments, and a line segment and a geometric shape. This function can also be applied to detect collisions with various obstacles of different shapes reasonably and efficiently. Furthermore, an obstacle avoidance function based on modified gradient projection method considering multi-task transformation is proposed. According to the different distances between the robot and the obstacle, it can be used in three situations: end obstacle avoidance task, end effector operation task, and end trajectory tracking task. This function can be applied to avoid obstacles both in the workspace and on the desired path. Finally, a simulation system is established to verify the collision detection and obstacle avoidance algorithms of the intelligent demolition robot. An experiment was conducted on the intelligent demolition robot. This robot can successfully achieve the expected trajectory with the method described in this article. Results of simulation and experiment demonstrate that obstacles both in workspace and on desired path can be detected and avoided properly.

Decomposition and fragmentation of conventional and biobased plastic wastes in simulated and real aquatic systems

AbstractPlastics play a crucial role in our daily lives. The challenge, however, is that they become waste and contribute to a global environmental problem, increasing concerns about pollution and the urgent need to protect the environment. The accumulation and fragmentation of plastic waste, especially micro- and nanoplastics in aquatic systems, poses a significant threat to ecosystems and human health. In this study, the decomposition and fragmentation processes of conventional and biobased plastic waste in simulated water bodies (waters with different pH values) and in real water systems (tap water and seawater) are investigated over a period of one and six months. Three types of plastic were examined: thermoplastic polyethylene terephthalate and thermoset melamine etherified resin in the form of nonwovens and biobased polylactic acid (PLA) in the form of foils. Such a comprehensive study involving these three types of plastics and the methodology for tracking degradation in water bodies has not been conducted before, which underlines the novelty of the present work. After aging of the plastics, both the solid fraction and the leachate in the liquid phase were carefully examined. The parameters studied include mass loss, structural changes and alterations in functional groups observed in the aged plastics. Post-exposure assessment of the fragmented pieces includes quantification of the microplastic, microscopic observations and confirmation of composition by in situ Attenuated Total Reflectance Fourier Transform Infrared Spectroscopy. The leachate analysis includes pH, conductivity, turbidity, total carbon and microplastic size distribution. The results highlight the importance of plastic waste morphology and the minor degradation of biobased PLA and show that microfibers contribute to increased fragmentation in all aquatic systems and leave a significant ecological footprint. This study underlines the crucial importance of post-consumer plastic waste management and provides valuable insights into strategies for environmental protection. It also addresses the pressing issue of plastic pollution and provides evidence-based measures to mitigate its environmental impact. Graphical abstract

Web application of an integrated simulation for aquatic environment assessment in coastal and estuarine areas

This paper introduces the web application-type Graphical User Interface that has been developed and also presents an application example. The introduced simulator conducts hydrodynamics and ecosystems in coastal and estuarine areas. It consists of (1) a hydrodynamic model that can simulate the current velocity, water temperature, salinity, and water level; (2) an ecosystem model that can simulate dissolved oxygen, phytoplankton, zooplankton, nutrients, fish, and bivalves; and (3) a benthic ecosystem model that can simulate elution. Web GUI is the first web system of aquatic environment simulation system that can both prepare calculation conditions and visualize them. Another significant feature is that it requires no installation and can be easily used by anyone to perform calculations. Thus, the proposed system helps fill the expertise gap experienced by potential users of the model. The use of standard systems, such as those discussed in this study, will facilitate evidence-based policymaking (EBPM).

A multi-task stations cooperative air quality prediction system for sustainable development

In recent years, A series of environmental problems caused by air pollution have attracted widespread attention. Air quality forecasting has become an indispensable part of people’s daily life. However, the traditional air quality prediction (AQP) model WRF-CMAQ simulation system faces several challenges: (1) the fuzziness of pollutant formation mechanism; (2) the hardness of integrating the features of meteorological conditions; (3) the difficulty of cooperating among monitoring stations. To deal with these challenges, we propose a multi-task station cooperative air quality prediction (MTSC-AQP) system for sustainable development. The MTSC-AQP system contains three modules: air quality analysis, a WRF-LSTM module for initial pollutant prediction, and multi-station cooperative AQP. Through air quality analysis, it can calculate the air quality index (AQI) and analyze the correlation between pollutants and meteorological conditions. Then, the WRF-LSTM module integrates spatio-temporal multi-source data to forecast the initial concentration of pollutants. Finally, the system incorporates the gravity model to predict the final AQI. Extensive experiments conducted on real-world datasets show the effectiveness of forecasting the AQI by using the MTSC-AQP system.

Data-Driven Material Exploration of High Conductive Fluorides By Multi-Element Substitution

Barium fluoride (BaF2) has a fluorite-type structure and is attracting high attention as a solid electrolyte material for fluoride ion batteries that can be used under high voltage. Previous research has reported that the ionic conductivity increases by 4 to 5 orders of magnitude by partially replacing Ba in BaF2 with La. This indicates that elemental substitution is effective in improving the ionic conductivity of BaF2. However, single element substitution only explore much limited experimental space in elements, so that it should be difficult to discover innovative conductive compositions. By replacing various elements at the same time, the experimental space can be largely expanded and the possibility of finding the optimal composition increases. But, the combination numbers of multi-element substituted compositions are much over 10000, and exhaustive exploration of them is unrealistic. Therefore, we aimed to efficiently search for multi-element substitution compositions with high conductivity by creating a material-conductivity correlated exploring system under data-driven approach using large number of fluoride material database. The purpose of this research was to discover solid solution compositions in the multi-element substituted fluorides and understanding of the relationship between substitutional elements and solid solution formation.Approximately 20 types of multi-element substitution composition candidates were initially picked up using the D-optimal design (Dopt). The target compositions were synthesized to collect their XRD data. Next, we extracted the "number of peaks" from the XRD data as a feature values to express the degree of solid solution in the sample. Furthermore, we made a regression model with the element types and addition ratios as explanatory variables and the "number of peaks" as the objective variable. Using the regression model, approximately 20 candidate were predicted to form solid solution, in which we called as data-driven design of experiments (DOE) approach. Finally, the materials were actually prepared according to the proposal in DOE. The identification results were projected using the principal component analysis (PCA) to draw the material space.The obtained material space shows the number of peaks in the samples prepared using DOE decreased compared to those using Dopt. In detail, the Dopt-derived sample had 41.83 peaks, while the DOE-derived sample had 19.65 peaks on average. This result indicates that we were able to efficiently find preferable compositions that are advantageous for solid solution formation by using the data-driven method.In the presentation, we will also report the high-throughput composition exploration, their conductivities, and also development of virtual X-ray diffraction simulation system for over 10,000 kinds of multi-substituted composition of the fluorides. Acknowledgement s This research was partially supported by Knowledge Hub Aichi, the GIMRT Program of the Institute for Materials Research, Tohoku University (Proposal No. 202303-CRKKE-0048).

Orthogonal signal correction assisted multivariate regression approach for the estimation of uranium and acidity in PUREX process streams

A direct UV–Visible absorbance spectrophotometric method was developed for the simultaneous determination of uranium and nitric acid concentration in the PUREX process samples. The simulated system consisted of uranium and nitric acid in concentration range corresponding to reprocessing of spent nuclear fuel discharged from nuclear reactor was prepared. The absorbance of these samples was measured in the range of 400–470 nm at a scan speed of 100 nm/s and resultant spectra were recorded. The changes in wavelength maxima of U(VI) absorption spectrum at different nitric acid concentration was utilized to determine the concentration of uranium and nitric acid in the sample by orthogonal signal correction assisted principal component regression. After the principle component regression the RMSEP for test data (Uranium: 3–21 g/L and acidity: 2–12 M) were 0.7 g/L and 0.4 M respectively. This method is superior to conventional method being followed for routine analysis of plant control samples in view of minimizing the generation of radioactive analytical waste consisting other corrosive reagents and reducing radiation exposure to operators during analysis. This method is amenable for online monitoring also.

Fabricated devices for performing bacterial-fungal interaction experiments across scales.

Diverse and complex microbiomes are found in virtually every environment on Earth. Bacteria and fungi often co-dominate environmental microbiomes, and there is growing recognition that bacterial-fungal interactions (BFI) have significant impacts on the functioning of their associated microbiomes, environments, and hosts. Investigating BFI in vitro remains a challenge, particularly when attempting to examine interactions at multiple scales of system complexity. Fabricated devices can provide control over both biotic composition and abiotic factors within an experiment to enable the characterization of diverse BFI phenotypes such as modulation of growth rate, production of biomolecules, and alterations to physical movements. Engineered devices ranging from microfluidic chips to simulated rhizosphere systems have been and will continue to be invaluable to BFI research, and it is anticipated that such devices will continue to be developed for diverse applications in the field. This will allow researchers to address specific questions regarding the nature of BFI and how they impact larger microbiome and environmental processes such as biogeochemical cycles, plant productivity, and overall ecosystem resilience. Devices that are currently used for experimental investigations of bacteria, fungi, and BFI are discussed herein along with some of the associated challenges and several recommendations for future device design and applications.

High-Definition Dynamic Voltage Restorer Systems Using Equivalent Time Sampling Techniques and Circular Structural Memory Filters

Due to advances in power electronics technology and the evolution of automation devices, the number of electrical devices that are sensitive to power quality is rapidly increasing, and for this reason, users are increasing their demand for high quality. To meet power quality demands, many power conversion devices are used, including dynamic voltage restorers (DVRs). DVRs are recognized as devices that can effectively manage problems such as voltage segments, swells, and harmonics. DVR control requires many samples for harmonic compensation, which has the disadvantage of being complicated to implement due to fast digital signal processing computation and the application of the cyclic discrete Fourier transform. In this paper, a high-precision DVR system configuration is proposed that compensates for harmonics using a periodically equivalent time-interval sampling technique and a novel circular-structured memory filter. The proposed circular-structured multi-pointer memory filter is an effective filter algorithm for high-precision input voltage measurement because it can remove noise and compensate for the delay of the phase angle of the filter in voltage measurement. A simulation and DVR prototype system were built, and the feasibility and effectiveness of the phase angle multi-filter voltage detection method and the compensation method were verified by experiments.

Virtual coupling control of photovoltaic-energy storage power generation system for efficient power support

The key to achieving efficient and rapid frequency support and suppression of power oscillations in power grids, especially with increased penetration of new energy sources, lies in accurately assessing the inertia and damping requirements of the photovoltaic energy storage system and establishing a controllable coupling relationship between the virtual synchronous generator and the main network. In this paper, the inertia and damping requirements of the photovoltaic energy storage system are estimated using frequency safety warnings and power oscillation suppression constraints. Furthermore, the oscillation characteristics of the power system, which include photovoltaic and energy storage in the presence of periodic load disturbances, are analyzed. Based on this analysis, a coupled virtual synchronous controller for energy storage is proposed. This controller employs a forced oscillation suppression technique through natural frequency shifting, and establishes a controllable power coupling relationship between the photovoltaic energy storage system and the main network to achieve the desired frequency shift. Finally, a simulation system incorporating conventional generators and a photovoltaic energy storage system controlled with the proposed strategy is built to test the frequency support and power oscillation suppression behaviors of the grid-connected power generation system.

A Modular Multi-Technology Generator Model for EMT Stability Studies in Power Systems with Large Amounts of Converter-based Generation

This paper explores the integration of converter- based generation (CBG) into power systems, analyzing their impact on power system stability. For this purpose, a simulation tool consisting of comprehensive, aggregated models integrating several generation technologies into a power system for dynamic simulation has been developed to represent three prominent generation technologies: synchronous generation (SG), grid- following converters (GFL), and grid-forming converters (GFM). The proposed modular multi-technology generator model allows to set the penetration level of each technology at any bus of the system, thus facilitating stability assessment of power systems with large amounts of CBG. Electromagnetic-type (EMT) studies are carried out in a small test system to analyse the impact of the GFL/GFM generation ratio on power system stability. The results show the capability of the proposed tool for testing the impact of the generation mix in the stability of the system, highlighting the importance of GFM generation to operate in with low amounts or in absence of synchronous generators in the system.

Assessing the impact of communication delays for Autonomous Intersection Management systems

Communication is essential for Cooperative Intelligent Transportation Systems (C-ITS) to achieve better road efficiency, especially for Autonomous Intersection Management (AIM) which coordinates vehicles to pass the intersection safely and efficiently. Communication delays cause severe safety crises (i.e., collisions) and vehicular-performance degradation regarding intersection capacities and vehicular delays. Targeting the delays, network researchers have been working on low-latency communication technologies, and C-ITS researchers have proposed delay-tolerant AIM systems to avoid collisions in intersections. The impacts of communication delays are observed and discussed in the literature; however, models and assessments of the delay requirements for AIM are needed to provide insights for future network and C-ITS research. Here, we model the impact of communication delays on vehicular performance at an autonomous intersection and validate the models with the simulation results from over two million experiments, two types of multi-lane intersections (a typical 4-legged intersection and a roundabout), and four AIM systems. The simulations are conducted with SUMO simulator and AIM systems, where communication delays are inserted into the message exchanges during the simulation. The models are represented in linear, quintic, and cubic polynomials, showing that communication delay between 0 to 100 milliseconds is linearly related to vehicular performance in terms of intersection capacity and vehicular delay. According to the models, we show that by reducing communication delay from 100 to 10 milliseconds, the capacity degradation can be reduced from 7-10% to 0.7-1.0%. Moreover, communication delays must be less than 247 milliseconds to allow AIM systems to outperform traditional traffic lights.

A Novel Training Model for Superficial Temporal Artery- Middle Cerebral Artery Anastomosis Using Microsurgical Techniques

To create a reusable and inexpensive training model with technological tools that simulates cerebral bypass surgery and a sensor system that provides tactile feedback to the surgeon. Furthermore, we aimed to evaluate the anastomotic stability and contribution to the surgeon's learning curve. We created a superficial temporal artery-middle cerebral artery bypass simulation model using chicken and turkey brachial arteries. A cranium model was printed with a three-dimensional printer for craniotomy and cerebral parenchyma was created by pouring silicone into the cranial mold. A blood flow simulation system was also prepared. Pressure-sensitive sensors were placed on parenchyma and tactile conditioning was performed via audible warning from the sensors. Twenty-four anastomosis were performed with different sutures and hand tools. Anastomosis completion times and durability and the number of touches and pressures applied to the parenchyma were recorded. The stability of the anastomoses was evaluated by increasing the pressure in the blood flow simulation system, so usefulness of the training model was evaluated. The time required for anastomosis completion decreased as the number of practices increased (P<0.05). As the number of practices increased, the number of parenchymal touches decreased (P<0.05). With practice, the time required for anastomosis completion and number of parenchymal touches decreased. Thus, the model is useful, inexpensive, reusable, easily accessible, and contributes to the surgeon's learning curve. Our model with pressure-sensitive sensors can be used for microsurgery practice, enabling the surgeons to gain tactile conditioning and evaluate anastomotic stability and leakage.

Knowledge graph-based representation and recommendation for surrogate modeling method

Surrogate models have been widely used in engineering design for approximating a simulation system with high computational cost. Complex system design typically is a multi-stage and multi-discipline design problem, which requires a large number of surrogate models. The choice of surrogate modeling method (SMM) is critical as it directly impacts the performance of both the surrogate models and the designed systems. With the growing variety of SMMs, designers face challenges in selecting the appropriate methods for their specific applications. To address this, we propose a representation and recommendation framework for surrogate modeling methods based on knowledge graph. Firstly, we develop an ontology to formally represent core concepts involved in the recommendation for surrogate modeling methods, including surrogate modeling method, surrogate model, and data sets,etc. Secondly, we extract 460 samples from 46 benchmark functions using Latin hypercube sampling to construct a knowledge graph with 8,343 nodes and 16,100 relationships, which involves 7,820 surrogate models generated from 17 surrogate modeling methods. Finally, we propose a knowledge graph-based recommendation method for surrogate modeling method named KGRSMM to facilitate the selection of an appropriate surrogate modeling method. We test the efficacy of KGRSMM using examples of theoretical problems and engineering problems of hot rod rolling respectively. It is shown in the results that KGRSMM is capable of recommending surrogates with appropriate accuracy, robustness, and time to satisfy designers’ preferences.

Implementation of a Cuckoo Search intelligent simulation system for node placement problem in Wireless Mesh Networks: Performance evaluation for tuning hyperparameters [formula omitted] and [formula omitted]

In recent years, Internet of Things (IoT) is the hottest topics in the research and implementation of wireless networks. Most of the research of IoT-based applications is related with the data collecting and processing using sensors and actuators. As the network environment for connecting IoT devices are considered WLAN or Bluetooth. The Wireless Mesh Networks (WMNs) are the most suitable networks for the Internet of Things (IoT) due to their numerous benefits. However, they have several issues related to wireless communications. One solution is the optimization of position and location of mesh routers. But finding the best location of mesh routers is an NP-hard problem. To deal with this issue, we propose and implement an intelligent simulation system based on the Cuckoo Search (CS) algorithm called WMN-CS. In the implemented system, the hyperparameters should be tuned to find better solutions. This paper evaluates the WMN-CS performance for various combination of hyperparameters by tuning host bird discovery rate (pa) and scale parameter (γ). The simulation results show that a better performance is achieved when the scale parameter (γ) is between 0.07 and 0.09 and the host bird recognition rate (pa) is between 0.900 and 0.925.

Parameter identification of photovoltaic current loop parameters based on parameter separation

Abstract Accurate control parameters are the basis for analyzing the fault characteristics of PV, but due to technical confidentiality reasons, equipment manufacturers do not provide accurate parameters, so they need to be identified. This paper aims to solve the problem of poor accuracy of the integration coefficient identification of a current loop with low sensitivity by proposing a parameter identification method for a photovoltaic current loop based on parameter separation. By analyzing the current response of the fault and fault recovery stages, the scale coefficient kp and the integration coefficient ki are separated and identified separately. Finally, a simulation system was built on the PSCAD/EMTDC platform, and the validity of the method was verified by using the simulation data.

Ship accident re-enactment based on AIS data and virtual reality: a case study of “Xiang Xiang Yin Huo 0410” ship accident

Abstract The recurrence of ship accidents was an effective method of maritime investigation. To analyze the cause of the sinking of “Xiang Xiang Yin Huo 0410”, a vessel accident recurrence simulation system was constructed by integrating an automatic identification system and virtual reality technology to replicate the navigation and accident process of the accident vessel. The voyaging process of the accident vessel was reduplicated in the simulation system according to the AIS information, such as speed, course, and turning speed. The mathematical model of accident vessel maneuvering was established by using MMG to reproduce the ship maneuvering scheme based on the maneuvering data before the accident obtained through on-the-spot investigation. The navigation and sinking process of the accident vessel were reproduced visually, and the cause of the accident was judged and analyzed intuitively by constructing a three-dimensional model of the accident vessel and a mathematical model of ocean waves. The actual application indicates that the ship’s navigation and accident process under external force, combined with the ship’s maneuvering data, automatic identification data, and hydrometeorological data, was consistent with the actual accident process of the accident. The method proposed in this paper provided a new idea for the investigation and analysis of maritime accidents.

The bactericidal effect of punicalagin concentrations, high pressure conditions and their combination on Escherichia coli O157: H7

Punicalagin (PUN), as a natural antimicrobial agent, is the most abundant phenolic compound in pomegranate peel. In this study, the bactericidal effect of PUN concentrations, high pressure conditions and their combination on E. coli O157: H7 were investigated. The bacteria were suspended in physiological saline solution with PUN concentrations of 1–3 mg/mL and treated by high pressures of 100–300 MPa for 2–10 min. The synergistic effect of the combination treatment was demonstrated with the maximum enhancement value of 4.10. Cell morphology changes, membrane permeability enhancement and swimming motility inhibition were observed in the combination treatment. Adenosine, adenine, cyclic GMP in purine metabolism and glutathione in glutathione metabolism pathways were differential metabolites with content changes, which were related with treatment methods (HPP or PUN) but not the degree of bacteria inactivation. This study provided a more effective and innovative method for killing E. coli in simulation system. Industrial relevanceHPP was the most commercialized emerging non-thermal processing technology and PUN was the natural antibacterial agent from pomegranate peel waste. The combination of HPP and PUN had synergistic bactericidal effect on E. coli with less processing intensity, which was very suitable for the products those contained thermosensitive components with maximum sensory characteristics preserved and less preservative added.

Development of a noninvasive olfactory stimulation fMRI system in marmosets

Olfactory dysfunction is associated with aging and the earliest stages of neurodegenerative diseases, such as Alzheimer’s and Parkinson’s diseases; it is thought to be an early biomarker of cognitive decline. In marmosets, a small non-human primate model used in brain research, olfactory pathway activity during olfactory stimulation has not been well studied because of the difficulty in clearly switching olfactory stimuli inside a narrow MRI. Here, we developed an olfactory-stimulated fMRI system using a small-aperture MRI machine. The olfactory presentation system consisted of two tubes, one for supply and one for suction of olfactory stimulants and a balloon valve. A balloon valve installed in the air supply tube controlled the presentation of the olfactory stimulant, which enabled sharp olfactory stimulation within MRI, such as 30 s of stimulation repeated five times at five-minute intervals. The olfactory stimulation system was validated in vivo and in a simulated system. fMRI analysis showed a rapid increase in signal values within 30 s of olfactory stimulation in eight regions related to the sense of smell. As these regions include those associated with Alzheimer’s and Parkinson’s diseases, olfactory stimulation fMRI may be useful in clarifying the relationship between olfactory dysfunction and dementia in non-human primates.

Research on digital twin architecture of ship maneuverability simulation for system model

Abstract With the development of civil and military requirements, ship maneuverability simulation systems tend to be complicated, and the traditional simulation methods are difficult to meet in terms of the development needs of digitalization. The joint simulation technology based on the FMI standard can realize the digital twin of complex system models, which can solve the difficult problem of multidisciplinary and multi-sub-system integration simulation. This paper analyzes the current domestic and foreign research content on joint simulation technology, digital twin technology, and complex system modeling, takes a typical Z-type maneuverability test as an example, establishes a multidisciplinary and cross-platform subsystem model, and designs and develops a ship maneuverability joint simulation algorithm and system software for the complex system model. The simulation results verify the effectiveness of the system modeling technique and the joint simulation technique based on FMI. Based on this system, the whole framework of the ship maneuverability simulation system, which is based on digital twins, is designed. According to the development requirements of ship maneuverability simulation, the main design framework, key technology, and implementation approach of this research method are given, which provides basic methods and ideas for the digital research of ship maneuverability simulation.