Simulation Parameters Research Articles

Simulation of high efficiency hybrid FTO/TiO2/CH3NH3SnI3/RGO based solar cell using SCAPS-1D

A hybrid structure of fluorine-doped tin oxide (FTO)/titanium dioxide (TiO2)/methylammonium tin triiodide (CH3NH3SnI3)/reduced graphene oxide (RGO) based solar cell has been designed and simulated using SCAPS-1D simulation tool. The three layers of the solar cell have been organized like TiO2 acting as an electron transporting layer (ETL), CH3NH3SnI3 serving as the photon absorption layer, and reduced graphene oxide (RGO) acting as the hole transporting layer (HTL). The thickness of the ETL and HTL layers are maintained at 300 nm each. The absorption layer thickness has been optimized and kept at 450 nm to get enhanced efficiency. The experimental absorption data from the referred articles of the materials are incorporated with the simulation to obtain convincing result. Simulated device parameters, such as efficiency, open-circuit voltage, short-circuit current density, and fill factor have been found to be 19.30 %, 1.070 V, 41.66 mA/cm2, and 43.27 %, respectively at 300 K temperature. Moreover, a study of the absorption layer defect density (109–1017 cm−2) has also been conducted for the device, revealing an almost inverse proportionality with the efficiency of the device. In addition, the device FTO/TiO2/CH3NH3SnI3/RGO has been examined with the various temperature range from 270 K to 400 K. The device, with a high offset band structure and high mobility ETL and HTL layers, shows promising candidate for solar cell applications.

Executing realistic earthquake simulations in unreal engine with material calibration

Earthquakes significantly impact societies and economies, underscoring the need for effective search and rescue strategies. As AI and robotics increasingly support these efforts, the demand for high-fidelity, real-time simulation environments for training has become pressing. Earthquake simulation can be considered as a complex system. Traditional simulation methods, which primarily focus on computing intricate factors for single buildings or simplified architectural agglomerations, often fall short in providing realistic visuals and real-time structural damage assessments for urban environments. To address this deficiency, we introduce a real-time, high visual fidelity earthquake simulation platform based on the Chaos Physics System in Unreal Engine, specifically designed to simulate the damage to urban buildings. Initially, we use a genetic algorithm to calibrate material simulation parameters from Ansys into the Unreal Engine’s fracture system, based on real-world test standards. This alignment ensures the similarity of results between the two systems while achieving real-time capabilities. Additionally, by integrating real earthquake waveform data, we improve the simulation’s authenticity, ensuring it accurately reflects historical events. All functionalities are integrated into a visual user interface, enabling zero-code operation, which facilitates testing and further development by cross-disciplinary users. We verify the platform’s effectiveness through three AI-based tasks: similarity detection, path planning, and image segmentation. This paper builds upon the preliminary earthquake simulation study we presented at IMET 2023, with significant enhancements, including improvements to the material calibration workflow and the method for binding building foundations.

Exploiting Axisymmetry to Optimize CFD Simulations—Heave Motion and Wave Radiation of a Spherical Buoy

Simulating the free decay motion and wave radiation from a heaving semi-submerged sphere poses significant computational challenges due to its three-dimensional complexity. By leveraging axisymmetry, we reduce the problem to a two-dimensional simulation, significantly decreasing computational demands while maintaining accuracy. In this paper, we exploit axisymmetry to perform a large ensemble of Computational Fluid Dynamics (CFDs) simulations, aiming to evaluate and maximize both accuracy and efficiency, using the Reynolds Averaged Navier–Stokes (RANS) solver interFOAM, in the opensource finite volume CFD software OpenFOAM. Validated against highly accurate experimental data, extensive parametric studies are conducted, previously limited by computational constraints, which facilitate the refinement of simulation setups. More than 50 iterations of the same heaving sphere simulation are performed, informing efficient trade-offs between computational cost and accuracy across various simulation parameters and mesh configurations. Ultimately, by employing axisymmetry, this research contributes to the development of more accurate and efficient numerical modeling in ocean engineering.

Drone Swarm Classification from ISAR Imaging

In today's technology, the use of drones has become very popular for they can be easily purchased over the Internet and can be easily developed. With drones that have wide usage areas, swarm structures have become popular. However, this has brought about some problems. The issue of drone detection has emerged in order to prevent the uncontrolled use of drone swarms in the airspace. Drone swarm detection is important to prevent dangerous accidents or criminal acts. In this study, a new classification algorithm is proposed with deep learning using inverse synthetic aperture radar (ISAR) images of drone swarms based on various formation swarm types. ISAR images are created using ANSYS simulation. Additionally, high frequency structural simulator (HFSS) - shooting bouncing ray (SBR+) solver is used for high-speed computation. Radar and simulation parameters to obtain ISAR images are discussed. Especially, down-range and cross-range resolution parameters are taken into account to achieve high resolution. ISAR images are classified using deep learning methods in terms of formation. Formation types include Line, Square, Cross, and Triangle. The convolutional neural network (CNN) model is used to solve classification problems. The model consists of train, validation, and test steps. Classification performance results are presented with high accuracy. The developed method can be used for anti-drone technologies.

Modelling drought in South Africa: meteorological insights and predictive parameters

South Africa has grappled with recurring drought scenarios for over two decades, leading to substantial economic losses. Droughts in the Western Cape between 2015 and 2018, especially in Cape Town was declared a national disaster, resulting in the strict water rationing and the “day zero” effect. This study presents a set of simulations for predicting drought over South Africa using Artificial Neural Network (ANN), using Standard Precipitation Index (SPI) as the drought indicator in line with the recommendations of the World Meteorological Organization (WMO). Furthermore, different meteorological variables and an aerosol parameter were used to develop the drought set in four distinct locations in South Africa for a 21-year period. That data used include relative humidity (rh), temperature (tp), soil wetness (sw), evapotranspiration (et), evaporation (ev) sea surface temperature (st), and aerosol optical depth (aa). The obtained R2 values for SPI3 ranged from 0.49 to 0.84 and from 0.22 to 0.84 for SPI6 at Spring Bok, Umtata 0.83 to 0.95 for SPI3, and 0.61 to 0.87 for SPI6; Cape Town displayed R2 values from 0.78 to 0.94 for SPI3 and 0.57 to 0.95 for SPI6, while Upington had 0.77–0.95 for SPI3, and 0.78–0.92 for SPI6. These findings underscore the significance of evapotranspiration (et) as a pivotal parameter in drought simulation. Additionally, the predictive accuracy of these parameter combinations varied distinctly across different locations, even for the same set of parameters. This implies that there is no single universal scheme for drought prediction. Hence, the results are important for simulating future drought scenarios at different parts of South Africa. Finally, this study shows that ANN is an effective tool that can be utilized for drought studies and simulations.

Impact of network topology changes on information source localization

Well-established methods of locating the source of information in a complex network are usually derived with the assumption of complete and exact knowledge of network topology. We study the performance of three such algorithms (Limited Pinto–Thiran–Vetterli Algorithm — LPTVA, Gradient Maximum Likelihood Algorithm — GMLA and Pearson Correlation Algorithm — PCA) in scenarios that do not fulfill this assumption by modifying the network before localization. This is done by adding superfluous new links, hiding existing ones, or reattaching links following the network’s structural Hamiltonian. Our results show that GMLA is highly resilient to adding superfluous edges, as its precision falls by more than statistical uncertainty only when the number of links is approximately doubled. On the other hand, if the edge set is underestimated or reattachment has taken place, the performance of GMLA drops significantly. In such a scenario, PCA is preferable, retaining most of its performance when other simulation parameters favor successful localization (high density of observers, highly deterministic propagation). It is also generally more accurate than LPTVA and orders of magnitude faster. The differences between localization algorithms can be intuitively explained, although further theoretical research is needed.

The effects of graphene oxide nanoparticles on the mechanical and thermal properties of polyurethane/polycaprolactone nanocomposites; a molecular dynamics approach

Using molecular dynamics simulations using LAMMPS and other tools, this work examined the effect of graphene oxide nanoparticles on the mechanical and thermal properties (TPs) of polyurethane/polycaprolactone nanocomposites. The simulations examined the atomic, MP, and thermodynamic properties of atomic structures while examining and equilibrating them. After 10 ns of equilibration at 300 K and 1 bar, samples were convergent and the simulation parameters were confirmed. The addition of GO-NPs significantly enhanced TPs and MPs, with optimal improvements observed at a 2 % concentration. Specifically, increasing GO-NP content from 0.5 % to 2 % resulted in increases in heat flux from 680.95 to 714.09 W/m2, thermal conductivity from 0.69 to 0.93 W/m·K, and Young's modulus from 5.91 to 6.63 MPa. This is while increasing GO-NP content from 0.5 % to 2 % resulted in decreases in both the mean square displacement and glass transition temperature (Temp) to 0.22 Å2and 318 K, respectively. However, further increasing the GO-NP concentration to 5 % led to a decrease in HF and TC, likely due to nanoparticle agglomeration, which also reduced mechanical strength and increased MSD and Tg. This study underscores the importance of optimizing GO-NP concentration, with 2 % identified as the most effective for enhancing the properties of PU/PCL/GO-NCs.

Investigation of the Time-Dependent Deformation of Recycled Aggregate Concrete in a Water Environment.

The water environment greatly affects the creep deformation of recycled aggregate concrete (RAC). Hence, a humidity-stress-damage coupling numerical model was used for investigating the time-dependent deformation of RAC in the water environment in this study. Firstly, uniaxial compression and water absorption tests were performed to determine the calculation parameters of the creep numerical simulation of RAC in a water environment. Experimental results indicate that the elastic modulus and compressive strength drop as the water content increases. Then, the time-dependent deformation of RAC in a water environment was studied using a numerical simulation test of compressive creep when multiple stress levels were applied, and the critical stress for accelerated creep and the long-term strength of RAC were obtained. Finally, the influence of confining pressures on the long-term deformation of RAC in a water environment was discussed. When there is no confining pressure, the long-term strength of RAC is 23.53 MPa. However, when a confining pressure of 3.921 MPa is loaded onto RAC, the long-term strength of RAC is 47.052 MPa, which increases by 100%. Increasing confining pressures has an obvious effect on ensuring the long-term stable application of RAC in a water environment. Compared with the creep test, the method adopted in this study saves time and money and provides the theoretical basis for evaluating the time-dependent deformation of RAC in a water environment.

Improving UASS pesticide application: optimizing and validating drift and deposition simulations.

As unmanned aerial spraying systems (UASS) usage grows rapidly worldwide, a critical research study was conducted to optimize the simulation of UASS applications, aiming to enhance pesticide delivery efficiency and reduce environmental impact. The study examined several key aspects for accurate simulation of UASS application with lattice Boltzmann method (LBM). Based on these discussions, the most suitable grid size and simulation parameters were selected to create a robust model for optimizing UASS performance in various pest management scenarios, potentially leading to more targeted and sustainable pest control practices. The effect of stability parameter, grid size around the rotor and near ground, and parameters at wake flow were carefully analyzed to improve the precision of pesticide drift predictions and deposition patterns. Optimal grid sizes were identified as 0.2 m generally, 0.025 m near rotors, and a 0.1 + 0.2 m scheme for ground proximity, with finer grids improving accuracy but increasing computation time. Wake resolution and threshold significantly influenced simulation results, while wake distance had minimal impact beyond a certain point. The LBM's accuracy was validated by comparing simulated downwash flow and droplet deposition with field test data. This study optimized UASS simulation parameters, balancing computational efficiency with accuracy. The validated model enhances our ability to design more effective UASS for pest management, potentially leading to more precise and targeted pesticide applications. These advancements contribute to the development of sustainable pest control strategies, aiming to reduce pesticide usage and environmental impact while maintaining crop protection efficacy. © 2024 Society of Chemical Industry.

Optimizing Wireless Power Transfer Systems: A Simulation-Based Approach Using CST Studio Suite

In the rapidly evolving field of wireless power transfer (WPT), achieving systems that combine efficiency with reliability is crucial. This paper presents a simulation-based approach using CST Studio Suite to optimize WPT systems for wireless battery chargers in electric bikes. The study fixes the size of both the transmitter and receiver coils, the spacing between turns, and the number of coil turns, with a set distance of 20mm between the transmitter and receiver coils. CST Studio Suite is employed to determine the parameters of resonance capacitors and the optimal switching frequency to enhance WPT efficiency. This powerful simulation tool allows researchers and engineers to design resonance compensation circuits more effectively, optimizing power transfer efficiency. The simulation conducted with CST Studio Suite confirms its effectiveness in designing a WPT system, achieving an impressive peak efficiency of 99.61% at the resonant frequency. The paper demonstrates CST Studio Suite's capability to accurately predict WPT system performance by developing detailed electromagnetic models and fine-tuning simulation parameters to reflect real operating conditions. Iterative simulations resulted in optimized practices, validated by select results that highlight the method's effectiveness and showcase an optimal balance between system complexity and efficiency. These findings provide a strategic framework for future WPT system development, offering practical guidance for researchers and industry professionals aiming to advance WPT technology.

Studying Past Earthquakes with Modern Techniques: Ground-Motion Simulations for the 11 January 1693 Noto Earthquake in Italy

Abstract The 1693 Noto earthquake, which struck on 11 January, is one of the Italy’s largest and most devastating earthquakes. According to the Italian Parametric Earthquake Catalogue, it reached a maximum intensity of 11 on the Mercalli–Cancani–Sieberg scale and had an estimated magnitude of M 7.3. Nevertheless, its precise location and source definition remain subjects to debate due to the complexity of the seismic sequence and lack of geological evidence. A series of potential seismic sources differing in location, dimension, and kinematics have been proposed in the literature based on seismotectonic data and interpretations. The goal of this work is to perform a retrospective experiment to verify which of the proposed seismic sources have a better fit with the observed intensity data. To do so, novel simulation techniques are used to study this historical earthquake. We generated ground-motion scenarios for each proposed source model through a stochastic finite-fault simulation approach. Then, the simulated ground-motion parameters were converted to intensities using two different ground-motion intensity conversion equations for Italy. Finally, we compared these converted intensities with the observed intensity data in terms of normalized root mean square errors and converted intensities from ground-motion models. Our results generally show good consistency between converted intensities from the simulated and predicted ground motions, whereas the observed intensities fit better to converted ones from the peak ground velocity rather than peak ground acceleration. Our analysis reveals that the source model reproducing the best of the macroseismic data of the Noto earthquake is the Canicattì–Villasmundo fault system with a magnitude of 7.1.

CHARMM36 All-Atom Gas Model for Lipid Nanobubble Simulation.

Lipid nanobubbles with different gas cores may integrate the biocompatibility of lipids, powerful physicochemical properties of nanobubbles, and therapeutic effects of gas molecules, which thus promote enormous biomedical applications such as ultrasound molecular imaging, gene/drug delivery, and gas therapy. In order for further more precise applications, the exact molecular mechanisms for the interactions between lipid nanobubbles and biological systems should be studied. Molecular dynamics (MD) simulation provides a powerful computational tool for this purpose. However, previous state-of-the-art MD simulations of free gas nanobubble/lipid nanobubble employed the vacuum as their gas cores, which is not suitable for studying the interactions between functional lipid nanobubbles and biological systems and revealing the biological roles of gas molecules. Hence, in this work, we developed and optimized the CHARMM36 all-atom gas parameters for six gases including N2, O2, H2, CO, CO2, and SO2, which accurately reproduced the gas density at different pressures as well as the spontaneous formation of gas nanobubbles. Subsequent applications of these gas parameters for lipid nanobubble simulations also reproduced the self-assembly process of the lipid nanobubble. We further developed a Python script to generate all-atom lipid nanobubble simulation systems, which was proven to be efficient for all-atom MD simulations of lipid nanobubbles and to be able to capture the exact dynamics of gas molecules at the gas-lipid and lipid-water interfaces of the lipid nanobubble. In summary, the all-atom gas models proposed in this work are suitable for simulating free gas nanobubbles and lipid nanobubbles, which are supposed to overcome the shortcomings of previous state-of-the-art MD simulations with the vacuum replacing the gas core and play key roles in revealing the molecular-level interactions between lipid nanobubbles and biological systems.

Research on FBG curvature sensor based on PET substrate and silicone package

To address the low repeatability and accuracy in single package form and lack of strain transfer theory analysis in composite material form of fiber Bragg grating (FBG) curvature sensors, a flexible curvature sensing test scheme for FBG based on polyethylene terephthalate (PET) substrate and silicone package is proposed. Firstly, the factors affecting the strain response of the FBG are obtained through the analysis of the multilayer strain transfer theory, and the relationship between different material encapsulation forms and the strain response of the FBG is explored through finite element simulation. Secondly, the FBG curvature sensor with optimized simulation parameters is fabricated in package, and the curvature calibration verification experiments are performed on the sensor. Finally, the experimental results show that the deviation index of the FBG curvature sensor is between −1.89 % and 1.62 %, which indicates a better repeatability consistency. Based on the computation, the maximum sensitivity of the FBG curvature sensor is 13.32 µε/m−1 when the spacing between the fiber and the substrate layer is 2.50 mm, and its error is 0.30 % comparing with the simulation of the same condition, which supports the accuracy of the theoretical analysis and simulation of the multilayer strain transfer of the encapsulated FBG sensor conducted in this paper, meanwhile, the optimal encapsulation process of the FBG curvature sensor is obtained.

Evaluating thermal sensation in outdoor environments - Different methods of coupling CFD and radiation modelling with a human body thermoregulation model

Thermal comfort is vital in outdoor spaces for social interaction and recreation. With increases in heat wave events and global warming, it becomes more essential to be able to assess heat stresses. Outdoor spaces have asymmetrical and transient environmental factors such as solar radiation, long-wave radiation and natural wind, which makes the thermal perception outdoors largely different from indoors. Traditional thermal comfort models have some limitations and less accuracy when applied to outdoor spaces. The current study focuses on developing a method to assess the human thermal interaction with the complex outdoor environment and the consequent thermal sensation. Computational Fluid Dynamics (CFD) and radiation modelling are coupled to the Joint System Thermoregulation (JOS-3) model to assess the skin temperature, based on which thermal sensation is predicted using the updated thermal comfort model developed by UC Berkeley. The proposed methodology is applied to assess the thermal sensation of human subjects standing in front of an outdoor radiant cooling hub. The important finding is that the one-way coupling, i.e., no skin temperature feedback to the CFD simulation, works reasonably well in the outdoor environment, and that convective heat transfer coefficients available from experimental studies can be coupled with CFD simulated environmental parameters to obtain more reliable convective heat exchange to avoid meshing the geometry of a human body in CFD and further reduce the computing cost.

Predicting unparalleled degradation progression in PV module using simulation of shunt resistance experimental data

Datasets of various levels of solar cell shunt resistances were available in the literature, resulting in a linear correlation to forecast shunt resistance (Rsh) degradation and failures in photovoltaic (PV) cells. However, some PV failures initiate mildly with one or few cells in a PV module and then distribute to other cells which leads to a major power reduction as well as critical safe operation. Simulating these experimental datasets gives the potential to catch these unparalleled degradations in PV modules at early stages, preventing them from turning critical. In response, SPICE simulation was used to study the effect of PV cells with low shunt resistance in a PV module. The simulation follows four steps, focusing on the electrical output of Current-Voltage (I-V) curve of the solar cells: (a) simulation of the experimental healthy I-V curve of a PV cell; (b) simulation of experimental eleven I-V curves at reduced levels of shunt resistance; (c) simulation of a PV module by using the simulation parameters obtained from the former steps; and (d) lastly, simulation of four different scenarios depending on the number of faulty cells in a PV module. The effect of these scenarios on the I-V curve parameters is compared. Results revealed it is feasible to detect unparalleled degradations, spotting the failed cells (reduced Rsh) in a PV module by implementing a linear model correlating open-circuit voltage (VOC) with short-circuit-current (ISC) (acting as a proxy of Irradiance) at different shunt resistance levels. This model can be simulated as a robust indicator of PV abnormalities and is appropriate for employment in online PV monitoring systems.

The Time-Varying Characteristics of Relative Permeability in Oil Reservoirs with Gas Injection

Relative permeability is a critical parameter in reservoir numerical simulation and production prediction, intimately associated with reservoir architecture and fluid property. During gas injection development, substantial alterations in reservoir properties and fluid phase behavior induce dynamic changes in relative permeability. Clearly characterizing the time-varying features of relative permeability is very useful for an understanding of how gas injection influences fluid mobility within the reservoir and enhances recovery rates. In this paper, core displacement experiments are firstly conducted to obtain the characteristics of the relative permeability of oil and gas under various development stages and displacement conditions, further delineating the comprehensive shifts in reservoir properties at different gas injection stages. Subsequently, a novel reservoir numerical simulation method is proposed that considers the spatial and temporal segmentation of relative permeability curves in the reservoir simulation. Finally, a practical application is presented to clarify the effects of injection and production parameters on the development performance of gas flooding oil reservoirs. The results show the following: (i) Significant time-varying characteristics of relative permeability occur throughout gas injection development, in the early stages of gas injection, where most of the reservoir is at the gas injection front, and a rightward shift in relative oil and gas permeability indicates that gas injection promotes oil mobility. Conversely, in the later stages of gas injection, as the reservoir reaches the trailing edge of gas injection, the change trend in relative oil and gas permeability reverses, shifting leftward, thereby exacerbating the gas breakout phenomena. (ii) Increasing the rate of gas injection causes relative oil and gas permeability to move leftward, effectively enhancing the gas volume sweep coefficient and microscopic oil displacement efficiency at lower injection speeds while reducing development performance at higher injection speeds. (iii) An increase in gas injection pressure causes relative oil and gas permeability to shift rightward, and although it reduces residual oil saturation and enhances microscopic oil displacement efficiency, it also intensifies gas breakout phenomena and lowers the gas volume sweep coefficient. This paper provides theoretical guidance and technical support for the design of gas injection strategies, optimization of injection and production parameters, and production forecasting.

Application of Computer Simulation Technology in the Development of Tractor Transmission Systems

Tractors are the most important agricultural power machinery. With the development of tractors toward large-scale and electrification, the design of modern tractor transmission systems increasingly relies on computer simulation technology. However, tractor transmission systems, especially power-shift transmissions and CVTs, are highly complex industrial products that involve specialized knowledge from multiple disciplines. Engineers and researchers find it difficult to choose the correct mathematical model and grasp the trend of technological development when applying simulation technology. To address this issue, we conducted a systematic review of the field and carried out the following work: First, the types and basic principles of tractor transmission systems were introduced; Second, the modeling methods and applications of computer simulation technology in the structural optimization, power-shift control, and energy saving of tractor transmission systems were reviewed; Finally, the method of obtaining simulation parameters through system identification was introduced. Although computer simulation technology has been applied in the development of all tractor transmission systems, there are still some issues that need attention, such as the lack of established shift quality evaluation indicators and driving cycles suitable for tractors, which are crucial for the reliability of simulation results but are rarely studied. These issues need to be addressed in future work.

Simulating Bacterial Membrane Models at the Atomistic Level: A Force Field Comparison.

Molecular dynamics (MD) simulations are currently an indispensable tool to understand both the dynamic and nanoscale organization of cell membrane models. A large number of quantitative parameters can be extracted from these simulations, but their reliability is determined by the quality of the employed force field and the simulation parameters. Much of the work on parametrizing and optimizing force fields for biomembrane modeling has been focused on homogeneous bilayers with a single phospholipid type. However, these may not perform effectively or could even be unsuitable for lipid mixtures commonly employed in membrane models. This work aims to fill this gap by comparing MD simulation results of several bacterial membrane models using different force fields and simulation parameters, namely, CHARMM36, Slipids, and GROMOS-CKP. Furthermore, the hydrogen isotope exchange (HIE) method, combined with GROMOS-CKP (GROMOS-H2Q), was also tested to check for the impact of this acceleration strategy on the performance of the force field. A common set of simulation parameters was employed for all of the force fields in addition to those corresponding to the original parametrization of each of them. Furthermore, new experimental order parameter values determined from NMR of several lipid mixtures are also reported to compare them with those determined from MD simulations. Our results reveal that most of the calculated physical properties of bacterial membrane models from MD simulations are substantially force field and lipid composition dependent. Some lipid mixtures exhibit nearly ideal behaviors, while the interaction of different lipid types in other mixtures is highly synergistic. None of the employed force fields seem to be clearly superior to the other three, each having its own strengths and weaknesses. Slipids are notably effective at replicating the order parameters for all acyl chains, including those in lipid mixtures, but they offer the least accurate results for headgroup parameters. Conversely, CHARMM provides almost perfect estimates for the order parameters of the headgroups but tends to overestimate those of the lipid tails. The GROMOS parametrizations deliver reasonable order parameters for entire lipid molecules, including multicomponent bilayers, although they do not reach the accuracy of Slipids for tails or CHARMM for headgroups. Importantly, GROMOS-H2Q stands out for its computational efficiency, being at least 3 times faster than GROMOS, which is already faster than both CHARMM and Slipids. In turn, GROMOS-H2Q yields much higher compressibilities compared to all other parametrizations.

Dynamic impact analysis of the time-delay levitation control system on maglev vehicle system after adding smith predictor

The time delay (TD) in the levitation control system significantly affects the dynamic performance of the closed-loop system in electromagnetic suspension (EMS) maglev vehicles. Excessive TD can cause levitation instability, making it essential to explore effective mitigation methods. To address this issue, a Smith Predictor (SP) is integrated into the traditional PID levitation control system. The combination of theoretical analysis and numerical simulation is employed to assess the stability of the time-delay levitation control system after the integration of the Smith Predictor. Theoretical analysis reveals that when TD exceeds a critical threshold, the levitation system becomes unstable. The addition of SP alters the root trajectory of the system characteristic equation from positive to negative, and recovers the levitation system to stable status. Assuming complete knowledge of the dynamic system, the TD compensation value in the SP becomes a key parameter that determines its performance. A minimum effective value (MEV) for TD compensation is identified, correlating with the system's stability region. Under the influence of TD, more complex systems and higher running speeds of the maglev vehicle lead to a narrower stable region and a larger MEV for TD compensation. Given the simulation parameters in this paper, with a system TD of 15 ms and a maximum vehicle speed of 160 km/h, the MEV for TD compensation in the SP should be set at 12 ms.

Assessing key parameters in simultaneous simulation of rapid kinetics of chlorophyll a fluorescence and trans-thylakoid electric potential difference.

Our study attempts to address the following questions: among numerous photosynthetic modules, which parameters notably influence the rapid chlorophyll fluorescence (ChlF) rise, the so-called O-J-I-P transient, in conjunction with the P515 signal, as these two records are easily obtained and widely used in photosynthesis research, and how are these parameters ranked in terms of their importance? These questions might be difficult to answer solely through experimental assays. Therefore, we employed an established photosynthesis model. Firstly, we utilized the model to simulate the measured rapid ChlF rise and P515 kinetics simultaneously. Secondly, we employed the sensitivity analysis (SA) tool by randomly altering model parameters to observe their effects on model output variables. Thirdly, we systematically identified significant parameters for both or one of the kinetics across various scenarios. A novel aspect of our study is the application of the Morris method, a global SA tool, to simultaneously assess the significance of model parameters in shaping both or one of the kinetics. The Morris SA technique enables the quantification of how much a specific parameter affects O-J-I-P transient during particular time intervals (e.g., J, I, and P steps). This allowed us to theoretically analyze which step is more significantly influenced by the parameter. In summary, our study contributes to the field by providing a comprehensive analysis of photosynthesis kinetics and emphasizing the importance of parameter selection in modelling this process. These findings can inform future research efforts aimed at improving photosynthesis models and advancing our understanding of photosynthetic processes.