System Model Research Articles

Adaptive direct RBFNN consensus control for a class of unknown nonlinear underactuated systems

The consensus tracking control of a class of nonlinear underactuated multi-agent systems with uncertainties is studied in this paper. A radial basis function neural network (RBFNN)-based direct adaptive control algorithm is designed. Unlike many previous articles in which a neural network is employed to identify the unknown nonlinear functions in the system model or controller, this method directly approximates the ideal control law by a neural network, making the control law simpler. Based on the neural network direct control algorithm, a controller with a single-parameter learning scheme is designed. This new control method reduces the computational burden by reducing the amount of computational data. Finally, the closed-loop system is proved to be ultimately uniformly bounded stable; the application examples of the controllers are given by simulation.

A Novel Electric Vehicle Thermal Management System Based on Charging Station Heat Pump System During Fast Charging

Abstract A battery thermal management system based on a charging station heat pump system is proposed to improve battery charging efficiency during high-power DC charging. The system provides coolant of appropriate temperature through the charging station heat pump system. It enables the battery to be charged at the optimal temperature for charging, which improves the charging efficiency and reduces the charging time. The two system models are modeled and analyzed using numerical simulation software, and the temperature characteristics and charging time of the proposed system and the original battery thermal management system based on the electric vehicle heat pump system are analyzed under five different temperature conditions. The results show that the proposed system has fast response, high efficiency, and can maintain the maximum temperature below 50°C. Compared with the original system, the proposed system reduces the charging time of the power battery by 10.6%, 17.9%, and 24.6% at 0°C, −10°C, and −20°C operating conditions, respectively. It also saves 1.4% and 6.0% of the charging time at 20°C and 40°C operating conditions, respectively. Comparing the charging time for the power battery at each stage, the proposed system mainly reduces the charging time in the range of 0-20% of the battery state of charge compared to the original system.

Modeling and Simulation of an Integrated Synchronous Generator Connected to an Infinite Bus through a Transmission Line in Bond Graph

Most electrical energy generation systems are based on synchronous generators; as a result, their analysis always provides interesting findings, especially if an approach different to those traditionally studied is used. Therefore, an approach involving the modeling and simulation of a synchronous generator connected to an infinite bus through a transmission line in a bond graph is proposed. The behavior of the synchronous generator is analyzed in four case studies of the transmission line: (1) a symmetrical transmission line, where the resistance and inductance of the three phases (a,b,c) are equal, which determine resistances and inductances in coordinates (d,q,0) as individual decoupled elements; (2) a symmetrical transmission line for the resistances and for non-symmetrical inductances in coordinates (a,b,c) that result in resistances that are individual decoupled elements and in a field of inductances in coordinates (d,q,0); (3) a non-symmetrical transmission line for resistances and for symmetrical inductances in coordinates (a,b,c) that produce a field of resistances and inductances as individual elements decoupled in coordinates (d,q,0); and (4) a non-symmetrical transmission line for resistances and inductances in coordinates (a,b,c) that determine resistances and inductance fields in coordinates (d,q,0). A junction structure based on a bond graph model that allows for obtaining the mathematical model of this electrical system is proposed. Due to the characteristics of a bond graph, model reduction can be carried out directly and easily. Therefore, reduced bond graph models for the four transmission line case studies are proposed, where the transmission line is seen as if it were inside the synchronous generator. In order to demonstrate that the models obtained are correct, simulation results using the 20-Sim software are shown. The simulation results determine that for a symmetrical transmission line, currents in the generator in the d and q axes are −25.87 A and 0.1168 A, while in the case of a non-symmetrical transmission line, these currents are −26.14 A and 0.0211 A, showing that for these current magnitudes, the generator is little affected due to the parameters of the generator and the line. However, for a high degree of non-symmetry of the resistances in phases a, b and c, it causes the generator to reach an unstable condition, which is shown in the last simulation of the paper.

EVOLUTIONARY TRANSFORMATION OF THE QUALITY ASSURANCE SYSTEM OF DEFENSE GOODS, SERVICES AND WORKS IN UKRAINE

It is inadvisable to limit the quality assurance system for defense-related goods/services/works solely to a set of mechanisms; it must necessarily encompass vertical and horizontal connections both among the relevant functional blocks (subsystems) and between the entities involved in their operation. In the context of not only geopolitical challenges but also the ongoing daily struggle against an aggressor state, it is critically important to establish an effective and efficient system for controlling the quality of defense-related goods/services/works. This article is dedicated to the systematization of the characteristics of transformational changes and the outlining of methodological foundations for the effective functioning of the quality assurance system for defense- related goods, works, and services in Ukraine. A conceptual three-level model of the quality assurance system for defense-related goods, services, and works has been formulated by distinguishing the essential parameters of their operation at the global, national, and micro levels. The peculiarities of forming a quality assurance system for defense-related products at both the micro and macro levels have been justified. It has been emphasized that the effectiveness of the evolutionary transformation of the quality assurance system for defense-related products at all levels of its representation should be characterized by the penetration of market forces into the mechanism of its operation. Attention has been drawn to the substantive directions for implementing the requirements of NATO standards in the AQAP series (AQAP 2110) within the framework of a corporate quality management system in accordance with ISO 9001. Since there are no legal or legislative requirements mandating the implementation of quality management systems, the enhancement and improvement of the efficiency of internal business and production processes should be a direct concern of the enterprise itself, driven by market forces. At the same time, the state should create stimulating mechanisms, for instance, by employing soft regulation; in military procurement specifications, it should mandate the presence of a certificate of compliance with AQAP requirements for the enterprises involved. The expected effects of the transformation of the studied system include economic, organizational, and worldview effects. A comparative analysis of the evolutionary transformation of the quality assurance system for defense-related goods, services, and works has been conducted based on the following criteria: the object and subject of control; the function performed by the control subject; the breadth of the controlling institution’s functionality; the perimeter of the controlling institution’s authority; the level of depth of control; and the level of subordination of the controlling institution to the Ministry of Defense. Despite the complexity of the task declared at the state level regarding the functioning of the state guarantee system for the quality of defense-related products, which is influenced not only by objective factors but also by worldview factors, its implementation has the potential to significantly enhance the quality of Ukraine’s defense products and substantially improve Ukraine’s rankings in terms of transparency, institutional maturity, and the ecological soundness of economic relations in the defense sector.

Nonlinear performance of aerospace face gear-planetary gear transmission system under multi-factors including the effect of temperature

Face gear-planetary gear transmission system combines the advantages of both planetary and face gears. And has enormous advantages in the aerospace field. The meshing of gear teeth generates heat through friction, resulting in thermal deformation and affects nonlinear dynamic characteristics. Exploring the temperature effect on the nonlinear dynamics of the transmission system of face gear-planetary gear is of great significance. Considering the temperature variations effect and time-varying meshing stiffness, tooth side clearance as well as meshing error, a nonlinear dynamic model of the face gear-planetary gear system is established. The nonlinear dynamic characteristics of the system were described by maximum Lyapunov exponent diagram, phase diagram, bifurcation diagram et al. The findings indicate that the effect of temperature variations on the gear system is related to the values of the time-varying mesh stiffness coefficient and mesh damping ratio. When the mesh damping ratio is small, the influence of temperature changes on the bifurcation characteristics of the system is more obvious. As the time-varying mesh stiffness value increases, the temperature rise gradually stabilizes in the range of 15°C to 42°C; Under certain temperature changes, as the meshing damping ratio and time-varying meshing stiffness coefficient increase, the face gear planetary gear system gradually tends to stabilize.

Exploring the decision-making process in model development: focus on the Arctic snowpack

Abstract. The Arctic poses many challenges for Earth system and snow physics models, which are commonly unable to simulate crucial Arctic snowpack processes,such as vapour gradients and rain-on-snow-induced ice layers. These limitations raise concerns about the current understanding of Arctic warming and its impact on biodiversity, livelihoods, permafrost, and the global carbon budget. Recognizing that models are shaped by human choices, 18 Arctic researchers were interviewed to delve into the decision-making process behind model construction. Although data availability, issues of scale, internal model consistency, and historical and numerical model legacies were cited as obstacles to developing an Arctic snowpack model, no opinion was unanimous. Divergences were not merely scientific disagreements about the Arctic snowpack but reflected the broader research context. Inadequate and insufficient resources, partly driven by short-term priorities dominating research landscapes, impeded progress. Nevertheless, modellers were found to be both adaptable to shifting strategic research priorities – an adaptability demonstrated by the fact that interdisciplinary collaborations were the key motivation for model development – and anchored in the past. This anchoring and non-epistemic values led to diverging opinions about whether existing models were “good enough” and whether investing time and effort to build a new model was a useful strategy when addressing pressing research challenges. Moving forward, we recommend that both stakeholders and modellers be involved in future snow model intercomparison projects in order to drive developments that address snow model limitations currently impeding progress in various disciplines. We also argue for more transparency about the contextual factors that shape research decisions. Otherwise, the reality of our scientific process will remain hidden, limiting the changes necessary to our research practice.

Between green hills and green bills: Unveiling the green shades of sustainability and burden shifting through multi-objective optimization in Swiss energy system planning

The Paris Agreement is the first-ever universally accepted and legally binding agreement on global climate change. It is a bridge between today’s and climate-neutrality policies and strategies before the end of the century. Critical to this endeavor is energy system modeling, which, while adept at devising cost-effective carbon-neutral strategies, often overlooks the broader environmental and social implications. This study introduces an innovative methodology that integrates life-cycle impact assessment indicators into energy system modeling, enabling a comprehensive assessment of both economic and environmental outcomes. Focusing on Switzerland’s energy system as a case study, the model reveals that optimizing key environomic indicators can lead to significant economic advantages, with system costs potentially decreasing by 15% to 47% by minimizing potential impacts from the current system still operating with fossil technologies to an alternative only relying on renewable and where the impact are mainly related to the construction of the infrastructure. However, a system optimized solely for economic efficiency, despite achieving 63% reduction in carbon footprint compared to 2020, shows a potential risk of burden shift to other environmental issues. The adoption of multi-objective optimization in this approach nuances the exploration of the complex interplay between environomic objectives and technological choices. The results illuminate pathways towards more holistically optimized energy systems, effectively addressing trade-offs across environmental problems and enhancing societal acceptance of the solutions to this century’s defining challenge.

Arctic climate response to European radiative forcing: a deep learning study on circulation pattern changes

Abstract. Heterogeneous radiative forcing in mid-latitudes, such as that exerted by aerosols, has been found to affect the Arctic climate, though the mechanisms remain debated. In this study, we leverage deep learning (DL) techniques to explore the complex response of the Arctic climate system to local radiative forcing over Europe. We conducted sensitivity experiments using the Max Planck Institute Earth System Model (MPI-ESM1.2) coupled with atmosphere–ocean–land-surface components. Large-scale circulation patterns can mediate the impact of the forcing on Arctic climate dynamics. We employed a DL-based clustering approach to classify large-scale atmospheric circulation patterns. To enhance the analysis of how these patterns impact the Arctic climate, the poleward moist static energy transport (PMSET) associated with the atmospheric circulation patterns was incorporated as an additional similarity metric in the clustering process. Furthermore, we developed a novel method to analyze the circulation patterns' contributions to various climatic parameter anomalies. Our findings indicate that the negative radiative forcing over Europe alters existing circulation patterns and their occurrence frequency without introducing new ones. Specifically, our analysis revealed that while the regional radiative forcing alters the occurrence frequencies of the circulation patterns, these changes are not the primary drivers of the forcing's impact on the Arctic parameters. Instead, it is the shifts in the mean spatial characteristics of the atmospheric circulation patterns, induced by the forcing, that predominantly determine the effects on the Arctic climate. Our methodology facilitates the uncovering of complex, nonlinear interactions within the climate system, capturing nuances that are often obscured in broader seasonal anomaly analyses. This approach enables a deeper understanding of the dynamics driving observed climatic anomalies and their links to specific atmospheric circulation patterns.

A first order in time wave equation modeling nonlinear acoustics

In this paper we focus on a small amplitude approximation of a Navier-Stokes-Fourier system modeling nonlinear acoustics. Omitting all third and higher order terms with respect to certain small parameters, we obtain a first order in time system containing linear and quadratic pressure and velocity terms. Subsequently, the well-posedness of the derived system is shown using the classical method of Galerkin approximation in combination with a fixed point argument. We first prove the well-posedness of a linearized equation using energy estimates and then the well-posedness of the nonlinear system using a Newton-Kantorovich type argument. Based on this, we also obtain global in time well-posedness for small enough data and exponential decay. This is in line with semigroup results for a linear part of the system that we provide as well.

Effectiveness Evaluation Method Using System of Systems Architecture Description of Aircraft Health Management in Aircraft Maintenance Program

This study proposes a system modeling method for aircraft maintenance program development that adopts condition-based maintenance using aircraft health management (AHM) based on a systems engineering approach, which considers AHM as a system of systems. The metamodel is tailored on the basis of the Unified Architecture Framework (UAF) and the NASA Systems Modeling Handbook for Systems Engineering. It is described using the modeling tool "Balus 2.0" (Levii, Inc). The applicability and effectiveness of a maintenance program adopting AHM is analyzed on the basis of the Maintenance Steering Group-3 (MSG-3), and its effectiveness is evaluated using the proposed system modeling method. The proposed method considers the uncertainty of the aircraft maintenance environment related to airline operations in addition to the uncertainty of the aircraft system. The effectiveness of the proposed system is investigated through a sample problem that considers a tire system using a pressure monitoring system as AHM based on the MSG-3 approach. Finally, the limitations of the proposed method are discussed.

МЕТОДИКА ЕКСПЕРИМЕНТАЛЬНИХ ДОСЛІДЖЕНЬ СПРОЩЕНОЇ МОДЕЛІ БПЛА ТИПУ БІКОПТЕР

Modern aircraft are usually statically unstable objects. Due to this, increased maneuverability and controllability is achieved. One of the promising directions is the development and implementation of UAVs of the bicopter or convertible type. Their movement is provided by two screw electric drives. The non-identity of the engine parameters and the peculiarities of the movement of UAVs of this type cause special requirements for the control systems. To ensure sustainability, modeling and conducting research with the help of various tools is necessary. The most available models of unstable movements are pendulum devices of various types, which allow studying stabilization processes. The purpose of the work is to use a computer stabilization system of a simplified bicopter UAV model by changing the thrust of the engines. The tasks of the work are: development of a computer stabilization system for the angle of pitch or roll, creation and verification of stabilization algorithms on a laboratory bench using an ARDUINO controller, research of the functional capabilities of angular stabilization circuits. The object of the study: a computer control system of an aircraft that performs the task of stabilizing a UAV. Subject of research: processes of stabilization of the angular position of a simplified bicopter model. When performing the work, a control law using angular and angular velocity feedback was chosen. A variant of aircraft stabilization when using engine thrust control is proposed. The control law of the computer stabilization system, which is implemented on the ARDUINO controller, was selected and tested. The applied value of the work is the further use of the obtained results in modern samples of bicopters.

Dynamic modeling and response analysis of misaligned rotor system with squeeze film dampers under maneuver loads

Dynamic modeling and response analysis of misaligned rotor system with squeeze film dampers under maneuver loads

Identification of Residential and Commercial Area using Convolutional Neural Network

Abstract— Image classification of land use using aerial scene classification has become increasingly common around the world. Utilizing the power of Convolutional Neural Networks (CNNs), identification of various city township areas using satellite imagery has become more efficient compared to the previous manual labeling. The objective of this research is to build a convolutional neural network model for residential and commercial area identification. In the research, we also adopted Inception V3 and VGG16 to develop two transfer learning models for the identification system. The Inception V3-based model achieved the highest overall accuracy value of 100%, showing its effectiveness in accurate residential and commercial area identification. The proposed CNN model achieved an accuracy of 99%, while the VGG-16 model with all configurations being frozen achieved 99% accuracy.

Optimizing Touchless Fingerprint Identification: A Machine Learning Approach to Modelling and Performance Evaluation

This paper explored the modelling and performance analysis of a smartphone-based fingerprint identification system using Convolutional Neural Networks (CNN). The research developed a theoretical framework to validate picture-based fingerprint identification as a feasible alternative to traditional touch-based methods. A modified Automated Fingerprint Identification System (AFIS) model served as the study's foundation. To enhance the model's capabilities, data from two databases, IIT India and SOCOFing, were utilized. The evaluation of the CNN architecture focused on mobile device fingerprint recognition. It emphasized key processes such as data pre-processing, model training, and the optimization of the CNN through a Siamese-CNN approach to boost accuracy and efficiency. Python scripts developed for this purpose were converted to Android code using TensorFlow for deployment on Android devices. Performance metrics, including identification accuracy, processing speed, and resource utilization, were analysed to determine the system's feasibility. The results demonstrated that CNN-based fingerprint identification systems hold significant promise for delivering robust and reliable biometric authentication on smartphones, highlighting both their practical applications and limitations. decrease medical as well as financial burden, hence improving the management of cirrhotic patients. These predictors, however, need further work to validate reliability.

Reliability modelling of a grid integration system with dual operational and quintuple critical systems

Reliability modelling of a grid integration system with dual operational and quintuple critical systems

Preface to the special issue on model-driven engineering and system analysis and modelling

Preface to the special issue on model-driven engineering and system analysis and modelling

Inter-Model Spread in Representing the Impacts of ENSO on the South China Spring Rainfall in CMIP6 Models

A major challenge for climate system models in simulating the impacts of El Niño–Southern Oscillation (ENSO) on the interannual variations of East Asian rainfall anomalies is the wide inter-model spread of outputs, which causes considerable uncertainty in physical mechanism understanding and short-term climate prediction. This study investigates the fidelity of 40 models from Phase 6 of the Coupled Model Intercomparison Project (CMIP6) in representing the impacts of ENSO on South China Spring Rainfall (SCSR) during the ENSO decaying spring. The response of SCSR to ENSO, as well as the sea surface temperature anomalies (SSTAs) over the tropical Indian Ocean (TIO), is quite different among the models; some models even simulate opposite SCSR anomalies compared to the observations. However, the models capturing the ENSO-related warm SSTAs over TIO tend to simulate a better SCSR-ENSO relationship, which is much closer to observation. Therefore, models are grouped based on the simulated TIO SSTAs to explore the modulating processes of the TIO SSTAs in ENSO affecting SCSR anomalies. Comparing analysis suggests that the warm TIO SSTA can force the equatorial north–south antisymmetric circulation in the lower troposphere, which is conducive to the westward extension and maintenance of the western North Pacific anticyclone (WNPAC). In addition, the TIO SSTA enhances the upper tropospheric East Asian subtropical westerly jet, leading to anomalous divergence over South China. Thus, the westward extension and strengthening of WNPAC can transport sufficient water vapor for South China, which is associated with the ascending motion caused by the upper tropospheric divergence, leading to the abnormal SCSR.

Efficient Estimation of Climate State and Its Uncertainty Using Kalman Filtering with Application to Policy Thresholds and Volcanism

Abstract We present the Energy Balance Model – Kalman Filter (EBM-KF), a hybrid model projecting and assimilating the global mean surface temperature (GMST) and ocean heat content anomaly (OHCA). It combines an annual energy balance model (difference equations) with 17 parameters drawn from the literature and a statistical Extended Kalman Filter assimilating GMST and OHCA, either observed timeseries or simulated by earth system models. Our motivation is to create an efficient and natural estimator of the climate state and its uncertainty, which we believe to be Gaussian at a global scale. We illustrate four applications: 1) EBM-KF generates a similar estimate to the 30-year time-averaged climate state 15 years sooner, or a model-simulated hindcasts’ annual ensemble average, depending on whether volcanic forcing is filtered or not. 2) EBM-KF conveniently assesses annually likelihoods of crossing a policy threshold. For example, based on temperature records up to the end of 2023, p=0.0017 that the climate state was 1.5°C over preindustrial, but there is a 16% likelihood that the GMST in 2023 itself could have been over that threshold. 3) A variant of the EBM-KF also approximates the spread of an entire climate model large ensemble using only one or a few ensemble members. 4) All variants of the EBM-KF are sufficiently fast to allow thorough sampling from non-Gaussian probabilistic futures, e.g., the impact of rare but significant volcanic eruptions. This sampling with the EBM-KF better determines how future volcanism may affect when policy thresholds will be crossed and what an ensemble with thousands of members exploring future intermittent volcanism reveals.

Spatiotemporal upscaling of sparse air-sea pCO2 data via physics-informed transfer learning.

Global measurements of ocean pCO2 are critical to monitor and understand changes in the global carbon cycle. However, pCO2 observations remain sparse as they are mostly collected on opportunistic ship tracks. Several approaches, especially based on direct learning, have been used to upscale and extrapolate sparse point data to dense estimates using globally available input features. However, these estimates tend to exhibit spatially heterogeneous performance. As a result, we propose a physics-informed transfer learning workflow to generate dense pCO2 estimates that are grounded in real-world measurements and remainphysically consistent. The models are initially trained on dense input predictors against pCO2 estimates from Earth system model simulation, and then fine-tuned to sparse SOCAT observational data. Compared to the benchmark direct learning approach, our transfer learning framework shows major improvements of up to 56-92%. Furthermore, we demonstrate that using models that explicitly account for spatiotemporal structures in the data yield better validation performances by 50-68%. Our strategy thus presents a new monthly global pCO2 estimate that spans for 35 years between 1982-2017.

Upstream influence of midlatitude jet stream biases in boreal summer

AbstractClimate models exhibit biases in the mean state and in variability across different regions of the Earth. For example, atmosphere‐only models have a poleward bias in summertime jet streams across the Northern Hemisphere (NH). This can result from many processes, including misrepresentation of Rossby waves that can propagate in different directions and thereby interact with jet streams. However, Rossby‐wave biases can result from biased background state of the climate system as well. The propagation speed of Rossby waves depends on jet stream strength, thus a poleward displacement of the jet stream can hinder westward propagation of Rossby waves at higher latitudes and displace eastward propagating Rossby waves (downstream development). These biases then impact other regions resulting in biased atmospheric circulation across the NH. Indeed, in this study we confirm this via regional nudging experiments within the Norwegian Earth System Model. Namely, nudged horizontal winds over the North Pacific can improve Rossby wave statistics and thereby atmospheric circulation over Eurasia (i.e., upstream). However, nudging over the North Atlantic has little effect on boreal summer atmospheric circulation. This implies that improving biases over the North Pacific is crucial for a better representation of modelled boreal summer circulation over Eurasia.