Accurate System Model Research Articles

Sensitivity analysis and calibration of a Lagrangian particle tracking using GPS-tagged drifters

A Lagrangian particle tracking module within a hydrodynamic model in Environmental Fluid Dynamic Code Plus (EFDC+) was used to track the movements of large floating marine carcasses. This research used data from six GPS-tagged drifters collected in April 2017 by NOAA to conduct a sensitivity analysis and calibration on the Lagrangian particle tracking module. These analyses investigate the influence of specific parameters, notably wind drag and number of vertical layers in the model. Changing these parameters resulted in observable changes in the trajectories of tracked particles. The results of the sensitivity experiments were compared using the Skill Score statistic. Adding wind drag to the LPT was a pivotal alteration, augmenting the model predictive capabilities by increasing the Skill Score value from 0.23 to 0.47. Further tests examined the impact of varying wind drag coefficients (A and B) which are the slope and y-intercept of a linear equation used to describe overall drag coefficient (CD) as a function of wind velocity. These coefficients are employed in EFDC + software and altering them revealed that coefficient A has a larger impact on the model predictions compared to coefficient B. Finally, the transition from a 2D to a 3D model had a more significant impact on the results compared to increasing the number of vertical layers in the 3D configuration. However, the findings indicated that changing the model from 2D to 3D may not yield a large enough improvement in accuracy that would justify the additional computational demands associated with a 3D model when running long periods of time (several years). The study results have valuable implications for developing accurate and precise models of hydrodynamic systems emphasizing the importance of wind in LPT models and the efficiency of 2D vs 3D models for LPT simulations in the Mississippi Sound area.

Straw movement and flow field in a crushing device based on CFD-DEM coupling with flexible hollow straw model

To accurately predict and evaluate the performance of the crushing device of no-till planter, it is strongly necessary to establish an accurate interaction model of the straw-crushing device coupling system. The traditional model has low prediction accuracy and cannot simulate the fracture characteristics of the straw during the operation of the crushing device due to the straw being usually regarded as a rigid body or a combination of rigid ball joints. In this paper, a breakable and flexible hollow straw model is first proposed and a CFD-DEM coupling model of the straw group-crushing device considering the effect of gas-solid two phase is also established. Afterwards, the corresponding verification experiments are conducted and the effect of working and structural parameters on the straw-throwing weight and crushing length is also analysed. It is demonstrated that the relative error between the simulation results and the experimental results for the straw crushing length is 7.7%, and the relative error for the straw-throwing weight is 7.1%, thereby verifying the correctness of the CFD-DEM model. Finally, the response surface method is also used to determine the optimal working and structural parameters of the crushing device and corresponding field tests verification is also conducted. Results indicate that the optimal parameter combination is as: the rotational speed of the crushing spindle is 2400 rpm, the inlet clearance is 30 mm and the starting point angle is 45°. Moreover, the crushing length after optimisation is 6.6% smaller than before optimisation, and the straw-throwing weight is increased by 11.5%.

High-Resistance Connection Fault Diagnosis in Ship Electric Propulsion System Using Res-CBDNN

The fault detection and diagnosis of a ship’s electric propulsion system is of great significance to the reliability and safety of large modern ships. The traditional fault diagnosis method based on mathematical models and expert knowledge is limited by the difficulty of establishing an accurate model of the complex system, and it is easy to cause false alarms. Data-driven methods, such as deep learning, can automatically learn from the mass of data, extract and analyze fault characteristics, and create a more objective distinction system state. A deep learning fault diagnosis model based on ResNet feature extraction capability and bidirectional long-term memory network timing processing capability is proposed to realize fault diagnosis of high resistance connections in ship electric propulsion systems. The results show that the res-convolutional BiLSTM deep neural network (Res-CBDNN) can fully integrate the advantages of the two networks, efficiently process fault current data, and achieve high-performance fault diagnosis. The accuracy of Res-CBDNN can be kept above 85% in a noisy environment, and it can effectively monitor the high resistance connection fault of ship electric propulsion systems.

Optimal control of a heat pump-based energy system for space heating and hot water provision in buildings: Results from a field test

This paper presents and discusses results from a field test that uses model predictive control (MPC) to optimise the operation of a multi-source heat pump-based energy system for space heating and hot water provision in a building. The objective of the optimisation is to minimise the electricity consumption of the heat pump using flexibility from the heat sources, the space heating and domestic hot water tanks while ensuring end-user comfort and system constraints. The field test was performed in the context of the RES4BUILD project. The integrated system consists of photovoltaic-thermal (PVT) collectors, an advanced vapour-compression multi-source heat pump and water buffer tanks for space heating and hot water needs. The heat pump utilises a solar buffer, a borehole thermal energy storage (BTES) and air as heat sources. The solar buffer is supplied with heat from the PVT collectors. The optimal operation of the system is obtained by monitoring and controlling the interactions between the different system components using MPC, taking into account the weather and heat load forecasts. Results have shown that MPC has a potential and added value for the optimal operation of multi-source heat pumps in real-life while considering system constraints and user behaviour to ensure thermal comfort. However, significant effort and expert knowledge are needed to develop the sufficiently accurate system models required by this control approach. The outcomes and conclusions of this work are therefore a basis for further development of such control approaches to improve their replicability and feasibility on a large scale, considering the diverse nature of energy system components in buildings.

Structural Coarse-Graining via Multiobjective Optimization with Differentiable Simulation.

In the realm of multiscale molecular simulations, structure-based coarse-graining is a prominent approach for creating efficient coarse-grained (CG) representations of soft matter systems, such as polymers. This involves optimizing CG interactions by matching static correlation functions of the corresponding degrees of freedom in all-atom (AA) models. Here, we present a versatile method, namely, differentiable coarse-graining (DiffCG), which combines multiobjective optimization and differentiable simulation. The DiffCG approach is capable of constructing robust CG models by iteratively optimizing the effective potentials to simultaneously match multiple target properties. We demonstrate our approach by concurrently optimizing bonded and nonbonded potentials of a CG model of polystyrene (PS) melts. The resulting CG-PS model effectively reproduces both the structural characteristics, such as the equilibrium probability distribution of microscopic degrees of freedom and the thermodynamic pressure of the AA counterpart. More importantly, leveraging the multiobjective optimization capability, we develop a precise and efficient CG model for PS melts that is transferable across a wide range of temperatures, i.e., from 400 to 600 K. It is achieved via optimizing a pairwise potential with nonlinear temperature dependence in the CG model to simultaneously match target data from AA-MD simulations at multiple thermodynamic states. The temperature transferable CG-PS model demonstrates its ability to accurately predict the radial distribution functions and density at different temperatures, including those that are not included in the target thermodynamic states. Our work opens up a promising route for developing accurate and transferable CG models of complex soft-matter systems through multiobjective optimization with differentiable simulation.

Design of Car Control Algorithm Based on Deep Reinforcement Learning

Reinforcement learning algorithm is widely used in the field of agent control because of its high robustness and strong adaptability. The problem of Mountain-Car-v0 is a classic control problem, which attracts the attention of many researchers. Traditional control methods, for example, PID control, are widely used in industrial and robot control fields, but they show obvious limitations in the face of such highly nonlinear and unknown environments. Therefore, this paper proposes a car control algorithm based on deep reinforcement learning, aiming at overcoming the shortcomings of traditional methods and realizing efficient control of complex dynamic systems. By combining the automatic extraction of high-dimensional features of deep learning with the dynamic decision-making mechanism of reinforcement learning, an adaptive control algorithm is designed, which can continuously learn and optimize in the interaction with the environment without establishing an accurate system model in advance. The experimental results show that compared with traditional control methods and other intelligent control strategies, the proposed algorithm shows higher efficiency and better adaptability in the task of Mountain-car-V0, and obviously improves the success rate of the Mountain-Car-v0.

Vibrational Circular Dichroism Spectroscopy with a Classical Polarizable Force Field: Alanine in the Gas and Condensed Phases.

Polarizable force fields are an essential component for the chemically accurate modeling of complex molecular systems with a significant degree of fluxionality, beyond harmonic or perturbative approximations. In this contribution we examine the performance of such an approach for the vibrational spectroscopy of the alanine amino acid, in the gas and condensed phases, from the Fourier transform of appropriate time correlation functions generated along molecular dynamics (MD) trajectories. While the infrared (IR) spectrum only requires the electric dipole moment, the vibrational circular dichroism (VCD) spectrum further requires knowledge of the magnetic dipole moment, for which we provide relevant expressions to be used with polarizable force fields. The AMOEBA force field was employed here to model alanine in the neutral and zwitterionic isolated forms, solvated by water or nitrogen, and as a crystal. Within this framework, comparison of the electric and magnetic dipole moments to those obtained with nuclear velocity perturbation theory based on density-functional theory for the same MD trajectories are found to agree well with one another. The statistical convergence of the IR and VCD spectra is examined and found to be more demanding in the latter case. Comparisons with experimental frequencies are also provided for the condensed phases.

Additively manufactured Ni-20Cr to V functionally graded material: Computational predictions and experimental verification of phase formations

A database for the Cr-Ni-V system was constructed by modeling the binary Cr-V and ternary Cr-Ni-V systems using the CALPHAD approach aided by density functional theory (DFT)-based first-principles calculations and ab initio molecular dynamics (AIMD) simulations. To validate this new database, a functionally graded material (FGM) using Ni-20Cr and elemental V was fabricated using directed energy deposition additive manufacturing (DED AM) and experimentally characterized. The deposited Ni-20Cr was pure fcc phase, while increasing the amount of V across the gradient resulted in the formation of sigma phase, followed by the bcc phase. The experimentally measured phase data was compared with computational predictions made using a Cr-Ni-V thermodynamic database from the literature as well as the database developed in the present work. The newly developed database was shown to better predict the experimentally observed phases due to its accurate modeling of binary systems within the database and the ternary liquid phase, which is critical for accurate Scheil calculations.

Development of an Automated Experimental System for Thermomechanical and Electrical Characterization of NiTi Shape Memory Alloys

BackgroundComprehensive datasets quantifying the coupled thermo-mechanical and electrical properties of shape memory alloys (SMAs) are lacking, as are standardized techniques for robust characterization. This hampers accurate modeling and design of SMA-based components. Objective: This work develops an automated experimental system to enable simultaneous measurement of stress-strain-temperature behavior and electrical resistivity evolution in NiTi SMA wires under controlled stress conditions. Methods: Customized test frames apply precise mechanical stresses while allowing for in situ electrical measurements and infrared imaging during complete thermal cycling protocols. Specialized instrumentation including a Keithley 2002 multimeter, Agilent E3631A programmable power supply, and FLIR A615 thermal camera are integrated with LabVIEW-based software routines for complete automation of the characterization process. Rigorous metrology principles are implemented throughout the measurement procedure to improve accuracy, repeatability, and consistency compared to prior manual techniques. Results: Extensive datasets are generated which reveal pronounced stress-dependencies in key SMA material parameters including transformation temperatures, recoverable strain, and electrical resistivity. A 3D regression model describes the comprehensive relationship between resistivity, temperature, and applied stress across the entire characterization domain. Conclusions: The automated measurement framework and methodology establishes a foundation for high-fidelity, reliable acquisition of coupled SMA property data. This will enable more accurate modeling and design of components and systems incorporating SMA actuation or sensing functions.

Fully actuated system approach for spacecraft rendezvous system with actuator saturation

This paper explores the application of a fully actuated system (FAS) approach to a spacecraft rendezvous system with actuator saturation. A FAS model for the spacecraft rendezvous system is developed, followed by the design of an extended state observer (ESO) to estimate unmeasurable states and the system's nonlinear terms. The saturation issue is addressed using the convex hull method. By the designed ESO, a state feedback controller for the system is designed using the FAS approach, resulting in a constant linear closed-loop system whose eigenvalues can be arbitrarily assigned. This method simplifies the controller design process and provides a more accurate system model without the linearisation of nonlinear terms, thereby improving control accuracy. Simulation results show the effectiveness and usefulness of the proposed method.

Design of Lumped Disturbance Observer in Current Loop of IPMSM Based on Recursive Integral Sliding Mode Surface

To overcome the problem of current control effect being reduced by unideal factors in a motor control system, such as motor parameter variation, inverter dead time, nonlinearity of the system, etc., a sliding mode disturbance observer for an interior permanent magnet synchronous motor is proposed in this paper. The model of an interior permanent magnet synchronous motor with unideal factors is designed, and the unideal factors are unified into lumped disturbances of motor stator voltage. Then, the observer for lumped disturbance is designed. A recursive integral sliding surface is used to replace the terminal sliding surface to avoid the noise sensitivity and singularity problem of the traditional terminal sliding mode observer. The observer can estimate the lumped disturbance of the current loop without relying on the accurate system model in finite time. Moreover, the structure of the current loop does not need to be adjusted while using the observer to observe and compensate for disturbances. Experiments are carried out to verify the effectiveness of the proposed observer.

Research on the Control Strategy for Handling Stability of Electric Power Steering System with Active Front Wheel Steering Function

<div>Due to the presence of uncertain disturbances in the actual steering system, disturbances in the system may affect the handling stability of the vehicle. Therefore, this article proposes an integrated steering system control strategy with stronger anti-disturbance performance. When disturbances exist in the system, the proposed control strategy effectively reduces the attitude changes during the vehicle steering process. In the upper-level control strategy, a variable transmission ratio curve is designed to coordinate the high-speed handling stability and low-speed steering sensitivity of the vehicle. On this basis, a sideslip angle observer is proposed based on the extended state observation theory, which does not depend on an accurate system model, thus determining the intervention timing of the active front wheel steering system. In the lower-level control strategy, DR-PI/DR-PID controllers are designed for the integrated steering system. Finally, experiments are conducted in the CarSim/Simulink joint simulation environment. The results indicate that compared to traditional PI/PID controllers and advanced ADRC controllers, DR-PI/DR-PID controllers can effectively suppress step disturbances, sinusoidal disturbances, and white noise disturbances while ensuring the dynamic response characteristics of the system. Under double-lane conditions, the root mean square values of the yaw rate and sideslip angle decrease by 4.35% to 12.3% and 3.21% to 7.72%, respectively. Therefore, the designed control strategy can improve handling stability while ensuring the robustness of the system.</div>

Investigation on tillage resistance and soil disturbance in wet adhesive soil using discrete element method with three-layer soil-plough coupling model

To reveal the interaction between plough and wet adhesive soil, it's essential to establish an accurate coupling model of the soil-plough system. Traditional Edinburgh elasto-plastic adhesive contact model neglects the adhesion effect when two interactive particles are separated, leading to lower simulation accuracy. Here, an improved contact model of wet adhesive soil by introducing the adhesive force is proposed. Then a novel discrete element model of a three-layer soil-plough coupling system is established, where the liquid bridge force and the adhesion effect are considered. Furthermore, the corresponding soil bin tests are also conducted to demonstrate the effectiveness of the proposed model. Finally, the effect of soil adhesion, ploughing speeds and ploughing depths on the tillage resistance and soil disturbance is analyzed. Results show that the prediction accuracy of the proposed model increases by about 7%, moreover, the soil disturbed area decreases, while the tillage resistance increases with increasing soil adhesion.

Adaptive control of transonic buffet and buffeting flow with deep reinforcement learning

The optimal control of flow and fluid–structure interaction (FSI) systems often requires an accurate model of the controlled system. However, for strongly nonlinear systems, acquiring an accurate dynamic model is a significant challenge. In this study, we employ the deep reinforcement learning (DRL) method, which does not rely on an accurate model of the controlled system, to address the control of transonic buffet (unstable flow) and transonic buffeting (structural vibration). DRL uses a deep neural network to describe the control law and optimizes it based on data obtained from interaction between control law and flow or FSI system. This study analyzes the mechanism of transonic buffet and transonic buffeting to guide the design of control system. Aiming at the control of transonic buffet, which is an unstable flow system, the control law optimized by DRL can quickly suppress fluctuating load of buffet by taking the lift coefficient as feedback signal. For the frequency lock-in phenomenon in transonic buffeting flow, which is an unstable FSI system, we add the moment coefficient and pitching displacement to feedback signal to observe pitching vibration mode. The control law optimized by DRL can also effectively eliminate or reduce pitching vibration displacement of airfoil and buffet load. The simulation results in this study show that DRL can adapt to the control of two different dynamic modes: typical forced response and FSI instability under transonic buffet, so it has a wide application prospect in the design of control laws for complex flow or FSI systems.

Kriging-based Model Predictive Control for Lower-limb Rehabilitation Robots

Model predictive control (MPC) has emerged as a predominant method in the realm of control systems; yet, it faces distinct challenges. First, MPC often hinges on the availability of a precise and accurate system model, where even minor deviations can drastically affect the control performance. Second, it entails a high computational load due to the need to solve complex optimization problems in real time. This study introduces an innovative method that harnesses the probabilistic nature of Gaussian processes (GPs), offering a solution that is robust, adaptive, and computationally efficient for optimal control. Our methodology commences with the collection of data to learn optimal control policies. We then proceed with offline training of GPs on these data, which enables these processes to accurately grasp system dynamics, establish input–output relationships, and, crucially, identify uncertainties, thereby informing the MPC framework. Utilizing the mean and uncertainty estimates derived from GPs, we have crafted a controller that is capable of adapting to system deviations and maintaining consistent performance, even in the face of unforeseen disturbances or model inaccuracies. The convergence of the closed-loop system is assured through the application of the Lyapunov stability theorem. In our numerical experiments, the exemplary performance of our approach is demonstrated, notably in its capacity to adeptly handle the complexities of dynamic systems, even with limited training data, underlining a significant leap forward in MPC strategies.

Subglottal Impedance-Based Model Parameter Estimation via System Identification

Accurate mathematical modeling of different systems of the human body stands as a key issue in medical and bioengineering applications. This paper specifically considers the continuous-time parameter estimation of the impedance-based mathematical model — a mechano-acoustic representation of the subglottal system. This approach allows the customization of the model for each patient. A key advantage of having a patient-dependent model of the subglottal system is to facilitate the ambulatory non-invasive monitoring of the glottal airflow and the assessment of vocal functions, using an accelerometer on the neck skin surface. For this model of the subglottal system, the glottal airflow is the excitation signal, while the acceleration on the neck skin is the system response. In this study, continuous-time parameter estimation of the impedance-based model for the subglottal system is applied, using both frequency response and sampled data through system identification techniques.

Optimizing the Simulation of Conveyor Systems through Digital Shadow Integration to Increase Assembly Efficiency

In today's highly competitive industrial environment, continuous improvement of efficiency and optimization of processes is crucial. This paper presents an approach to the optimization of conveyor systems that uses the concept of a digital shadow. A digital shadow, as an exact digital replica of a physical conveyor system, enables detailed simulation and analysis of real operational data, providing a basis for in-depth analysis and identification of areas for improvement. The aim of this approach is not only to improve the understanding of the dynamics and performance of existing conveyor systems, but also to increase the overall efficiency through predictive simulations and optimization algorithms. In this work, we demonstrate how the integration of a digital shadow into the simulation process can contribute to a better reaction to changes in the production environment, to the reduction of downtime and to the optimization of production flows. Our methodology combines data collection / analysis, and enables the creation of accurate and flexible models of conveyor systems. These models are then used in simulations that help identify optimal settings for different production scenarios and predict potential problems before they occur. The results of applying our approach on a test laboratory line show a significant improvement in efficiency and a reduction in operating costs. This study provides important insights and practical guidelines for engineers and production managers focused on the use of digital shadow to increase the efficiency of conveyor systems. It also contributes to the development of intelligent production technologies in the era of Industry 4.0.

Exploring HCl-HCl interactions: QZVPP calculations, improved Lennard-Jones potential, and second virial coefficient analysis for thermodynamics and industrial applications.

In this paper, we present a comprehensive analysis of HCl-HCl interactions, including QZVPP calculations, energy fitting, conformation validation, and the determination of the second virial coefficient B using improved Lennard-Jones (ILJ) potential parameters. To acquire accurate interaction energies, initial QZVPP calculations are performed on approximately 1851 randomly generated HCl-HCl conformations. Then, these energies are used to fit an improved Lennard-Jones potential energy surface, allowing for a robust description of HCl-HCl interactions. The ILJ potential parameters are then used to validate particular HCl dimer conformations, ensuring their stability and consistency with experimental observations. The correlation between calculated and experimental conformations strengthens the validity of the ILJ potential parameters. In addition, the second viral coefficient B is calculated at various temperatures using the ILJ potential. The obtained B values are compared to experimental data, demonstrating close agreement, and validating the ILJ potential's ability to accurately capture the intermolecular interactions and gas-phase behavior of the HCl-HCl system. The results of this study demonstrate the effective implementation of QZVPP calculations, energy fitting, and ILJ potential parameters in validating HCl-HCl conformations and accurately determining the second virial coefficient B. The high degree of concordance between calculated B values and experimental data demonstrates the validity of the ILJ potential and its suitability for modeling HCl-HCl interactions. This research contributes to a greater comprehension of HCl-HCl interactions and their implications for numerous chemical and atmospheric processes. The validated conformations, energy fitting method, and calculated second virial coefficients provide valuable instruments for future research and pave the way for more accurate modeling and simulations of HCl-HCl systems.

District Cooling System Control for Providing Operating Reserve Based on Safe Deep Reinforcement Learning

Heating, ventilation, and air conditioning (HVAC) systems are well proved to be capable to provide operating reserve for power systems. As a type of large-capacity and energy-efficient HVAC system (up to 100 MW), district cooling system (DCS) is emerging in modern cities and has huge potential to be regulated as a flexible load. However, strategically controlling a DCS to provide flexibility is challenging, because one DCS services multiple buildings with complex thermal dynamics and uncertain cooling demands. Improper control may lead to significant thermal discomfort and even deteriorate the power system's operation security. To address the above issues, we propose a model-free control strategy based on the deep reinforcement learning (DRL) without the requirement of accurate system model and uncertainty distribution. To avoid damaging “trial & error” actions that may violate the system's operation security during the training process, we further propose a safe layer combined to the DDPG to guarantee the satisfaction of critical constraints, forming a safe-DDPG scheme. Moreover, after providing operating reserve, DCS increases power and tries to recover all the buildings' temperature back to set values, which may cause an instantaneous peak-power rebound and bring a secondary negative impact on power systems. Therefore, we design a self-adaption reward function within the proposed safe-DDPG scheme to constrain the peak-power effectively. Numerical studies based on a realistic DCS demonstrate the effectiveness of the proposed methods.

Base on -Learning Pareto Optimality for Linear Itô Stochastic Systems With Markovian Jumps

This article investigate the cooperative differential game (CDG) for continuous-time linear Itô stochastic systems with markovian jumps (SSMJ) to obtain the Pareto solutions. Different from most existing works studying nonzero-sum games, this article studies the CDG on the quadratic infinite horizon for the Itô-type SSMJ with unknown system matrix and transition probability. A novel <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$Q$</tex-math> </inline-formula> -learning online algorithm is developed, which consists of that (i) the optimal control problem is equivalent to solving a stochastic algebraic Riccatic equation (ARE); (ii) the joint cost function is approximated by a critic neural network (NN) and Pareto efficient is approximated by two actor NNs. The rigorous stability analysis shows that the system state for SSMJ and the NN weight errors are uniformly ultimately bounded (UUB). Finally, the theory analysis is validated by a numerical example with detailed discussions. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —In practical applications, many systems are often affected by the change of external environment or the failure of internal components, which leads to the random jump of system parameters. Markovian jump system can effectively describe the above problems. And when the system model is disturbed by the internal parameters, the system control input and external environment, the random errors of state measurement, and other random factors, the deterministic model can no longer accurately describe the controlled system. Therefore, the SSMJ can describe practical problems more accurately. By cooperation, in general, the cost one specific player incurs is not uniquely determined anymore. If all players decide, for example, to use their control variables to reduce the cost of player 1 as much as possible, a different minimum is attained for player 1 compared with that in the case where all players agree collectively to help a different player in minimizing his cost. So, depending on how the players choose to ‘divide’ their control efforts, a player incurs different ‘minima’. Therefore, we will design an online learning algorithm to obtain pareto solutions with different weights. On the other hand, in practice, it is difficult to obtain an accurate system model. In order to solve this problem, a novel scheme is designed by using <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$Q$</tex-math> </inline-formula> -learning technology, which does not need system matrix.