Nonlinear Simulation Model Research Articles

Thermohaline staircase formation in the diffusive convection regime: a theory based upon stratified turbulence asymptotics

We describe a mechanism that leads to the spontaneous formation of a thermohaline staircase in the high-latitude oceans. Our analysis of this mechanism is based upon a model in which uniform gradients of temperature and salinity are assumed and is applied to a simplified mean-field model of stratified turbulence. Detailed analysis employs a parametrization of turbulent diapycnal diffusivities (Bouffard & Boegman, Dyn. Atmos. Oceans, vol. 61, 2013, pp. 14–34). This parametrization is apparently unique in that it distinguishes between the diapycnal diffusivities for heat and salt on the basis of their Prandtl (Schmidt) numbers. Our model predicts that the temperature and salinity profiles will be susceptible to linear instability if the buoyancy Reynolds number lies in the range 0.18–91, and a nonlinear mean-field model simulation demonstrates that it evolves into a well-defined thermohaline staircase that matches the characteristics of those found in the high-latitude oceans. The criterion for initial instability is furthermore shown to be consistent with the observed regional variability of staircase occurrence in the Arctic Ocean as determined by the most recent observational datasets.

Time‐varying robustness analysis of launch vehicles under thrust perturbations

AbstractThis article presents a robustness analysis for launch vehicles during atmospheric ascent. It is based on recent advances in the worst‐case analysis of uncertain, finite horizon linear time‐varying (LTV) systems. Integral quadratic constraints are used to represent the uncertainties leading to a worst‐case gain condition based on a parameterized Riccati differential equation (RDE). This framework readily covers certain parametric uncertainties, for example, aerodynamic uncertainties. However, the effects of thrust perturbations are inherently harder to address. They directly result in mass and balance uncertainties, and a deviation from the planned trajectory. The latter amounts to a shift from the nominal/reference trajectory that has to be accounted for. Since thrust is proportional to the mass change of the launch vehicle, the thrust uncertainty is modeled as an external disturbance acting on the system. In order to cover the deviation from the reference trajectory, a dynamic uncertainty model is added in the analysis. A worst‐case aerodynamic loads and lateral drift analysis under wind disturbances is performed. The LTV results are validated against Monte Carlo simulations of the space launcher's nonlinear model.

Intelligent Speed Regulation Control in Switched Reluctance Motor of Electric Vehicle Based on Neural Network Parameter Identification

This paper studies the intelligent speed regulation control of switched reluctance motor of electric vehicle based on neural network parameter identification. Starting with the analysis of the performance of switched reluctance motor, the nonlinear flux linkage characteristic inversion model and torque characteristic model of switched reluctance motor are established based on BP neural network. This paper studies and improves the fast self configuration algorithm of BP neural network. Finally, the nonlinear simulation model of switched reluctance motor is established under Matlab/Simulink. The model can be used for further control research. In this paper, the integrated control method of instantaneous torque control based on torque observation and three-step commutation control is studied, and the simulation analysis is carried out. The results show that this method can effectively reduce the torque ripple of switched reluctance motor and improve the performance of its drive system.

Unscented Kalman filter for airship model uncertainties and wind disturbance estimation.

An airship is lighter than an air vehicle with enormous potential in applications such as communication, aerial inspection, border surveillance, and precision agriculture. An airship model is made up of dynamic, aerodynamic, aerostatic, and propulsive forces. However, the computation of aerodynamic forces remained a challenge. In addition to aerodynamic model deficiencies, airship mass matrix suffers from parameter variations. Moreover, due to the lighter-than-air nature, it is also susceptible to wind disturbances. These modeling issues are the key challenges in developing an efficient autonomous flight controller for an airship. This article proposes a unified estimation method for airship states, model uncertainties, and wind disturbance estimation using Unscented Kalman Filter (UKF). The proposed method is based on a lumped model uncertainty vector that unifies model uncertainties and wind disturbances in a single vector. The airship model is extended by incorporating six auxiliary state variables into the lumped model uncertainty vector. The performance of the proposed methodology is evaluated using a nonlinear simulation model of a custom-developed UETT airship and is validated by conducting a kind of error analysis. For comparative studies, EKF estimator is also developed. The results show the performance superiority of the proposed estimator over EKF; however, the proposed estimator is a bit expensive on computational grounds. However, as per the requirements of the current application, the proposed estimator can be a preferred choice.

Increasing Superstructure Optimization Capacity Through Self-Learning Surrogate Models

Simulation-based optimization models are widely applied to find optimal operating conditions of processes. Often, computational challenges arise from model complexity, making the generation of reliable design solutions difficult. We propose an algorithm for replacing non-linear process simulation models integrated in multi-level optimization of a process and energy system superstructure with surrogate models, applying an active learning strategy to continuously enrich the database on which the surrogate models are trained and evaluated. Surrogate models are generated and trained on an initial data set, each featuring the ability to quantify the uncertainty with which a prediction is made. Until a defined prediction quality is met, new data points are continuously labeled and added to the training set. They are selected from a pool of unlabeled data points based on the predicted uncertainty, ensuring a rapid improvement of surrogate quality. When applied in the optimization superstructure, the surrogates can only be used when the prediction quality for the given data point reaches a specified threshold, otherwise the original simulation model is called for evaluating the process performance and the newly obtained data points are used to improve the surrogates. The method is tested on three simulation models, ranging in size and complexity. The proposed approach yields mean squared errors of the test prediction below 2% for all cases. Applying the active learning approach leads to better predictions compared to random sampling for the same size of database. When integrated in the optimization framework, simpler surrogates are favored in over 60% of cases, while the more complex ones are enabled by using simulation results generated during optimization for improving the surrogates after the initial generation. Significant time savings are recorded when using complex process simulations, though the advantage gained for simpler processes is marginal. Overall, we show that the proposed method saves time and adds flexibility to complex superstructure optimization problems that involve optimizing process operating conditions. Computational time can be greatly reduced without penalizing result quality, while the continuous improvement of surrogates when simulation is used in the optimization leads to a natural refinement of the model.

The influence of suspension nonlinearities on fatigue assessment of vehicle structure

Load spectrums for the fatigue analysis were created using suspension responses generated in a simulation of vehicle and suspension vertical dynamics nonlinear model for different conditions of vehicle use. The next stage presented was the use of finite element method and analysis of obtained stresses with its transformation to a set of cycles that are used in the determination of fatigue characteristics. The qualitative and quantitative analysis of the stresses field in the vehicle structure and suspension elements was done and later the influence of suspension responses on the fatigue assessment for most loaded parts of suspension and vehicle structure. Lastly conclusions were drawn from the results describing qualitative and quantitative influence of different road class and load conditions on fatigue assessment of vehicle structure and suspension components. Conclusion on the proposed and used methodology also was drawn.

Gramian-Aware Control Approach for Atmospheric Gust Harvesting

Micro-aerial vehicles (MAVs) lack the endurance times demanded by typical mission applications, and previous work to minimize flight path disturbances has also quantified their high atmospheric sensitivities. This study introduces an approach to modulate the sensitivity through real-time parameter variation that translates into a net energy gain of the vehicle. By applying a control-theoretic disturbance sensitivity framework and observability gramian via a “gust capture metric,” the formulation modulates vehicle gust response to sensed favorable (or unfavorable) vertical gusts. The approach is implemented in a nonlinear simulation and an experimental test environment for a representative 21 g MAV. These results include the development of a system-identified MAV flight dynamics model and the development of an experimental facility to provide automated, repeatable indoor flight tests over a gust field. Nonlinear model simulation indicates that the control law provides altitude gain over an idealized gust field, consistent with theoretical analysis. The experimental flight facility was equipped with a gust field generation capability, and its velocity and turbulence intensity distribution was quantified. The experimental flight tests evaluating altitude gain under vehicle controller modification show agreement with theoretical and simulation results and indicate the importance of turbulence distribution in atmospheric gust harvesting experiments.

A Model Predictive Control Approach to Operation Optimization of an Ultracapacitor Bank for Frequency Control

This paper presents a nonlinear dynamic simulation model of an ultracapacitor (UC) bank and the associated control system. The control system at hand consists of two levels: the lower level controls the inverter of the UC bank, while the upper control level is responsible for providing charging/discharging active power set points to be followed by the lower control level. This paper focuses on the development of the upper control level for frequency control. Specifically, two simulation case studies are developed so as to assess the performance of the proposed control framework. In the first case study the upper control level is developed using a classical Proportional-Integral-Derivative (PID) controller. In the second case study the upper control level is devised using a Model Predictive Control (MPC) algorithm based on internal linear prediction model of a nonlinear UC bank. In both cases, a nonlinear UC bank simulation model is used. The simulation case studies are modelled and tested in Matlab/Simulink. The response of the MPC-controlled UC bank is compared to the 3 existing PID-control algorithms for frequency control. The simulation results show that the MPC algorithm outperforms the conventional PID controllers.

Quantifying Food Loss and Waste in Saudi Arabia

Using the FAO model calculations proposed by Gustavsson et al. (2013) and FAO (2014), food loss and waste (FLW) is measured in Saudi Arabia with a special focus on wheat, rice, dates, poultry, vegetables, fruits, fish, and meat. Results show that the overall FLW rate is 33.1%, where the food loss rate is 14.2%, and the food waste rate is 18.9%. Acceding to the disaggregated results, we find that FLW rates are distributed as follows: 29.7% for wheat, 33.6% for rice, 21.4%, for dates 29.1% for poultry, 39.5% for vegetables, 39.6% for fruits, 33% for fish, and 31.3% for meat. The Sustainable Development Goal (SDG 12.3) target is to reduce the rates of food loss and waste by 50% in 2030, and to help achieve that goal, we employed a nonlinear optimisation simulation model with the objective function of reducing FLW by 50% over the period 2020–2030. Based on the findings achieved, recommendations are made to cover the various aspects of the whole food supply chain (FSC) and to aim at more efficiency and higher levels of productivity. Our findings have significant implications by estimating the FLW baseline indicator and providing the different stakeholders of FSC with the optimal actions to do to reduce FLW rates.

Simultaneous identification of groundwater pollution source spatial–temporal characteristics and hydraulic parameters based on deep regularization neural network-hybrid heuristic algorithm

The prerequisites for groundwater pollution remediation include providing accurate identification of hydraulic parameters of the contaminated site and retrospecting pollution source release history from sparse and discrete observation data. However, expensive time running and high nonlinearity of groundwater solute transport simulation model hinder precise and fast identification. To overcome expensive running cost, a deep regularization neural network (DRNN) was introduced to emulate the highly nonlinear simulation model. A hybrid heuristic algorithm, integrating a particle swarm optimization (PSO) and a differential evolution markov chain (DEMC) method was employed to provide reliable identification results of pollution source characteristics and hydraulic parameters. In particular, DEMC conducted a global search and PSO performed a local search. To evaluate transferability for various groundwater pollution cases, the proposed method was utilized to provide an identification for a point source pollution case and a non-point source pollution case. The results indicated that DRNN was capable of emulating input–output relationship of a highly nonlinear and high time expense simulation model of two cases with a R2 of 0.997 and 0.984, respectively, accelerating identification process. Furthermore, when the PSO was combined with DEMC, the optimization was accelerated while escaping from the local optimum, substantially improving the efficiency and accuracy of the identification task.

Validation of a Prototype Global 4D-Var Data Assimilation System for the MPAS-Atmosphere Model

AbstractA four-dimensional variational (4D-Var) data assimilation (DA) system is developed for the global nonhydrostatic atmospheric dynamical core of the Model for Prediction Across Scales (MPAS). The nonlinear forward and adjoint models of the MPAS-Atmosphere dynamic core are included in a Python-driven structure to formulate a continuous 4D-Var DA system, shown to effectively minimize the cost function that measures the distances between the nonlinear model simulations and observations. In this study, three idealized experiments with a six-hour assimilation window are conducted to validate and demonstrate the numerical feasibilities of the 4D-Var DA system for both uniform- and variable-resolution meshes. In the first experiment, only a single point observation is assimilated. The resulting solution shows that the analysis increments have highly flow-dependent features. The observations in the second experiment are all model prognostic variables that span the entire global domain, the purpose of which is to check how well the initial conditions six hours prior to the observations can be reversely inferred. The differences between the analysis and the referenced "truth" are significantly smaller than those calculated with the first guess. The third experiment assimilates the mass field only, i.e., potential temperatures in the case of MPAS-Atmosphere, and examines the impacts on the wind field and the mass field under initial conditions. Both the wind vectors and potential temperatures in the analysis agree more with the referenced "truth" than the first guess because the adjustments made to the initial conditions are dynamically consistent in the 4D-Var system.

Launcher flight control design using robust wind disturbance observation

The development of effective load relief strategies is key to the improvement of launcher flight performance as it enables a joint increase of wind resilience and decrease of mass. This is particularly relevant for reusable launchers, which are aimed at maximising operational availability and payload capacity. Yet, despite various load relief advances in the aeronautics and wind energy sectors, classical feedback-only techniques remain the state-of-practice for launchers. In this article, an improved load relief functionality for reusable vehicles is proposed based on the use of a disturbance observer for on-board wind anticipation and a load relief compensator driven by the estimate of the wind for its amelioration.Two space systems are used to demonstrate the capabilities of the proposed approach. First, it is applied to a 3 degrees-of-freedom nonlinear simulation model of DLR’s EAGLE vertical-flight demonstrator. Then, it is applied to a 6 degrees-of-freedom nonlinear simulation model of a generic lightweight, reusable launch vehicle. For both cases, the results highlight the benefits of using this type of wind-estimation/load-relief compensation schemes. Further, for the second case, which uses thrust vector control and planar fins for ascent and descent attitude control, it is also shown that the use of fins during ascent (which is not common practice), can further improve launcher performance.

Virtual Connectomic Datasets in Alzheimer's Disease and Aging Using Whole-Brain Network Dynamics Modelling.

Large neuroimaging datasets, including information about structural connectivity (SC) and functional connectivity (FC), play an increasingly important role in clinical research, where they guide the design of algorithms for automated stratification, diagnosis or prediction. A major obstacle is, however, the problem of missing features [e.g., lack of concurrent DTI SC and resting-state functional magnetic resonance imaging (rsfMRI) FC measurements for many of the subjects]. We propose here to address the missing connectivity features problem by introducing strategies based on computational whole-brain network modeling. Using two datasets, the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset and a healthy aging dataset, for proof-of-concept, we demonstrate the feasibility of virtual data completion (i.e., inferring "virtual FC" from empirical SC or "virtual SC" from empirical FC), by using self-consistent simulations of linear and nonlinear brain network models. Furthermore, by performing machine learning classification (to separate age classes or control from patient subjects), we show that algorithms trained on virtual connectomes achieve discrimination performance comparable to when trained on actual empirical data; similarly, algorithms trained on virtual connectomes can be used to successfully classify novel empirical connectomes. Completion algorithms can be combined and reiterated to generate realistic surrogate connectivity matrices in arbitrarily large number, opening the way to the generation of virtual connectomic datasets with network connectivity information comparable to the one of the original data.

Required Moment Sets: Enhanced Controllability Analysis for Nonlinear Aircraft Models

Attainable moment sets (AMS) are a powerful method to assess aircraft controllability. However, as attainable moment sets only take into account the achievable moment set of the control effectors, they do not assess the required moment set to fulfill the aircraft mission requirements. This paper proposes to calculate a corresponding required moment set (RMS) which defines a set of moments sufficient for fulfilling aircraft controllability requirements in the mission flight envelope. The paper applies the required moment set approach to a nonlinear simulation model of an electric vertical take off vehicle (eVTOL) transition drone in hover. By comparing the required moment set to the AMS of the aircraft model, moment set margins are derived and used to assess the controllability of the considered aircraft. The results indicate that the combined evaluation directly identifies critical moment channels and margins, which is advantageous when compared to a pure AMS-based evaluation. The proposed approach enables the execution of simulation-based assessments in aircraft design and flight control development. In the early stages of aircraft design, required moment sets can support sizing, positioning and tilting of control effectors (e.g., propulsive elements) to fit the AMS to the actual required force and moment set for the specific system.

Controllable transmission networks under demand uncertainty with modular FACTS

The transmission system operators (TSOs) are responsible to provide secure and efficient access to the transmission system for all stakeholders. This task is gradually getting challenging due to the demand growth, demand uncertainty, rapid changes in generation mix, and market policies. Traditionally, the TSOs try to maximize the technical performance of the transmission network via building new overhead lines or physical hardening. However, obtaining public acceptance for building new lines is not an easy step in this procedure. For this reason, the TSOs try to capture the capabilities of existing assets. This paper investigates the use of modular FACTS devices (to alter the line characteristics) for improving the capability of transmission network in serving the uncertain demand without the need for building new overhead lines. The proposed method considers the uncertainty of demands and controls the utilization of existing transmission assets. The mathematical results obtained are validated with a complete non-linear simulation model of three transmission networks.

Design of Intelligent Integrated Simulation for Marine Electric Propulsion System

Based on integrated simulation technology of both marine electric propulsion system and artificial intelligence, a design of intelligent integrated simulation system of marine electric propulsion based on artificial neural network, fuzzy comprehensive evaluation and fuzzy neural network is proposed in this paper. The intelligent module mainly consists of a nonlinear simulation model, simulation evaluation and model optimization. Thanks to adopting an intelligent module, the accuracy of the simulation system is improved. The software implementation of intelligent integrated simulation for marine electric propulsion system is also discussed. The intelligent integrated simulation system of the marine electric propulsion system can guide the related design of electric propulsion system. The design of intelligent integrated simulation system of marine electric propulsion system can also guide the intelligent integrated simulation design of other systems.

Исследование и оптимизация систем управления объектами атомной энергетики на основе имитационных моделей

Matters concerned with development of a fully variable nonlinear simulation model of a double-loop system for automatically controlling the steam pressure in the main steam header (MSH) of a VVER type reactor based NPP power unit’s secondary circuit are considered. A two-stage procedure for optimizing the control system dynamic tuning is described, which uses the linear system frequency analysis method at the first stage and a numerical simulation and optimization method at the second stage, which takes into account the nonlinearities of the control equipment and control object. An evolutionary algorithm of multiparametric optimization with respect to a complex optimality indicator than takes into account the specific requirements for the system is used. The system performance efficiency in a wide range of the power unit outputs has been studied.

Ідентифікація моделей маркетингу взаємодії сільськогосподарських підприємств з виробництва біопалива

The purpose of the article is to study the formation factors of the biofuels domestic market in the network of Ukrainian agricultural enterprises based on interaction marketing. The materials of the article are focused on the study of both the causes of dysfunction of the biofuels domestic market and the search for new factors in the formation of such a market. Research methods. The research methodology considered the motivation factor implemented in the form of a separate marketing policy of «interaction» as the basis for the formation of the biofuels market in Ukraine, where the main market agents were considered agricultural enterprises (as biofuel producers, consumers and business partners).In the course of the research, in addition to a number of general scientific methods of economic research, an experiment was used to assess the interest of managers in typical (for Vinnytsia region) agricultural enterprises in partnerships in the business of biofuels production. The solution of the problem provided influence modeling of separate factors on attitudes of the enterprises-recipients concerning partnership interaction in researched business on manufacture of biofuels on: 1) linear Markov analytical model; 2) a nonlinear Markov model with a nonstationary matrix of transition probabilities; 3) generalized Markov imitation (multi-version) model of transitions; 4) linear Markov simulation model with individual parameters of the elements; 5) nonlinear Markov simulation model with «training» of the elements. Thus, the dynamics of the model was determined by the change in the attitudes of many recipients about cooperation depending on: 1) the intensity of information links about the benefits and risks of cooperation; 2) the presence of the implementation system of motivational factors (production standards, a special type of marketing policy and partnership scheme). Research results. An empirical reflection of the agency base among this category is determined, which allows us to conclude that the number of potential business participants significantly exceeds the critical level, and the motivational factors for the partnership are at least significant. In contrast to the national biofuels market, where – in the absence of any protectionist policy – the business economy has shown low competitiveness in recent years, these results suggest the possibility of forming a new market type – agency, where business will be formed among agricultural enterprises, united by certain integration schemes on the basis of interaction marketing policy of the enterprise-business initiator. The efficiency basis of this market will obviously be the effect of minimizing the value chain both in terms of the participants’ number and the value added itself, which will significantly reduce the cost of production. Scientific novelty. Firstly, the question of the forming possibility – as an alternative – a new type of biofuels market in Ukraine in the absence, unlike all other countries, a state policy to support the industry; secondly, substantiation of the idea of a special marketing policy of the organizational level of the agricultural enterprise as a business initiator – the policy of «interaction»; third, the identification of the agency basis of this market in relation to domestic agricultural enterprises. Practical significance. The applied value of the results is considered as a basis for forming a business for the production of biofuels in the network of agricultural enterprises in the context of empirical forecasting of potential partners and the role of motivating factors for building such partnership schemes. Tabl.: 2. Figs.: 6. Refs.: 17.

Nonlinear Digital Simulation Models of Switched Reluctance Motor Drive

The paper deals with nonlinear simulation models of a drive consisting of the four-phase 8/6 doubly salient switched reluctance motor (SRM), the four-phase dissymmetric bridge power converter and the closed-cycle rotor speed control strategy carried out by the pulse width modulation (PWM) with variable angle and combined control scheme with the PI algorithm. All presented considerations are based on a MATLAB-SIMULINK platform. The nonlinear mathematical model of the analyzed SRM drive was obtained as a combination of the two dimensional (2D) finite element model (FEM) of the motor and the nonlinear model of the electrical network of the power supply circuit. The main model and its seven sub-modules, such as the controller module, one phase simulation module, rotor position angle transformation module, power system module, phase current operation module, “subsystem” module, and electromagnetic torque of one phase operation module, are described. MATLAB functions store the magnetization curves data of the motor obtained by the 2D FEM electromagnetic field calculations, as well as the data of magnetic co-energy curves of the motor calculated from the magnetization curves. The 2D specimen insert method is adopted in MATLAB functions for operating the flux linkage and the magnetic co-energy at the given phase current and rotor position. The phase current waveforms obtained during simulations match with the tested experimentally phases current waveforms at the same rotor speed and the same load basically. The simulated rotor speed curves also agree with the experimental rotor speed curves. This means that the method of suggested nonlinear simulation models of the analyzed SRM drive is correct, and the model is accurate.

Sizing Optimization of Pneumatic Actuation Systems Through Operating Point Analysis

Abstract This paper reports a study on the static and dynamic behavior of pneumatic actuation systems, resulting in a comprehensive view of the influence of the system parameters on the energy efficiency and dynamic performance. The operating point approach based on the steady-state analysis of a pneumatic actuation system is used for developing analytical expressions to describe the relationship between the piston diameter and the system performance, including displacement time, stroke end velocity, and energy efficiency. The validity of the proposed equations is demonstrated by comparison with results from a test rig. Sensitivity analysis using a nonlinear dynamic simulation model indicated that a specific operating region exists, where good energy efficiency and the maximum dynamic performance are achieved. Moreover, the results show that an oversized system becomes more inefficient in both energetic and dynamic aspects. The results obtained provide a very consistent foundation for developing a method for pneumatic system sizing.