Dynamic State Variables Research Articles

Dynamic response simulation and experiment for gamma-type Stirling engine

This study presents a dynamic model of a gamma-type Stirling engine and examines variations of the crank rotational speed against time (Dynamic response). Kinematic relations of the engine linkages were determined and Lagrange formulations were formed for rigid body dynamics. Working gas pressures on each surface of the rigid bodies were calculated over time using a third order thermodynamic analysis code. A test setup was established for measuring dynamic and thermodynamic state variables of ST500engine. First, experiment data was used to validate the thermodynamic code results. Then the dynamic formulations combined with the thermodynamic code were simultaneously solved and the result is compared to ST500 dynamic response. Finally, using the whole dynamic model, new aspects of Stirling engine behavior were investigated. The dynamic–thermodynamic combination of the model gives the opportunity to instantly calculate a precise open-loop response, which is the essential part of designing proper load controllers for inherently unstable Stirling engines.

유체항력 계산을 통한 자율무인잠수정의 운동성능 예측과 실험

In this study, a dynamics model was developed to predict the motion performance of an Autonomous Underwater Vehicle (AUV). The dynamics model includes basic dynamic state variables of the hull and force terms to determine the motion of the AUV. The affecting terms for the forces are hydrostatic force, added mass, hydrodynamic damping, lift and drag forces. The force terms can be calculated using analytical and Computational Fluid Dynamics methods. For the underwater motion simulation, a simple PD controller was used. Also, the AUV was tested in a water tank and near sea for the partial verification of the fluid drag force coefficients and way-point tracking motions.

Dynamic modeling the composting process of the mixture of poultry manure and wheat straw

Due to lack of understanding of the complex nature of the composting process, there is a need to provide a valuable tool that can help to improve the prediction of the process performance but also its optimization. Therefore, the main objective of this study is to develop a comprehensive mathematical model of the composting process based on microbial kinetics. The model incorporates two different microbial populations that metabolize the organic matter in two different substrates. The model was validated by comparison of the model and experimental data obtained from the composting process of the mixture of poultry manure and wheat straw. Comparison of simulation results and experimental data for five dynamic state variables (organic matter conversion, oxygen concentration, carbon dioxide concentration, substrate temperature and moisture content) showed that the model has very good predictions of the process performance. According to simulation results, the optimum values for air flow rate and ambient air temperature are 0.43 l min−1 kg−1OM and 28 °C, respectively. On the basis of sensitivity analysis, the maximum organic matter conversion is the most sensitive among the three objective functions. Among the twelve examined parameters, μmax,1 is the most influencing parameter and X1 is the least influencing parameter.

Lateral Handling Improvement with Dynamic Curvature Control for an Independent Rear Wheel Drive EV

The integrated longitudinal and lateral dynamic motion control is important for four wheel independent drive (4WID) electric vehicles. Under critical driving conditions, direct yaw moment control (DYC) has been proved as effective for vehicle handling stability and maneuverability by implementing optimized torque distribution of each wheel, especially with independent wheel drive electric vehicles. The intended vehicle path upon driver steering input is heavily depending on the instantaneous vehicle speed, body side slip and yaw rate of a vehicle, which can directly affect the steering effort of driver. In this paper, we propose a dynamic curvature controller (DCC) by applying a newly-defined parameter, the dynamic curvature of the path, derived from vehicle dynamic state variables; yaw rate, side slip angle, and speed of a vehicle. The proposed controller, combined with DYC and wheel longitudinal slip control, is to utilize the dynamic curvature as a target control parameter for a feedback, avoiding estimating the vehicle side-slip angle. The effectiveness of the proposed controller, in view of stability and improved handling, has been validated with numerical simulations and a series of experiments during cornering engaging a disturbance torque driven by two rear independent in-wheel motors of a 4WD micro electric vehicle.

Sub-tropical seagrass ecosystem metabolism measured by eddy covariance

MEPS Marine Ecology Progress Series Contact the journal Facebook Twitter RSS Mailing List Subscribe to our mailing list via Mailchimp HomeLatest VolumeAbout the JournalEditorsTheme Sections MEPS 529:75-90 (2015) - DOI: https://doi.org/10.3354/meps11314 Sub-tropical seagrass ecosystem metabolism measured by eddy covariance Matthew H. Long1,2,*, Peter Berg1, Karen J. McGlathery1, Joseph C. Zieman1 1Department of Environmental Sciences, University of Virginia, 291 McCormick Rd, Charlottesville, Virginia 22904, USA 2Present address: Department of Marine Chemistry and Geochemistry, Woods Hole Oceanographic Institution, 266 Woods Hole Road, Woods Hole, Massachusetts 02543, USA *Corresponding author: mlong@whoi.edu ABSTRACT: The metabolism of seagrass ecosystems was examined at 4 sites in south Florida, USA, using the eddy covariance technique under in situ conditions. Three sites were located across a phosphorus-driven productivity gradient to examine the combined effects of dynamic variables (irradiance, flow velocity) and state variables (sediment phosphorus and organic content, seagrass biomass) on ecosystem metabolism and trophic status. Gross primary production and respiration rates varied significantly across Florida Bay in the summer of 2012 with the lowest rates (64 and –53 mmol O2 m–2 d–1, respectively) in low-phosphorus sediments in the northeast and the highest (287 and –212 mmol O2 m–2 d–1, respectively) in the southwest where sediment phosphorus, organic matter, and seagrass biomass are higher. Seagrass ecosystems offshore of the Florida Keys had similar large daily production and respiration rates (397 and –17 mmol O2 m–2 d–1, respectively) and were influenced by flow through the permeable offshore sediments. Across all sites, net ecosystem metabolism rates indicated that the seagrass ecosystems were autotrophic in the summertime. Substantial day-to-day variability in metabolic rates was found due to variations in irradiance and flow velocity. At all sites the relationship between photosynthesis and irradiance was linear and did not show any sign of saturation over the entire irradiance range (up to 1400 µmol photons m–2 s–1). This was likely due to the efficient use of light by the large photosynthetic surface area of the seagrass canopy, an effect which can only be examined by in situ measurements that integrate across all autotrophs in the seagrass ecosystem. KEY WORDS: Seagrass · Eddy covariance · Metabolism · Carbon cycling Full text in pdf format PreviousNextCite this article as: Long MH, Berg P, McGlathery KJ, Zieman JC (2015) Sub-tropical seagrass ecosystem metabolism measured by eddy covariance. Mar Ecol Prog Ser 529:75-90. https://doi.org/10.3354/meps11314 Export citation RSS - Facebook - Tweet - linkedIn Cited by Published in MEPS Vol. 529. Online publication date: June 08, 2015 Print ISSN: 0171-8630; Online ISSN: 1616-1599 Copyright © 2015 Inter-Research.

Fast Adaptive Image Encryption using Chaos by Dynamic State Variables Selection

new image encryption scheme based on high dimensional compound chaotic systems is proposed in this paper. Common chaotic image encryption, will perform encryption algorithm on pixels one by one which make the process slower, in this paper we called logistic map to choose and encrypt appropriate number of pixels makes the algorithm swifter and much more reliable by enlarging the key length.

Low voltage two-state-variable memristor model of vacancy-drift resistive switches

We illustrate a heuristic two-state-variable memristor model of charged O vacancy-drift resistive switches that include the effects of internal Joule heating on both the electronic transport and the drift velocity (i.e., switching speed) of vacancies in the switching material. The dynamical state variables correspond to the cross-sectional area of a conducting channel in the device and the gap between the end of the channel and one of the electrodes. The model was calibrated against low voltage pulse-sweep and state-test data collected from a TaO x memristor so that the contributions of the channel gap, area and temperature to switching can be analyzed. The model agrees well with experimental results for long switching times and low-to-intermediate voltage operation.

Numerical simulation of composting process for mixture of organic fraction of municipal solid waste and poultry manure

The objectives of this study were the following: to combine nth-order kinetic model with mass/heat balance equations and apply mathematical model on the composting process for a highly heterogeneous mixture of organic fraction of municipal solid waste and poultry manure, to estimate the kinetic parameters, to test the mathematical model with two independent sets of experimental data, to determine the optimum value of initial moisture content and to identify the key kinetic parameters. Experimental results showed that used volume of reactor with a thermal insulation allowed the self-heating of the substrate. The effect of temperature on the reaction rate constant was described by the following equation, kT=0.015×[1.018−2.8−0.002(T−20)]. Obtained value of the reaction order was 1.6. Comparisons of experimental and model data for four dynamic state variables showed very good agreement during the process. Organic matter conversion was the most accurately predicted dynamic state variable. The optimal value of initial moisture content was determined (45%). The sensitivity analysis showed that two of five parameters have significant impacts on the model results.

Biases in multistable displays as dynamic state variables

Biases in multistable displays as dynamic state variables

A fast chaos-based image encryption scheme with a dynamic state variables selection mechanism

In recent years, a variety of chaos-based image cryptosystems have been investigated to meet the increasing demand for real-time secure image transmission. Most of them are based on permutation–diffusion architecture, in which permutation and diffusion are two independent procedures with fixed control parameters. This property results in two flaws. (1) At least two chaotic state variables are required for encrypting one plain pixel, in permutation and diffusion stages respectively. Chaotic state variables produced with high computation complexity are not sufficiently used. (2) The key stream solely depends on the secret key, and hence the cryptosystem is vulnerable against known/chosen-plaintext attacks. In this paper, a fast chaos-based image encryption scheme with a dynamic state variables selection mechanism is proposed to enhance the security and promote the efficiency of chaos-based image cryptosystems. Experimental simulations and extensive cryptanalysis have been carried out and the results prove the superior security and high efficiency of the scheme.

Dynamic programming for a Markov-switching jump–diffusion

We consider an optimal control problem with a deterministic finite horizon and state variable dynamics given by a Markov-switching jump–diffusion stochastic differential equation. Our main results extend the dynamic programming technique to this larger family of stochastic optimal control problems. More specifically, we provide a detailed proof of Bellman’s optimality principle (or dynamic programming principle) and obtain the corresponding Hamilton–Jacobi–Belman equation, which turns out to be a partial integro-differential equation due to the extra terms arising from the Lévy process and the Markov process. As an application of our results, we study a finite horizon consumption–investment problem for a jump–diffusion financial market consisting of one risk-free asset and one risky asset whose coefficients are assumed to depend on the state of a continuous time finite state Markov process. We provide a detailed study of the optimal strategies for this problem, for the economically relevant families of power utilities and logarithmic utilities.

Generalized Memristive Device SPICE Model and its Application in Circuit Design

This paper presents a SPICE model for memristive devices. It builds on existing models and is correlated against several published device characterization data with an average error of 6.04%. When compared to existing alternatives, the proposed model can more accurately simulate a wide range of published memristors. The model is also tested in large circuits with up to 256 memristors, and was less likely to cause convergence errors when compared to other models. We show that the model can be used to study the impact of memristive device variation within a circuit. We examine the impact of nonuniformity in device state variable dynamics and conductivity on individual memristors as well as a four memristor read/write circuit. These studies show that the model can be used to predict how variation in a memristor wafer may impact circuit performance.

A physical model of switching dynamics in tantalum oxide memristive devices

We present resistive switching model for TaOx memristors, which demonstrates that the radius of a tantalum rich conducting filament is the state variable controlling resistance. The model tracks the flux of individual oxygen ions and permits the derivation and solving of dynamical and static state equations. Model predictions for ON/OFF switching were tested experimentally with TaOx devices and shown to be in close quantitative agreement, including the experimentally observed transition from linear to non-linear conduction between RON and ROFF. This work presents a quantitative model of state variable dynamics in TaOx memristors, with direct comparison to high-speed resistive switching data.

Closed-form likelihood expansions for multivariate time-inhomogeneous diffusions

The aim of this paper is to find approximate log-transition density functions for multivariate time-inhomogeneous diffusions in closed form. There are many empirical evidences supporting that the data generating process governing dynamics of many economics variables might vary over time because of economic climate changes or time effects. One possible way to explain the time-dependent dynamics of state variables is to model the drift or volatility terms as functions of time t as well as state variables. A way to find closed-form likelihood expansion for a multivariate time-homogeneous diffusion has been developed by Aït-Sahalia (2008). This research is built on his work and extends his results to time-inhomogeneous cases. We conduct Monte Carlo simulation studies to examine performance of the approximate transition density function when it is used to obtain ML estimates. The results reveal that our method yields a very accurate approximate likelihood function, which can be a good candidate when the true likelihood function is unavailable as is often the case.

Parameterization of High Resolution Vegetation Characteristics using Remote Sensing Products for the Nakdong River Watershed, Korea

Mesoscale regional climate models (RCMs), the primary tool for climate predictions, have recently increased in sophistication and are being run at increasingly higher resolutions to be also used in climate impact studies on ecosystems, particularly in agricultural crops. As satellite remote sensing observations of the earth terrestrial surface become available for assimilation in RCMs, it is possible to incorporate complex land surface processes, such as dynamics of state variables for hydrologic, agricultural and ecologic systems at the smaller scales. This study focuses on parameterization of vegetation characteristics specifically designed for high resolution RCM applications using various remote sensing products, such as Advanced Very High Resolution Radiometer (AVHRR), Système Pour l’Observation de la Terre-VEGETATION (SPOT-VGT) and Moderate Resolution Imaging Spectroradiometer (MODIS). The primary vegetative parameters, such as land surface characteristics (LCC), fractional vegetation cover (FVC), leaf area index (LAI) and surface albedo localization factors (SALF), are currently presented over the Nakdong River Watershed domain, Korea, based on 1-km remote sensing satellite data by using the Geographic Information System (GIS) software application tools. For future high resolution RCM modeling efforts on climate-crop interactions, this study has constructed the deriving parameters, such as FVC and SALF, following the existing methods and proposed the new interpolation methods to fill missing data with combining the regression equation and the time series trend function for time-variant parameters, such as LAI and NDVI data at 1-km scale.

Dynamics of Type-3 Wind Turbine Generator Models

The influence of wind turbine generators (WTGs) on power system dynamic performance is becoming increasingly important as wind generation grows. The dynamic behavior of WTGs should therefore be thoroughly understood. The paper analyzes dynamic models of type-3 WTGs, and in particular the WECC generic model. The behavior of such models is governed by interactions between the continuous dynamics of state variables, and discrete events associated with limits. It is shown that these interactions can be quite complex, and may lead to switching deadlock that prevents continuation of the trajectory. Switching hysteresis is proposed for eliminating deadlock situations. Various type-3 WTG models include control blocks that duplicate integrators. It is shown that this leads to non-uniqueness in the conditions governing steady-state, and may result in pre- and post-disturbance equilibria not coinciding. It also gives rise to a zero eigenvalue in the linearized WTG model.

Hydrologic controls on phosphorus dynamics: A modeling framework

A process-based modeling framework is developed to examine the control that the soil moisture and soil carbon dynamics have on phosphorus (P) availability. The model accounts for soil moisture controls on several key fluxes that regulate P availability, including the decomposition rate of litter and humus, the mortality rate of microbes, the plant P demand and leaching of P from the rooting zone. To examine these dynamics over a wide range of time scales (i.e., from daily to seasonal to annual) we couple a zero-dimensional stochastic soil moisture model with a soil carbon model and a soil P model, all of which are run at the daily time scale. The soil carbon model accounts for the dynamics of the litter pool, humus pool, and microbial biomass pool, while the soil P model includes two inorganic P pools and four organic P pools that are coupled with the soil carbon pools. A mechanism is included in this framework to account for the contribution of enzymatic release to P availability, which has been shown to be an important source of P under conditions of low P availability. The model is applied to a Cerrado ecosystem located in Central Brazil where the phosphorus, carbon and hydrologic cycles have been well documented. Despite this application to a specific system, the modeling framework developed here is general and could be applied to any P-limited ecosystem for studies that seek to examine the controls on P-availability over time scales ranging from weeks to years. Results indicate that the model accurately captures the key driving variables controlling both the short-term (i.e., day to week) and long-term (i.e., year to decade) phosphorus, soil moisture and carbon dynamics. The effect of stochastic rainfall variability on P cycling is examined by running long-term simulations to obtain a probabilistic characterization of the state variables and fluxes under different rainfall regimes. The effect of a change in the precipitation regime suggests that such changes can significantly affect the dynamics of state variables and fluxes controlling P-availability. Moreover, results from the model illustrate the importance of the microbial retention of P, as a P cycling and conserving mechanism for P-limited ecosystems.

Assimilation of multiple data sets with the ensemble Kalman filter to improve forecasts of forest carbon dynamics

The ensemble Kalman filter (EnKF) has been used in weather forecasting to assimilate observations into weather models. In this study, we examine how effectively forecasts of a forest carbon cycle can be improved by assimilating observations with the EnKF. We used the EnKF to assimilate into the terrestrial ecosystem (TECO) model eight data sets collected at the Duke Forest between 1996 and 2004 (foliage biomass, fine root biomass, woody biomass, litterfall, microbial biomass, forest floor carbon, soil carbon, and soil respiration). We then used the trained model to forecast changes in carbon pools from 2004 to 2012. Our daily analysis of parameters indicated that all the exit rates were well constrained by the EnKF, with the exception of the exit rates controlling the loss of metabolic litter and passive soil organic matter. The poor constraint of these two parameters resulted from the low sensitivity of TECO predictions to their values and the poor correlation between these parameters and the observed variables. Using the estimated parameters, the model predictions and observations were in agreement. Model forecasts indicate 15 380-15 660 g C/ m2 stored in Duke Forest by 2012 (a 27% increase since 2004). Parameter uncertainties decreased as data were sequentially assimilated into the model using the EnKF. Uncertainties in forecast carbon sinks increased over time for the long-term carbon pools (woody biomass, structure litter, slow and passive SOM) but remained constant over time for the short-term carbon pools (foliage, fine root, metabolic litter, and microbial carbon). Overall, EnKF can effectively assimilate multiple data sets into an ecosystem model to constrain parameters, forecast dynamics of state variables, and evaluate uncertainty.

Asynchronous anti-noise hyper chaotic secure communication system based on dynamic delay and state variables switching

This Letter designs an asynchronous hyper chaotic secure communication system, which possesses high stability against noise, using dynamic delay and state variables switching to ensure the high security. The relationship between the bit error ratio (BER) and the signal-to-noise ratio (SNR) is analyzed by simulation tests, the results show that the BER can be ensured to reach zero by proportionally adjusting the amplitudes of the state variables and the noise figure. The modules of the transmitter and receiver are implemented, and numerical simulations demonstrate the effectiveness of the system.

A synthetic hydrologic‐response dataset

AbstractSynthetic data have long been employed in hydrology for model development and testing. The objective of this study was to generate a synthetic dataset of hydrologic response with higher spatial and temporal resolution than could presently be obtained in the field, spanning a longer period than the typical duration of monitoring campaigns in experimental catchments. The synthetic dataset was generated for a rangeland catchment with the Integrated Hydrology Model (InHM), and is presented for future use by the community. The InHM boundary‐value problem is based upon the previously reported hypothetical reality of Tarrawarra‐like hydrologic response. Whereas the emphasis in developing the hypothetical reality was on parameterising InHM to reproduce observations from the Tarrawarra catchment, the emphasis in generating the synthetic dataset is on developing an internally valid hydrologic‐response dataset that extends well beyond the period of observations at Tarrawarra. The synthetic dataset spans 11 years of continuous forcing and response data (e.g. integrated response, distributed fluxes, state variable dynamics). The dataset should be useful for a wide range of problems including evaluation of simple rainfall runoff modelling techniques, design of measurement networks, development of data‐assimilation algorithms, and studies on information theory. The dataset is available at: ftp://pangea.stanford.edu/pub/loague/. Copyright © 2011 John Wiley & Sons, Ltd.