Time Constant Model Research Articles

A simple dynamic time constant model for both summer overheating and winter heating analysis

As new buildings have high levels of insulation it is important to ensure that solar and other internal gains do not make the interiors too hot in summer. This is well known. But assessing overheating at an early design stage is not so easy. The Thermal Mass Parameter (TMP) in the regulations is based on a 24 h cyclic assessment of the depth of heat storage in a material. This paper examines a simpler method using the whole thermal mass, simple steady state calculations and a time constant that can take into account the heat gains over a number of days, such as in a heat wave, not just over 24 h. This simple dynamic time constant model (SDTCM) would be a more realistic model to assess overheating and performance for Future Homes and Buildings Standards, now under consultation in the UK. The model can equally be applied to assess winter heating and for dynamic simulation to consider control strategies. Practical application This paper describes a simple model that can be used for determining heating sizing in winter and overheating in summer. It relates to assessing the thermal mass via the time constant relating to the dynamic response of the building. This will be useful in initial design and possibly regulation.

Two-time scale dynamic closed-loop scheduling for gas supply network with multiple air separation units

The gas supply network with multiple air separation units (ASUs) in steel enterprises provides the essential gas products for iron- and steel-making processes. Steel enterprises must make scheduling decisions for their gas supply networks to meet the fast-changing downstream demands. Therefore, the dynamic characteristics of the process must be considered during short-interval production scheduling to ensure more reasonable scheduling decisions. A two-scale dynamic scheduling method is first developed in this paper to consider the process dynamics. In this method, scheduling decisions are made in the slow time scale, and the nonlinear dynamic characteristics are described using a varied time constant dynamic model in the fast time scale. Then, an autoregressive moving average (ARMA) model is established to address the unavoidable model mismatch, which explicitly predicts and integrates future mismatches into the scheduling model. Moreover, a moving horizon closed-loop framework is designed to integrate the state feedback, mismatch modeling, and scheduling decision modification so that scheduling decisions can be modified in real time to avoid potential safety hazards. The results of the industrial application indicate that the proposed method can provide better scheduling decisions in case studies of model mismatch and unknown leakage compared with existing methods.

Deep Learning from Phylogenies for Diversification Analyses.

Birth-death (BD) models are widely used in combination with species phylogenies to study past diversification dynamics. Current inference approaches typically rely on likelihood-based methods. These methods are not generalizable, as a new likelihood formula must be established each time a new model is proposed; for some models, such a formula is not even tractable. Deep learning can bring solutions in such situations, as deep neural networks can be trained to learn the relation between simulations and parameter values as a regression problem. In this paper, we adapt a recently developed deep learning method from pathogen phylodynamics to the case of diversification inference, and we extend its applicability to the case of the inference of state-dependent diversification models from phylogenies associated with trait data. We demonstrate the accuracy and time efficiency of the approach for the time-constant homogeneous BD model and the Binary-State Speciation and Extinction model. Finally, we illustrate the use of the proposed inference machinery by reanalyzing a phylogeny of primates and their associated ecological role as seed dispersers. Deep learning inference provides at least the same accuracy as likelihood-based inference while being faster by several orders of magnitude, offering a promising new inference approach for the deployment of future models in the field.

A Mathematical Model for Zika Virus Infection and Microcephaly Risk Considering Sexual and Vertical Transmission

We establish a compartmental model for Zika virus disease transmission, with particular attention paid to microcephaly, the main threat of the disease. To this end, we consider separate microcephaly-related compartments for affected infants, as well as the role of asymptomatic carriers, the influence of seasonality and transmission through sexual contact. We determine the basic reproduction number of the corresponding time-dependent model and time-constant model and study the dependence of this value on the mosquito-related parameters. In addition, we demonstrate the global stability of the disease-free periodic solution if R0<1, whereas the disease persists when R0>1. We fit our model to data from Colombia between 2015 and 2017 as a case study. The fitting is used to figure out how sexual transmission affects the number of cases among women as well as the number of microcephaly cases. Our sensitivity analyses conclude that the most effective ways to prevent Zika-related microcephaly cases are preventing mosquito bites and controlling mosquito populations, as well as providing protection during sexual contact.

Eight-Week Functional Training with Ascending AMRAP Model and FOR TIME Constant Load Model to Increase Abdominal Muscle Strength and Maximal Oxygen Consumption Levels in Adolescent Males

The study purpose was to investigate the effect of functional training with the ascending AMRAP model and with the FOR TIME constant load model on increasing abdominal muscle strength and maximal oxygen consumption (VO2max) in adolescent males.
 Materials and methods. This study used the True-Experimental method with the research design of the Randomized Control Group Pretest-Posttest Design. A total of 21 adolescent males aged 18–21, body mass index (BMI) 18.5–24.9 kg/m2, normal blood pressure, normal resting heart rate, normal oxygen saturation (SpO2) participated in this study and were divided into three groups, namely CTRLG (n = 7; control group), FSTAG (n = 7; ascending AMRAP functional training group), and FSTFG (n = 7; FOR TIME constant load functional training group). Both the ascending AMRAPfunctional training and the FOR TIME constant load functional training interventions were carried out with a frequency of 3 times/week for 8 weeks. Statistical analysis used the one-way ANOVA test with the Statistical Package for Social Science (SPSS) software version 21.
 Results. The results showed that there was a significant difference in the mean delta (Δ) increase in abdominal muscle strength (p ≤ 0.05). The mean delta (Δ) increase in VO2max also showed a significant difference (p ≤ 0.05).
 Conclusions. Based on the results of the study, it was shown that the administration of functional training intervention with the ascending AMRAP model and functional training intervention with the FOR TIME constant load model which was carried out 3 times/week for 8 weeks increased abdominal muscle strength and maximal oxygen consumption levels.

Ordinary differential equation based neural network coupled with random forest in the quality assessment of hand hygiene processes

We describe a novel approach for quality assessment of hand hygiene process based on combination of ordinary differential equation (ODE) neural network and random forest. The continuous-time recurrent neural network (RNN) with ODE hidden nodes is utilized. Unlike traditional continuous-time RNNs, the ODE network in this scheme applies an input-dependent varying time-constant models referred as liquid time-constant (LTC) RNN. It expresses stable and bounded behavior and yields superior expressivity. The random forest is attractive for findings of multiple trees in classification. It is built from multiple LTC networks, each network is corresponding to each tree. The experimental results showed that the proposed approach attains the recognition accuracy of 98.9% for single handwashing step and 78% for the whole handwashing process.

Effect of Stochastic Modeling for Inter-Frequency Biases of Receiver on BDS-3 Five-Frequency Undifferenced and Uncombined Precise Point Positioning

The third generation of the Beidou navigation satellite system (BDS-3) broadcasts navigation signals of five frequencies. Focusing on the deep integration of five-frequency signals, we applied the joint BDS-3 five-frequency undifferenced and uncombined precise point positioning (UC-PPP) to analyze the receiver inter-frequency biases (IFB). Firstly, 12 Multi-GNSS Experiment tracking (MGEX) stations are selected to investigate the time-varying characteristics of receiver IFB and, according to random characteristics, three random modeling schemes are proposed. Secondly, the effects of three stochastic modeling methods on zenith tropospheric delay, ionospheric delay, floating ambiguity, and quality control are analyzed. Finally, the effects of three IFB stochastic modeling methods on positioning performance are evaluated. The results showed that the amplitude in the IFB for B2b is 5.139 m, B2a is 1.964 m, and B1C is 0.950 m by measuring one week’s observation data. The IFB stochastic modeling method based on random walks can shorten the PPP convergence time by 4~12%, diminish the false alarm of quality control, and improve the positioning accuracy. The random walk model is recommended to simulate the variation of IFB, which can not only overcome the disadvantage of the time constant model being unable to accurately describe the time-varying characteristics of the IFB, but also avoid reducing the strength of the kinematic PPP positioning model due to the large process noise of the white noise model.

Electrocution Risk of Capacitive Discharge Shocks: Application to Electric Vehicle Charging.

It is difficult to electrocute (induce ventricular fibrillation) with capacitive discharge shocks. With small capacitance values, the high voltages required for the necessary charge are rarely seen in industrial situations (e.g. electric vehicle charging stations). On the other hand, with large capacitance values, the discharge time is so great that the shock couples inefficiently with the cardiac cells. The update to IEC 60479-2 sets the C1 "mostly-safe" charge limit of 3 mC for a short "impulse function" pulse. We calculated the equivalent capacitor stored charge for an arbitrary capacitance value using the simple single membrane time constant model for the cardiac response. The peak membrane response was set equal to that of the 3 mC impulse function response to calculate the safe values for stored charge, voltage, and energy. The total stored charge, per se, cannot be used simplistically to estimate the danger of a capacitive discharge shock. A capacitive-discharge shock cannot be accurately compared to a rectangular shock with a duration equal to the shock time constant. The greater the capacitance, the larger the fraction of wasted charge in coupling to the heart and thus the shorter equivalent duration compared to the shock time constant. For a capacitive discharge shock this translates to a stored charge of 3 mC increasing up to 9 mC for a 10 capacitor using the assumed 575 load for an electric-vehicle (EV) charging station. In the area of interest for 1 - 10 the safe voltage ranges from 1300 to 4700 V, which includes the 1500-VDCscope of EV charger standard IEC 61851-23. For C > 100 the voltage asymptote is 700 V.

Redshift evolution of the underlying type Ia supernova stretch distribution

The detailed nature of type Ia supernovae (SNe Ia) remains uncertain, and as survey statistics increase, the question of astrophysical systematic uncertainties arises, notably that of the evolution of SN Ia populations. We study the dependence on redshift of the SN Ia SALT2.4 light-curve stretch, which is a purely intrinsic SN property, to probe its potential redshift drift. The SN stretch has been shown to be strongly correlated with the SN environment, notably with stellar age tracers. We modeled the underlying stretch distribution as a function of redshift, using the evolution of the fraction of young and old SNe Ia as predicted using the SNfactory dataset, and assuming a constant underlying stretch distribution for each age population consisting of Gaussian mixtures. We tested our prediction against published samples that were cut to have marginal magnitude selection effects, so that any observed change is indeed astrophysical and not observational in origin. In this first study, there are indications that the underlying SN Ia stretch distribution evolves as a function of redshift, and that the age drifting model is a better description of the data than any time-constant model, including the sample-based asymmetric distributions that are often used to correct Malmquist bias at a significance higher than 5σ. The favored underlying stretch model is a bimodal one, composed of a high-stretch mode shared by both young and old environments, and a low-stretch mode that is exclusive to old environments. The precise effect of the redshift evolution of the intrinsic properties of a SN Ia population on cosmology remains to be studied. The astrophysical drift of the SN stretch distribution does affect current Malmquist bias corrections, however, and thereby the distances that are derived based on SN that are affected by observational selection effects. We highlight that this bias will increase with surveys covering increasingly larger redshift ranges, which is particularly important for the Large Synoptic Survey Telescope.

Small‐signal‐based study of a two stage AC/DC and DC/DC battery charger using battery one time constant model

This study focuses on considering battery one time constant model in the design of controllers of a two-stage AC/DC and DC/DC battery charger using the small-signal approach. For this aim, small-signal models of DC/DC and AC/DC converters and also the battery model are obtained. Moreover, the effect of disturbance terms are analysed to be omitted in controller loops. Simulation and experimental results of a 1 kW laboratory prototype show appropriate performance of designed controllers in tracking reference signals. Also, tuned controllers using this approach have guaranteed unity power factor and low total harmonic distortion. Furthermore, a complete charge cycle of a lithium-ion battery evaluates the performance of the converter in different constant current/voltage controlling modes. In comparison with restive model consideration, results show a more accurate model with grater damping ration and better stability characteristics.

Comparison of a radially resolved dynamic inflow pitch step experiment to mid-fidelity simulations and BEM

In this paper a detailed comparison of the experimental and numerical results of a scaled wind turbine model in a wind tunnel subjected to fast pitching steps leading to the so-called dynamic inflow effect is presented. We compare results of an Actuator Line LES tool, a vortex code and four engineering models, to the experiment. We perform one and two time constant model analysis of axial wake induction and investigate the overshooting of integral loads. Our results show, that the effect is captured better by the two time constant models than by the one time constant models. Also the experiment and mid-fidelity simulations are best described by a two time constant fit. We identify the best dynamic inflow model to be the 0ye model. Different possibilities for the improvement of dynamic inflow models are discussed.

Smart City Moving Target Tracking Algorithm Based on Quantum Genetic and Particle Filter

In the application of moving target tracking in smart city, particle filter technology has the advantages of dealing with nonlinear and non-Gaussian problems, but when the standard particle filter uses resampling method to solve the degradation phenomenon, simply copying the particles will cause local optimization difficulties, resulting in unstable filtering accuracy. In this paper, a particle filter algorithm combined with quantum genetic algorithm (QGA) is proposed to solve the above problems. Aiming at the problem of particle exhaustion in particle filter, the algorithm adopts the method of combining evolutionary algorithm. Each particle in particle filter is regarded as a chromosome in genetic algorithm, and the fitness of each chromosome corresponds to the weight of particle. For each particle state with weight, the particle is first binary coded with qubit and quantum superposition state, and then quantum rotation gate is used for selection, crossing, mutation, and other operations, after a set number of iterations, the final particle set with accuracy and better diversity. In this paper, the filter state estimation and RMSF of N=50 and N=100 for nonlinear target tracking and the comparison of real state and state estimation trajectory in time-constant model under nonlinear target tracking are given. It can be seen that in nonlinear state, the quantum genetic and particle filter (QGPF) algorithm can achieve a higher accuracy of state estimation, and the filtering error of QGPF algorithm at each time is relatively uniform, which shows that the algorithm in this paper has better algorithm stability. Under the time-constant model, the algorithm fits the real state and realizes stable and accurate tracking.

Comparison of one and two time constant models for lithium ion battery

The fast and accurate modeling topologies are very much essential for power train electrification. The importance of thermal effect is very important in any electrochemical systems and must be considered in battery models because temperature factor has highest importance in transport phenomena and chemical kinetics. The dynamic performance of the lithium ion battery is discussed here and a suitable electrical equivalent circuit is developed to study its response for sudden changes in the output. An effective lithium cell simulation model with thermal dependence is presented in this paper. One series resistor, one voltage source and a single RC block form the proposed equivalent circuit model. The 1 RC and 2 RC Lithium ion battery models are commonly used in the literature are studied and compared. The simulation of Lithium-ion battery 1RC and 2 RC Models are performed by using Matlab/Simulink Software. The simulation results in his paper shows that Lithium-ion battery 1 RC model has more maximum output error of 0.42% than 2 RC Lithium-ion battery model in constant current condition and the maximum output error of 1 RC Lithium-ion battery model is 0.18% more than 2 RC Lithium-ion battery model in UDDS Cycle condition. The simulation results also show that in both simple and complex discharging modes, the error in output is much improved in 2 RC lithium ion battery model when compared to 1 RC Lithium-ion battery model. Thus the paper shows for general applications like in portable electronic design like laptops, Lithium-ion battery 1 RC model is the preferred choice and for automotive and space design applications, Lithium-ion 2 RC model is the preferred choice. In this paper, these simulation results for 1 RC and 2 RC Lithium-ion battery models will be very much useful in the application of practical Lithium-ion battery management systems for electric vehicle applications.

Determining the input impedance of ECG amplifiers using accurate electrode modelling

Objective: The influence of the skin-electrode-amplifier interface and the input impedance of ECG recording amplifiers on transient response performance is investigated using accurate modeling of surface contact electrodes. The paper aims to establish the input impedance requirements of ECG recording amplifiers based on the electrical properties of the electrodes. Approach: The IEC 60601 standard stipulates the performance requirements for electrocardiographs. Analyses and simulation of both dc and ac modes of coupling the electrodes to the input of the amplifier have been undertaken using an accurate double time-constant model of the electrodes in order to establish design constraints for amplifier input impedance to meet this performance specification. These have been backed up with bench tests. Main Results: Investigations carried out indicate that the minimum 10 MΩ resistance at the amplifier input recommended in the specification is insufficient when using some modern adhesive electrodes and increasingly popular un-gelled or dry electrodes. Design constraints are established based on the electrical properties of the electrodes. These constraints suggest that the input impedance of the amplifier should be as high as 10 GΩ and the amplifier ac coupled cut-off frequency should not to be higher than 0.05 Hz for all the electrode models considered. Significance: Signal distortions in the form of false S-wave creation and depression of the S-T segment have been observed when the previously recommended 10 MΩ input impedance is used in the amplifier. This distortion can lead to clinical misdiagnosis but can be avoided if the design constraints established in the paper are adopted.

Impedimetric detection of Pseudomonas aeruginosa attachment on flexible ITO-coated polyethylene terephthalate substrates

Biofilm monitoring in environmental and biomedical applications remains a challenge. Currently, conventional biochemical methods do not provide a quick quantitative measure of attached biomass. Thus, there is a need for rapid in situ detection tools for routine biofilm characterization. Electrochemical impedance spectroscopy (EIS) characterizes the electroactivity of bacteria within a biofilm and has been extensively used to monitor strong electroactive biofilms. Yet, studies on weak electricigens such as Pseudomonas aeruginosa remain underrepresented. Here, conductive indium tin oxide coated polyethylene terephthalate (ITO:PET) sheets were used as flexible growth substrates instead of more conventional carbonaceous or gold materials. EIS was compared with standard optical methods for the detection of P. aeruginosa biofilms formed on ITO:PET under static growth conditions. Relaxation time analysis showed a dominant time constant at approximately 1 s and confirmed the validity of a two time constant equivalent circuit model for the biofilm impedance. The interfacial resistance calculated from the equivalent circuit analysis showed a rapid drop after bacterial attachment whereas capacitance of the biofilm was masked by the capacitance of ITO:PET. The trends for interfacial resistance and capacitance were independent to the geometry of the ITO:PET working electrode. Moreover, most robust behaviour was observed for rectangular electrodes. EIS across a broad range of potentials with and without inhibitors showed a marked difference between the interfacial resistance of viable and energy inhibited biofilms. Moreover, EIS of exopolysaccharide Δpsl mutant showed a substantial drop in current. Overall, our results indicated that EIS enabled the detection of biofilm formation across large surface areas as early as 24 h after inoculation for the weak electroactive species P. aeruginosa using a flexible polymeric substrate.

Survival probabilities of wintering Eurasian Woodcocks Scolopax rusticola in northern Spain reveal a direct link with hunting regimes

The management of game species relies on robust estimates of hunting-related mortality. A relative measure of this mortality can be obtained by comparing survival estimates of animals across similar areas with different hunting pressures. We conducted live recapture-dead recovery analyses on wintering Eurasian Woodcocks Scolopax rusticola (hereinafter “Woodcock”) in provinces of Gipuzkoa (GIP) and Alava (ALA), two neighboring regions of northern Spain. The two regions have a similar number of hunting licences issued on a per day basis, but while hunting is limited to 3 days per week in ALA, in GIP it is allowed on a daily basis, resulting in a ca. 50% longer period of exposure of game species to hunting-related mortality here. We used a model based on monthly survival estimates to test whether the mortality of Woodcock varied between the two regions. Mean (± SE from a time-constant model) annual survival of Woodcocks was estimated to be 0.37 (± 0.04) and 0.56 (± 0.04) in GIP and ALA, respectively. If we assumed that this difference was only due to the longer period of exposure to hunting, mortality was increased by ca. 10% per additional day of hunting per week. Moreover, we also found that survival was positively associated with temperature in one of the study zones (ALA), suggesting that a high hunting pressure can override the effect of climate-dependent fluctuations. However, further research into fecundity and dispersal is necessary to assess the viability and sustainability of the wintering Woodcock populations under the current hunting regimes in these two zones.

A Review of Different Electric Equivalent Circuit Models and Parameter Identification Methods of Lithium-Ion Batteries

Lithium-ion batteries have many advantages in compare to other conventional batteries. Because of that, they have many applications in different sectors such as portable electronic devices, electric vehicles and other fields. To assure that the operation and performance of lithium-ion batteries are reliable, safe and economic, the approximation of state of charge, cycle life and other parameters for lithium-ion batteries are necessary. Equivalent circuit models are an important tool in the field of research in lithium-ion batteries. Several researcher have suggested a variety of equivalent circuit models. Some of these models are simple whereas some others are complicated. This paper presents a review of different equivalent circuit models and parameters identification methods in lithium-ion batteries for energy storage applications. These models include the internal resistance model, one time constant model, two time constants model, Thévenin model, PNGV (partnership for a new generation of vehicles) model, DP (dual polarization) model, improved PNGV model, tree seeds algorithm, electrochemical impedance spectroscopy method, and Randles’ model.

Dynamic cerebral autoregulation estimates derived from near infrared spectroscopy and transcranial Doppler are similar after correction for transit time and blood flow and blood volume oscillations

We analysed mean arterial blood pressure, cerebral blood flow velocity, oxygenated haemoglobin and deoxygenated haemoglobin signals to estimate dynamic cerebral autoregulation. We compared macrovascular (mean arterial blood pressure-cerebral blood flow velocity) and microvascular (oxygenated haemoglobin-deoxygenated haemoglobin) dynamic cerebral autoregulation estimates during three different conditions: rest, mild hypocapnia and hypercapnia. Microvascular dynamic cerebral autoregulation estimates were created by introducing the constant time lag plus constant phase shift model, which enables correction for transit time, blood flow and blood volume oscillations (TT-BF/BV correction). After TT-BF/BV correction, a significant agreement between mean arterial blood pressure-cerebral blood flow velocity and oxygenated haemoglobin-deoxygenated haemoglobin phase differences in the low frequency band was found during rest (left: intraclass correlation=0.6, median phase difference 29.5° vs. 30.7°, right: intraclass correlation=0.56, median phase difference 32.6° vs. 39.8°) and mild hypocapnia (left: intraclass correlation=0.73, median phase difference 48.6° vs. 43.3°, right: intraclass correlation=0.70, median phase difference 52.1° vs. 61.8°). During hypercapnia, the mean transit time decreased and blood volume oscillations became much more prominent, except for very low frequencies. The transit time related to blood flow oscillations was remarkably stable during all conditions. We conclude that non-invasive microvascular dynamic cerebral autoregulation estimates are similar to macrovascular dynamic cerebral autoregulation estimates, after TT-BF/BV correction is applied. These findings may increase the feasibility of non-invasive continuous autoregulation monitoring and guided therapy in clinical situations.

Electrical impedance spectroscopy of multiwall carbon nanotube–PDMS composites under compression

Composites made of elastomers and carbon nanotubes are promising components of soft electronics due to their enhanced permittivity, conductivity and piezoresistive characteristics. It is well established that the conductivity increases dramatically near the percolation threshold, where the nanotubes are more densely packed and electron tunneling between them can occur. However, less is known about the changes in electrical characteristics of the nanotube network when the composites are compressed. Does the electrical network change in any way upon compression? Here, electrical impedance spectroscopy is used to characterize composites made of polydimethylsiloxane and multiwalled carbon nanotubes in order to determine potential changes in the electrical nanotube network under compression. The force-dependent impedance spectrogram is recorded, thus allowing one to detect any changes in the distribution of resistive and capacitive elements using a distributed time constant model, which reflect properties of the underlying nanotube network. For both high and low concentrations, a simple single-relaxation time electrical equivalent circuit with force-dependent resistance and capacitance provides a good fit to the experimental data, thus demonstrating that the distribution of resistive and capacitive remains the same during compression. This is attributed to randomly aligned clusters of carbon nanotubes. Knowing how the electrical network changes under compression is of particular importance when designing piezoresistive sensors that utilize lower concentrations of nanotubes.

Constructing supplier learning curves to evaluate relational gain in engineering projects

Learning curve models analyze how experience relates to performance over time, and rates vary widely across industries, organisations, and processes. Very little empirical research addresses learning curves for suppliers in engineering projects. Hence, we consider learning within strategically managed buyer–supplier relationships, focusing on the potential for ‘relational gain’ in terms of learning performance for longer term strategic suppliers (LTSP). A mixed-method approach is adopted employing 2 phases. First, empirical performance data is collected as well as contextual information regarding the managerial actions behind the learning curve. Second, the empirical data is analysed using the time constant learning curve model. Different supplier relational categories are mobilised to assess any relational gain in the rates of learning. The findings give learning parameters across different categories, showing that LTSPs outperform other less collaborative categories across a range of learning and performance dimensions. Suppliers who are not formally part of the firm’s development programme, but are requested by clients or have specialist capabilities, do not perform well at the outset of a project but accelerate rapidly along the learning curve. There is a need for project based organizations to both strategically manage long term relationships with suppliers, as well as quickly integrate new suppliers. Better performing learning curves act as benchmarks to show the relative relational gains to be achieved by investment and integration approaches.