Time-dependent Power Research Articles

Theoretical Investigation of Bandwidth in Multimode Step-Index Silica Photonic Crystal Fibers

Solving the time-dependent power flow equation (PFE) provides a useful method to study the transmission bandwidth of step-index silica photonic crystal fibers (SI SPCFs). The transmission bandwidth of these kinds of fibers is determined for different air-hole structures (different numerical apertures (NAs)) and different distribution widths of the Gaussian launch beam. The results indicate that the lower the NA of SI SPCFs, the higher the bandwidth (for example, for a lower NA of SI SPCFs, a bandwidth that is eight times larger is obtained at a fiber length of 3500 m). The narrower launch beam at short fiber lengths results in a wider bandwidth. The longer the fiber (>300 m), the much less the effect of the launch beam width on the bandwidth. The bandwidth becomes independent of the width of the launch beam distribution at the fiber length at which a steady-state distribution (SSD) is reached. These results are useful for some potential applications, such as high capacity transmission optical fiber systems.

Demonstration of combined reduced order model and deep neural network for emulation of a time-dependent reactor transient

In this paper, a Reduced-Order Model (ROM) was constructed to approximate nuclear reactor neutronic transient behaviour. The ROM was developed by first using the Principal-Component Analysis (PCA) to reduce the output space of the response of interest, i.e, the time-dependent reactor power. A Deep-Neural Network (DNN) is then employed to build a surrogate model that maps the model inputs, i.e, the kinetic data, to the reduced-order output. While many previous applications of machine learning have focused on using algorithms to generate the response of singular Figures of Merit (such as peak clad or peak fuel temperatures over an entire transient) relatively few works have developed methods that can replicate the transient output of computer codes over the duration of the simulations. The difficulty in using machine learning to reconstruct the entire transient response is that, given the duration of the transient and the relatively fine time steps used, the output space can be very large thus making standard machine learning methods difficult to apply. In this work, PCA is first used to identify the key components of the output responses such that DNN can then be used to map the input parameters to the Principal component time-dependent behaviour. Then the neural network can reconstruct these components and emulate the output transient response based on the input parameters selected. Thus the methodology can provide time-dependent output results rather than single figures of merit. In this work, the LRA benchmark was used as a demonstration problem. The Detran time-dependent diffusion solver was used to generate the training and test data for a power control-rod ejection scenario. The results show that the combined ROM-DNN methodology is able to reconstruct the output power trajectory with high accuracy and with significantly reduced computational overhead.

Fully Distributed Event-Triggered Cooperative Output Regulation of Multi-Agent Systems under Jointly Connected Digraphs

This paper addresses the cooperative output regulation problem of heterogeneous linear multi-agent systems (MASs) with <i>jointly connected digraphs</i> under the assumption that the exosystem matrix is only known by the neighbors of the exosystem. First, a novel event-based adaptive distributed observer with time-dependent power functions as the event-triggering threshold is proposed to estimate the exosystem matrix and the exogenous signal simultaneously. A new analytical method based on the weighted mean-value theorem for integrals and Bellman-Gronwall lemma is developed to prove the estimation errors converge to zero asymptotically. Second, a distributed dynamic control law is proposed based on the event-based adaptive observer, and it is shown that the tracking errors of the resulting closed-loop system converge to zero asymptotically. Two distinctive advantages of this work are (i) the event-triggered control (ETC) protocol is fully distributed, and (ii) a positive minimum inter-event time (MIET) is explicitly given so that Zeno behavior is strictly excluded. Finally, the effectiveness of the proposed fully distributed ETC protocol is illustrated by a numerical example.

Integrating dynamic Bayesian network and physics-based modeling for risk analysis of a time-dependent power distribution system during hurricanes

Hurricane is one of the major natural hazards that bring significant damages and failures to the power distribution system for many coastal regions. For better decision-making, pre-hazard maintenance and post-hazard restoration of the power distribution system can be improved through the risk analysis of the distribution system. However, as a natural material, wood deteriorates with time. Therefore, the performance of the power distribution system could vary significantly during their life cycles. To quantify the time-dependent nature of the existing power distribution system due to material deterioration, a dynamic Bayesian network (DBN) is implemented for the risk analysis of a power distribution system against hurricanes. To capture the failure probabilities of the poles in the power distribution system, fragility surfaces for the poles are generated through physics-based fragility analysis, incorporating physical properties of the pole-wire components and the pole-wire system topology. The failure rate of the distribution system is obtained through the Bayesian network (BN) at each time step. With the observed failure data of poles, the material properties and the fragility surfaces of the components in the power distribution system are updated through Bayesian inferring. More specifically, the particle filter (PF) algorithm is utilized. To efficiently save computational cost in BN reasoning, the Gaussian process (GP) algorithm is adopted to build surrogate models to predict poles’ behaviors. Predictors are selected from the feature space to further reduce computational time and to enhance the prediction performance of the surrogate models through the automatic relevance determination (ARD) method. The failure rate is underestimated by 11% when the system deterioration process is ignored given a ten-year interval of hurricane events.

Modeling Optimal Cadence as a Function of Time during Maximal Sprint Exercises Can Improve Performance by Elite Track Cyclists

In track cycling sprint events, optimal cadence PRopt is a dynamic aspect of fatigue. It is currently unclear what cadence is optimal for an athlete’s performance in sprint races and how it can be calculated. We examined fatigue-induced changes in optimal cadence during a maximal sprint using a mathematical approach. Nine elite track cyclists completed a 6-s high-frequency pedaling test and a 60-s isokinetic all-out sprint on a bicycle ergometer with continuous monitoring of crank force and cadence. Fatigue-free force-velocity (F/v) and power-velocity (P/v) profiles were derived from both tests. The development of fatigue during the 60-s sprint was assessed by fixing the slope of the fatigue-free F/v profile. Fatigue-induced alterations in PRopt were determined by non-linear regression analysis using a mono-exponential equation at constant slope. The study revealed that PRopt at any instant during a 60-s maximal sprint can be estimated accurately using a mono-exponential equation. In an isokinetic mode, a mean PRopt can be identified that enables the athlete to generate the highest mean power output over the course of the effort. Adding the time domain to the fatigue-free F/v and P/v profiles allows time-dependent cycling power to be modelled independent of cadence.

Radially-dependent ignition process of a pulsed capacitively coupled RF argon plasma over 300 mm-diameter electrodes: multi-fold experimental diagnostics

A synergistic combination of multi-diagnostic methods are proposed to investigate temporal evolution of electrical and plasma parameters at various radial positions over 300 mm-diameter electrodes during the pre-ignition, ignition, and post-ignition phases of a pulsed capacitively coupled radio-frequency (RF) argon discharge. The electron density, n e, and the optical emission intensity (OEI) of argon at 750.4 nm at different radial positions are measured time-resolved by using a hairpin probe and an optical probe, respectively. A B-dot probe is employed to determine the waveforms of the azimuthal magnetic field at different radii, from which the waveforms of the axial current density at corresponding radial positions are derived based on Ampere’s law. Then, the time evolution of the power density at various radii can be calculated, provided that the voltage drop between the electrodes is independent of radius. Meanwhile, the time-dependent total power deposited into the reactor is calculated with the voltage and the current waveforms measured by a voltage and a current probe at the power feeding point. It was found that during pre-ignition phase, the OEI and n e cannot be measurable due to extremely low power deposition when the system exhibits pure capacitive impedance. During the ignition phase, the OEI, the power density, and the current density exhibit the most significant increase at the electrode center, while the time evolution of n e measured by the hairpin probe seems to exhibit a relatively weak radial dependence during this phase. In particular, at r ⩽ 8 cm, the OEI at every radius was observed to change with time in the same manner as the power density during the ignition phase, because the RF power is absorbed primarily by electrons, which dissipate their energy via inelastic collisions. Shortly, the profile of n e becomes edge-high during the post-ignition phase and remains thereafter until the end of the pulse-on period. Methodologically, the synergistic diagnostics lay the foundation for extensive studies on spatiotemporal evolution of plasma ignition process under broader conditions, e.g. electronegative gas, lower working gas pressure and very high driving frequency, widely used by practical etching process.

Calculation of Bandwidth of Multimode Step-Index Polymer Photonic Crystal Fibers.

By solving the time-dependent power flow equation, we present a novel approach for evaluating the bandwidth in a multimode step-index polymer photonic crystal fiber (SI PPCF) with a solid core. The bandwidth of such fiber is determined for various layouts of air holes and widths of Gaussian launch beam distribution. We found that the lower the NA of SI PPCF, the larger the bandwidth. The smaller launch beam leads to a higher bandwidth for short fibers. The influence of the width of the launch beam distribution on bandwidth lessens as the fiber length increases. The bandwidth tends to its launch independent value at a particular fiber length. This length denotes the onset of the steady state distribution (SSD). This information is useful for multimode SI PPCF applications in telecommunications and optical fiber sensing applications.

Isothermal titration calorimetric assessment of lignin conversion by laccases.

Lignin valorization may offer a sustainable approach to achieve a chemical industry that is not completely dependent on fossil resources for the production of aromatics. However, lignin is a recalcitrant, heterogeneous, and complex polymeric compound for which only very few catalysts can act in a predictable and reproducible manner. Laccase is one of those catalysts and has often been referred to as an ideal “green” catalyst, as it is able to oxidize various linkages within lignin to release aromatic products, with the use of molecular oxygen and formation of water as the only side product. The extent and rate of laccase‐catalyzed lignin conversion were measured using the label‐free analytical technique isothermal titration calorimetry (ITC). IITC provides the molar enthalpy of the reaction, which reflects the extent of conversion and the time‐dependent power trace, which reflects the rate of the reaction. Calorimetric assessment of the lignin conversion brought about by various fungal and bacterial laccases in the absence of mediators showed marked differences in the extent and rate of conversion for the different enzymes. Kraft lignin conversion by Trametes versicolor laccase followed Michaelis–Menten kinetics and was characterized by the following thermodynamic and kinetic parameters ΔH ITC = −(2.06 ± 0.06)·103 kJ mol−1, KM = 6.6 ± 1.2 μM and Vmax = 0.30 ± 0.02 U/mg at 25°C and pH 6.5. We envision calorimetric techniques as important tools for the development of enzymatic lignin valorization strategies.

Regulating Grip Forces through EMG-Controlled Protheses for Transradial Amputees

This study aims to evaluate different combinations of features and algorithms to be used in the control of a prosthetic hand wherein both the configuration of the fingers and the gripping forces can be controlled. This requires identifying machine learning algorithms and feature sets to detect both intended force variation and hand gestures in EMG signals recorded from upper-limb amputees. However, despite the decades of research into pattern recognition techniques, each new problem requires researchers to find a suitable classification algorithm, as there is no such thing as a universal ’best’ solution. Consideration of different techniques and data representation represents a fundamental practice in order to achieve maximally effective results. To this end, we employ a publicly-available database recorded from amputees to evaluate different combinations of features and classifiers. Analysis of data from 9 different individuals shows that both for classic features and for time-dependent power spectrum descriptors (TD-PSD) the proposed logarithmically scaled version of the current window plus previous window achieves the highest classification accuracy. Using linear discriminant analysis (LDA) as a classifier and applying a majority-voting strategy to stabilize the individual window classification, we obtain 88% accuracy with classic features and 89% with TD-PSD features.

Retentive capacity of power output and linear versus non-linear mapping of power loss in the isotonic muscular endurance test

The limit of dynamic endurance during repetitive contractions has been referred to as the point of muscle fatigue, which can be measured by mechanical and electrophysiological parameters combined with subjective estimates of load tolerance for revealing the human real-world capacity required to work continuously. In this study, an isotonic muscular endurance (IME) testing protocol under a psychophysiological fatigue criterion was developed for measuring the retentive capacity of the power output of lower limb muscles. Additionally, to guide the development of electrophysiological evaluation methods, linear and non-linear techniques for creating surface electromyography (sEMG) models were compared in terms of their ability to estimate muscle fatigue. Forty healthy college-aged males performed three trials of an isometric peak torque test and one trial of an IME test for the plantar flexors and knee and hip extensors. Meanwhile, sEMG activity was recorded from the medial gastrocnemius, lateral gastrocnemius, vastus medialis, rectus femoris, vastus lateralis, gluteus maximus, and biceps femoris of the right leg muscles. Linear techniques (amplitude-based parameters, spectral parameters, and instantaneous frequency parameters) and non-linear techniques (a multi-layer perception neural network) were used to predict the time-dependent power output during dynamic contractions. Two mechanical manifestations of muscle fatigue were observed in the IME tests, including power output reduction between the beginning and end of the test and time-dependent progressive power loss. Compared with linear mapping (linear regression) alone or a combination of sEMG variables, non-linear mapping of power loss during dynamic contractions showed significantly higher signal-to-noise ratios and correlation coefficients between the actual and estimated power output. Muscular endurance required in real-world activities can be measured by considering the amount of work produced or the activity duration via the recommended IME testing protocol under a psychophysiological termination criterion. Non-linear mapping techniques provide more powerful mapping of power loss compared with linear mapping in the IME testing protocol.

Theoretical Investigation of the Influence of Wavelength on the Bandwidth in Multimode W-Type Plastic Optical Fibers with Graded-Index Core Distribution.

The bandwidth of multimode W-type plastic optical fibers (POFs) with graded-index (GI) core distribution is investigated by solving the time-dependent power flow equation. The multimode W-type GI POF is designed from a multimode single-clad (SC) GI POF fiber upon modification of the cladding layer of the latter. Results show how the bandwidth in W-type GI POFs can be enhanced by increasing the wavelength for different widths of the intermediate layer and refractive indices of the outer cladding. These fibers are characterized according to their apparent efficiency to reduce modal dispersion and increase bandwidth.

Development of electrical substitution Fourier transform spectrometry for absolute optical power measurements.

We have demonstrated the first continuous-scan electrical substitution Fourier transform spectrometer (ES-FTS), which serves initially as an apparatus for absolute spectral responsivity calibrations of detectors over the wavelength range from 1.5 µm to 11 µm. We present data on the realization of a spectral detector-comparator system with high accuracy, high dynamic range, high spectral resolution and fast measurement in the infrared region, which is tied directly to an absolute power scale through electrical substitution. The ES-FTS apparatus employs a commercial Fourier transform spectrometer and a custom electrical substitution bolometer detector to enable spectrally-resolved absolute optical power measurements. A generalization of electrical substitution techniques enables determination of the voltage waveform that must be applied to the bolometer's electrical heater to cancel the optical signal from a Michelson interferometer in order to quantify the time-dependent optical power incident on the bolometer. The noise floor of the electrical substitution bolometer is on the order of 10 pW/Hz½ and its response is expected to be linear from the noise floor to 1 mW. A direct comparison between a pyroelectric standard detector and the ES-FTS has been performed, and experimental results reported here show great potential for this technique.

Investigation of bandwidth in multimode graded-index plastic optical fibers.

A new method is proposed for investigating the bandwidth in multimode graded-index plastic optical fibers (GI POFs). By numerically solving the time-dependent power flow equation, bandwidth is reported for a varied launch conditions (radial offsets) of multimode GI POF. Our theoretical results are supported by the experimental results which show that bandwidth decreases with increasing radial offset. This decrease is more pronounced at short fiber lengths. At fiber length close to the coupling length Lc at which an equilibrium mode distribution (EMD) is achieved, this decrease becomes slower, indicating that mode coupling improves bandwidth at larger fiber lengths. With further increase of fiber length, bandwidth becomes nearly independent of the radial offset, indicating that a steady-state distribution (SSD) is achieved. Such a fiber characterization can be applied to optimize fiber performance in POF links.

Time-dependent power law function for the post-dealloying chemical coarsening of nanoporous gold derived using small-angle X-ray scattering

Dealloyed nanoporous gold spontaneously coarsens when exposed to moderate temperatures (thermal coarsening), or to non-inert gases or liquids at room temperature (chemical coarsening). Although considerable research has been done on post-dealloying thermal coarsening, little attention has been paid to post-dealloying chemical coarsening, occurring not during, but after dealloying. Here, we use small- and wide-angle X-ray scattering (SAXS/WAXS) techniques to study the post-dealloying chemical coarsening behavior of dealloyed nanoporous gold samples aged in nitric acid over the course of 6 days at room temperature. A quantitative relationship between the ligament size and coarsening time is derived. Typically, our SAXS data reveals that the ligament size in nanoporous gold chemically coarsens according to a ∼1/4 time-dependent power law, which is characteristic of a surface diffusion-dominated process. The derived relationship between the ligament size and coarsening time will guide the design of nanoporous gold with desired structure size to optimize its (electro)chemical properties.

Thermal Analysis and Design of Electronics Systems Across Scales Using State-Space Modeling Technique

Under a given set of boundary conditions (BCs), the thermal performance of an electronic system is generally evaluated based on its steady-state response to constant power loads and thermal BCs that are time-averaged values of the actual transient or cyclic loads and BCs. Such analysis may produce accurate results if the time dependence of the power cycles and thermal BCs is small. Ideally, transient thermal analyses with actual time-dependent BCs and power cycles should be performed to determine the steady-state behavior. While being less overwhelming compared to laboratory experiments, fully time-dependent computational fluid dynamics (CFD) analysis still requires a large amount of CPU time. In order to overcome this large computational cost, several approximate models, such as resistor-capacitor ( R- C) thermal network approaches, have been developed. Although reasonably accurate, these models require rigorous curve-fitting effort followed by an optimization process, which only makes them practical for relatively simple systems. The present study builds a state-space model applicable to heat transfer problems and makes comparisons with the R- C networks. The state-space model is later applied to determine the transient thermal behavior of a complex system, namely, a multidie SOIC chip over a printed circuit board (PCB), with a significant reduction in CPU time and no compromise on the accuracy. Finally, as a demonstration of systemic thermal design, an optimization exercise is performed on the above state-space model, in which the power cycles on individual die elements are controlled to limit the maximum temperature on the package die.

Response of a proton exchange membrane fuel cell to step changes in mass flow rates

AbstractTransient regime effects are particularly important in fuel cells designed for vehicles. Three‐dimensional modeling of a proton exchange membrane fuel cell with a serpentine channel is presented, and the response of the fuel cell to a step‐change in the mass flow rates is analyzed by using the computational fluid dynamics techniques. After a validation study of the mathematical and numerical model, step increases of 20% in mass flow rates are applied to the inlet boundary conditions, and time dependent power and current density responses of the fuel cell are analyzed. Polarization curves are generated for the assessment of the fuel cell performance, and their variations in time are presented. The results show that current and power densities increase with time at low cell voltage values due to concentration losses; however, increases in power and current are negligible at high voltages.

Diagnosis of Alzheimer's Disease by Time-Dependent Power Spectrum Descriptors and Convolutional Neural Network Using EEG Signal.

Using strategies that obtain biomarkers where early symptoms coincide, the early detection of Alzheimer's disease and its complications is essential. Electroencephalogram is a technology that allows thousands of neurons with equal spatial orientation of the duration of cerebral cortex electrical activity to be registered by postsynaptic potential. Therefore, in this paper, the time-dependent power spectrum descriptors are used to diagnose the electroencephalogram signal function from three groups: mild cognitive impairment, Alzheimer's disease, and healthy control test samples. The final feature used in three modes of traditional classification methods is recorded: k-nearest neighbors, support vector machine, linear discriminant analysis approaches, and documented results. Finally, for Alzheimer's disease patient classification, the convolutional neural network architecture is presented. The results are indicated using output assessment. For the convolutional neural network approach, the accurate meaning of accuracy is 82.3%. 85% of mild cognitive impairment cases are accurately detected in-depth, but 89.1% of the Alzheimer's disease and 75% of the healthy population are correctly diagnosed. The presented convolutional neural network outperforms other approaches because performance and the k-nearest neighbors' approach is the next target. The linear discriminant analysis and support vector machine were at the low area under the curve values.

Modelling and simulating thunderstorm/downburst winds using S-transform and discrete orthonormal S-transform

Fluctuating winds from intense thunderstorm/downburst are often modelled as the amplitude modulated evolutionary stochastic processes and simulated based on the spectral representation method. Aspects of the modelling and simulation include assessing time and frequency dependent power spectrum, and the identification of the amplitude and frequency modulations are not straightforward. The present study uses the S-transform and discrete orthonormal S-transform to evaluate the time-frequency power spectral density (TFPSD) functions and coherence functions of nonstationary winds. The results for a thunderstorm/downburst event indicate that the coherence is time-dependent. The use of the TFPSD functions and a newly developed simulation method to generate winds acted on single and multiple points with a time-dependent coherence is shown. A comparison of the mean of the TFPSD of the simulated winds to its corresponding target is used to validate the adequacy of the simulation models. The use of the simulated records to assess the record-to-record variability on the maximum responses of a nonlinear inelastic single-degree-of-freedom system is given.

Fluctuations in irreversible quantum Otto engines

We derive the general probability distribution function of stochastic work for quantum Otto engines in which both the isochoric and driving processes are irreversible due to finite time duration. The time-dependent work fluctuations, average work, and thermodynamic efficiency are explicitly obtained for a complete cycle operating with an analytically solvable two-level system. The effects of the irreversibility originating from finite-time cycle operation on the thermodynamic efficiency, work fluctuations, and relative power fluctuations are discussed.

Internal Temperature of Skin when Surface Temperature Is Controlled with an Electromagnetic Beam

We study the thermal effect on skin exposed to an electromagnetic beam of time-dependent power. We consider two types of beam power time schedules. In the controlled temperature exposure, the skin surface temperature is increased quickly to a prescribed level using a high beam power; then the surface temperature is maintained at the prescribed level by adjusting the beam power adaptively. In the constant power exposure, the applied beam power is relatively low and stays unchanged over the time. We start both types of exposures at the same time and compare their internal temperatures of skin when they have the same surface temperature. In a non-dimensionalized formulation, we show that at the moment when both exposure types reach the same prescribed surface temperature level, the controlled temperature exposure has a higher internal temperature at all depths. This conclusion is mathematically rigorous and is independent of skin material properties.