Time Delay Characteristics Research Articles

Shock tube studies on ignition delay and combustion characteristics of oxygenated fuels under high temperature

A comparative study on ignition delay time and combustion characteristics of four typical oxygenated fuel/air mixtures of dimethyl ether (DME), diethyl ether (DEE), ethanol and E92 ethanol gasoline was conducted through the chemical shock tube. The fuel/air mixtures were measured under the ignition temperature of 1100 to 1800 K, initial pressure of 0.3 MPa and the equivalence ratios of 0.5, 1.0 and 1.5. The experimental results show that the ignition delay time of these four oxygenated fuels satisfies the Arrhenius relation. The reaction H + O2 = OH + O has a high sensitivity in four fuel/air mixtures during high-temperature ignition, which makes the ignition delay lengthen with the increase of the equivalence ratios. By comparing the ignition delay of four fuels, ether fuels have excellent ignition performance and ether functional group has better ignition promotion than hydroxyl group. Moreover, the carbon chain length also significantly promotes the ignition. Due to the accumulation of a large number of active intermediates and free radicals during the long ignition delay time before ignition, the four fuels all have intense deflagration and generate the highest combustion peak pressure at the relatively low ignition temperature (1150-1300 K). For DME, DEE and ethanol, due to the high content of oxygen in their molecules, the combustion peak pressure and luminous intensity increased with the equivalence ratio, and the combustion is intense after ignition. E92 ethanol gasoline with low oxygen content has a lower combustion peak pressure and a longer combustion duration than the other three fuels, and its highest combustion peak pressure appears in the stoichiometric ratio. The combustion process of E92 ethanol gasoline is more oxygen-dependent than the other three fuels.

Full-wave analysis and design of optical fractional-order temporal differentiators based on ultra-compact microring resonator

Optical temporal differentiators (DIFFs) based on on-chip all-pass microring resonators (APMRRs) are the fundamental all-optical signal processing blocks for differentiation of the complex envelope of optical pulses. This paper presents, for the first time to our knowledge, an accurate full-wave analysis for the ultra-compact APMRR-based optical fractional-order temporal DIFFs. The intensity transmission and phase response of the APMRR, and normalized intensity in the output of the temporal DIFFs for ultra-fast optical pulses have been obtained numerically with a high accuracy compared with those of mathematical model. The critically coupled, under-coupled and over-coupled APMRRs have been modelled as mathematical DIFFs. Key factors such as inverse phase response behaviour of the over-coupling regime, time-delay and time-reversal characteristics, finite slope of the phase response in the under and over-coupling regimes, and dependence of the differentiation order to the input pulse full-width at half-maximum (FWHM) have been analysed in detail.

Comprehensive Experimental and Simulation Study of the Ignition Delay Time Characteristics of Binary Blended Methane, Ethane, and Ethylene over a Wide Range of Temperature, Pressure, Equivalence Ratio, and Dilution

A comprehensive experimental and kinetic modeling study of the ignition delay time (IDT) characteristics of some binary blends of C1–C2 gaseous hydrocarbons such as methane/ethylene, methane/ethane...

The On-Chip D-LMS Filter Design Method of Wireless Sensor Node Based on FPGA

In the real-time position technology of underground shallow source, the signal denoising performance of wireless sensor nodes directly determines the location speed and accuracy of underground burst point. Because of the complexity and randomness of the underground medium and the fact that underground explosion is a nonstationary transient process, the problems of low convergence rate and poor steady-state performance of the filter exist when the existing LMS algorithm is used for signal denoising. In light of the above concerns, this paper comes up with a signal denoising algorithm and hardware implementation method based on D-LMS (delay-LMS). Firstly, according to the autocorrelation function characteristic of random signal, using the principle that the autocorrelation function time delay characteristic of narrowband signal such as explosion vibration signal is better than that of wideband random signal such as ground noise, the D-LMS filter algorithm is constructed by introducing the time delay parameter. Secondly, the selection method of key parameters in D-LMS hardware implementation is analyzed. Thirdly, the corresponding hardware circuit is designed by FPGA, and the simulation is carried out. Numerical simulation and experimental verification show that compared with the existing LMS improved algorithm, the D-LMS algorithm proposed in this paper has higher denoising stability and better denoising effect. Compared with the signal postprocessing method based on the host computer, the signal denoising speed of this method is significantly improved. This method will provide a powerful theoretical method to solve the problem of high-precision and fast source positioning and provide technical support for the development of high-speed and real-time source positioning instruments.

A Way to Evaluation the Process Immunity Time of Customers’ Sensitive Industries Processes

The premise of voltage sag mitigation for sensitive industrial processes is to reasonably assess the economic losses caused by voltage sag. And the process immunity time is the key information in the process of economic loss assessment. Existing research usually obtains process immunity time through monitoring systems or overall testing. However, in the actual situation, due to various constraints, the traditional method of obtaining the process immunity time is inconvenient. To get PIT more conveniently, a sensitive process immunity evaluation method based on response information flow is proposed in this paper. This method regards sensitive industrial processes as a composite system. According to the function of each part of the system and the response characteristics of voltage sag, it can be divided into three modular combinations, which are delayed structure, competitive structure, and conductive structure. According to the delay time and module combination characteristics of module response to the voltage sag, the propagation path of response information flow caused by sag can be determined, and the process immunity time can be evaluated.Finally, a case study of the painting process of a car factory in southern China shows that the proposed method can be applied to the sensitive process immune time assessment.

Entropy production analysis in two-phase cavitation flows with thermodynamic cavitation model

Entropy production is an important parameter for the design and optimization of turbo machines, which is short of intuitiveness in previous studies. The present article investigated the mechanisms of entropy production in cavitation flows around a hydrofoil. The objectives of this research are to (1) reveal the thermodynamic effects with advanced cavitation model, (2) investigate dynamic characteristics of different entropy production terms, (3) analyze the interactions between vorticity and local entropy production rate based on vorticity transport equation. Firstly, water from 298 K to 423 K is investigated around a NACA0015 hydrofoil to reveal thermodynamic effects on different temperatures. Then, in order to study the energy loss due to cavitation, the entropy production theory is firstly applied on two-phase cavitation flows with high temperature water based on homogenous model and its unsteady characteristics is revealed combining with cavity structure. The density corrected turbulence model (DCM) and advanced thermodynamic cavitation model adopted in this research are validate corresponding to experiment. The analysis of local entropy production rate (EPR) indicated that the entropy production rate induced by direct dissipation (EPDD) and turbulent dissipation (EPTD) has the characteristics of spatial and time delay compared with the evolution of cavity instabilities. Moreover, entropy production rate induced by temperature gradients and wall shear stress are slight, while entropy production rate induced by velocity gradients are main part toward total entropy production rate (TEPR).

A Comprehensive Experimental and Simulation Study of Ignition Delay Time Characteristics of Single Fuel C1–C2 Hydrocarbons over a Wide Range of Temperatures, Pressures, Equivalence Ratios, and Dilutions

A comprehensive experimental and modeling study of the ignition delay time (IDT) characteristics of some single prominent C1–C2 hydrocarbons including methane, ethane, and ethylene has been perform...

Continuous and recurrent pattern dynamic neural networks recognition of electrophysiological signals

In the last few years, recurrent and continuous algorithms have became key factors in the solution of diverse pattern recognition problems. The main goal of this study is to introduce four classes of recurrent and continuous artificial neural networks (ANN) that can be implemented for pattern recognition of electrophysiological signals. Such networks are generally known as dynamic neural networks (DNN). The proposed DNN based pattern recognizer uses biosignals raw data as input. This processing method allows capturing the signal time dynamics, which is considered as an intrinsic characteristic of physiology signals. Therefore, recurrent and differential ANN structures were developed to construct different versions of dynamic automatic pattern recognizer. The first one describes the application of Recurrent Neural Networks (RNN) to enforce the biosignal analysis which evolves over time with a fixed sampling period. Three different DNNs with continuous dynamics are introduced. Differential neural network (DifNN) with the capability of learning the evolution of the signal in continuous time, a time-delay neural network (TDNN) for classification is implemented to consider the time-delayed characteristics of the electrophysiological signals and a complex valued neural network (CVNN) which considered the signals to be classified may be pre-processed with a frequency analysis technique. Two different databases of diverse physiological signals are used in this study to validate the application of dynamic neural networks. A first database considers electromiographic (EMG) signals which are tested using the DifNN, TDNN and CVNN. The second database includes gait in Parkinson's disease database signals which are used in the evaluation procedure of RNN. Two validation methods are used to justify the application of dynamic ANNs as pattern recognizer for the EMG activities and the health level classification of patients suffering from Parkinson's: generalization-regularization and the k-fold cross validation. The accuracy estimation and the confusion matrix evaluation confirm the superiority of the proposed approach compared to classical feed-forward ANN pattern recognizer. The particular case of the RNN is also implemented in a 32-bits micro-controller embedded device.

Time-delay signature characteristics of the chaotic output from an optoelectronic oscillator by introducing an optical feedback.

In this work, via autocorrelation function (ACF) and permutation entropy (PE) methods, we numerically investigate the time-delay signature (TDS) characteristics of the chaotic signal output from an optoelectronic oscillator (OEO) after introducing an extra optical feedback loop. The results demonstrate that, for such a chaotic system, both the optoelectronic feedback with a delay time of T1 and the optical feedback with a delay time of T2 contribute to the TDS of generated chaos. The TDS of the chaotic signal should be evaluated within a large time window including T1 and T2 by the strongest peak in the ACF curve of the chaotic signal, and the strongest peak may locate at near T1 or T2. Through mapping the evolution of the TDS in the parameter space of the optical feedback strength and time, certain optimized parameter regions for achieving a chaotic signal with a relatively weak TDS can be determined.

Collaborative scheduling and flexibility assessment of integrated electricity and district heating systems utilizing thermal inertia of district heating network and aggregated buildings

In the traditional scheduling of integrated electricity and district heating systems (IEDHS) with high penetrations of wind power, large amounts of wind power curtailments can still occur. Exploring the underlying flexibilities within the existing infrastructure can be beneficial to the further integration of wind power. The thermal inertia of district heating network and aggregated buildings can provide such flexibility. This paper proposes a collaborative scheduling model of integrated electricity and district heating systems considering the thermal inertia of district heating network and aggregated buildings and a flexibility assessment method of the integrated electricity and district heating systems. With the detailed thermal model of the aggregated buildings and the transmission time delay characteristics of heating network pipelines, a thermal inertia model of the district heating systems is proposed. Then, the scheduling model considering thermal inertia of district heating network and the aggregated buildings is formulated as a quadratic programming problem, the objective function of which is to minimize the operating cost of integrated electricity and district heating systems. Four scheduling cases based on whether to consider the transmission time delay characteristics of heating network pipelines or the adjustable indoor temperature of aggregated buildings are established, and a flexibility assessment method of different cases for the electricity system and coupling component is proposed. The validity of the model is verified by cases studies. Numerical results show that the proposed optimization scheduling model can make use of the heat storage capacity of district heating network and aggregated buildings, reduce operating costs, increase the flexibility of electricity system and effectively promote wind power integration.

Sub-nanosecond threshold switching dynamics in GeSb2Te4 phase change memory device

Conventional Ge–Sb–Te based phase change memory (PCM) devices offer promising attributes, including fast non-volatile memory operations, higher endurance and sub-ns re-crystallization/re-amorphization capabilities for the next generation high speed non-volatile memory. The threshold switching process in the amorphous phase of the PCM device enables breakdown of electrical resistance followed by rapid flow of device current, leading to Joules heating induced crystallization, known as SET operation. The speed of SET operation is, therefore, governed by voltage dependent threshold switching dynamics and the delay time (td) characteristics at the timescale of nanoseconds. Although GeSb2Te4 offers faster crystal growth velocity, realization of fast threshold switching capabilities remains unexplored. In this work, the ultrafast threshold switching dynamics of a prototypical GeSb2Te4 PCM device are investigated. The present experimental results demonstrate an exponential reduction of td upon increasing the applied voltage (VA) above its threshold voltage (VT). The td has been reduced to a strikingly lower value of 0.5 ns for VA of 1.6 times of VT, which enables faster SET operation within 3 ns as evidenced by a permanent change in the device resistance. These experimental findings of sub-nanosecond threshold switching dynamics and faster SET operation in the GeSb2Te4 device pave a way for designing a PCM device with ns programming speed for future electronics.

Operation Optimization of Multi-District Integrated Energy System Considering Flexible Demand Response of Electric and Thermal Loads

Multi-district integrated energy system (IES) can make full use of the complementary characteristics of district power and thermal system, and loads in different districts. It can improve the flexibility and economy of system operation, which has a good development prospect. Firstly, based on the general energy transfer model of the district heating network (DHN), the DHN system is described by the basic equations of the heating network and nodes considering the characteristics of the transmission time delay and heat loss in pipelines. A coupling model of DHN and multi-district IES is established. Secondly, the flexible demand response (FDR) model of electric and thermal loads is established. The load characteristics of each district in IES are studied. A shiftable load model based on the electric quantity balance is constructed. Considering the flexibility of the heat demand, a thermal load adjustment model based on the comfort constraint is constructed to make the thermal load elastic and controllable in time and space. Finally, a mixed integer linear programming (MILP) model for operation optimization of multi-district IES with the DHN considering the FDR of electric and thermal loads is established based on the supply and demand sides. The result shows that the proposed model makes full use of the complementary characteristics of electric and thermal loads in different districts. It realizes the coordinated distribution of thermal energy among different districts and improves the efficiency of thermal energy utilization through the DHN. FDR effectively reduces the peak-valley difference of loads. It further reduces the total operating cost by the coordinated operation of the DHN and multi-district IES.

Negative Pressure Wave Leakage Location Algorithm Based on Difference Cross-Correlation Delay Estimation

Accurate estimate the time difference of the leakage negative pressure wave signals reached both sides of the sensor at the two sides of the leakage point is the key to accurate location of the leakage point. Based on the theoretical analysis of cross-correlation time delay estimation algorithm and characteristics of negative pressure wave signal, a cross-correlation delay estimation method for pipeline leakage negative pressure wave signal is proposed. Through wavelet transform, signal conversion and cross correlation function analysis of discrete negative pressure wave signal sequence obtained by high-speed sampling, the time difference of the negative pressure wave signal monitored by the sensors on both sides of the leakage point can be accurately obtained. In the laboratory’s 102.8 meters pipeline, leakage was simulated through the valve, the time delay estimation and leak location experiment of the sensor at the two ends of the pipeline were analyzed, and compared with the wavelet analysis method. The experimental results show under the condition that the leakage is 10%, 5% and 2% of the total pipeline volume, the cross-correlation analysis algorithm has a positioning error interval of 0.97%-1.46%, which is better than the current positioning error 0.48%-2.43% of the wavelet analysis algorithm. At the same time, it avoids the influence of different wavelet types and analysis scales in the wavelet analysis algorithm on positioning accuracy, and is more suitable for pipeline leakage monitoring system to real-time online monitoring needs, has a wide application value.

Clog detection method in metal tubes using group delay characteristics in electromagnetic waves

AbstractIn large‐scale factories or power plants metal tube clogging sometimes leads to serious accidents. However, metal tube testing for the detection of foreign objects requires a long testing time and considerable effort. Therefore, we proposed an original inspection method of a single metallic tube using electromagnetic wave reflection in the tube. However, for branched tubes, it is difficult to estimate the position of the clog by electromagnetic wave reflection. To evaluate the position of clog in the branched tubes, we proposed a new detection method using the group delay time in electromagnetic waves. The group delay time in electromagnetic waves is slightly affected by the bifurcation area. To verify the new method, the group delay time characteristics were measured in the branched tubes. The position of clog could be easily detected by the proposed method.

Potential effects of internal physio-ecological changes on the online biomonitoring of water quality: The behavior responses with circadian rhythms of zebrafish (Danio rerio) to different chemicals

The online biomonitoring of aquatic accidental pollution is very important to realize the assessment of complex toxicity. However, the monitoring results would be affected greatly by the internal physio-ecological changes of test organisms, and circadian rhythms might contribute greatly to this kind of effects. In the present study, the behavior responses of zebrafish (Danio rerio) to different concentrations of Deltamethrin, Atrazine, and Thallium (Tl) in 15 days were investigated using an online behavior monitoring system. The results showed that the average behavior strength (BS) value of dark period (0.71 ± 0.16) was lower than that of light period (0.88 ± 0.09) in the control group. Similar pattern was observed in all other treatments with negative relationship between exposure concentrations and mean BS values. It is concluded that the 24 h circadian rhythms in the behavior responses of zebrafish (Danio rerio) could be observed clearly in the online biomonitoring system, and the online monitoring results would be affected obviously in the characteristics of behavior periodicity abnormal and time delay. Therefore, it is suggested that internal physio-ecological characteristics of organisms must be considered once they have the chance to play roles in bio-induced technologies. More investigations are warranted to clear the effects of internal physio-ecological changes on the exported results.

Relay‐based identification scheme for processes with non‐minimum phase and time delay

This study presents the frequency-domain and state-space approaches for modelling and identification of processes exhibiting non-minimum phase and time delay characteristics. An asymmetrical relay is fed back to unknown process to bring sustained oscillatory responses or limit cycle at the process output. Utilising the relay settings and limit cycle information, a set of mathematical expressions for the identification of process dynamics in terms of non-minimum phase second-order plus time delay, non-minimum phase first-order plus time delay, non-minimum phase integrating first-order plus time delay models is deduced. As compared to the methods reported in recent literature, the proposed set of explicit expressions is completely free from the simultaneous solution of a set of non-linear equations and does not need any prior guess for initial value of the unknown process model parameters. Simulations of benchmark examples from literature and experimental results from coupled tanks system are included to demonstrate the efficacy of the proposed relay-based identification algorithms.

Associative Memories With Synaptic Delays.

In this paper, we introduce a new concept of associative memories in which synaptic connections of the self-organizing neural network learn time delays between input sequence elements. Synaptic connections represent both the synaptic weights and expected delays between the network inputs. This property of synaptic connections facilitates recognition of time sequences and provides context-based associations between sequence elements. Characteristics of time delays are learned and are updated each time an input sequence is presented. There are no separate learning and testing modes typically used in other neural networks, as the network starts to predict the next input element as soon as there is no expected input signal. The network generates output signals useful for associative recall and prediction. These output signals depend on the presented input context and the knowledge stored in the graph. Such a mode of operation is preferred for the organization of episodic memories used to store the observed episodes and to recall them if a sufficient context is provided. The associative sequential recall is useful for the operation of working memory in a cognitive agent. Test results demonstrate that the network correctly recognizes the input sequences with variable delays and that it is more efficient than other recently developed sequential memory networks based on associative neurons.

Robustness of complex networks: Cascading failure mechanism by considering the characteristics of time delay and recovery strategy

To study the robustness of complex networks while encountering random failures or deliberate attacks, the cascading failure model is constructed by considering failure propagation with probability, which can depict the dynamic failure process. In particular, the characteristics of time delay and repetitive failure are taken into account in our model, and the network comprehensive robustness index (RI) is further designed according to valid survival edges and nodes. Additionally, the probability recovery strategy is proposed as well, and it is implemented in four typical networks, including the BA network, WS network, NC network and ER network. Different parameters in our model, including α, f, R, and β, are applied in simulation experiments to reveal their effects on RI in the cascading failure process. The simulation results show that nodes’ recovery abilities increase with R, which reduce the impacts of cascading failures and produce good network robustness. Meanwhile, the time delay increases with parameter α, and the size of the cascading failure decreases accordingly, which indicates that the larger that the time delay is, the stronger the RI. Additionally, the speed of the cascading failure process and the size of the cascading failure both present an increasing trend when parameter f increases gradually in the cascading failure. This indicates that the failure probability apparently impacts the RI. We also analyze the turning point t for RI(t) during the cascading failure process.

Enhancing Long-Lived Particles Searches at the LHC with Precision Timing Information.

We explore the physics potential of using precision timing information at the LHC in searches for long-lived particles (LLPs). In comparison with the light standard model particles, the decay products of massive LLPs arrive at detectors with time delays around the nanosecond scale. We propose new strategies to take advantage of this time delay feature by using initial state radiation to time stamp the collision event and require at least one LLP to decay within the detector. This search strategy is effective for a broad range of models. In addition to outlining this general approach, we demonstrate its effectiveness with the projected reach for two benchmark scenarios: a Higgs boson decaying into a pair of LLPs, and pair production of long-lived neutralinos in the gauge mediated supersymmetry breaking models. Our strategy increases the sensitivity to the lifetime of the LLP by two orders of magnitude or more and particularly exhibits a better behavior with a linear dependence on the lifetime in the large lifetime region compared to traditional LLP searches. The timing information significantly reduces the standard model background and provides a powerful new dimension for LLP searches.

Robust Combined Lane Keeping and Direct Yaw Moment Control for Intelligent Electric Vehicles with Time Delay

In order to enhance the tracking performance and improve the stability of intelligent electric vehicles, combined Lane keeping and direct yaw moment control is a good choice. In this paper, an uncertain model of intelligent electric vehicles for combing the lane keeping and direct yaw moment control is deduced, in which time delay and data dropouts are involved. Since the intelligent electric vehicles have the features of time delay and strong uncertainties, a novel robust guaranteed cost combined lane keeping and direct yaw moment control system is constructed to manage the lateral motion of intelligent electric vehicles. The asymptotic stability of combined lane keeping and direct moment control system is verified based on the Lyapunov stability theory. Simulation tests are carried out to demonstrate the feasibility of the proposed control approach, and the results indicate that the presented robust combined control approach can accurately achieve the lane tracking capability, stability and maneuverability of intelligent electric vehicles.