Real-time Experimental Data Research Articles

A cost effective approach to design of energy efficient residential buildings

The objective of this study is to identify cost-optimal efficiency packages at several levels of building energy savings. A two-story residential building located in Jordan is selected as a case study. DesignBuilder software is used to predict the annual energy usage of a two-story residence in Irbid, Jordan. Real-time experimental data from a single isolated controlled room was used to verify the proposed model. In addition to energy analysis, the economic, environmental, and social benefits of the proposed design have been investigated. The sequential search optimization approach is used to estimate the minimum cost of the building while considering various design scenarios. In addition, the impact of various energy conservation techniques on residential buildings is assessed, and the payback period for each program is calculated. Ultimately, the optimal combination of design to achieve energy efficiency measures has been identified in several climate regions. The simulations results predict that the annual electricity consumption can be reduced up to 50% if the proper combinations of energy conservation measures are selected at the lowest cost. The payback period is 9.3 years. Finally, energy efficiency measures can lead to a total of 9470 jobs/year job opportunities.The study provide practical framework to link between energy performance criteria and economic goals of building. Linking the energy performance requirements to economic targets provides guidelines for homeowners, contractors, and policymakers for making a suitable decision regarding the retrofitting of existing residential buildings. The study focuses on developing new methodologies that support minimizing costs during a building's lifecycle while maximizing environmental benefits which can not be identified by a series of parametric analyses using individual energy-efficient measures.

Customized data center cooling system operating at significant outdoor temperature fluctuations

This elaboration presents the configuration of the cooling system of the POLCOM Data Center which utilizes commercially available components, proposes steering strategy and analyzes their functionality. The designed architecture of cooling and dedicated control system is presented to demonstrate the novelty and customization with respect to the demanding outdoor temperatures existing in the climate of the Małopolska Province. The cooling system control and operation is illustrated by a comprehensive analysis of compressor and freecooling modes. The discussion on embedded functionality of chillers is undertaken. According to features and limitations of the complex solution, the cooling system reached the annual average coefficient of performance of 8.63 in 2015 (increase of 110% compared to the year 2014), operating 65% of the time during the year in compressor mode and 35% in freecooling mode. This coefficient in compressor mode amounted to 4.39 while in freecooling mode totaled 16.50. It was proved that in the real case under consideration they generated losses in electricity consumption amounting to 557MWh per year. The real-time experimental data collected from the commercial Data Center installation are used to present a unique operation of a such complex system.

The upgrade of data processing and storage system for EAST NBI

Inspired by the ITER organization's CODAC system, the current experimental system architecture is being progressively redesigned using the experimental physics and industrial control system (EPICS) to meet the needs of future long pulse experiments. As part of the overall experimental system, the data processing and storage system (DPSS) is mainly used to provide services of data processing and data storage for the experimental system. However, with the data processing mechanism of the previous DPSS, the previous experimental system is unsatisfactory in terms of real-time experimental data feedback, and the data processing mechanism of the previous DPSS is not compatible with the new architecture. Given that the original DPSS no longer fits the current experimental characteristics in terms of both functionality and demand so that it is necessary to redesign and upgrade the original DPSS to adapt to the new architecture. In view of the soft real-time and distributed characteristics of EPICS, the upgraded DPSS will be redesigned based on the data processing mechanism of EPICS. And it uses an adaptive strategy that allows the experimental system to automatically match more appropriate data uploading modes according to the rate of data acquisition. For this purpose, a data transmission protocol based on the TCP/IP protocol has been proposed. With regard to software optimization, the upgraded DPSS uses technologies of double-buffer and MMAP to optimize data exchanging between memory spaces. In terms of data storage, the upgraded DPSS adopts the segment strategy to manage experimental data efficiently. In addition, to prevent problems caused by abnormal circumstances, a guarantee mechanism for data storage is provided in the upgraded DPSS. After being tested, the upgraded DPSS will later be integrated into the general control system of EAST NBI.

SVM, ANN, and PSF modelling approaches for prediction of iron dust minimum ignition temperature (MIT) based on the synergistic effect of dispersion pressure and concentration

Data-driven models for predicting fire and explosion-related properties have been improved greatly in recent years using machine-learning algorithms. However, choosing the best machine learning approach is still a challenging task. Therefore, in this study, the predictability comparisons have been made with the different machine learning methods used to model the MIT for iron dust. The MIT of iron dust was determined using the Godbert-Greenwald furnace for seventy unique combinations of dispersion pressures and dust concentrations. The data has been divided into 'Training Set' and 'Testing Set'. The implementation and efficacy of machine learning and statistical approaches have been demonstrated through real-time experimental results. The support vector machines (SVM) regression models were trained with various kernel functions to enhance the performance of the resultant model. The cubic kernel function was found suitable for training SVMs. Besides, a feed-forward artificial neural network with the backpropagation algorithm and a polynomial surface fit model have also been developed to predict the MIT. For statistical phenomena, such as MIT, predictive modelling based on real-time experimental data is critical. If an accurate estimate of the combustible dust's minimum ignition temperature is confirmed, it is possible to ensure that the temperatures of the surrounding hot surfaces do not rise to that level, preventing an explosion. An overall comparison of predictive models has been given with unseen test data set. All the trained models yielded comparable results with unseen test data set. However, the SVM model with Bayesian optimizer approach can effectively assess the risk of ignition based on dust MIT under the influence of dispersion pressure and dust cloud concentration among all the approaches adopted in this study.

Material Model for Predicting Dynamic Response of Copper and Nickel at Very High Strain Rates Under Cold Spray Conditions

Numerous material models have been proposed to describe material properties under impact-induced extreme conditions. All these models suffer from the fact that the tests used to provide the required empirical parameters were performed on bulk material at limited strain rates, much lower than those found in the cold spray process. In this work, recent published results available in the literature that were obtained using advanced imaging techniques are used to modify material plasticity models to accurately predict the material plastic flow stress under cold spray conditions. The capability of two frequently used material models, Johnson–Cook and Preston–Tonks–Wallace models, to predict real-time experimental data for copper and nickel particles is evaluated. A series of finite element simulations are conducted and the comparison is made with the coefficient of restitution from laser-induced projectile impact test data. The original empirical parameters in Preston–Tonks–Wallace model are re-calibrated so that the coefficient of restitution matches the laser-induced projectile impact test data, providing an enhanced quality in the particle impact behavior predictions. Additionally, experiments and modeling are conducted to explore the bonding mechanism and rebound phenomena of copper powder cold sprayed on low carbon steel substrate.

A Hybrid Steady-State Compressor Model for Real-Time Applications in Performance Monitoring, Control and Optimization

In this study, a hybrid steady-state compressor model is proposed that can be used in the real-time performance monitoring, control and optimization of the vapor compression cycle. In the proposed model, first, a detailed analysis of the mass flow rate is presented, which is based on the volumetric efficiency concept and the assumption of a polytropic compression process. Then, discharge temperature of the refrigerant and power consumption of the compressor are also investigated. Three semiempirical models are constructed respectively. Further, to tune the unknown empirical parameters of the models, a social learning particle swarm optimization (SLPSO) algorithm is developed by using the real-time experimental data. An experimental apparatus of a refrigerant system is tested to validate the proposed models. The experimental results demonstrate that the proposed models accurately predict the performance of real-time operating compressors. Meanwhile, the models identified by the SLPSO algorithm are more accurate than those identified by the traditional least-squares method.

Data Augmentation Method for Switchgear Defect Samples Based on Wasserstein Generative Adversarial Network

The problem of sample imbalance will lead to poor generalization ability of the deep learning model algorithm, and the phenomenon of overfitting during network training, which limits the accuracy of intelligent fault diagnosis of switchgear equipment. In view of this, this paper proposes a data augmentation method for switchgear defect samples based on Wasserstein generative adversarial network with the partial discharge live detection data of the substation and the real-time switchgear partial discharge simulation experimental data. This method can improve the imbalanced distribution of data, and solve the problems such as the disappearance of gradients and model collapses in the classic generative adversarial network model, and greatly improve the stability of training. Verification through examples and comparison with traditional data augmentation methods. The results show that the data augmentation method mentioned in this paper can more effectively reduce the data imbalance, improve the performance of data-driven technology, and provide data support for subsequent fault diagnosis of switchgear equipment.

Comparison of Complexity and Predictability of a Cellular Automaton Model in Excitable Media Cardiac Wave Propagation Compared with a FitzHugh-Nagumo Model

Background: Excitable media are spatially distributed systems that propagate signals without damping. Examples include fire propagating through a forest, the Belousov-Zhabotinsky reaction, and cardiac tissue. (1) Excitable media generate waves which synchronize cardiac muscle contraction with each heartbeat. Spatiotemporal patterns formed by excitation waves distinguish healthy heart tissues from diseased ones. (3) Discrete Greenberg-Hastings Cellular- Automaton (CA) (1) and the continuous FitzHugh- Nagumo (FHN) model (7) are two methods used to simulate cardiac wave propagation. However, previous observations have shown that these models are not accurately predictive of experimental results as a function of time. We hypothesize that cardiac simulations deviate from the experimental data at a rate that depends on the complexity of the experimental data’s initial conditions (I.C.).Methods: To test this hypothesis, we investigated two types of propagating waves with different complexities: a planar (i.e. simple) and a spiral wave (i.e. complex). With the same I.C., we first compared simulation results of a Greenberg-Hastings Cellular Automaton (GH-CA) model to that a FitzHugh-Nagumo (FHN) continuous model which we used as a surrogate for experimental data. We then used median-filtered real-time cardiac tissue experimental data to initialize the GH-CA model and observe the divergence of wave propagation in the simulation and the experiment.Results: The alignment between the CA model of a planar wave and the FHN model remains constant, while the degree of overlap between the CA and FHN models decreases for a spiral wave as a function of time. CA simulation initialized by a planar wave real-time cardiac tissue data propagates like the experimental data, however, this is not the case for the spiral wave experimental data.Conclusion: We were able to confirm our hypothesis that the divergence between the two models is due to initial condition (I.C.) complexity.Discussion: We discuss a promising strategy to represent a GH-CA model as a Convolutional Neural Network (CNN) to enhance predictability of the model when an initial condition is given by the experimental data with a higher level of complexity.

Real-time classification of L-H transition and ELM in KSTAR

In order to automate the control of ELM and plasma density, an algorithm for classification of L-H transition and the first ELM burst in real-time has been developed with KSTAR campaign data. We applied a Long Short-term Memory (LSTM) which is a special kind of recurrent neural networks for storing long time-series data to classify the L-H transition phenomena and the first ELM burst. For training the LSTM classifier, we used Dα and line-averaged electron density diagnostic data that indicate the characteristic patterns of the L-H transition. After the training, we tested two versions for real-time experimental data and post-processed data. In addition, through the results related to the long intermediate states, we examined whether the statistical approach can reflect physical aspect of the KSTAR plasmas.

Thermodynamic Analysis of Solar Low-Temperature Differential Stirling Engine Considering Imperfect Regeneration and Thermal Losses

Abstract Low-temperature differential Stirling engines (LTDSE) are the gamma-type Stirling engines that can produce useful work from source temperatures less than 350 K, making them a preferred choice/device for solar energy utilization. An improved mathematical model to evaluate the performance of the solar-operated LTDSE has been developed by incorporating the top heat loss coefficient correlation with the finite-time thermodynamic model of the Stirling engine. In order to realize the internal imperfections of the thermodynamic Stirling cycle, the effect of the imperfect regeneration process is incorporated. Input parameters such as absorber plate temperature, irradiation, and geometrical features of the solar LTDSE are taken from real-time experimental data available in the literature. The effect of convective and radiation heat transfer coefficients of working fluid on maximum power output and thermal efficiency is determined to be significant and marginal, respectively. A comprehensive study of various working fluids and regenerator materials is carried out to investigate their impact on the performance of solar LTDSE. Helium is the best-working fluid, among air, hydrogen, ethane, and nitrogen for the considered model. Copper exhibited maximum regenerator effectiveness compared with Monel 400, aluminum, SS-304L.

Fault detection and fault severity calculation for rotor windings based on spectral, wavelet and ratio computation analyses of rotor current signals for a doubly fed induction generator in wind turbines

Doubly fed induction generators (DFIGs) are negatively affected by inter-turn short-circuit faults (ITSCFs) of rotor windings, and considering the safety and continuous operation of the wind turbine surveillance of DFIGs is of immense importance. For this purpose, an experimental platform was designed to detect ITSCFs in rotor windings. An experimental set-up with a 100 kW three-phase wound rotor induction machine and a condition monitoring system was constructed to obtain real-time experimental data. Different tapping points were set up to induce different ITSCFs. The rotor current was used for the analysis of the ITSCFs. First, spectral analysis was performed, and the ensuing results suggest that some harmonic frequency components were generated during the ITSCFs in addition to the fundamental frequency component. Additionally, the RMS value of the rotor current was measured during the process. In the next step, a wavelet coefficient of detail level 9 was found to be effective in analysing such harmonic frequency components in the rotor current. In the end, a new method based on the RMS value of the wavelet coefficient and the RMS value of the rotor current was proposed that can calculate the fault severity. It is concluded that such signal processing techniques are appropriate to detect ITSCFs and determine the fault severity in rotor windings in DFIGs.

Robust μ-Synthesis With Dahl Model Based Feedforward Compensator Design for Piezo-Actuated Micropositioning Stage

In this article, a combined feedback-feedforward control design scheme is presented to enhance the tracking performance of a piezo-actuated micropositioning stage by compensating the nonlinear hysteretic behavior of the piezoelectric actuator and model uncertainties of the system. Detailed investigation of the presented control scheme is performed not only in simulation by analyzing the robust stability and robust performance but also in real-time with motion trajectories of multiple frequencies. To design the presented control scheme, first of all, the dynamic model of the system is identified from the real-time experimental data by using the recursive least squares parameter adaptation algorithm. Then, Dahl hysteresis model is considered to represent the nonlinear hysteretic behavior of the piezoelectric actuator. To deal with this hysteresis nonlinearity, Dahl feedforward compensator is designed without involving inverse model calculations to avoid any computational complexity. This feedforward compensator is then combined with $\mu $ -synthesis robust feedback controller which is designed in the presence of model uncertainties of the system. The presented control scheme ensures the boundedness of the closed-loop signals and the desired tracking performance of the considered micropositioning stage. Finally, experimental tests are conducted with motion trajectories of multiple frequencies for the validation of the control scheme. An average improvement of 95% in compensating the hysteresis nonlinearity and 80% in reducing the tracking error is achieved which demonstrates the efficacy of the presented control scheme.

How to share real time experimental data using online tools

How to share real time experimental data using online tools

Estimating the State-of-Charge of Lithium-Ion Battery Using an H-Infinity Observer Based on Electrochemical Impedance Model

The lithium-ion batteries in the electric vehicles are nonlinear systems with complex electrochemical dynamics, and estimation of battery state-of-charge (SOC) is affected by factors such as environmental temperature and battery current. Considering the above problems, the accurate estimation of battery SOC has always been a difficult and the critical issue of battery management system (BMS). In this paper, the constant phase element (CPE) is introduced to the traditional time domain circuit model by analyzing the electrochemical impedance spectra of lithium-ion batteries. Accordingly, an equivalent circuit model based on electrochemical impedance is constructed by using fractional order theory, which has specific physical significant, leading to the improved estimation accuracy to represent battery voltage. Moreover, the polarization resistance in the model is replaced by Butler-Volmer (BV) equation, which can solve the problem caused by large current and temperature variation during the actual operation of electric vehicles. Next, based on the model, an $\text{H}\infty $ observer is designed for battery SOC estimation, and the proposed SOC observer is tested by real-time experimental data of battery. The efficiency of the proposed model and observer are validated by some simulations and experiment tests.

Application of Generalized Regression Neural Network in Predicting the Performance of Natural Convection Solar Dryer

Abstract The performance evaluation of a natural convection solar dryer is a complex one because of the transient and non-linear nature of atmospheric conditions. In this comparative study, a smart neural network -based tool was developed for estimating the performance of such a transient nature solar dryer. For this purpose, a series of experimental studies are conducted through four successive days and compared with the generalized regression neural network (GRNN) modeling. GRNN architecture proposed in this study consists of three inputs (time duration, irradiance, and ambient temperature) and four outputs (drying chamber temperature, the mass of moisture removed, drying rate, and dryer efficiency). Such generalized regression neural network architecture was trained, tested, and validated with real-time experimental variable data sets. The results of the GRNN model are in good agreement with experimental results. The overall accuracy of the proposed GRNN model in predicting the performance is 96.29%.

Determining the Fatigue Characteristics of Material on the Basis of Stress-Wave Emission

Assessment of the fatigue of test samples on the basis of stress-wave emission is considered. Experiments are conducted on the basis of previously published material regarding the use of synergetically organized acoustic emission. Experimental data for various materials demonstrate that the acoustic-emission signal permits ongoing determination of mechanical characteristics and, in particular, the fatigue. The samples for fatigue tests are prepared from five grades of steel and also from BrAZh9-34 bronze. Five experiments are conducted for each metal. The experimental samples are subjected to loads in small increments. In each step, the emission signal is recorded, and the next series of dislocations is prepared for subsequent loading of the crystal surface and the emission of a stress wave. Thus, energetically prepared dislocations ensuring sufficient power for stable signal recorded are emitted. The experimental data obtained on the basis of the synergetically organized acoustic emission are compared with the fatigue limit of the material determined from empirical formulas in terms of the strength. According to the Fisher test, the experimental results are adequate at the 5% significance level. Assessment of the experimental data for the fatigue limit obtained from the acoustic-emission signal by means of the Cochran test indicates that the dispersion of the experimental measurements is uniform for all the materials considered. The conclusion is that determination of the fatigue limit of a material on the basis of synergetically organized acoustic emission yields real-time experimental data on the fatigue limit, with sufficient precision.

Online identification of a rotary wing Unmanned Aerial Vehicle from data streams

Until now the majority of the neuro and fuzzy modeling and control approaches for rotary wing Unmanned Aerial Vehicles (UAVs), such as the quadrotor, have been based on batch learning techniques, therefore static in structure, and cannot adapt to rapidly changing environments. Implication of Evolving Intelligent System (EIS) based model-free data-driven techniques in fuzzy system are good alternatives, since they are able to evolve both their structure and parameters to cope with sudden changes in behavior, and performs perfectly in a single pass learning mode which is suitable for online real-time deployment. The Metacognitive Scaffolding Learning Machine (McSLM) is seen as a generalized version of EIS since the metacognitive concept enables the what-to-learn, how-to-learn, and when-to-learn scheme, and the scaffolding theory realizes a plug-and-play property which strengthens the online working principle of EISs. This paper proposes a novel online identification scheme, applied to a quadrotor using real-time experimental flight data streams based on McSLM, namely Metacognitive Scaffolding Interval Type 2 Recurrent Fuzzy Neural Network (McSIT2RFNN). Our proposed approach demonstrated significant improvements in both accuracy and complexity against some renowned existing variants of the McSLMs and EISs.

IDENTIFICATION, FUZZY CONTROLLER FOR ELECTRICAL DISCHARGE MACHINE SERVO SYSTEM

This paper utilizes a system identification to model EDM using Arduino and fuzzy controller. System identification is a useful tool for identifying the model depending on the input and output data of the controller. However, derivation of modeling process from first principles is often difficult due to its complexity. The proposed method is used for identifying the mathematical model of EDM from the real time experimental data from gather data from the systemy feeding the DC motor sine sweep or white noise inputs. The model identification of EDM servo system was conducted by using MATLAB/Simulink program and after that the obtained transfer function for the system. The proposed controller has been applied by using FLC .The results showed that the controller can work well with a quick response, no overshoot output and high control precision.

Comparative analysis of iTRAQ-based proteomes for cocoons between the domestic silkworm (Bombyx mori) and wild silkworm (Bombyx mandarina)

The domestic silkworm Bombyx mori was domesticated from wild silkworm B. mandarina about 5000 years ago. Long-term domestication and breeding selection have led to the weight of domestic silkworm cocoon being ten times greater than that of wild silkworm cocoon. However, we found that wild silkworm cocoon has significantly much more abundance of sericin proteins than domestic silkworm cocoon. Strikingly, we found that many protease inhibitors have been significantly down-regulated in domestic silkworm cocoon compared with its wild progenitor by using isobaric tag for relative and absolute quantitation (iTraq) approach. Both real-time qPCR experiment and transcriptome data in silk gland also verified these results. In addition, our data indicated that protease inhibitor activity of wild silkworm cocoon was stronger than that of domestic silkworm cocoon, implying that relatively up-regulated protease inhibitors in wild silkworm cocoon may play protection roles against microorganisms. Thus, these protease inhibitors up-regulated in wild silkworm cocoon may have potential values in sericulture and medical practice. SignificanceThis study revealed that there is a big difference in abundance of sericin proteins in cocoon between domestic and wild silkworms. Strikingly, many protease inhibitors have been significantly down-regulated in domestic silkworm cocoon compared with its wild progenitor. This may be due to different environments where the two silkworms live. In addition, the results also implied that relatively up-regulated protease inhibitors in wild silkworm cocoon may play protection roles against microorganisms. Thus, these protease inhibitors may have potential values in sericulture and medical practice.

Risk Assessment of a Multiplexed Carbon Nanotube Network Biosensor

In this paper, we report on the fabrication and performance analysis of multiplexed carbon nanotube (CNT) network-based biosensors for detecting real-time deoxyribonucleic acid (DNA) hybridization. We have presented a tailor-made application of analytic hierarchy process (AHP) that matches the risk assessment of such CNT network biosensors. In this respect, we are introducing AHP in the biosensor area for the first time predicting at risk severity assessment for various analyte (DNA) concentrations. Most importantly, we are presenting AHP that allows for an analysis based on multi-factors (multi-criteria) which is augmented intuition along with real-time experimental data and expert proficiency.