scholarly journals Model-Based Condenser Fan Speed Optimization of Vapor Compression Systems

Energies ◽  
2020 ◽  
Vol 13 (22) ◽  
pp. 6012
Author(s):  
Sebastian Angermeier ◽  
Christian Karcher
Keyword(s):  
Steady State ◽  
Optimal Operation ◽  
Vapor Compression ◽  
Total Energy Consumption ◽  
Proposed Model ◽  
Wide Range ◽  
Fan Speed ◽  
Main Components ◽  
The Ideal ◽  
Vapor Compression Systems

Vapor compression systems (VCS) cover a wide range of applications and consume large amounts of energy. In this context, previous research identified the optimization of the condenser fans speed as a promising measure to improve the energy efficiency of VCS. The present paper introduces a steady-state modeling approach of an air-cooled VCS to predict the ideal condenser fan speed. The model consists of a hybrid characterization of the main components of a VCS and the optimization problem is formulated as minimizing the total energy consumption by respectively adjusting the condenser fan and compressor speed. In contrast to optimization strategies found in the literature, the proposed model does not relay on algorithms, but provides a single optimization term to predict the ideal fan speed. A detailed experimental validation demonstrates the feasibility of the model approach and further suggests that the ideal condenser fan speed can be calculated with sufficient precision, assuming constant evaporating pressure, compressor efficiency, subcooling, and superheating, respectively. In addition, a control strategy based on the developed model is presented, which is able to drive the VCS to its optimal operation. Therefore, the study provides a crucial input for set-point optimization and steady-state modeling of air-cooled vapor compression systems.

Universal Computer Model for Studying the Dynamics of a Two-Motor Scraper Conveyor

2021 ◽  
Vol 64 (2) ◽  
pp. 56-64
Author(s):  
Dmitry Shprekher ◽  
◽  
Gennady Babokin ◽  
Alexandr Zelenkov ◽  
Dmitry Ovsyannikov ◽  
...  
Keyword(s):  
Computer Model ◽  
Design Stage ◽  
Design Parameters ◽  
Coal Face ◽  
Mass System ◽  
Early Design Stage ◽  
Proposed Model ◽  
Wide Range ◽  
Scraper Conveyor ◽  
Main Components

The article proposes to study the dynamics of the scraper conveyor (SC), which is one of the main components of the mechanized complex of the coal face, to use a universal computer model in which the multi-mass system of the traction body (TB) with concentrated parameters is replaced by a script in the form of a MATLAB function, the code for modeling the system of equations of the TB in which is developed using the Matlab pro-gramming language. With the help of Simulink blocks, models of electric motors, transmissions, drive sprockets, as well as the elementary masses of TB are created. This solution made it possible to change the number of elementary masses in a wide range, and to ensure the study of dynamic processes in the electromechanical system of the SC with a predetermined accuracy. The most common type of multi-motor conveyor is considered: two-drive, with head and end drives connected through transmissions and sprockets by an infinite chain with scrapers. The simulation of the direct start modes at full load and empty load was carried out. The results showed that the proposed model provided 2 times faster simulation of the developed model compared to the model of a conveyor made up of the same number of individual elementary masses, while the accuracy of the simulation in terms of the speed of movement of the chain is 5% in the interval with the real conveyor. It is concluded that it is necessary to develop an effective method of controlling the head and tail drives of the scraper conveyor in order to equal their load. The results of the simulation can be used to predict the fatigue life and determine the optimal pretensioning force at an early design stage, when only a few design parameters are known.

Spectroscopic Studies of Asparaginyl-tRNA Synthetase from Entamoeba histolytica

2019 ◽  
Vol 26 (6) ◽  
pp. 435-448
Author(s):  
Priyanka Biswas ◽  
Dillip K. Sahu ◽  
Kalyanasis Sahu ◽  
Rajat Banerjee
Keyword(s):  
Circular Dichroism ◽  
Steady State ◽  
Entamoeba Histolytica ◽  
Protein Concentration ◽  
Trna Synthetase ◽  
Solvent Exposure ◽  
Steady State Fluorescence ◽  
State Process ◽  
Wide Range ◽  
Circular Dichroïsm

Background: Aminoacyl-tRNA synthetases play an important role in catalyzing the first step in protein synthesis by attaching the appropriate amino acid to its cognate tRNA which then transported to the growing polypeptide chain. Asparaginyl-tRNA Synthetase (AsnRS) from Brugia malayi, Leishmania major, Thermus thermophilus, Trypanosoma brucei have been shown to play an important role in survival and pathogenesis. Entamoeba histolytica (Ehis) is an anaerobic eukaryotic pathogen that infects the large intestines of humans. It is a major cause of dysentery and has the potential to cause life-threatening abscesses in the liver and other organs making it the second leading cause of parasitic death after malaria. Ehis-AsnRS has not been studied in detail, except the crystal structure determined at 3 Å resolution showing that it is primarily α-helical and dimeric. It is a homodimer, with each 52 kDa monomer consisting of 451 amino acids. It has a relatively short N-terminal as compared to its human and yeast counterparts. Objective: Our study focusses to understand certain structural characteristics of Ehis-AsnRS using biophysical tools to decipher the thermodynamics of unfolding and its binding properties. Methods: Ehis-AsnRS was cloned and expressed in E. coli BL21DE3 cells. Protein purification was performed using Ni-NTA affinity chromatography, following which the protein was used for biophysical studies. Various techniques such as steady-state fluorescence, quenching, circular dichroism, differential scanning fluorimetry, isothermal calorimetry and fluorescence lifetime studies were employed for the conformational characterization of Ehis-AsnRS. Protein concentration for far-UV and near-UV circular dichroism experiments was 8 µM and 20 µM respectively, while 4 µM protein was used for the rest of the experiments. Results: The present study revealed that Ehis-AsnRS undergoes unfolding when subjected to increasing concentration of GdnHCl and the process is reversible. With increasing temperature, it retains its structural compactness up to 45ºC before it unfolds. Steady-state fluorescence, circular dichroism and hydrophobic dye binding experiments cumulatively suggest that Ehis-AsnRS undergoes a two-state transition during unfolding. Shifting of the transition mid-point with increasing protein concentration further illustrate that dissociation and unfolding processes are coupled indicating the absence of any detectable folded monomer. Conclusion: This article indicates that GdnHCl induced denaturation of Ehis-AsnRS is a two – state process and does not involve any intermediate; unfolding occurs directly from native dimer to unfolded monomer. The solvent exposure of the tryptophan residues is biphasic, indicating selective quenching. Ehis-AsnRS also exhibits a structural as well as functional stability over a wide range of pH.

scholarly journals The IDEAL household energy dataset, electricity, gas, contextual sensor data and survey data for 255 UK homes

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Martin Pullinger ◽  
Jonathan Kilgour ◽  
Nigel Goddard ◽  
Niklas Berliner ◽  
Lynda Webb ◽  
...  
Keyword(s):  
Survey Data ◽  
Energy Demand ◽  
Secondary Data ◽  
Hot Water ◽  
Power Measurement ◽  
Sensor Data ◽  
Energy Awareness ◽  
Household Energy ◽  
Wide Range ◽  
The Ideal

AbstractThe IDEAL household energy dataset described here comprises electricity, gas and contextual data from 255 UK homes over a 23-month period ending in June 2018, with a mean participation duration of 286 days. Sensors gathered 1-second electricity data, pulse-level gas data, 12-second temperature, humidity and light data for each room, and 12-second temperature data from boiler pipes for central heating and hot water. 39 homes also included plug-level monitoring of selected electrical appliances, real-power measurement of mains electricity and key sub-circuits, and more detailed temperature monitoring of gas- and heat-using equipment, including radiators and taps. Survey data included occupant demographics, values, attitudes and self-reported energy awareness, household income, energy tariffs, and building, room and appliance characteristics. Linked secondary data comprises weather and level of urbanisation. The data is provided in comma-separated format with a custom-built API to facilitate usage, and has been cleaned and documented. The data has a wide range of applications, including investigating energy demand patterns and drivers, modelling building performance, and undertaking Non-Intrusive Load Monitoring research.

scholarly journals Identification of DC Thermal Steady-State Differential Inductance of Ferrite Power Inductors

Energies ◽  
2021 ◽  
Vol 14 (13) ◽  
pp. 3854
Author(s):  
Salvatore Musumeci ◽  
Luigi Solimene ◽  
Carlo Stefano Ragusa
Keyword(s):  
Steady State ◽  
Input Voltage ◽  
Switching Frequency ◽  
Ohmic Losses ◽  
Steady State Temperature ◽  
Wide Range ◽  
Average Current ◽  
Power Inductors ◽  
Saturable Inductor ◽  
Thermal Steady State

In this paper, we propose a method for the identification of the differential inductance of saturable ferrite inductors adopted in DC–DC converters, considering the influence of the operating temperature. The inductor temperature rise is caused mainly by its losses, neglecting the heating contribution by the other components forming the converter layout. When the ohmic losses caused by the average current represent the principal portion of the inductor power losses, the steady-state temperature of the component can be related to the average current value. Under this assumption, usual for saturable inductors in DC–DC converters, the presented experimental setup and characterization method allow identifying a DC thermal steady-state differential inductance profile of a ferrite inductor. The curve is obtained from experimental measurements of the inductor voltage and current waveforms, at different average current values, that lead the component to operate from the linear region of the magnetization curve up to the saturation. The obtained inductance profile can be adopted to simulate the current waveform of a saturable inductor in a DC–DC converter, providing accurate results under a wide range of switching frequency, input voltage, duty cycle, and output current values.

Equivariant Adversarial Network for Image-to-image Translation

2021 ◽  
Vol 17 (2s) ◽  
pp. 1-14
Author(s):  
Masoumeh Zareapoor ◽  
Jie Yang
Keyword(s):  
State Of The Art ◽  
Generative Models ◽  
Generative Model ◽  
Target Domain ◽  
Adversarial Network ◽  
Proposed Model ◽  
Image Translation ◽  
Great Performance ◽  
Representative Model ◽  
The Ideal

Image-to-Image translation aims to learn an image from a source domain to a target domain. However, there are three main challenges, such as lack of paired datasets, multimodality, and diversity, that are associated with these problems and need to be dealt with. Convolutional neural networks (CNNs), despite of having great performance in many computer vision tasks, they fail to detect the hierarchy of spatial relationships between different parts of an object and thus do not form the ideal representative model we look for. This article presents a new variation of generative models that aims to remedy this problem. We use a trainable transformer, which explicitly allows the spatial manipulation of data within training. This differentiable module can be augmented into the convolutional layers in the generative model, and it allows to freely alter the generated distributions for image-to-image translation. To reap the benefits of proposed module into generative model, our architecture incorporates a new loss function to facilitate an effective end-to-end generative learning for image-to-image translation. The proposed model is evaluated through comprehensive experiments on image synthesizing and image-to-image translation, along with comparisons with several state-of-the-art algorithms.

SET-VALUED PERFORMANCE APPROXIMATIONS FOR THE QUEUE GIVEN PARTIAL INFORMATION

2020 ◽  
pp. 1-23
Author(s):  
Yan Chen ◽  
Ward Whitt
Keyword(s):  
Steady State ◽  
Decay Rate ◽  
Waiting Time ◽  
Partial Information ◽  
Upper And Lower Bounds ◽  
Interarrival Time ◽  
Limited Information ◽  
Wide Range ◽  
The Mean ◽  
Third Moment

In order to understand queueing performance given only partial information about the model, we propose determining intervals of likely values of performance measures given that limited information. We illustrate this approach for the mean steady-state waiting time in the $GI/GI/K$ queue. We start by specifying the first two moments of the interarrival-time and service-time distributions, and then consider additional information about these underlying distributions, in particular, a third moment and a Laplace transform value. As a theoretical basis, we apply extremal models yielding tight upper and lower bounds on the asymptotic decay rate of the steady-state waiting-time tail probability. We illustrate by constructing the theoretically justified intervals of values for the decay rate and the associated heuristically determined interval of values for the mean waiting times. Without extra information, the extremal models involve two-point distributions, which yield a wide range for the mean. Adding constraints on the third moment and a transform value produces three-point extremal distributions, which significantly reduce the range, producing practical levels of accuracy.

scholarly journals Prediction of Dead Oil Viscosity: Machine Learning vs. Classical Correlations

Energies ◽  
2021 ◽  
Vol 14 (4) ◽  
pp. 930
Author(s):  
Fahimeh Hadavimoghaddam ◽  
Mehdi Ostadhassan ◽  
Ehsan Heidaryan ◽  
Mohammad Ali Sadri ◽  
Inna Chapanova ◽  
...  
Keyword(s):  
Machine Learning ◽  
Viscosity Data ◽  
Oil Viscosity ◽  
Pvt Data ◽  
Proposed Model ◽  
Wide Range ◽  
Engineering Problems ◽  
Functional Forms ◽  
Insight Into

Dead oil viscosity is a critical parameter to solve numerous reservoir engineering problems and one of the most unreliable properties to predict with classical black oil correlations. Determination of dead oil viscosity by experiments is expensive and time-consuming, which means developing an accurate and quick prediction model is required. This paper implements six machine learning models: random forest (RF), lightgbm, XGBoost, multilayer perceptron (MLP) neural network, stochastic real-valued (SRV) and SuperLearner to predict dead oil viscosity. More than 2000 pressure–volume–temperature (PVT) data were used for developing and testing these models. A huge range of viscosity data were used, from light intermediate to heavy oil. In this study, we give insight into the performance of different functional forms that have been used in the literature to formulate dead oil viscosity. The results show that the functional form f(γAPI,T), has the best performance, and additional correlating parameters might be unnecessary. Furthermore, SuperLearner outperformed other machine learning (ML) algorithms as well as common correlations that are based on the metric analysis. The SuperLearner model can potentially replace the empirical models for viscosity predictions on a wide range of viscosities (any oil type). Ultimately, the proposed model is capable of simulating the true physical trend of the dead oil viscosity with variations of oil API gravity, temperature and shear rate.

Sign in / Sign up

Export Citation Format

Share Document