RBF-trained POD-accelerated CFD analysis of wind loads on PV systems

2017 ◽  
Vol 27 (3) ◽  
pp. 660-673 ◽  
Author(s):  
Victor Huayamave ◽  
Andres Ceballos ◽  
Carolina Barriento ◽  
Hubert Seigneur ◽  
Stephen Barkaszi ◽  
...  
Keyword(s):  
Real Time ◽  
Large Scale ◽  
Operating Conditions ◽  
Time Response ◽  
Cfd Analysis ◽  
Pv System ◽  
Data Set ◽  
Content Type ◽  
Pv Systems ◽  
Design Variables

Purpose Wind loading calculations are currently performed according to the ASCE 7 standard. Values in this standard were estimated from simplified models that do not necessarily take into account relevant flow characteristics. Thus, the standard does not have provisions to handle the majority of rooftop photovoltaic (PV) systems. Accurate solutions for this problem can be produced using a full-fledged three-dimensional computational fluid dynamics (CFD) analysis. Unfortunately, CFD requires enormous computation times, and its use would be unsuitable for this application which requires real-time solutions. To this end, a real-time response framework based on the proper orthogonal decomposition (POD) method is proposed. Design/methodology/approach A real-time response framework based on the POD method was used. This framework used beforehand and off-line CFD solutions from an extensive data set developed using a predefined design space. Solutions were organized to form the basis snapshots of a POD matrix. The interpolation network using a radial-basis function (RBF) was used to predict the solution from the POD method given a set of values of the design variables. The results presented assume varying design variables for wind speed and direction on typical PV roof installations. Findings The trained POD–RBF interpolation network was tested and validated by performing the fast-algebraic interpolation to obtain the pressure distribution on the PV system surface and they were compared to actual grid-converged fully turbulent 3D CFD solutions at the specified values of the design variables. The POD network was validated and proved that large-scale CFD problems can be parametrized and simplified by using this framework. Originality/value The solar power industry, engineering design firms and the society as a whole could realize significant savings with the availability of a real-time in situ wind-load calculator that can prove essential for plug-and-play installation of PV systems. Additionally, this technology allows for automated parametric design optimization to arrive at the best fit for a set of given operating conditions. All these tasks are currently prohibited because of the massive computational resources and time required to address large-scale CFD analysis problems, all made possible by a simple but robust technology that can yield massive savings for the solar industry.

Financial distress determinants among SMEs: empirical evidence from Sweden

2020 ◽  
Vol 47 (3) ◽  
pp. 547-560 ◽  
Author(s):  
Darush Yazdanfar ◽  
Peter Öhman
Keyword(s):  
Financial Crisis ◽  
Financial Distress ◽  
Large Scale ◽  
Global Financial Crisis ◽  
Binary Logistic Regression ◽  
Data Availability ◽  
Cross Sectional ◽  
Data Set ◽  
Content Type ◽  
The Global Financial Crisis

PurposeThe purpose of this study is to empirically investigate determinants of financial distress among small and medium-sized enterprises (SMEs) during the global financial crisis and post-crisis periods.Design/methodology/approachSeveral statistical methods, including multiple binary logistic regression, were used to analyse a longitudinal cross-sectional panel data set of 3,865 Swedish SMEs operating in five industries over the 2008–2015 period.FindingsThe results suggest that financial distress is influenced by macroeconomic conditions (i.e. the global financial crisis) and, in particular, by various firm-specific characteristics (i.e. performance, financial leverage and financial distress in previous year). However, firm size and industry affiliation have no significant relationship with financial distress.Research limitationsDue to data availability, this study is limited to a sample of Swedish SMEs in five industries covering eight years. Further research could examine the generalizability of these findings by investigating other firms operating in other industries and other countries.Originality/valueThis study is the first to examine determinants of financial distress among SMEs operating in Sweden using data from a large-scale longitudinal cross-sectional database.

scholarly journals Different Techniques to Mitigate Partial Shading in Photovoltaic Panels

Energies ◽  
2021 ◽  
Vol 14 (13) ◽  
pp. 3863
Author(s):  
Tiago Alves ◽  
João Paulo N. Torres ◽  
Ricardo A. Marques Lameirinhas ◽  
Carlos A. F. Fernandes
Keyword(s):  
Cell Model ◽  
Research Work ◽  
Experimental Tests ◽  
Operating Conditions ◽  
Pv System ◽  
Partial Shading ◽  
System Architectures ◽  
Pv Systems ◽  
Advantages And Disadvantages ◽  
Pv Panel

The effect of partial shading in photovoltaic (PV) panels is one of the biggest problems regarding power losses in PV systems. When the irradiance pattern throughout a PV panel is inequal, some cells with the possibility of higher power production will produce less and start to deteriorate. The objective of this research work is to present, test and discuss different techniques to help mitigate partial shading in PV panels, observing and commenting the advantages and disadvantages for different PV technologies under different operating conditions. The motivation is to contribute with research, simulation, and experimental work. Several state-of-the-artsolutions to the problem will be presented: different topologies in the interconnection of the panels; different PV system architectures, and also introducing new solution hypotheses, such as different cell interconnections topologies. Alongside, benefits and limitations will be discussed. To obtain actual results, the simulation work was conducted by creating MATLAB/Simulink models for each different technique tested, all centered around the 1M5P PV cell model. The several techniques tested will also take into account different patterns and sizes of partial shading, different PV panel technologies, different values of source irradiation, and different PV array sizes. The results will be discussed and validated by experimental tests.

Detection of COVID-19 cases through X-ray images using hybrid deep neural network

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Rajit Nair ◽  
Santosh Vishwakarma ◽  
Mukesh Soni ◽  
Tejas Patel ◽  
Shubham Joshi
Keyword(s):  
Real Time ◽  
Binary Classification ◽  
Data Set ◽  
Content Type ◽  
X Ray ◽  
Internal Parameters ◽  
Average Accuracy ◽  
Chest X Ray ◽  
Multi Class Classification

Purpose The latest 2019 coronavirus (COVID-2019), which first appeared in December 2019 in Wuhan's city in China, rapidly spread around the world and became a pandemic. It has had a devastating impact on daily lives, the public's health and the global economy. The positive cases must be identified as soon as possible to avoid further dissemination of this disease and swift care of patients affected. The need for supportive diagnostic instruments increased, as no specific automated toolkits are available. The latest results from radiology imaging techniques indicate that these photos provide valuable details on the virus COVID-19. User advanced artificial intelligence (AI) technologies and radiological imagery can help diagnose this condition accurately and help resolve the lack of specialist doctors in isolated areas. In this research, a new paradigm for automatic detection of COVID-19 with bare chest X-ray images is displayed. Images are presented. The proposed model DarkCovidNet is designed to provide correct binary classification diagnostics (COVID vs no detection) and multi-class (COVID vs no results vs pneumonia) classification. The implemented model computed the average precision for the binary and multi-class classification of 98.46% and 91.352%, respectively, and an average accuracy of 98.97% and 87.868%. The DarkNet model was used in this research as a classifier for a real-time object detection method only once. A total of 17 convolutionary layers and different filters on each layer have been implemented. This platform can be used by the radiologists to verify their initial application screening and can also be used for screening patients through the cloud. Design/methodology/approach This study also uses the CNN-based model named Darknet-19 model, and this model will act as a platform for the real-time object detection system. The architecture of this system is designed in such a way that they can be able to detect real-time objects. This study has developed the DarkCovidNet model based on Darknet architecture with few layers and filters. So before discussing the DarkCovidNet model, look at the concept of Darknet architecture with their functionality. Typically, the DarkNet architecture consists of 5 pool layers though the max pool and 19 convolution layers. Assume as a convolution layer, and as a pooling layer. Findings The work discussed in this paper is used to diagnose the various radiology images and to develop a model that can accurately predict or classify the disease. The data set used in this work is the images bases on COVID-19 and non-COVID-19 taken from the various sources. The deep learning model named DarkCovidNet is applied to the data set, and these have shown signification performance in the case of binary classification and multi-class classification. During the multi-class classification, the model has shown an average accuracy 98.97% for the detection of COVID-19, whereas in a multi-class classification model has achieved an average accuracy of 87.868% during the classification of COVID-19, no detection and Pneumonia. Research limitations/implications One of the significant limitations of this work is that a limited number of chest X-ray images were used. It is observed that patients related to COVID-19 are increasing rapidly. In the future, the model on the larger data set which can be generated from the local hospitals will be implemented, and how the model is performing on the same will be checked. Originality/value Deep learning technology has made significant changes in the field of AI by generating good results, especially in pattern recognition. A conventional CNN structure includes a convolution layer that extracts characteristics from the input using the filters it applies, a pooling layer that reduces calculation efficiency and the neural network's completely connected layer. A CNN model is created by integrating one or more of these layers, and its internal parameters are modified to accomplish a specific mission, such as classification or object recognition. A typical CNN structure has a convolution layer that extracts features from the input with the filters it applies, a pooling layer to reduce the size for computational performance and a fully connected layer, which is a neural network. A CNN model is created by combining one or more such layers, and its internal parameters are adjusted to accomplish a particular task, such as classification or object recognition.

Fast and accurate detection of surface defect based on improved YOLOv4

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Jiawei Lian ◽  
Junhong He ◽  
Yun Niu ◽  
Tianze Wang
Keyword(s):  
Feature Extraction ◽  
Real Time ◽  
Surface Defect ◽  
Steel Ingot ◽  
Industrial Applications ◽  
Data Sets ◽  
Data Set ◽  
Processing Technologies ◽  
Content Type ◽  
Public Data

Purpose The current popular image processing technologies based on convolutional neural network have the characteristics of large computation, high storage cost and low accuracy for tiny defect detection, which is contrary to the high real-time and accuracy, limited computing resources and storage required by industrial applications. Therefore, an improved YOLOv4 named as YOLOv4-Defect is proposed aim to solve the above problems. Design/methodology/approach On the one hand, this study performs multi-dimensional compression processing on the feature extraction network of YOLOv4 to simplify the model and improve the feature extraction ability of the model through knowledge distillation. On the other hand, a prediction scale with more detailed receptive field is added to optimize the model structure, which can improve the detection performance for tiny defects. Findings The effectiveness of the method is verified by public data sets NEU-CLS and DAGM 2007, and the steel ingot data set collected in the actual industrial field. The experimental results demonstrated that the proposed YOLOv4-Defect method can greatly improve the recognition efficiency and accuracy and reduce the size and computation consumption of the model. Originality/value This paper proposed an improved YOLOv4 named as YOLOv4-Defect for the detection of surface defect, which is conducive to application in various industrial scenarios with limited storage and computing resources, and meets the requirements of high real-time and precision.

Different firm responses to the COVID-19 pandemic shocks: machine-learning evidence on the Vietnamese labor market

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Lam Hoang Viet Le ◽  
Toan Luu Duc Huynh ◽  
Bryan S. Weber ◽  
Bao Khac Quoc Nguyen
Keyword(s):  
Machine Learning ◽  
Labor Market ◽  
Large Scale ◽  
Government Support ◽  
Policy Implications ◽  
Machine Learning Techniques ◽  
Firm Characteristics ◽  
Data Set ◽  
Content Type ◽  
Firm Responses

PurposeThis paper aims to identify the disproportionate impacts of the COVID-19 pandemic on labor markets.Design/methodology/approachThe authors conduct a large-scale survey on 16,000 firms from 82 industries in Ho Chi Minh City, Vietnam, and analyze the data set by using different machine-learning methods.FindingsFirst, job loss and reduction in state-owned enterprises have been significantly larger than in other types of organizations. Second, employees of foreign direct investment enterprises suffer a significantly lower labor income than those of other groups. Third, the adverse effects of the COVID-19 pandemic on the labor market are heterogeneous across industries and geographies. Finally, firms with high revenue in 2019 are more likely to adopt preventive measures, including the reduction of labor forces. The authors also find a significant correlation between firms' revenue and labor reduction as traditional econometrics and machine-learning techniques suggest.Originality/valueThis study has two main policy implications. First, although government support through taxes has been provided, the authors highlight evidence that there may be some additional benefit from targeting firms that have characteristics associated with layoffs or other negative labor responses. Second, the authors provide information that shows which firm characteristics are associated with particular labor market responses such as layoffs, which may help target stimulus packages. Although the COVID-19 pandemic affects most industries and occupations, heterogeneous firm responses suggest that there could be several varieties of targeted policies-targeting firms that are likely to reduce labor forces or firms likely to face reduced revenue. In this paper, the authors outline several industries and firm characteristics which appear to more directly be reducing employee counts or having negative labor responses which may lead to more cost–effect stimulus.

scholarly journals Influence of Parasitic Parameters on DC–DC Converters and Their Method of Suppression in High Frequency Link 35 kV PV Systems

Energies ◽  
2019 ◽  
Vol 12 (19) ◽  
pp. 3743
Author(s):  
Rui Li ◽  
Fangyuan Shi ◽  
Xu Cai ◽  
Haibo Xu
Keyword(s):  
High Frequency ◽  
Large Scale ◽  
Pv System ◽  
Zero Voltage Switching ◽  
Pv Systems ◽  
Magnetic Inductance ◽  
Parasitic Parameters ◽  
Saturable Inductor ◽  
Secondary Side ◽  
Pv Power Generation

Photovoltaic (PV) power generation has shown a trend towards large-scale medium- or high-voltage integration in recent years. The development of high-frequency link PV systems is necessary for the further improvement of system efficiency and the reduction of system cost. In the system, high-frequency high-step-up ratio LLC converters are one of the most important parts. However, the parasitic parameters of devices lead to a loss of zero-voltage switching (ZVS) in the LLC converter, greatly reducing the efficiency of the system, especially in such a high-frequency application. In this paper, a high-frequency link 35 kV PV system is presented. To suppress the influences of parasitic parameters in the LLC converter in the 35 kV PV system, the influence of parasitic parameters on ZVS is analyzed and expounded. Then, a suppression method is proposed to promote the realization of ZVS. This method adds a saturable inductor on the secondary side to achieve ZVS. The saturable inductor can effectively prevent the parasitic elements of the secondary side from participating in the resonance of the primary side. The experimental results show that this method achieves a higher efficiency than the traditional method by reducing the magnetic inductance.

Computational modeling of Weibo user influence based on information interactive network

2016 ◽  
Vol 40 (7) ◽  
pp. 867-881 ◽  
Author(s):  
Dingguo Yu ◽  
Nan Chen ◽  
Xu Ran
Keyword(s):  
Social Networks ◽  
Computational Model ◽  
Large Scale ◽  
Mobile Internet ◽  
Activity Level ◽  
Message Content ◽  
Data Set ◽  
Sina Weibo ◽  
Content Type ◽  
User Influence

Purpose With the development and application of mobile internet access, social media represented by Weibo, WeChat, etc. has become the main channel for information release and sharing. High-impact users in social networks are key factors stimulating the large-scale propagation of information within social networks. User influence is usually related to the user’s attention rate, activity level, and message content. The paper aims to discuss these issues. Design/methodology/approach In this paper, the authors focused on Sina Weibo users, centered on users’ behavior and interactive information, and formulated a weighted interactive information network model, then present a novel computational model for Weibo user influence, which combined multiple indexes such as the user’s attention rate, activity level, and message content influence, etc., the model incorporated the time dimension, through the calculation of users’ attribute influence and interactive influence, to comprehensively measure the user influence of Sina Weibo users. Findings Compared with other models, the model reflected the dynamics and timeliness of the user influence in a more accurate way. Extensive experiments are conducted on the real-world data set, and the results validate the performance of the approach, and demonstrate the effectiveness of the dynamics and timeliness. Due to the similarity in platform architecture and user behavior between Sina Weibo and Twitter, the calculation model is also applicable to Twitter. Originality/value This paper presents a novel computational model for Weibo user influence, which combined multiple indexes such as the user’s attention rate, activity level, and message content influence, etc.

Evaluation of Fuel Preparation Systems for Lean Premixing-Prevaporizing Combustors

1986 ◽  
Vol 108 (2) ◽  
pp. 391-395
Author(s):  
W. J. Dodds ◽  
E. E. Ekstedt
Keyword(s):  
System Design ◽  
Large Scale ◽  
Fuel Injection ◽  
Operating Conditions ◽  
Turbine Engine ◽  
Fuel Injectors ◽  
Design Concepts ◽  
Mixture Preparation ◽  
Design Variables ◽  
System Length

A series of tests was conducted to provide data for the design of premixing-prevaporizing fuel-air mixture preparation systems for aircraft gas turbine engine combustors. Fifteen configurations of four different fuel-air mixture preparation system design concepts were evaluated to determine fuel-air mixture uniformity at the system exit over a range of conditions representative of cruise operation for a modern commercial turbofan engine. Operating conditions, including pressure, temperature, fuel-air ratio, and velocity had no clear effect on mixture uniformity in systems which used low-pressure fuel injectors. However, performance of systems using pressure atomizing fuel nozzles and large-scale mixing devices was shown to be sensitive to operating conditions. Variations in system design variables were also evaluated and correlated. Mixture uniformity improved with increased system length, pressure drop, and number of fuel injection points per unit area. A premixing system compatible with the combustor envelope of a typical combustion system and capable of providing mixture nonuniformity (standard deviation/mean) below 15% over a typical range of cruise operating conditions was demonstrated.

Czech civil sector face-to-face with freelance activism

2016 ◽  
Vol 36 (11/12) ◽  
pp. 774-791
Author(s):  
Pavol Frič ◽  
Martin Vávra
Keyword(s):  
Large Scale ◽  
Civil Society Organizations ◽  
Political Consumerism ◽  
Data Set ◽  
Content Type ◽  
Individual Level ◽  
Face To Face ◽  
Online Activism ◽  
Civil Sector ◽  
Relationship Of

Purpose The purpose of this paper is to answer following question: what is the relationship between member activism performed through civil society organizations (CSOs) and individualized freelance activism (in form of online activism, everyday making, political consumerism or checkbook activism) independent of organizational framework? Is it a relationship of mutual competition or support? Design/methodology/approach Analysis is carried out on data from 2009 questionnaire-based survey on volunteering, representative for adult Czech population. The data set allowed the authors to relate member activism with freelance activism and in case of member activism distinguish the type of organization and the level of its professionalization. Findings Dominant pattern the authors identified in data is mutual support of both types of volunteering documented by significant overlap of these forms of public engagement. The most striking is the overlap for active members of new advocacy NGOs and the weakest for traditional clubs. Regression analysis shows that on an individual level “mixed activism” (compared with “pure freelance activism”) is linked with higher education and higher confidence in civic organizations. Originality/value The civil practice of individualized freelance activism was described and analysed by various authors as an activity of specific types of activist, but there has not yet been any research giving reflection on such a large scale of freelance activism types as in the analysis. The authors set them together in contrast to the member (collective, organized) form of civic activism and also took into account the influence of professionalization and type of CSOs.

Real-Time Prediction of Choke Health Using Production Data Integrated with AI

2021 ◽  
Author(s):  
Ahmed Alghamdi ◽  
Olakunle Ayoola ◽  
Khalid Mulhem ◽  
Mutlaq Otaibi ◽  
Abdulazeez Abdulraheem
Keyword(s):  
High Pressure ◽  
Real Time ◽  
Large Scale ◽  
Production Systems ◽  
Rate Of Change ◽  
Production Data ◽  
Ann Model ◽  
Sand Production ◽  
Data Set ◽  
Pressure Drops

Abstract Chokes are an integral part of production systems and are crucial surface equipment that faces rough conditions such as high-pressure drops and erosion due to solids. Predicting choke health is usually achieved by analyzing the relationship of choke size, pressure, and flow rate. In large-scale fields, this process requires extensive-time and effort using the conventional techniques. This paper presents a real-time proactive approach to detect choke wear utilizing production data integrated with AI analytics. Flowing parameters data were collected for more than 30 gas wells. These wells are producing gas with slight solids production from a high-pressure high-temperature field. In addition, these wells are equipped with a multi-stage choke system. The approach of determining choke wear relies on training the AI model on a dataset constructed by comparison of the choke valve rate of change with respect to a smoother slope of the production rate. If the rate of change is not within a tolerated range of divergence, an abnormal choke behavior is detected. The data set was divided into 70% for training and 30% for testing. Artificial Neural Network (ANN) was trained on data that has the following inputs: gas specific gravity, upstream & downstream pressure and temperature, and choke size. This ANN model achieved a correlation coefficient above 0.9 with an excellent prediction on the data points exhibiting normal or abnormal choke behaviors. Piloting this application on large fields, where manual analysis is often impractical, saves a substantial man-hour and generates significant cost-avoidance. Areas for improvement in such an application depends on equipping the ANN network with long-term production profile prediction abilities, such as water production, and this analysis relies on having an accurate reading from the venturi meters, which is often the case in single-phase flow. The application of this AI-driven analytics provides tremendous improvement for remote offshore production operations surveillance. The novel approach presented in this paper capitalizes on the AI analytics for estimating proactively detecting choke health conditions. The advantages of such a model are that it harnesses AI analytics to help operators improve asset integrity and production monitoring compliance. In addition, this approach can be expanded to estimate sand production as choke wear is a strong function of sand production.

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