Search results

The purpose of this paper is to assist an organization to restructure as a bi‐modal organization in order to achieve sustainability in today's highly complex business world.

The paper is conceptual and is based on relevant literature and the authors' research and practice.

Although fluid self‐organizing networks are the natural state for humankind, in most organizations “organizing” entails the process of autopoiesis. This process does not produce the open fluid organization that is required for success in today's business world. While autopoiesis is taking place, informal socialization is taking place across the organization's interpersonal networks. Under supportive conditions, this leads to the development of a bi‐modal organization where one or more open systems may emerge and co‐exist concurrently with the autopoietic system; these open systems include fluid networks and complex adaptive system. The bi‐modal organization achieves sustainability by balancing a certain amount of organization versus a certain amount of instability, leading to predictability with disorder, and planned long‐term strategy achieved through many concurrent short‐term actions.

Future research will involve an empirical study that will further examine the bi‐modal organization, its development, and its properties.

The systems that surround a business organization now and for the foreseeable future are highly dynamic, competitive, and socially individualized, and demand a new organizational form and competencies that may only be exhibited by a bi‐modal organization based on an open system. The paper describes how an organization can restructure to become a bi‐modal organization.

The paper should help improve quality of work‐life and organizational structure.

The paper describes a new organizational form designed to flourish in today's complex business contexts.

The purpose of this paper is to present a fast nonlinear solver for the prediction of transients in network flows.

Broyden method‐based nonlinear solvers are developed to solve the system of conservation equation for fluids by judiciously exploiting physical coupling among the equations.

To demonstrate the feasibility and robustness of the solvers, two test cases of practical engineering interest were solved. The results obtained by the solvers were verified against analytical results for a simplified case. The performance of the solvers was found to be comparable or better than existing solvers.

The proposed solver enables predictions of fluid and thermal transients in complex flow networks feasible in reduced computational time.

This paper aims to propose an adaptive unstructured finite volume procedure for efficient prediction of propellant feedline dynamics in fluid network.

The adaptive strategy is based on feedback control of errors defined by changes in key variables in two subsequent time steps.

As an evaluation of the proposed approach, two feedline dynamics problems are formulated and solved. First problem involves prediction of pressure surges in a pipeline that has entrapped air and the second is a conjugate heat transfer problem involving prediction of chill down of cryogenic transfer line. Numerical predictions with the adaptive strategy are compared with available experimental data and are found to be in good agreement. The adaptive strategy is found to be efficient and robust for predicting feedline dynamics in flow network at reduced CPU time.

This study uses an adaptive reduced-order network modeling approach for fluid network.

Energy consumption has always been the most serious issue to consider while deploying wireless sensor networks (WSNs). Sensor nodes are limited in power, computational capacities and memory so reporting the occurrence of specific events, such as fire or flooding, as quickly as possible using minimal energy resources is definitely a challenging issue. The purpose of this paper is to propose a new, reactive and energy‐efficient scheme for reporting events. In this scheme, nodes that detect a certain event will organize themselves into a cluster, elect a clusterhead that will collect data from the cluster members, aggregate it and forward it to the mobile sink.

In order to evaluate the scheme, a new sensor node model was designed, where the network layer is implemented from scratch. This layer contains the state process model of the algorithm which was made available through a high‐fidelity process modeling methodology.

Simulation results show that a high‐event notification delivery ratio and a significant energy saving is achieved by deploying the proposed sensor node model; comparisons with existing methods show the efficiency of using the new scheme.

The new contribution in this paper is a novel, reactive and energy‐efficient scheme for reporting events over WSNs. The concept introduced in this paper will decrease energy consumption inside the network and, thus, improve its lifetime.

The purpose of this paper is to present a method for thermal computations of power devices based on a coupling between thermal and pressure networks. The concept of the coupling as well as the solution procedure is explained. The included examples demonstrate that the new method can be efficiently used for design of transformers and other power devices.

The bidirectional propagation of temperature signal is introduced to the pressure network, which enables control of the power flow and a close coupling to the thermal network. The solution method is based on automatic splitting of the network definition (netlist) into two separate networks and iteratively solving the model using the Newton-Raphson approach as well as the adaptive relaxation enhanced by the direction change control.

The proposed approach offers reliable convergence behaviour even for models with unknown direction of the fluid flow (bidirectional flows). The accuracy is sufficient for engineering applications and comparable with the computational fluid dynamics method. The computation times in the range of milliseconds and seconds are attractive for using the method in engineering design tools.

The new method can be considered as a foundation for a consistent network modelling system of arbitrary thermodynamic problems including fluid flow. Such a modelling system can be used directly by device designers since the complexity of thermodynamic formulations is encapsulated in predefined network elements while the numerical solution is based on a standard network description and solvers (Spice).

The purpose of the study is to design and develop an pervasive and smart Internet of Things (IoT)-based sensor system to monitor he real-time intravenous (IV) fluid bag level.

This paper investigates such issue and performs several experiments to develop a non-invasive, semi-automatic system to monitor IoT-based IV fluid level in real-time.

The outcome of this study is a prototype hardware that includes an ESP8266 based embedded Web server to disseminate the fluid exhaust status flag to its connected users. Nurses can get the prompt intimation about the status of IV fluid bag whether it is about to get empty.

IoT is the backbone of the proposed system. Multi-master system need to be studied in future.

Non-invasive and real-time IoT-based novel technique is developed with power-efficient and cost-effective pervasive sensors.

This is applicable for pervasive and assistive e-health-care services by care givers and medical professionals.

The deployed system is controlled by ATtiny85 with help of LM35 temperature sensor. The results show a promising future of the proposed development in enhancing IoT-based smart health-care service in the coming days.

The application of dry-type transformers is growing in the market because the technology is non-flammable, safer and environmentally friendly. However, the unit dimensions are normally larger and material costs become higher, as no oil is present for dielectric insulation or cooling. At designing stage, a transformer thermal model used for predicting temperature rise is fundamental and the modelling of cooling system is particularly important. This paper aims to describe a thermal model used to compute dry transformers with different cooling system configurations.

The paper introduces a fast-calculating thermal and pressure network model for dry-transformer cooling systems, preliminarily verified by analytical methods and advanced CFD simulations, and finally validated with experimental results.

This paper provides an overview of the network model of dry-transformer cooling system, describing its topology and its main variants including natural or forced ventilation, with or without cooling duct in the core, enclosure with roof and floor ventilation openings and air barriers. Finally, it presents a formulation for the new heat exchanger element.

The network approach presented in this paper allows to model efficiently the cooling system of dry-type transformers. This model is based on physical principles rather than empirical assessments that are valid only for specific transformer technologies. In comparison with CFD simulation approach, the network model runs much faster and the accuracies still fall in acceptable range; therefore, one is able to utilize this method in optimization procedures included in transformer design systems.

The paper aims to develop an efficient data-driven modeling approach for the hydroelastic analysis of a semi-circular pipe conveying fluid with elastic end supports. Besides the structural displacement-dependent unsteady fluid force, the steady one related to structural initial configuration and the variable structural parameters (i.e. the variable support stiffness) are considered in the modeling.

The steady fluid force is treated as a pipe preload, and the elastically supported pipe-fluid model is dealt with as a prestressed hydroelastic system with variable parameters. To avoid repeated numerical simulations caused by parameter variation, structural and hydrodynamic reduced-order models (ROMs) instead of conventional computational structural dynamics (CSD) and computational fluid dynamics (CFD) solvers are utilized to produce data for the update of the structural, hydrodynamic and hydroelastic state-space equations. Radial basis function neural network (RBFNN), autoregressive with exogenous input (ARX) model as well as proper orthogonal decomposition (POD) algorithm are applied to modeling these two ROMs, and a hybrid framework is proposed to incorporate them.

The proposed approach is validated by comparing its predictions with theoretical solutions. When the steady fluid force is absent, the predictions agree well with the “inextensible theory”. The pipe always loses its stability via out-of-plane divergence first, regardless of the support stiffness. However, when steady fluid force is considered, the pipe remains stable throughout as flow speed increases, consistent with the “extensible theory”. These results not only verify the accuracy of the present modeling method but also indicate that the steady fluid force, rather than the extensibility of the pipe, is the leading factor for the differences between the in- and extensible theories.

The steady fluid force and the variable structural parameters are considered in the data-driven modeling of a hydroelastic system. Since there are no special restrictions on structural configuration, steady flow pattern and variable structural parameters, the proposed approach has strong portability and great potential application for other hydroelastic problems.

This paper aims to examine the dynamics of the communication network established within a multi-team system (MTS) and analyzes its implications for the MTS processes, emergent states and performance during a search and rescue operation.

The authors take a bifocal approach and combine both network analysis metrics and a qualitative analysis of the message content in addressing the research questions.

The findings illustrate the emergence of a decentralized network and that the extent to which decentralization is conducive toward MTS performance also depends on the density of the communication lines established among the component teams (CTs) and the communication content. Low density of the communication network was associated with impaired MTS processes and low effectiveness. Node centrality metrics indicate a limited connectivity between the leader of the operation and the MTS CTs, also with negative impact on MTS performance. Whereas, informal CTs become central to the MTS communication network and positively influence MTS performance during the last episodes of the mission.

This paper is among the first to use a social network approach to the study of MTS functioning and illustrates how the fluid structure of the MTS alters communication networks, which in turn influence other MTS processes, emergent states and overall performance.

A novel approach using artificial neural networks (ANNs) for predicting temperature profiles evaluated 27 wells in the Gulf of Mexico. Two artificial neural network models were developed that predict the temperature of the flowing fluid at any depth in flowing oil wells. Back propagation was used in training the networks. The networks were tested using measured temperature profiles from the 27 oil wells. Both neural network models successfully mapped the general temperature‐profile trends of naturally flowing oil wells. The highest accuracy was achieved with a mean absolute relative percentage error of 6.0 per cent. The accuracy of the proposed neural network models to predict the temperature profile is compared to that of existing correlations. Many correlations to predict temperature profiles of the wellbore fluid, for single‐phase or multiphase flow, in producing oil wells have been developed using theoretical principles such as energy, mass and momentum balances coupled with regression analysis. The Neural Network 2 model exhibited significantly lower mean absolute relative percentage error than other correlations. Furthermore, in order to test the accuracy of the neural network models to that of Kirkpatrick’s correlation, a mathematical model was developed for Kirkpatrick’s flowing temperature gradient chart.