Output Quantities Research Articles

Global sensitivity analysis of a coupled multiphysics model to predict surface evolution in fusion plasma–surface interactions

We construct a global sensitivity analysis framework for a coupled multiphysics model used to predict the changes in material properties and surface morphology of helium plasma-facing components in future fusion reactors. The model combines the particle dynamics simulator F-TRIDYN, that predicts the helium implantation profile, with the cluster dynamics simulator Xolotl, that predicts the growth and evolution of subsurface helium gas bubbles. In order to keep the sensitivity analysis tractable, we first construct a sparse, high-dimensional polynomial chaos expansion surrogate model for each output quantity of interest, which allows the efficient extraction of sensitivity information. The sensitivity analysis is performed for two problem settings: one for ITER-like conditions, and one that resembles conditions inside the PISCES-A linear plasma device. We present a systematic comparison of important parameters, for both F-TRIDYN and Xolotl in isolation as well as for the coupled model, and discuss the physical interpretation of these results.

Universal research index: An inclusive metric to quantify scientific research output

Scientometrics and bibliometrics, the subfields of library and information science, deal with the quantity and quality of research outputs. Currently, various scientometric indices are being used to quantify and compare research outputs. The most widely known is the h-index. However, this index and its derivatives suffer from dependence on the mere count of a scholar's highly cited publications. To remedy this deficiency, we developed a novel index, the Universal Research Index (UR-Index) (https://usern2021.github.io/UR-Index/) by which every single publication has its own impact on the total score. We developed this index by surveying international top 1 % cited scientists in various disciplines and included additional component variables such as publication type, leading role of a scholar, co-author count, and source metrics to this scientometric index. We acknowledge that unconscious biases built into the component variables included in the UR-Index might put research from specific groups at a disadvantage, thus continued efforts to improve equitable scholarly impact in science and academia are encouraged.

A review of inverse data envelopment analysis: origins, development and future directions

AbstractData envelopment analysis (DEA) is a widely used mathematical programming approach for assessing the efficiency of decision-making units (DMUs) in various sectors. Inverse DEA is a post-DEA sensitivity analysis approach developed initially for solving resource allocation. The main objective of inverse DEA is to determine the optimal quantity of inputs and/or outputs for each DMU under input and/or output perturbation (s), which would allow them to reach a given efficiency target. Since the early 2000s, inverse DEA has been extended theoretically and applied successfully in different areas including banking, energy, education, sustainability and supply chain management. In recent years, research has demonstrated the potential of inverse DEA for solving novel inverse problems, such as estimating merger gains, minimizing production pollution, optimizing business partnerships and more. This paper provides a comprehensive survey of the latest theoretical and practical advancements in inverse DEA while also highlighting potential areas for future research and development in this field. One such area is exploring the use of heuristic algorithms and optimization techniques in conjunction with inverse DEA models to address issues of infeasibility and nonlinearity. Moreover, applying inverse DEA to new sectors such as healthcare, agriculture and environmental and climate change issues holds great promise for future research. Overall, this paper sets the stage for further advancements in this promising approach.

Energy storage complementary control method for wind‐solar storage combined power generation system under opportunity constraint

AbstractDue to the different complementarity and compatibility of various components in the wind‐solar storage combined power generation system, its energy storage complementary control is very important. In order to ensure the stable operation of the system, an energy storage complementary control method for wind‐solar storage combined power generation system under opportunity constraints is proposed. The wind power output value is obtained. Aiming at the maximum similarity between the total output of wind power storage and the planned output curve, combined with the opportunity constraints and the output and electric quantity constraints of energy storage devices, the energy storage complementary control model is established, and the model is solved by improving the adaptive particle swarm optimization algorithm. The experimental results show that the total output of the wind‐solar storage combined power generation system is consistent with the expected output, and the utilization rate of wind‐solar resources is effectively improved. It has good convergence performance, and can better meet the needs of actual power work.

Contribution to Uncertainty Propagation Associated with On-Site Calibration of Infrasound Monitoring Systems

To improve the confidence and quality of measurements produced by regional and international infrasound monitoring networks, this work investigates a methodology for propagating uncertainty associated with on-site measurement systems. We focus on the propagation of sensor calibration uncertainties. The proposed approach is applied to synthetic infrasound signals with known back azimuth and trace velocity, recorded at the array elements. Relevant input uncertainties are investigated for propagation targeting the incoming signals (noise), instrumentation (microbarometers, calibration system, wind noise reduction system), and the time-delay-of-arrival (TDOA) model (frequency band). Uncertainty propagation is performed using the Monte Carlo method to obtain the corresponding uncertainties of the relevant output quantities of interest, namely back azimuth and trace velocity. The results indicate that, at high frequencies, large sensor uncertainties are acceptable. However, at low frequencies (<0.1 Hz), even a 2∘ sensor phase uncertainty can lead to errors in the back azimuth of up to 5∘ and errors in the trace velocity of 20 m/s.

Policy incentives, government subsidies, and technological innovation in new energy vehicle enterprises: Evidence from China

As the first step toward establishing the development of new energy vehicles (NEVs) as a national strategic priority, China's New Energy Vehicle Pilot City (NEVPC) policy has far-reaching significance for helping to develop China's capabilities in NEV industry. The existing literature focuses on the city- and industry-level impacts of this policy but ignores the important role of individual enterprises as innovation agents. In addition, in assessing the policy's effects technological innovation for NEV enterprises is typically measured in terms of output quantity, with no emphasis on innovation efficiency. This study uses balanced panel data from 2006 to 2020 covering 196 listed Chinese NEV enterprises to examine the mechanism and impact of the NEVPC policy on technological innovation within NEV enterprises. We show that the policy has achieved the goal of expanding government subsidies and had a significantly positive impact on the number of technological innovations produced by these enterprises, via a partial mediation effect. However, neither the policy nor the mechanism of government subsidies is effective in improving the efficiency of technological innovations for NEV enterprises. These results have important implications for formulating and improving policies for the NEV industry.

Research on the Influence of Digital Economy on Technological Innovation: Evidence from Manufacturing Enterprises in China

Digital economy is a key breakthrough in the new round of the industrial revolution, and technological innovation is the key to economic growth and wealth creation by enterprises. The influence of the digital economy on the innovation of manufacturing enterprises is directly related to solving the problem of inadequate development. Therefore, it is of great theoretical value and practical significance to study the influence of the digital economy on enterprise technological innovation. Taking Chinese manufacturing companies during 2016–2020 as a sample, this paper measured the technological innovation level of enterprises by the quantity and quality of innovation output of manufacturing enterprises and measured the development degree of the digital economy by the four dimensions of digital economy infrastructure, digital industrialization, industrial digitalization, and digital governance. The influence of the digital economy on enterprise technological innovation was researched through the Moran index and panel regression model. Moran’s indexes were all greater than 0, and the data were concentrated in the first and third quadrants of the scatter chart, and the value dropped from 0.186 to 0.152, indicating that provincial digital economic development presents a phenomenon of aggregation in spatial distribution, which gradually decreases along the eastern coast to the western region and gradually moves from aggregation to dispersion. The regression coefficients are significant at least at the level of 10%, indicating that the digital economy can effectively promote the output quantity and quality of technological innovation in manufacturing enterprises. The digital economy has a double-promotion effect on technological innovation. In promoting the technological innovation of manufacturing enterprises, infrastructure, digital industrialization, and industrial digitization play a dual and positive role as well as digital governance. In addition, the digital economy and its four dimensions have a first-order and second-order time-lag effect, which has a double-promotion effect on enterprises’ technological innovation. In general, the digital economy can promote enterprise technological innovation.

FE‐based modeling of a mesoscale piezoelectric motor

AbstractThe manufacturing of systems at the mesoscale on the order of millimeters to centimeters is costly and challenging. To simplify the development process, the finite element method (FEM) can be used to compare and evaluate different design variations at an early stage. This method also allows for gaining a more precise understanding of the system's behavior. In robotic applications it is the torque that is of the greatest interest as a sufficiently high torque mitigates the need for a gearbox, which is very difficult to manufacture and integrate at small scales. Given this background, the present work aims to develop a finite element model for the numerical representation of a class of rotational piezoelectric motors based on multiple unimorph arms. The influence of different design parameters on the resulting torque is analyzed utilizing this model. The motor design consists of a planar rotor and a flat spring, a stator with three unimorph arms, as well as a shaft. With a total integrated motor thickness of 0.8 mm, the weight is approximately 200 milligrams. The rotation is mainly caused by the tip contact between the arms of the stator and the rotor itself. Experiments with prototypes have shown that bidirectional rotation is possible with the design, which is then investigated in more detail using the numerical model. There are several challenges here in the non‐linearity due to the contact between the individual components and the high‐frequency excitation of the motor in the kHz range.The modeling also requires validation experiments to determine the resulting properties for the coupled system of structural and piezoelectric components. This includes both the measurement of the unimorph arms and the measurement of the natural frequencies of the stator with the piezoelectric unimorphs by a laser scanning vibrometer and displacement sensors. The scanning vibrometer also provides the opportunity to compare the vibration modes, as an important non‐local output quantity, in order to achieve good agreement between the numerical and experimental results.In the next stage, the model is to serve as the basis for multi‐objective optimization to achieve maximum torque in the smallest possible volume. Further miniaturization possibilities will also be investigated in future studies.

A novel approach combining bootstrapped non-intrusive reduced order models and unscented transform for the robust and efficient CFD analysis of accidental gas releases in congested plants

The risk assessment for safety-critical, complex systems is a very challenging computational problem when it is performed with high-fidelity models, e.g. CFD, like in the case of accidental gas releases in congested systems. Within this framework, a novel CFD approach, named Source Box Accident Model, has been recently proposed to efficiently model such phenomena by splitting the simulation of the gas release and its subsequent dispersion in the system in two steps. In this view, the present paper proposes a non-intrusive, Proper Orthogonal Decomposition-Radial Basis Functions reduced order model that exploits the two-step nature of the SBAM approach, to mimic the behaviour of the original, long-running CFD model code at a significantly lower computational cost. Moreover, the paper presents a methodology combining the bootstrap and unscented transform approaches to efficiently assess the ROM uncertainty in the safety-critical simulation output quantities of interest, e.g. the flammable volume. The results obtained in a test case involving a high pressure, accidental gas release in an off-shore Oil & Gas plant are in very satisfactory agreement with those produced by CFD, with a relative error smaller than 10% and a reduction in the computational time of about three orders of magnitude.

Reliability analysis of mechanisms with mixed uncertainties using polynomial chaos expansion

AbstractIn order to quantify the effect of mixed uncertainties on the reliability of mechanisms, this paper proposes a method for analyzing the reliability of mechanisms with mixed uncertainties using polynomial chaos expansion. Based on the performance margin theory, a mechanism reliability model considering mixed uncertainties is developed, and the Sobol indices can be easily calculated by an arbitrarily polynomial chaos expansion (aPCE) of the reliability function to quantify the independent contribution of each parameter to the global sensitivity and the effect of parameter coupling. Additionally, this paper also introduces a mixed uncertainties propagation method based on PCE, which treats cognitive uncertainties as fuzzy variables, converts the fuzzy variable into an interval variable using cut set theory, and transforms the PCE containing interval uncertainties into a Bernstein polynomial to calculate the membership function of the output quantity and the non‐probabilistic reliability index by area method. The final case study of a simplified automaton motion mechanism illustrates that the proposed method is effective and convincing, and the mechanism reliability decreases with increasing mixed uncertainties. The mechanism reliability index considering mixed uncertainties will give a more conservative reliability estimation.

An adaptive linear active disturbance rejection control method for HVDC transmission system

With the advantages of large transmission capacity, low losses, rapid and flexible power regulation, and the ability to achieve non-synchronous networking, High Voltage Direct Current transmission systems are playing an increasingly important role in long distance transmission and grid interconnection. However, under certain circumstances, such as disturbances in the DC transmission system and sudden load changes, the converter based on PI control in DC transmission system suffer from slow response speed and poor stability. In order to improve the stability of the system and the response speed of DC voltage, this paper proposes an adaptive linear active disturbance rejection control (A-LADRC) method. The method is based on the analysis of the relationship between key bandwidth parameters and output quantities in linear active disturbance rejection control and enables adaptive adjustment of the controller bandwidth parameters for fast regulation of the DC voltage and to improve the stability of the DC voltage in the event of disturbances. Finally, the proposed control method is validated by simulation, and the results show that the proposed control method can effectively improve the response speed and robustness of the DC voltage, which has good practical engineering value.

An Empirical Study on the Landscape of Mining and Mineral Processing (MMP) With Big Data

Over the last two decades, mining and mineral processing (MMP) has changed dramatically. Little is known about the bibliometric analysis of MMP. To this end, this study used the big data analysis to investigate the quantity and quality of scientific outputs in MMP over the past 21-year timespan. This study used IBM SPSS Statistics 25.0 and VOSviewer software to research on the 20 journals from Science Citation Index Expanded (SCI-Expanded) of Web of Science Core Collection (WOSCC). VOSviewer software was used to identify the visualization contributions of scientific outputs over 21-year timespan. Totally, the big data analysis shows people of China have the highest cumulative IFs, but their mean IFs are relatively low and are ranked in fourth place. Visually, people of Chin ranked the first in total link strength (2967), but not in links (86), which is the third place among Top15 countries. From the perspective of quality, it cannot rank the first. Thus, people of China should put more effort into improving the quality of scientific outputs.

Econometric Modeling of Input-Driven Output Risk through a Versatile CES Production Function

The conventional functional form of the Constant-Elasticity-of-Substitution (CES) production function is a general production function nesting a number of other forms of production functions. Examples of such functions include Leontief, Cobb–Douglas, and linear production functions. Nevertheless, the conventional form of the CES production specification is still restrictive in multiple aspects. One example is the fact that the marginal effect of increasing input use always has to be to increase the variability of output quantity by the conventional construction of this function. This paper proposes a generalized variant of the CES production function that allows for various input effects on the probability distribution of output. Failure to allow for this possible input–output risk structure is indeed one of the limitations of the conventional form of the CES production function. This limitation may result in false inferences about input-driven output risk. In light of this, the present paper proposes a solution to this problem. First, it is shown that the familiar CES formulation suffers from very restrictive structural assumptions regarding risk considerations, and that such restrictions may lead to biased and inefficient estimates of production quantity and production risk. Following the general theme of Just and Pope’s approach, a CES-based production-function specification that overcomes this shortcoming of the original CES production function is introduced, and a three-stage Nonlinear Least-Squares (NLS) estimation procedure for the estimation of the proposed functional form is presented. To illustrate the proposed approaches in this paper, two empirical applications in irrigation and fertilizer response using the famous Hexem–Heady experimental dataset are provided. Finally, implications for modeling input-driven production risks are discussed.

CONTRIBUTION OF GOVERNMENT, RESEARCH AND FINANCIAL INSTITUTIONS TOWARDS DEVELOPMENT OF HYDROPONIC FARMING IN INDIA

In the world around 9 million people die because of hunger and its related diseases. Of the world population 24% live in an area where they cant get sufficient food to survive. ButIndia is a country which exports Rs.9,47,930.77lakh worth of fresh fruits and vegetables and floriculture worth Rs.69,603.49 lakh for the financial year 2021-22 and import exotic fruit and vegetables from other countries. This is because of enrichment in the agricultural methods and techniques that helps in increasing the productivity of the farmers and hydroponic method of farming is one among them. Hydroponic farming is the system of farming where crops are grown without soil but by using water, media and nutrient solution. In hydroponic faming water required is 80-90% less than the traditional farming or soil based farming. Land required is also only 20% of the land which is required for traditional farming to produce same quantity of output. Though the Initial investment and operating expenses are high to install and maintain hydroponic farming, yield and returns are also high.Since India started exporting more food produce and started growing exotic fruits, vegetable and flowers, this would boost the growth of economy of a country. To support, empower and encourage the farmers government of various states are providing subsidy facility up to 25%, 40%, 50% and 75%( percent vary from state to state), since the initial invest and operating expenses are high.Banks and financial institutions are providing loans and financial support with less interest rates and easy repayment modes. In few states, government is proving free training programs for women and physically disabled persons to empower them and to make them independent.

Design and Experimental Research on Centralized Lubrication and Waste Oil Recovery System for Wind Turbines

Lubrication plays a key role in increasing availability of wind turbines, extending unit life and reducing operating costs. In view of the problems of valve core lag, grease hardening and difficulty in removing waste oil in a centralized lubrication system, an improved centralized lubrication system and waste oil recovery system were designed in this study. Discharge of waste grease in the bearing cavity was simulated under different vacuum conditions. It was shown that vacuum degree of bearing cavity is proportional to oil output speed of waste grease. Performance and fatigue reliability tests of the waste grease suction and drainer device test platform were conducted over 12,000 fatigue cycles. The results show that the vacuum degree error of the waste grease suction and drainer device before and after the test is less than 5%, and the power oil pressure, oil output pressure and oil output quantity of the test product are stable, indicating that the designed waste grease suction and drainer device has excellent sealing and reliability. The waste grease suction and drainer device can eliminate grease discharge resistance in the bearing cavity, facilitating discharge of waste oil and improving wind turbine operation efficiency.

Proper modelling of industrial production systems with unintended outputs: a different perspective

The question of how to properly model production systems with unintended outputs has proven both controversial and of particular interest to the productivity and efficiency community. The paper explains why some of the arguments put forward in these controversies are hardly convincing for industrial and other processes. Among other things, there is a lack of clear conceptual labelling of the different types of joint production, especially coupled production, which is the main source of undesirable and other unintended outputs, unless neglected. It is largely ignored that the desirability of such by-products may depend on the quantity produced. This is also true for reduction processes such as waste incineration or end-of-life vehicle dismantling, which in turn generate new unintended outputs. As a rule, industrial material and energy balances are modelled implicitly. Koopmans’ activity analysis is the standard approach in modelling production systems with undesirable outputs in the literature of business economics on sustainable production and supply chain management. With data envelopment analysis (DEA), instead of entire production possibilities, it is sufficient to know only certain local properties in the relevant range of input and output quantities of the observed activities. This lowers the challenge to verify their empirical validity.

Scenario modeling to predict changes in land use/cover using Land Change Modeler and InVEST model: a case study of Karaj Metropolis, Iran

Models for land cover/land use simulation are appropriate and important tools for decision-makers, helping them build future plausible landscape scenarios. Due to the fact that the simulation results of different models may be different, it is sometimes difficult for users to choose a suitable model. Therefore, in this study, an integrated approach is used, combining the data obtained from remote sensing and GIS with Land Change Modeler (LCM) and Integrated Valuation of Ecosystem Services and Tradeoffs (InVEST) models to simulate and predict land cover/land use changes for 2028 in Karaj metropolis (Northern Iran as a poor region-in terms of data-which is under intense and rapid urbanization. In this sense, three land cover/land use maps related to the study area were primarily generated using satellite image data for the period 2006, 2011, and 2017. They were used as a basis to define two scenarios: business-as-usual (BAU) scenario and participatory plausible scenario (PPS) for 2028. Afterwards, the necessary input data used in running of both models were prepared and, then, the outputs of the models were interpreted and compared. According to the results, while human-made coverage and low-density grasslands increased by about 74% and 12%, respectively, it was from 2006 to 2017 that agricultural lands, gardens, and high-density grasslands decreased by 42%, 34%, and 7%, respectively. According to the business-as-usual scenario, which was projected using the LCM model, the increase in human-made cover will continue by about 29% by 2028, and the reduction rate of agricultural lands, gardens, and low-dense and dense grasslands will experience decrease by about 20%, 3%, 11%, and 9%, respectively. The participatory plausible scenario for 2028, which was defined using the InVEST model, confirmed the same results, but having different quantities. Accordingly, while human-made cover will increase by about 73%, the reduction rate of agricultural lands, gardens, and low-dense and dense grasslands will decrease by about 41%, 10%, 16%, and 1%, respectively. The output quantities of InVEST scenario model seem to be closer to reality with less uncertainty, because this model estimates the quantity of demand for land and its suitability for different uses, based on the views of different stakeholders, and considers landscape development future policies and plans. In contrast, the LCM model is based solely on trend extrapolation from the past to current time and changes in the landscape structure.

Price re-interpretations of the basic IO quantity models result in the ultimate input-output equations

This note shows that Leontief's well-known demand-driven input–output (IO) quantity model may also be interpreted as the almost unknown revenue-pull IO price model, but measured in value terms instead of in prices. It is also shown how these two demand-driven models may be combined into a single ultimate demand-driven IO equation. An analogous result holds for the supply-driven quantity model and the cost-push price model, which results in a single ultimate supply-driven IO equation. The new price interpretation of the Leontief quantity model opens up hitherto unused possibilities to simulate interindustry demand-driven inflation processes, just as the price interpretation of the Ghosh quantity model enables simulations of supply-driven inflation processes.

Study on the methods of measuring power of the rotating mechanisms

This article is about the methods of measuring the power of rotating mechanisms, in which the sensors that measure the angular parameters of rotating mechanisms are analyzed. Also, several methods of measuring the power of rotating mechanisms are given in the article. The method of power dissipation through angular acceleration and torque is analyzed in more detail. Connections of input and output quantities are given.

Decentralized Relay Feedback Identification of TITO systems

The article focuses on the identification of systems with two inputs and two outputs (TITO systems) where strong cross-couplings exist. The transfer functions of all links of TITO systems are determined from the frequency response points obtained from the time courses of input and output quantities during decentralized relay control. The proposed procedure uses the so-called shifting method, which allows to obtain multiple points of the frequency characteristic from one experiment, thus allowing the estimation of linear models with multiple parameters. In addition, the article proposes a new procedure using the shifting method to obtain the steady state gains of individual transfer functions. The application of this procedure is shown on the instance of a methanol-ethanol distillation column.