System Performance Indicators Research Articles

Performance assessment of a modified air-gap and water-gap membrane distillation systems

This work experimentally evaluates and compares the performance of four membrane distillation (MD) units with and without an internal gap rotating impeller blade. The novelty of the current study lies in the installed impeller within the condensation cavity and the impeller design parameters. The investigated MD units include a novel modified water-gap MD (M-WGMD) system featuring an internal impeller blade; a novel modified air-gap MD (M-AGMD) system with an internally rotating impeller blade; a regular water-gap MD (WGMD) process; and a conventional air-gap MD (AGMD) system. The evaluated system performance index includes gained output ratio (GOR) and vapor flux. The performance of the four MD systems is assessed and compared against each other under the same test conditions for various system design and operating variables, including impeller blade material, impeller blade angle, saline feed temperature, coolant temperature, and saline feed flowrate. Findings indicate that M-WGMD and WGMD systems (modified and conventional) produce higher flux than M-AGMD and AGMD units. Meanwhile, air-gap MD units (AGMD and M-AGMD) exhibits better energy efficient (higher GOR) than WGMD and M-WGMD systems. Furthermore, modified MD systems recorded superior performance compared to conventional MD systems and the blade angle effect is more influential than the blade material. At 80 °C feed temperature and 3200 mg/L feed salinity, the M-WGMD reaches a maximum flux of 128.39 kg/m2h and a GOR of 0.7702. On the other hand, the WGMD attained a flux of 67.32 kg/m2h and a GOR of 0.6199. The M-AGMD recorded a flux of 30.20 kg/m2h and a GOR of 0.8696. For the AGMD system, the GOR and the flux values are 0.7357 and 15.58 kg/m2h, respectively.

Techno-economic-environmental analysis and fluid selection of transcritical organic Rankine cycle with zeotropic mixtures

Waste heat recovery is regarded as highly efficacious in ameliorating energy and environmental problems brought on by rapid economic and societal growth. The organic Rankine cycle (ORC), specifically transcritical ORC (tORC) garners significant attention for its potential in waste heat power generation. Researchers still seek to further improve performance for tORC, such as using zeotropic mixtures. Aimed at selecting the working fluid pairs that can bring better performance for tORC, this study provides a more comprehensive performance evaluation of tORC systems by developing a novel multi-attribute integrated assessment model that encompasses techno-economic-environmental aspects. To establish this model, the detailed mathematical models of the tORC are established first and used to obtain system performance indicators. Then, a refined analytic hierarchy process to assign weights to various performance indicators to complete the multi-attribute integrated assessment model. With the utilization of the proposed model, a thorough comparative analysis of the tORC system with ten groups of zeotropic mixtures is performed. Thus, the results of the working fluid selection can be obtained. Results show that R245fa/R600 and R600/R600a are more recommended than other working fluid pairs, with R600/R600a being the preferable choice when prioritizing environmental impact. While R245fa/R1234yf has the worst thermodynamic and environmental performance and R245fa/R227ea owns the worst economic performance. In conclusion, the findings show that employing a zeotropic mixture combines the benefits of the pure working fluids that comprise it. Compared to previous studies, this study provides a more comprehensive look at the selection of working fluids for tORC systems. It can offer valuable insights for selecting the appropriate working fluid more effectively for future applications.

Multivariate Relationships Between Health Outcomes and Health System Performance Indicators: An Integrated Factor Analysis With Canonical Correlations

ObjectivesThis study aimed to investigate the relationships between sets of variables related to health system performance indicators and health outcomes. MethodsThe relationships between a set of health outcomes and a set of health system performance indicators of a developing country were examined using multivariate statistical analysis techniques. A combinative strategy of explanatory factor analysis and the canonical correlation coefficient was used to define linear structural relationships between study variables. Province-based data were gathered from2 official statistical records of the Turkish Statistical Institute for the year 2019. Life expectancy at birth, infant mortality rate, and crude death rate were accepted as health outcome indicators. ResultsThe explanatory factor analysis indicated 2 independent variable groups, namely (1) health-related human resources and capacity and (2) health service utilization characteristics. The results of the canonical correlation analysis illustrated good performance to define sparse linear combinations of the 2 groups of variables. There existed strong positive correlations between health outcomes and health-related human resources and capacity indicators (rc = 0.83; P < .001) and health service utilization indicators (rc = 0.59; P < .001). ConclusionsThe results of this study support the view that there is a linear and strong positive relationship between health outcomes and health-related human resources and capacity indicators. Further studies will combine big data analytics with multivariate statistical analysis techniques by studying large health system performance data sets.

КОРПОРАТИВНА КУЛЬТУРА АВІАКОМПАНІЙ В СУЧАСНИХ УМОВАХ

Airlines are required to not only adapt to shifting market conditions and technological improvements, but also to rethink and construct their own corporate culture in order to stay up with the current stage of growth in the aviation industry, which is facing significant upheavals. This is vital in order for airlines to be able to keep up with the current stage of development. In addition to the pandemics-specific challenges that were brought on by COVID-19, and the war in Ukraine, the concurrent socioeconomic crisis brought about an additional focus that was of utmost significance. In order to determine the influence that the pandemic of the coronavirus disease and the war have had on the management of the corporate culture of airlines, the goal of this essay is to investigate the impact. The aim of this paper is to analyze the ways in which corporate culture influences many aspects of airline operations. These aspects include the internal interaction and motivation of employees, as well as the outward image and communications with consumers. Tools for managing airline corporate culture during a crisis include communication platforms, psychological support programs, skills training, leadership development initiatives, and airline employee opinion and feedback tools. These tools help airlines adapt to change during a crisis, maintain a healthy corporate culture, and support their employees. Setting clear goals and regular monitoring of performance indicators are important for timely adaptation of corporate culture management strategies. It is appropriate to use the KPI system of airlines for the development of corporate culture in the conditions of modern challenges. The KPI system is offered as follows: KPI 1: Level of employee engagement; KPI 2: Reducing the level of stress among employees; KPI 3: Level of training and development of employees; KPI 4: Improvement of leadership skills in management; KPI 5: Improvement of corporate culture based on feedback. There is a possibility that the study will be useful to academic scholars, analysts, and businesses. Potential route for further exploration, regarding the development of corporate culture in the face of both exogenous and endogenous unpredictability, is the digitization of the key performance indicator system that is adopted by airlines.

Determining quality indicators for projects of information protection of information activity objects

The design and analysis of the effectiveness of modern integrated information protection systems (IIPS) is complicated by the influence of the designer's subjective decisions regarding methods and means of protection, the need to determine the quality criteria for the functioning of the designed objects. The work is aimed at improving the technology of automated design of IIPS by determining the quality of projects. When creating a database (DB) of objects with such "qualitative" performance indicators of information protection systems, it is possible to compare existing and new protection objects and to adjust the protection projects of existing objects. Moreover, the ontological properties of active and threat-resistant objects are taken into account. To illustrate the use of the methodology for determining and comparing the quality of projects, an example of comparing the quality of projects obtained in different ways is given. One way currently operating involves the use of expert evaluation of the quality of protection projects for existing facilities. The second way is intended for objects defined as objects of protection of the general structure (OPGS) and involves a principally objective assessment of design quality using known quality diagrams and control of Ishikawa and Pareto design consequences. As a result of the given example, it was determined that the quality of projects according to quality diagrams and control of design consequences should increase by more than two times. The proposed method of comparing the quality of information protection projects for different objects or different protection projects of one object allows to improve the process of creating information protection projects. At the same time, it is possible to exclude the human controller from the process of determining the reliability of object protection. It also allows you to automate the design process, reduce the time and cost of design costs

Development and Implementation of a Unified Care Model Performance Framework in the Yishun Zone Regional Population Health System

Background / Objective: Governance and Leadership Subsystem (GLS) of health systems governs but also directs Service Delivery Subsystem (SDS) transformation. Performance management (PM) is a key GLS activity. Coherent PM frameworks improve SDS governance, health system performance and ultimately population health outcomes.&#x0D; Yishun Health (YH) is an integrated care organization within National Healthcare Group (NHG) caring for 320,000 residents in northern Singapore as the Yishun Zone Regional Population Health System (YZ). YH designed and deployed the Unified Care Model (UCM) as future-state SDS model to integrate and transform its care/services with care partners. A redesigned PM framework going beyond disease-condition services, specialty-departments, and institutions-partners was required.&#x0D; Method: Population segmentation was achieved using NHG River-of-Life segmentation model, dividing YZ residents into four Lifelong Care segments (LLC) and one Episodic Care segment (EC). Employing UCM, LLC and EC were further sub-segmented into a cascade of ever smaller populations of unique care needs to envision future SDS needs/demand-supply matching in the form of integrated microsystems of care/services delivery.&#x0D; UCM Performance Framework (UCMPF) was conceptualized by adapting IHI’s Quadruple Aim as annual outcomes for LLC microsystems and Dartmouth’s Clinical Value Compass as 90-day outcomes for EC microsystems. Ability to govern integrated microsystems was then modelled using clinical logic models, enabling curation of input, process, and output indicators into domains of standardized care strategies i.e. “Horizontal Logic”. “Vertical Logic” was then employed to aggregate relevant LLC or EC microsystem input, process, output or outcome indicators up the cascade of UCM population segments and higher order systems to envision future-state GLS, such as i) person and need/demand centric mesosystems e.g. LLC and EC or a community geographically, or ii) provider-centred mesosystems e.g. site-based disease condition-services or program, specialty-departments, institution-partners, culminating ultimately in whole health system or macrosystem performance indicators. UCMPF dual logic enabled stakeholders to visualize interdependencies in whole system PM indicators and more coherently co-create performance indicators at all levels of YZ.&#x0D; &#x0D; &#x0D; Results: Exceeding 1,300 existing care/services indicators across disease condition-services or program, specialty-departments, institution-partners were discovered and systemically visualized using UCMPF in LLC and EC Indicator Menus, revealing how current care/services indicators impacted input, process, output and outcome of LLC and EC at different levels of the SDS. Numerous leadership conversations were conducted to envision future GLS activities using UCMPF, spawning many working level discussions, which continue to be ongoing between senior clinicians and administrators to prioritize indicators for PM. Technical definitions for prioritized indicators was also initiated to support informatics systems and enterprise data warehouse dashboard redesign. &#x0D; Discussion/Conclusion: UCMPF employs a systemic design and visualization approach to mitigate complexity in whole system PM. LLC and EC Menus reduce cognitive difficulty for stakeholders to relate their existing care/services measurement efforts with micro and mesosystem levels of the UCM and jumpstarts conversations whether existing care/services contribute or not towards YZ outcomes i.e. macrosystem. UCMPF therefore supports our ongoing care/services integration activity, and the co-creation of future GLS for YZ SDS is hypothesized to ultimately accelerate health system transformation towards better population health outcomes in YZ.&#x0D;

Creating environmental performance indicators to assess corporate sustainability and reward employees

Assessing environmental performance, measuring the circular economy and quantifying decarbonization have become crucial in green management in recent years. Both scholars and practitioners have focused on different strategies to create performance indicators capable of tackling all these aspects. At the same time, companies have started connecting managers’ and employees’ incentives to the achievement of corporate goals. We analysed a case study of an Italian food company which uses environmental performance indicators to assess corporate sustainability and reward managers and employees. Through a series of interviews with members from all departments, we identified around 200 different key performance indicators, i.e., performance, commitment, control, reactivity, economic, consent, transversal and multifunctional. A final dashboard of around 70 indicators was then created and linked to employees’ rewards. A tailor-made methodology was, thus, used to define target for the company’s management by objectives, taking into account the trend of each indicator. This is the first attempt to investigate how a company can create an environmental performance indicator system to assess corporate sustainability and reward employees through management by objectives. Our study further extends not only the academic literature, but also provides a practical and operative contribution to all the companies that aim to create a new key performance indicator system. Lastly, we merged the management and strategies for promoting green practices amongst employees through a reward system with the environmental performance of each organisational department by creating a methodology to define management by objectives.

EEG-based schizophrenia classification using penalized sequential dictionary learning in the context of mobile healthcare

Mobile healthcare is an appealing approach based on the Internet of Medical Things (IoMT) and cloud computing. It can lead to unobstructed, economical, and patient-centric healthcare solutions. The key performance indicators of such systems are dimensionality reduction, computational effectiveness, low latency, and accuracy. In this context, a novel approach is devised for EEG-based schizophrenia, a severe mental disorder that adversely affects a person’s behavior and classification. A multichannel EEG recording with suitable granularity is required for precise analysis. It can increase exponentially the data dimensionality plus complexity and computational load. The proposed solution attains an interesting trade-off between dimensionality reduction plus computational effectiveness versus accuracy. It uses the penalized sequential dictionary learning (PSDL) that incorporates channel selection. First, PSDL learns a dictionary from the input data and evaluates its performance on all EEG channels. Based on this evaluation, a subset of six channels is selected for further training in the dictionary. The proposed PSDL algorithm then incorporates a penalty term that enhances the power of the learned dictionary on the selected channels. We evaluate the proposed approach on the multi-channel EEG dataset from the Institute of Psychiatry and Neurology in Warsaw, Poland. A performance comparison is also made with counterparts. The models’ performance depends on the EEG signals’ complexity. Therefore, we tried to make our models robust and straightforward, achieving appropriate performance with minimal computational cost. The proposed method reduces the dimension in two steps. First, the count of channels is reduced to 68.42%. In the second step, the kept information, 31.58% of channels, is further reduced to 83.75% using dictionary learning. The proposed framework secures a remarkable data dimension reduction and a lower computational cost and latency than the counterparts while attaining the sparse representation classification accuracy of 89.12%. These findings are promising and confirm the potential of investing in incorporating the proposed method in contemporary mobile healthcare solutions.

Evaluating the digitalization capability and display of KPIs for two fertilizer plants of Petrovietnam

The study partially funded by the Vietnam Oil and Gas Group (Petrovietnam) assesses the infrastructure, OT/IT systems, and data handling at two fertilizer plants. Although well-equipped with automation control technology, the plants lack seamless integration, connectivity, and encounter limitations in communication between systems. To address these issues, the study advocates for the integration of digital technology, emphasizing its potential for enhancing efficiency, facilitating optimal management, and guiding strategic direction. Following an assessment of infrastructure, key performance indicator (KPI) system requirements, and data management, the study proposes an initial application of digital technology to link KPI displays. The outlined scope, initial costs, investment phases, and implementation schedule indicate a total initial investment of around VND 37 - 39 billion, accompanied by annual maintenance costs ranging from VND 3.7 to 4.1 billion. Expected economic and social benefits involve improved management at both Petrovietnam and plant levels through the KPI system, with an estimated energy cost savings of 1%, equivalent to roughly VND 44.6 billion. The project aims to position the fertilizer plants and Petrovietnam as leaders in technology, contributing to sustainability and enhancing their global industry reputation.

Energy, exergy, environmental, and economic (4E’s) analysis of a solar driven humidification-dehumidification desalination system: An experimental investigation

A solar-driven humidification-dehumidification (SDH-DH) desalination system has shown to be a highly efficient and viable solution to meet the freshwater needs of a community. However, the solar air heater based on flat plate collector, imposes constraints on their efficiency. The current work focuses on the development of an SDH-DH desalination system. This system is comprised of a solar air heater that utilizes a double-ends open evacuated tube collector, a humidifier equipped with cellulose pad packing material, and a dehumidifier based on a direct evaporative cooler. The current SDH-DH desalination system is operated in two modes, namely open loop and closed loop, at four distinct process air flow rates. The system's performance indices are evaluated based on energy, exergy, environmental and economic analysis. The solar air heater is capable of generating hot air at an approximate average temperature of 100 °C, while maintaining an airflow rate of 150 kg/h. This enhances the efficiency of the humidifier, resulting in a freshwater production rate of about 2.43 L/m2/day at a cost of 0.027 $/L. Moreover, the use of an evaporative cooler-based dehumidifier effectively mitigates the issue of fouling that arises during the condensation process. The system achieved the maximum mean energy efficiency of 25.39 % and exergy efficiency of 4.59 % in a closed loop configuration, with an air flow rate of 150 kg/h. The total CO2 reduction amounts to 119.08 tons during the designated duration.

Optimal management strategies of renewable energy systems with hyperexponential service provisioning: an economic investigation

The current research proposes optimal management strategies for queueing modeling-based renewable energy systems with hyper-exponentially distributed maintenance/repair under the assumption of an admission control policy. Using the concept and steps of the matrix-analytical method, the steady-state probability distribution associated with energy systems is explicitly presented. A relatively straightforward computation that can help with modeling wind energy generation, investigating wind farm performance, optimizing energy based on system storage, reliability inspection, service maintenance planning, and numerous other purposes can be employed to mathematically derive several system performance indicators. The investigation findings are validated via quantitative outcomes, illustrative possesses, and a step-by-step recursive methodology for efficient management of the renewable energy system. Additionally, considering multiple governing parameter values, the nature-inspired optimization technique, Cuckoo Search (CS), is employed to demonstrate the optimum anticipated cost of renewable energy system. A comparison with other metaheuristics and semi-classical approaches is also presented to establish the best convergence results. In order to help system designers, policymakers, engineers, and researchers, several numerical examples are also provided to construct more practical strategies based on the production of energy, storage, and system management. The economic, parametric, and performance investigation findings are highlighted, and the opportunities and recommendations for further research are provided. In a nutshell, the outcomes of the present analysis can be adopted to formulate the most effective economic strategies and regulate decision-making processes in the energy sectors.

Development of a system of key performance indicators in the management system of aviation training complex

The relevance of the research topic is due to the fact that Air Training Complexes (ATC) are systems of many interconnected business processes, where the violation of one of the processes can hinder the achievement of the company's goals. Modern approaches to managing the commercial activities of companies define key performance indicators (KPIs) as a system of indicators that assess the degree of achievement of certain business objectives. Our study of the commercial activity of AZAL "Flight Training Centre" shows that the system of key performance indicators is not used to assess the effectiveness of commercial activity, although the sphere of ATC services requires increased attention to the construction of effective management with the possibility of assessing the results. In addition, the authors propose the use of Dupond's analysis as a tool for comprehensive assessment of the efficiency of the commercial activity of the ATC in order to identify the weaknesses of the ATC and timely development of measures to improve and increase the efficiency of its commercial activity. The purpose of this study is to develop a system of KPI indicators for a comprehensive assessment of the effectiveness of the management of the commercial activities of aviation training complexes, taking into account the specifics of the airline and the main areas of activity, using the tools of strategic management and marketing. Research methods - in the process of research the authors used both empirical and theoretical research methods, qualitative methods of interpretation and explanation of the characteristics of the studied economic object, methods of system analysis and decomposition. Significance of the study - the theoretical and practical results obtained can be used by ATC management as a tool for assessing and improving the efficiency of business activities. Results of the research - as a result of the research conducted on the basis of the analysis of the commercial activity of AZAL "Flight Training Centre" ATC, a system of key performance indicators of commercial activity in the following main areas has been developed: financial, production, customer relations and development indicators.

Multiobjective Integrated Optimal Control for Nonlinear Systems.

The multiobjective optimal control method optimizes the performance indexes of nonlinear systems to obtain setpoints, and designs a controller to track the setpoints. However, the stepwise optimal control method that independently analyzes the optimization process may obtain unfeasible and difficult to track setpoints, which will reduce the operation and control performance of the systems. To solve this problem, a multiobjective integrated optimal control (MIOC) strategy is proposed for nonlinear systems in this article. The main contributions of MIOC are threefold. First, in the framework of multiobjective model predictive control, an integrated control structure with a comprehensive cost function and a collaborative optimization algorithm is designed to achieve the coordinate optimal control. Second, for the time inconformity of setpoints and control laws caused by the characteristic of tracking control, the different prediction horizons are designed for the comprehensive cost function. Then, the collaborative optimization algorithm is proposed for the comprehensive cost function to achieve the integrated solution of setpoints and control laws to enhance the operation and control performance of nonlinear systems. Third, the stability and control performance analysis of MIOC is provided. Finally, the proposed MIOC method is applied for a nonlinear system to demonstrate its effectiveness.

Performance-Driven Fault Detection for Uncertain Takagi–Sugeno Fuzzy Feedback Control Systems

This paper mainly studies the performance-driven fault detection for general uncertain Takagi-Sugeno fuzzy feedback control systems, where the infinite-horizon quadratic index is introduced to describe the concerned system performance. Different from the existing output-driven fault detection methods, the proposed approach aims to detect the performance degradations induced by faults. For this purpose, the performance index embedded with the information of system dynamics is reformulated for linear feedback control systems with reference inputs. The performance residual, which is adopted as the evaluation function, is derived based on the Bellman equation and further represented into an explainable form. Then, for uncertain linear feedback control systems, the boundaries of the performance residual are analyzed via the linear matrix inequality technique and an advantaged threshold setting scheme is proposed with the aid of randomized algorithm. Concerning the main objective, a novel approximation method which combines the fuzzy blend of local performance indices and the radial basis function neural network is developed to approximate the global performance index for Takagi-Sugeno fuzzy systems. Based on the result in linear case, the performance residual is constructed along the closed-loop system trajectory and the threshold is determined for the fuzzy control systems with reference inputs and model uncertainties. Finally, simulation studies are provided to show the effectiveness of the proposed theoretical results.

Performance Index Model of Main Structure (CASE STUDY IN IPDMIP)

Purpose: This research intends to build a performance index model of main stucture by case study in IPDMIP. Theoretical reference: It is important to evaluate the percentage weights of 6 (six) variables in the Irrigation System Performance Index (ISPI) assessment so that irrigation managers get assessment results close to actual conditions in the field, improving the quality of irrigation system management. Method: The methodology or the main structure model in this study was analyzed by using SEM-PLS software and GRG analysis. The selection of a research location in the Central Authority Irrigation Area was by considering the same data availability as previous research. In this research, the author used the Smart-PLS (Partial Least Square) application to analyze and reduce variables, which were then analyzed again using the GRG (Generalized Reduced Gradient) method to calculate non-linear equations. Result and Conclusion: The result shows that in this equation, there is only 1 (one) variable, 3 (three) dimensions, and 13 (thirteen) indicators analyzed. After analyzing with SEM PLS, followed by GRG (Generalized Reduced Gradient) analysis with the solver in Microsoft Excel, changes in variable weights occur, so the new model equation for the main structure performance index (Y) is 0.348, with structure body dimensions of + 0.492 and door &amp; gear to operate of +0.160. Mud Bags and drain doors apply to the Central Authority. Implication of research: Based on the results of previous research, where the assessment weight of the variable of the main irrigation network (with a weight of 0.744) is greater than the tertiary irrigation network (with a weight of 0.256). Originality/ value: the role of the main irrigation network is more dominant in channeling water from the intake to the tertiary irrigation network. However, previous research has not been completed since the main structure still has a level below it and has not been included in the scope of previous research.

The Application of Whale Optimization Algorithm in Array Antennas

Abstract With the continuous improvement of various radio system performance indicators, the research work on antenna has become particularly important. According to different scenarios and requirements, practical projects also need the corresponding antennas to produce different radiation patterns. By reasonably setting the parameters of the array antenna, the target radiation pattern can be obtained to meet real life applications. When the array antenna has a large number of basic units and the expected far-field pattern is complicated, the design of the array antenna becomes a complicated optimization problem. To solve this problem, Whale Optimization Algorithm (WOA) is proposed. WOA is not only simple and fast, but can also get the global optimal solution. Therefore, WOA has developed rapidly in recent years. However, the application of this algorithm in the field of antenna design is still relatively rare, thus using WOA to solve the optimization problem of array antenna design is very valuable.

Effects of Aging on Taxi Service Performance: A Comparative Study Based on Different Age Groups

Rapid population aging has significantly impacted labor supply and posed substantial challenges for the taxi industry, which have not yet been fully comprehended. Here, for the first time, we employ a large-scale dataset of taxi driver operations from China, establishing a comprehensive indicator system of taxi service performance, encompassing economic, environmental, and safety aspects. Through the application of multivariate regression models and other statistical analysis techniques, we have thoroughly investigated the mechanisms through which aging influences taxi service performance. Our research reveals that older drivers, despite exhibiting higher operational efficiency and greater inclination towards stability, underperform in time efficiency metrics, such as income per hour worked, owing to a more conservative working style. Furthermore, aging manifests negative effects on safety and environmental performance. Adjusting the driving strategies of older taxi drivers, such as regulating daily working hours and refining passenger-searching area preferences, can help mitigate these adverse impacts.

Numerical investigation of the effect of nanoparticle aggregation on the performance of concentrated photovoltaic-solar thermal collectors with spectral filtering

Solar energy, particularly solar thermal technology, has gained popularity as a possible long-term replacement to fossil fuels. The application of concentrated photovoltaic-solar thermal (CPV/T) collectors, which are improved by spectral filter fluids (SFF) and nanotechnology, has the potential to provide both higher thermal power for heating and cooling as well as improved electrical power generation. This work contributes new insight by quantifying the influence of nanoparticle agglomeration on collector performance and highlighting the challenges associated with the heterogeneous distribution of nanoparticles in CPV/T systems. The study employed coupled Eulerian multiphase modeling and discrete ordinate (DO) radiation modeling to examine slip velocity, nanoparticle diameter (including agglomeration), and suspension concentration. Population balance modeling (PBM) was utilized to determine the nanoparticle size distribution, and the obtained results were validated through comparison with experimental and numerical studies. When neglecting the effect of agglomeration and breakage of the non-solar participating media, the maximum error for this configuration was found to be 3.58% when compared to experimental work. From the solar participating study, in terms of electrical energy production, the best performance obtained was 16.64% with a volume fraction of 0.005% when considering agglomeration and breakage. It was also found that the Sauter diameter increases with volume fraction as the tendency for nanoparticle agglomeration increases. This study provides a broader view of the application of multiphase modeling in solar participating and non-solar participating media and, additionally, provides insight on the effect of various boundary conditions on the key system performance indicators. To extend this work, the flow Reynolds number can be increased in addition to varying the type of working fluid.

Adaptive event‐triggered fault detection for Markov jump nonlinear systems with time delays and uncertain parameters

AbstractThis article proposes a fault detection (FD) method for a class of Markov jump nonlinear systems with time delays and uncertain parameters. The FD problem is transformed into a filtering question by designing a FD filter that satisfies appropriate performance index. An adaptive event‐triggered mechanism is introduced to conserve network resources, and the impact of time‐varying and network‐induced delay on the system is fully considered. Then, stochastical stability and appropriated performance index of the residual system are guaranteed using the Lyapunov–Krasovskii functional theory, discrete Wirtinger inequality and extended reciprocally convex matrix inequality. Finally, a simulation example verifies the feasibility and effectiveness of the designed FD filter.

Remaining useful life prediction for motor systems by iteratively updated wiener process based on closed-loop performance degradation monitoring

Driven by the need for remaining useful life prediction of degraded motor systems with feedback controllers, a real-time updated Wiener stochastic process is adopted to model the performance degradation of motor systems. First, a closed-loop performance index of the motor system is derived incorporating the multiple slow time-varying characteristic of motor parameters. On this basis, the drift coefficient and diffusion coefficient of the Wiener degradation model are updated to obtain the prior maximum likelihood function with available historical data. It is followed by the iterative optimization of nonlinear feature parameter in the Wiener degradation model with taking the prior maximum likelihood function as the cost equation. The effectiveness of proposed remaining useful life prediction architecture for closed-loop motor systems is demonstrated by motor systems.