System Parameters Research Articles

Bio inspired technique for controlling angle of attack of aircraft

<p>This paper deals with the design of a proportional–integral (PI) controller for controlling the angle of attack of flight control system. For the first time teaching learning-based optimization (TLBO) algorithm is applied in this area to obtain the parameters of the proposed PI controller. The design problem is formulated as an optimization problem and TLBO is employed to optimize the parameters of the PI controller. The superiority of proposed approach is demonstrated by comparing the results with that of the conventional methods like genetic algorithm (GA) and particle swarm optimization (PSO). It is observed that TLBO optimized PI controller gives better dynamic performance in terms of settling time, overshoot, and undershoot as compared to GA and PSO based PI controllers. The various performance indices like mean square error (MSE), integral absolute error (IAE), and integral time absolute error (ITAE) are improved by using the TLBO soft computing techniques. Further, robustness of the system is studied by varying all the system parameters from −50% to +50% in step of 25%. Analysis also reveals that TLBO optimized PI controller gains are quite robust and need not be reset for wide variation in system parameters.</p>

Optimizing active chilled beam systems in the South Korean Climate: Comparative analysis of cooling energy and thermal comfort by set-point temperature

In this study, the feasibility of implementing the active chilled beam system (ACBS) in South Korea has been analyzed, and an appropriate control strategy has been proposed. While ACBS is increasingly adopted in North America and Europe, its spread in South Korea has been slow due to different climatic characteristics and a lack of diverse understanding and analysis. Furthermore, research discussing the effects of key system parameters on ACBS is notably scarce. Therefore, this study aims to assess the impact of air handling unit discharge air temperature (AHU DAT) and ACBS entering chilled water temperature (ACBS ECWT) on ACBS and propose a control strategy suitable for the South Korean climate. The applicability of ACBS, compared with existing HVAC systems using this control strategy, has been evaluated. The study incorporates two methodologies: firstly, the impact of control variables has been assessed, and a suitable control strategy has been proposed through a sensitivity analysis of these variables. Secondly, the feasibility of applying ACBS, in comparison with conventional HVAC systems, has been evaluated from the perspectives of energy and thermal comfort. It has been found that system parameters, tailored for the South Korean climate, can maintain a level of thermal comfort like existing HVAC systems while achieving up to 18% in energy savings. Overall, this research not only demonstrates the potential applicability of ACBS in hot and humid climates like South Korea but also emphasizes the need for controlling key system parameters

Bifurcation and multi-stability analysis of microwave engineering systems: Insights from the Burger–Fisher equation

The present article depicts the research being done on the microwave effect realized by the often-used Burger-Fisher equation. A particular view is on the emergence of multi-stability and the bifurcations associated with microwave engineering systems. A novel rational, trigonometric, and hyperbolic form of the traveling waves is furnished by the resolution of the non-linear issue with the aid of the advanced exp (−Ψ(η))-expansion function parameterization. Firstly, we will solve the exact form of the Burger-Fisher equation, which clarifies the behavior of the equation in different conditions. Subsequently, bifurcation analysis techniques will be employed to study the nuanced relationship between the system parameters and the emergence of multistability phenomena. Through our results, we exposed the complicated essential features of microwave physics and established crucial parameters that determine a system’s behavior. Next, we cover control principle issues and practical applications in microwave engineering problems. This model accommodates the essential features of multi-stable dynamic systems and provides an important framework for creating microwave devices and circuits.The main aim of this research work is to analyze the phenomenon of multi-stability and bifurcation in microwave engineering systems by applying Burger-Fisher equation. The study intends to obtain new solutions to the PDEs through the application of a new method, the exp (−Ψ(η))-expansion function method that uses rational, trigonometric, and hyperbolic traveling wave solutions. Existing research of the Burger-Fisher equation is not explicit and by solving the exact form the research aims at exploring the different conditions under which the equation behaves and studying the delicate interactions between the parameters of the multistable system.

Investigation of nitrogen conversion in ditch-treated greywater: Contributions of biochar and aeration

This study aimed to assess the contribution of different combinations of uninterrupted aeration and tidal flow modes, as well as shallow and deep biochar landfill methods, to nitrogen removal pathways in ditches. Compared to natural ditches, the addition of biochar and aeration resulted in higher water pollution removal rates, lower Greenhouse Gas (GHG) emissions, and reduced sediment leaching. The nitrogen mass balance revealed that Total Nitrogen removal efficiency of each ditch group was 25.33% for CA-C, 43.75% for DBT-C, 49.08% for UBT-C, 30.75% for DBA-C, and 45.16% for UBA-C. Compared to the control group (CA-C), the tidal flow group with deep biochar landfill (DBT-C)demonstrated the highest efficiency in total nitrogen removal. To elucidate the fate of nitrogen elements, potential transformation processes of unexplained nitrogen were explored. The primary outflow pathway of nitrogen pollution in ditches is through liquids. The nitrogen composition in the liquid of each ditch indicated that for shallow biochar configurations (UBT-C/UBA-C), denitrification might be the main factor limiting TN removal efficiency. In contrast, for deep biochar configurations (DBT-C/DBA-C), hydrolysis and nitrification of organic nitrogen were identified as limiting factors, with tidal flow alleviating these limitations. Overall, the combination of shallow biochar and tidal flow aeration in a ditch is the most effective method for removing rural greywater, providing valuable insights for the development of design parameters in greywater removal systems.

A novel design approach for structures with inerter systems based on non-stationary seismic excitations

Existing research predominantly focuses on developing design methods for the structural configuration of inerter systems. However, the authors find that these optimization methods often overlook the non-stationary characteristics associated with seismic response. Additionally, traditional approximation algorithms yield significant errors in non-stationary processes, resulting in inaccurate predictions of the structural responses with inerter systems, which compromises the effectiveness of the optimization designs. This paper proposes a novel approach that combines the parameters of the inerter system with a modified Vanmarcke approximation to obtain unified calculation formulas for inerter parameters. The accuracy and computational efficiency in solving spectral moments are crucial for the feasibility of this method. The study presents an efficient calculation method to accurately determine non-stationary response spectral moments of structures with inerter systems. Using the complex modal method (CMM) and pseudo-excitation method (PEM), a unified solution for structural responses under seismic actions is obtained. The quadratic decomposition of the power spectral density function (QD-PSDF) is employed to derive analytical solutions for response spectral moments and non-geometric spectral characteristics (NGSC). Substituting the obtained moment values into the parameter calculation formulas allows effective determination of the inerter system parameters. Finally, Finite Element Method (FEM) modeling verifies the validity and general applicability of this approach. The results demonstrate that the proposed method accounts for non-stationary characteristics of structures with inerter systems under seismic excitations, enhancing seismic design reliability and structural safety. Moreover, the obtained inerter system parameters at each floor effectively achieve the target performance while providing a broader range of design options for enhanced flexibility in system configuration.

Effect of AP and AN on the combustion and injection performance of Al-H2O gelled propellant

Aluminum-water propellants (Al-H2O propellants), representing a novel class of solid propellants, demonstrate the merits of cost efficiency and reduced feature signal characteristics. However, the conventional formulations of Al-H2O propellants are hampered by the generation of substantial condensed residues. In our investigation, we explored the incorporation of oxidizers into the Al-H2O propellant grain, aiming to enhance combustion and injection performance. Employing a multifaceted experimental approach, we conducted thermal gravimetric analysis, laser ignition experiments, and ignition tests within a lab-scale solid rocket motor (SRM) firing to systematically examine the effects of varying content of ammonium perchlorate (AP) and ammonium nitrate (AN) on the combustion and injection performance of Al-H2O propellants. Our findings indicated that integrating AP and AN at a mass fraction of 3 % each notably curtailed ignition delay time by approximately 67 % and 90 %, respectively, and concurrently decreased burning rates by approximately 50 % and 58 %. Significantly, it has been observed that a composition incorporating a 5 % mass fraction of AP enhances the combustion efficiency of the Al-H2O propellant system by approximately 2 %. Conversely, the integration of a 5 % mass fraction of AN into the same propellant matrix results in an augmentation of the injection efficiency by an estimated 47 %. Empirical evidence validating the augmentative impacts of AP and AN on the performance of Al-H2O propellants has been substantiated through a series of motor hot firing experiments. Furthermore, the combustion behavior of Al-H2O propellants has been elucidated through an analysis of the combustion physical mechanism of Al particles. The thermal decomposition of AP yields a substantial volume of oxidizing gases, which effectively accelerates the combustion rate of the Al particles, subsequently leading to an enhancement in the overall combustion efficiency of the propellant. Conversely, the decomposition of AN results in an increased production of nitrogen gas, thereby augmenting the velocity of gas flow and, consequently, elevating the injection efficiency of the propellant. This finding holds promise for guiding the developmental trajectory of Al-H2O propellants and refining the design parameters of propulsion systems.

Analytical method for random seismic response of structures with tuned parallel inerter mass system and optimal parameters based on reliability

Analytical method for random seismic response of structures with tuned parallel inerter mass system and optimal parameters based on reliability

A ranking improved teaching-learning-based optimization algorithm for parameters identification of photovoltaic models

Solar energy is an important clean energy source, primarily applied for photovoltaic (PV) power generation. The precise identification of PV system parameters is critical for system control and simulation, posing a challenge due to the models' non-linearity, implicitness, and multiple optimal properties. So, a ranking improved teaching-learning-based optimization (RITLBO) is developed in this work to solve the problem of identifying the parameters of the PV model. RITLBO is a meta-heuristic algorithm based on teaching-learning-based optimization (TLBO) that simulates classroom teacher-student interaction. In RITLBO, learners are classified into inferior and superior groups based on their fitness ranking. During the teacher phase, superior learners emulate the top three agents with the highest fitness for local search, while inferior learners engage in guided mutual learning for global search, effectively utilizing computing resources. In the learner phase, superior learners receive guided information, while inferior learners engage in broader information exchange, balancing exploration and exploitation. RITLBO and fourteen algorithms are used to identify the parameters for five different PV models to confirm that the RITLBO is effective. Statistical results and analysis demonstrate that RITLBO is accurate and reliable in identifying PV model parameters. RITLBO offers promising prospects in optimizing PV system parameters through its unique strategies.

Integrating Solar Induced Fluorescence with High Throughput Plant Screening for Advanced Phenotyping of plants

There is an urgent need to address the escalating impacts of climate change, particularly the exacerbation of drought conditions, which pose significant threats to global food security and agricultural sustainability. Innovative solutions are imperative, and one such solution involves integrating advanced technologies like the "PlantArray" system to monitor and enhance plant physiological responses to water scarcity. The "PlantArray" system enables the precise measurement of critical whole-plant physiological traits such as transpiration rate, canopy stomatal conductance (Gs canopy), and growth rate with an exceptional spatiotemporal resolution. Augmenting this system with photosynthesis measurements offers an additional layer of information, facilitating a more focused interpretation of the system parameters. To overcome the limitations of single-leaf photosynthesis measurement techniques, this study employs a remote sensing approach to rapidly scan numerous samples at multiple time points, revealing insights into drought stress responses of S. licopersicum lines. An ultra-spectral spectroradiometer mounted on a mobile cart was positioned above an experimental matrix comprising drought-stressed S. licopersicum obsolete and mutagenic lines. Our findings reveal that the vegetation index PRI exhibited greater sensitivity to drought stress compared to other vegetation and photosynthesis remote sensing indices. Photosynthesis indices demonstrated increased sensitivity to daily biomass accumulation and served as predictors of final plant yield. Interestingly, Solar-Induced Fluorescence (SIF) parameters, solely indicative of photosynthesis-emitted fluorescence, exhibited no correlation with stress levels or final biomass production. This study articulates the potential to monitor plant responses to agricultural stressors through real-time physiological tracking across complete diel cycles, thereby enriching our understanding of plant-environment interactions. Ultimately, this integrated system shows promise in screening and developing crop cultivars with ideal physiological and photosynthetic traits, vital to cultivating resilient crops in extreme droughts and weather conditions.

Power quality enhancement using fully informed particle swarm optimization based DSTATCOM in distribution systems

To compensate for the reactive power, inverter-based conditioners have been utilized in recent years due to their faster response. Distribution static synchronous compensator (DSTATCOM) has been utilized to enhance power quality in power system that is an inverter-based device that is widely utilized. To control this type of equipment, a proportional integrated (PI) controller has been utilized to control most of the equipment with respect to certain parameters. The performance of the controller basically does not meet the expectations because of the dynamics and nonlinearity of a system parameters. In this present paper, a probabilistic neural network has been used in a controller with a fully informed particle swarm optimization (FIPSO) algorithm to generate a suitable weight for controlling the axes of various parameters of DSTATCOMs. Using MATLAB/Simulink software, simulations were performed, and the responses were monitored with particular regard to the reference reactive parameter. The results are compared. DSTATCOM improves power system damping.

Process Simulation, Thermodynamic and System Optimization for the Low-Carbon Alcohols Production via Gasification of Second-Generation Biomass

Biomass-to-liquid fuels technology offers a promising method for high-value biomass conversion, addressing environmental toxicity and fossil fuel non-renewability. However, challenges such as low system efficiency and identifying efficiency losses persist. In this study, a comprehensive process model of a low-carbon alcohols production system via biomass gasification was developed, based on the first demonstration project in China. The innovation of this study lies in its detailed experimental validation, as the model simulation was performed using laboratory data and verified with pilot-scale platform data ensuring high accuracy. Additionally, the study conducted a thorough sensitivity analysis of system parameters, energy, and exergy assessments to find the proper operating conditions, including equivalent ratio, biomass type, and reactor temperature and pressure. The simulation results demonstrated an energy efficiency of 34.67% and an exergy efficiency of 31.24%. Through operating parameters and heat recovery measures, these efficiencies increased by 11.66% and 8%, respectively. This research not only obtains improved operating parameters for the pilot-scale platform but also provides actionable insights for enhancing the yields of target products and upgrading low-grade energy utilization. These findings offer valuable guidance for the commercialization of bio-syngas alcohols production systems, highlighting significant advancements in efficiency and system performance.

Combined tuned mass damper and vibro-impacting nonlinear energy sink for the seismic protection of frame structures

It is well established that, for tuned mass dampers, a high mass ratio relative to the protected structure is essential for effective performance under seismic excitation in most scenarios. This paper proposes a novel method to enhance the seismic effectiveness of a tuned mass damper without increasing the mass ratio. The proposed approach introduces an innovative device in which a tuned mass damper is serially coupled with a vibro-impacting mass that slides with negligible friction before impacting the boundaries of its runway. This system is then connected to the first storey of the frame structure via a Kelvin-Voigt visco-elastic device. The study employs a dynamically equivalent four-degree-of-freedom model, with its governing equations derived through a direct formulation approach. Initially, the coupled system is subjected to harmonic excitation, and the resulting behaviour is represented using frequency-response curves, which depict the maximum displacements of the structure in relation to the excitation frequency. Following this, a subsequent analysis involves subjecting the system to three distinct earthquake records to evaluate its performance under seismic excitation. To evaluate the effectiveness of the proposed protective device in reducing the displacements of the frame structure during seismic events, the displacements of the first storey and the drifts of the superstructure are compared to those of a frame structure without the external device. The findings indicate that the proposed device performs effectively across a wide range of system parameters, proving to be especially effective for low- and medium-rise frame structures.

Thermoelectric cooling of a finite reservoir coupled to a quantum dot

We investigate nonequilibrium transport of charge and heat through an interacting quantum dot coupled to a finite electron reservoir. Both the quantum dot and the finite reservoir are coupled to conventional electric contacts, i.e., infinite electron reservoirs, between which a bias voltage can be applied. We develop a phenomenological description of the system, combining a rate equation for transport through the quantum dot with standard expressions for bulk transport between the finite and infinite reservoirs. The finite reservoir is assumed to be in a quasiequilibrium state with a time-dependent chemical potential and temperature which we solve for self-consistently. We show that the finite reservoir can have a large impact on the stationary state transport properties, including a shift and broadening of the Coulomb diamond edges. We also demonstrate that there is a region around the conductance lines where a heat current flows out of the finite reservoir. Our results reveal the dependence of the temperature that can be reached by this thermoelectric cooling on the system parameters, in particular the coupling between the finite and infinite reservoirs and additional heat currents induced by electron-phonon couplings, and can thus serve as a guide for experiments on quantum-dot-enabled thermoelectric cooling of finite electron reservoirs. Finally, we study the full dynamics of the system, with a particular focus on the timescales involved in the thermoelectric cooling. Published by the American Physical Society 2024

Juvenile and total reproductive values for sexual reproduction under any genetic system

Abstract Reproductive value (RV) is the expected contribution of genes by an individual or class of individuals to the gene pool in the distant future, and it plays a crucial role in understanding adaptation in long-term evolution. Class RVs are linked to the genetic system and to life history: for diploid genetics, Grafen derived expressions for relative RVs of female and male juveniles, and for the absolute RVs of all females and of all males. Subsequently, Gardner presented a derivation for relative RVs of juvenile females and males under haplodiploidy. Here we generalise these results to any genetic system for biparental sexual reproduction, such that RV is explicitly linked to parameters of the genetic system and life history. The earlier results by Grafen and Gardner arise as special cases. Finally, we derive expressions for absolute juvenile and total reproductive values of both sexes under any genetic system.

Adaptive load balancing strategies in service composition for improved system performance

Service composition enables the integration of multiple services to create new functionalities, optimizing resource utilization and supporting diverse applications in critical domains such as safety-critical systems, telecommunications, and business operations. This paper addresses the challenges in comparing load-balancing algorithms within service composition environments and proposes a novel dynamic load-balancing algorithm designed specifically for these systems. The proposed algorithm aims to improve response times, enhance system efficiency, and optimize overall performance. Through a simulated service composition environment, the algorithm was validated, demonstrating its effectiveness in managing the computational load of a BMI calculator web service. This dynamic algorithm provides real-time monitoring of critical system parameters and supports system optimization. In future work, the algorithm will be refined and tested across a broader range of scenarios to further evaluate its scalability and adaptability. By bridging theoretical insights with practical applications, this research contributes to the advancement of dynamic load balancing in service composition, offering practical implications for high-tech system performance.

The Integrative Single-Case Design as a Biosemiotic-Systemic Research Tool in Psychoneuroimmunology.

Psychoneuroimmunology (PNI) is a field of research that deals with the interactions between psyche, nervous, endocrine, and immune systems. Investigating these complex PNI relationships under asecologically validas possible conditions ("life as it is lived") necessitates a paradigm change in research. This shift placesfactors such as "time" and "subjective meaning" of personal experiences at the center of the research methodology. For this purpose, the biosemiotic-systemic research design "Integrative Single-Case Study" was developed. Initial results from healthy individuals as well as patients with systemic lupus erythematosus (SLE), breast cancer, and rheumatoid arthritis(RA) support the validity of the research approach. Specifically, in connection with the occurrence ofemotionally meaningful everyday incidents, we repeatedlyobserved stress system reactions, which were(1) delayed over several days, (2) cyclically patterned, (3) anticipatory, and (4) opposing, depending on a)whether participants experienced emotionally positive or negative everyday incidents, b)whether they were healthy or ill, and c)which stress system parameter was investigated. In this chapter, the Integrative Single-Case Study design is introduced as a holistic research option, presented in detail in its methodology, and critically discussed in terms of its limitations.

PID control algorithm based on multistrategy enhanced dung beetle optimizer and back propagation neural network for DC motor control.

Traditional Proportional-Integral-Derivative (PID) control systems often encounter challenges related to nonlinearity and time-variability. Original dung beetle optimizer (DBO) offers fast convergence and strong local exploitation capabilities. However, they are limited by poor exploration capabilities, imbalance between exploration and exploitation phases, and insufficient precision in global search. This paper proposes a novel adaptive PID control algorithm based on enhanced dung beetle optimizer (EDBO) and back propagation neural network (BPNN). Firstly, the diversity of exploration is increased by incorporating a merit-oriented mechanism into the rolling behavior. Then, a sine learning factor is introduced to balance the global exploration and local exploitation capabilities. Additionally, a dynamic spiral search strategy and adaptive [Formula: see text]-distribution disturbance are presented to enhance search precision and global search capability. The BPNN is employed to fine-tune both PID and network parameters, leveraging its powerful generalization and learning ability to model nonlinear system dynamics. In the simplified motor experiments, the proposed controller achieved the lowest overshoot (0.5%) and the shortest response time (0.012s), with a settling time of 0.02s and a steady-state error of just 0.0010. In another set of experiments, the proposed controller recorded an overshoot and response time of 0.7% and 0.0010s, across five DC motor tests. These results demonstrate the proposed adaptive PID control algorithm has superior performance in optimizing control system parameters, as well as improving system robustness and stability.

Unfolding Electrolyzer Characteristics to Reveal Solar-to-Chemical Efficiency Potential: Rapid Analysis Method Bridging Electrochemistry and Photovoltaics.

Development of photovoltaic-electrochemical (PV-EC) systems for energy storage and industry decarbonization requires multidisciplinary collaborative efforts of different research groups from both photovoltaic and electrochemical research communities. Consequently, the evaluation of the solar-to-chemical or solar-to-fuel efficiency of a new electrolyzer (EC) as a part of a PV-EC system is a time-consuming task that is challenging in a routine optimization loop. To address this issue, a new rapid assessment method is proposed. This method employs power balance requirements to unfold the input EC characteristics into the parameter space of PV-EC systems. The system parameters, composed with the EC output characteristics, yield the solar-to-chemical efficiency attainable by the electrolyzer in combination with any PV device under any irradiance at any relative PV-to-EC scaling and any mode of power coupling. This comprehensive overview is achieved via a mathematically simple conversion of the EC characteristics in any spreadsheet software. The method, designed to streamline the development and minimize the efforts of both the photovoltaic and electrochemical communities, is demonstrated via the analysis of CO2-reduction electrolyzer characteristics and verified with dedicated PV-EC experiments.

Preventing malware propagation in wireless sensor networks: Hybrid optimization algorithm for controlling

Malware transmission is a significant security issue in WSN, however, the influence of the attack and defensive processes on malware propagation is rarely taken into account in traditional malware propagation prevention methods. Advanced methods are in need to stop the propagation of malware of sensor nodes. With the formulation of representing dynamics among states, a new decision-making problem as the optimal control problem via hybrid optimization algorithm. The proposing model is termed as Butterfly Updated Bald Eagle Optimization based Prevention of Malware Propagation in Wireless Sensor Network (BUBEO-PMPWSN). In the proposed controlling system, optimal system parameters are analyzed via the BUBEO for preventing malware propagation in WSN. Particularly, the sensor node states considered are Susceptible, Infectious, Infectious and sleeping, recovered, Recovered and sleeping, and finally Dead. The system parameter tuning will be under the evaluation of fitness calculation under probability of infectious sensor node becoming recovered and the probability of infectious sensor node entering sleeping state. This optimal tuning strategy ensures the preventing of malware propagation. Finally, the performance of proposed BUBEO-PMPWSN model is evaluated and validated successfully by comparing other state-of-the-art models. The BUBEO-PMPWSN achieved 250 recovered nodes for time 500, while the HGS, BOA, HBA, COOT, and HHO scored 123, 115, 236, 172, and 180, respectively, for recovered nodes.

Algorithm for monitoring water quality parameters in optical systems based on artificial intelligence data mining.

Due to the increasingly serious water environment pollution, the difficulty of Water Quality Monitoring (abbreviated as WQM for convenience) is also constantly increasing, which puts forward more requirements for the capabilities of various aspects of WQM systems. However, the current WQM method has drawbacks such as slow speed, long monitoring time, complex operation, poor stability, and the inability to obtain accurate information on water pollution in the first time, as well as the generation of toxic and harmful secondary pollutants after some measurement parameters are tested. To address these issues and ensure water quality safety, this paper investigated the algorithm for monitoring water quality parameters using artificial intelligence data mining optical systems. This article applied an artificial intelligence data mining system to detect water quality and designed various system through this method to improve system performance. To verify the actual effectiveness of artificial intelligence data mining systems, this article selected 10 water plants as experimental research subjects and compared the differences between traditional WQM methods and WQM methods based on artificial intelligence data mining systems in terms of WQM time, accuracy, sensitivity, and protective performance. The experimental results showed that the optical system based on artificial intelligence data mining took an average of 2.7 days in WQM and the average accuracy was 85.95%. The average sensitivity value was 84.19% and the average protective score was 8.46 points. This indicated that artificial intelligence data mining optical technology had vital significance and value for WQM.