Behavior Of System Research Articles

Novel adaptive control approach to fractal fractional order deforestation model and its impact on soil erosion.

Deforestation exerts profound ramifications on soil quality and biodiversity, thereby exerting substantial economic repercussions. The depletion of organic matter and structural integrity of soil following tree removal for agricultural purposes underscores the severity of this issue. In elucidating the soil pollution stemming from deforestation, this research employs a sophisticated five-compartment SDIFR model integrating fractal dimension and fractional order dynamics. The rigorous analysis, including the application of Picard Lindelof's fixed point theorem, establishes the existence and uniqueness of explicit solutions. Furthermore, the examination of local and global stability sheds light on the system's behavior, delineating between pollution-free equilibrium and pollution-extinct equilibrium states. To regulate system behavior, an adaptive control framework grounded in fractal fractional order is proposed, leveraging the Adams-Bashforth numerical approximation scheme for implementation. Through numerical simulations, the study underscores the pivotal role of parameters, thus substantiating the significance of the proposed model in comprehensively addressing the complexities of soil pollution induced by deforestation.

A model for scheduling the resource deployment in a multi-stage ramp-up production system

Rapid and continuous changes in customer product requirements affect demand, requiring the supply chain to be more responsive. A new product ramp-up is integral to responsiveness, where it is paramount to implement it successfully and manage it effectively. A smooth ramp-up process minimises problems and delays, leading to lower costs and higher profitability. This study develops and analyses a model that describes a multi-stage ramp-up production system to identify the most cost-effective policy for controlling multiple ramp-ups. We propose a search-based optimisation approach to solve the problem. Through numerical analyses, we develop a decision framework to classify the patterns of resource deployment and planning, aiming to make the ramp-up process efficient and responsive to increasing demand. Additionally, we conduct sensitivity analyses to examine how variations in input parameters affect system behaviour. Our findings offer managerial implications and insights based on the numerical results.

Time Domain Design of a Marine Target Tracking System Accounting for Environmental Disturbances

Environmental disturbances represent significant challenges to the performance and accuracy of autonomous systems, especially in marine environments, where their impact varies based on disturbance severity and the employed guidance law. This paper comprehensively investigates a marine target tracking system using time-domain simulations incorporating realistic environmental disturbances. Three guidance laws and four key performance indicators are analysed to evaluate system performance under disturbed and ideal conditions. A robust and systematic evaluation pipeline is developed and applied to a case study featuring a scaled tugboat model. This approach provides a reliable method to assess tracking accuracy and robustness in adverse conditions. The results are selected from a wide range of possibilities to show the effect of the disturbances on the selected target tracking motion control scenario with two manoeuvres and two environmental conditions. The results are measured through the selected key performance indicators, and several phases are identified for each manoeuvre to extend the analysis not only to the global KPI values but also to the partial values of defined phases. They reveal the quantitative effects of environmental disturbances, exposing different system behaviours and trends. These findings demonstrate the effectiveness of the proposed pipeline in quantifying tracking system performance, delivering useful understandings of the system under environmental disturbances. The broader implications of this study are substantial, offering enhanced predictive accuracy for the performance of the analysed systems, particularly in the context of target tracking. Furthermore, introducing numerical key performance indicators facilitates a more rigorous comparison of different system characteristics, enabling informed decisions in designing and optimising autonomous operations in challenging environments.

Exploring dynamics, stability, and bifurcation in a coupled damped oscillator with piezoelectric energy harvester

In this work, we analyze and investigate the mathematical modeling of a dynamical system connected to a piezoelectric device. Piezoelectric transducers are established as highly effective energy harvesting (EH) devices, often employed in real-world mechanical systems. The structure of the dynamical model contains a damped Duffing oscillator acting as the major component, which is connected to an unstretched pendulum and simultaneously to the piezoelectric harvester. To derive the governing equations of motion (EOM), Lagrange’s equations are applied based on the system’s generalized coordinates, in which they are analytically solved using the multiple scales (MS) perturbation method up to the second approximation. Moreover, the solutions achieved are compared with numerical ones to increase transparency and demonstrate the accuracy of the approximate solutions. In addition, comprehensive graphical representations have been produced to investigate the nonlinear stability of the modulation equations. Phase portraits, bifurcation diagrams, and Lyapunov spectra are displayed to depict various system behaviors, alongside Poincaré maps for deeper understanding. The various stability ranges are also explored and discussed. In conclusion, mechanical vibrations are transformed into electricity by the piezoelectric transducer connected to the dynamic model, which has widespread applications and includes crystal oscillators, medical ultrasound, gas igniters, and displacement transducers.

Characterizing the interaction effects of modular components on transtibial prosthesis stance-phase mechanical behavior

Background: Despite evidence that passive prosthesis mechanical properties can directly affect user experience, prosthetists have access to minimal information regarding the mechanical interactions between a prosthetic foot and proximal modular componentry. Objectives: This study quantified the stance phase mechanical behavior of a transtibial prosthetic system through the addition of passive modular componentry to a dynamic response (DR) foot. Study Design: Repeated measures, mechanical characterization. Methods: Maximum displacement and energy return were measured with a materials test machine simulating initial, mid, and terminal stances. Twelve conditions were tested: a DR foot in combination with a hydraulic ankle at 2 resistance settings and 3 different shock-absorbing pylons (SAPs). The roll-over shape of the DR foot with and without hydraulic ankle was measured using a test rig. Results: Adding modular passive components altered displacement and energy return, displaying independent and interaction effects. Generally, the hydraulic ankle and SAP reduced energy return (up to 18%) but decreased (up to 51%) and increased (up to 88%) displacement, respectively, while the combined properties were more complex. Roll-over shape radii decreased with increasing load for the foot alone but exhibited a nonlinear response with the addition of the ankle. Conclusions: Inclusion of modular components in a transtibial prosthetic system can have complex mechanical interactions that independently affect the system's response to load. It is important for clinicians to be aware of the cumulative effects of these interactions to inform the tuning of transtibial prosthesis mechanical behavior. Combinations of hydraulic ankles and SAPs can help clinicians adjust the prosthesis to achieve a balance between user comfort and energy return.

Multilevel modeling and control of dynamic systems.

The underlying changes of complex dynamic systems are affected by numerous highly interdependent components with nonlinear interactions and cannot be suitably predicted by merely analyzing their individual parts. Recent technological developments (reducing the size of actuators and sensors, increasing speed and productivity) make it possible to monitor and handle systems on time, even during transient processes. In many practical applications, to effectively study rapidly evolving systems, their structure has to be examined at three levels: Micro-Scale (Short-Term), Meso-Scale (Intermediate-Term), and Macro-Scale (Long-Term). This multiscale framework provides a powerful tool for understanding and managing the intricate dynamics of complex systems. Macro-scale and micro-scale approaches are conventionally utilized in general system modeling and control theory, especially within the fields of networked control and multi-agent systems. By leveraging the meso-scale perspective, it is possible to balance detailed component analysis with overarching system behaviors, thereby enhancing the efficiency and effectiveness of operational strategies in complex systems. This paper presents and discusses a formal meso-level depiction of dynamics and control, assuming a finite number of attractors form and remain stable over time progress. Integral characteristics and dynamics of clusters are defined, simplifying control synthesis while maintaining problem formulation. A significant challenge is the model error due to changing cluster structures linked to randomized estimation and optimization algorithms valid under unknown disturbances. To demonstrate the practicality of this framework, the approach is applied to control tasks of a group of robots, leading to scalable and effective control strategies.

A Survey on Design Space Exploration Approaches for Approximate Computing Systems

Approximate Computing (AxC) has emerged as a promising paradigm to enhance performance and energy efficiency by allowing a controlled trade-off between accuracy and resource consumption. It is extensively adopted across various abstraction levels, from software to architecture and circuit levels, employing diverse methodologies. The primary objective of AxC is to reduce energy consumption for executing error-resilient applications, accepting controlled and inherently acceptable output quality degradation. However, harnessing AxC poses several challenges, including identifying segments within a design amenable to approximation and selecting suitable AxC techniques to fulfill accuracy and performance criteria. This survey provides a comprehensive review of recent methodologies proposed for performing Design Space Exploration (DSE) to find the most suitable AxC techniques, focusing on both hardware and software implementations. DSE is a crucial design process where system designs are modeled, evaluated, and optimized for various extra-functional system behaviors such as performance, power consumption, energy efficiency, and accuracy. A systematic literature review was conducted to identify papers that ascribe their DSE algorithms, excluding those relying on exhaustive search methods. This survey aims to detail the state-of-the-art DSE methodologies that efficiently select AxC techniques, offering insights into their applicability across different hardware platforms and use-case domains. For this purpose, papers were categorized based on the type of search algorithm used, with Machine Learning (ML) and Evolutionary Algorithms (EAs) being the predominant approaches. Further categorization is based on the target hardware, including Field-Programmable Gate Arrays (FPGAs), Application-Specific Integrated Circuits (ASICs), general-purpose Central Processing Units (CPUs), and Graphics Processing Units (GPUs). A notable observation was that most studies targeted image processing applications due to their tolerance for accuracy loss. By providing an overview of techniques and methods outlined in existing literature pertaining to the DSE of AxC designs, this survey elucidates the current trends and challenges in optimizing approximate designs.

A general egg-limited and search-limited host–parasitoid model with proportional host refuge

Motivated by the article [Getz and Mills. Host–parasitoid coexistence and egg-limited encounter rates. Am Nat. 1996;148:301–315], in this paper, we explore a discrete model involving a host and a parasitoid with search and egg limitations and arbitrary host-escape function. We added proportional refuge for the hosts to the model. We focus on the system's behavior at the equilibrium points and the nearby regions. In addition to the topological classification of these points, we examined local behavior. In the case of the extinction equilibrium, we obtain the global result. We describe the dynamical behavior scenarios in the neighborhood of the non-isolated exclusion equilibrium point (1:1 resonant). For the unique coexisting equilibrium, we prove the emergence of the Neimark–Sacker bifurcation and calculate the first Lyapunov exponent. This bifurcation can be either super or sub-critical. We have also established the occurrence of the Chenciner bifurcation. Our findings indicate that proportional refuge may or may not stabilize the system, and the choice of host-escape function plays a crucial role in shaping the system's dynamics. We also provide numerical examples to support our theoretical results.

Managing human-AI collaborations within Industry 5.0 scenarios via knowledge graphs: key challenges and lessons learned

In this paper, we discuss technologies and approaches based on Knowledge Graphs (KGs) that enable the management of inline human interventions in AI-assisted manufacturing processes in Industry 5.0 under potentially changing conditions in order to maintain or improve the overall system performance. Whereas KG-based systems are commonly based on a static view with their structure fixed at design time, we argue that the dynamic challenge of inline Human-AI (H-AI) collaboration in industrial settings calls for a late shaping design principle. In contrast to early shaping, which determines the system's behavior at design time in a fine granular manner, late shaping is a coarse-to-fine approach that leaves more space for fine-tuning, adaptation and integration of human intelligence at runtime. In this context we discuss approaches and lessons learned from the European manufacturing project Teaming.AI, https://www.teamingai-project.eu/, addressing general challenges like the modeling of domain expertise with particular focus on vertical knowledge integration, as well as challenges linked to an industrial KG of choice, such as its dynamic population and the late shaping of KG embeddings as the foundation of relational machine learning models which have emerged as an effective tool for exploiting graph-structured data to infer new insights.

Spectral absorbance changes in the photoreceptors of the juvenile masked greenling, Hexagrammos octogrammus (Pisces, Hexagrammidae).

When changing habitat during migration or ontogenesis, fish encounter changes of the visual environment, among which the most important is the spectral composition of light and turbidity. This leads to changes in the behavior, morphology, and physiology of the visual system, in particular, spectral sensitivity, which is determined by the properties of visual pigments. The masked greenling Hexagrammos octogrammus has an additional factor that shapes effective spectral sensitivity-the presence of densely bright orange cornea which reversibly changes its density depending on the state of light/dark adaptation. However, it is unknown to what extent the properties of visual pigments are matched to spectral properties of the developing cornea at the final stage of metamorphosis during migration of juveniles from pelagic to coastal environments. The findings of this microspectrophotometric study show that the range of the spectral sensitivity of rods and cones in juveniles is much wider than in adults, with a shift in the sensitivity of a significant part of the cells to shorter wavelengths. The reason for this is the large variation in the ratio of chromophores A1:A2 in the pigment mixture in each cell and possible expression of new opsins. This also indicates the asynchrony of pigment transformations in different types of photoreceptors and the incompleteness of these transformations on the eve of the transition to bottom life in shallow water.

Reliability optimization through soft digital twins

This paper presents a framework for developing a “soft digital twin” (SDT) for reliability optimization through modeling and simulation. The focus is on demonstrating the effectiveness of the SDT in modeling maintenance task processes within mission-critical facilities, which is particularly essential for reliable operations in critical infrastructure such as nuclear facilities. The need for efficient completion of maintenance tasks (MTs) to ensure high reliability and minimal downtime underscores the challenge faced by managers, who desire a predictive tool akin to a “crystal ball” for optimal resource configuration in the face of uncertainties from equipment failures, staffing issues, and supply chain disruptions. The proposed SDT functions as this predictive tool, leveraging the simulation’s versatility to provide insights into resource configurations and staff planning. This work extends the prior research by incorporating additional model validation, sensitivity testing, and analysis of the impact of various resource changes on the system. It employs a combination of data-driven frameworks and stochastic modeling methods to construct an adaptive SDT capable of accommodating changes in system behavior. The work provides a framework for the construction of SDT, testing and validation of its results, and its application to a case study of a mission-critical facility focused on replication and then optimizing of two key performance indicators based on human resource mixtures. Initial findings suggest that the SDT yields reliable results comparable to retrospective test datasets, offering the potential to minimize MTs’ time in the system while maximizing throughput within specified time frames through scenario experimentation and optimization.

Extrinsic fluctuations in the p53 cycle.

Fluctuations are inherent to biological systems, arising from the stochastic nature of molecular interactions, and influence various aspects of system behavior, stability, and robustness. These fluctuations can be categorized as intrinsic, stemming from the system's inherent structure and dynamics, and extrinsic, arising from external factors, such as temperature variations. Understanding the interplay between these fluctuations is crucial for obtaining a comprehensive understanding of biological phenomena. However, studying these effects poses significant computational challenges. In this study, we used an underexplored methodology to analyze the effect of extrinsic fluctuations in stochastic systems using ordinary differential equations instead of solving the master equation with stochastic parameters. By incorporating temperature fluctuations into reaction rates, we explored the impact of extrinsic factors on system dynamics. We constructed a master equation and calculated the equations for the dynamics of the first two moments, offering computational efficiency compared with directly solving the chemical master equation. We applied this approach to analyze a biological oscillator, focusing on the p53 model and its response to temperature-induced extrinsic fluctuations. Our findings underscore the impact of extrinsic fluctuations on the nature of oscillations in biological systems, with alterations in oscillatory behavior depending on the characteristics of extrinsic fluctuations. We observed an increased oscillation amplitude and frequency of the p53 concentration cycle. This study provides valuable insights into the effects of extrinsic fluctuations on biological oscillations and highlights the importance of considering them in more complex systems to prevent unwanted scenarios related to health issues.

Evaluating What Others Say: The Effect of Accuracy Assessment in Shaping Mental Models of AI Systems

Forming accurate mental models that align with the actual behavior of an AI system is critical for successful user experience and interactions. One way to develop mental models is through information shared by other users. However, this social information can be inaccurate and there is a lack of research examining whether inaccurate social information influences the development of accurate mental models. To address this gap, our study investigates the impact of social information accuracy on mental models, as well as whether prompting users to validate the social information can mitigate the impact. We conducted a between-subject experiment with 39 crowdworkers where each participant interacted with our AI system that automates a workflow given a natural language sentence. We compared participants' mental models between those exposed to social information of how the AI system worked, both correct and incorrect, versus those who formed mental models through their own usage of the system. Specifically, we designed three experimental conditions: 1) validation condition that presented the social information followed by an opportunity to validate its accuracy through testing example utterances, 2) social information condition that presented the social information only, without the validation opportunity, and 3) control condition that allowed users to interact with the system without any social information. Our results revealed that the inclusion of the validation process had a positive impact on the development of accurate mental models, especially around the knowledge distribution aspect of mental models. Furthermore, participants were more willing to share comments with others when they had the chance to validate the social information. The impact of inaccurate social information on altering user mental models was found to be non-significant, while 69.23% of participants incorrectly judged the social information accuracy at least once. We discuss the implications of these findings for designing tools that support the validation of social information and thereby improve human-AI interactions.

Experimental determination of maximum shear stress in Mobius® Breez perfusion microbioreactors and comparative analysis with stirred tank bioreactors

Perfusion processes have experienced increased popularity in recent years due to their ability to sustain high cell densities and productivities in biopharmaceutical production, offering advantages over traditional batch and fed-batch cultivation methods. The Mobius® Breez microbioreactor significantly reduces experimental effort by downsizing the classical volume of perfusion bioreactors to the mL range and thus represents a valuable tool for process development. However, miniaturization has raised questions regarding comparability with traditional bioreactors in terms of the physical environment, such as hydrodynamic shear stress. Therefore, the maximum hydrodynamic shear stress, cultivation performance, and membrane-wall contact were evaluated to elucidate the system's behavior. Findings reveal two distinct operational conditions, distinguished by the presence or absence of membrane-wall contact, resulting in varying levels of hydrodynamic stress. Conditions lacking membrane contact demonstrate stress levels within safe operating thresholds for CHO cells, while those involving membrane contact exceed these thresholds, potentially leading to cell damage. Through the identification of critical frequencies of membrane motion, this study offers insights for optimizing microbioreactor operation and enhancing overall bioprocess efficiency.

Gut microbiota modulates depressive-like behaviors induced by chronic ethanol exposure through short-chain fatty acids

BackgroundChronic ethanol exposure (CEE) is recognized as an important risk factor for depression, and the gut-brain axis has emerged as a key mechanism underlying chronic ethanol exposure-induced anxiety and depression-like behaviors. Short-chain fatty acids (SCFAs), which are the key metabolites generated by gut microbiota from insoluble dietary fiber, exert protective roles on the central nervous system, including the reduction of neuroinflammation. However, the link between gut microbial disturbances caused by chronic ethanol exposure, production of SCFAs, and anxiety and depression-like behaviors remains unclear.MethodsInitially, a 90-day chronic ethanol exposure model was established, followed by fecal microbiota transplantation model, which was supplemented with SCFAs via gavage. Anxiety and depression-like behaviors were determined by open field test, forced swim test, and elevated plus-maze. Serum and intestinal SCFAs levels were quantified using GC-MS. Changes in related indicators, including the intestinal barrier, intestinal inflammation, neuroinflammation, neurotrophy, and nerve damage, were detected using Western blotting, immunofluorescence, and Nissl staining.ResultsChronic ethanol exposure disrupted with gut microbial homeostasis, reduced the production of SCFAs, and led to anxiety and depression-like behaviors. Recipient mice transplanted with fecal microbiota that had been affected by chronic ethanol exposure exhibited impaired intestinal structure and function, low levels of SCFAs, intestinal inflammation, activation of neuroinflammation, a compromised blood-brain barrier, neurotrophic defects, alterations in the GABA system, anxiety and depression-like behaviors. Notably, the negative effects observed in these recipient mice were significantly alleviated through the supplementation of SCFAs.ConclusionSCFAs not only mitigate damage to intestinal structure and function but also alleviate various lesions in the central nervous system, such as neuroinflammation, and reduce anxiety and depression-like behaviors, which were triggered by transplantation with fecal microbiota that had been affected by chronic ethanol exposure, adding more support that SCFAs serve as a bridge between the gut and the brain.

An Attempt to Predict Transparent Armor Ballistic Performance through Quasi-Static Punch Shear Test

Abstract Background Transparent armor systems are traditionally designed following a trial-and-error approach, which involves high development costs associated with ballistic testing. This research article presents a novel methodology, termed quasi-static multi-punch shear testing, within the domain of transparent armor systems. Objective The primary aim is to establish a correlation between multi-hit ballistic tests at Level III-A according to the NIJ 0108.01 standard, achieved through an adaptation of the single-shot ballistic limit methodology, and the quasi-static multi-punch shear testing. The objective is to utilize a simple experimental methodology that provides insights into the multi-hit ballistic behavior of transparent armors. Methods Parameters such as absorbed energy and observed damage mechanisms were utilized to assess the potential relationship between these tests. Transparent armor samples that underwent testing using the quasi-static multi-punch shear test were subsequently cross-sectioned using a water jet cutting machine to facilitate visualization of material damage. In addition, drawing on insights from quasi-static multi-punch shear testing results, the K-means clustering algorithm was employed to predict the likelihood of a specific transparent armor system passing a multi-hit ballistic test. Results Various damage mechanisms were observed as a function of the punch displacement, and correlations were made with the load–displacement curves. Furthermore, the implementation of the K-means clustering algorithm successfully classified transparent armor into two groups: those that passed the ballistic test and those that did not. Conclusions This research significantly advances understanding of transparent armor system behavior under multi-hit conditions and offers a promising predictive tool for evaluating their performance through straightforward and cost-effective experimentation.

In Situ Solid-State Dewetting of Ag-Au-Pd Alloy: From Macro- to Nanoscale.

Metal alloy nanostructures represent a promising platform for next-generation nanophotonic devices, surpassing the limitations of pure metals by offering additional "buttons" for tailoring their optical properties by compositional variations. While alloyed nanoparticles hold great potential, their scalability and underexplored optical behavior still limit their application. Here, we establish a systematic approach to quantifying the unique optical behavior of the AgAuPd ternary system while providing a direct comparison with its pure constituent metals. Computationally, we analyze their electronic structure and uncover the transition of Pd d states to Pd/Ag hybridized s states in the bulk form, explaining the similar optical properties observed between Pd and AgAuPd. Experimentally, we fabricate pure metal and fully alloyed nanoparticles through solid-state dewetting, a scalable method. During the process, we trace the optical transition in the systems from the initial thin film stage to the final nanoparticle stage with in situ ellipsometry. We reveal the interplay between optical properties influenced by chemical interdiffusion and localized surface plasmon resonance arising from morphological changes with ex situ surface characterizations. Additionally, we analytically implement a metallic layer derived from the ternary system in a trilayer device, resulting in a single-time and irreversible color filter, to demonstrate an application encompassing a lithography-free and cost-effective route for nanophotonic devices.

A comprehensive numerical model for aero-hydro-mooring analysis of a floating offshore wind turbine

This paper presents a comprehensive study of a Floating Offshore Wind Turbine (FOWT), requiring multidisciplinary expertise in floating platform hydrodynamics, mooring system dynamics, and wind turbine aerodynamics. We introduce a fully coupled numerical model, focusing specifically on the NREL's (National Renewable Energy Laboratory's) 5 MW OC4 FOWT. The model is validated through both numerical simulations using the Computational Fluid Dynamics (CFD) based software OpenFOAM and experimental results. Key findings demonstrate the model's accuracy in forecasting the aerodynamic behaviors of the turbine, the platform's response to motions, and the behavior of the mooring system across diverse wind and sea state scenarios. Furthermore, the study enhances the understanding of FOWT's stability and efficiency by examining the influence of different Center of gravity (COG) heights. Results show that reduction in COG height has a minor effect on heave and surge motion but significantly decreases pitch motion and mooring line tension, thereby improving static stability and reducing the impact of wave loads on dynamic responses. Additionally, the results show that this reduction in COG height enhances the aerodynamic power output, suggesting that optimized FOWT designs could achieve improved energy capture efficiency. These insights optimize FOWT design and efficiency, enhancing renewable energy performance.

Three perspectives on entropy dynamics in a non-Hermitian two-state system

Abstract A comparative study of entropy dynamics as an indicator of physical behavior in an open two-state system with balanced gain and loss is presented. To begin with, we illustrate the phase portrait of this non-Hermitian model on the Bloch sphere, elucidating the changes in behavior as one moves across the phase transition boundary, as well as the emergent feature of unidirectional state evolution in the spontaneously broken PT-symmetry regime. This is followed by an examination of the purity and entropy dynamics. Here we distinguish the perspective taken in utilizing the conventional framework of Hermitian-adjoint states from an approach that is based on biorthogonal-adjoint states and a third case based on an isospectral mapping. In this it is demonstrated that their differences are rooted in the treatment of the environmental coupling mode. For unbroken PT symmetry of the system, a notable characteristic feature of the perspective taken is the presence or absence of purity oscillations, with an associated entropy revival. The description of the system is then continued from its PT-symmetric pseudo-Hermitian phase into the regime of spontaneously broken symmetry, in the latter two approaches through a non-analytic operator-based continuation, yielding a Lindblad master equation based on the PT charge operator C. This phase transition indicates a general connection between the pseudo-Hermitian closed-system and the Lindbladian open-system formalism through a spontaneous breakdown of the underlying physical reflection symmetry.

Thermal performance analysis of a domestic-size absorption cooling system incorporating nanofluid with thermal insulation in hot climate of Algeria

The increased demand for energy consumption for air conditioning, due to rising temperatures, has created challenges related to thermal comfort in residential environments .In the present study, a simulation of an air conditioning system for a small building was conducted, considering various absorption cooling scenarios to assess system performance through dynamic and economic analysis. The proposed system employs a parabolic trough collector in conjunction with a storage tank, designed to cool a 12 m² room in Adrar, Algeria. The study aims to identify the optimal scenario based on temperature, heat, and system coefficient of performance indicators. A computer program was developed using characteristic equations and finite difference methods to analyze the system's behavior with different nanofluid concentrations and thermal insulation. Simulation results show that scenario with nanofluid and insulation the best performance is achieved with a 4% nanofluid concentration, a 9 m² solar collector, a 0.3 m³ storage tank, and 0.05 m thick polystyrene insulation. The system can operate for 9 h, maintaining an indoor temperature of 27°C with a peak cooling load of 3 kW. The economic analysis estimates an investment cost of 4535.4$ and Net Present Value 3,370.40$ with a payback period of 12 years and 3 months.