Modeling Of Systems Research Articles

Systems Modeling of the Water-Energy-Food-Ecosystems Nexus: Insights from a Region Facing Structural Water Scarcity in Southern Spain

The complex relationship between water, energy, food, and ecological systems, known as the WEFE nexus, has emerged as a major topic in the debate about sustainable economic development and resource management. This subject is of special interest in Mediterranean coastal areas as rapid economic expansion driven by population growth, higher influx of tourists, and intensification of agriculture is leading to structural water scarcity conditions. However, addressing the diverse range of issues associated with the nexus is a difficult task due to the existence of intricate interconnections, interdependencies, and nonlinearities within and across its various components. Accordingly, this case study applies a combination of participatory systems modeling and network analysis tools to yield insights into the complexity of this nexus in Axarquia, a region with features that make it an example of water-stressed jurisdictions in the Mediterranean. Overall, our results provide a strong foundation to understand the dynamics that govern this nexus in regions where the availability of freshwater resources is a significant concern. Furthermore, they lay the groundwork for the development of models and scenarios to simulate the impact of various policies and interventions on the overall system.

Analysis on the vibration signals of a novel double-disc crack rotor-bearing system with single defect in inner race

Previous studies have paid little attention to the dynamic modeling of multi-fault systems with both bearing defect and cracked rotor. In the current research, a double-disc rotor model with a breathing crack and fault bearing was established using the finite element method (FEM). The dual-impulse phenomenon associated with inner race defect was addressed as a time-varying external excitation. The effects of varying crack depths and inner race spall lengths were further investigated. The maximum error between simulation and experiment was found to be less than 5% for the dual-impulse time spacing. The calculated results indicate that the inner race defect causes a slight increase in the rotational frequency frotor and 2frotor, followed by a decrease in the 3frotor at low speeds, as observed using Fast Fourier Transform (FFT) and Short-Time Fourier Transform (STFT). The influence of changes in crack depth is greater than the bearing defect in the system, a finding that is also confirmed at 1/2 critical speed. The phenomenon of frequency modulation observed in the simulation was also verified experimentally. The ball passing frequency on inner raceway fBPFI, frotor and mixed frequency components can be used to distinguish whether there is inner race defect.

Space group prediction of complex alloy systems by product-based neural networks

Complex alloy systems exhibit some unique properties, many of which are attributed to ordering phenomena. At the atomic scale, this phenomenon refers to the occupancy probability of particles at the lattice sites. From the perspective of symmetry, it corresponds to different space groups. This study uses several machine learning algorithms to predict some common space groups of complex alloy systems. Under a large data set and a difficult classification task, the relevant models achieved excellent results on the test set. In the traditional support vector machine algorithm model, the prediction can reach the first-class level. Further, through the Product-based Neural Networks method under the wide and deep framework, the bottleneck of the traditional algorithm is broken through, and the prediction ability of the model is further improved. The average Area under curve value of the model can reach 99 %, and the prediction ability of multiple space groups has been improved. This study can not only provide more ideas for cross-scale modeling of complex systems, but the related models can also provide guidance for specific alloy design.

Heat Transfer Modeling and Optimal Thermal Management of Electric Vehicle Battery Systems

Heat Transfer Modeling and Optimal Thermal Management of Electric Vehicle Battery Systems

Hybrid modeling of multibody systems: Comparison of two discrepancy models for trajectory prediction

Hybrid modeling of multibody systems: Comparison of two discrepancy models for trajectory prediction

System Design, Modelling, Energy Analysis, and Industrial Applications of Ohmic Heating Technology

AbstractThe food and drink industry is gradually gaining sustainability pressure due to the high carbon emissions rate and limited availability of traditional energy supplies. Thus, it is essential to consider energy efficiency and decarbonisation of the ever-expanding food and drink industry. Ohmic heating technology is a highly energy-efficient and quick method of electrically heating food products. It uses the resistance of the food material to convert electrical energy into heat. However, the heating rate of ohmic heating depends on several process variables, such as electrical conductivity, voltage gradient, and voltage frequency. Therefore, it is important to study and understand the effects of these process variables to optimise the modelling and control of ohmic heating systems to obtain desirable output from food products. Hence, this study presents a review of the effects of process parameters on nutritional and organoleptic qualities, product yields, and energy efficiency of ohmic heating, along with its various industrial applications. The technology shows promising advancements in colour and nutritional quality, increasing energy efficiency, shorter processing times, and higher product yields compared to conventional heating processes.

Analytical approach to piezoelectric model synthesis with the use of Cauer’s method for system design

Background Piezoceramic materials have unique property which enables direct and bilateral conversion between mechanical and electrical energy. This ability facilitates significant miniaturisation of technology and opens many opportunities in design of new actuators and energy harvesters. Mathematical modelling of piezoelectric modules is notoriously hard due to complex constitutive equations defining mechanical and electrical energy conversion. Methods The article presents research on a new synthesis method based on the Cauer’s method. Mechanical damping is introduced with the use of Rayleigh’s approximation. A discrete electromechanical model is formed based on the Mason’s piezoelectric model. The proposed approach allows modelling of piezoelectric systems based on a set of characteristic frequencies. The method allows a more general approach to the problem of modelling new systems, as opposed to application-oriented methods seen in literature. A non-standard model analysis method using edge graphs and structural numbers is also verified as a potential alternative for matrix-based method. The authors compare their precision and computation requirements. Results The structural method of mechanical model analysis gave identical results as the reference matrix method. However, the non-classical algorithm took much longer to calculate and was using more memory. The electromechanical model analysis has shown an error of 5% in comparison to resonance frequencies taken from a reference plate specification. The calculated magnitude of displacement was well above the capability of a 3.5mm thick piezoelectric plate. Conclusions The synthesis method presented in this paper allows synthesizing piezoelectric cascade models based on limited information in form of characteristic frequencies. Currently this method allows a coarse approximation of the real piezoelectric parameters with limited number of inputs. The additional method of analysis based on structural numbers offers a promising alternative to matrix calculations but requires a more thorough investigation of the computational power required to determine whether it can compete with existing algorithms.

Mesofractal Modeling of Biosystems & Organic Spintronics

Mesoscopic modeling of complex systems involves thermodynamic nonequilibrium of discrete scaling. Further from quantum correlation on a chip retrieved quantum nonlinear optics with single photons enabled by strongly interacting atoms. Accompanied by mesofractals as the development of meso & micro size fractal structures is required to mimic various biological systems for various functions. Showed through fluorapatite in gelatin‐based nanocomposite, fractal in DNA knots driven by balance of fission & fusion in mtDNA/mitochondrial DNA mechanism, for optical engines for light energy detection described the proportional integral derivative [PI(D)]‐controller set in microbial cells to HCCI/Homogeneous Charge Compression Ignition.

Novel Hybrid Optimization Technique for Solar Photovoltaic Output Prediction Using Improved Hippopotamus Algorithm

This paper introduces a novel hybrid optimization technique aimed at improving the prediction accuracy of solar photovoltaic (PV) outputs using an Improved Hippopotamus Optimization Algorithm (IHO). The IHO enhances the traditional Hippopotamus Optimization (HO) algorithm by addressing its limitations in search efficiency, convergence speed, and global exploration. The IHO algorithm used Latin hypercube sampling (LHS) for population initialization, significantly enhancing the diversity and global search potential of the optimization process. The integration of the Jaya algorithm further refines solution quality and accelerates convergence. Additionally, a combination of unordered dimensional sampling, random crossover, and sequential mutation is employed to enhance the optimization process. The effectiveness of the proposed IHO is demonstrated through the optimization of weights and neuron thresholds in the extreme learning machine (ELM), a model known for its rapid learning capabilities but often affected by the randomness of initial parameters. The IHO-optimized ELM (IHO-ELM) is tested against benchmark algorithms, including BP, the traditional ELM, the HO-ELM, LCN, and LSTM, showing significant improvements in prediction accuracy and stability. Moreover, the IHO-ELM model is validated in a different region to assess its generalization ability for solar PV output prediction. The results confirm that the proposed hybrid approach not only improves prediction accuracy but also demonstrates robust generalization capabilities, making it a promising tool for predictive modeling in solar energy systems.

Substrate-Assisted Atomic Dispersion of Cobalt for Alkaline Water Electrolysis.

Atomically dispersed single-atom catalysts have recently attracted broad research interest due to their high atom efficiency and unique catalytic performance. In this study, atomic dispersion of cobalt is achieved using a chemical bath deposition method on a highly stable alkali titanate film (Ti/KTiO). These films were characterized using a variety of techniques, with atomic dispersion confirmed via grazing incidence X-ray absorption spectroscopy and ab initio modeling of single-atom systems. This modeling indicated that the alkali ion incorporated into the film facilitates atomic dispersion. Experimentally, the Ti/KTiO-supported Co(OH)2 catalysts exhibited remarkable electrochemical performance, with an overpotential of 163 mV to achieve a current density of 10 mA cm-2 with a catalyst loading of ∼0.1 mg cm-2 and high stability. These results show the potential of Ti/KTiO/Co(OH)2 catalysts for atomically efficient hydrogen production.

A Coupled Human and Natural Systems (CHANS) framework integrated with reinforcement learning for urban flood mitigation

To address the challenge of escalating urban flood risk and the deficiency in effective flood emergency management, this study introduces a novel Coupled Human and Natural Systems (CHANS) modelling framework that employs hierarchical reinforcement learning to optimise mobile pump scheduling and placement for urban flood risk mitigation. The CHANS framework integrates hydrodynamic and agent-based models within a multi-GPU computing environment for high-resolution, real-time flood inundation modelling and risk assessment to enrich Reinforcement Learning (RL) training. In the application to Ninh Kieu District in Can Tho City, Vietnam, the new RL-enabled modelling framework is used to evaluate optimal mobile pumping strategies for concurrent pluvial flooding and post-flooding events against the traditional deployment approaches. Results demonstrate that RL-based strategies can significantly enhance flood risk reduction, outperforming traditional methods by achieving 2× and 4× improvements in the concurrent and post-flooding periods/events, respectively. Incorporating human factors and adapting to local conditions, the RL agent provides valuable insights into mobile pump scheduling and deployment strategies. Sensitivity analysis confirms the robustness of the CHANS modelling framework and underscores the role of RL in optimising mobile pump scheduling and placement where traditional rule-based strategies are challenging.

Active learning concerning sampling cost for enhancing AI-enabled building energy system modeling

Machine learning is widely recognized as a promising data-driven modeling technique for the model-based control and optimization of building energy systems. However, the generalizability of data-driven models often faces significant challenges, as the available training data from building operations usually only covers a limited range of working conditions. Active learning can proactively test unseen and informative working conditions to enrich the training set by adding new data samples, leading to improved generalization performance of data-driven models. A novel distance and information density-based sample strategy is developed that accounts for the real-time status of building operation and outdoor environment. Based on Mahalanobis distance, this strategy determines the sampling value of an unlabeled sample (unseen working condition) by assessing its similarity to both the training samples and other unlabeled samples. As collecting sufficiently representative samples can be difficult, costly, and time-consuming, a distance-based sampling cost metric is proposed to compare the efficiency of different sampling methods, considering the detrimental effects of the actively sampling process on the normal operation of building energy systems. This paper presents a comprehensive and in-depth comparison of five active learning methods, including one incorporating the distance-based sampling strategy, by conducting data experiments on the data collected from the cooling towers of a real high-rise building. The results show that active learning can effectively identify informative data samples and improve the generalization performance of data-driven models. The research outcomes are valuable for enhancing AI-enabled data-driven modeling of building energy systems with substantial decreases in costs on data sampling.

Domain Decomposition for Data-Driven Reduced Modeling of Large-Scale Systems

This paper focuses on the construction of accurate and predictive data-driven reduced models of large-scale numerical simulations with complex dynamics and sparse training datasets. In these settings, standard, single-domain approaches may be too inaccurate or may overfit and hence generalize poorly. Moreover, processing large-scale datasets typically requires significant memory and computing resources, which can render single-domain approaches computationally prohibitive. To address these challenges, we introduce a domain-decomposition formulation into the construction of a data-driven reduced model. In doing so, the basis functions used in the reduced model approximation become localized in space, which can increase the accuracy of the domain-decomposed approximation of the complex dynamics. The decomposition furthermore reduces the memory and computing requirements to process the underlying large-scale training dataset. We demonstrate the effectiveness and scalability of our approach in a large-scale three-dimensional unsteady rotating-detonation rocket engine simulation scenario with more than 75 million degrees of freedom and a sparse training dataset. Our results show that compared to the single-domain approach, the domain-decomposed version reduces both the training and prediction errors for pressure by up to 13% and up to 5% for other key quantities, such as temperature, and fuel, and oxidizer mass fractions. Lastly, our approach decreases the memory requirements for processing by almost a factor of four, which in turn reduces the computing requirements as well.

Modeling and Stability Analysis of Fuel Cell-Based Marine Hybrid Power Systems

Modeling and Stability Analysis of Fuel Cell-Based Marine Hybrid Power Systems

Оптимизация региональных планов адаптации к изменению климата в Арктической зоне России на основе прогностического моделирования

Based on the Russian Arctic example, an attempt was made to update the developed regional plans for adaptation to climate change, taking into account currently available scientific knowledge, observational data, climate scenarios and the results of mathematical modeling of natural and socio-economic systems — recipients of climate risks. The authors using the developed methods have calculated the geocryological risk index for the macro-regions of the Russian permafrost zone, as well as the vulnerability index of economic systems for municipalities of the Russian Arctic.

Charging dependent electro-chemo-mechanical coupling behavior of polymer electrolytes containing immobilized anions

Theoretical models have been developed to explore the electrodeposition stability between Li metal and salt-doped polymer electrolytes. However, there is still limited investigation on the coupling behavior between mechanics and electrochemistry in those novel nano-structured polymers with immobilized anions. In this work, we employ a multiphysics modeling framework to predict the electro-chemo-mechanical behavior of polymer electrolytes containing immobilized anions. We analyze the impacts of charging conditions, stress coupling and the immobilization of anions on their ion transport, electrical and mechanical properties during charging. Strengthening stress coupling is demonstrated to improve the diffusion-driven stability of electrodeposition, by enhancing limiting applied current densities. Compared with stress coupling, immobilizing anions surprisingly sustains the non-zero interfacial Li+-ion concentrations even at high applied currents, thereby mitigating the potential diffusion-driven instability of electrodeposition. Both stress/strain and overpotentials also get reduces with increasing the concentrations of immobilized anions. This theoretical work provides insights into the strategy of redesigning polymer electrolytes, and also lays foundation for the multiphysics modeling of electrodes and full-cell battery systems.

Vapor-Liquid Equilibrium of System Comprising Green Solvents: A Holistic Review

Abstract: The design and operation of distillation columns is based on vapor-liquid equilibrium data, which is a necessity for the chemical industry. In recent years, chemical industry has embraced green chemistry and sustainable development. Furthermore, green solvents are more environmentfriendly and cleaner than conventional solvents and thus offer a good alternative. Very limited work has been reported in the literature that focuses on the generation of isobaric/isothermal vapor-liquid equilibrium (VLE) data of systems comprising green solvents. In this paper, reported VLE data are explored for three emerging green solvents, such as cyclopentyl methyl ether (CPME), γ- valerolactone (GVL), and 2-methyltetrahydrofuran (2-MeTHF). Emerging green solvents have favorable environmental, health, and safety characteristics, making them attractive alternatives for a wide range of applications. The study focuses on two critical separations; the extraction of formic acid from Power-to-X chemical processes and purification of acetic acid from chemical synthesis or fermentation processes. Both processes are integral parts of the chemical industry's sustainable development. To facilitate these separations, accurate VLE data for these green solvents with acetic acid/formic acid systems are essential. The paper reviews literature related to VLE data for systems involving these green solvents. It provides insights into the experimental conditions, equipment, analysis methods, thermodynamic models, and error-minimizing functions used in the previous studies. The researchers can refer to this information as a useful reference prior to the VLE experimentation and modeling of systems comprising these three green solvents. Moreover, the paper presents an overview of recent research on green solvents and their applications, illustrating their versatility and potential for various industrial processes. Research efforts are needed to generate VLE data for green solvent systems and support the chemical industry in transitions towards more sustainable practices.

Modern Methods and Solutions for Water Resources Management in the Wadi-Watir Region (Egypt)

The main problem of water resources in Egypt is the increasing demand and decreasing supply, especially after the construction of the Ethiopian Renaissance Great Dam. One of the best solutions is the use of unusual alternative management techniques, including as a major one, rainwater harvesting and distribution. This paper examines the use of unconventional approaches to water management in arid regions based on rainfall accumulation. Using Geographic Information Systems (GIS) and hydrological modelling, the suitability of the Wadi-Watir catchment (Egypt) for rainwater storage was assessed and optimal locations for the construction of storage structures were identified. The results of the study showed that 19% (666 km2) of the study area is highly suitable for the aims and objectives; 16% (573 km2) of the catchment area has limited realisability. Twelve sites were found to be suitable for the construction of storage dams. Fourteen sites are identified as optimal for the placement of percolation (filtration) reservoirs along watercourses. The area considered ideal for above-ground percolation (filtration) tanks is 25.9 km2. The optimum area for farm ponds is 1.34 km2. As a result, the construction of water storage structures is proposed to improve water management in the Wadi-Watir region. The implementation of the rainwater harvesting structures (SRF) was divided into three phases depending on the severity of the flash flood hazard. The first, second and third phases can meet 62.24% (34.24 mil m3) of the water demand. This approach is a new and modern solution to the problem of water scarcity under socio-economic and environmental pressures while achieving the goals of sustainable development in Egypt. The article is based on the dissertation research of Mohamed Mostafa Ezzeldin Abdelrahim (MGSU, dissertation council № 24.2.339.07, March 2024).

A novel tensor-based modal decomposition method for reduced order modeling and optimal sparse sensor placement

Data-driven modal decomposition methods have become fundamental algorithms in constructing reduced order models (ROMs), which serve as a critical tool in the digital twin modeling of large complex systems. Classical modal decomposition methods are particularly adept at handling matrix-form input data that contains columnized snapshots without retaining spatial structure and neighborhood information. Focusing on addressing this problem, this paper proposed a Tensor-Based Modal Decomposition (TBMD) method to deal with the high-order tensor-form input data. TBMD method is effective in finding the potential low-order manifolds of a given high-order dynamical system with low energy loss within fewer decomposited modes. Moreover, TBMD is extended to be used in the field of optimal sensor placement via the development of a tensor-based QR factorization method and a tensor-based compressive sensing algorithm. The tensor-based QR factorization method is able to consider the spatial structure and neighborhood information of the basis matrices data in tensor form. This is the first time that a compressive sensing model has been discussed and solved for the frontal slice-wise product of a tensor and a vector. Subsequently, comprehensive case studies are conducted based on the cylinder wake dataset, airfoil vortex dataset, sea surface temperature dataset, eigenfaces dataset and turbulent channel flow dataset. The experiment results show that TBMD can accurately capture lower-order manifolds, and the tensor-based QR factorization is effective in optimizing sensor placement with fewer sensors while maintaining high reconstruction accuracy. The proposed methods in this paper is promising to be utilized in the construction of large-scale complex system digital twins, in which TBMD can serve as a fundamental algorithm in constructing ROM.

Synthesis of expert matrices in inductive system-analytical research based on fuzzy logic algorithm

The object of research is the process of inductive modeling of complex systems. The studies that were conducted related to the application of algorithms of the fuzzy logic theory were to coordinate the conclusions of top-level experts in system information-analytical research (SIAR) in the tasks of innovative design. The possibilities of constructing elements of expert matrices of results, as well as evaluating the effectiveness of such applications, are defined. Thanks to this, obtaining formal expert evaluations in numerical form became possible. Experimental studies have confirmed that the proposed approach to the application of fuzzy logic algorithms to the construction of matrices of expert evaluations of SIAR results is quite effective and simple to implement. In addition, this approach fits well into the general paradigm of the Group Method of Data Handling (GMDH). In particular, it was established that the possibility of «retraining» such a block without significant efforts of professional experts can have a positive result, as well as have a good effect on the economic and time parameters of the research project. The main calculation formulas for the algorithm for building a fuzzy system using a neural network in a system with two rules are given. The construction of a fuzzy information output system trained on expert evaluations in the Matlab system is shown. As a result, a technologically acceptable standard deviation of 0.28268 mg/l was obtained. It has been established that by accumulating a database (knowledge) and/or using an information monitoring system, it is possible to «additionally train» a fuzzy system periodically or according to the established quality criterion in the program mode, without involving experts in this process. Thus, there are reasons to assert the importance of using a fuzzy system as one of the tools in inductive SIAR procedures