Deployment Of Systems Research Articles

Active Security Surveillance and Object Detection

Active security surveillance and object detection have become crucial components in modern security systems, driven by advancements in computer vision and machine learning. These systems are designed to enhance real-time monitoring and threat detection across various environments, such as public spaces, transportation hubs, and critical infrastructure. Active security surveillance systems continuously scan and analyze video feeds or sensor data to identify potential security threats, unauthorized activities, or anomalies. They employ advanced algorithms for motion detection, facial recognition, and behavioural analysis, allowing for the rapid identification of suspicious activities or individuals. Object detection, a key element of these systems, involves the identification and classification of objects within a given scene. This is typically achieved through deep learning techniques, such as convolutional neural networks (CNNs), which can accurately detect and categorize objects like vehicles, weapons, or unattended bags. The integration of object detection with active surveillance enables more precise and automated responses to potential threats, reducing reliance on human operators and minimizing the risk of oversight. The adoption of active surveillance and object detection technologies has led to significant improvements in security management, offering enhanced situational awareness and faster response times. However, these technologies also raise concerns regarding privacy, data security, and ethical implications, particularly in their deployment in public and private spaces. Addressing these concerns requires careful consideration of data governance, transparency, and regulatory compliance to balance security needs with individual rights. As technology continues to evolve, ongoing research and development are essential to improving the accuracy, efficiency, and ethical deployment of active security surveillance and object detection systems.

Network Time Protocol based Black Box Timing Accuracy Analysis for Internet of Things Devices

With large-scale Cyber-Physical Systems (Internet of Things) deployments, the low-cost networked devices are becoming ubiquitous. Time stamping of events is a basic requirement and it is crucial for distributed/isolated systems/devices to work in unison. The system clocks of these devices are not very stable and their behaviour is affected by ageing and environmental conditions. A clock’s timing behaviour is generally analysed independently by comparison to a highly stable standard reference clock using specialized instruments. Network Time Protocol (NTP) is widely used for time synchronization over computer networks. As the NTP server clock is more stable in comparison to a client’s system clock, the variation in the end device’s clock offset values with respect to the NTP server clock over a long time can be considered for analysing the behaviour of the end device’s clock. This paper presents a method for analysing clock behaviour based on NTP messages. A computer programme is developed, which utilizes an open-source NTP client library to exchange NTP messages and records end device’s system clock’s offset variations over time for a given periodicity to analyse the behaviour. This black box analysis approach does not require any direct access to the end device’s clock circuit. The solution may be useful for analysing clock behaviour for long-term stability under actual environmental conditions, for deciding time synchronization periodicity to optimize the number of synchronization packet exchanges, and for pre-deployment timing stability assessment of devices for deployment scenarios having intermittent/non-availability of periodic time synchronization.

Hybrid Control Policy for Artificial Pancreas Via Ensemble Deep Reinforcement Learning.

The artificial pancreas (AP) has shown promising potential in achieving closed-loop glucose control for individuals with type 1 diabetes mellitus (T1DM). However, designing an effective control policy for the AP remains challenging due to the complex physiological processes, delayed insulin response, and inaccurate glucose measurements. While model predictive control (MPC) offers safety and stability through the dynamic model and safety constraints, it lacks individualization and is adversely affected by unannounced meals. Conversely, deep reinforcement learning (DRL) provides personalized and adaptive strategies but faces challenges with distribution shifts and substantial data requirements. We propose a hybrid control policy for the artificial pancreas (HyCPAP) to address the above challenges. HyCPAP combines an MPC policy with an ensemble DRL policy, leveraging the strengths of both policies while compensating for their respective limitations. To facilitate faster deployment of AP systems in real-world settings, we further incorporate meta-learning techniques into HyCPAP, leveraging previous experience and patient-shared knowledge to enable fast adaptation to new patients with limited available data. We conduct extensive experiments using the FDA-accepted UVA/Padova T1DM simulator across three scenarios. Our approaches achieve the highest percentage of time spent in the desired euglycemic range and the lowest occurrences of hypoglycemia. The results clearly demonstrate the superiority of our methods for closed-loop glucose management in individuals with T1DM. The study presents novel control policies for AP systems, affirming the great potential of proposed methods for efficient closed-loop glucose control.

The Governance and Optimization of Urban Flooding in Dense Urban Areas Utilizing Deep Tunnel Drainage Systems: A Case Study of Guangzhou, China

With urban expansion, traditional drainage systems in densely populated cities face significant challenges, leading to frequent flooding and pollution issues. Deep tunnel drainage systems emerge as an innovative approach, offering underground storage for excess precipitation and alleviating urban inundation. This research investigates the deployment of a deep tunnel system in Guangzhou’s densely populated urban core. By integrating with existing networks, this system aims to curtail over-flow contamination and boost sewage-handling capacity. Successful implementation hinges on the thorough evaluation and synchronization with broader urban development objectives. In Guangzhou, where traditional methods fall short, deep tunnels present a viable option. This study explores techniques for identifying drainage deficiencies, devising enhancements, and refining citywide strategies. Economic analysis indicates that deep tunnels are more cost-effective than conventional drainage upgrades, offering long-term benefits for land conservation and drainage efficiency. Following implementation, these systems markedly enhance sewage management, diminish overflow incidents, and improve pollution mitigation. Although initial investments are substantial, the enduring advantages in land preservation and drainage efficiency are significant. Thus, deep tunnel systems emerge as a practical flood control solution for high-density urban areas like Guangzhou, fostering sustainable metropolitan growth.

Methodology of deployment of dependable FPGA based artificial intelligence as a service

The subject of study in this article is the models, methods, and principles of organization of entire lifecycle of Artificial Intelligence (AI) as a Service implemented with the use of Field Programmable Gate Array (FPGA). The purpose of this work is the improvement of the methodology of the deployment of dependable FPGA-based Artificial Intelligence as a Service by creating a complex of mutually agreed concept, principles, models, and methods considering the specifics of the use of heterogeneous computations of Artificial Intelligence and the possibility of realizing the unified protection of FPGA implementations. Tasks: to clarify the taxonomy of the dependability term within the proposed methodology; to propose the concept of deployment of dependable FPGA-based heterogeneous computations of Artificial Intelligence as a Service; to formulate the principle of tracing changes in FPGA projects and integrated environments during the entire lifecycle; to formulate the principle of unification of protection of FPGA implementations of heterogeneous computing of Artificial Intelligence as a Service; to formulate the principle of the product-service assessment of the availability of FPGA as a Service; and to discuss the promising directions of heterogeneous computations of AI. According to the tasks, the following results were obtained. The existing concepts of dependable systems deployment are discussed. The concept of the deployment of computations of Artificial Intelligence as a Service, which is obtained based on the improvement of paradigms of the creation and deployment of dependable systems and services, is proposed. The principle of tracing of changes, which assumes the updating of requirements during the lifecycle of FPGA projects, is proposed. The principle of the unification of protection, which combines and joins the consideration of various unique features of the FPGA instance to protect the implementation and the set of cyberthreats for the service as a whole, is proposed. The principle of the product-service assessment, which considers parameters and indicators of availability, is proposed. The perspective of the progress of non-electronic mediums for heterogeneous computations with the use of a photonic implementations of Artificial Intelligence computations to ensure improved performance and reduced energy consumption is discussed. Conclusions. One of the main contributions of this research is that in the proposed methodology, the set of principles, models, and methods of deployment of Artificial Intelligence as a Service under conditions of changing requirements and integrated environments, and the need for mechanism of licensing protection of each instance of the system are developed, which allows to reduce model uncertainty by considering various stages of the lifecycle of dependable FPGA implementation using heterogeneous computations.

Nanosecond laser cleaning method for high-capacity fast storage and data multiplexing in ARM architecture

As the application of laser cleaning technology continues to expand, various fields have put forward higher requirements for the performance and quality of the operation, and at present, laser cleaning has problems such as low efficiency in handling repetitive tasks, and the system deployment and maintenance costs are high. This paper proposes a nanosecond laser cleaning method based on ARM architecture with high-capacity fast storage and data reuse, integrating high-capacity fast storage technique and data reuse strategy to optimize the laser cleaning process. In this paper, SDRAM is used to store and record the laser scanning path, Cache is integrated to weaken the data calling delay and improve the system response speed, and the scanning cycle is finely adjusted by combining positional modulation laser frequency technology to avoid the edge erosion caused by the motor deceleration and to ensure the homogeneity and accuracy of laser cleaning. The results indicate that, under the same output point conditions, the dynamic phase difference spiral algorithm in this paper outperforms the linear filling algorithm in terms of speed and data filling efficiency, reducing the burden of repetitive task calculations. While maintaining surface erosion uniformity during cleaning, excessive erosion is prevented, leading to a comprehensive enhancement in the overall efficiency and performance of laser cleaning operations.

The Advancement of Artificial Intelligence's Application in Hybrid Solar and Wind Power Plant Optimization: A Study of the Literature

The harnessing of solar, wind, and hydroelectric energy sources has rendered them easily accessible renewable resources, owing to their abundant availability. There is a growing body of research evincing interest in the deployment of hybrid renewable energy systems. Over recent decades, adopting hybrid technologies has engendered a positive trend, marked by broader considerations of configurations and applications within these systems. This study analytically examines the potential of hybrid solar and wind energy harvesting devices. As such, the project aims to extensively evaluate relevant literature and statistical analysis of data extracted from journal papers published between 2004 and 2023. A specific objective is to develop a complete database matrix surrounding multiple categories, including component configurations, methodological approaches, and supporting software infrastructures. Moreover, an assessment of the socio-economic, environmental, and ecological impacts of these systems is undertaken to ascertain their salience. Furthermore, this inquiry delves into the optimization strategies of these systems leveraging artificial intelligence methodologies. Critical lacunae identified during this review pertain to more emphasis on optimization metrics for PV-wind hybrid energy systems, impeding a holistic understanding of their implications on energy, economics, environment, and society. Our findings underscore prevalent methodologies such as computational modelling utilizing software suites like MATLAB/Simulink, HOMER, and others to derive empirical data. Additionally, parametric analyses emerge as the predominant approach, characterized by the application of algorithms such as Particle Swarm Optimization (PSO), Fuzzy Logic Control (FLC), and Genetic Algorithms (GA), among others. PV-wind hybrid energy systems are classified into autonomous and grid-interconnected configurations, with primary components comprising PV-wind generators. The anticipated trajectory suggests a burgeoning development of these hybrid energy harvesting systems, underpinned by their potential as clean, sustainable, and eco-friendly energy sources.

Formal Methods in Industry

Formal methods encompass a wide choice of techniques and tools for the specification, development, analysis, and verification of software and hardware systems. Formal methods are widely applied in industry, in activities ranging from the elicitation of requirements and the early design phases all the way to the deployment, configuration, and runtime monitoring of actual systems. Formal methods allow one to precisely specify the environment in which a system operates, the requirements and properties that the system should satisfy, the models of the system used during the various design steps, and the code embedded in the final implementation, as well as to express conformance relations between these specifications. We present a broad scope of successful applications of formal methods in industry, not limited to the well-known success stories from the safety-critical domain, like railways and other transportation systems, but also covering other areas such as lithography manufacturing and cloud security in e-commerce, to name but a few. We also report testimonies from a number of representatives from industry who, either directly or indirectly, use or have used formal methods in their industrial project endeavours. These persons are spread geographically, including Europe, Asia, North and South America, and the involved projects witness the large coverage of applications of formal methods, not limited to the safety-critical domain. We thus make a case for the importance of formal methods, and in particular of the capacity to abstract and mathematical reasoning that are taught as part of any formal methods course. These are fundamental Computer Science skills that graduates should profit from when working as computer scientists in industry, as confirmed by industry representatives.

United in Green: A Bibliometric Exploration of Renewable Energy Communities

In recent years, the domain of renewable energy communities has experienced dynamic growth, spurred by European Union (EU) legislation that became law for all 27 Member States in June 2021. This legislative framework intensified research efforts aimed at discovering new methods for sustainable energy sources through the development of individual and collective energy communities. Each EU country has implemented distinct frameworks for renewable energy communities, leading to varied legislative approaches. This has spurred exponential investment, facilitating the deployment of photovoltaic and battery energy storage systems, offering significant economic and environmental benefits to community members. Against this backdrop, the purpose of this analysis is to investigate academic publications related to renewable energy communities. Using a dataset extracted from the ISI Web of Science database, this study employs a bibliometric approach to identify the main authors, affiliations, and journals and analyze collaboration networks, as well as discern key topics and the countries involved. The analysis reveals an annual growth rate of 42.82%. Through thematic maps, WordClouds, three-field plots, and a review of the top 10 globally cited documents, this study provides a comprehensive perspective on the evolving domain of renewable energy communities.

Three-dimensional deployment strategy for a multi-BWBUG cooperative system at deep depths

ABSTRACT This study investigates the issue of the three-dimensional deployment for blended-wing-body underwater gliders (BWBUGs) at deep depths. Initially, an underwater three-dimensional deployment optimisation model is formulated, considering factors such as the three-dimensional coverage ratio and the balance of node communication energy consumption. Subsequently, a sine-logistic chaos strategy with excellent ergodicity is proposed. Thereafter, the dynamic decision-assisted heuristic method (DDHM) is proposed based on the dynamic decision framework, sine-logistic chaos strategy, and opposition-based learning. The well-designed dynamic decision framework effectively avoids the premature convergence and local optimum of the algorithm. Additionally, the multi-objective variant of DDHM, referred to as MDDHM, is established utilising the archival mechanism. Lastly, the performance of the DDHM and MDDHM is evaluated by simulation experiments. Statistical analysis demonstrates that DDHM is highly effective for the underwater three-dimensional deployment of the multi-BWBUG cooperative system. Furthermore, its overall performance surpasses that of other comparative methods.

PVSails: Harnessing Innovation With Vertical Bifacial PV Modules in Floating Photovoltaic Systems

AbstractIn the context of offshore floating photovoltaic systems (FPVs), this paper explores the use of bifacial photovoltaic modules installed in the vertical position. The energy harvested from the rear face of vertically configured bifacial PV modules compensates for the reduced production at the front face of the module, and this demonstrates the potential of bifacial technology for offshore applications. By comparison, most existing horizontally tilted bifacial FPV systems gain only a small benefit in production from the rear face of the module due to the minimum radiation received, and what also must be taken into consideration is the negative effect of significant soiling owing to the low tilt angle of the PV modules. Hence, to overcome these drawbacks, we have developed the innovative “PVSail” concept, which explores the deployment of vertical FPV systems on floats, buoys, or poles/minipiles. Floating vertical bifacial PV systems (VBPVs) have huge potential to harness all the energy generation capabilities enhance by reflected light, especially from snow‐covered surfaces in northern regions. Our analysis considers a patented mooring and vertical PV system that allows the VBPV structure to align with the prevailing wind direction to shed wind loads, and our numerical analysis explores the potential of VBPV applied to Catania in Italy and Nigg Bay in the United Kingdom. Our analysis study has revealed that across an azimuth angle range (0°–180°), vertical bifacial modules experience roughly a 9% decrease in energy yield at Catania and about a 5% energy yield gain in higher latitude regions like Nigg Bay. Additionally, increasing the latitude of the installation location of VBPV reduces the energy yield sensitivity to the orientation, that is, azimuth angle. The PVSail concept opens the door to novel deployment possibilities in offshore renewable energy projects.

Novel Thermoreversible Reverse-Phase-Shift Foam With Deployment System for Treatment of Penetrating Globe Trauma in a Newly Described Porcine Model.

The initial management of penetrating ocular injuries is a major sight-threatening problem for both civilian and military medicine. A novel device (Eye-Aid) temporarily tamponades leakage from such injuries while being easy to remove upon arrival to specialized ophthalmologic care. Eye-Aid consists of a protective eye shield with an adhesive backing that connects to a portable canister containing rapidly deployable thermoresponsive foam. The aim of this study was to compare the use of the novel Eye-Aid device to control in a new live swine ocular injury model. Bilateral penetrating ocular injuries were created on 14 male Yorkshire swine in a standardized manner using a 16-gauge needle device to puncture the central cornea and cause a full-thickness wound. Researchers randomized eye intervention side, with the contralateral eye used as paired control. Two minutes after the injury, the eye shield components of the Eye-Aid system, which has a sticky pad for attachment to the skin and a luer-lock for foam deployment, were placed bilaterally. Eight minutes after the injury, foam was deployed for the intervention eye according to the device instructions for use. For the control eye, no additional procedures were performed. Six hours post-injury, end A-scan and intraocular pressure (IOP) were measured. Primary study outcome was change in axial length of the globe. Secondary outcomes were as follows: (1) Presence of full anterior chamber collapse, defined as a lack of measurable anterior lens capsule-reflex (ALC-reflex) on A-scan and (2) change in IOP. Outcomes were analyzed as paired intra-animal data, with intervention and control data for each animal. A paired t-test was used to analyze the difference in axial length change and IOP change between treatment groups, whereas a conditional logistic regression was used to analyze dichotomous ALC-reflex outcome and estimate the odds ratio associated with the Eye-Aid device. A significant difference (P < .0001) in mean change in axial length between intervention (-210 μm) and control (-1,202 μm) groups was found. There was a significant difference in ALC-reflex presence, with 79% of eyes having an ALC-reflex in the intervention group, compared to 14% in the control (P = .008). IOP remained higher in the intervention group, with a mean change of -1.5 mmHg for the intervention group compared to -4.0 mmHg in the control (P = .0001). This study describes the first development of an in vivo large animal ocular injury model that realistically approximates the emergent time course and pathophysiology of patients with full-thickness corneal open globe injuries. It also gives the first description of using thermoreversible hydrogel foam for such injuries. Eye-Aid was found to be significantly better than control for treatment of such injuries, based on measurements of both structure and pressure. Assuming that the absence of an ALC-reflex demonstrates complete anterior chamber collapse, the Eye-Aid group demonstrated a 79% eye "save" rate compared to only 14% in the control group, as described earlier. This results in a Number Needed to Treat of 3 for this finding. Eye-Aid additionally demonstrated several characteristics that would be beneficial in a device targeted for emergent deployment by non-ophthalmologists.

Design and hydrodynamic analysis of an automated polymer composite mattress deployment system for offshore oil and gas pipeline protection

The protection of offshore oil and gas pipelines has become increasingly important due to its application in maritime transportation. Currently there are few deployment devices for pipelines and their operation was complicated. Therefore, a novel automatic polymer composite mattress deployment system is designed in this study, and its hydrodynamic performance is analyzed based on computational fluid dynamics (CFD). Firstly, the conceptual design of the system is introduced. Then, modeling and simulation are carried out on the system, including its forward movement, lateral movement, and heave movement at various speeds. Significant surface pressure was brought to the system during forward and lateral movement by the open frame design, with the maximum pressure during lateral movement reaching 719.46 Pa. During the heave movement, the mattress blocks the water flow, causing negative pressure on the surface of the system. The huge flow separation generated significant drag, with a maximum value of 12620.3 N. However, the mattress avoids the impact of water flow on the system. The longitudinal length of the mattress means the Reynolds number is greater than the other movements. The proportion of pressure drag further increases, reaching a maximum of 98.7%. This research provides new perspectives and solutions for the protection technology of pipelines.

Exploring the Processing Paradigm of Input Data for End-to-End Deep Learning in Tool Condition Monitoring.

Tool condition monitoring technology is an indispensable part of intelligent manufacturing. Most current research focuses on complex signal processing techniques or advanced deep learning algorithms to improve prediction performance without fully leveraging the end-to-end advantages of deep learning. The challenge lies in transforming multi-sensor raw data into input data suitable for direct model feeding, all while minimizing data scale and preserving sufficient temporal interpretation of tool wear. However, there is no clear reference standard for this so far. In light of this, this paper innovatively explores the processing methods that transform raw data into input data for deep learning models, a process known as an input paradigm. This paper introduces three new input paradigms: the downsampling paradigm, the periodic paradigm, and the subsequence paradigm. Then an improved hybrid model that combines a convolutional neural network (CNN) and bidirectional long short-term memory (BiLSTM) was employed to validate the model's performance. The subsequence paradigm demonstrated considerable superiority in prediction results based on the PHM2010 dataset, as the newly generated time series maintained the integrity of the raw data. Further investigation revealed that, with 120 subsequences and the temporal indicator being the maximum value, the model's mean absolute error (MAE) and root mean square error (RMSE) were the lowest after threefold cross-validation, outperforming several classical and contemporary methods. The methods explored in this paper provide references for designing input data for deep learning models, helping to enhance the end-to-end potential of deep learning models, and promoting the industrial deployment and practical application of tool condition monitoring systems.

A blockchain transaction mechanism in the delay tolerant network

Current blockchain systems have high requirements on network connection and data transmission rate, for example, nodes have to receive the latest blocks in time to update the blockchain, nodes have to immediately broadcast the generated block to other nodes for consensus, which restricts the blockchain to run only on real-time connection networks, but the existence of delay tolerant networks poses a great challenge to the deployment of blockchain systems. To address this challenge, a novel blockchain transaction mechanism is proposed. First, the block structure is modified by adding a flag, and on this basis, the definition of the extrachain is proposed. Secondly, based on the blockchain transaction process, transaction verification and consensus algorithms on the extrachain are presented. Thirdly, both the extrachain selection algorithm and appending algorithm are proposed, so that the extrachain can be appended to the blockchain fairly and safely. Finally, an extrachain transmission scheme is presented to broadcast the blocks generated in the delayed network to the normal network. Theoretical analysis and simulation experiments further illustrate the efficiency of the proposed mechanism.

Retraction Note: Deployment of smart home management system at the edge: mechanisms and protocols

Retraction Note: Deployment of smart home management system at the edge: mechanisms and protocols

Decision support for augmented reality-based assistance systems deployment in industrial settings

AbstractThe successful deployment of augmented reality (AR) in the industry for on-the-job guidance depends heavily on factors such as the availability of required expertise, existing digital content and other deployment-related criteria such as a task’s error-proneness or complexity. Particularly in idiosyncratic manufacturing situations involving customised products and diverse complex and non-complex products and its variants, the applicability and attractiveness of AR as a worker assistance system is often unclear and difficult to gauge for decision-makers. To address this gap, we developed a decision support tool to help prepare customised deployment strategies for AR-based assistance systems utilising manual assembly as the main example. Consequently, we report results from an interview study with sixteen domain experts. Furthermore, when analysing captured expert knowledge, we found significant differences in criteria weighting based on task complexity and other factors, such as the effort required to obtain data.

MADDPG-Based Deployment Algorithm for 5G Network Slicing

One of the core features of 5G networks is the ability to support multiple services on the same infrastructure, with network slicing being a key technology. However, existing network slicing architectures have limitations in efficiently handling slice requests with different requirements, particularly when addressing high-reliability and high-demand services, where many issues remain unresolved. For example, predicting whether actual physical resources can meet network slice request demands and achieving flexible, on-demand resource allocation for different types of slice requests are significant challenges. To address the need for more flexible and efficient service demands, this paper proposes a 5G network slicing deployment algorithm based on the Multi-Agent Deep Deterministic Policy Gradient (MADDPG). Firstly, a new 5G network slicing deployment system framework is established, which measures resources for three typical 5G network slicing scenarios (eMBB, mMTC, uRLLC) and processes different types of slice requests by predicting slice request traffic. Secondly, by adopting the multi-agent approach of MADDPG, the algorithm enhances cooperation between multiple service requests, decentralizes action selection for requests, and schedules resources separately for the three types of slice requests, thereby optimizing resource allocation. Finally, simulation results demonstrate that the proposed algorithm significantly outperforms existing algorithms in terms of resource efficiency and slice request acceptance rate, showcasing the advantages of multi-agent approaches in slice request handling.

Study of mechanical, optoelectronic, and thermoelectric characteristics of Be/Mg ions Based double perovskites A2FeH6 (A= Be, Mg) for hydrogen storage applications

This study addresses the hydrogen storage capabilities and mechanical, optoelectronic, and thermoelectric features of double perovskite hydrides A2FeH6 (A = Be, Mg). The structural aspects suggest the cubic symmetry of studied hydrides. The formation enthalpy value calculation has confirmed the structures' thermal stability. The gravimetric ratios of Be2FeH6 and Mg2FeH6 hydrides are 7.50 wt% and 5.43 wt%, respectively. The desorption temperatures are predicted to be 230.32 K and 208.67 K, respectively. The mechanical and thermophysical characteristics have been elaborated, which demonstrate mechanical stability, higher stiffness, ductile behavior, and higher temperature stability of both materials. The examined electronic properties have revealed band gap values of 2.89 and 3.08 eV, respectively. The optical properties have been assessed to predict the appropriateness of these hydrides for deployment in photovoltaic systems. The thermoelectric characteristics have been evaluated to examine the carrier transport mechanism and efficiency for converting heat into electricity. Finally, the investigated properties and hydrogen storage capacity of these compounds suggest that they are suitable for hydrogen storage and have the potential to play a key role in many energy generation applications.

Semi-Analytical Model for Hybrid and Redox Targeting Flow Battery Systems

Redox flow battery represents an economically viable energy storage technology that can integrate intermittent renewable energies from solar and wind power into existing electric grids. Besides the conventional all-liquid redox flow battery, there are two different architectures of flow batteries: hybrid and redox targeting flow batteries. Because the reactive solid phase is involved in the system, the energy storage density increases significantly. Therefore, they are considered as ones of the potential solutions for the future large-scale deployment of long-duration energy storage systems (LDES). A digital twin (DT) has been demonstrated as a promising numerical framework and platform to assist in the design, deployment, and operation of LDES. DTs usually require a fast-response model to provide an estimation of the flow battery's performance, which can be a fast-forward physics-based model or a machine learning-based regression model. Many pioneering works and our previous studies have introduced several fast-forward physics-based models for the conventional all-liquid redox flow battery in both inorganic and organic systems. In this study, a semi-analytical model, which can adapt to DTs, is proposed to provide an estimation of the performance of the hybrid and the redox targeting flow battery at different operating conditions.