System-level Model Research Articles

A Power Analysis Method for Self-Interference Signal Components in Full-Duplex Transceivers Under Constant/Nonconstant Modulus Signal Stimulation

The existence of multiple self-interference (SI) signal components, particularly the nonlinear ones, seriously constrains the performance of self-interference cancellation (SIC) methods. To decrease the complexity of SIC methods in full-duplex devices, this article proposes a power analysis method for SI signal components in a full-duplex transceiver. The proposed method comprises a separate analysis algorithm and a system-level power model. Initially, the algorithm is conducted to obtain the spectrum of the linear and nonlinear components in the power amplifier (PA) output signal. Once the linear-to-nonlinear power ratio (LNPR) has been obtained, a system-level power model is constructed by taking both the transmitter noise and analog-to-digital converter (ADC) quantization noise into account. The proposed power model allows for the allocation of SIC method performance in multiple domains during the design of full-duplex transceivers at the top level, thereby reducing the overall system complexity. The simulation results demonstrate that in a full-duplex transceiver with only antenna isolation, the power of the SI signal component is susceptible to alterations due to the operating waveform and transmission power. Finally, the accuracy of the power analysis method is verified through measurement and Simulink.

MULTI-AEROELASTIC PHENOMENA MODELLING PART I: THE DEVELOPMENT OF WHOLE LPC MODEL FOR CIVIL TURBOFAN ENGINE

Abstract Aeroelastic phenomena in the low pressure compression (LPC) system of civil turbofan engines can cause a significant high cycle fatigue (HCF) risk to LPC components. Currently, the mechanical integrity risk to the LPC components is assessed by performing multiple, discrete aeromechanical simulations, which require large resources and incur high time cost. Therefore, the development of a comprehensive modelling simulation approach is necessary to assess multiple phenomena and components simultaneously that can provide accurate results within design timescales. This paper presents the methodology for generating a common, system level model for investigating multiple aeroelastic phenomena in fan and outlet guide vanes (OGVs). The development of a high fidelity, full annulus CFD model of the whole low pressure compression system is described. Time accurate unsteady CFD simulations are then conducted at multiple flight conditions. These information-rich simulation models are interrogated to extract various parameters of interest to assess multiple aeroelastic phenomena such as, 1EO fan forced response, fan alternating passage divergence (APD) forced response, OGV buffet, and OGV resonant forced response etc. In the second part of this paper, the application of this modelling methodology is demonstrated to evaluate and to better understand the efficacy of OGV asymmetric cyclic patterns.

A system-level model reveals that transcriptional stochasticity is required for hematopoietic stem cell differentiation

HSCs differentiation has been difficult to study experimentally due to the high number of components and interactions involved, as well as the impact of diverse physiological conditions. From a 200-node network, that was grounded on experimental data, we derived a 21-node regulatory network by collapsing linear pathways and retaining the functional feedback loops. This regulatory network core integrates key nodes and interactions underlying HSCs differentiation, including transcription factors, metabolic, and redox signaling pathways. We used Boolean, continuous, and stochastic dynamic models to simulate the hypoxic conditions of the HSCs niche, as well as the patterns and temporal sequences of HSCs transitions and differentiation. Our findings indicate that HSCs differentiation is a plastic process in which cell fates can transdifferentiate among themselves. Additionally, we found that cell heterogeneity is fundamental for HSCs differentiation. Lastly, we found that oxygen activates ROS production, inhibiting quiescence and promoting growth and differentiation pathways of HSCs.

Multi-criteria sensitivity study and optimization of a two-stage preheated SOFC system considering thermal characteristics

At present, the pivotal challenges hindering the commercialization of the Solid Oxide Fuel Cell (SOFC) lie in system design and matching, thermal management safety, and performance optimization. To address these issues, a SOFC system-level model for the optimal control is proposed, integrating a two-stage preheater design, the electrochemical reaction mechanisms, and the influence of thermoelectric coupling effects on electrode temperature distributions. Furthermore, a comprehensive multi-criteria sensitivity analysis is conducted, incorporating thermodynamic, economic, and environmental indicators. The research demonstrates that there exist distinct mechanisms underlying the impact of varying trends in fuel utilization ratio and excess air ratio on the performance indicators of the SOFC system. Then, a detailed sensitivity analysis is undertaken, exploring the response trends of performance indicators under varying parameters. To validate the dynamic behavior of the objective functions, an intelligent learning approach based on the Artificial Neural Network is proposed. Additionally, a Multi-Objective Grey Wolf Optimization process is introduced, aimed at enhancing the comprehensive performance of the SOFC system. The results of this study demonstrate that the proposed multi-objective optimization strategy achieves a reduction of 12.12% in maximum temperature gradient, 28.00% in Levelized Cost of Energy, and 4.76% in Mass Specific Emission at the optimal operating point. This underscores the effectiveness of the approach in balancing and optimizing the trade-offs between thermal stability, economic feasibility, and environmental impact of the SOFC system.

An Electrochemical System Model for Aqueous Organic Flow Batteries: The TEMPTMA/MV System

Flow batteries store the energy in the liquid electrolytes, which can be pumped through the battery stacks and stored in external tanks. This technology provides exciting solutions for large-scale, low-cost energy storage with the advantages of decoupling output power and storage capacity [1]. In recent decades, the commercial use of flow batteries has been rapidly developing as the need for long-duration energy storage has increased. To alleviate any concerns about the corrosivity and cost of metal-based electrolytes, such as the well-established vanadium flow batteries, recent researchers have shown great interest in seeking alternative redox couples for aqueous organic flow batteries (AOFB) due to their potentially improved safety by utilising near-neutral aqueous solutions, relatively lower cost using earth-abundant organic compounds and high flexibility with their wide diversity of molecular structures [2]. An aqueous organic flow battery using TEMPTMA/MV, one of the TEMPO/MV derivatives first proposed in 2016 [3], has claimed high capacity and power density while maintaining excellent stability.While most literature emphasises microscale or cell-scale experiments and modelling for aqueous organic flow batteries, very little literature to date has reported on a detailed stack or system-level model. To this end, this work proposes a detailed electrochemical stack and system model for the TEMPTMA/MV aqueous organic flow batteries, which can be applied to model commercial scale stacks and improve their design. The electrochemical model is based on the equivalent circuit method, used to determine the resistances, currents, voltages and efficiencies of aqueous organic flow batteries, considering shunt currents, ion diffusion, self-discharge and degradation side reactions. The model can also simulate capacity fade and concentration variations under multiple cycles to investigate the long-term operation of aqueous organic flow batteries. The results demonstrate that ion diffusion and degradation play a predominant role in the capacity fade of the TEMPTMA/MV aqueous organic flow batteries, while the accumulation of diffused ions across the membrane suppresses the ion crossover and tends to stabilise the coulombic capacity loss in the long run.The model utilises TEMPTMA/MV as a demonstration but can also be adapted to other non-mixing aqueous organic flow batteries to predict and optimise the performance. The proposed electrochemical stack and system model can provide valuable information and insights for the design optimisation of aqueous organic flow batteries. Reference [1] C. Menictas and M. Skyllas-Kazacos, “Next-Generation Vanadium Flow Batteries,” Flow Batteries: From Fundamentals to Applications, vol. 2, pp. 673–687, 2023.[2] J. Luo, B. Hu, M. Hu, Y. Zhao, and T. L. Liu, “Status and Prospects of Organic Redox Flow Batteries toward Sustainable Energy Storage,” ACS Energy Letters, vol. 4, no. 9. American Chemical Society, pp. 2220–2240, Sep. 13, 2019. doi: 10.1021/acsenergylett.9b01332.[3] T. Janoschka, N. Martin, M. D. Hager, and U. S. Schubert, “An Aqueous Redox-Flow Battery with High Capacity and Power: The TEMPTMA/MV System,” Angewandte Chemie - International Edition, vol. 55, no. 46, pp. 14427–14430, Nov. 2016, doi: 10.1002/ANIE.201606472.

FuzzAGG: A Fuzzing-Driven Attack Graph Generation Framework for Industrial Robot Systems

As industrial robot systems (IRS) are increasingly utilized in smart factories, their information security issues have become particularly critical. Attack graphs, an essential system-level risk modeling technique, traditionally rely on predefined risk attributes and exploitation rules for their generation. However, this approach fails to meet the needs for attack graph generation and analysis in environments with missing risk data. To address this issue, this paper proposes a fuzzing-driven attack graph generation framework, FuzzAGG. This framework aims to provide an efficient and accurate method for generating attack graphs under conditions of incomplete risk data, thereby supporting information security analysis and risk assessment of IRS. In this paper, a risk data model (RDM) is constructed using the Meta Attack Language to achieve a structured description of the risk data of IRS. A fuzzing test case generation algorithm based on the MU-SeqGAN model is proposed, which can generate test cases suitable for the state machines of IRS and map them to specific Risk Data Model Objects (RDMOs). Additionally, a conversion unit is designed to integrate all RDMOs into a risk description file, which is then used by the generation unit to construct a graphical attack graph. In performance tests, FuzzAGG is able to achieve automated construction of IRS attack graphs containing 1000 state nodes in 42min and maintain 88% risk coverage. Taking the IRS of a PCB automated production line the effectiveness of the FuzzAGG framework is validated. The results demonstrate that FuzzAGG can automatically generate and validate an attack graph containing 184 attribute nodes and atomic attack nodes in 8min with high operational efficiency, proving the practicality and reliability of this method in automated attack graph generation.

Arch2End: Two-Stage Unified System-Level Modeling for Heterogeneous Intelligent Devices

Arch2End: Two-Stage Unified System-Level Modeling for Heterogeneous Intelligent Devices

‘Positive disruption’: catalysing system-wide work on health inequalities through embedded research

Abstract Background This presentation explores embedded research as an innovative model for bridging system-level knowledge to action gaps in public health. Embedded researchers (ERers) are university-employed researchers co-located in public health organisations, who work to boost capacity in these organisations to tackle inequalities by better use of evidence in decision-making, evaluation and service improvement Methods Drawing on the initial phase of the role and wider literature, we qualitatively reflect on strengths and limits of a system-level ERer model. Most ERer evidence draws on a one-to-one organisational model. This presentation explores a different model of an ERer embedded in and working across a public health system (health services, municipality, third sector) in a large city in north England Results Our focus is on catalytic features of the ERer role (1) positionality: being embedded at system-level gives fuller understanding of inter-organisational connections and of the implications of emergent system changes; (2) convening: bringing together partners improves mutual awareness and builds interest in collectively pursuing research aligned to identified population health needs and public health priorities in the city; (3) co-production to build capacity: working with a new cross-organisational partnership to collectively address issues constraining opportunities to pursue system-level research; working with the municipality to explore the production/use of research to inform decisions on approaches towards health inequalities. This includes work on links between public health and other functions focused on social determinants of health Conclusions This presentation outlines distinctive consequences of system-level embedded research in a city-wide public health system. It critically appraises the transferability of this model of knowledge production and mobilisation at the intersection of system and cross-organisational action on health inequalities. Key messages • System-level embedded research is an innovative model for helping to bridge system-level knowledge to action gaps in public health. • The model has catalytic benefits and transferability potential, although this needs to be considered in relation to the specific context for proposed system-level embedded research.

System-Level Dynamic Model of Redox Flow Batteries (RFBs) for Energy Losses Analysis

This paper presents a zero-dimensional dynamic model of redox flow batteries (RFBs) for the system-level analysis of energy loss. The model is used to simulate multi-cell systems considering the effect of design and operational parameters on energy loss and overall performance. The effect and contribution of stack losses (e.g., overpotential and crossover losses) and system losses (e.g., shunt currents and pumps) to total energy loss are examined. The model is tested by using literature data from a vanadium RFB energy storage. The results show that four parameters mainly affect RFB system performance: manifold diameter, stack current, cell standard potential, and internal resistance. A reduction in manifold diameter from 60 mm to 20 mm reduced shunt current loss by a factor of four without significantly increasing pumping loss, thus boosting round-trip efficiency (RTE) by 10%. The increase in stack current at a low flow rate increases power, while the cell standard potential and internal resistance play a crucial role in influencing both power and energy output. In summary, the modeling activities enabled the understanding of critical aspects of RFB systems, thereby serving as tools for system design and operation awareness.

Using wave based SEA methods to model noise and vibration in underwater structures across a broad frequency range

Modelling noise and vibration transmission in underwater structures is often challenging due to the large size of the structures and the need for analysis across a broad frequency range. Finite Element and Boundary Element methods are useful at lower frequencies but are typically not computationally tractable for system level models at mid and high frequencies. Statistical Energy Analysis (SEA) methods are widely used for modeling structures in air at mid and high frequencies. However, traditional SEA methods often do not contain enough detail to describe wave propagation in heavy fluid loaded structures in underwater applications. This paper discusses the use of a more general 'wave based' SEA method that uses periodic structure theory to accurately calculate the wavetypes of typical underwater structures. The paper illustrates the method by initially considering simple structural sections and showing the effects of: curvature, heavy fluid-loading, panel stiffeners/ribs. The method is then applied to a simplified full scale submarine model and comparisons are made with a nodal finite element model. The domain of validity and the hypotheses of the method are then discussed.

Turbopump Parametric Modelling and Reliability Assessment for Reusable Rocket Engine Applications

The development of modern reusable launchers, such as the Themis project with its LOX/LCH4 Prometheus engine, CALLISTO—a reusable VTVL-launcher first-stage demonstrator with a LOX/LH2 RSR2 engine, and SpaceX’s Falcon 9 with its Merlin 1D engine, underscores the need for advanced control algorithms to ensure reliable engine operation. The multi-restart capability of these engines imposes additional requirements for throttling, necessitating an extended controller-validity domain to safely achieve low thrust levels across various operating regimes. This capability also increases the risk of component failure, especially as engine parameters evolve with mission profiles. To address this, our study evaluates the dynamic reliability of reusable rocket engines (RREs) and their subcomponents under different failure modes using multi-physics system-level modelling and simulation, with a particular focus on turbopump components. Transient condition modelling and performance analysis, conducted using EcosimPro-ESPSS software (version 6.4.34), revealed that turbopump components maintain high reliability under nominal conditions, with turbine blades demonstrating significant fatigue life even under varying thermal and mechanical loads. Additionally, the proposed predictive model estimates the remaining useful life of critical components, offering valuable insights for improving the longevity and reliability of turbopumps in reusable rocket engines. This study employs deterministic, thermally dependent structural simulations, with key control objectives including end-state tracking of combustion chamber pressure and mixture ratios and the verification of operational constraints, exemplified by the LUMEN demonstrator engine and the LE-5B-2 engine class.

High-Resolution Magic-Angle Spinning Nuclear Magnetic Resonance Identifies Impairment of Metabolism by T-2 Toxin, in Relation to Toxicity, in Zebrafish Embryo Model.

Among the widespread trichothecene mycotoxins, T-2 toxin is considered the most toxic congener. In the present study, we utilized high-resolution magic-angle spinning nuclear magnetic resonance (HRMAS NMR), coupled to the zebrafish (Danio rerio) embryo model, as a toxicometabolomics approach to elucidate the cellular, molecular and biochemical pathways associated with T-2 toxicity. Aligned with previous studies in the zebrafish embryo model, exposure to T-2 toxin was lethal in the high parts-per-billion (ppb) range, with a median lethal concentration (LC50) of 105 ppb. Exposure to the toxins was, furthermore, associated with system-specific alterations in the production of reactive oxygen species (ROS), including decreased ROS production in the liver and increased ROS in the brain region, in the exposed embryos. Moreover, metabolic profiling based on HRMAS NMR revealed the modulation of numerous, interrelated metabolites, specifically including those associated with (1) phase I and II detoxification, and antioxidant pathways; (2) disruption of the phosphocholine lipids of cell membranes; (3) mitochondrial energy metabolism, including apparent disruption of the tricarboxylic acid (TCA) cycle, and the electron transport chain of oxidative phosphorylation, as well as "upstream" effects on carbohydrate, i.e., glucose metabolism; and (4) several compensatory catabolic pathways. Taken together, these observations enabled development of an integrated, system-level model of T-2 toxicity in relation to human and animal health.

Numerical Modeling of a Polymer Electrolyte Membrane Fuel Cell for Electric Aircraft Propulsion

Electrified propulsion systems with hydrogen-fueled fuel cells can potentially reduce carbon dioxide emissions from aircraft. However, achieving a substantial increase in the gravimetric/volumetric power density of fuel cell systems poses a considerable challenge. In this study, we focus on high temperature polymer electrolyte membrane fuel cells (HT-PEMFCs) working between 100 and 200°C. To design an optimized PEMFC structure for an electric aircraft, we have developed a numerical model that employs the finite element method (FEM) (COMSOL Multiphysics) to couple electrochemistry, gas diffusive and convective transports, and heat transfer in fuel cell components such as separators, gas flow channels, gas diffusion layers, catalyst layers, and a PEM. We thereby derive the current distributions associated with three-dimensional hydrogen, oxygen, and water vapor concentration distributions, as well as temperature distributions. Moreover, we develop a machine learning model from the data outputs of the FEM model, which serves as training data, to reduce the computational costs for the fuel cell stack design incorporated in a system-level numerical model.

Numerical Modeling of a Polymer Electrolyte Membrane Fuel Cell for Electric Aircraft Propulsion

Electrified propulsion systems with hydrogen-fueled fuel cells can potentially reduce carbon dioxide emissions from aircraft. However, achieving a substantial increase in the gravimetric/volumetric power density of fuel cell systems poses a considerable challenge. In this study, we focus on high temperature polymer electrolyte membrane fuel cells (HT-PEMFCs) working between 100 and 200°C. To design an optimized PEMFC structure for an electric aircraft, we have developed a numerical model that employs the finite element method (FEM) (COMSOL Multiphysics) to couple electrochemistry, gas diffusive and convective transports, and heat transfer in fuel cell components such as separators, gas flow channels, gas diffusion layers, catalyst layers, and a PEM. We thereby derive the current distributions associated with three-dimensional hydrogen, oxygen, and water vapor concentration distributions, as well as temperature distributions. Moreover, we develop a machine learning model from the data outputs of the FEM model, which serves as training data, to reduce the computational costs for the fuel cell stack design incorporated in a system-level numerical model.

Multidisciplinary design optimization of an autonomous underwater vehicles based on random uncertainty

Herein, discipline decomposition was performed for an autonomous underwater vehicle (AUV) based on multidisciplinary design optimization (MDO), and its design parameters were determined. Parameterized analysis of the hull-form and structure disciplines was performed, and an approximate model of these disciplines was established with a high fitting accuracy. An analysis model of propulsion, energy, and system disciplines was developed based on the formula method. Based on collaborative optimization as well as discipline and system level models, a deterministic MDO framework for AUVs was established. Subsequently, an optimized solution was obtained. By considering the random uncertainty of design variables, an MDO framework for the uncertainty of AUVs was established and optimized solutions were obtained. The distribution of solutions obtained from uncertainty optimization was more concentrated and the distribution range was smaller than that obtained using deterministic optimization. Robustness analysis was performed on the initial scheme, typical deterministic optimization schemes, and typical uncertainty optimization schemes. Results showed that fluctuations in design variables may lead to constraints that exceed boundary conditions in the deterministic optimization design scheme. Using uncertainty and objective function optimization, the robustness of the overall scheme of AUVs was improved.

Multiscale modeling shows how 2’-deoxy-ATP rescues ventricular function in heart failure

2'-deoxy-ATP (dATP) improves cardiac function by increasing the rate of crossbridge cycling and Ca[Formula: see text] transient decay. However, the mechanisms of these effects and how therapeutic responses to dATP are achieved when dATP is only a small fraction of the total ATP pool remain poorly understood. Here, we used a multiscale computational modeling approach to analyze the mechanisms by which dATP improves ventricular function. We integrated atomistic simulations of prepowerstroke myosin and actomyosin association, filament-scale Markov state modeling of sarcomere mechanics, cell-scale analysis of myocyte Ca[Formula: see text] dynamics and contraction, organ-scale modeling of biventricular mechanoenergetics, and systems level modeling of circulatory dynamics. Molecular and Brownian dynamics simulations showed that dATP increases the actomyosin association rate by 1.9 fold. Markov state models predicted that dATP increases the pool of myosin heads available for crossbridge cycling, increasing steady-state force development at low dATP fractions by 1.3 fold due to mechanosensing and nearest-neighbor cooperativity. This was found to be the dominant mechanism by which small amounts of dATP can improve contractile function at myofilament to organ scales. Together with faster myocyte Ca[Formula: see text] handling, this led to improved ventricular contractility, especially in a failing heart model in which dATP increased ejection fraction by 16% and the energy efficiency of cardiac contraction by 1%. This work represents a complete multiscale model analysis of a small molecule myosin modulator from single molecule to organ system biophysics and elucidates how the molecular mechanisms of dATP may improve cardiovascular function in heart failure with reduced ejection fraction.

Fundamental equations and hypotheses governing glomerular hemodynamics.

The glomerular filtration rate (GFR) is the outcome of glomerular hemodynamics, influenced by a series of parameters: renal plasma flow, resistances of afferent arterioles and efferent arterioles (EAs), hydrostatic pressures in the glomerular capillary and Bowman's capsule, and plasma colloid osmotic pressure in the glomerular capillary. Although mathematical models have been proposed to predict the GFR at both the single-nephron level and the two-kidney system level using these parameters, mathematical equations governing glomerular filtration have not been well-established because of two major problems. First, the two-kidney system-level models are simply extended from the equations at the single-nephron level, which is inappropriate in epistemology and methodology. Second, the role of EAs in maintaining the normal GFR is underappreciated. In this article, these two problems are concretely elaborated, which collectively shows the need for a shift in epistemology toward a more holistic and evolving way of thinking, as reflected in the concept of the complex adaptive system (CAS). Then, we illustrate eight fundamental mathematical equations and four hypotheses governing glomerular hemodynamics at both the single-nephron and two-kidney levels as the theoretical foundation of glomerular hemodynamics. This illustration takes two steps. The first step is to modify the existing equations in the literature and establish a new equation within the conventional paradigm of epistemology. The second step is to formulate four hypotheses through logical reasoning from the perspective of the CAS (beyond the conventional paradigm). Finally, we apply the new equation and hypotheses to comprehensively analyze glomerular hemodynamics under different conditions and predict the GFR. By doing so, some concrete issues are eliminated. Unresolved issues are discussed from the perspective of the CAS and a desinger's view. In summary, this article advances the theoretical study of glomerular dynamics by 1) clarifying the necessity of shifting to the CAS paradigm; 2) adding new knowledge/insights into the significant role of EAs in maintaining the normal GFR; 3) bridging the significant gap between research findings and physiology education; and 4) establishing a new and advanced foundation for physiology education.

System-level optimisation of hybrid energy powered irrigation system

Renewable energy-powered irrigation systems have emerged as sustainable solutions, particularly for farmers in off-grid areas. While existing research often highlights tank storage-based systems as the most cost-effective option, large-scale deployment of water tanks incurs significant costs and maintenance challenges. Additionally, there is limited research on the feasibility and optimisation of battery-based irrigation systems, which are often deemed costly despite their potential benefits. This study addresses this gap by identifying the optimal storage solution for hybrid energy-powered irrigation systems through a system-level optimisation model. The model evaluates the suitability of three storage options: direct-coupled water tank storage, battery-coupled storage, and a hybrid battery-tank storage system. Optimisation criteria include life cycle cost (LCC), loss of power supply probability (LPSP), and loss of load probability (LOLP), ensuring a comprehensive assessment of both cost and reliability. Results indicate that the hybrid battery-tank storage system is the most reliable, followed by battery-only storage, while tank-only storage, despite its lower initial cost, poses scalability and maintenance challenges. The LCC over a 25-year project lifetime is £31 k for battery-tank, £26 k for battery-only, and £23.3 k for tank-only systems. Despite the lower cost of tank storage, its complexity and maintenance make it the least preferred option for large-scale systems.

High-Performance Lithium-Sulfur Batteries with Atomic V- and Co-Modified Ketjen Black-Sulfur Composite Cathode

Lithium-sulfur (Li-S) batteries are potential alternatives to lithium-ion batteries, which are known to have certain drawbacks. This has drawn special technological attention. Sulfur’s low electrical conductivity, shuttling of the intermediate polysulfides, the slow conversion reaction kinetics of various lithium polysulfides (LiPS), and the major volume change of sulfur in the lithiation reaction are some of the main issues that still need to be resolved before Li-S batteries can be used in practical applications. Additionally, it is likely because there are not enough prognostic models determining energy densities at the cell and pack level based on materials and cell design, most of the articles do not report system-level metrics, specific energy (Wh kg-1), and energy density (Wh L-1).To overcome these problems, atomic vanadium (V) and cobalt (Co) modified ketjen black-sulfur composite (VCKBS) was prepared and used as a cathode in Li-S batteries. The synthesized composite was characterized by various techniques, e.g., XRD, Raman, SEM, HR-TEM, HAADF-STEM, and XPS spectra. The polysulfide adsorption test showed that V had increased the adsorption ability, and cyclic voltammetry displayed the increased catalytic activity resulting from Co, offering a large number of interfacial active sites and ensuring smooth electron transfer. Cycling performance tests showed an initial specific capacity of 1329 mAh g−1, maintained at 1249 mAh g−1 after 100 cycles. Under a higher sulfur loading (2.4 mg cm−2), at 0.2 C, Li-S cells demonstrated an initial specific capacity of 1201 mAh g−1 and retained 985 mAh g−1 after 300 cycles with a low fading rate of 0.059% per cycle. The rate performance tests showed that the prior capacity is nearly recovered, going from 1C back to 0.1C, demonstrating the superiority of the VCKBS material.In addition to the experimental characterization of the Li-S cell performance, energy densities were also predicted by a proposed system-level performance model. The 1st and 100th discharge capacities were used to forecast the system-level specific energies and energy densities. The system-level specific energies and energy densities based on the 100th discharge capacity of VCKBS cathodes had improvements of 1342 and 568%, respectively. This suggests that an easy route to construct the highly stable cathode material may pave the way for the development of highly efficient Li-S batteries. Acknowledgments Hira Fazal acknowledges support from the Türkiye Scholarship/Research Fellowship Program sponsored by the Prime Ministry of Turkey Presidency for Turks Abroad and Related Communities (21PK070917). Damla Eroglu acknowledges support from the Istanbul Development Agency (Award No: TR10/21/YEP/0001). We also thank the National Natural Science Foundation of China (22171180) and the Science and Technology Commission of Shanghai Municipality (20520710400).

A novel scale-bridging method for MSMA linking continuum thermodynamics constitutive formulations to lumped system-level models

This work introduces a novel scale-bridging method between a continuum thermodynamics constitutive model and a lumped system-level model for magnetic shape memory alloys (MSMA). With this method, system models for real-time operations are generated based on virtual experiments using the constitutive model. The proposed method addresses the fact that, while constitutive models for MSMA typically only require small sets of parameters as input, their evaluation is still computationally expensive. System models for control engineering, however, require extensive experimental parameterization, while their evaluation is highly time-efficient. The proposed scale-bridging method has the potential to combine a small parameterization effort and a low computational cost of the real-time system model. Additionally, the constitutive model is utilized to investigate whether it can determine the individual behavior of MSMA samples. This is important since the inherent model parameters, while valid for ideal single crystals, deviate for non-ideal MSMA sample behavior. To this end, the MSMA constitutive model, based on a global variational principle originally proposed by Kiefer et al is supplemented by various extensions, including a more robust algorithmic treatment. A parameter identification procedure is introduced to optimize the constitutive model parameters based on an outer hysteresis curve for a particular load case. By conducting virtual experiments with the constitutive model, data sets are generated to parameterize Preisach hysteresis models as numerical approximations of the constitutive models. The resulting hysteresis models are compared with physical experiments using an MSMA test bench for different load cases. It is shown that the proposed scale-bridging method successfully generates hysteresis models derived from constitutive models. While maintaining accuracy comparable to strictly phenomenological models across various load cases (as validated through physical MSMA test bench experiments), these models require significantly less parameterization effort than classical system models. This translates to faster model creation and broader applicability.