System-level Model Research Articles

MitoMAMMAL: a genome scale model of mammalian mitochondria predicts cardiac and BAT metabolism

Abstract Motivation Mitochondria perform several essential functions in order to maintain cellular homeostasis and mitochondrial metabolism is inherently flexible to allow correct function in a wide range of tissues. Dysregulated mitochondrial metabolism can therefore affect different tissues in different ways which presents experimental challenges in understanding the pathology of mitochondrial diseases. System-level metabolic modelling is therefore useful in gaining in-depth insights into tissue-specific mitochondrial metabolism, yet despite the mouse being a common model organism used in research, there is currently no mouse specific mitochondrial metabolic model available. Results In this work, building upon the similarity between human and mouse mitochondrial metabolism, we have created mitoMammal, a genome-scale metabolic model that contains human and mouse specific gene-product reaction rules. MitoMammal is therefore able to model mouse and human mitochondrial metabolism. To demonstrate this feature, using an adapted E-Flux algorithm, we first integrated proteomic data extracted from mitochondria of isolated mouse cardiomyocytes and mouse brown adipocyte tissue. We then integrated transcriptomic data from in vitro differentiated human brown adipose cells and modelled the context specific metabolism using flux balance analysis. In all three simulations, mitoMammal made mostly accurate, and some novel predictions relating to energy metabolism in the context of cardiomyocytes and brown adipocytes. This demonstrates its usefulness in research relating to cardiac disease and diabetes in both mouse and human contexts. Availability and implementation MitoMammal is formatted in SBML3. The code required for constraint-based modelling used in this work is implemented in Python 3 and is available as a Jupyter Notebook. The mitoMammal metabolic model, along with Jupyter notebooks and data used in this work are available at: https://gitlab.com/habermann_lab/mitomammal. Supplementary Information Supplementary data are available at Bioinformatics Advances online.

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.

A system-level CFD simulation model for investigating the energy dissipation mechanism of seawater pump and rotary energy recovery device in SWRO desalination system

Seawater reverse osmosis (SWRO) technology has been universally adopted because of its high efficiency, economic benefits, easy maintenance, and high-pressure seawater pump (HPSP) and energy recovery device (ERD) are essential components, which determine the specific energy consumption of SWRO system. However, it is still unclear about energy dissipation mechanism and mixing characteristics from the perspective of system. For that, this thesis proposes a novel system-level Computational Fluid Dynamics (CFD) model considering all interfaces in HPSP and integrated energy recovery and pressure boost device (IERPBD). To profoundly investigate the system performance under various operating conditions, the pressure and composition distribution, power loss of each interface and mixing rate are numerically calculated and analyzed. Additionally, experiment of the effect of temperature on SWRO system performance is conducted to practically verify the proposed model, which indicates that the presented model could precisely reflect the energy dissipation and mixing characteristics. Besides, it is advised that the rotating speed of HPSP and IERPBD should not be set too high which could increase the mixing rate and energy consumption, and higher temperature might lower the quality of produced freshwater. This study offers a feasible way to reveal the energy dissipation mechanism of SWRO desalination system.

Thermal Energy Storage With Horizontal Thermocline Tank for Sludge Drying Application

Solar thermal technologies can be an attractive option for the decarbonization of process heat applications; in particular, thermal energy storage is a high value proposition for continuous processes that may operate at a range of thermal conditions. A thermal-fluid model is developed to investigate the buoyant flow in a horizontal thermocline storage tank for a sludge-drying application. The process conditions and tank geometry considered are unique compared to the domestic water heating application typically considered in the literature and well suited for parabolic trough collectors. It was found that good separation of hot and cold fluids can be achieved with the long horizontal tank geometry depending on the inlet configuration. Uniform flow distribution along the length of the tank is required to establish a thermocline. Assuming desirable inlet conditions can be achieved, the tank outlet temperature predicted by the thermal-fluid model shows good agreement with the system-level performance model.

Multiscale multiphysics modeling of ammonia-fueled solid oxide fuel cell: Effects of temperature and pre-cracking on reliability and performance of stack and system

Ammonia is a promising carbon-free fuel for solid oxide fuel cells (SOFCs). However, direct feeding of ammonia into the SOFC may lead to serious degradation due to the nitriding. Conversely, the pre-cracking of ammonia introduces complexity to the system design and causes increased power losses in the system. Therefore, it is crucial to explore all these aspects simultaneously. In this context, this study introduces a novel multiscale modeling approach by integrating a 3D multiphysics simulation of the ammonia-fueled SOFC stack with system-level modeling to investigate the reliability and performance of the stack and system. Two different cell technologies developed for low temperature (LT) and high temperature (HT) operation are investigated at LT (600 – 700°C) and HT (700 – 800°C) ranges. The results indicate that fuel inlet temperature should be 55°C and 18°C higher than the minimum temperature in 0% pre-cracking case for HT and LT cases, respectively. The increase in the required air flow rate for cooling in the 100% pre-cracking case compared to the 0% pre-cracking case is around 100% and 216% for the HT and LT cases, respectively. However, stack power production and power losses in the system components are comparable for LT and HT cases which leads to similar system performance. A larger share of the active area is affected by nitriding in LT cases than HT ones. However, a smaller cracking ratio at LT (∼ 82%) compared to HT conditions (∼ 92%) is needed for elimination of nitriding. While the LT and HT cases are comparable in terms of system power production, the lower stack outlet temperatures in LT cases require novel and more expensive catalysts for ammonia pre-cracking and HT cases need more expensive steels.

Multi-hazard life-cycle consequence analysis of deteriorating engineering systems

Probabilistic life-cycle consequence (LCCon) analysis (e.g., assessment of repair costs, downtime, or casualties over an asset’s service life) can enable optimal life-cycle management of critical assets under uncertainties. This can lead to effective risk-informed decision-making for future disaster management (i.e., risk mitigation and/or resilience-enhancing strategies/policies) implementation. Nevertheless, despite recent advances in understanding, modeling, and quantifying multiple-hazard (or multi-hazard) interactions, most available LCCon analytical formulations fail to accurately compute the exacerbated consequences which may stem from incomplete or absent repair actions between different interacting hazard events. This paper introduces a discrete-time, discrete-state Markovian framework for efficient multi-hazard LCCon analysis of deteriorating engineering systems (e.g., buildings, infrastructure components) that appropriately accounts for complex interactions between natural hazard events and their effects on a system’s performance. The Markovian assumption is used to model the probability of a system being in any performance level (i.e., limit state) after multiple hazard events inducing either instantaneous and/or gradual deterioration and after potential repair actions through implementing stochastic (transition) matrices. LCCon estimates are then obtained by combining limit state probabilties with suitable system-level consequence models in a computationally efficient manner. The proposed framework is illustrated for two case studies subject to earthquake and flood events as well as environment-induced corrosion during their service life. The first is a reinforced concrete building and the second is a simple transportation road network with a reinforced concrete bridge.

A System-Level Reliability Growth Model for Efficient Inspection Planning of Offshore Wind Farms

Fatigue damage can lead to failures of structural systems. To reduce the failure risk and enhance the reliability of structural systems, inspection and maintenance interventions are required, and it is important to develop an efficient inspection strategy. This study, for the first time, develops a system-level reliability growth model to establish efficient inspection planning. System-level reliability growth is defined as an increase in the percentage of the system reliability index with and without inspection. The probabilistic S-N approach is used to obtain the reliability index without inspection. Moreover, advanced risk analysis and Bayesian inference techniques are used to obtain the reliability index with inspection. The optimal inspection planning is obtained by maximizing system-level reliability growth. This model is applied to an offshore wind farm. The results show that inspection efficiency can be improved by increasing the number of repair objects in response to a ‘detection’ inspection outcome, changing the inspection object for each inspection, and increasing the inspection quality. The maximum system-level reliability growth gained from one additional inspection decreases as the number of inspections increases. This study quantifies the inspection efficiency of offshore wind farms by explicit system-level reliability growth computation, offering valuable insights for promoting sustainable energy solutions.