Search Results (1,468)

Ultrasonic thickness resonance can be effectively used to detect and quantify the level of corrosion in steel nuclear storage containers as well as other corrosion-prone thin-walled structures, such as pipes and storage tanks. Electro-Magnetic Acoustic Transducers (EMATs) have several advantages over more traditional piezoelectric-based transducers; namely, they can be used in a non-contact fashion on robotic platforms, allowing for measurements regardless of surface conditions or temperature. The major challenge of EMAT application is the power required to counteract the low actuation efficiency, which is achieved with a high-power ultrasonic pulse generator and a transformer circuit. Resonance techniques, in which most of the energy is concentrated near structural resonance frequencies, are preferable to improve efficiency of electro-magnetic acoustic measurements. This methodology was applied to 316L stainless steel thin plates subjected to uniform corrosion as well as pitting corrosion imitating different damage scenarios in a nuclear waste container. The resonant peak frequency shift was found to be proportional to the severity of corrosion for minimally corroded samples. However, the complete disappearance of the resonance peak was observed in the samples with severe corrosion damage. The EMAT liftoff distance was studied to quantify its effect on the amplitude, spread, and frequency of resonant peaks. Recommendations for use of EMATs for assessing corrosion damage are presented. The study demonstrates the success of frequency-based detection of corrosion damage in 316L stainless steel used in fabrication of nuclear waste storage containers. Full article

The safety and stability of onboard Liquid Hydrogen (LH₂) storage systems depend strongly on gas–liquid two-phase flow, heat transfer, and phase change under sloshing; however, the coupled influence of filling ratio and sloshing on thermo-hydrodynamic behavior remains underexplored. We develop a Volume of Fluid (VOF)-based two-phase Computational Fluid Dynamics (CFD) model in ANSYS Fluent to quantify interfacial dynamics, pressure response, and temperature-field evolution in LH₂ tanks subjected to sinusoidal acceleration for filling ratios from 10% to 90%. Increasing the filling ratio strengthens net condensation in the ullage and thus intensifies depressurization. As the filling ratio increases from 10% to 90%, the pressure reduction over the 2.0 s sloshing process increases from 0.418 kPa to 2.410 kPa, and the corresponding initial depressurization rate rises from 0.209 to 1.205 kPa s⁻¹. Free-surface motion decreases with filling ratio: at 10%, large interface excursions can induce gas-cavity formation and splashing, increasing the risk of intermittent propellant supply, whereas at 90% the interface is constrained and oscillations are suppressed. Higher filling ratios lead to faster ullage cooling and larger temperature oscillations. The liquid warms modestly, and its warming rate decreases nonlinearly with filling ratio, consistent with the larger effective thermal mass at higher fillings. Overall, the obtained mechanistic understanding can support the engineering design of onboard LH₂ tanks, including filling-ratio selection and thermal-management optimization under sloshing conditions. Full article

Liquid storage tanks are widely used in sectors such as water treatment, oil and gas, food processing, and chemical manufacturing. Knowing the exact amount of liquid in a tank is essential for ensuring safety, preventing spills, and optimizing process control; therefore, the liquid level in a tank must be maintained at a precise reference point. This is where liquid level control for tanks becomes crucial and constitutes a fundamental problem in the industrial sector due to nonlinearities, multivariable coupling, and stochastic disturbances. Given the drawbacks of available control methods, such as classical Model Predictive Control (MPC), which are highly dependent on model accuracy and struggle to reject complex stochastic noise, predicting random disturbances represents a major technological challenge. A new approach is proposed to specifically address the problem and challenge of the four-tank system, where water levels in two lower tanks must be controlled by two pumps, often with varying delays and significant parameter disturbances. To establish a relationship between expected performance and MPC parameters, this approach uses a novel hybrid nonlinear MPC, Extended State Observer, and Physics-Informed Neural State Estimation (NMPC-ESO-PINSE) architecture. A Physics-Informed Neural State Estimation (PINSE) layer, chosen for its learning capacity, is designed to filter sensor noise by applying Bernoulli’s physical laws, while an Extended State Observer (ESO) is integrated to capture and compensate for unmodeled uncertainties in the process. Finally, a proposed hybrid (NMPC-ESO-PINSE) strategy leverages these clean, physically consistent state estimations to solve a non-convex optimization problem via Sequential Quadratic Programming (SQP), computing optimal pump voltages. Extensive numerical simulations demonstrate the superior resilience of this decoupled framework against parametric drifts and continuous noise sequences, yielding a +27.36% reduction in global Root Mean Square Error (RMSE) compared to standard NMPC, accelerating the closed-loop settling time to 15.2 s, and restricting transient overshoot to just 0.18%. Full article

Ammonia is a promising zero-carbon energy carrier for maritime decarbonisation, but its deployment is limited by safety risks that are not adequately addressed by conventional marine fuel safety frameworks. This study critically reviews safety assessment, risk management and control strategies for ammonia storage and handling in maritime applications using a PRISMA-informed literature synthesis. Evidence is analysed across hazard characterisation, storage configurations, transfer operations, risk assessment methods, mitigation barriers and regulatory frameworks. The review shows that ammonia safety is governed by coupled release–exposure–barrier interactions shaped by storage condition, tank configuration, pressure–temperature behaviour, material compatibility, transfer mode, ventilation, ship geometry and human intervention. Existing methods, including HAZID, HAZOP, risk matrices and QRA, support hazard screening and prioritisation, but remain limited in representing flashing two-phase releases, dense gas dispersion, confined-space accumulation, exposure duration, ventilation effectiveness and safeguard timing under maritime conditions. CFD, FTA, Bayesian approaches and Monte Carlo analysis offer higher analytical resolution, but their reliability is constrained by limited validation data, uncertain leak-frequency inputs and simplified assumptions for human exposure and emergency response. Effective risk control therefore requires a toxicity-centred barrier strategy linking containment integrity, ammonia-compatible materials, gas and process monitoring, emergency shutdown, ventilation, water-based mitigation, PPE, competency-based training and emergency planning. Current regulatory and classification guidance provides an essential foundation but remains fragmented and insufficiently aligned with ammonia-specific requirements for exposure modelling, safety-zone definition and approval pathways. This review contributes a maritime-specific synthesis of ammonia storage and handling safety by connecting hazard behaviour, storage design, transfer operations, risk assessment limitations, control-barrier logic and regulatory approval needs. The findings highlight the need for validated source-term models, full-scale release and dispersion data, exposure-based safety criteria and harmonised regulatory pathways to support the safe and scalable use of ammonia in maritime decarbonisation. Full article

Limited access to clean water and reliable electricity infrastructure remains a major challenge in many remote regions of Indonesia, particularly for building-scale domestic use. Conventional water treatment systems are often constrained by high operational costs and dependence on grid power, highlighting the need for sustainable and autonomous infrastructure solutions. This study presents the design, development, and performance evaluation of an integrated solar-powered clean water treatment system for smart building applications in remote areas using a Research and Development (R&D) approach. The proposed system combines off-grid polycrystalline photovoltaic panels with a multi-stage water treatment process consisting of a floss (mud) filter, activated carbon filter, water hyacinth cellulose bio-filter, ultraviolet (UV) sterilization unit, storage tank, and an IoT-based real-time water quality monitoring system. System performance was evaluated through microbiological, physical, and chemical water quality testing, with monitoring conducted via Wi-Fi-enabled sensors connected to the Blynk platform. The results demonstrate substantial improvements in treated water quality. Escherichia coli and total coliform bacteria were eliminated (100% reduction). Total dissolved solids (TDSs) decreased from 450 mg/L to 218 mg/L (51.6%), and dissolved manganese was reduced from 30 mg/L to 0.01 mg/L (99.97%), while nitrate levels decreased by 50%. Water pH and temperature remained stable and within regulatory limits. All treated water parameters complied with national clean water standards for hygiene and sanitation. The system operated independently using solar energy and achieved a clean water production capacity of 1000–1500 L/day. These findings indicate that the proposed system is a feasible, cost-effective, and sustainable civil engineering solution for clean water infrastructure in remote building environments. Full article

Ensuring safe and palatable drinking water is critical for long-duration space travel and part of NASA’s 2022 strategic goals. This study investigated whether the formation of iodoform occurred when iodine reacts with trace levels of dissolved organic carbon (DOC) leaching from spacecraft water system components. A simplified model of the International Space Station’s Environmental Control and Life Support System was constructed, focusing on disinfection. The system included water storage in low-density polyethylene (LDPE) bags followed by activated carbon block filtration. Three scenarios were tested: iodine treatment in the storage tank, iodine treatment in-line after storage, and a control with no iodine. Preliminary results showed I₂ concentrations of 0.1–5.42 mg/L prior to filtration, which decreased below detection after filtration. DOC concentrations ranged from below detection to 1.1 mg/L. Concentrations of iodoform, determined by gas chromatography–mass spectrometry, were assessed to observe potential risks to spacecraft drinking water quality. Iodine-based disinfection did result in significant iodoform formation or increased leaching of DOC. This study supports that long-term water storage can be achieved using iodine disinfection and LDPE storage. These results also inform the use of iodine disinfection in emergency situations by drinking water managers when water supply is interrupted in disaster situations. Full article

During fast-fill, large type IV polymer composite hydrogen storage tanks experience significant temperature gradients associated with both the compression of the gas and a Joule–Thomson effect that can compromise vessel integrity, significantly affecting overall safety. In order to remedy this concern, the current work proposes a novel active mixing approach in which the tank rotates, which leads to enhanced internal convective heat transfer and consequently minimizes temperature gradients. Transient CF simulations were performed using the Redlich–Kwong real-gas equation of state, capturing the high-pressure thermodynamic behavior of hydrogen precisely. The study, based on the 1000 s fast-refueling of a tank of 20.56 m³ internal volume, was carried out to assess the tangential speeds of rotation at 10, 30, and 50 rad/s, respectively. Results also show that thermal performance has a strongly nonlinear dependence on rotational speed. At 10 rad/s, a reasonably even temperature profile develops with a much lower energy cost. The most significant suppression of peak temperatures, and therefore the most efficient cooling, is seen at 30 rad/s. Nevertheless, when the rotation speed further elevates to 50 rad/s, abundant viscous dissipation heating results in an unwanted secondary temperature increase while partially counteracting the benefits brought about by improved mixing. On the whole, the results indicate that an ideal operating window more closely correlated with 30 rads/s is seen to provide the most beneficial compromise between temperature uniformity, maximum temperature limitation, and energy consumption for rapid refueling of large composite hydrogen storage systems. Full article

Focusing on the thermodynamic response of onboard liquid hydrogen tanks under dynamic sloshing conditions, this study investigates the flow-thermal coupling mechanism between the gas-phase space and the main chamber by establishing a numerical model that includes the gas-phase space. The results show that the gas-phase space enhances the initiative and efficiency of system pressure regulation through pressure-difference-driven mass transfer. The evolution of the gas–liquid two-phase temperature field sequentially undergoes four typical stages: pressure-difference-driven jet dominance, thermal stratification maintenance, turbulent mixing, and thermal stratification disappearance. The magnitude of the initial pressure difference significantly affects the temperature response and pressure equilibration time of the two chambers. The gas-phase space achieves thermal uniformity in approximately 4.1 s under sloshing, demonstrating its role as a “dynamic thermal buffer.” The research reveals the critical function of the gas-phase space in the dynamic thermal management of liquid hydrogen storage tanks, providing guidance for enhancing the safety and stability of the onboard hydrogen storage system. Full article

The high-pressure spherical gas storage tank in a wind tunnel energy storage and gas supply system is a critical pressure-bearing component of the wind tunnel operation system. The linearized stress in its critical control region is a key parameter for structural safety assessment. Therefore, investigating and evaluating the linearized stress and its associated uncertainty in this region is essential for enhancing operational safety. In this study, a three-dimensional finite element model of the spherical tank was developed, and the critical control region was identified through stress linearization. The operating internal pressure, working temperature, and shell wall thickness were treated as random input variables. Based on the stress linearization results, the stability of the critical control location was assessed. For physically homogeneous intervals, a non-intrusive polynomial chaos expansion surrogate model was constructed, and a conditional uncertainty propagation model for the linearized stress was established. Compared with the Monte Carlo and GUM methods, the non-intrusive polynomial chaos expansion method achieves substantially higher computational efficiency while producing consistent evaluation results. The uncertainty analysis shows that the operating internal pressure is the dominant contributor to the uncertainty of the linearized stress, followed by the effective wall thickness of the spherical shell. In contrast, the working temperature has a minor effect, and the interactions among the input variables are weak. Full article

This paper addresses the current development status of a innovative direct high-pressure electrolyser (DHPEL, operating up to 700 bar) and its integration into a microgrid system in which solar energy constitutes the primary energy source and a hybrid energy storage system, comprising a battery and hydrogen, is employed. The DHPEL under development enables the direct production and storage of hydrogen at high pressures, thereby obviating the need for intermediate mechanical compression. In combination with standardized pressure vessels (300–350 bar) or the increasingly widespread use of CFRP-based high-pressure storage tanks (up to 700 bar), the DHPEL concept represents a technically and economically attractive option for microgrids with hybrid energy storage. The hybrid storage concept is based on functional differentiation between the storage media: the battery is intended to act predominantly as a buffer or short-term storage unit, and the hydrogen is designated for long-term energy storage. In principle, this configuration facilitates an autonomous energy supply relying exclusively on renewable energy sources; this is achieved by enabling the surplus solar energy generated in summer to be converted into hydrogen and subsequently utilized in winter. A rule-based energy-management algorithm is presented, prioritizing hydrogen production from surplus energy during the summer period and aiming to minimize interaction with the public electricity grid. This is particularly relevant for high-latitude regions, such as Germany, where solar irradiation is significantly lower in winter than in summer. A quasi-optimal sizing of all components in the microgrid, along with a realistic techno-economic assessment of the overall system, is performed using an energy-management model implemented in Simulink and utilised with realistic boundary conditions. A case study utilizing realistic solar generation and empirically derived electrical load profiles demonstrates the technical and economic viability of seasonal energy shifting from summer to winter (resulting in an autarky degree exceeding 1) within an economically acceptable cost range. Full article

In the practical operation of air-source heat pump heating systems coupled with phase-change energy storage tanks, wide fluctuations in outdoor temperatures often cause issues such as excessive heating, frequent unit start–stops, and low operational efficiency. Traditional start–stop control strategies struggle to balance heating quality with system energy savings. To enhance the system’s energy efficiency across all operating conditions and improve the stability of indoor temperatures, this study introduces a straightforward and easy-to-implement return water temperature zone control strategy. Using physical reference points, a three-zone control approach for return water temperature was created, which integrates outdoor temperature feedback along with combined indoor temperature adjustments. The proposed strategy’s effectiveness was confirmed through comparative experiments that split the heating season into two parts: one employing traditional control and the other using the zone control method. The results show that, compared to empirical start–stop control, the segmented control strategy increased the system’s average coefficient of performance (COP) from 3.06 to 3.11, representing a 1.63% improvement; reduced indoor temperature deviation from 1.4 °C to 1.2 °C, a 14.2% decrease; and narrowed the amplitude of extreme temperature deviations from 7.9 °C to 3.9 °C, a 50.6% reduction. Total electricity consumption for the entire heating season was approximately 4191 kWh. These findings indicate that the proposed control strategy effectively improves system energy efficiency and indoor temperature stability while meeting heating demands. It significantly suppresses excessive heating during transitional seasons and enhances heating reliability under extreme low-temperature conditions. This study involves low retrofitting costs and balances both energy-saving and comfort objectives, providing a practical, engineering-ready solution for the intelligent control of air-source heat pump heating systems. Full article

To address the issues of high energy consumption, high carbon emissions, and the waste of associated energy (AE) in coal mine production, which severely hinder global sustainable development goals, this paper proposes a novel low-carbon economic collaborative optimal scheduling model for a coal mine integrated energy system (CMIES) oriented towards sustainable energy transitions. First, a refined utilization model for AE encompassing coal mine gas, ventilation air methane (VAM), and mine groundwater (GW) is constructed, and a tiered carbon emission trading mechanism (TCET) is introduced to constrain carbon emissions and promote ecological sustainability. Second, a concentrated solar power (CSP) plant is integrated to break the rigid “power determined by heat” constraint of a traditional combined heat and power (CHP) unit, thereby enhancing the system’s scheduling flexibility and renewable energy integration. Meanwhile, abandoned mines are retrofitted into solvent storage tanks to construct an integrated flexible carbon capture system (IFCCS), achieving sustainable reuse of mining wastelands. Finally, to tackle the multi-source, heterogeneous uncertainties on both the source and load sides, a hybrid risk assessment method combining information gap decision theory (IGDT) and conditional value at risk (CVaR) is proposed. Case study results demonstrate that, compared to traditional energy supply modes, the proposed model reduces carbon emissions and total costs in the mining area by 66.04% and 15.97%, respectively. This significantly improves resource utilization efficiency and ecological benefits, providing a highly viable pathway for the sustainable development and clean transition of coal mine operations. Furthermore, the proposed hybrid assessment method can effectively assist decision-makers in achieving a refined trade-off between operating costs and system robustness under varying risk preferences. Full article

Marine transportation and storage of liquid hydrogen (LH2) has gained increasing interest, while potential LH2 membrane-type tanks could utilize 316L corrugated austenitic stainless-steel sheets. The corrugation process results in a strain-induced martensitic transformation in the material, introducing rapid diffusion pathways for hydrogen atoms and promoting the formation of hydrogen-trapping sites that alter hydrogen transport and reduce the material’s resistance to hydrogen embrittlement. In this study, 316L sheets were subjected to different levels of uniaxial pre-strain (10, 20, 30, and 40%) with two different strain-rates, to replicate the varying degrees of pre-deformation caused by the corrugation. Microstructural analysis using Electron Backscatter Diffraction (EBSD) (Thermo Fisher Scientific, Waltham, MA, USA) and X-Ray Diffraction (XRD) (Bruker, Billerica, MA, USA) combined with quantitative phase analysis using the Rietveld Method on XRD data, provided valuable insights into the induced phase transformations. Cathodic hydrogen charging method was implemented on as-received and pre-strained material, followed by slow strain rate tensile testing (SSRT) and thermal desorption spectroscopy (TDS) to examine the hydrogen effect on each condition. Experimental results indicated that although 316L exhibits considerable phase stability, it undergoes strain-induced phase transformation resulting in a significant amount of martensite, reaching 5% in the 40% pre-strained condition. Pre-deformation increased hydrogen embrittlement, as evidenced by fractographic analysis which indicated a Relative Reduction of Area (RRA) of 0.83, and by increased hydrogen uptake. These findings contribute to a better understanding of phase transformations and the role of hydrogen in austenitic stainless steels. Full article

The increasing expansion of ubiquitous sensing systems has created large streams of time-series data that are difficult for non-technical users to interpret. Large Language Models (LLMs) offer a promising interface for transforming sensor data into natural language insights, particularly in distributed environments where users may lack familiarity with data analysis. However, models optimized for text generation often struggle to interpret raw time-series signals, producing responses that are generic, inaccurate, or poorly grounded in the data. This study evaluates a prompt structure based on the Retrieval-Augmented Generation (RAG) framework for interpreting sensor-derived time-series data from water-consumption monitoring systems installed in household storage tanks. The prompt integrates statistical summaries, sensor metadata, and contextual information about household water-use practices. Performance is evaluated using synthetic datasets representing a year of tank water-consumption measurements and a rubric-based evaluation framework applied by three independent language-model evaluators. Results show that augmenting prompts with structured contextual information improves the clarity and grounding of language model responses to sensor time-series data, increasing evaluation scores and reducing failure modes such as hallucination, contradiction with the data, and misuse of contextual information, as assessed by independent evaluator models. These findings highlight the potential of structured contextual prompting to support locally deployed language models that produce reliable and actionable interpretations of sensor time-series data. Full article

LNG storage tanks are essential facilities for large-scale storage and transportation of cryogenic energy. Because of the flammable, explosive, and ultra-low-temperature characteristics of liquefied natural gas, failures in such systems may result in serious consequences for operational safety and the surrounding environment. Effective identification and prioritization of potential failure modes are therefore crucial for safe operation. Failure mode and effects analysis (FMEA) has been widely applied in risk assessment, yet conventional FMEA methods still show limited capability in describing uncertain linguistic evaluation information, reflecting the reliability of expert judgments, and representing high-order coupling relationships among failure modes. To address these issues, this study develops a modified FMEA framework that integrates confidence-enhanced probabilistic linguistic modeling with weighted hypergraph propagation for LNG storage tank risk assessment. In the proposed framework, confidence-enhanced probabilistic linguistic term sets are employed to represent the fuzziness, probabilistic preference, and reliability differences contained in expert assessments. A confidence-adaptive scoring function is further constructed to strengthen the discrimination of risk quantification by capturing structural differences in probability distributions without introducing externally specified parameters. Meanwhile, the importance of risk factors is determined through a combined subjective–objective weighting strategy, and a weighted hypergraph propagation mechanism is established to characterize high-order structural associations among failure modes and to revise baseline risk levels through a node–hyperedge–node transmission process. A case study of a large LNG storage tank system in Tangshan, China, is carried out to examine the applicability and effectiveness of the proposed framework. The results demonstrate that the proposed method can effectively integrate complex expert evaluation information with structural coupling effects, while sensitivity and comparative analyses further confirm its robustness and suitability for failure risk prioritization in LNG storage tanks. Full article