Low cycle fatigue (LCF) is a critical degradation mechanism in reactor pressure vessel (RPV) steels, potentially compromising the structural integrity of nuclear power plants. This study investigates the microstructural evolution and defect formation in a Mn–Mo–Ni low‐alloy RPV steel subjected to LCF at a total strain amplitude of ± 0.7% at room temperature. Electron backscatter diffraction (EBSD) and positron annihilation lifetime spectroscopy (PALS) were employed to assess changes in dislocation substructure, grain orientation, and defect configurations after LCF loading. EBSD analysis revealed enhanced intragranular misorientations indicating significant dislocation activity and localized strain accumulation. PALS measurements identified an increase in positron lifetime components associated with microvoids and dislocations, suggesting the formation and growth of subsurface defects during fatigue cycling. The complementary use of EBSD and PALS provided a comprehensive understanding of the damage mechanisms operative during LCF.

A novel embedded optical fibre sensors network and integration strategy for in-situ monitoring of hydrogen storage 70 MPa type IV composite pressure vessels

Assessment of AM70 high-strength steel processed via wire arc additive manufacturing for pressure vessel applications: Role of spray and pulsed arc

Strength prediction of thin-walled composite pressure vessels under axial compression: Revealing buckling-induced failure competition

Abstract Embrittlement of reactor pressure vessel (RPV) steel caused by neutron irradiation has been evaluated using ductile-to-brittle transition temperature (DBTT) derived from surveillance tests (Charpy impact tests) during plant operation. For reliable structural integrity assessment of the RPV, incorporating adequate safety margins that take into account uncertainties inherent in surveillance Charpy impact tests are needed. In this study, a model to evaluate temperature dependence of Charpy absorbed energy variability using approximately 1900 datasets of unirradiated and irradiated materials manufactured in Japan and United States was developed. Next, probability distribution of Charpy ductile-to-brittle transition temperature at a 41 J energy level (T41J) was evaluated by estimating the probability distribution of Charpy test data using Monte Carlo sampling and Bayesian inference. From the detailed evaluation of the relationship between the number of specimens and T41J uncertainty, uncertainty of T41J was found to be almost the same in materials manufactured in Japan and U.S., and unchanged with neutron irradiation (no clear change in material inhomogeneity). Regarding product form on the other hand, uncertainty of T41J for base metal and weld metal was found to be comparable, indicating no significant difference, but the heat affected zone was shown to have large uncertainty.

This study investigates CFRP overwrapped pressure vessels featuring a bonded dome-cylinder structure. A stepped butt-joint structure without adhesive was employed as the bonding structure between the dome and cylinder section. The effects of joint length (step length) and joint position were examined through burst tests. After CFRP pressure vessels of four kinds simulating the dome/cylinder bonded configuration were produced, and subsequently subjected to burst tests. Using an X-ray CT scanner, internal damage in the pressure vessels was investigated after burst testing. The results indicate minimal influence of the step length L at the joint, and show that higher burst pressures were achieved when the joint was located closer to the dome section. Furthermore, progressive damage analyses based on finite element method with a cohesive zone model (CZM) were conducted to evaluate the experimentally obtained results. Damage behavior estimated from the numerical simulation roughly agreed with the experimentally obtained results.

This article compares the meshless method and the classical finite element method in the case of elastic-plastic analysis of pressure vessels. The finite element method has proven to be a robust approach over the years and has been continuously developed for both static and dynamic analyses, as well as linear and nonlinear applications. However, the use of the finite element method presents some intrinsic critical issues, such as the generation of the calculation grid or mesh and dependence, leading to the introduction of methods that do not require the explicit generation of a calculation grid, or meshless methods. The purpose of this paper is to review the performance of a meshless resolution compared with the traditional mesh-based approach, in the case of the verification of industrial HIPPS valves, focusing on the precision and quality of the results obtained as well as the operating cost, assessing the method’s operational advantages at an industrial level.

This study focuses on the analysis and optimization of filament-wound composite pressure vessels loaded with a combination of mechanical and thermal loading, which occurs during the curing process and subsequent cooling to the ambient temperature. The state of stress in the pressure vessel is determined using the analytical description based on the classical lamination theory supplemented by netting theory. The analytical description is used for generating robust input data for the data-driven deep learning evolutionary algorithm EvoDN2, to carry out machine learning followed by many-objective optimization, to compute the optimal tradeoff between the nominal pressure and weight of the pressure vessel, maintaining a high level of safety. The influence of material configurations on the design of pressure vessels is also studied, where four material configurations of E-glass/DA 4518U, T300/N5208, AS4/3501-6, and Kevlar 49/CYCOM 919 are considered. The results indicate that the usage of aramid fiber in the design has its limits, whereas the materials based on carbon-epoxy show an impressive performance and great strength-to-weight ratio.

This work presents a structured screening of seven alternative carbon fiber reinforced polymer (CFRP) systems for use in Type IV hydrogen pressure vessels. The objective was to reduce carbon fiber usage while maintaining mechanical integrity and industrial viability. The investigated configurations included matrix-modified epoxies, chemically functionalized fibers, and a high-strength fiber variant. All laminates were manufactured using wet filament winding and assessed through standardized mechanical and thermal testing. To capture industrial relevance beyond material performance, a criteria-based evaluation framework was applied, incorporating processability, cost, scalability, sustainability, and regulatory compatibility. The results show that most variants achieved structural performance comparable to the reference system. A combined nanofiller–silane matrix delivered the highest strength values but exhibited reduced process stability, while a silane-modified matrix provided a more balanced performance profile with favorable practical scores. A radar-based comparison across five evaluation axes highlights trade-offs between mechanical performance and implementation feasibility. The study demonstrates that multi-dimensional screening approaches are essential for identifying viable composite systems at an early development stage. The proposed evaluation framework is transferable in its methodological structure and decision logic, while absolute numerical scores remain application- and dataset-dependent.

Purpose Pressure vessels are vital components in manufacturing and energy systems, where early detection of degradation is essential to prevent catastrophic failures. Conventional acoustic emission (AE)-based prognostic methods depend heavily on experimental testing and finite element simulations, which are costly and time-intensive. This study proposes a fully computational, artificial intelligence (AI)-driven digital twin framework capable of performing both health-state classification and remaining useful life (RUL) prediction using physics-inspired synthetic AE data. Design/methodology/approach A multi-task convolutional neural network–long short-term memory (CNN–LSTM) architecture was developed to extract spatio-temporal features from synthetically generated AE waveforms. The AE signals were produced through a parameterized, physics-informed model representing energy attenuation and frequency variation during progressive material degradation. CNN layers capture spectral energy features, while LSTM layers learn temporal evolution patterns associated with structural deterioration. The framework, implemented entirely in Python, enables an end-to-end digital twin without external simulation platforms. Model convergence, residual analysis and t-distributed stochastic neighbor embedding visualizations were used to assess robustness and interpretability. Findings The model achieved 99% classification accuracy and a low RUL prediction error (root mean square error ˜ 5), demonstrating strong predictive capability and stable convergence. Latent-space visualization revealed distinct health-state clusters, validating interpretable degradation learning across synthetic AE data. Originality/value This work presents one of the first physics-informed, Python-based digital twin frameworks for AE-driven prognostics of pressure vessels. It eliminates dependence on costly experiments or finite element simulations, offering a scalable, data-efficient approach suitable for Industry 4.0 applications.

This study examines the influence of strip width on microstructure, weldability, and cracking behavior of NiCr20Mn3Nb cladding deposited on 20MnMoNi55 pressure vessel steel using the Submerged Arc Strip Cladding (SASC) process. Two industrial strip widths, 30 mm (SAW-W30) and 60 mm (SAW-W60), were compared to evaluate differences in heat-input distribution, weld-pool geometry, solidification behavior, and elemental segregation under post-weld heat-treatment (PWHT) conditions. While both conditions satisfied mechanical property requirements, SAW-W30 exhibited higher energy density, deeper fusion, and increased susceptibility to microfissuring in cross-bead tests. Microstructural characterization using scanning electron microscopy (SEM) and energy-dispersive spectroscopy (EDS) revealed pronounced niobium and manganese segregation along grain boundaries in SAW-W30, particularly in regions subjected to repeated thermal cycling, which acted as the primary mechanistic cause for the observed micro-fissuring. In contrast, SAW-W60 promoted more uniform solidification and superior cladding integrity. Quantitative EDS analysis demonstrated significantly higher Nb segregation ratios at grain boundaries in the narrower strip condition, particularly in bead-overlap regions, which directly correlated with the observed tendency toward microfissuring. The results provide practical guidelines for optimizing strip width selection in nuclear pressure vessel cladding by linking fabrication-induced segregation and microfissuring behavior to the expected long-term resistance to corrosion, oxidation, and irradiation-assisted degradation.

Abstract The Used Fuel Container (UFC) is a metal container for long-term management (i.e., disposal) of used nuclear fuel in a deep geological repository. The UFC employs a non-standard closure weld joint with a shallow partial penetration. The container was constructed following the principles of ASME Boiler and Pressure Vessel Code Section III, Division 3, and the weld is inspected by surface and volumetric non-destructive examination (NDE) methods, i.e., eddy current testing and ultrasonic testing. NDE acceptance criteria for this customized weld joint are developed based on the effect of porosity and its interaction with the partial penetration weld geometry. The safety margin of the proposed acceptance criteria is verified through tensile tests of welded specimens containing artificial defects of bounding geometry. The effectiveness of the acceptance criteria is further demonstrated in full-scale prototype manufacturing and external pressure tests.

Multi-objective optimization (MOO) plays a critical role in mechanical and industrial engineering, where conflicting design goals must be balanced under complex constraints. In this study, we introduce the Multi-Objective Giant Trevally Optimizer (MOGTO), a novel extension of the Giant Trevally Optimizer inspired by predatory foraging dynamics. MOGTO integrates predation-regime switching into a Pareto-based framework, enhanced with feasibility-aware archiving, knee-biased selection, and adaptive constraint handling. We benchmark MOGTO against established algorithms—NSGA-II, SPEA2, MOEA/D, and ParetoSearch—using synthetic test suites (ZDT1–3, DTLZ2) and classical engineering problems (welded beam, spring, and pressure vessel). Performance was assessed with Hypervolume (HV), Inverted Generational Distance (IGD), Spacing, and coverage metrics across 30 independent runs. The results demonstrate that MOGTO consistently achieves competitive or superior HV and IGD, maintains more uniform spacing, and generates larger feasible archives than the baselines. Particularly on constrained engineering problems, MOGTO yields more feasible non-dominated solutions, confirming its robustness and industrial applicability. These findings establish MOGTO as a reliable and general-purpose metaheuristic for multi-objective optimization in engineering design.

Abstract Accurate prediction of gamma flux in reactor internals is essential for structural assessments studies. This paper presents gamma spectrum measurements in a VVER-1000 full-scale mock-up at the LR-0 reactor, using a stilbene scintillator connected to the NGA-01 spectrometer. Deconvolution of the gamma spectrum was performed using Maximum Likelihood Estimation with a validated response matrix. Measurements were conducted in the reactor baffle and in front of the reactor pressure vessel (RPV) and compared with MCNP6.2 simulations using ENDF/B-VIII.0 and JEFF-3.3 libraries. Good agreement was observed in the baffle region below 5 MeV for ENDF/B-VIII.0. However, both libraries underpredicted the measured spectrum in front of the RPV, particularly at higher energies. These results indicate persisting deficiencies in gamma production data for key reactor materials and emphasize the need for continued validation

This study presents a hybrid framework integrating a physics-based corrosion model with a feedforward neural network (FNN) to predict corrosion rates and estimate the remaining useful life (RUL) of industrial pressure vessels for condition-based maintenance. Using non-destructive evaluation (NDE) wall thickness measurements from 24 inspection points over multiple years (2002–2008) and physics-based training data, a three-layer FNN with Monte Carlo dropout predicts localized corrosion rates, while exponential and linear degradation models project future wall thickness. The FNN achieved a coefficient of determination (R²) of 0.975 for corrosion rate prediction and a mean absolute error (MAE) of 0.1204 mm/year. For thickness prediction, the exponential model achieved R² = 0.99 with MAE = 0.0389 mm, outperforming the linear model (MAE) = 0.1350 mm. The framework was integrated with Fitness-for-Service (FFS) assessment based on API 579-1/ASME FFS-1 standards, enabling classification of vessel components and identification of sections requiring maintenance. This hybrid approach translates predictive analytics into standards-compliant engineering decisions for structural integrity management.

This paper proposes a novel meta-heuristic optimization algorithm - Geomagnetic Navigation Algorithm (GNA), which is inspired by the geomagnetic navigation mechanism of migratory birds. The algorithm constructs three core mechanisms: (1) Geomagnetic gradient dominance and multi-source cognitive modulation mechanism, which maps the direction of the magnetic inclination gradient to the global optimal solution and integrates information from three channels: elite memory, group social cognition, and magnetic pole reinforcement attraction; (2) Adaptive cognitive landmark chain correction mechanism, which dynamically selects the top three individuals of the population as cognitive landmark chains, and their guiding weights are randomly generated each generation to simulate the reliability assessment of landmarks, achieving multi-scale references from near to medium to long distances; (3) Triple heavy-tailed distribution bionic perturbation mechanism, which switches between Cauchy/Gaussian/pseudo-Levy distributions with equal probability to simulate three types of environmental disturbances: geomagnetic storm impact, magnetic field micro-fluctuation, and terrain exploration jumps. In the comparison with six algorithms such as WOA, TOC, and SCA on the CEC-2017 test set of 10/30/50 dimensions and the verification of engineering optimization problems such as pressure vessel design, GNA significantly outperforms the comparison algorithms. Experiments confirm that GNA has significant advantages in the balance of exploration and exploitation, resistance to local optimality, dimension adaptability, and handling of engineering constraints, providing an efficient solution for complex optimization problems.

Abstract Hydrogen storage in lightweight composite pressure vessels demands materials with excellent thermal stability, reliable dimensional properties, and consistent curing behavior. In this study, carbon fiber reinforced epoxy laminates based on 4 carbon fiber layers with epoxy bisphenol A diglycidyl ether and hardener Diethylenetriamine as (4C-DGEBA/DETA) and other with our novel schema of 4 carbon fibers and 3 polyethylene films hot pressed with identical epoxy system as (4C3PE–DGEBA/DETA) analyzed and compared by using thermogravimetric analysis (TGA), Dynamic-mechanical analysis (DMA), and differential scanning calorimetry (DSC). TGA identified the degradation onset and differences in thermal stability between the two systems, while DMA provided insights into temperature-dependent viscoelastic behavior, including stiffness retention and damping characteristics. DSC used to evaluate curing characteristics and glass transition temperatures, revealing links between resin chemistry and thermal performance. The comparison showed that the modified 4C3PE–DGEBA/DETA system exhibited superior thermal resistance and dimensional stability compared to the baseline 4C-DGEBA/DETA composite. These results also improve the understanding of composite behavior in liner-less (Type-V) hydrogen storage pressure vessels and guide material selection and design improvements, supported by SEM surface morphology analysis for a novel 4C3PE laminate geometry.

Theoretical prediction model for dust explosion pressure and flame propagation in vessel–pipeline systems under airflow transport conditions

This study presents an innovative approach to designing filament-wound conical pressure vessels loaded by internal pressure, combining analytical computations based on classical lamination theory with data-driven evolutionary algorithms. The most critical area of the whole cone is the balanced layer at the lower base, in which the safety level is evaluated using the failure index based on Hoffman’s strength criterion. The designed mathematical model related the geometrical parameters of the cone to the magnitude of internal pressure, representing the performance of the structure and safety expressed by the failure index. The optimization loop runs on two modules –Evolutionary Deep Neural Networks EvoDN2 module for machine learning and creating a lighter surrogate model, together with Constrained Reference Vectors Evolutionary Algorithms (cRVEA) for multicriteria optimization. This was applied to three different material configurations: E-glass/DA4518U (glass-epoxy), T300/N5208 (carbon-epoxy) and AS4/3501-6 (carbon-epoxy). The results show that the critical area is invariant to the material configuration. In addition, it was shown that the material configurations with higher anisotropy achieve a more uniform distribution of the load in the layers of the conical three-layered wall.

Tuning asymmetric domes in thin-walled composite pressure vessels for enhanced burst strength