Product design constitutes an iterative process centered on user requirements. In this process, user requirements are collected, interpreted by designers and transformed into design objectives, which are then refined through solution generation and evaluation to yield the final product; this is typically an iterative human–computer interaction process. The intent understanding process converts vague user requirements into standardized expressions for designers. For complex products, existing intent understanding methods are hindered by excessive reliance on experience, lack of iterative mechanisms, and insufficient identification of critical performance attributes. To address these limitations, this paper proposes a computational methodology for element-based intent understanding, positioned as a foundational layer for future human–computer collaborative design systems. The concept of “elements” is introduced at three levels: function, performance, and structure—to aid in the standardization of design objectives. Relationships among elements are defined and used to construct design objectives, establishing a transformation model from elements to objectives. An iterative reconstruction method for element-objective transformation under design iteration is further proposed. The feasibility of this method is empirically validated via a reactor fuel-handling case study.

To address insufficient support for innovative methods under the model-based systems engineering (MBSE) framework during the conceptual design phase of nuclear power equipment, this paper proposes an innovative design strategy based on multi-layer problem decomposition. Three core contributions are presented. First, a five-layer hierarchical problem decomposition framework (Layer 0–4) is proposed and integrated into the MBSE architecture, enabling systematic transition from operational requirements to physical solutions. Second, hierarchical matching rules are established to map problem types at each layer to corresponding innovative tools: knowledge stimulation for Layer 1, DSM and FTA for Layer 2, TRIZ technical contradiction matrix for Layer 3, and TRIZ separation principles for Layer 4. Third, a complete solution set is developed for the HPR1000 reactor core detector removal equipment, including a torque-following control strategy, a cylinder spring pressure roller, and a pulley-based clamping mechanism. Using Arcadia and Capella, full-chain MBSE modeling is completed, and prototype tests demonstrate stable forming performance with a mean profile size of 164.20 mm (SD = 0.559 mm), below the 170 mm design limit. The results indicate that this strategy can effectively guide the innovative design of nuclear power equipment under the MBSE framework during the conceptual design phase.

The first step in the analysis of the contact mechanics and leakage prediction of metallic seals applied to nuclear reactor pressure vessels is the proper characterization of the surface topography. At present, two approaches are used for this characterization. On the one hand, there are non-parametric techniques such as HPD and PSD, which retain all the characteristics of the surfaces that have been measured, but the results are high dimensional; hence, they cannot be analyzed analytically. The other type is parametric fractal methods, where the parameters used are fractal dimension D and characteristic scale G, where the analytical derivations can be made; however, this leads to systematic deviations in the mechanical response due to some idealized assumptions, like isotropy, Gaussian distribution, and infinite self-similarity. In this article, we propose an equivalent fractal parameter inversion model (EFPIM) that does not rely on geometric fitting; instead, it fits the mechanical contact behavior of a physical surface. This inversion procedure reduces three errors simultaneously. Thus, the EFPIM does not use D and G as the geometrical fitting variables but rather redefines them as mechanically equivalent ones, the purpose of which is to minimize the difference between the W-M fractal surface and the real measured surface. To address the problem of constrained inversion, we adopt a genetic algorithm with BFGS. To prove its effectiveness, we carried out experiments on C-ring seal surfaces and found that the deviation in the contact area was reduced by an order of magnitude in comparison to traditional structure-function extraction, and the deviation in the approach and the maximum pressure were less than 2%. Moreover, the equivalent parameters shift systematically away from their geometric counterparts in the direction that compensates for the dominant non-ideal deficit of the W-M surface; when both parameters are free, the equivalent fractal dimension decreases, while the equivalent characteristic scale increases, compensating for the absent non-Gaussian deep valleys of ideal W-M surfaces. Existing models of analytical contact and leakage may be directly implemented using equivalent parameters and with accuracy comparable to that of FFT-based simulations, with the modest cost of the offline computations.

Rolling pairs are widely used in high-value equipment, making remaining useful life (RUL) prediction essential for reliability and safety. However, RUL prediction remains challenging due to complex wear mechanisms, scarce data, and insufficient representation of operating history. A hybrid method combining a Gamma process and a backpropagation (BP) neural network is proposed. Monte Carlo samples generated from Gamma and inverse power-law models fitted to accelerated-test data are used to augment the training data, while a history matrix represents time-varying conditions. The proposed method is evaluated using wear data from an enclosed, low-speed, heavy-load rotating component in a nuclear power plant. The results show that the proposed method achieves high accuracy in wear prediction and produces a continuous RUL trajectory under time-varying operating conditions. A continuous RUL trajectory is also produced under time-varying operating conditions. The method provides a practical framework for rolling pairs with limited degradation data and variable operating conditions.

Fretting wear at metal O-ring sealing interfaces involves the coupled processes of debris generation, entrapment, and oxidation. Existing accelerated life testing relies on similarity theory, yet it overlooks the dominant role of third-body behavior in governing wear regime transitions, so equivalence between test and service conditions lacks a physical basis. This study investigates reciprocating fretting wear of an Inconel 718/304 stainless steel pair and reinterprets wear evolution using third-body dynamics. Laser scanning confocal microscopy, energy-dispersive spectroscopy, and real-time friction monitoring reveal three distinct stages: debris generation with mild damage, oxide layer formation with steady wear, and oxide layer fracture with material spalling. The friction coefficient passes through running-in, steady-state, and sharp-rise phases, while oxygen content on the wear track rises from 2.1 wt.% to 18.3 wt.%. One-way ANOVA shows significant differences among all stages. Based on Berthier’s theory, a state-evolution model is developed that treats third-body oxidation degree and cumulative friction energy dissipation as equivalence criteria. Friction energy and wear mass correlate linearly (R² = 0.94), giving an effective wear coefficient of 6.3 × 10⁻⁷ mg/J and acceleration exponents of m₁ = 9.77 for pressure and m₂ = −8.71 for frequency. Independent validation shows that the model compresses test duration by 51%, with relative errors below 18% for wear mass, fractal dimension, and fractal roughness. By shifting the focus of accelerated testing from dimensional analysis to mechanism-preserving state tracking, this work provides a failure analysis framework that identifies the root cause of seal degradation as third-body oxidative spalling and offers practical preventive actions for nuclear metal seal reliability assessment.