Search Results (924)

An important question for turbomachine designers is how to deal with blade and flowpath geometric variabilities stemming from the manufacturing process or erosion during the component lifetime. The challenge consists of identifying where stringent manufacturing tolerances are absolutely necessary and where looser tolerances can be used as some geometric variations have little or no effects on performance while others do have a significant impact. Because numerical simulations based on Reynolds-averaged Navier–Stokes (RANS) equations are computationally expensive for a stochastic analysis, an alternative approach is proposed in which these simulations are complemented by cheaper through-flow simulations to provide a finer exploration of the range of variations, in particular in the context of robust design. The overall goal of the present study is to evaluate the adequacy of a viscous time-marching through-flow solver to predict geometric variability effects on compressor performance and, in particular, to capture the main trends. Although the computational efficiency of such a low-fidelity solver is useful for parametric studies, it is known that the involved assumptions and approximations associated with the through-flow (TF) approach introduce errors in the performance prediction. Thus, we first evaluate the model with respect to its underlying assumptions and correlations. To accomplish this, TF simulations are compared to RANS simulations applied to a modern low-pressure compressor designed by Safran Aero Boosters. On the one hand, the TF simulations are fed with the exact radial distribution of the correlation parameters using RANS input data in order to isolate the modeling error from correlation empiricism. Moreover, in the context of multi-fidelity optimization, such distributions can be predicted using the more detailed RANS simulations that are performed on selected operating points. On the other hand, correlations from the literature are assessed and improved. It is shown that the solver provides realistic predictions of performance but is highly sensitive to the underlying correlations. Then, two modeling aspects linked to the blade leading edge, namely incidence correction and camber line computation, are discussed. As geometric variability precisely at the blade leading edge has a significant impact on the performance, we assess how these two aspects influence the variability propagation in this region. Moreover, we propose a strategy to mitigate these model uncertainties, and geometric variabilities are introduced at the blade leading edge in order to quantify the resulting variation in performance. Finally, within the scope of this preliminary study, perturbations of the three-dimensional position of undeformed stator blades and deformations of the hub and shroud contours are introduced one factor at a time per simulation. Their range is defined based on the tolerance limits typically imposed in the industry and on observed manufacturing variability. It is found that the through-flow model broadly provides realistic predictions of performance variations resulting from the imposed geometric variations. These results are a promising first step towards the use of the through-flow modeling approach for geometric uncertainty quantification. Full article

Copper has been proposed as a container material for the disposal of used nuclear fuel in a number of countries worldwide. The container materials will be subject to various corrosion processes in a deep geological repository, including radiation-induced corrosion (RIC) resulting from the γ-irradiation of the near-field environment. A comprehensive model is being developed to predict the extent of RIC by coupling a radiolysis model to the interfacial electrochemical reactions on the container surface. An important component of the overall model is a radiolysis model to predict the time-dependent concentration of oxidizing and reducing radiolysis products. As a first step in the model development, various radiolysis models have been validated against experimental measurements of the concentrations of dissolved and gaseous radiolysis products. Experimental data are available for pure H₂O- and Cl⁻-containing solutions, with and without a gas headspace. The results from these experiments have been compared with predictions from corresponding radiolysis models, including the effects of the partitioning of gaseous species (O₂ and H₂) at the gas–solution interface. Different reaction schemes for the Cl⁻ radiolysis models are also compared. The validated radiolysis model will then be coupled with interfacial reactions on the copper surface and additional processes related to the presence of bentonite clay in Steps 2 and 3 of the overall model, respectively. Full article

Optimizing customer journeys is a critical challenge in e-commerce and financial services, attracting attention from marketing, operations research, and business analytics. Traditional customer analytics models, such as rule-based segmentation and regression models, rely heavily on structured transactional data, limiting their ability to capture latent behavioral patterns and adapt to multi-channel dynamics. These models often struggle to integrate unstructured data sources, failing to provide adaptive, personalized insights. To address these limitations, this study proposes a multi-stage data-driven framework integrating latent Dirichlet allocation (LDA) for behavioral insights, deep learning for predictive modeling, and heuristic algorithms for adaptive decision-making. Empirical validation using Taiwanese financial institution data shows a 15% improvement in predictive accuracy compared to traditional machine-learning models, significantly enhancing customer lifetime value (CLV) predictions and multi-channel resource allocation. This research highlights the practical value of integrating structured and unstructured data for improving customer analytics. Our framework leverages LDA to extract behavioral patterns from customer interactions, enriching predictive models and enhancing real-time decision-making in financial services. Robustness checks confirm the scalability and adaptability of this approach, offering a data-driven strategy for long-term value optimization in dynamic digital ecosystems. Full article

In one of our previous papers, we obtained a general class of potentials inside the nucleus, such that the singular solution of the Dirac equation for the S-states of hydrogen atoms outside the nucleus can be matched with the corresponding regular solution inside the nucleus (the proton) at the boundary. The experimental charge density distribution inside the proton generates a particular case of such potentials inside the proton. In this way, the existence of the second kind of hydrogen atom was predicted: atoms having only S-states. This theoretical prediction was then evidenced by several different types of atomic experiments and by astrophysical observations. In the present paper we provide the following new results. First, we show that the solution of the Dirac equation inside the proton can be (and is) found within the class of functions that are non-analytic at r = 0—in distinction to the traditional practice of limiting the search for the solution by the class of analytic functions. We demonstrate that this non-analytic solution inside the proton can be matched at the proton boundary R with the corresponding singular solution outside the proton regardless of the particular value of R. Second, we show that the interior and exterior solutions are scale-invariant with respect to the change of the boundary R between these two regions. Such invariance is the manifestation of a new symmetry—in addition to the previously discussed symmetries of the Dirac equation in the literature. Third, based on the new, more accurate results for the wave function inside and outside the proton, we revisit the resolution of the neutron lifetime puzzle initially outlined in our previous papers. On the basis of the more accurate calculations, we reconfirm that (A) the 2-body decay of neutrons produces overwhelmingly the SFHA (rather than the usual hydrogen atoms) and (B) the strengthened-in-this-way branching ratio for the 2-body decay of neutrons (compared to the 3-body decay) is in excellent agreement with the branching ratio required for reconciling the neutron lifetime values measured in the trap and beam experiments, so that the neutron lifetime puzzle seems to be indeed resolved in this way. Full article

Background/Objectives: It is increasingly recognized that traumatic life experiences render individuals more vulnerable to gastrointestinal (GI) symptoms and chronic bowel conditions like inflammatory bowel disease (IBD) and irritable bowel syndrome (IBS). In this study, we examined whether this effect is mediated by negative affectivity. Methods: A total of 281 participants recruited in the Netherlands, including 94 with IBD, 95 with IBS and 92 controls, were assessed for lifetime trauma, trait anxiety, depression, and GI (IBD/IBS) disease activity. Results: The results confirmed that negative affectivity fully mediated the association between trauma and GI symptomatology, with trauma and depression explaining 38–40% (IBD|IBS) of the variance in disease activity and trauma and anxiety explaining 31–33% (IBD|IBS) of the variance in disease activity. Upon correction for condition (patient/controls), the predictive capacity increased even further, with trauma and depression now accounting for 43–44% (IBD|IBS) and trauma and anxiety for 40% (IBD and IBS) of the GI symptom heterogeneity. Conclusions: The results are in line with studies linking trauma to negative affectivity and negative affectivity to a more aggressive GI disease course. More generally, they show that the somatic and affective consequences of trauma should not be considered in isolation but must be treated as a covariant whole. Full article

Lithium-ion batteries experience degradation with each cycle, and while aging-related deterioration cannot be entirely prevented, understanding its underlying mechanisms is crucial to slowing it down. The aging processes in these batteries are complex and influenced by factors such as battery chemistry, electrochemical reactions, and operational conditions. Key stressors including depth of discharge, charge/discharge rates, cycle count, and temperature fluctuations or extreme temperature conditions play a significant role in accelerating degradation, making them central to aging analysis. Battery aging directly impacts power, energy density, and reliability, presenting a substantial challenge to extending battery lifespan across diverse applications. This paper provides a comprehensive review of methods for modeling and analyzing battery aging, focusing on essential indicators for assessing the health status of lithium-ion batteries. It examines the principles of battery lifespan modeling, which are vital for applications such as portable electronics, electric vehicles, and grid energy storage systems. This work aims to advance battery technology and promote sustainable resource use by understanding the variables influencing battery durability. Synthesizing a wide array of studies on battery aging, the review identifies gaps in current methodologies and highlights innovative approaches for accurate remaining useful life (RUL) estimation. It introduces emerging strategies that leverage advanced algorithms to improve predictive model precision, ultimately driving enhancements in battery performance and supporting their integration into various systems, from electric vehicles to renewable energy infrastructures. Full article

Background: A grid search, at the cost of training and testing a large number of models, is an effective way to optimize the prediction performance of deep learning models. A challenging task concerning grid search is time management. Without a good time management scheme, a grid search can easily be set off as a “mission” that will not finish in our lifetime. In this study, we introduce a heuristic three-stage mechanism for managing the running time of low-budget grid searches with deep learning, sweet-spot grid search (SSGS) and randomized grid search (RGS) strategies for improving model prediction performance, in an application of predicting the 5-year, 10-year, and 15-year risk of breast cancer metastasis. Methods: We develop deep feedforward neural network (DFNN) models and optimize the prediction performance of these models through grid searches. We conduct eight cycles of grid searches in three stages, focusing on learning a reasonable range of values for each of the adjustable hyperparameters in Stage 1, learning the sweet-spot values of the set of hyperparameters and estimating the unit grid search time in Stage 2, and conducting multiple cycles of timed grid searches to refine model prediction performance with SSGS and RGS in Stage 3. We conduct various SHAP analyses to explain the prediction, including a unique type of SHAP analyses to interpret the contributions of the DFNN-model hyperparameters. Results: The grid searches we conducted improved the risk prediction of 5-year, 10-year, and 15-year breast cancer metastasis by 18.6%, 16.3%, and 17.3%, respectively, over the average performance of all corresponding models we trained using the RGS strategy. Conclusions: Grid search can greatly improve model prediction. Our result analyses not only demonstrate best model performance but also characterize grid searches from various aspects such as their capabilities of discovering decent models and the unit grid search time. The three-stage mechanism worked effectively. It not only made our low-budget grid searches feasible and manageable but also helped improve the model prediction performance of the DFNN models. Our SHAP analyses not only identified clinical risk factors important for the prediction of future risk of breast cancer metastasis, but also DFNN-model hyperparameters important to the prediction of performance scores. Full article

Background: Women with a familial predisposition to breast cancer (BC) are offered screening at earlier ages and more frequently than women from the general population. Methods: We evaluated the effect of screening mammography in 1552 BC cases with a hereditary predisposition to BC unexplained by BRCA1 or BRCA2 and 1363 unrelated controls. Participants reported their lifetime mammography exposures in a detailed questionnaire. Germline rare deleterious or predicted deleterious variants (D-PDVs) in 113 DNA repair genes were investigated in 82.5% of the women and classified according to the strength of their association with BC. Genes with an odds ratio (OR) < 0.9 was assigned to the Gene Group “Reduced”, those with OR ≥ 0.9 and ≤1.1 to Group “Independent”, and those with OR > 1.1 to Group “Increased”. Results: Overall, having been exposed to mammograms (never vs. ever) was not associated with BC risk. However, an increase in BC risk of 4% (95% CI: 1–6%) per additional exposure was found under the assumption of linearity. When grouped according to D-PDV carrier status, mammograms doubled the BC risk of women carrying a D-PDV in Group “Reduced”, as compared to those carrying a D-PDV in Group “Increased”. Conclusions: Our study is the first to investigate the joint effect of mammogram exposure and variants in DNA repair genes other than BRCA1 and BRCA2 in women at high risk of BC; therefore, further studies are needed to verify our findings. Even though mammographic screening reduces the risk of mortality from BC, the identification of populations that are more or less susceptible to ionizing radiation may be clinically relevant. Full article

This article introduces a new continuous lifetime distribution within the Gamma family, called the induced Xgamma distribution, and explores its various statistical properties. The proposed distribution’s estimation and prediction are investigated using Bayesian and non-Bayesian approaches under progressively Type-II censored data. The maximum likelihood and maximum product spacing methods are applied for the non-Bayesian approach, and some of their performances are evaluated. In the Bayesian framework, the numerical approximation technique utilizing the Metropolis–Hastings algorithm within the Markov chain Monte Carlo is employed under different loss functions, including the squared error loss, general entropy, and LINEX loss. Interval estimation methods, such as asymptotic confidence intervals, log-normal asymptotic confidence intervals, and highest posterior density intervals, are also developed. A comprehensive numerical study using Monte Carlo simulations is conducted to evaluate the performance of the proposed point and interval estimation methods through progressive Type-II censored data. Furthermore, the applicability and effectiveness of the proposed distribution are demonstrated through three real-world datasets from the fields of medicine and engineering. Full article

Sediment transport and shoreline changes causing shoreline morphodynamic evolution are key indicators of a coastal structure’s operational continuity. To reduce the computational costs associated with sediment transport modelling tools, a novel procedure based on the combination of a support vector machine for image classification and a trained neural network to extrapolate the shore evolution is presented here. The current study focuses on the coastal area over the Amir-Abad port, using high-resolution satellite images. The real conditions of the study domain between 2004 and 2023 are analysed, with the aim of investigating changes in the shore area, shoreline position, and sediment appearance in the harbour basin. The measurements show that sediment accumulation increases by approximately 49,000 m²/y. A portion of the longshore sediment load is also trapped and deposited in the harbour basin, disrupting the normal operation of the port. Afterwards, satellite images were used to quantitatively analyse shoreline changes. A neural network is trained to predict the remaining time until the reservoir is filled (less than a decade), which is behind the west arm of the rubble-mound breakwaters. Harbour utility services will no longer be offered if actions are not taken to prevent sediment accumulation. Full article

The present paper studies the degradation of an ethylene propylene diene monomer (EPDM) due to aging within an environment resembling the conditions inside the turbine stage of an electrically assisted turbocharger for fuel cell applications, with a special focus on the influence of the system resonance. Aging experiments were conducted on EPDM O-rings of different thicknesses and compression levels, evaluating their degradation and determining its impact on system functionality. The study also quantified changes in system resonance and created an Arrhenius law-based forecast model. The unique activation energy for the degradation processes was identified, providing insight into the optimal dimension and compression of EPDM O-rings. Full article

Research on lithium-ion batteries has been driven by the growing demand for electric vehicles to mitigate greenhouse gas emissions. Despite advances, batteries still face significant challenges in efficiency, lifetime, safety, and material optimization. In this context, the objective of this research is to develop a predictive model based on Deep deep-Learning learning techniques. Based on Deep Learning techniques that combine Transformer and Physicsphysics-Informed informed approaches for the optimization and design of electrochemical parameters that improve the performance of lithium batteries. Also, we present a training database consisting of three key components: numerical simulation using the Doyle–Fuller–Newman (DFN) mathematical model, experimentation with a lithium half-cell configured with a zinc oxide anode, and a set of commercial battery discharge curves using electronic monitoring. The results show that the developed Transformer–Physics physics-Informed informed model can effectively integrate deep deep-learning DNF to make predictions of the electrochemical behavior of lithium-ion batteries. The model can estimate the battery battery-charge capacity with an average error of 2.5% concerning the experimental data. In addition, it was observed that the Transformer could explore new electrochemical parameters that allow the evaluation of the behavior of batteries without requiring invasive analysis of their internal structure. This suggests that the Transformer model can assess and optimize lithium-ion battery performance in various applications, which could significantly impact the battery industry and its use in Electric Vehicles vehicles (EVs). Full article

In several geotechnical and geoenvironmental projects, fines containing expandable clay minerals such as expansive clay (EC) were added to sand as sealing materials to form liners or hydraulic barriers. Liner layers are generally exposed to different climatic conditions such as freeze-thaw (FT) during their service lifetime. The hydromechanical behavior of these layers under such circumstances is of great significance. In this study, the impact of fine contents of expansive soil on swelling, consolidation characteristics, and hydraulic conductivity under FT conditions is examined. Different clay liners with 20%, 30%, and 60% of EC content were designed. The specimens were initially subjected to successive FT cycles up to 15 in close system criteria. The results revealed that volumetric strains attained during successive FT cycles are proportional to the content and nature of expanding minerals (i.e., montmorillonite) and reached a 4.5% magnitude value for the liner with 60% clay. Vertical strains during wetting conditions have been reduced by about 90% after the first FT cycles, which implies significant destruction in the soil structure. The yield stress indicated a 60% change, along with increasing FT cycles. The hydraulic conductivity during an extended period of 100 days shows significant changes and deterioration due to FT actions. The outcome of this study will help to predict the lifetime behavior and performance of the liner, taking into account the stability under frost conditions. Full article

Wind turbine blades are subjected to cyclic loading conditions throughout their operational lifetime, making fatigue a critical factor in their design. Accurate prediction of the fatigue performance of wind turbine blades is important for optimizing their design and extending the operational lifespan of wind energy systems. This study aims to develop predictive models of laminated composite fatigue life based on experimental results published by Montana State University, Bozeman, Composite Material Technologies Research Group. The models have been trained on a dataset consisting of 855 data points. Each data point consists of the stacking sequence, fiber volume fraction, stress amplitude, loading frequency, laminate thickness, and the number of cycles of a fatigue test carried out on a laminated composite specimen. The output feature of the dataset is the number of cycles, which indicates the fatigue life of a specimen. Random forest (RF), extreme gradient boosting (XGBoost), categorical boosting (CatBoost), light gradient boosting machine (LightGBM), and extra trees regressor models have been trained to predict the fatigue life of the specimens. For optimum performance, the hyperparameters of these models were optimized using GridSearchCV optimization. The total number of cycles to failure could be predicted with a coefficient of determination greater than 0.9. A feature importance analysis was carried out using the SHapley Additive exPlanations (SHAP) approach. LightGBM showed the highest performance among the models (R² = 0.9054, RMSE = 1.3668, and MSE = 1.8682). Full article

The partial discharge (PD) measurement under pulse width modulation (PWM) voltage is a critical measurement of quality assessment for inverter-fed motors, as outlined in IEC 60034-18-41 and IEC 60034-18-42. One of the key parameters in PD measurement is the repetitive partial discharge inception voltage (RPDIV). This paper examines factors that influence the ageing process of hairpin windings in motors, with ageing tests designed using the Design of Experiment (DoE) method. The study focuses on the effects of electrical and thermal stresses on the ageing process. To achieve this, the failure rate, the RPDIV data, and the lifetime data are selected as the output responses. The findings highlights that RPDIV measurements alone cannot accurately predict the degree of ageing of hairpin windings. Specifically, RPDIV results are influenced not only by the quality of the hairpin windings under PWM voltage but also by other contributing factors. Furthermore, the change in RPDIV during the ageing process showed that the RPDIV measurement cannot predict the ageing degree of the hairpin winding. Experimental data on failure rates and lifetimes reveal that both electrical and thermal stresses significantly influence the ageing process, with a notable interaction between these factors. Among the three output responses, the failure rate provides a more accurate reflection of this interaction. To reliably estimate the lifetime of hairpin windings, more precise parameters are necessary. Further research is required to deepen the understanding of the underlying PD mechanisms under PWM voltage, which could enhance diagnostic and predictive capabilities for hairpin winding performance. Full article