Search Results (125)

Blast furnace (BF) ironmaking is a complex multiphase process involving gas–solid flow, heat transfer, chemical reactions, burden movement, and phase transformation under high-temperature conditions. Since many internal states of the blast furnace cannot be directly observed during operation, numerical simulation and mathematical modeling have become important tools for understanding furnace behavior and optimizing operational parameters. This paper reviews recent advances in blast furnace numerical simulation and internal state reconstruction methods. Existing approaches, including packed-bed flow models, cohesive zone reconstruction methods, burden distribution models, and temperature field prediction methods, are summarized and discussed. In addition, the evolution of blast furnace mathematical models from early one-dimensional steady-state formulations to modern three-dimensional multifluid and hybrid simulation approaches is reviewed. Recent developments in computational fluid dynamics (CFD), the discrete element method (DEM), digital twin, and data-driven modeling are also discussed. Compared with traditional simplified models, modern multidimensional and hybrid approaches show improved capability in describing asymmetric furnace inner states, multiphase transport behavior, and operational parameter effects under industrial conditions. However, challenges still remain in achieving computational efficiency, parameter calibration, multiphase coupling, and real-time industrial application. Future studies are expected to focus on the integration of mechanism-based simulation and intelligent data-driven methods to improve prediction accuracy, operational adaptability, and intelligent control capability in blast furnace ironmaking. Full article

While double-layer insulation structures are widely adopted, their thermal performance is critically dependent on the thermophysical behavior of the interstitial air cavity, a variable often oversimplified in current design practices. This article moves beyond generic material descriptions to investigate the specific mechanism of heat transfer transition within sealed air gaps sandwiched between graphite polystyrene boards. The innovation of this experiment lies in the rigorous isolation of air gap thickness as the primary independent variable within a 1 × 1 × 1 m closed building model, instrumented with high-precision GPRS temperature and humidity sensors to capture real-time thermal gradients under the authentic climate conditions of Anyang, Henan. The results demonstrate a non-monotonic relationship between gap thickness and effective thermal resistance, governed by the competition between molecular conduction and buoyancy-driven natural convection. Specifically, the data validates that a 20 mm air gap represents the statistically significant optimum, thereby maximizing insulation efficiency while minimizing radiative heat loss. Using this optimized structure reduces steady-state heat flux compared to monolithic equivalents and aligns with the energy conservation target. Unlike previous studies limited by simulation assumptions or short-term testing, this research provides empirically verified, long-term field data that bridges the gap between theoretical fluid dynamics and practical building envelope engineering. These findings offer a robust, physics-based reference for optimizing double-layer insulation systems in the Central Plains, directly supporting the low-carbon retrofitting of existing building stocks. Full article

Digital tools in aluminum alloy processing are computational, sensing-based, and data-driven methods used to understand, predict, monitor, optimize, and control how aluminum alloys are transformed into components. They support decisions across casting, deformation processing, heat treatment, welding, surface engineering, and additive manufacturing by linking processing conditions with geometry, microstructure, defects, properties, and service performance. In technical use, the term includes finite element method (FEM), computational fluid dynamics (CFD), CALculation of PHAse Diagrams (CALPHAD), microstructure models, machine-learning regressors, surrogate models, nondestructive digital inspection, image-analysis tools, and digital twins. These tools are most effective when they establish links among controllable processing variables, underlying metallurgical mechanisms, measurable quality indicators, and service-relevant performance outcomes. Full article

Rainfall is a critical factor inducing slope instability, and accurate prediction of the factor of safety (FOS) of slopes under rainfall conditions is of paramount importance for disaster prevention and mitigation. Conventional numerical simulation methods incur high computational costs, while individual machine learning models are often insufficient to adequately capture the nonlinear spatiotemporal evolution characteristics of multiple factors under coupled multi-physics fields. To address these limitations, this paper proposes a Transformer–LSTM prediction framework. First, a fluid–structure coupling model for rainfall-affected slopes is constructed using COMSOL, and multi-factor orthogonal experiments are performed to generate multi-dimensional time-series data. Subsequently, a Transformer–LSTM fusion deep learning model is built, in which LSTM is employed to extract the temporal dynamic characteristics of rainfall infiltration, and the self-attention mechanism of the Transformer is leveraged to enhance feature extraction and global dependency modeling of key disaster-causing factors. Experimental results demonstrate that the Transformer–LSTM model significantly outperforms traditional PSO-LSTM, PSO-SVM, and standalone Transformer or LSTM models in terms of both prediction accuracy and generalization capability. Its coefficient of determination (R²) remains above 0.94, and key evaluation metrics—including mean absolute error (MAE), root mean square error (RMSE), and mean absolute percentage error (MAPE)—attain the lowest values among the compared models. Furthermore, the SHAP (SHapley Additive exPlanations) interpretability framework is introduced to quantitatively elucidate the model’s predictive decision-making and to establish a physically grounded causal mapping with geotechnical mechanisms. It is confirmed that effective cohesion and slope angle exert a dominant interactive effect on the degradation of slope stability, providing data-driven support for wide-area monitoring of rainfall-induced landslides. Full article

α-synuclein (α-syn) aggregation in dopaminergic neurons is a central event in Parkinson’s disease (PD) pathogenesis. Immunotherapeutic strategies targeting α-syn, including passive and active approaches, aim to inhibit aggregation, propagation, and toxicity of pathological species while promoting their clearance via immune mechanisms. This review summarizes α-syn directed immunotherapies evaluated in in silico, in vitro, and in vivo models, as well as early phase clinical trials, focusing on how epitope selection and antibody formats influence efficacy, safety, and target engagement. Data on monoclonal antibody, peptide, and protein-based vaccines, and structure-guided immunogens were analyzed, integrating behavioral, neuropathological, proteomic, and structural outcomes alongside biomarker development for α-syn species in cerebrospinal fluid and peripheral compartments. Clinical evidence indicates that several candidates induce sustained anti-α-syn antibody responses with acceptable safety profiles and signs of pharmacodynamic engagement, including reductions in free or oligomeric α-syn. However, consistent long-term clinical benefits remain unproven, highlighting the gap between preclinical success and disease modification in humans. Advances in structural biology and proteomics support rational epitope selection and improved immunogen design, reinforcing α-syn-targeted immunotherapy as a promising yet experimental strategy for PD, and highlighting the need for mechanistically oriented, biomarker-driven clinical trials initiated in well-characterized prodromal and early-stage cohorts. Full article

Celestine (SrSO₄) is the principal ore of Sr and a sensitive tracer of diagenetic fluid–rock interaction in carbonate–evaporite successions. This study presents integrated petrographic mineral chemistry and Sr–S isotopic data for epigenetic celestine hosted by Asmari carbonates at Jabal Hafit, Al Ain (UAE), to constrain fluid source, and mechanisms of SrSO₄ precipitation during basin diagenesis. Field and SEM observations show celestine as stratabound, vug- and fracture-filling euhedral to subhedral crystals within dolomitised limestone, suggesting precipitation after initial lithification during early-to-mid burial diagenesis. Electron microprobe analyses show nearly stoichiometric SrSO₄ (55.15–57.30 wt.% SrO; 42.43–44.35 wt.% SO₃) with very low Ba and Ca. The characteristically high Sr/Ba signature of the celestine reflects a complex diagenetic history driven by efficient Sr remobilisation during carbonate recrystallisation within an inherently Ba-poor marine sequence. Measured ⁸⁷Sr/⁸⁶Sr ratios are tightly clustered (0.707841–0.707854) with a high degree of isotopic homogeneity, which indicates a stable, well-buffered fluid reservoir, while the absolute values align with an Oligocene marine signature. Sulphur isotope values (δ³⁴S = +27.3 to +29.1‰) are enriched relative to coeval marine sulphate, which could be attributed to closed-system Rayleigh fractionation driven by bacterial sulphate reduction. We propose that celestine precipitated from stable, marine-buffered burial brines, where supersaturation was achieved through coupled Sr enrichment from carbonate diagenesis and microbial modification of the sulphate reservoir. Full article

Hydraulic fracture network initiation and propagation in tight sandstone are strongly controlled by mineral heterogeneity and fluid–solid interaction. However, existing numerical models still have limited capability in simultaneously representing multi-mineral distributions and dynamically coupled fracture-fluid processes. In this study, a two-dimensional polygonal discrete element fluid–solid coupled model was established based on mineralogical images of tight sandstone. Compared with conventional continuum-based approaches, the proposed model is better suited to describing fracture initiation, branching, and network evolution in multi-mineral granular media. Under dimensionless operating conditions calibrated against field data, coupled and uncoupled formulations were systematically compared to evaluate the role of hydro-mechanical interaction during hydraulic fracturing. The coupled simulations generated consistently more fractures than the uncoupled simulations over the investigated injection-rate range, with an average increase of 28.7% and a maximum increase of 67.2%. Compared with the uncoupled model, the coupled model also predicted higher breakdown pressures and stronger fracture-tip pressure concentrations, and the breakdown pressure increased with injection rate. Under low injection rates, the coupled formulation reproduced pressure-buildup-driven fracture-tip advance, whereas the uncoupled formulation failed to sustain fracture propagation. Under higher injection rates, the coupled formulation produced multilayered and highly branched fracture networks, while the uncoupled formulation mainly generated simple first-order branching. These results demonstrate that hydro-mechanical coupling is a controlling mechanism for fluid-energy dissipation, fracture-tip pressure evolution, and complex fracture network formation in tight sandstone. This study provides an image-based polygonal DEM framework for evaluating hydro-mechanical fracture network evolution in multi-mineral tight sandstone. Full article

Biomechanics sits at the interface of engineering and medical sciences, offering essential insight into how tissues, organs, and biological systems respond to mechanical loading. This review brings together recent advances in musculoskeletal and cardiovascular biomechanics, illustrating how experimental techniques, computational modeling, and multiscale analysis are used to characterize load transfer, tissue deformation, fatigue, and injury mechanisms. In musculoskeletal applications, predictive simulations, wearable sensing technologies, and neuromechanical assessment tools support improved injury prevention, rehabilitation planning, and assistive device development. In the cardiovascular domain, patient-specific modeling, fluid–structure interaction analyses, and advanced imaging approaches clarify how hemodynamics, vessel wall mechanics, and device–tissue interactions influence disease progression, implant performance, and therapeutic outcomes. Emerging technologies including artificial intelligence, machine learning, digital twin frameworks, biofabrication, soft robotics, and self-powered sensing are enabling data-driven, real-time, and personalized interventions that connect mechanistic understanding with clinical practice. Despite these advances, challenges remain in accounting for individual variability, integrating multiscale data, and translating computational predictions into clinically validated solutions. By emphasizing interdisciplinary strategies that unite biomechanics, computational analytics, and innovative device engineering, this review outlines a pathway toward predictive, patient-centered healthcare and next-generation therapeutic and rehabilitation solutions. Full article

Modelling plays a critical role in many engineering applications. Partial differential equations (PDEs) are ubiquitous, describing various physical phenomena such as fluid flow, electromagnetism, and quantum mechanics. Although some of these equations have analytical solutions, many require high-fidelity simulations of parametric PDEs. In general, high-fidelity simulations are computationally expensive and often infeasible for real-time or multi-query applications. This challenge has led to the development of reduced-order models (ROMs). Over the past few decades, ROMs have emerged as a practical solution for simulating, controlling, and optimizing large-scale and complex dynamical systems. This paper introduces a novel Calibrated Intrusive Reduced-Order Modelling (CIROM) approach for the efficient and accurate simulation of the one-dimensional Burgers’ equation, employed as a canonical benchmark because it is a simplified fundamental partial differential equation that captures the behaviour of many real-world phenomena. The proposed method, combining the strengths of proper orthogonal decomposition (POD) and long short-term memory (LSTM) networks, effectively reduces computational complexity while addressing inherent instabilities in classical reduced-order models. Unlike traditional POD-ROMs, which often suffer from error accumulation and instability at high Reynolds numbers, the CIROM employs an iterative LSTM-based error correction mechanism to learn and compensate for truncation and projection errors. This study is benchmark-oriented and does not aim to provide a general PDE solver. The performance of the proposed method is rigorously evaluated across a broad range of Reynolds numbers, including interpolation and extrapolation scenarios, demonstrating robust extrapolation within moderate ranges. Comprehensive numerical experiments confirm that the CIROM outperforms both pure intrusive ROMs and purely data-driven LSTM models in terms of prediction accuracy, stability, and computational cost. Full article

The reliability of structural safety assessments for offshore wind turbines is often compromised by time-dependent corrosion effects and the high computational cost of fluid–structure interaction analysis. This study proposes a data-driven framework for predicting the degradation of offshore wind turbine support structures under time-dependent corrosion. First, the multi-faceted mechanisms of corrosion progression were analyzed to quantitatively evaluate the evolution of structural cross-sectional damage and residual load-bearing capacity. A structural mechanical equivalent method was then proposed and integrated with a high-fidelity fluid–structure coupled model that takes into account corrosion effects, and a corresponding time-dependent structural response database was established. Then, the data extrapolation techniques were applied to unsimulated response samples, enabling comprehensive assessment and accurate forecasting of structural states. Validation under different data sampling strategies shows that the dense strategy achieves the highest accuracy, with stress and deformation errors of 0.31% and 2.23%, the moderate strategy yields errors of 2.47% and 2.58%, while the sparse strategy results in larger errors of 3.31% and 8.71%, but still captures the overall evolution trend. It demonstrates that the proposed approach provides a reliable and efficient predictive tool for service-life assessment and structural response evaluation of offshore wind turbine support structures. Full article

Tunnel kilns are widely used in ceramic manufacturing due to their continuous operation, stable performance, and relatively high thermal efficiency. However, the firing stage remains highly energy-intensive and is a major source of environmental impact, necessitating advanced strategies for performance optimization and sustainability. This study presents a comprehensive and critical review of recent developments in tunnel kiln technology, focusing on heat transfer mechanisms, thermal modeling, process optimization, airflow management, energy recovery, computational fluid dynamics (CFD), and environmental sustainability. The literature shows that kiln performance is governed by strongly coupled interactions among fluid flow, heat transfer, combustion, and material transformations. Although significant progress has been achieved through analytical modeling, experimental studies, and numerical simulations, many approaches rely on simplified assumptions or isolated subsystem analyses, limiting their applicability to real industrial conditions. Key findings emphasize the importance of optimizing airflow distribution, kiln geometry, and product arrangement to enhance convective heat transfer and temperature uniformity. Energy optimization strategies—including waste heat recovery, combustion control, and reduction in kiln car thermal mass—demonstrate considerable potential, but their effectiveness depends on integrated, system-level implementation. Environmental analyses identify the firing stage as the primary source of greenhouse gas emissions, highlighting the need for coordinated energy and emission reduction strategies. In this context, Digital Twin and Industry 4.0 technologies offer promising capabilities for real-time monitoring, predictive control, and data-driven optimization. Generally, this review underscores the need to transition from isolated optimization approaches to integrated, multi-scale frameworks that combine advanced modeling, experimental validation, and intelligent digital systems to achieve sustainable and energy-efficient ceramic manufacturing. Full article

Gingival recession is commonly linked to alveolar bone dehiscence, inflammatory burden, traumatic brushing, or excessive orthodontic forces. However, recession is also observed in some patients despite apparently mild or “biologically acceptable” loading, particularly in thin periodontal phenotypes. Here, we propose the Gingival Creep Failure Theory, a hypothesis-driven conceptual framework in which gingival soft tissues undergo time-dependent viscoelastic deformation (creep) under sustained or repetitive tensile microstrain. Over time, accumulated deformation and microstructural fatigue may reduce recoil capacity and shift the gingival margin apically once tissue-level tolerance is exceeded. Gingival connective tissue is modeled as a fiber-reinforced, fluid-rich viscoelastic composite whose response depends on collagen architecture, cross-linking, proteoglycan-mediated hydration, and vascular support. In thin phenotypes characterized by reduced connective tissue volume and altered extracellular matrix (ECM) organization, creep progression is hypothesized to accelerate, lowering the threshold at which fatigue-related microdamage translates into clinically detectable marginal migration. Evidence from collagenous connective tissue biomechanics supports the plausibility that sub-failure sustained or cyclic loading can produce cumulative deformation and incomplete recovery; however, direct creep–fatigue data for human gingiva remain limited, underscoring the need for targeted validation studies. This hypothesis integrates soft tissue mechanics with periodontal phenotype biology and orthodontic loading patterns and proposes creep and microstructural fatigue as plausible time-dependent contributors to gingival recession in susceptible phenotypes. Because direct in vivo gingival strain and creep–fatigue measurements remain limited, the model should be interpreted as hypothesis-generating and in need of targeted clinical and experimental validation. Full article

Temporomandibular disorders (TMDs) are the prevalent causes of orofacial pain and dysfunction of the temporomandibular joint (TMJ) and masticatory muscles. Previous studies have revealed that proinflammatory cytokines play a key role in promoting inflammation, pain, and degeneration within the TMJ. In this context, the present systematic review synthesizes current evidence on various cytokines involved in the pathophysiology of TMDs and evaluates their associations with clinical signs and structural TMJ damage. A PRISMA-guided search (PROSPERO: CRD420251163290) was conducted in PubMed/MEDLINE, Embase, Scopus, and the Cochrane Library to identify human-based, in vivo, and in vitro studies (January 2014 to September 2025) that assessed the roles of proinflammatory cytokines in TMDs. The following data were extracted from the identified studies: cytokine profiles, sampling methods, clinical outcomes, and TMJ structural changes. Study quality and risk of bias were systematically evaluated. A total of 15 studies (clinical, animal, and mechanistic) were included in the review. Tumor necrosis factor-alpha (TNF-α), interleukin-1β (IL-1β), interleukin-6 (IL-6), and interleukin-17 (IL-17) consistently emerged as the major contributors to synovitis, cartilage degradation, nociceptive sensitization, and bone resorption. Human studies showed that high levels of TNF-α, IL-1β, and IL-6 and chemokines such as C-C motif chemokine ligand 2 (CCL2) and regulated on activation, normal T-cell expressed and secreted (RANTES) were associated with TMJ pain, restricted mandibular motion, crepitus, malocclusion, and erosive changes on imaging. An increased ratio of TNF to soluble TNF receptor in synovial fluid correlated with both pain and condylar damage, suggesting that loss of cytokine control contributes to progressive joint destruction. TMDs, particularly inflammatory and degenerative subtypes, are cytokine-driven pathologies rather than purely mechanical disorders. TNF-α, IL-1β, and IL-6 are the promising candidate biomarkers of local inflammation and structural joint pathology. Standardized longitudinal studies are required to validate cytokine-based diagnostics and develop anti-cytokine therapeutics. Full article

Microfluidic electrokinetic flows play a central role in applications such as lab-on-a-chip diagnostics, microelectronics cooling, and biomedical sample manipulation. These systems involve intricate heat transfer processes, including Joule heating from ionic currents, temperature-driven flow instabilities, and coupled thermal–fluid interactions, that crucially affect device performance, reliability, and scalability. Current challenges include non-equilibrium charge dynamics, incomplete thermophysical property data for complex fluids, and thermal crosstalk in integrated platforms. This review summarizes the literature published over the past 20 years, with occasional reference to earlier work, covering the fundamental mechanisms of heat generation and dissipation in electrokinetic microflows; analytical, numerical, and experimental approaches for characterizing thermal effects; and discussion on the limitations and application-driven opportunities. It also highlights open questions and future research directions and offers a comprehensive view of design principles and guidelines for developing robust, thermally optimized electrokinetic microfluidic technologies. Full article

In air jet looms, relay nozzles are critical components in governing airflow velocity and air consumption during the weft insertion process. Although computational fluid dynamics (CFD) offers high-fidelity simulation for aerodynamic analysis, its computational burden hinders its practicality in iterative aerodynamic design of relay nozzles. To address the challenge, this study proposes a data-driven framework integrating a Chebyshev polynomial Kolmogorov–Arnold Network (Chebyshev KAN) surrogate model with an Improved Multi-objective Red-billed Blue Magpie Optimizer (IMORBMO). The accuracy of the Chebyshev KAN model was benchmarked against conventional multilayer perceptrons (MLP), convolutional neural networks (CNN), and the standard Kolmogorov–Arnold Network (KAN). Experimental results demonstrate that the Chebyshev KAN model achieves the lowest mean absolute error (MAE) of 0.103 for airflow velocity and 0.115 for air consumption. Building upon the non-dominated sorting and crowding distance strategies, IMORBMO was developed, incorporating an adaptive mutation mechanism by information entropy for improvement of convergence, diversity, and uniformity of the Pareto-optimal solutions. Comprehensive evaluations on the ZDT and WFG benchmark suites confirm that the IMORBMO consistently attains the best and highly competitive performance, yielding the lowest generation distance (GD), inverted generational distance (IGD) values and the highest hypervolume (HV). Applied to the aerodynamic optimization of a relay nozzle, the proposed framework delivers an optimal aerodynamic design that increases airflow velocity by 10.5% while reducing air consumption by 15.4%, as verified by CFD simulation. The steady-state flow field was simulated by solving the Reynolds-Average NavierStokes equations with the k–ω turbulent model, utilizing Fluent 2025.R2. No-slip wall, inlet pressure and outlet pressures are boundary conditions to the relay nozzle surfaces. This work establishes a computationally efficient and accurate optimization paradigm that holds significant promise for aerodynamic design and other complex real-world engineering applications. Full article