Search Results (425)

The Pennes bioheat equation is the most widely used model for describing heat transfer in living tissue during thermal exposure. It is derived from the classical Fourier law of heat conduction and assumes energy exchange between blood vessels and surrounding tissues. The literature presents various numerical methods for solving the bioheat equation, with exact solutions developed for different boundary conditions and geometries. However, analytical models based on this framework are rarely reported. This study aims to develop an analytical three-dimensional model using MATHEMATICA software, with subsequent mathematical validation performed through COMSOL simulations, to characterize heat transfer in biological tissues induced by laser irradiation under various therapeutic conditions. The objective is to refine the conventional bioheat equation by introducing three key improvements: (a) incorporating a non-Fourier framework for the Pennes equation, thereby accounting for the relaxation time in thermal response; (b) integrating Dirac functions and the telegraph equation into the bioheat model to simulate localized point heating of diseased tissue; and (c) deriving a closed-form analytical solution for the Pennes equation in both its classical (Fourier-based) and improved (non-Fourier-based) formulations. This paper investigates the nuanced relationship between the relaxation time parameter in the telegraph equation and the thermal relaxation time employed in the bioheat transfer equation. Considering all these aspects, the optimal thermal relaxation time determined for these simulations was 1.16 s, while the investigated thermal exposure time ranged from 0.01 s to 120 s. This study introduces a generalized version of the model, providing a more realistic representation of heat exchange between biological tissue and blood flow by accounting for non-uniform temperature distribution. It is important to note that a reasonable agreement was observed between the two modeling approaches: analytical (MATHEMATICA) and numerical (COMSOL) simulations. As a result, this research paves the way for advancements in laser-based medical treatments and thermal therapies, ultimately contributing to more optimized therapeutic outcomes. Full article

The in situ emulsification synergistic self-profile control system has wide application prospects for efficient development on offshore oil reservoirs. During water flooding in Bohai heavy oil reservoirs, random emulsification occurs with superimposed Jamin effects. Effectively utilizing this phenomenon can enhance the efficient development of offshore oilfields. This study addresses the challenges hindering water flooding development in offshore oilfields by investigating the emulsification mechanism and key influencing factors based on oil–water emulsion characteristics, thereby proposing a novel in situ emulsification flooding method. Based on a fundamental analysis of oil–water properties, key factors affecting emulsion stability were examined. Core flooding experiments clarified the impact of spontaneous oil–water emulsification on water flooding recovery. Two-dimensional T1–T2 NMR spectroscopy was employed to detect pure fluid components, innovating the method for distinguishing oil–water distribution during flooding and revealing the characteristics of in situ emulsification interactions. The results indicate that emulsions formed between crude oil and formation water under varying rheometer rotational speeds (500–2500 r/min), water cuts (30–80%), and emulsification temperatures (40–85 °C) are all water-in-oil (W/O) type. Emulsion viscosity exhibits a positive correlation with shear rate, with droplet sizes primarily ranging between 2 and 7 μm and a viscosity amplification factor up to 25.8. Emulsion stability deteriorates with increasing water cut and temperature. Prolonged shearing initially increases viscosity until stabilization. In low-permeability cores, spontaneous oil–water emulsification occurs, yielding a recovery factor of only 30%. For medium- and high-permeability cores (water cuts of 80% and 50%, respectively), recovery factors increased by 9.7% and 12%. The in situ generation of micron-scale emulsions in porous media achieved a recovery factor of approximately 50%, demonstrating significantly enhanced oil recovery (EOR) potential. During emulsification flooding, the system emulsifies oil at pore walls, intensifying water–wall interactions and stripping wall-adhered oil, leading to increased T2 signal intensity and reduced relaxation time. Oil–wall interactions and collision frequencies are lower than those of water, which appears in high-relaxation regions (T1/T2 > 5). The two-dimensional NMR spectrum clearly distinguishes oil and water distributions. Full article

The titanium matrix composites (TMCs) fabricated via Directed Energy Deposition (DED) effectively overcome the issue of coarse columnar grains typically observed in additively manufactured titanium alloys. In this study, systematic annealing heat treatments were applied to in situ (TiB + TiC)/Ti-6Al-4V composites to refine the microstructure and tailor mechanical properties. The results reveal that the plate-like α phase in the as-deposited composites gradually transforms into an equiaxed morphology with increasing annealing temperature and holding time. Notably, when the annealing temperature exceeds 1000 °C, significant coarsening of the TiC phase is observed, while the TiB phase remains morphologically stable. Annealing promotes decomposition of acicular martensite and stress relaxation, leading to a reduction in hardness compared to the as-deposited state. However, the reticulated distribution of the TiB and TiC reinforcement phases contributes to enhanced tensile performance. Specifically, the as-deposited composite achieves a tensile strength of 1109 MPa in the XOY direction, representing a 21.6% improvement over the as-cast counterpart, while maintaining a ductility of 2.47%. These findings demonstrate that post-deposition annealing is an effective strategy to regulate microstructure and achieve a desirable balance between strength and ductility in DED-fabricated titanium matrix composites. Full article

Nuclear Magnetic Resonance (NMR) and Magnetic Resonance Imaging (MRI) are powerful techniques that have been employed to analyze foodstuffs comprehensively. These techniques offer in-depth information about the chemical composition, structure, and spatial distribution of components in a variety of food products. Quantitative NMR is widely applied for precise quantification of metabolites, authentication of food products, and monitoring of food quality. Low-field ¹H-NMR relaxometry is an important technique for investigating the most abundant components of intact foodstuffs based on relaxation times and amplitude of the NMR signals. In particular, information on water compartments, diffusion, and movement can be obtained by detecting proton signals because of H₂O in foodstuffs. Saffron adulterations with calendula, safflower, turmeric, sandalwood, and tartrazine have been analyzed using benchtop NMR, an alternative to the high-field NMR approach. The fraudulent addition of Robusta to Arabica coffee was investigated by ¹H-NMR Spectroscopy and the marker of Robusta coffee can be detected in the ¹H-NMR spectrum. MRI images can be a reliable tool for appreciating morphological differences in vegetables and fruits. In kiwifruit, the effects of water loss and the states of water were investigated using MRI. It provides informative images regarding the spin density distribution of water molecules and the relationship between water and cellular tissues. ¹H-NMR spectra of aqueous extract of kiwifruits affected by elephantiasis show a higher number of small oligosaccharides than healthy fruits do. One of the frauds that has been detected in the olive oil sector reflects the addition of hazelnut oils to olive oils. However, using the NMR methodology, it is possible to distinguish the two types of oils, since, in hazelnut oils, linolenic fatty chains and squalene are absent, which is also indicated by the ¹H-NMR spectrum. NMR has been applied to detect milk adulterations, such as bovine milk being spiked with known levels of whey, urea, synthetic urine, and synthetic milk. In particular, T2 relaxation time has been found to be significantly affected by adulteration as it increases with adulterant percentage. The ¹H spectrum of honey samples from two botanical species shows the presence of signals due to the specific markers of two botanical species. NMR generates large datasets due to the complexity of food matrices and, to deal with this, chemometrics (multivariate analysis) can be applied to monitor the changes in the constituents of foodstuffs, assess the self-life, and determine the effects of storage conditions. Multivariate analysis could help in managing and interpreting complex NMR data by reducing dimensionality and identifying patterns. NMR spectroscopy followed by multivariate analysis can be channelized for evaluating the nutritional profile of food products by quantifying vitamins, sugars, fatty acids, amino acids, and other nutrients. In this review, we summarize the importance of NMR spectroscopy in chemical profiling and quality assessment of food products employing magnetic resonance technologies and multivariate statistical analysis. Full article

Control of buoyancy-assisted convective flow and the associated thermal behavior of nanofluids in finite-sized conduits has become a great challenge for the design of many types of thermal equipment, particularly for heat exchangers. This investigation discusses the numerical simulation of the buoyancy-driven convection (BDC) of a nanofluid (NF) in a differently heated cylindrical annular domain with an interior cylinder attached with a thin baffle. The annular region is filled with non-Darcy porous material saturated-nanofluid and both NF and the porous structure are in local thermal equilibrium (LTE). Higher thermal conditions are imposed along the interior cylinder as well as the baffle, while the exterior cylinder is maintained with lower or cold thermal conditions. The Darcy–Brinkman–Forchheimer model, which accounts for inertial, viscous, and non-linear drag forces was adopted to model the momentum equations. An implicit finite difference methodology by considering time-splitting methods for transient equations and relaxation-based techniques is chosen for the steady-state model equations. The impacts of various pertinent parameters, such as the Rayleigh and Darcy numbers, baffle dimensions, like length and position, on flow, thermal distributions, as well as thermal dissipation rates are systematically estimated through accurate numerical predictions. It was found that the baffle dimensions are very crucial parameters to effectively control the flow and associated thermal dissipation rates in the domain. In addition, machine learning techniques were adopted for the chosen analysis and an appropriate model developed to predict the outcome accurately among the different models considered. Full article

This study aims to investigate the application of nuclear magnetic resonance (NMR) to characterize mortars containing recycled rubber waste as an eco-innovative material for sustainable construction. The primary objective was to analyze the way rubber granules influence hydration kinetics, microstructural development and pore structure. The innovative mortar formulations incorporated rubber granules, casein, natural hydraulic lime (NHL), and latex. NMR analysis revealed distinct T₂ relaxation time distributions correlated with different pore sizes and water states: shorter T₂ values demonstrate strongly bound water in small pores, while longer T₂ values are associated with loosely bound or free water in larger pores. The formulation with 3.5% NHL and 5% rubber granules exhibited optimal microstructural characteristics. These results reveal that NMR is a valuable, non-destructive tool for monitoring cementitious material evolution and supporting the use of tire-derived waste in eco-innovative mortar designs. Full article

This study introduces an innovative analytical solution to the time-fractional Cattaneo heat conduction equation, which models photothermal transport in metallic thin films subjected to short laser pulse irradiation. The model integrates the Caputo fractional derivative of order 0 < p ≤ 1, addressing non-Fourier heat conduction characterized by finite wave speed and memory effects. The equation is nondimensionalized through suitable scaling, incorporating essential elements such as a newly specified laser absorption coefficient and uniform initial and boundary conditions. A hybrid approach utilizing the finite Fourier cosine transform (FFCT) in spatial dimensions and the Laplace transform in temporal dimensions produces a closed-form solution, which is analytically inverted using the two-parameter Mittag–Leffler function. This function inherently emerges from fractional-order systems and generalizes traditional exponential relaxation, providing enhanced understanding of anomalous thermal dynamics. The resultant temperature distribution reflects the spatiotemporal progression of heat from a spatially Gaussian and temporally pulsed laser source. Parametric research indicates that elevating the fractional order and relaxation time amplifies temporal damping and diminishes thermal wave velocity. Dynamic profiles demonstrate the responsiveness of heat transfer to thermal and optical variables. The innovation resides in the meticulous analytical formulation utilizing a realistic laser source, the clear significance of the absorption parameter that enhances the temperature amplitude, the incorporation of the Mittag–Leffler function, and a comprehensive investigation of fractional photothermal effects in metallic nano-systems. This method offers a comprehensive framework for examining intricate thermal dynamics that exceed experimental capabilities, pertinent to ultrafast laser processing and nanoscale heat transfer. Full article

This study investigates the coupled effects of waste rock-to-tailings ratio (WTR) and curing temperature on the pore structure and mechanical performance of cemented tailings waste rock backfill (CTRB). Four WTRs (6:4, 7:3, 8:2, 9:1) and curing temperatures (20–50 °C) were tested. Low-field nuclear magnetic resonance (NMR) was used to characterize pore size distributions and derive fractal dimensions ($D_a$, $D_b$, $D_c$) at micropore, mesopore, and macropore scales. Uniaxial compressive strength (UCS) and elastic modulus (E) were also measured. The results reveal that (1) the micropore structure complexity was found to be a key indicator of structural refinement, while excessive temperature led to pore coarsening and strength reduction. $D_a$ = 2.01 reaches its peak at WTR = 7:3 and curing temperature = 40 °C; (2) at this condition, the UCS and E achieved 20.5 MPa and 1260 MPa, increasing by 45% and 38% over the baseline (WTR = 6:4, 20 °C); (3) when the temperature exceeded 40 °C, $D_a$ dropped significantly (e.g., to 1.51 at 50 °C for WTR = 7:3), indicating thermal over-curing and micropore coarsening; (4) correlation analysis showed strong negative relationships between total pore volume and mechanical strength ($R$ = −0.87 for ${\mathsf{\delta }}_a\mathrm{v}\mathrm{s}.\mathrm{U}\mathrm{C}\mathrm{S}),$ and a positive correlation between $D_a$ and UCS (R = 0.43). (5) multivariate regression models incorporating pore volume fractions, $T_2$ relaxation times, and fractal dimensions predicted $\mathrm{U}\mathrm{C}\mathrm{S}$ and E with R² > 0.98; (6) the hierarchical sensitivity of fractal dimensions follows the order micro-, meso-, macropores. This study provides new insights into the microstructure–mechanical performance relationship in CTRB and offers a theoretical and practical basis for the design of high-performance backfill materials in deep mining environments. Full article

This paper investigates data transmission in an Internet of Things (IoT) network, where multiple devices send environmental data to a remote base station through an unmanned aerial vehicle (UAV) relay. The UAV serves as an airborne intermediary that collects status information from distributed IoT devices (e.g., temperature readings in a real-time forest fire monitoring system) and forwards it to the base station. To capture the impact of data staleness, a novel Age of Information (AoI) and entropy-aware system loss is defined in terms of L-conditional cross-entropy, which quantifies the expected penalty caused by state misestimation. The scheduling problem, which aims to minimize the system loss defined by L-conditional cross-entropy, is formulated as a Restless Multi-Armed Bandit (RMAB) problem. By applying Lagrange relaxation, the objective function is decomposed into tractable sub-problems, enabling a low-complexity, gain-index-based scheduling strategy. Numerical simulations validate the effectiveness of the proposed algorithm in reducing the long-term average system loss. In particular, the gain-index-based policy achieves a significant reduction in average penalty compared to random, round-robin, periodic update, and MAX-AoI scheduling strategies, demonstrating its superior performance over these baselines. Full article

Lithium-ion batteries are extensively utilized in modern applications due to their high energy density, long cycle life, and efficiency. With the increasing demand for sustainable energy storage solutions, accurately estimating the State of Health (SOH) is essential to address challenges related to battery degradation and secondary life management. Electrochemical Impedance Spectroscopy (EIS) is a widely used diagnostic tool for evaluating battery performance due to its simplicity and cost-effectiveness. However, EIS often struggles to decouple overlapping electrochemical processes. The Distribution of Relaxation Times (DRT) method has emerged as a powerful alternative, enabling the isolation of key processes, such as ohmic resistance, SEI resistance, charge transfer resistance, and diffusion, thereby providing deeper insights into battery aging mechanisms. This paper presents a novel approach for estimating the State of Health (SOH) of batteries by leveraging DRT parameters across multiple State of Charge (SOC) levels. This study incorporates data from three lithium-ion batteries, each with distinct initial capacities, introducing variability that reflects the natural differences observed in real-world battery performance. By employing a Long Short-Term Memory (LSTM)-based machine learning model, the proposed framework demonstrates a superior accuracy in SOH prediction compared to traditional EIS-based methods. The results highlight the sensitivity of DRT parameters to SOH degradation and validate their effectiveness as reliable indicators for battery health. This research underscores the potential of combining a DRT analysis with AI-driven models to advance scalable, precise, and interpretable battery diagnostics. Full article

We demonstrate the reconstruction of battery electrochemical impedance spectroscopy (EIS) curves from time-domain pulse testing and the distribution of relaxation times (DRT) analysis. In the proposed approach, the DRT directly utilizes measured current data instead of simulated current patterns, thereby enhancing robustness against current variations and data anomalies. The method is demonstrated with a simulation, a single cylindrical battery cell experiment, and an experimental EIS of a completely assembled module of 448 cells. For the 3.7 kWh battery module, we applied a transient current pulse and analyzed the dynamic voltage responses. The EIS curves were reconstructed with DRT and compared to experiments across different states of charge (SoC). The experimental EIS data were corrected by a multistep calibration workflow in a frequency range from 50 mHz to 1 kHz, achieving error corrections of up to 80% at 1 kHz. The reconstructed impedances from the pulse test data are in good agreement with the EIS experiments in a broad frequency range, delivering relevant electrochemical information including the ohmic resistance and dynamic time constants of a battery module and its corresponding submodules. With the proposed workflow, rapid pulse tests can be used for extracting electrochemical information faster than standard EIS, with a 67% reduction in measurement time. This time-domain pulsing approach provides an alternative to EIS characterization, making it particularly valuable for battery monitoring, the classification of battery packs upon their return to the manufacturer, second-life applications, and recycling. Full article

Mg-Li alloy is widely used in aerospace and military equipment, but the research on its micromechanical mechanism is still insufficient. In this paper, molecular dynamics simulation is used to analyze the nano-indentation response and mechanical mechanism of single crystal Mg-Li. The deformation and hardness changes of Mg-Li under different loads were studied by load–displacement and hardness–depth curves. At the same time, the dislocation defects and equivalent stress are analyzed to reveal the dislocation formation and stress distribution in the process of deformation. Finally, under the condition of stress relaxation, the mechanical behavior of Mg-Li in the process of nano-indentation was revealed, and the deformation mechanism of Mg-Li alloy was studied. Full article

Aero-engine turbine vanes made from directionally solidified nickel-based superalloys often fail with crack formation from the external wall of cooling channels. Therefore, this study simulates the compressive load on the external wall of the vane and conducts a sequence of creep–fatigue evaluations at 980 °C to investigate the creep–fatigue damage mechanisms of a directionally solidified superalloy and to assess its life. It is found that at low strain ranges, creep damage is dominant, with creep cavities forming inside the specimen and fatigue sources mostly distributed in the specimen interior. As the strain range increases, the damage mechanism transitions from creep-dominated to creep–fatigue coupled damage, with cracks nucleating preferentially on the surface and exhibiting a characteristic of multiple fatigue sources. In the longitudinal (L) specimen, dislocations in multiple orientations of the {111}<110> slip system are activated simultaneously, interacting within the γ channels to form dislocation networks, and dislocations shear through the γ′ phase via antiphase boundary (APB) pairs. In the transverse (T) specimen, stacking intrinsic stacking faults (SISFs) accumulate within the limited {111}<112> slip systems, subsequently forming a dislocation slip band. The modified creep–fatigue life prediction model, incorporating strain energy dissipation and stress relaxation mechanisms, demonstrates an accurate fatigue life prediction under creep–fatigue coupling, with a prediction accuracy within an error band of 1.86 times. Full article

Aging experiments are pivotal for car manufacturers to ensure the reliability of their battery cells. However, realistic aging methods are time-consuming and resource-intensive, necessitating accelerated aging techniques. While these techniques reduce testing time, they can also lead to distorted results due to the partially reversible nature of cell behavior, which stems from the inhomogenization and rehomogenization of conducting salt and lithium distribution in the electrode. To accurately capture these phenomena, cell relaxation must be incorporated into the test design. This work investigates the impact of the test procedure and several stress factors, namely depth of discharge and C- rate, on the formation and rehomogenization of cell inhomogeneities. The experimental results reveal increasing cell inhomogenization, leading to growing reversible capacity losses, particularly under conditions with shorter cycling interruptions (check ups and rest phases). These reversible capacity losses are associated with a significant reduction in cycle life performance of up to 400% under identical cycling conditions but shorter cycling interruptions. Similar trends are observed for increasing cycle depths and C-rates. Optimized recovery cycles effectively mitigate cell inhomogenization, doubling cycle stability without requiring considerable additional testing time. Furthermore, a clear correlation is found between increasing inhomogenization and cell failure, with lithium stripping confirming the occurrence of lithium plating shortly before failure. These findings emphasize the critical importance of considering cell relaxation in cycle aging studies to ensure reliable and accurate lifetime predictions. Under realistic conditions, substantially enhanced cycle stability is expected. Full article

Comprehensive assessment of pore structure and multiphase water distribution is critical to the flow and transport process in coalbed methane (CBM) reservoirs. In this study, nuclear magnetic resonance (NMR) and multifractal analysis were integrated to quantify the multiscale heterogeneity of nine medium- and high-rank coals under water-saturated and dry conditions. By applying the box-counting method to transverse relaxation time (T₂) spectra, multifractal parameters were derived to characterize pore heterogeneity and residual water distribution. The influencing factors of pore heterogeneity were also discussed. The results show that pore structures in high-rank coals (HCs) exhibit a broader multifractal spectrum and stronger rightward spectrum than those of medium-rank coals, reflecting micropore-dominated heterogeneity and the complexity induced by aromatization in HCs. The vitrinite content enhances micropore development, increasing the heterogeneity and complexity of pore structure and residual water distribution. Inertinite content shows opposite trends compared to vitrinite content for the effect on pore structure and water distribution. Volatile yield reflects coal metamorphism and thermal maturity, which inversely correlates with pore heterogeneity and complexity. Residual water mainly distributes to adsorption pores and pore throats, shortening T₂ relaxation (bound water effect) and reducing spectral asymmetry. The equivalence of the multifractal dimension and singularity spectrum validates their joint utility in characterizing pore structure. Minerals enhance pore connectivity but suppress complexity, while moisture and ash contents show negligible impacts. These findings provide a theoretical reference for CBM exploration, especially in optimizing fluid transportation and CBM production strategies and identifying CBM sweet spots. Full article