Search Results (50)

Characterizing and simulating complex reservoirs, particularly unconventional resources with multiscale and non-homogeneous features, presents significant bottlenecks in cost, efficiency, and accuracy for conventional research methods. Consequently, there is an urgent need for the digital and intelligent transformation of the field. To address this challenge, this paper proposes that the core solution lies in the deep integration of physical mechanisms and data intelligence. We systematically review and define a new research paradigm characterized by the trinity of digital cores (geometric foundation), physical simulation (mechanism constraints), and artificial intelligence (efficient reasoning). This review clarifies the core technological path: first, AI technologies such as generative adversarial networks and super-resolution empower digital cores to achieve high-fidelity, multiscale geometric characterization; second, cross-scale physical simulations (e.g., molecular dynamics and the lattice Boltzmann method) provide indispensable constraints and high-fidelity training data. Building on this, the methodology evolves from surrogate models to physics-informed neural networks, and ultimately to neural operators that learn the solution operator. The analysis demonstrates that integrating these techniques into an automated “generation–simulation–inversion” closed-loop system effectively overcomes the limitations of isolated data and the lack of physical interpretability. This closed-loop workflow offers innovative solutions to complex engineering problems such as parameter inversion and history matching. In conclusion, this integration paradigm serves not only as a cornerstone for constructing reservoir digital twins and realizing real-time decision-making but also provides robust technical support for emerging energy industries, including carbon capture, utilization, and sequestration (CCUS), geothermal energy, and underground hydrogen storage. Full article

Although physics-informed neural networks (PINNs) offer a novel, mesh-free paradigm for computational fluid dynamics (CFD), existing models often suffer from poor stability and insufficient accuracy, particularly when dealing with complex flows at high Reynolds numbers. To address this limitation, we propose, for the first time, a novel hybrid architecture, PINN-MRT, which integrates the multi-relaxation-time lattice Boltzmann method (MRT-LBM) with PINNs. The model embeds the MRT-LBM evolution equation as a physical constraint within the loss function and employs a unique dual-network architecture to separately predict macroscopic conserved variables and non-equilibrium distribution functions, enabling both forward and inverse problem-solving through a composite loss function. Benchmark tests on the lid-driven cavity flow demonstrate the superior performance of PINN-MRT. In inverse problems, it remains stable at Reynolds numbers up to 5000 with parameter inversion errors below 15%, whereas standard PINN and single-relaxation-time PINN-LBM models fail at a Reynolds number of 1000 with errors exceeding 80%. In purely physics-driven forward problems, PINN-MRT also provides stable solutions at a Reynolds number of 400, while the other models completely collapse. This study confirms that incorporating mesoscopic kinetic theory into PINNs effectively overcomes the stability bottlenecks of conventional approaches, providing a more robust and accurate architecture for CFD and paving the way for solving more challenging fluid dynamics problems. Full article

The paper uses the model of the coupled Boltzmann transport equations, numerically modeled using the lattice Boltzmann method, which describes the impact of an ultra-short laser on thin metal surfaces. The main reason for using this method is the need to create an appropriate model on a very small scale, both in terms of space and time. In the present study, a three-dimensional model is used, for which, in the case of optimization or identification tasks, the characteristics of the obtained numerical solution are quite different from those of other numerical methods and models. The proposed and numerically implemented task of identifying parameters characterizing the laser pulse, such as the power of the stationary laser source, the laser-beam absorptivity coefficient, and the radius of the laser beam, appropriately illustrates the problem. The problem has also been solved for ideal deterministic (no noise) and randomly disturbed (with noise) values of measured temperatures. Three different optimization algorithms are used to solve the inverse task. Several variants of the identification tasks, differing in terms of the number of temperature measurement points, are solved. The results and effectiveness of the identification tasks are compared for the best solution, as well as the statistical measures. Full article

In high-temperature testing scenarios that rely on contact, fine-wire thermocouples demonstrate commendable dynamic performance. Nonetheless, their thermal inertia leads to notable dynamic nonlinear inaccuracies, including response delays and amplitude reduction. To mitigate these challenges, a novel dynamic error correction approach is introduced, which combines a Continuous Restricted Boltzmann Machine, Deep Belief Network, and Physics-Informed Neural Network (CDBN-PINN). The unique heat transfer properties of the thermocouple’s bimetallic structure are represented through an Inverse Heat Conduction Equation (IHCP). An analysis is conducted to explore the connection between the analytical solution’s ill-posed nature and the thermocouple’s dynamic errors. The transient temperature response’s nonlinear characteristics are captured using CRBM-DBN. To maintain physical validity and minimize noise amplification, filtered kernel regularization is applied as a constraint within the PINN framework. This approach was tested and confirmed through laser pulse calibration on thermocouples with butt-welded and ball-welded configurations of 0.25 mm and 0.38 mm. Findings reveal that the proposed method achieved a peak relative error of merely 0.83%, superior to Tikhonov regularization by −2.2%, Wiener deconvolution by 20.40%, FBPINNs by 1.40%, and the ablation technique by 2.05%. In detonation tests, the corrected temperature peak reached 1045.7 °C, with the relative error decreasing from 77.7% to 5.1%. Additionally, this method improves response times, with the rise time in laser calibration enhanced by up to 31 ms and in explosion testing by 26 ms. By merging physical constraints with data-driven methodologies, this technique successfully corrected dynamic errors even with limited sample sizes. Full article

The Unit Inverse Maxwell–Boltzmann (UIMB) distribution is introduced as a novel single-parameter model for data constrained within the unit interval ($0,1$), derived through an exponential transformation of the Inverse Maxwell–Boltzmann distribution. Designed to address the limitations of traditional unit-interval distributions, the UIMB model exhibits flexible density shapes and hazard rate behaviors, including right-skewed, left-skewed, unimodal, and bathtub-shaped patterns, making it suitable for applications in reliability engineering, environmental science, and health studies. This study derives the statistical properties of the UIMB distribution, including moments, quantiles, survival, and hazard functions, as well as stochastic ordering, entropy measures, and the moment-generating function, and evaluates its performance through simulation studies and real-data applications. Various estimation methods, including maximum likelihood, Anderson–Darling, maximum product spacing, least-squares, and Cramér–von Mises, are assessed, with maximum likelihood demonstrating superior accuracy. Simulation studies confirm the model’s robustness under normal and outlier-contaminated scenarios, with MLE showing resilience across varying skewness levels. Applications to manufacturing and environmental datasets reveal the UIMB distribution’s exceptional fit compared to competing models, as evidenced by lower information criteria and goodness-of-fit statistics. The UIMB distribution’s computational efficiency and adaptability position it as a robust tool for modeling complex unit-interval data, with potential for further extensions in diverse domains. Full article

Understanding tortuosity is essential for accurately modeling fluid flow in complex porous media, particularly in the sub-surface reservoir rock; therefore, tortuosity estimation was evaluated using three approaches: Streamline streamline simulations via the Lattice Boltzmann Method (LBM), geometric pathfinding using Dijkstra’s algorithm, and empirical modeling based on pore-structure parameters. The analysis encompassed 1963 micro-Computed Tomography (micro-CT) images of Brazilian pre-salt carbonate and sandstone samples, with the effective porosity extracted from LBM velocity fields, isolating flow-contributing pores, establishing streamline tortuosity as the reference standard. Sandstones exhibited relatively narrow tortuosity ranges (Dijkstra: 1.29–1.75; Streamline: 1.18–2.61; Empirical: 1.18–4.42), whereas carbonates display greater heterogeneity (Dijkstra: 1.00–3.18; Streamline: 1.00–3.68; Empirical: 1.59–4.93). Model performance assessed using the corrected Akaike Information Criterion (AICc) revealed that the best agreement with the data was achieved by the semi-empirical model incorporating coordination number and minimum throat length (AICc = −113.11), followed by the Dijkstra-based geometrical approach (−99.74) and the empirical porosity-based model (202.23). There was a nonlinear inverse correlation between tortuosity and effective porosity across lithologies. This comprehensive comparison underscores the importance of incorporating multiple pore-scale parameters for robust tortuosity prediction, improving the understanding of flow behavior in heterogeneous reservoir rocks. Full article

Because of the involvement of π-electron cyclic constituents in their side chains, the so-called aromatic residues give rise to a number of strong, narrow, and well-resolved lines spread over the middle wavenumber (1800–600 cm⁻¹) region of the Raman spectra of peptides and proteins. The number of characteristic aromatic markers increases with the structural complexity (Phe → Tyr → Trp), herein referred to as (F_i = 1, …, 6) in Phe, (Y_i = 1, …, 7) in Tyr, and (W_i = 1, …, 8) in Trp. Herein, we undertake an overview of these markers through the analysis of a representative data base gathered from the most structurally simple tripeptides, Gly-Xxx-Gly (where Xxx = Phe, Tyr, Trp). In this framework, off-resonance Raman spectra obtained from the aqueous samples of these tripeptides were jointly used with the structural and vibrational data collected from the density functional theory (DFT) calculations using the M062X hybrid functional and 6-311++G(d,p) atomic basis set. The conformation dependence of aromatic Raman markers was explored upon a representative set of 75 conformers, having five different backbone secondary structures (i.e., β-strand, polyproline-II, helix, classic, and inverse γ-turn), and plausible side chain rotamers. The hydration effects were considered upon using both implicit (polarizable solvent continuum) and explicit (minimal number of 5–7 water molecules) models. Raman spectra were calculated through a multiconformational approach based on the thermal (Boltzmann) average of the spectra arising from all calculated conformers. A subsequent discussion highlights the conformational landscape of conformers and the wavenumber dispersion of aromatic Raman markers. In particular, a new interpretation was proposed for the characteristic Raman doublets arising from Tyr (~850–830 cm⁻¹) and Trp (~1360–1340 cm⁻¹), definitely excluding the previously suggested Fermi-resonance-based assignment of these markers through the consideration of the interactions between the aromatic side chain and its adjacent peptide bonds. Full article

We investigate spin-to-charge conversion via the Edelstein effect in a 2D Rashba electron gas using the semiclassical Boltzmann approach. We analyze the magnetization arising from the direct Edelstein effect, taking into account an anisotropic Rashba model. We study how this effect depends on the effective masses and Rashba spin–orbit coupling parameters, extracting analytical expressions for the high electronic density regime. Indeed, it is possible to manipulate the anisotropy introduced into the system through these parameters to achieve a boost in the Edelstein response compared to the isotropic Rashba model. We also discuss the theoretical framework to study the inverse Edelstein effect and calculate self-consistently the electric current induced by the proximity of the system to a ferromagnet. These results provide insights into the role of Rashba spin–orbit coupling and anisotropic effects in spin–charge conversion phenomena. Full article

Carbon-fiber composites with thermoplastic matrices offer many processing and performance benefits in aerospace applications, but the long relaxation times of polymers make it difficult to predict how the structure of the matrix depends on its chemistry and how it was processed. Coarse-grained models of polymers can enable access to these long-time dynamics, but can have limited applicability outside the systems and state points that they are validated against. Here we develop and validate a minimal coarse-grained model of the aerospace thermoplastic poly(etherketoneketone) (PEKK). We use multistate iterative Boltzmann inversion to learn potentials with transferability across thermodynamic states relevant to PEKK processing. We introduce tabulated EKK angle potentials to represent the ratio of terephthalic (T) and isophthalic (I) acid precursor amounts, and validate against rheological experiments: The glass transition temperature is independent to T/I, but chain relaxation and melting temperature is. In sum we demonstrate a simple, validated model of PEKK that offers 15× performance speedups over united atom representations that enables studying thermoplastic processing-structure-property-performance relationships. Full article

This study analyzes the impact of slip-dependent zeta potential on the heat transfer characteristics of nanofluids in cylindrical microchannels with consideration of thermal radiation effects. An analytical model is developed, accounting for the coupling between surface potential and interfacial slip. The linearized Poisson–Boltzmann equation, along with the momentum and energy conservation equations, is solved analytically to obtain the electrical potential field, velocity field, temperature distribution, and Nusselt number for both slip-dependent (SD) and slip-independent (SI) zeta potentials. Subsequently, the effects of key parameters, including electric double-layer (EDL) thickness, slip length, nanoparticle volume fraction, thermal radiation parameters, and Brinkman number, on the velocity field, temperature field, and Nusselt number are discussed. The results show that the velocity is consistently higher for the SD zeta potential compared to the SI zeta potential. Meanwhile, the temperature for the SD case is higher than that for the SI case at lower Brinkman numbers, particularly for a thinner EDL. However, an inverse trend is observed at higher Brinkman numbers. Similar trends are observed for the Nusselt number under both SD and SI zeta potential conditions at different Brinkman numbers. Furthermore, for a thinner EDL, the differences in flow velocity, temperature, and Nusselt number between the SD and SI conditions are more pronounced. Full article

Complex networks, from neuronal assemblies to social systems, can exhibit abrupt, system-wide transitions without external forcing. These endogenously generated “noise-induced transitions” emerge from the intricate interplay between network structure and local dynamics, yet their underlying mechanisms remain elusive. Our study unveils two critical roles that nodes play in catalyzing these transitions within dynamical networks governed by the Boltzmann–Gibbs distribution. We introduce the concept of “initiator nodes”, which absorb and propagate short-lived fluctuations, temporarily destabilizing their neighbors. This process initiates a domino effect, where the stability of a node inversely correlates with the number of destabilized neighbors required to tip it. As the system approaches a tipping point, we identify “stabilizer nodes” that encode the system’s long-term memory, ultimately reversing the domino effect and settling the network into a new stable attractor. Through targeted interventions, we demonstrate how these roles can be manipulated to either promote or inhibit systemic transitions. Our findings provide a novel framework for understanding and potentially controlling endogenously generated metastable behavior in complex networks. This approach opens new avenues for predicting and managing critical transitions in diverse fields, from neuroscience to social dynamics and beyond. Full article

We follow the Boltzmann-Clausius-Maxwell (BCM) proposal to establish the generalized second law (GSL) that is applicable to a system of any size, including a single particle system as our example establishes, and that supercedes the celebrated second law (SL) of increase of entropy of an isolated system. It is merely a consequence of the mechanical equilibrium (stable or unstable) principle (Mec-EQ-P) of analytical mechanics and the first law. We justify an irreversibility priciple that covers all processes, spontaneous or not, and having both positive and negative nonequilibrium temperatures temperatures T defined by ${(dQ/dS)}_E$. Our novel approach to establish GSL/SL is the inverse of the one used in classical thermodynamics and clarifies the concept of spontaneous processes so that $dS\ge 0$ for $T>0$ and $dS<0$ for $T<0$. Nonspontaneous processes such as creation of internal constraints are not covered by GSL/SL. Our demonstration establishes that Mec-EQ-P controls spontaneous processes, and that temperature (positive and negative) must be considered an integral part of dissipation. Full article

The space- and temperature-dependent electron distribution $n(\mathbf{r},T)$ determines optoelectronic properties of disordered semiconductors. It is a challenging task to get access to $n(\mathbf{r},T)$ in random potentials, while avoiding the time-consuming numerical solution of the Schrödinger equation. We present several numerical techniques targeted to fulfill this task. For a degenerate system with Fermi statistics, a numerical approach based on a matrix inversion and one based on a system of linear equations are developed. For a non-degenerate system with Boltzmann statistics, a numerical technique based on a universal low-pass filter and one based on random wave functions are introduced. The high accuracy of the approximate calculations are checked by comparison with the exact quantum-mechanical solutions. Full article

In the Multi-Axis Differential Absorption Spectroscopy (MAX-DOAS) trace gas and aerosol profile inversion algorithm, the vertical resolution and the observation information obtained through a series of continuous observations with multiple elevation angles (EAs) can affect the accuracy of an aerosol profile, thus further affecting the results of the gas profile. Therefore, this study examined the effect of the vertical resolution of an aerosol profile and EAs on the NO₂ profile retrieval by combining simulations and measurements. Aerosol profiles were retrieved from MAX-DOAS observations and co-observed using light detection and ranging (Lidar). Three aerosol profile shapes (Boltzmann, Gaussian, and exponential) with vertical resolutions of 100 and 200 m were used in the atmospheric radiative transfer model. Firstly, the effect of the vertical resolution of the input aerosol profile on the retrieved aerosol profile with a resolution of 200 m was studied. The retrieved aerosol profiles from the two vertical resolution aerosol profiles as input were similar. The aerosol profile retrieved from a 100 m resolution profile as input was slightly overestimated compared to the input value, whereas that from a 200 m resolution input was slightly underestimated. The relative deviation of the aerosol profile retrieved from the 100 m resolution as input was higher than that of the 200 m. MAX-DOAS observations in Hefei city on 4 September 2020 were selected to verify the simulation results. The aerosol profiles retrieved from the oxygen collision complex (O₄) differential slant column density derived from MAX-DOAS observations and Lidar simulation were compared with the input Lidar aerosol profiles. The correlation between the retrieved and input aerosol profiles was high, with a correlation coefficient R > 0.99. The aerosol profiles retrieved from the Lidar profile at 100 and 200 m resolutions as input closely matched the Lidar aerosol profiles, consistent with the simulation result. However, aerosol profiles retrieved from MAX-DOAS measurements differed from the Lidar profiles due to the influence of the averaging kernel matrix smoothing, the different location and viewing geometry, and uncertainties associated with the Lidar profiles. Next, NO₂ profiles of different vertical resolutions were used as input profiles to retrieve the NO₂ profiles under a single aerosol profile scenario. The effect of the vertical resolution on the retrieval of NO₂ profiles was found to be less significant compared to aerosol retrievals. Using the Lidar aerosol profile as the a priori aerosol information had little effect on NO₂ profile retrieval. Additionally, the retrieved aerosol profiles and aerosol optical depths varied under different EAs. Ten EAs (i.e., 1, 2, 3, 4, 5, 6, 8, 15, 30, and 90°) were found to obtain more information from observations. Full article

Physics-Informed Neural Networks (PINNs) improve the efficiency of data utilization by combining physical principles with neural network algorithms and thus ensure that their predictions are consistent and stable with the physical laws. PINNs open up a new approach to address inverse problems in fluid mechanics. Based on the single-relaxation-time lattice Boltzmann method (SRT-LBM) with the Bhatnagar–Gross–Krook (BGK) collision operator, the PINN-SRT-LBM model is proposed in this paper for solving the inverse problem in fluid mechanics. The PINN-SRT-LBM model consists of three components. The first component involves a deep neural network that predicts equilibrium control equations in different discrete velocity directions within the SRT-LBM. The second component employs another deep neural network to predict non-equilibrium control equations, enabling the inference of the fluid’s non-equilibrium characteristics. The third component, a physics-informed function, translates the outputs of the first two networks into physical information. By minimizing the residuals of the physical partial differential equations (PDEs), the physics-informed function infers relevant macroscopic quantities of the flow. The model evolves two sub-models that are applicable to different dimensions, named the PINN-SRT-LBM-I and PINN-SRT-LBM-II models according to the construction of the physics-informed function. The innovation of this work is the introduction of SRT-LBM and discrete velocity models as physical drivers into a neural network through the interpretation function. Therefore, the PINN-SRT-LBM allows a given neural network to handle inverse problems of various dimensions and focus on problem-specific solving. Our experimental results confirm the accurate prediction by this model of flow information at different Reynolds numbers within the computational domain. Relying on the PINN-SRT-LBM models, inverse problems in fluid mechanics can be solved efficiently. Full article