Flow Method Research Articles

Dense Continuous-Time Optical Flow From Event Cameras.

We present a method for estimating dense continuous-time optical flow from event data. Traditional dense optical flow methods compute the pixel displacement between two images. Due to missing information, these approaches cannot recover the pixel trajectories in the blind time between two images. In this work, we show that it is possible to compute per-pixel, continuous-time optical flow using events from an event camera. Events provide temporally fine-grained information about movement in pixel space due to their asynchronous nature and microsecond response time. We leverage these benefits to predict pixel trajectories densely in continuous time via parameterized Bézier curves. To achieve this, we build a neural network with strong inductive biases for this task: First, we build multiple sequential correlation volumes in time using event data. Second, we use Bézier curves to index these correlation volumes at multiple timestamps along the trajectory. Third, we use the retrieved correlation to update the Bézier curve representations iteratively. Our method can optionally include image pairs to boost performance further. To the best of our knowledge, our model is the first method that can regress dense pixel trajectories from event data. To train and evaluate our model, we introduce a synthetic dataset (MultiFlow) that features moving objects and ground truth trajectories for every pixel. Our quantitative experiments not only suggest that our method successfully predicts pixel trajectories in continuous time but also that it is competitive in the traditional two-view pixel displacement metric on MultiFlow and DSEC-Flow. Open source code and datasets are released to the public.

Cross-correlation-based convolutional neural network with velocity regularization for high-resolution velocimetry of particle images

Particle image velocimetry (PIV) stands as a pivotal experimental technique in fluid dynamics, enabling the visualization and analysis of fluid flows. Traditional methods for extracting velocity fields from particle images often rely on window-cross correlation PIV or, more recently, optical flow techniques rooted in intensity conservation principles. However, the former approach suffers from low resolution, whereas the latter is hampered by computational inefficiency and a high susceptibility to noise. Recent studies have demonstrated the effectiveness of convolutional neural networks (CNNs) in processing particle images to obtain high-resolution and high-accuracy velocity fields, though traditional CNN architectures are still not satisfying in accuracy. The present study introduces an enhanced network, En-FlowNetC, based on the cross correlation-based CNN FlowNetC, specifically designed to process PIV particle images and achieve high-accuracy, high-resolution velocity fields. It incorporates a velocity regularization and is trained and validated on canonical datasets. The results indicate that En-FlowNetC surpasses traditional CNN networks in accuracy and markedly outperforms the classic Horn–Schunck optical flow method in both complex and simple flow scenarios. Furthermore, this study confirms the beneficial impact of velocity regularization, when judiciously applied, on network accuracy. The proposed modifications compared to the original FlowNetC are also examined in the ablation experiments. Overall, En-FlowNetC provides an effective deep-learning solution for PIV analysis, paving the way for future endeavors aimed at achieving highly accurate and resolved velocimetry.

Advancing Tumor Therapy: Development and Utilization of Protein-Based Nanoparticles.

Protein-based nanoparticles (PNPs) in tumor therapy hold immense potential, combining targeted delivery, minimal toxicity, and customizable properties, thus paving the way for innovative approaches to cancer treatment. Understanding the various methods available for their production is crucial for researchers and scientists aiming to harness these nanoparticles for diverse applications, including tumor therapy, drug delivery, imaging, and tissue engineering. This review delves into the existing techniques for producing PNPs and PNP/drug complexes, while also exploring alternative novel approaches. The methods outlined in this study were divided into three key categories based on their shared procedural steps: solubility change, solvent substitution, and thin flow methods. This classification simplifies the understanding of the underlying mechanisms by offering a clear framework, providing several advantages over other categorizations. The review discusses the principles underlying each method, highlighting the factors influencing the nanoparticle size, morphology, stability, and functionality. It also addresses the challenges and considerations associated with each method, including the scalability, reproducibility, and biocompatibility. Future perspectives and emerging trends in PNPs' production are discussed, emphasizing the potential for innovative strategies to overcome current limitations, which will propel the field forward for biomedical and therapeutic applications.

Impact of operation strategies on performance of integrated kang-floor heating system in severe cold areas

Efficiently and cost-effectively meeting the heating requirements of residents in severely cold regions by utilizing solar energy is currently a pressing need. Therefore, a solar heating system that integrated Kang-floor heating was established and tested for a passive solar house in Gan-nan Tibetan. This study reveals the effects of adjusting the flow/intermittent time periods and implementing variable flow rate operations on performance of system, to improve energy efficiency and optimize sleeping comfort. Findings indicated decreasing flow/intermittent time to 15/15 min had strengthened the average heat transfer from the water side, and mitigated the influence of returning water on internal water temperature of the tank, ultimately enhancing heat supply. The heating period was divided into three stage (18–24, 0–4, 4–8 o'clock), with a specific flow rate assigned to each stage. Under five flow rate configurations(A-E) with identical initial conditions: during constant flow rate, the greater the initial supply water temperature of the system, the more heat was transferred to room, and the higher heating efficiency was achieved, however, under variable flow rate, the effect of the water temperature on heating efficiency was significantly less evident. Mode B (low-to-high flow rate) had the highest heating efficiency and heat supply. The heating rate and cooling rate of Kang surface, temperature variations were notably less compared to the other four modes. Shortening the flow/intermittent periods and implementing variable flow method could improve the system's heat supply and sleep comfort with limited capital and resource investment.

Real-Time Simulation System for Small Scale Regional Integrated Energy Systems

Regional Integrated Energy Systems (RIESs) integrate wide spectrum of energy sources and storage with optimized energy management and further pollution reduction. This paper presents a real-time simulation system for RIESs powered by multiple digital signal processors (DSPs) with different means of data exchange. The RIES encompasses the DC microgrid (DMG), the district heat network (DHN), and the natural gas network (NGN). To realize multi-energy flow simulation, averaged switch models are investigated for different types of device-level units in the DMG, and the unified energy path method is used to build circuit-dual models of the DHN and NGN. A hierarchical island strategy (HIS) and a multi-energy dispatch strategy (MEDS) are proposed to enhance the energy flow control and operating efficiency. The two-layer HIS can adjust the operating status of device-level units in real time to achieve bus voltage stability in the DMG; MEDS uses energy conversion devices to decouple multi-energy flows and adopts the decomposed flow method to calculate the flow results for each network. The real-time simulation hardware platform is built, and both electricity-led and thermal-led experiments are carried out to verify the accuracy of models and the effectiveness of the proposed strategy. The proposed system with an energy management strategy aims to provide substantial theoretical and practical contributions to the control and simulation of RIESs, thus supporting the advancement of integrated energy systems.

Discrete energy balance equation via a symplectic second-order method for two-phase flow in porous media

We propose and analyze a second-order partitioned time-stepping method for a two-phase flow problem in porous media. The algorithm is a refactorization of Cauchy's one-leg θ-method: the implicit backward Euler method on [tn,tn+θ], and a linear extrapolation on [tn+θ,tn+1]. In the backward Euler step, the decoupled equations are solved iteratively, with the iterations converging linearly. In the absence of the chain rule for time-discrete setting, we approximate the change in the free energy by the product of a second-order accurate discrete gradient (chemical potential) and the one-step increment of the state variables. Similar to the continuous case, we also prove a discrete Helmholtz free energy balance equation, without numerical dissipation. In the numerical tests we compare this symplectic implicit midpoint method (θ=1/2) with the classic backward Euler method, and two implicit-explicit time-lagging schemes. The midpoint method outperforms the other schemes in terms of rates of convergence, long-time behavior and energy approximation, for both small and large values of the time step.

Positivity-preserving discontinuous spectral element methods for compressible multi-species flows

We introduce a novel positivity-preserving numerical stabilisation approach for high-order discontinuous spectral element approximations of compressible multi-species flows. The underlying stabilisation method is the adaptive entropy filtering approach (Dzanic and Witherden, J. Comput. Phys., 468, 2022), which is extended to the conservative formulation of the multi-species flow equations. We show that the straightforward enforcement of entropy constraints in the filter yields poor results around species interfaces and propose an adaptive switch for the entropy bounds based on the convergence properties of the pressure field which drastically improves its performance for multi-species flows. The proposed approach is shown in a variety of numerical experiments applied to the multi-species Euler and Navier–Stokes equations computed on unstructured grids, ranging from shock-fluid interaction problems to three-dimensional viscous flow instabilities. We demonstrate that the approach can retain the high-order accuracy of the underlying numerical scheme even at smooth extrema, ensure the positivity of the species density and pressure in the vicinity of shocks and contact discontinuities, and accurately predict small-scale flow features with minimal numerical dissipation.

A second-order in time, BGN-based parametric finite element method for geometric flows of curves

Over the last two decades, the field of geometric curve evolutions has attracted significant attention from scientific computing. One of the most popular numerical methods for solving geometric flows is the so-called BGN scheme, which was proposed by Barrett et al. (2007) [8], due to its favorable properties (e.g., its computational efficiency and the good mesh property). However, the BGN scheme is limited to first-order accuracy in time, and how to develop a higher-order numerical scheme is challenging. In this paper, we propose a fully discrete, temporal second-order parametric finite element method, which integrates with two different mesh regularization techniques, for solving geometric flows of curves. The scheme is constructed based on the BGN formulation and a semi-implicit Crank-Nicolson leap-frog time stepping discretization as well as a linear finite element approximation in space. More importantly, we point out that the shape metrics, such as manifold distance and Hausdorff distance, instead of function norms, should be employed to measure numerical errors. Extensive numerical experiments demonstrate that the proposed BGN-based scheme is second-order accurate in time in terms of shape metrics. Moreover, by employing the classical BGN scheme as mesh regularization techniques, our proposed second-order schemes exhibit good properties with respect to the mesh distribution. In addition, an unconditional interlaced energy stability property is obtained for one of the mesh regularization techniques.

Метод потоков в диагностике угроз национальной экономической безопасности России

The work identifies the problem of discrepancy between the rapidly growing importance of national economic security (NES) of Russia and the degree of its scientific elaboration according to such criteria as clarity in defining the subject of NES, methodology for studying NES, mechanisms for connecting scientific research with education and practice of NES. The article is devoted to the study of the dynamics of teacher and scientist staff. The research is carried out using a new method developed by the author – the Flow Method. The novelty of the Flow Method begins with a change in the idea of the object of study. In traditional approaches, an object is fixed, and is studied from different sides in different periods of time; the Flow Method focuses the researcher’s attention on the movement of resource flows in the economic environment. In such a movement of resources, the following are distinguished: inflows, outflows, accumulations. Normally, the movement of resources is balanced. It is appropriate to consider sanctions as the most important component of a hybrid war, aimed at unbalancing the resources of the national economy by blocking flows. The study showed that the representation of the movement of resources by the Flow Method differs from traditional representations in statistics and accounting. Approbation of the Method in diagnosing the state of personnel in scientific and educational institutions showed strategic threats to the national security of the Russian Federation. Since 2015, the number of scientists and teachers with an academic degree has been continuously decreasing. The flow method allows us to dispel the myth that if scientists leave region A, they will simply work in another region. This study refutes such a hypothesis. Scientists, in particular, can change their specialty, leave the country, etc. The noted situation is similar for all 16 cities with a population of over a million in Russia, including Moscow and St. Petersburg and 18 constituent entities of the Russian Federation. The influx of young scientists is decreasing while the workforce continues to age. If we ignore the results obtained, then in 5–10 years, for example, the Omsk region will begin to lose the ability to train specialists with higher education, not to mention candidates and doctors of science. A similar situation awaits other regions.

Decentralized Operations of Industrial Complex Microgrids Considering Corporate Power Purchase Agreements for Renewable Energy 100% Initiatives in South Korea

With the rise of environmental policies and advanced technologies, power systems are transitioning from centralized to decentralized systems, incorporating more distributed energy resources (DERs). This shift has increased interest in the operational functions of microgrids (MGs). The “Renewable Energy 100%” (RE100) campaign is pushing companies to adopt renewable energy. In South Korea, industrial complex microgrids (ICMGs) aim to achieve RE100 through corporate power purchase agreements (PPAs) with renewable energy providers. ICMGs need to operate in both grid-connected and islanded modes, facing challenges in power transactions due to different operating agents. This study proposes a decentralized optimal power flow (OPF) method using the separable augmented Lagrangian relaxation (SALR) algorithm to solve these power transaction problems without disclosing internal information. The proposed method decomposes the centralized OPF problem into subproblems for each ICMG and solves them in a distributed manner, sharing only transaction prices and amounts. Numerical results from the case study validate the effectiveness of the proposed method.

Effect of Hydrate Saturation on Permeability Anisotropy for Hydrate-Bearing Turbidite Sediments Based on Pore-Scale Seepage Simulation

The permeability of natural gas hydrate (NGH) turbidite reservoirs typically exhibits significant anisotropy, with anisotropy being a crucial basis for evaluating reservoir production. The presence of hydrates, as a crucial constituent of the solid framework, not only impacts the overall permeability but also influences the permeability anisotropy. To investigate the saturation sensitivity of permeability anisotropy, a series of simulations are performed by integrating particle flow and computational fluid dynamics methods to construct the homogeneous and layered numerical samples and compute the evolution of permeability anisotropy. It is shown that the permeability is isotropic for homogeneous sediments and the isotropy remains unchanged regardless of variations in hydrate saturation. The permeability of layered sediments, in contrast, exhibits significant anisotropy due to the presence of dominant channels within the coarse layer. For uniformly distributed hydrates, the more effective blockage in coarse layers results in a reduction in anisotropy. While for preferentially distributed hydrates, the excess blocking of coarse layers makes the dominant channels transfer to the fine layers, the further blocking causes a U-shaped anisotropy–saturation curve characterized by a decrease–increase transformation. During the reservoir production process, the preponderance channels blocked by hydrates will be cleared and the horizontal permeability will significantly increase. As a result, the production efficiency of horizontal wells may exceed expectations. The findings offer a parameter support for production estimation and environmental assessment.

Educational Interface of Load Flow Methods Using Matlab Graphic User’s Interface Software

this paper introduces a new created programapplication built based on MATLAB programing languageto solve power system networks at steady state problems,which include power flow studies with significant approach.The load flow solution uses iterative techniques that arenamely, Newton-Raphson, Gauss-Seidel and Fast Decouple.After obtaining their results, line changes like lineswitching, tap changer and bus changing such as busdeletion or any specific information at the same bus can beeasily achieved. Moreover, the users can enter their ownsystem, and save it to apply the pervious analysis. Thisfriendly interactive program designed to be simple for theusers with accurate results and it can be applied for anynetwork.

Analyzing Convergence and Accuracy of Load Flow Methods in the IEEE 39-Bus Power System

This research paper presents a comprehensive comparative analysis of load flow methods applied to the IEEE 39 bus system, focusing on Newton-Raphson, Gauss-Seidel, and two variations of the Fast Decoupled method, XB and BX. The study evaluates the convergence behavior and iteration performance of these methods across two widely used software platforms: MATLAB and Power World Simulator. Through extensive simulations, the research investigates the convergence characteristics and computational efficiency of each method, providing insights into their respective advantages and limitations. The findings offer valuable guidance for power system engineers and researchers in selecting suitable load flow methods for analyzing complex power systems.

The economic value of sustainability certification for sugarcane farms

Certifications related to sustainability in agribusiness are a crucial tool for promoting agricultural processes with a higher level of socio-environmental responsibility. However, their economic impact on small and medium-sized rural properties remains a topic of discussion. This research, with its comprehensive approach, aims to assess the economic potential generated by Bonsucro certification, an innovation process in sustainability. This process introduces pioneering evaluation criteria to meet the specific sustainability demands in the sugarcane sector on small and medium-sized properties. The approach was quantitative, using the Discounted Cash Flow method supported by Monte Carlo Simulation, where four scenarios were developed considering the small and medium producers without certification and with Bonsucro certification. The results obtained not only allow for obtaining the Economic Value of certified and non-certified producers but also for profiling the modal producer (who possesses traditional characteristics of the region), identifying convergence variables that are common characteristics among them and that impact land management, such as education, average age, investment capacity, among others. The findings demonstrate that small and medium-sized rural properties certified by Bonsucro create more economic-financial and sustainable value than uncertified rural properties. These findings can serve as a powerful motivator for private entities and associations of rural producers to implement certification, thereby empowering small and medium-sized rural producers to address relevant issues that have impacted the sustainability of sugarcane production, bringing significant results in terms of economic viability and socio-environmental impact.

Unsteady heat transfer of long gas insulated transmission lines in tunnel: Model and analysis

The temperature of the cable core and cable casing is crucial for the safety and efficiency of the transmission system. Accurately predicting the distribution and variation of the transmission temperature field in the gas insulated transmission lines (GIL) tunnel is essential to ensure the long GIL tunnel transmission system's safe and stable operation. This paper addresses the challenge of calculating unsteady heat transfer flow in extra-long GIL transmission tunnels. The paper proposes a model, and a rapid solution method for extra-long GIL transmission heat transfer flow. In addition, the temperature variation during the operation of 1000 kV GIL has been analyzed. The results indicated that the conductor and cable casing temperature gradually increases and tends to be relatively stable with the operation time. The research results can provide theoretical basis and data support for the regulation of GIL tunnel transmission operation.

Distributed optimal coordination algorithm for nonlinear second-order multi-agent systems and its application to vehicle platoon

This paper addresses the problem of distributed optimal coordination over second-order multi-agent systems, where the dynamics are nonlinear and unknown. To solve this problem, a state-based observer is designed to handle the unknown matched and unmatched nonlinear terms. Using this observer and the proportional–integral control concept, a distributed optimal coordination algorithm with fixed control parameters is constructed through the consensus scheme and gradient flow method. With the help of the Routh–Hurwitz stability criterion, the exponential convergence proof is guaranteed under the condition that the nonlinear term and the convex local cost function are all locally Lipschitz. Finally, we present four simulation examples, including the vehicle platoon’s cooperative control, to demonstrate the efficiency of our proposed algorithms.

Two-loop mixed QCD-electroweak amplitudes for Z+jet production at the LHC: bosonic corrections

We present a calculation of the bosonic contribution to the two-loop mixed QCD-electroweak scattering amplitudes for Z-boson production in association with one hard jet at hadron colliders. We employ a method to calculate amplitudes in the ’t Hooft-Veltman scheme that reduces the amount of spurious non-physical information needed at intermediate stages of the computation, to keep the complexity of the calculation under control. We compute all the relevant Feynman integrals numerically using the Auxiliary Mass Flow method. We evaluate the two-loop scattering amplitudes on a two-dimensional grid in the rapidity and transverse momentum of the Z boson, which has been designed to yield a reliable numerical sampling of the boosted-Z region. This result provides an important building block for improving the theoretical modelling of a key background for monojet searches at the LHC.

Adaptive convergence enhancement strategies for Newton–Raphson power flow solutions in distribution networks

AbstractThis study introduces a series of strategies to enhance convergence in the Newton–Raphson power flow method for unbalanced distribution networks. The proposed approach incorporates graph theory, Kron reduction, and current injection techniques. The network components, including transmission conductors, power transformers, power conductors, shunt capacitors/reactors, and demand loads, were merged into the bus impedance matrix. Using two identification processes (based on mismatches for bus voltages and bus power injections) and optimal multipliers (μ), the proposed approach estimates the initial bus voltages by approximating the converged solutions. To verify the effectiveness of the proposed approach, the authors performed a comparative analysis of five IEEE test feeders in various scenarios. The results confirmed the effectiveness of the proposed approach, particularly for unbalanced distribution systems with diverse transformer connections.

Enhanced Kepler Optimization Method for Nonlinear Multi-Dimensional Optimal Power Flow

Enhanced Kepler Optimization Method for Nonlinear Multi-Dimensional Optimal Power Flow

Raman spectrometer with vertical flow method for solutions containing organic solvents

The vertical flow (VF) method improves generation and collection efficiency in Raman spectroscopy. It enhances all Raman signals, including undesired signals of organic solvents having a considerably large Raman cross section. We constructed a Raman spectrometer using the VF method to overcome this drawback and introduced a spatial line rejection mask to eliminate unnecessary bands. In addition, the design of the VF unit was improved to resist organic solvents. A VF unit with a 60-µm pinhole enhanced the signal 168 times. The spatial mask effectively eliminated the large Raman bands of the solvent and enabled a longer exposure time. The increase in the dynamic range improved the signal-to-noise ratio by 10 % in methanol and acetonitrile measurements. Raman spectrometer with the VF method and spatial mask enables us to record the Raman spectrum of solute molecules without the disturbance of solvent bands.