Flow Analysis Research Articles

Image registration for accurate electrode deformation analysis in operando microscopy of battery materials

Operando imaging techniques have become increasingly valuable in both battery research and manufacturing. However, the reliability of these methods can be compromised by instabilities in the imaging setup and operando cells, particularly when utilizing high-resolution imaging systems. The acquired imaging data often include features arising from both undesirable system vibrations and drift, as well as the scientifically relevant deformations occurring in the battery sample during cell operation. For meaningful analysis, it is crucial to distinguish and separately evaluate these two factors. To address these challenges, we employ a suite of advanced image-processing techniques. These include fast Fourier transform analysis in the frequency domain, power spectrum-based assessments for image quality, as well as rigid and non-rigid image-registration methods. These techniques allow us to identify and exclude blurred images, correct for displacements caused by motor vibrations and sample holder drift and, thus, prevent unwanted image artifacts from affecting subsequent analyses and interpretations. Additionally, we apply optical flow analysis to track the dynamic deformation of battery electrode materials during electrochemical cycling. This enables us to observe and quantify the evolving mechanical responses of the electrodes, offering deeper insights into battery degradation. Together, these methods ensure more accurate image analysis and enhance our understanding of the chemomechanical interplay in battery performance and longevity.

Label-free 3D optical angiography via time-frequency domain analysis of focal modulated dynamic blood flow

Accurate three-dimensional (3D) blood flow imaging with high spatiotemporal resolution and significant detection depth is essential for studying vascular structure-related diseases. In this Letter, we introduce a label-free 3D optical angiography technique via time-frequency domain analysis (TFDA) of focal modulated dynamic blood flow. First, a low-magnification telecentric lens is used for sparse axial sampling within a large depth-of-field range to obtain a coarse estimate of vascular depth. Then, based on the frequency-depth characteristics of dynamic blood flow signals, a TFDA-based focusing evaluation function is established in combination with Lambert–Beer's law, achieving a mean absolute percentage error of 2.06%. Finally, validation on a 3-day-old chicken embryo demonstrated a lateral spatial resolution of 2.95 μm and imaging time of 11.5 s for a 4.95 × 4.95 × 0.7 mm3 sample. Our method provides effective blood flow depth localization by assessing focal modulation intensity relative to focal plane and blood flow position, offering promising support for vascular disease research.

Hydrogeological analysis of topography-driven groundwater flow in a low temperature geothermal aquifer system in the Julian Alps, Slovenia

Abstract Groundwater flow and heat distribution was investigated in the regional karstic-fissured aquifer-aquitard system near Lake Bled in the Slovenian, eastern Julian Alps. The area features thermal springs with temperatures of 19–23 °C which are exploited by abstraction wells. The occurrence of low-temperature geothermal systems, which are common in the Alps, are associated with specific hydrogeological conditions, such as vertical hydraulic connectivity between different geological formations, relatively large elevation differences along flow paths, and the concentrated upwelling of geothermal water to the surface. The occurrence of the low-temperature geothermal field is explained by the presence of a hydraulically conductive fault along with a regional groundwater flow pattern that supports deep groundwater circulation. Hydraulic measurements and temperature data were collected from springs and wells in the area to support the analysis of flow patterns, together with the construction of a basin-scale 2D numerical flow and heat transport simulation. The diverse topographic and geological conditions result in a multi-scale groundwater flow system. The discharge of thermal waters in the Lake Bled area is a consequence of the upwelling of deep groundwater induced by a combination of the ~ 650 m difference in hydraulic head and hydrogeological heterogeneity and anisotropy, related to faulting of the geological formations. In addition, individual flow subsystems were found to significantly affect the natural heat distribution and travel times within the basin-scale system. The study highlights the combination of a basin scale approach taking into consideration local to regional-scale heterogeneities and faults in order to better understand the hydrogeological behaviour of Alpine groundwater systems.

EVCS Demand Management using Multi-Objective Power Flow Analysis with Renewable Distributed Generators

Abstract The optimal placement and sizing of multiple distributed generator units in strategic locations are essential for effective energy management of Electric vehicle charging station (EVCS) in modern power systems. This study aims to minimize power losses, optimize Renewable based Distributed Generator (RDG) sizing, and reduce fuel costs of conventional generators while also considering the impact on voltage stability by using multi objective Newton-Raphson (NR) power flow method. Two meta-heuristic multi-objective Grey Wolf Optimizer (MOGWO) and Multi-Objective Differential Evolution (MODE) techniques have been merged with NR power flow analysis. In this paper two scenarios are analyzed: the base case under normal conditions and with EVCS where the load is increased by 1.1 times the base load. The results demonstrate that the MOGWO approach outperforms the MODE method, yielding more favorable outcomes in both scenarios. Notably, the MOGWO method results in smaller RDG sizes compared to MODE in both normal and EVCS loading conditions. Transmission losses are reduced by 68.12% under normal conditions and by 72.7% under EVCS loading conditions with the MOGWO approach. The effectiveness of the proposed method has been evaluated using MATLAB software on the standard IEEE 57-bus system. Additionally, the base case results have been compared with other algorithms, including SCA, DA, NSGA-II, and MOCS, to validate the performance of the proposed MOGWO approach.

Point-of-Care-Testing NO3-N Detection Technology with Selected Transition-Metal-Based Colorimetric Sensor Arrays.

Nitrate-nitrogen (NO3-N) is a major contaminant in groundwater and seawater. Significant amounts of ammonia are oxidized to nitrate through nitrification, leading to an imbalance in the nitrogen cycle and causing nitrate pollution in water bodies. Controlling NO3-N levels is a significant challenge for both marine aquaculture and human health. Traditional measurement methods, such as ion chromatography and continuous flow analysis, require pretreatment steps to detect NO3-N in complex matrices, which is time-consuming. However, in this study, we developed a transition-metal-based sensor capable of measuring NO3-N concentrations on-site without the need for pretreatment. We analyzed the color change of transition-metal-based sensors over time and obtained color data by mixing transition metals (Mn, V, Fe, Co, Cr, Cu, and Ni) with solvents and additives at fixed ratios, and combining them with standard solutions of NO3-N at concentrations of 1, 2, 3, 5, 10, 20, 30, 40, 50, 75, and 100 ppm. We selected sensors that exhibited linearly increasing color velocity with increasing NO3-N concentrations and developed an array sensor using the selected sensors. The performance of the array was validated by comparing its results with those of hierarchical cluster analysis (HCA) based on color data and compositional analysis, confirming its ability to detect NO3-N in complex matrices. Additionally, by creating a large data set of color change patterns of the array sensor, we can develop selective array sensors for detecting specific substances, surpassing the capability of merely measuring the NO3-N concentration.

Urban Regeneration: Economic and Social Impacts of a Multifunctional Sports Park in Reggio Calabria

Urban areas increasingly face complex challenges in achieving sustainability, particularly in balancing economic, social, cultural, and environmental needs. Despite their significant potential, multifunctional infrastructures remain underexplored, especially in socio-economically disadvantaged and marginal contexts, where they could play a crucial role in urban regeneration processes. This research, based on a case study of an unused urban infrastructure in Southern Italy, adopts a methodological framework to transform these abandoned urban spaces into integrated multifunctional infrastructures capable of providing social, sports, cultural, educational, and recreational services. The proposed framework offers a comprehensive decision-making tool for planners and policymakers, enabling the assessment of multifunctional infrastructures as strategic assets for urban regeneration. The study employs Discounted Cash Flow Analysis (DCFA) and Cost-Benefit Analysis (CBA) to assess the economic feasibility and social sustainability of the proposed project. Investment and management costs were analyzed and compared against both direct and indirect economic benefits, revealing scenarios that support the feasibility of public–private investment. Furthermore, sensitivity analysis corroborates the robustness of the model, demonstrating its applicability under variable economic and social conditions. In conclusion, the evaluation results highlight the transformative potential of multifunctional infrastructures, highlighting their ability to generate significant social and economic impacts.

Thermal analysis of transient MHD ferrofluid flow and natural convection in a Porous cavity with a cylindrical barrier

Thermal analysis of transient MHD ferrofluid flow and natural convection in a Porous cavity with a cylindrical barrier

Lagrangian analysis of submesoscale flows from sparse data using Gaussian Process Regression for field reconstruction

Lagrangian analyses of oceanic flows provide insight into the various transport pathways in the ocean. This analysis typically relies on a dense set of trajectories that can be computed using high-resolution velocity fields, which are often not available during field experiments. Instruments like drifters and floats are often employed to overcome the limitations imposed by satellite- and radar-based velocity fields, to understand the transport pathways in the ocean. However, the sparsity in available drifter-trajectory data proves prohibitive to obtaining a comprehensive map of the Lagrangian characteristics of the underlying flow. To circumvent these issues, we use Gaussian Process Regression (GPR) to obtain velocity fields from sparse drifter data to generate synthetic trajectories and subsequently estimate two Lagrangian metrics, FTLE and dilation rate. A detailed error analysis is performed for drifter clusters deployed within various dynamical regions in the analytic Bickley jet system. The uncertainties in velocity reconstruction obtained from the GPR method, averaged along particle trajectories, locate Lagrangian confidence regions that are applicable both to synthetic trajectories and the dilation rate field. A sensitivity analysis reveals the role played by factors such as the spatial sampling density and temporal resolution of the drifter data, as well as the effect of position uncertainty as a result of GPS inaccuracy. The method is then applied to the drifter data from the Lagrangian Submesoscale Experiment in 2016 to locate convergent filaments. The results present a marked improvement over direct estimation of area-averaged dilation rates using drifter clusters.

Thermodynamics and solar radiative analysis in Jeffery-Hamel flow through non-parallel channel by novel improved residual power series method

Thermodynamics and solar radiative analysis in Jeffery-Hamel flow through non-parallel channel by novel improved residual power series method

Fluid-structure interaction analysis of blood flow in collapsible channels: Implications for sports performance

Fluid-structure interaction analysis of blood flow in collapsible channels: Implications for sports performance

Enhancing the reliance of emergency power supply systems for nuclear facilities using hybrid system

The performance of an atomic facility depends on the efficient supply of electricity, particularly emergency loads like monitoring and control equipment, radiation safety systems, and emergency lights. Most nuclear facilities rely on diesel generators to supply emergency loads during grid outages. Due to the diesel generator's imperfections, such as its starting time, it may fail to deliver power because it is unavailable due to maintenance, failure to start, or failure to run and supply the load. It cannot immediately supply the critical loads, resulting in a blackout and the release of radioactive substances into the environment. To address the previous issues, this paper proposes an improved method to enhance the reliability of nuclear facilities for providing electricity to safety and critical consumers during normal and emergency operating modes. The approach incorporates a photovoltaic (PV) system/battery, and its robustness and performance are tested using load flow and transient stability analysis. The simulation results demonstrated the effectiveness and speed of the proposed method when compared to the traditional method, as the emergency consumers were successfully powered within a very short time without fluctuations, and the voltage reduction and frequency were within the nominal values. The electrical transient analyzer program (ETAP) is used to validate these results.

Flow and heat transfer analysis of direct contact heat exchanger based on the multi-field synergy principle

Flow and heat transfer analysis of direct contact heat exchanger based on the multi-field synergy principle

Abstract TMP86: Stratifying risk of hemorrhage of pedatriac brain arteriovenous malformation using quantitative color-coded digital subtraction angiography and iflow analysis

Background: Pediatric brain arteriovenous malformation (bAVM) patients often present with intracranial hemorrhage. While certain angioarchitectural features, such as aneurysms, deep venous drainage, a single draining vein, venous outflow stenosis, or small nidus size, increase hemorrhage risk, they do not fully predict it. Hemodynamic factors, particularly those assessed by quantitative color-coded digital subtraction angiography (cDSA), have also been linked to hemorrhage risk. This study evaluated the relationship between nidal mean transit time (MTT) and hemorrhage history in pediatric bAVM patients. Methods: We retrospectively analyzed 40 pediatric bAVM patients using flow analysis software (syngo iFlow®; Siemens) to generate cDSA images. A neurointerventional radiologist identified regions of interest (ROIs) in each bAVM’s major arterial afferent and venous efferent. ROI peak time and MTT were obtained. ROI peak time was defined as the time that contrast intensity of the selected ROI reached the peak value and MTT was defined as the difference between venous and arterial peak times. Multivariable logistic regression analysis assessed the association between MTT and prior hemorrhage, adjusting for bAVM size. Results: Among the 40 patients (median age 13 years ; 42.5% female), 47.5% (n=19) had a history of hemorrhage. The median bAVM size was 2.7 cm (IQR: 1.8-4.2), and the median nidal MTT was 0.97 seconds (IQR: 0.79-1.27). In logistic regression analysis, each 0.25-second increase in nidal MTT time was associated with a 24% reduction in the risk of prior hemorrhage (OR=0.76, 95% CI: 0.53-1.08, p=0.122), although not statistically significant. Larger bAVM size was significantly associated with a lower risk of prior hemorrhage (OR=0.48 per cm increase, 95% CI: 0.27-0.87, p=0.014). Conclusion: Our findings suggest that prolonged nidal MTT may be associated with a reduced risk of prior bleeding in pediatric bAVM patients after accounting for bAVM size. Larger bAVM size was significantly associated with a lower likelihood of prior hemorrhage.

Mitigating cavitation effects on Francis turbine performance: A two-phase flow analysis

Mitigating cavitation effects on Francis turbine performance: A two-phase flow analysis

Neural network-assisted analysis of MHD boundary layer flow and thermal radiation effects on SWCNT nanofluids with Maxwellian and non-Maxwellian models

Neural network-assisted analysis of MHD boundary layer flow and thermal radiation effects on SWCNT nanofluids with Maxwellian and non-Maxwellian models

Waste flow analysis to improve waste management systems: Case studies in Diponegoro University

Implementation of solid waste management in educational institutions is one of the main challenges faced in achieving sustainable development. However, far too little attention has been paid to managing solid waste effectively in educational institutions. Thus, this research tries to map and characterize solid waste produced through waste flow analysis (WFA). A case study of waste flow mapping at Diponegoro University revealed that the waste management infrastructure in Diponegoro University has yet to achieve equilibrium, with around 50% of the waste ending up in landfills. Waste management has not been implemented effectively, leading to only half of the recyclable waste being processed monthly. The findings indicate that the Waste Flow Analysis (WFA) effectively assesses the existing waste management conditions at Diponegoro University. The findings of this study could serve as a reference for implementing comprehensive waste management in educational institutions.

A fluid flow model for the software defined wide area networks analysis

The advancement of IT systems necessitates efficient communication methods essential across various sectors, from streaming platforms to cloud-based solutions and Industry 4.0 applications. Enhancing Quality of Service (QoS) in computer networks by focusing on bandwidth and communication delay is critical. Mechanisms like Active Queue Management (AQM) techniques are used, but more advanced solutions are needed to utilize the technological advances in communication technologies. Such advancements are Software-Defined Networks (SDN). Introduced by the SDN decoupling between the control plane and the data plane, it enables advanced real-time traffic shaping and centralized traffic management. This shift allows dynamic routing and improved QoS mechanisms, with research exploring multi-path routing. This paper proposes an extension to the Fluid Flow analysis model for complex networks. This modification allows for the simulation of various networking topologies and can be used to test novel routing and active queue management algorithms in more detail. The obtained numerical analysis demonstrates the model’s advantages over traditional methods, enabling the exploration of new scenarios.

The architecture of the human default mode network explored through cytoarchitecture, wiring and signal flow.

The default mode network (DMN) is implicated in many aspects of complex thought and behavior. Here, we leverage postmortem histology and in vivo neuroimaging to characterize the anatomy of the DMN to better understand its role in information processing and cortical communication. Our results show that the DMN is cytoarchitecturally heterogenous, containing cytoarchitectural types that are variably specialized for unimodal, heteromodal and memory-related processing. Studying diffusion-based structural connectivity in combination with cytoarchitecture, we found the DMN contains regions receptive to input from sensory cortex and a core that is relatively insulated from environmental input. Finally, analysis of signal flow with effective connectivity models showed that the DMN is unique amongst cortical networks in balancing its output across the levels of sensory hierarchies. Together, our study establishes an anatomical foundation from which accounts of the broad role the DMN plays in human brain function and cognition can be developed.

A Methodology Simplifying River Habitat Quality Assessment through Regression Analysis: Application in Slovakia

In the past, river restoration mainly focused on flood protection and somewhat ignored the degradation of river ecosystems, e.g., the habitats of river organisms. In recent decades, however, there has been an increased effort to carefully assess the quality of aquatic habitats as well. The authors have collected data from mountain and sub-mountain streams, including the habitat preferences of fish, topographic surveys, hydrometric measurements, and hydraulic modelling. These data were used to model the quality of stream habitats using the System of Environmental Flow Analysis (SEFA). SEFA, however, may be perceived as impractical for routine usage since the data it requires are extensive and time-consuming to obtain. For simplification, a regression analysis was conducted using only part of SEFA’s input data to balance reliability and data requirements of the computations. The general contribution of the paper is a demonstration of working with small datasets, addressing the challenges of analyzing the quality of river habitat through techniques such as boosting and regularization in regression analyses. The study confirmed a satisfactory agreement between the SEFA model’s results and the proposed regression methods, especially when using the boosting machine learning algorithm for the regression analysis (with a correlation of 0.9). The regression method significantly reduced the input data necessary to evaluate the quality of a habitat compared to the SEFA model. This permits an assessment of the ecological state of streams not only in a scientific context, but also in standard engineering practice.

Abstract B014: Dual checkpoint blockade mitigates the immunosuppressive effects of radiotherapy amplified by experimental liver metastasis

Abstract Background: Stereotactic Radiotherapy (RT) is commonly used to manage tumor burden and progression in metastatic cancer patients, often presenting with synchronous liver metastases (LM). Our previous work demonstrated LM is associated with poor responses to checkpoint inhibitor (CPI) and induces systemic tumor-specific immune tolerance in an immunocompetent LM murine model. RT can either stimulate or suppress immune responses, but its immunomodulatory effects and whether it can complement immunotherapy in the context of LM remain unclear. We hypothesize RT in the presence of LM may have unique systemic immunomodulatory effects and aim to measure its impact on innate and adaptive antitumor immunity. Methods: Mice were injected with MC38-Luc cells both subcutaneously (SQ) and intrahepatically to generate SQ-LM mice to mimic multiple disease sites in LM patients. A commonly used dual-SQ model was generated as tumor-site control, as it reliably produces the abscopal response with RT+CPI treatment. SQ tumor of each RT groups were treated with one ablative dose of 14Gy using an image-guided small animal irradiator. Tumor growth of both irradiated and non-irradiated tumor by in vivo imaging were recorded for the following treatment group: No treatment (no-tx), RT alone, RT+aPD-1, RT+dual-CPI (aPD-1+aCTLA-4). Additionally, NRG mice were used to rule out lymphocyte effects. Finally, innate and adaptive responses in both tumor sites were analyzed by 24-color spectral cytometry. Results: SQ RT reduced the local tumor growth compared to no-tx control. Compared to dual-SQ mice, RT-targeted to SQ tumors in the SQ-LM mice had reduced local efficacy. SQ RT paradoxically accelerated growth at the non-irradiated LM site in 3 out of 9 cases, compared to no-tx. The distant LM promoting effect from RT was reproduced in NRG mice, suggesting a T/B cell-independent pro-tumor mechanism. Combining aPD-1 to SQ RT treatment did not significantly enhance response at the LM site nor yield significant abscopal effects. However, combining dual CPI with SQ RT produced significant efficacy improvements over no-tx control at both local and abscopal sites, achieving ∼40% complete response rate. Spectral flow analysis revealed distinct immune modulation as a result RT, revealing SQ RT increased CD80/86 expression on MHC II⁺ monocytes exclusively at the SQ site and decreased ICOS⁺ and Ki67⁺ on CD8⁺ T cells at the liver tumor. In contrast, triple combination therapy enhanced ICOS⁺ CD8⁺ T cells at both sites, compared to control groups. Conclusions: RT targeting to SQ tumor in the presence of LM may inadvertently foster systemic immunosuppression through non-redundant innate immune pathways, promoting growth of non-irradiated liver tumors. However, RT+aPD-1+aCTLA-4 treatment counteracted this effect, suggesting a promising approach to overcome the adverse impact of RT in the context of LM. Data supports dual CPI therapy mitigates potential negative immune impacts from RT in patients with LM, warranting further clinical investigation. Ethics Animal study IACUC approval AN202115 by UCSF Citation Format: Han Zang, Asha Bunyan, Luke Kim, Dhruvi Mistry, Karan Bhatt, Shao Tao, James Lee. Dual checkpoint blockade mitigates the immunosuppressive effects of radiotherapy amplified by experimental liver metastasis. [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Translating Targeted Therapies in Combination with Radiotherapy; 2025 Jan 26-29; San Diego, CA. Philadelphia (PA): AACR; Clin Cancer Res 2025;31(2_Suppl):Abstract nr B014.