Flow Estimation Research Articles

A New Development of Cross-Correlation-Based Flow Estimation Validated and Optimized by CFD Simulation

The accurate measurement of mass flow rates is important in nuclear power plants. Flow meters have been invented and widely applied in several industries; however, the operating environment in advanced nuclear power plants is especially harsh due to high temperatures, high radiation, and potentially corrosive conditions. Traditional flow meters are largely limited to deployment at the outlet of pumps, on pipes, or in limited geometries. Cross-correlation function (CCF) flow estimation, on the other hand, can estimate the flow velocity indirectly without any specific instruments for flow measurement and in any geometry of the flow region. CCF flow estimation relies on redundant instruments, typically temperature sensors, in series in the direction of flow. One challenge for CCF flow estimation is that the accuracy of the flow measurement is mainly determined by inherent, common local process variation across the sensors, which may be small compared to the uncorrelated measurement noise. To differentiate the process variations from the uncorrelated noise, this research implements periodic fluid injection at a different temperature than the bulk fluid before the temperature sensors to amplify process variation. The feasibility and accuracy of this method are investigated through flow loop experiments and Computational Fluid Dynamics (CFD) simulations. This paper focuses on a CFD simulation model to verify the previous experimental results and optimize CCF flow estimation with different configurations. The optimization study is carried out to perform a grid search on the optimal location of the sensor pair under different flow rates. The CFD results show that the optimal sensor spacing depends on the flow rate being measured and provides guidance for sensor location implementation under various anticipated flow rates.

Estimating inter-regional trade: the radiation model versus the gravity model

The estimation of interregional trade flows within a country is confined to the context of multi-regional input–output models. Despite the remarkable relevance of these trade flows for regional incomes, most countries have almost no official data, with Korea being an exception used here. In addition to survey-based estimation methods, there are non-survey methods, some based on gravity models, which can be used to estimate inter-regional trade flows. This paper compares the gravity model with the novel radiation model. Preliminary results show that the accuracy of the radiation model’s estimation is superior to that of the gravity model, with the invaluable benefit of not needing to estimate regression coefficients, unlike the gravity model. This finding provides a generalisation of the radiation model for estimating interregional trade in contexts where there is no prior multi-regional input–output table without restrictions derived from coefficient estimation as in the gravity model.

Evaluating changes in flood frequency due to climate change in the Western Cape, South Africa

This study assesses the impact of climate change on flood frequency across seven sites in the Western Cape province of South Africa. The calibrated Water Resources Simulation Model (WRSM)/Pitman hydrological model was run using precipitation inputs from two representative concentration pathways (RCP) scenarios (RCP 4.5 and 8.5) using a combination of eight global circulatory models (GCM) for the two periods (2030–2060 and 2070–2100). GCMs were statistically downscaled using the delta change (DC), linear scaling (LS) and quantile delta mapping (QDM) approaches. Average daily discharge was estimated from each downscaled daily precipitation dataset using the Pitman/WRSM model with the Fuller and Sangal estimation methods used to calculate daily instantaneous peak flows. Flood frequency curves (FFC) were generated using the annual maximum series (AMS) for the GCM ensemble mean and individual GCMs for the return periods between 2 and 100 years. FFCs generated based on LS and QDM downscaling methods were aligned for the GCM ensemble mean in terms of the direction of FFCs. Further analysis was conducted using outputs based on the QDM approach, given its suitability in projecting peak flows. Under this method, both Fuller and Sangal FFCs exhibited a decreasing trend across the Jonkershoek and Little Berg River sites; however, estimated quantiles for low-probability events were higher under the Fuller method. This study noted the variation in FFCs from individual GCMs compared to the FFC representing the GCM ensemble mean. Further research on climate change flood frequency analysis (FFA) in South Africa should incorporate other advanced downscaling and instantaneous peak flow estimation (IPF) methods.

GloFP-MSF: monocular scene flow estimation with global feature perception

GloFP-MSF: monocular scene flow estimation with global feature perception

Quantifying form resistance is essential for estimating summer low and bankfull flow from stream survey channel morphology

Reliable estimates of low flow and flood discharge at ungaged locations are required for evaluating stream flow alteration, designing culverts and stream crossings, and interpreting regional surveys of habitat and biotic condition. Very few stream gaging stations are located on small, remote streams, which typically have complex channel morphology. Adequate gaging is also lacking on larger streams that are remote, smaller than those typically gaged, or have channel morphology not conducive to installation of gages. Complex channels typically contain large scale hydraulic roughness elements that dominate flow patterns (i.e., form roughness), making it difficult to measure channel cross-section area and water velocity, or to measure channel volume even where discharge is known. In channels with large channel form roughness, it is equally difficult to estimate discharge using commonly applied equations based on slope and channel dimensions or basin area. We employed a novel approach that explicitly accounts for hydraulic resistance from large wood and riffle-pool morphology (form roughness) in calculating low flow and bankfull discharge from stream and river physical habitat data collected from 4229 stream and river sites in the conterminous US (CONUS) sampled in 2008–9 and 2013–14 as part of the US Environmental Protection Agency's National Rivers and Streams Assessment (NRSA). Hydraulic resistance derived from form roughness clearly dominated resistance derived from bed particles (particle resistance) during summer low flows in wadeable streams across the spectrum of channel slopes and substrate sizes smaller than boulders. Under bankfull conditions, the influence of form resistance relative to particle resistance was diminished, but form resistance still dominated except in large low gradient rivers lacking complex channels, and in streams or rivers with boulder-size bed particles. We validated our hydraulic resistance estimates by comparing measured discharges with calculated discharges that used those hydraulic resistance estimates along with measured NRSA channel morphology data. Morphology-based summer discharge (low flow) estimates and direct field measurements of discharge in 2333 wadeable CONUS streams showed reasonable agreement (median difference < 2.5×) for discharges ranging from 3.6 × 10−5 to 123 m3/s and drainage areas of 0.12 to 171,000 km2. In a subset of 759 of NRSA's larger wadeable stream and non-wadeable river sites where nearby U.S. Geological Survey (USGS) gage data were available and adequate, our morphology-based summer discharge estimates agreed fairly well (median difference < 2.0×) with USGS 20-yr average August mean flows ranging from 0.003 to 16,000 m3/s. Similarly, morphology-based estimates of bankfull flow ranging from 0.3 to 100,000 m3/s agreed reasonably well with the 1.5-yr recurrence interval flood in these gaged sites (median deviation <2.2×). These findings demonstrate the importance of quantifying flow resistance from large-scale form roughness features in natural channels and provide a novel approach for estimating discharge from widely available survey data. This will allow examination of discharge and its ecological influence across the full range of stream and river sizes sampled by NRSA or other synoptic surveys where comprehensive measures of biota, physical habitat, and chemistry are also made. Although these morphology-based estimates exhibit some variability, they are adequate for examining regional patterns in discharge and flow alteration and their association with instream biota and anthropogenic disturbances, providing summer low and bankfull flow information where reliable estimates are lacking.

Curie point depth and heat flow estimations for geothermal energy exploration in parts of southern Nigeria’s inland basins

Curie point depth and heat flow estimations for geothermal energy exploration in parts of southern Nigeria’s inland basins

Three-dimensional generative adversarial networks for turbulent flow estimation from wall measurements

Different types of neural networks have been used to solve the flow sensing problem in turbulent flows, namely to estimate velocity in wall-parallel planes from wall measurements. Generative adversarial networks (GANs) are among the most promising methodologies, due to their more accurate estimations and better perceptual quality. This work tackles this flow sensing problem in the vicinity of the wall, addressing for the first time the reconstruction of the entire three-dimensional (3-D) field with a single network, i.e. a 3-D GAN. With this methodology, a single training and prediction process overcomes the limitation presented by the former approaches based on the independent estimation of wall-parallel planes. The network is capable of estimating the 3-D flow field with a level of error at each wall-normal distance comparable to that reported from wall-parallel plane estimations and at a lower training cost in terms of computational resources. The direct full 3-D reconstruction also unveils a direct interpretation in terms of coherent structures. It is shown that the accuracy of the network depends directly on the wall footprint of each individual turbulent structure. It is observed that wall-attached structures are predicted more accurately than wall-detached ones, especially at larger distances from the wall. Among wall-attached structures, smaller sweeps are reconstructed better than small ejections, while large ejections are reconstructed better than large sweeps as a consequence of their more intense footprint.

Automated Quantitative Image-Derived Input Function for the Estimation of Cerebral Blood Flow Using Oxygen-15-Labelled Water on a Long-Axial Field-of-View PET/CT Scanner.

The accurate estimation of the tracer arterial blood concentration is crucial for reliable quantitative kinetic analysis in PET. In the current work, we demonstrate the automatic extraction of an image-derived input function (IDIF) from a CT AI-based aorta segmentation subsequently resliced to a dynamic PET series acquired on a Siemens Vision Quadra long-axial field of view scanner in 10 human subjects scanned with [15O]H2O. We demonstrate that the extracted IDIF is quantitative and in excellent agreement with a delay- and dispersion-corrected sampled arterial input function (AIF). Perfusion maps in the brain are calculated and compared from the IDIF and AIF, respectively, showed a high degree of correlation. The results demonstrate the possibility of defining a quantitatively correct IDIF compared with AIFs from the new-generation high-sensitivity and high-time-resolution long-axial field-of-view PET/CT scanners.

Potential Legacy of SWOT Mission for the Estimation of Flow–Duration Curves

Flow–duration curves (FDCs) provide a compact view of the historical variability of river flows, reflecting climate conditions and the main hydrologic features of river basins. The Surface Water and Ocean Topography (SWOT) satellite mission will enable the estimation of river flows globally, by sensing rivers wider than 100 m with a sampling recurrence from 3 to 21 days. This study investigated the lifetime mission potential for FDC estimation through the comparison between remotely-sensed and empirical FDCs. We employed the Global Runoff Data Center dataset and derived SWOT-like river flows by selecting gauging stations of rivers wider than 100 m with more than 10-year long daily river flow time series. Overall, 1200 gauged river cross-sections were examined. For each site, we created a set of 24 SWOT-simulated FDCs (i.e., based on different sampling recurrences, mean biases, and random errors) to be compared against their empirical counterparts through the Nash–Sutcliffe efficiency and the mean relative error. Our results show that climate and the sampling recurrence play a key role on the performance of SWOT-based FDCs. Tropical and temperate climates performed the best, whereas arid climates mostly revealed higher uncertainties, especially for high- and low-flows.

Recent Advances in Bio-Inspired Vision Sensor: A Review

Event-based cameras, also known as biologically inspired visual sensors, are capable of capturing real-time scene changes efficiently. Unlike traditional frame-based cameras, event cameras solely report triggered pixel-level brightness changes which are referred to as events. Event-based cameras show many advantages such as high temporal resolution, low latency, and high dynamic range, making them very attractive in robotics and computer vision, especially in challenging scenarios that are too demanding for traditional cameras. In this paper, we provide a comprehensive overview of the emerging field of event-based vision, focusing on the operation principle, sampling mechanisms, and algorithms that take advantage of their superior features. We also delve into the various tasks for which event cameras are utilized, such as object tracking, optical flow estimation, 3D reconstruction, SLAM, image reconstruction, and recognition. Additionally, we highlight the challenges and future opportunities for event cameras, seeking a more efficient way for machines to perceive and interact with the world.

Soil pores in preferential flow terminology and permeability equations

AbstractLinkages between the micro‐scale of soil water and landscape scale of hydrological data could be improved with the analysis of soil factors in preferential flow rates. This rearrangement of the terminology on soil pore size from published literature focused on the relationship between aggregate and pore size. In the range of pore size relevant to water flow (&gt;0.005 mm), a 2:1 ratio of aggregate to pore diameter approximated the mean of proposed pore size categories. Major functional change points in soil pore size were identified where water becomes mobile in soil (0.005 mm), where preferential flow among aggregate surfaces begins (0.3 mm), and where water flows without soil interaction (bypass flow ∼1.0 mm). A number of published equations supported the application of soil pore size in permeability estimation for modeling hydraulic conductivity. Common understanding of soil pore terminology would support water flow estimation from soil to landscape scales.

The thermal structure of the Colombian lithosphere: A regional and basin-scale analysis

It is well-established that the thermal state of the lithosphere strongly influences various regional and local geological processes, including crustal deformation, hydrocarbon maturation, hydrogen generation, and geothermal phenomena. Moreover, the thermal structure exhibits high sensitivity to tectonic features, a property of particular significance in Colombia, where three main tectonic plates converge, and lithospheric tearing has been documented. In this contribution, we focus on elucidating the impact of plate architecture on the thermal field in central-eastern Colombia at both shallow and deep levels. To accomplish this, we constructed a series of two-dimensional profiles and derived a numerical solution of the heat equation using the conservative finite difference method. As constraints, we incorporate an inferred distribution of rocks in the deep crust and upper mantle based on global and local lithospheric thickness models. Material parameters for the various rocks, both exposed and inferred, were obtained from the literature. Additionally, we used superficial heat flow estimates and apparent geothermal gradients compiled by the Colombian Geological Survey.Our results suggest a significant influence of the lithospheric Caldas tear on the thermal state of Colombia, with the breaking off occurring in the Nazca plate under the Eastern Cordillera range around 5°N. The modeled asthenospheric heat flow remains approximately 25 mWm−2, except in the northern Eastern Cordillera range, where the background heat flow increases rapidly to 40 mWm−2. Consequently, our model predicts partial melting in the lower crust to the north and a thermally unstable lower crust to the south of the Caldas tear. The material parameters that best fit the surface data suggest the presence of a basement moderately enriched in radioactive elements in the Eastern Llanos basin. After accounting for compaction, we also confirm a strong influence of the tectonic setting on the thermal state of sedimentary basins.

The prognostic value of right ventricular outflow tract velocity time integral in patients with pulmonary hypertension.

Right ventricular (RV) failure is one of the leading causes of death in patients with pulmonary hypertension (PH). Conventional echocardiographic parameters are not included in risk stratification and follow-up for prognostic assessment due to PH's diverse nature and the RV's complex geometry. RV outflow tract velocity time integral (RVOT VTI) is a simple, non-invasive estimate of pulmonary flow and an echocardiographic surrogate of RV stroke volume. In this study, we aimed to define the prognostic value of RVOT VTI in PH patients. Sixty-three subjects with idiopathic PAH (IPAH) (n=23), connective tissue disease-associated PAH (CTD-associated PAH) (n=19) and chronic thromboembolic pulmonary hypertension (CTEPH) (n=21) were retrospectively included. A comprehensive two-dimensional echocardiographic evaluation, including RVOT-VTI measurement, was performed during the follow-up and the New York Heart Association functional class (NYHA FC), 6min walk distance (6MWD) and brain natriuretic peptide (BNP) levels were recorded. The median age of the whole cohort was 63years (52-68), and 47 (74.6%) of the patients were women. The median follow-up period was 20months (11-33), and 20 (31.7%) patients died in this period. BNP values were higher [317 (210-641) vs 161 (47-466), P=0.02], and 6MWD values were lower [197.5±89.5 vs 339±146.3, P<0.0001] in the non-survivor group, and the non-survivor group had a worse NYHA-FC (P=0.02). Among echocardiographic data, tricuspid annular plane systolic excursion (TAPSE) (15.4±4.8 vs 18.6±4.2, P=0.01) and RVOT VTI (11.9±4.1 vs 17.2±4.3, P<0.0001) values were lower whereas right atrial area (RAA) (26.9±10.1 vs 22.2±7.1, P=0.04) values were higher in the non-survivor group. The area under curve of the RVOT VTI for predicting mortality was 0.82 [95% confidence interval (CI) 0.715-0.940, P<0.0001], and the best cut-off value was 14.7cm with a sensitivity of 80% and specificity of 77%. Survival was significantly lower in subjects with RVOT VTI≤14.7cm (log-rank P<0.0001). Survival rates for patients with RVOT VTI≤14.7cm were 70% at 1year, 50% at 2years, %29 at 3years and 21% at 5years. The univariate determinants of all-cause mortality were BNP [hazard ratio (HR) 1.001 (1.001-1.002), P=0.001], 6MWD [HR 0.994 (0.990-0.999), P=0.012] and NYHA-FC III-IV [HR 3.335 (1.103-10.083), P=0.03], TAPSE [HR 0.838 (0.775-0.929), P=0.001], RAA [HR 1.072 (1.013-1.135), P=0.016] and RVOT VTI [HR 0.819 (0.740-0.906), P<0.0001]. RVOT VTI was found to be the only independent determinant of mortality [HR 0.857 (0.766-0.960), P=0.008]. The decreased RVOT VTI predicts mortality in patients with PH and each 1mm decrease in RVOT VTI increases the risk of mortality by 14.3%. This parameter might serve as an additional parameter in the follow-up of these patients especially when 6MWD and NYHA-FC could not be determined.

Phylogeny and genetic diversity of Echinometra sea urchin in Taiwan

ABSTRACT Four closely related Echinometra species exist in the Indo-Pacific region, but little is known about their population genetics in Taiwan, an area rich in marine biodiversity. Echinometra dominates the Douziping coral reef off Liuqiu Island, where urchin barrens are also present. To understand the relationship between Echinometra species and urchin barrens, we investigated the genetic diversity of Echinometra species in Taiwan by using a nondestructive DNA extraction method on the spines of sea urchins. Mitochondrial DNA results showed that E. sp. C is the dominant species in Taiwan, with relatively high interspecific genetic distance. The E. sp. C is the only species in Douziping, and its population is expanding in accordance with the significantly low R2 value, the significantly negative Fu’s Fs values, and mismatch distribution analysis results. Gene flow estimates between the urchin populations in Taiwan and those in the neighbouring waters of Okinawa suggested that the Kuroshio Current could play an important role in Echinometra dispersal. This implies strongly connectivity between these populations. This study reports the first case of E. sp. C-associated urchin barrens in the tropics.

Graph Neural Networks for Pressure Estimation in Water Distribution Systems

AbstractPressure and flow estimation in water distribution networks (WDNs) allows water management companies to optimize their control operations. For many years, mathematical simulation tools have been the most common approach to reconstructing an estimate of the WDNs hydraulics. However, pure physics‐based simulations involve several challenges, for example, partially observable data, high uncertainty, and extensive manual calibration. Thus, data‐driven approaches have gained traction to overcome such limitations. In this work, we combine physics‐based modeling and graph neural networks (GNN), a data‐driven approach, to address the pressure estimation problem. Our work has two main contributions. First, a training strategy that relies on random sensor placement making our GNN‐based estimation model robust to unexpected sensor location changes. Second, a realistic evaluation protocol that considers real temporal patterns and noise injection to mimic the uncertainties intrinsic to real‐world scenarios. As a result, a new state‐of‐the‐art model, GAT with Residual Connections, for pressure estimation is available. Our model surpasses the performance of previous studies on several WDNs benchmarks, showing a reduction of absolute error of ≈40% on average.

Self-Supervised Deep Blind Video Super-Resolution.

Existing deep learning-based video super-resolution (SR) methods usually depend on the supervised learning approach, where the training data is usually generated by the blurring operation with known or predefined kernels (e.g., Bicubic kernel) followed by a decimation operation. However, this does not hold for real applications as the degradation process is complex and cannot be approximated by these idea cases well. Moreover, obtaining high-resolution (HR) videos and the corresponding low-resolution (LR) ones in real-world scenarios is difficult. To overcome these problems, we propose a self-supervised learning method to solve the blind video SR problem, which simultaneously estimates blur kernels and HR videos from the LR videos. As directly using LR videos as supervision usually leads to trivial solutions, we develop a simple and effective method to generate auxiliary paired data from original LR videos according to the image formation of video SR, so that the networks can be better constrained by the generated paired data for both blur kernel estimation and latent HR video restoration. In addition, we introduce an optical flow estimation module to exploit the information from adjacent frames for HR video restoration. Experiments show that our method performs favorably against state-of-the-art ones on benchmarks and real-world videos.

Challenges and opportunities in traffic flow prediction: review of machine learning and deep learning perspectives

In recent days, traffic prediction has been essential for modern transportation networks. Smart cities rely on traffic management and prediction systems. This study utilizes state-of-the-art deep learning and machine learning techniques to adjust to changing traffic conditions. Modern DL models, such as LSTM and GRU, are examined here to see whether they may enhance prediction accuracy and provide valuable insights. Repairing problems and errors connected to weather requires hybrid models that integrate deep learning with machine learning. These models need top-notch training data to be precise, flexible, and able to generalize. Researchers are continuously exploring new approaches, such as hybrid models, deep learning, and machine learning, to discover traffic flow data patterns that span several places and time periods. Our current traffic flow estimates need improvement. Some expected benefits are fewer pollutants, higher-quality air, and more straightforward urban transportation. With machine learning and deep learning, this study aims to improve traffic management in urban areas. Long Short-Term Memory (LSTM) models may reliably forecast traffic patterns.

Initial estimate of AC optimal power flow with graph neural networks

Optimal power flow (OPF) is a crucial task in power system management and control; accurate and time-efficient solutions for OPF are necessary to ensure cost-efficient and reliable power system operation. We introduce a novel solution to solving alternating current OPF (ACOPF), a nonlinear and nonconvex optimization problem, by combining the speed of a trained deep learning model with the accuracy of iterative solvers. The proposed framework uses a graph neural network (GNN) to exploit the graph structure of a power system in conjunction with proximal policy optimization, a deep reinforcement learning algorithm, to compute initial guesses for an interior point solver (IPS), providing a warm start, allowing the solver to converge in fewer iterations. Existing literature that explores warm start ACOPF solutions using machine learning choose to compute initial guesses that are trained to be feasible and cost-minimizing. Our approach trains the GNN-based reinforcement learning agent to produce an output that minimizes IPS convergence time by designing a reward function that is a function of the IPS convergence time. We evaluate the proposed framework using IEEE test case environments, using PyPower’s IPS-based ACOPF solver and a GNN-based framework that computes ACOPF solutions directly as baselines, demonstrating significantly improved convergence times.

GLHDR: HDR video reconstruction driven by global to local alignment strategy

Reconstructing High Dynamic Range (HDR) video from alternating exposure Low Dynamic Range (LDR) sequence is an exceptionally challenging task. It not only demands the reliable reconstruction of missing information caused by occlusion or motion without introducing artifacts but also balances the exposure differences between frames to ensure a visually pleasing reconstructed HDR video. Unfortunately, existing methods are typically complex and struggle with unavoidable artifacts and noise, especially when dealing with low-exposed scenes. To tackle this formidable challenge, we propose a two-stage HDR video reconstruction method that employs a global to local alignment strategy. Firstly, we utilize iterative optical flow estimation and hybrid weighting to achieve global alignment, ensuring well-reconstructed in majority of areas. Secondly, the recursive refinement network further addresses locally misaligned areas, reconstructing HDR frames from bottom to top and recursively refining them to yield faithful reconstruction results. Extensive experimental results demonstrate that our method generates the HDR video with fine details and superior visually, surpassing the state-of-the-art method across diverse scenes.

Adaptive observer design for coupled ODE–hyperbolic PDE systems with application to traffic flow estimation

This paper studies the state estimation problem for a class of coupled linear ODE–hyperbolic PDE systems with unknown in-domain parameters. Based on the swapping transformation and the least squares parameter estimation method, a Luenberger-type adaptive boundary observer is designed to estimate the ODE–PDE states under the unknown parameters. The exponential convergence conditions w.r.t. the observer feedback gains are given by employing the Lyapunov technique. Finally, an application to traffic state estimation of the Aw–Rascle–Zhang (ARZ) model involving the unknown relaxation time and the boundary input speed deviation is provided to validate the theoretical result.