Flow Technique Research Articles

Developing a high-rate ABE fermentation system for rice straw: CSTR with immobilization on granular-activated carbon with ex situ pervaporation

This study assessed the performance of two continuous stirred tank reactors (CSTRs) to immobilize Clostridium acetobutylicum DSM 792 and Clostridium beijerinckii DSM 6422 on granular activated carbon. Dilution rates (D) from 0.05 to 0.60 h−1 were tested using a sugar mixture to mimic rice straw hydrolysate. Immobilization was shown to be a highly effective technique for processing high flows without cell washout. Both CSTRs demonstrated significantly higher butanol productivity than their free-cell counterparts, achieving the remarkable value of 2.29 g L−1 h−1 with C. beijerinckii at D = 0.60 h−1. Rice straw hydrolysate was fed to the high-rate CSTR, reaching a butanol productivity of 2.07 g L−1 h−1 at D = 0.30 h−1. A novel downstream configuration of ex situ gas stripping followed by two pervaporation cycles operated under rough vacuum was explored in discontinuous mode, that reduced the energy demand by 63 % of conventional distillation methods. This configuration holds great promise for the advance of the lignocellulosic biorefinery process.

Deep Q-Learning-Based Mud Ring Optimization Approach for Optimal Power Flow in Islanded DC Microgrid

Microgrid (MG) is the basic element that recreates a significant role in integrating renewable energy sources (RES). Direct current (DC) MG has advantages over alternating current (AC) for many applications. A DC-MG is connected to the islanded mode through power electronic converters, which are also an important component for RES integration. Due to environmental factors, RES will struggle to load the conditions. Branches of DC-MG have battery energy storage systems (BESSs) installed for compensating the supply–load imbalance. Even though BESS are installed, accurate maintenance is still required to maintain a balanced power flow. This work proposes an adaptive deep Q-learning (DQL) based mud ring optimization technique (DQMR) for optimal power flow in islanded DC-MG. DQL is effective for the task with discrete and low-dimensional action spaces, and mud ring algorithm (MRA) is to clear up large-scale optimization problems and power flow problems. The implementation is done through MATLAB/Simulink model. The error is minimized to 6% by this method, and the losses are also reduced. IEEE-9 bus system and IEEE-30 bus system are used in the proposed work to validate the performance.

Application of artificial neural networks to predict the particle-scale contact force of photoelastic disks

Measuring the contact force of photoelastic granular materials in a two-dimensional system either has a high dependence on the experimental parameters or requires expensive computations. This study proposes a supervised machine learning strategy that constructs a universal relation between the features of a stressed photoelastic disk and the corresponding total contact force acting on it via an artificial neural network (ANN). Most of the common circumstances of particle–particle contact that occurs in real photoelastic granular flow experiments are considered in the data. The ANN reached a test R-squared value of about 0.96. Therefore, the proposed machine learning framework is an efficient and accurate force measurement technique for two-dimensional photoelastic granular flows.

Utilizing Craft Techniques in Zero Waste Fashion Design Clothing

The role of research in design is to develop the potential that drives the creation of novel value in design. In the research process for design, the majority of designer-students have difficulties in determining the starting point or initial idea of a design, so they have the potential not to have a train of thought regarding what needs to be done in realizing the design and do not know the next process. One aspect that can enrich design ideas and novelty in fashion products is the application of craft techniques. With the application of craft techniques, the data and facts obtained become increasingly massive. So that the series of ideas-research-design requires a clear mapping of the flow of thinking in the creative process. Today's student-designers cannot be limited to just one research method, knowing that conceptual ideas can start from anywhere, such as design concept ideas that start from craft techniques in a linear mapping flow. This can be a reference regarding the linear flow of ideas-research-design that is carried out in zero waste fashion design to find out its potential, especially in the application of craft techniques. This research was conducted qualitatively by reviewing literature data and looking at examples of case studies from the student-designer work process using the linear method. Observations are made periodically for students who have designed the application of craft techniques to zero waste fashion design, to become research subjects and find out their creative process. The result of this study is an analysis of fashion product design methods with the application of craft techniques that have been carried out in a linear way of thinking as a reference for designers in producing innovative works.

Fractional higher order thin film equation with linear mobility: gradient flow approach

We prove existence of weak solutions of a fractional thin film type equation with linear mobility in any space dimension and for any order of the equation. The proof is based on a gradient flow technique in the space of Borel probability measures endowed with the Wasserstein distance.

Status and Prospects for Development of Groundwater Flow and Transport Modeling Techniques to Address the Long-Term Radiation Safety Issues

Status and Prospects for Development of Groundwater Flow and Transport Modeling Techniques to Address the Long-Term Radiation Safety Issues

Preliminary Studies on a Lightweight Porous Cement-Based Composite – Gel Concrete

This paper presents some preliminary results of research on light, highly porous cement composites – gel concretes. A material based on Portland cement was tested, the very high porosity of which (over 60%) was obtained by direct gelatinization of starch in a liquid cement slurry. A composite based solely on cement and concretes produced with the addition of metakaolin or zeolite was tested. The basic properties of the concretes obtained in this way were determined, i.e., the volume density in a dry state, the thermal properties, and the compressive strength. In the case of the thermal properties, tests were performed on specimens dried to constant mass, while the test itself was carried out at an average temperature of 10°C. The tests employed an Isomet 2114 apparatus, which uses a non-stationary heat flow technique. The thermal conductivity coefficients and the volumetric specific heat were determined. The compressive strength tests were carried out on cubic specimens with a side of 4 cm after 28 days of curing by air-drying. Four specimens of each type of composite were tested. For composites based solely on Portland cement, the sorption properties of the material were also tested. For this purpose, the method of dynamic water vapor sorption (DVS) was used. As a result, graphs of the sorption and desorption of individual composites of different densities were obtained. Preliminary qualitative tests were also carried out using an electron microscope. The use of the starch gelatinization process directly in the cement slurry made it possible to obtain a very homogeneous material, in which the initial, temporary structure was starch gel, around which, after exceeding the setting time of the cement, the target cement-based structure was formed – gel concrete. The tested materials are innovative, having excellent thermal properties, comparable mechanical properties to lightweight concretes of the same densities, and is formed using an easily available, relatively cheap admixture in the form of starch. They can be used to produce small masonry elements, such as blocks for external walls or other typical prefabricated lightweight concrete elements.

Utilization of agricultural waste as hydroponic media for the Deep Flow Technique system and combination of nutrients for shallot plants from seeds

Hydroponics is one of the planting systems without using soil as a medium. This system is developing in urban areas where the availability of land is very limited. Suitable planting media and the availability of nutrients to the needs of plants are important in cultivating plant with hydroponic systems. This study aims to determine the effect of planting media and the combination of AB Mix nutrients on the growth and yield of shallots from seeds in the Deep Flow Technique (DFT) system. The research was conducted in Maku village, Dolo sub-district, Sigi district, Central Sulawesi, from February to May 2021. The research used a Split Plot Design, where the main plot was the planting media and the subplots were the combination of nutrients. The planting media used were rockwool, husk charcoal, and cocopeat. While the nutrients are given vegetable nutrition for three months, fruit nutrition for three months, vegetable nutrition for two months + fruit nutrition for one month, vegetable nutrition for one month + fruit nutrition one month with four replications. The observation components were plant height, number of leaves, number of tubers per clump, tuber weight per clump, tuber weight per seed and tuber diameter. The results showed that the treatment of planting media types and nutrient combinations had different effects on each observation component. The best treatment was husk charcoal media given vegetable nutrition at the age of one month and fruit nutrition at the age of two months.

Prediction technique for flow boiling heat transfer and critical heat flux in both microgravity and Earth gravity via artificial neural networks (ANNs)

This study is part of the Flow Boiling and Condensation Experiment (FBCE) and utilizes flow boiling data collected in both microgravity onboard the International Space Station (ISS) and Earth gravity at different channel orientations. The goal is to develop a prediction technique for heat transfer and critical heat flux (CHF) for flow boiling in both microgravity and Earth gravity using artificial neural networks (ANNs). The working fluid, n-perfluorohexane or FC-72, flows through a rectangular channel of 114.6 mm heated length, 2.5 mm heated width, and 5.0 mm unheated height with either one or two walls heated. The consolidated FBCE database for heat transfer coefficient comprises 29,226 datapoints spanning a mass velocity of 173 – 3200 kg/m2s, pressure of 102 – 238 kPa, subcooling of 0 – 44°C, and thermodynamic equilibrium quality of -0.60 – 0.95 (spanning from highly subcooled to high-quality saturated boiling). Following a statistical analysis of various input parameters relevant to flow boiling and optimization of key model parameters, a fully connected feed-forward ANN is developed to predict Nutp. It predicts the entire test database with an overall mean absolute error (MAE) of just 7.99% with consistent and accurate predictions in each subset. Similarly, 641 CHF datapoints from FBCE were consolidated into a database spanning a mass velocity of 99 – 3212 kg/m2s, inlet pressure of 97 – 239 kPa, inlet subcooling of 0 – 46°C, inlet thermodynamic equilibrium quality of -0.61 – 0.86, and CHF values of 4 – 54 W/cm2. A separate ANN is developed by following the same methodology as heat transfer, and it predicts dimensionless CHF, BoCHF, with an overall MAE of just 12.05%. Existing seminal correlations are assessed for subsets of the two consolidated FBCE databases, and the ANNs are shown to have better accuracies in each subset of the database. The ANNs’ high prediction accuracy, in conjunction with their ability to predict physical parametric trends in previously unseen data, shows their potential as prediction tools for both heat transfer and CHF for flow boiling in microgravity and Earth gravity.

Sustained pharyngeal inflation in infant airway-Flexible bronchoscopy measurements.

Sustained pharyngeal inflation (SPI) with pharyngeal oxygen flow and nasal closure (PhO2-NC) technique create positive inflation pressure in the airway. This study measured the peak inflation pressure (PIP) levels and image changes with SPI-assisted flexible bronchoscopy (SPI-FB) and compared the effects in the pharyngeal space and mid-tracheal lumen. This prospective study enrolled 20 participants aged 6 months to 3 years. Each participant underwent sequential SPI-FB of four different durations (0, 1s, 3s, and 5s) for three cycles. We used a 3.8 mm OD flexible bronchoscope to measure and analyze PIP levels, images, and lumen dimension scores. A total of 480 data were collected. The mean (SD) age and body weight were 12.0 (11.5) months and 7.8 (7.5) kg, respectively. The mean (IQR) PIPs were 4.2 (2.0), 18.5 (6.1), 30.6 (13.5), and 46.1 (25.0) cmH2O in the pharynx and 5.0 (1.6), 17.5 (6.5), 28.0 (12.3), 46.0 (28.5) cmH2O in the mid-trachea at SPI durations of 0, 1s, 3s, and 5s, respectively. The PIP levels had a positive correlation (p <0.001) with different SPI durations in both pharynx and trachea, and were nearly identical (p = 0.695, 0.787, and 0.725 at 1s, 3s, and 5s, respectively) at the same duration except the 0 s (p = 0.015). Lumen dimension scores also significantly increased with increasing SPI durations (p <0.05) in both locations. The identified lesions significantly increased as PIP levels increased (p <0.001). Conclusion: SPI-FB using PhO2-NC with durations up to 3s is safe and informative technique that provides controllable PIP, dilates airway lumens, and benefits lesion detection in the pharyngeal space and mid-tracheal lumen.

A bi-objective optimization model to decide the lines attending a bus-stop with high passenger demands

A public transportation network must change dynamically along the time due to the variation of the mobility of the population in interurban areas. This research addresses the challenge of serving newly detected zones with high passenger demand. We present a bi-objective optimization model to determine the feasibility of meeting new demands within the existing lines of the public transportation network. The two objectives considered are to minimize passenger overload on the selected lines and minimize travel time increases for these lines. Subsequently, a polynomial-time algorithm utilizing network flow techniques is introduced to solve the bi-objective problem. The algorithm is then incorporated into the decision-making system of Transportes Interurbanos de Tenerife, S.A. We also explore the company’s utilization of this tool and demonstrate its current implementation in a real case scenario where a high-demand area is detected.

Deep Learning Approach for Human Action Recognition Using a Time Saliency Map Based on Motion Features Considering Camera Movement and Shot in Video Image Sequences

In this article, a hierarchical method for action recognition based on temporal and spatial features is proposed. In current HAR methods, camera movement, sensor movement, sudden scene changes, and scene movement can increase motion feature errors and decrease accuracy. Another important aspect to take into account in a HAR method is the required computational cost. The proposed method provides a preprocessing step to address these challenges. As a preprocessing step, the method uses optical flow to detect camera movements and shots in input video image sequences. In the temporal processing block, the optical flow technique is combined with the absolute value of frame differences to obtain a time saliency map. The detection of shots, cancellation of camera movement, and the building of a time saliency map minimise movement detection errors. The time saliency map is then passed to the spatial processing block to segment the moving persons and/or objects in the scene. Because the search region for spatial processing is limited based on the temporal processing results, the computations in the spatial domain are drastically reduced. In the spatial processing block, the scene foreground is extracted in three steps: silhouette extraction, active contour segmentation, and colour segmentation. Key points are selected at the borders of the segmented foreground. The last used features are the intensity and angle of the optical flow of detected key points. Using key point features for action detection reduces the computational cost of the classification step and the required training time. Finally, the features are submitted to a Recurrent Neural Network (RNN) to recognise the involved action. The proposed method was tested using four well-known action datasets: KTH, Weizmann, HMDB51, and UCF101 datasets and its efficiency was evaluated. Since the proposed approach segments salient objects based on motion, edges, and colour features, it can be added as a preprocessing step to most current HAR systems to improve performance.

Surrogate‐based optimization for active drag reduction of turbulent boundary layer flows

AbstractTwo surrogate‐based optimization strategies using support vector regression (SVR) and Gaussian process regression (GPR) as surrogates are investigated to guide the design of actuation parameters for active drag reduction techniques in turbulent boundary layer flows encountered at civil airplanes in cruise flight and high‐speed trains. As an approximation, the turbulent flow over a flat plate subjected to spanwise traveling transversal sinusoidal surface waves is simulated by wall‐resolved large‐eddy simulations (LESs). These simulation data are used to model the dependence of the objective drag reduction on the actuation parameters, that is, the optimization variables. In this work, the previous purely exploitative approach of SVR‐based ridgeline optimization is extended to GPR‐based Bayesian optimization to further automate the simulation‐driven tuning of the actuation parameters.

Optimal dispatch model for aviation-fuel vehicles in refuelling operations

Taiwan Taoyuan international airport has been devoted to becoming a green airport in recent years, aiming to reduce carbon dioxide emissions (CDEs) from ground handling services. Refuelling operations play a pivotal role in airport operations and, given the short and frequent nature of flight takeoffs and landings, demand substantial work. Different aircraft types and flight lengths require varying refuelling operations, and the inefficient and unrealistic dispatch of aviation-fuel vehicles can lead to flight delays. In this study, time–space network flow techniques were used to formalise the flow of aviation-fuel vehicles in both space and time. Based on the problem characteristics and by adding appropriate constraints, the aim of this work was to minimise CDEs and construct an optimal model for the refuelling operation scheduling of aviation-fuel vehicles. The proposed model was evaluated through a case study, and conclusions and recommendations are presented.

A novel load flow method for islanded microgrids with optimum droop coefficients

The integration of renewable energy resources into the power system has led to growing interest in the field of microgrids. These microgrids can operate in grid-connected or islanded modes.Load flow analysis is an important step for the operation and planning of the power system. The traditional load flow techniques, which use the concept of a reference bus, are not valid in microgrids, particularly when operating in islanded mode. Thus, in this study, a modified Gauss-Seidel method has been implemented in which to find the optimum values for droop coefficients, different optimisation techniques have been used. The value of droop gain impacts the load distribution among various DGs, as the fast action of droop is indicated by a higher value of droop gain. But the inappropriate value of droop gain affects the stability of the system, so typically the values are kept low due to steady state stability concerns. The optimum droop gain helps to keep the voltage and frequency within the permissible limits. The voltage profile has been improved with optimal gains for the droop coefficients, and about 4% loss reduction has been reported, proving the effectiveness of the proposed method. The method has been tested on 6-bus, 38-bus, and 40-bus islanded microgrid systems.

Nocturnal Water Use Partitioning and Its Environmental and Stomatal Control Mechanism in Caragana korshinskii Kom in a Semi-Arid Region of Northern China

As an important aspect of plant water consumption, nocturnal water use (En) behavior provides reliable information on the effect of plantation carbon and water budgets at stand and regional scales. Therefore, quantifying En and its environmental and stomatal controlling mechanisms is urgent to establish adaptation strategies for plantation management in semiarid regions. With the help of the sap flow technique, our study investigated the seasonal variations in canopy transpiration and canopy conductance in a Caragana korshinskii Kom plantation. Environmental variables were measured concurrently during the growing seasons of 2020 and 2021. The results indicated that the average En values were 0.10 mm d−1 and 0.09 mm d−1, which accounted for 14% and 13% of daily water use, respectively, over two years. The proportions of nocturnal transpiration (Tn) to En were approximately 49.76% and 54.44%, while stem refilling (Re) accounted for 50.24% and 45.56% of En in 2020 and 2021, respectively, indicating that C. korshinskii was able to draw on stored stem water to support transpiration. En was predominantly affected by nocturnal canopy conductance (Gc–n), air temperature (Ta–n) and wind speed (u2-n). In contrast, Gc–n and Ta–n explained the highest variation in Tn and nocturnal vapor pressure (VPDn), and u2-n explained the highest variation in Re. Total effects of the five environmental and stomatal variables explained 50%, 36% and 32% of En, Tn and Re variation, respectively. These findings could enable a better understanding of nocturnal water use dynamics and their allocation patterns in C. korshinskii plantations on the Bashang Plateau. Moreover, our results reveal the water use strategies of artificial shrubs and highlight the importance of incorporating nocturnal water use processes into large-scale ecohydrological models in semiarid regions.

An analysis of the influence of a generic building on tornadic flow fields using high-frequency PIV and point velocity measurements

The current practice of implementing tornadic wind actions on structures in building codes is not well established compared to atmospheric boundary layer winds. Given there is a lack of velocity measurements in actual tornados close to the surface, there is a body of research that is currently looking into physical simulations of tornado-like vortices (TLVs). Our study contributes to this field of research by looking into the aerodynamic effects a generic building exerts on TLVs that are stationary and positioned above the building. Our study also investigated the differences among two distinctively different velocity measurement techniques in TLV flows—Cobra probe and particle image velocimetry (PIV). We further examine how different TLV intensities affect the results by considering TLVs that correspond to (Enhanced Fujita) EF1- and EF2-rated tornadoes in the atmosphere. We show that the velocity fluctuations in Cobra probe records are higher than in PIV measurements despite both velocity records being sampled at 200 Hz. The discrepancy is due to the inherent spatial filtering used in PIV data processing. A larger discrepancy between the two measurement techniques was observed in the EF1 TLV. The presence of the building affected EF1 more than EF2 TLV due to the larger core diameter of the latter vortex. The diameter of the circumference of the maximum tangential velocity in EF1 and EF2 TLVs undisturbed by the building was 1.6 and 2.1 times larger than the diagonal length of the building. By comparing our results against prior research on the same TLVs, but without a building in the flow, we concluded that the building had the strongest effect in amplifying vertical velocity in the EF2 TLV. The significance of this result to wind loading and structural responses requires further wind engineering analyses. The circular symmetry of EF1 was more affected than in EF2 TLV due to its smaller size relative to the building.

An enhanced optimizer of social network search for multi-dimension optimal power flow in electrical power grids

This paper presents an enhanced computational optimizer of the Social Network Search Technique (ESNST) for multi-dimension Optimal Power Flow (OPF) in Electrical Power Grids (EPGs). The SNST is motivated by social network users who have various moods: conversation, imitation, innovation, and disputation. The proposed ESNST is introduced with two strategies. The first is the aggressive exploit process, which aims to increase the number of people seeking the best possible perspective to develop SNST performance. The second strategy proposes an adaptive parameter to aid in higher exploitation towards the end of iterations. This paper considers the multi-dimension OPF in EPGs with several minimization objectives for fuel costs, grid losses, and produced emissions. Not only that but also the system's power demands and losses must be satisfied. The performance of the proposed SNST and ESNST is evaluated on IEEE standard 30-bus, 57-bus, 118-bus, and practical West Delta Region EPGs. The results reveal the solution quality and robustness of the proposed ESNST compared with the SNST and other relevant techniques reported in the literature.

A novel cold spray process flow technique - a numerical investigation

The particle deposition rates on the coating surface for three different geometries during the high-pressure cold spray processes were studied and verified in the paper. Analyzes were performed for three different techniques, based on the same de Laval nozzle geometry, but each with a different method of supplying working gas to the system. One case of geometry is called the based case and described the classical flow commonly used in industry. The second case called, the vortex case employs the six attached tubes located at the very beginning of the converging section. The last one, called the combined case, uses classical and vortex cases and is defined as the use of a direct nozzle inlet along with additional tubes for the working gas. For all cases, the spraying process took approximately 0.05 seconds, where the value of the deposition rate was calculated using an internal UDF code algorithm written and implemented in the Ansys FLUENT module. The working fluid was the air combined with the spherical copper particles with a diameter of 20μm. The combined variant achieved the highest value of the accretion rate compared to the other two methods and was 33.6 kgm2. On the contrary, the lowest rate was achieved by the vortex method with a rate of 10.7 kgm2. The distribution of the particles sprayed in this combined method forms a cone; in the basic method, the distribution is uniform and in the vortex method, no repeating structure is formed. It was proven that the combined method also gives the highest accretion rate values.

A Novel Doppler Unfolding Technique Using Optical Flow

Abstract Doppler radars measure Doppler velocity within the [−VN, VN] range, where VN is the Nyquist velocity. Doppler velocities outside this range are “folded” within this interval. All Doppler “unfolding” techniques use the folded velocities themselves. In this work, we investigate the potential of using velocities derived from optical flow techniques applied to the radar reflectivity field for that purpose. The analysis of wind speed errors using six months of multi-Doppler wind retrievals showed that 99.9% of all points are characterized by errors smaller than 26 m s−1 below 5-km height, corresponding to a failure rate of less than 0.1% if optical flow winds were used to unfold Doppler velocities for VN = 26 m s−1. These errors largely increase above 5-km height, indicating that vertical continuity tests should be included to reduce failure rates at higher elevations. Following these results, we have developed the Two-step Optical Flow Unfolding (TOFU) technique, with the specific objective to accurately unfold Doppler velocities with VN = 26 m s−1. The TOFU performance was assessed using challenging case studies, comparisons with an advanced Doppler unfolding technique using higher Nyquist velocities, and 6 months of high VN (47.2 m s−1) data artificially folded to 26 m s−1. TOFU failure rates were found to be very low. Three main situations contributed to these errors: high low-level wind shear, elevated cloud layers associated with high winds, and radar data artifacts. Our recommendation is to use these unfolded winds as the first step of advanced Doppler unfolding techniques. Significance Statement The potential of using optical flow winds operationally to accurately unfold Doppler velocities is demonstrated in this work. The operational significance is that the Nyquist velocity can confidently be reduced to 26 m s−1, allowing for extended first trip radar maximum range and reduced contamination from dual pulse repetition frequency artifacts.