Optimal Speed Research Articles

Bi-objective ship speed optimization based on machine learning method and discrete optimization idea

With the introduction of increasingly stringent carbon dioxide (CO2) emission regulations in the shipping industry, shipping companies face significant pressure to reduce emissions while ensuring profitable ship operations. A practical bi-objective speed optimization (BSO) algorithm for container ships is developed based on the variable weight idea, the discrete optimization idea, and the ideal point method to meet the urgent needs of shipping companies to improve operating profits and reduce emissions. By constructing two BSO models of ship main engine fuel consumption-ship sailing time and ship operating costs (SOC)-ship CO2 emissions (SCE), the effectiveness of the BSO algorithm is confirmed through extensive optimization experiments. The results show that compared to the historical sailing results, the BSO reduces the SOC by 4.43 % without causing a significant increase in the SCE, and reduces the SCE by 0.25 %. Moreover, the results demonstrate the effectiveness and feasibility of the BSO in reducing the cost of ship operations while reducing the impact on the environment. The results provide valuable references for shipping companies in the trade-off analysis between economic and environmental benefits and optimization decision-making.

Energy-saving operation in urban rail transit: A deep reinforcement learning approach with speed optimization

The energy consumption of urban rail transit plays a significant role in the operating costs of trains. It is particularly crucial to decrease the energy consumption of the traction power supply in subway systems, as it accounts for approximately half of the total energy consumption of the subway operating organization. To overcome the limitations of traditional real-time speed profile generation methods and the limited exploration capabilities of popular reinforcement learning algorithms in the speed domain, this paper presents the Energy-Saving Maximum Entropy Deep Reinforcement Learning (ES-MEDRL) algorithm. The ES-MEDRL algorithm incorporates Lagrange multipliers and maximum policy entropy as penalties to formulate a novel objective function. This function aims to intensify exploration in the speed domain, minimize train traction energy consumption, and ensure a balance between ride comfort, punctuality, and safety within the subway system. This leads to the optimization of speed profile strategies. To further reduce energy consumption, this paper proposes a secondary optimization strategy for the energy-saving speed profile. This approach involves trading acceptable travel time for improved energy efficiency. To validate the performance of the proposed model and algorithm, numerical experiments are conducted using the Yizhuang Line of the Beijing Metro. The findings demonstrate a minimum 20 % increase in energy efficiency with the ES-MEDRL algorithm compared to manual driving. This algorithm can guide energy-efficient train operations at the planning level.

Identifying Patterns of Primary Care In-Person and Telemedicine Use in the Veterans Health Administration: A Latent Class Analysis.

The Veterans Health Administration increased synchronous telemedicine (video and telephone visits) in primary care in response to the COVID-19 pandemic. Our objective was to determine veteran use patterns of in-person and telemedicine primary care when all modalities were available. A retrospective cohort analysis. We performed a latent class analysis of primary care visits over a 1-year period to identify veteran subgroup (i.e., class) membership based on amount of primary care use and modality used. Then, we used multinomial logistic regression with a categorical outcome to identify patient characteristics associated with class identification. A random national sample consistingof 564,580 primary care empaneled veterans in June 2021. Latent class membership. We identified three latent classes: those with few primary care visits that were predominantly telephone-based (45%), intermediate number of visits of all modalities (50%), and many visits of all modalities (5%). In an adjusted model, characteristics associated with the "few" visits class, compared to the intermediate class, were older age, male sex, White race, furtherdriving distance to primary care, higher Gagne, optimal internet speed, and unmarried status (OR 1.002, 1.52, 1.13, 1.004, 1.04, 1.05, 1.06, respectively; p < .05). Characteristics associated with membership in the "many" visits class, compared to the intermediate class, were Hispanic race, higher JEN Frailty Index and Gagne (OR 1.12, 1.11, 1.02, respectively; p < .05), and higher comorbidity by Care Assessment Need score quartile (Q2 1.73, Q3 2.80, Q4 4.12; p < 0.05). Veterans accessing primary care in-person or via telemedicine do so primarily in three ways: (1) few visits, predominantly telephone; (2) intermediate visits, all modalities, (3) many visits, all modalities. We found no groups of veterans receiving a majority of primary care through video.

Optimizing friction stir processing parameters for aluminium alloy 2024 reinforced with SiC particles: A taguchi approach of investigation

The present investigation focuses on the microstructural behavior and mechanical properties of Aluminium alloy 2024 reinforced with SiC nanoparticles by applying the Friction Stir Processing (FSP) technique. Taguchi L9 orthogonal array was used to find the optimal process parameters. The experiment used the expected best process parameters, which confirmed the predicted highest value for the mechanical characteristic. Traverse speed, axial load, and rotating speed are the main factors affecting aluminum metal matrix composite. Hence, this experiment examined the effects of five different tool spinning speeds, specifically 800, 900, 1000, 1100, and 1200 rpm. Weld samples are fabricated wherein the stir zone contains SiC nanoparticles. The results of the study indicate that the optimal welding rotational speed of 1000 rpm has a significant impact on the microstructure, wear rate, and microhardness. Specifically, it was observed that the stirred zone exhibited enhanced wear resistance compared to other zones. The study's findings revealed that after implementing friction stir processing (FSP), a well-defined shear zone was observed on the advancing side of the stir zone. Additionally, it is found that the uniform dispersion and strong bonding of SiC particles with an aluminium matrix further enhance wear resistance.

Advancing forensic-based investigation incorporating slime mould search for gene selection of high-dimensional genetic data

Modern medicine has produced large genetic datasets of high dimensions through advanced gene sequencing technology, and processing these data is of great significance for clinical decision-making. Gene selection (GS) is an important data preprocessing technique that aims to select a subset of feature information to improve performance and reduce data dimensionality. This study proposes an improved wrapper GS method based on forensic-based investigation (FBI). The method introduces the search mechanism of the slime mould algorithm in the FBI to improve the original FBI; the newly proposed algorithm is named SMA_FBI; then GS is performed by converting the continuous optimizer to a binary version of the optimizer through a transfer function. In order to verify the superiority of SMA_FBI, experiments are first executed on the 30-function test set of CEC2017 and compared with 10 original algorithms and 10 state-of-the-art algorithms. The experimental results show that SMA_FBI is better than other algorithms in terms of finding the optimal solution, convergence speed, and robustness. In addition, BSMA_FBI (binary version of SMA_FBI) is compared with 8 binary algorithms on 18 high-dimensional genetic data from the UCI repository. The results indicate that BSMA_FBI is able to obtain high classification accuracy with fewer features selected in GS applications. Therefore, SMA_FBI is considered an optimization tool with great potential for dealing with global optimization problems, and its binary version, BSMA_FBI, can be used for GS tasks.

Critical parameters to standardize the size and concentration determination of nanomaterials by nanoparticle tracking analysis

The size and concentration are critical for the diagnostic and therapeutic applications of nanomaterials but the accurate measurement remains challenging. Nanoparticle tracking analysis (NTA) is widely used for size and concentration determination. However, highly repeatable standard operating procedures (SOPs) are absent. We adopted the “search-evaluate-test” strategy to standardize the measurement by searching the critical parameters. The particles per frame are linearly proportional to the sample concentration and the measured results are more accurate and repeatable when the concentration is 108-109 particles/ml. The optimal detection threshold is around 5. The optimal camera level is such that it allows clear observation of particles without diffractive rings and overexposure. The optimal speed is ≤ 50 in AU and ∼ 10 μl/min in flow rate. We then evaluated the protocol using polydisperse polystyrene particles and we found that NTA could discriminate particles in bimodal mixtures with high size resolution but the performance on multimodal mixtures is not as good as that of resistive pulse sensing (RPS). We further analyzed the polystyrene particles, SiO2 particles, and biological samples by NTA following the SOPs. The size and concentration measured by NTA differentially varies to those determined by RPS and transmission electron microscopy.

Integrated design optimization method for pavement structure and materials based on further development of finite element and particle swarm optimization algorithm

The maintenance design for existing asphalt pavements, characterized by their typical structure and material composition, falls short of meeting the diverse requirements of pavements. There is an urgent need for a more rational design approach that can achieve accurate pavement design while also reducing construction costs. This paper focuses on selected conventional road surface structures and conducts an analysis of how pavement structure and materials impact fatigue life and construction expenses. It employs Python to perform three-dimensional finite element road modeling, analysis, and data extraction at specific locations, using normalized fatigue equation to calculate pavement fatigue life. The optimization variables selected are the modulus E and thickness h, with pavement maintenance cost serving as the optimization target function. The particle swarm optimization algorithm is employed for optimizing these variables. A comparative analysis is carried out for five improved optimization algorithms, optimizing combinations of inertia weight, learning factors, particle speed, number of particles, and iteration times. The results reveal that the enhanced optimization algorithms significantly improve iteration trends, featuring velocity limitations and natural selection mechanisms. Between 0 and 20 iterations, all algorithms search for the optimal solution over a wider interval with greater fluctuations. Between 20 and 30 iterations, each algorithm gradually converges towards its solution, stabilizing in the later stages. Based on optimization speed and actual engineering cost benchmarks, the NDPSO algorithm is selected, resulting in a 52.8% reduction in actual engineering costs. Adjustments to the particle count and iteration number of the NDPSO algorithm are made based on computational cost and optimization accuracy, thus choosing 20 particles and 50 iterations as the recommended parameter combination. By combining automated finite element modeling, analysis, and particle swarm optimization, this paper offers a novel and effective automated design reference for extending the lifespan of asphalt pavement structures.

Comparison of the PSO Algorithm and JAYA Algorithm for Economic Load Dispatch

The electrical power industry has undergone the significant changes, leading to a competitive market and the need for optimal economic dispatch solutions. As energy resources becoming scarce, prices rise, environmental concerns intensify, and electricity consumption increases, classical optimization techniques struggle to handle these complex issues. This project compares particle swarm optimization (PSO) and JAYA optimization method for economic load dispatch (ELD) problems in the electrical power industry. PSO has gained recognition as an effective solution for ELD problems in the last decade. The aim of this study is to show that Jaya optimization is the best optimization methodology for ELD problems. By comparing the performance, convergence speed, and accuracy of PSO and JAYA optimization, this paper aims to provide insights into the superiority of JAYA optimization. Key Words: Economic Load dispatch, Optimization method, PSO algorithm, JAYA algorithm, Incremental fuel cost

Time-domain fatigue damage assessment for wind turbine tower bolts under yaw optimization control at offshore wind farm

Yaw optimization control is recognized as the most effective active wake control strategy for enhancing the overall power generation of wind farms. However, the potential adverse effects of yaw optimization control on the fatigue life of wind turbines remain unclear. This study examined the effect of yaw optimization control on the fatigue life of offshore wind turbines using tower bolts. Initially, the yaw misalignment of the wind turbines was optimized using the open-source FLORIS package to maximize wind farm power generation. Subsequently, the time-varying load of the wind turbines was obtained through the OpenFAST software, using the optimal yaw misalignment, wind speed, and turbulence intensity at the hub height as inputs. The fatigue life of the wind turbines was then calculated by integrating the Schmidt–Neuper engineering stress algorithm, rain flow counting method, and Miner-Palmgren fatigue damage accumulation theory. Finally, a case study of the Horns Rev I large-scale offshore wind farm, comprising 80 wind turbines, revealed that yaw optimization control could significantly enhance the annual power generation of the wind farm by approximately 1.818%. However, optimization comes at the cost of reducing the fatigue life of wind turbine tower bolts by approximately 3–9 years.

Multi-robot navigation based on velocity obstacle prediction in dynamic crowded environments

PurposeThe purpose of this paper is to propose a new velocity prediction navigation algorithm to develop a conflict-free path for robots in dynamic crowded environments. The algorithm BP-prediction and reciprocal velocity obstacle (PRVO) combines the BP neural network for velocity PRVO to accomplish dynamic collision avoidance.Design/methodology/approachThis presented method exhibits innovation by anticipating ahead velocities using BP neural networks to reconstruct the velocity obstacle region; determining the optimized velocity corresponding to the robot’s scalable radius range from the error generated by the non-holonomic robot tracking the desired trajectory; and considering acceleration constraints, determining the set of multi-step reachable velocities of non-holonomic robot in the space of velocity variations.FindingsThe method is validated using three commonly used metrics of collision rate, travel time and average distance in a comparison between simulation experiments including multiple differential drive robots and physical experiments using the Turtkebot3 robot. The experimental results show that our method outperforms other RVO extension methods on the three metrics.Originality/valueIn this paper, the authors propose navigation algorithms capable of adaptively selecting the optimal speed for a multi-robot system to avoid robot collisions during dynamic crowded interactions.

A piezoelectric energy harvester with parallel connection using beams of different lengths to improve output performance

The purpose of this paper is to design an energy harvester to improve output performance. The theoretical analysis of the piezoelectric energy harvester has been performed. Reducing the length of one cantilever beam, thereby changing the relative impact position, causing the amplitude of the two cantilever beams to be different, and making the waveform of two beams different. Some experiments have been tested to verify the feasibility of the device and compare the differences with Plan A. Based on the experiment, it can be concluded that the output voltage is higher at both high and low speeds. When the rotation speed is 255 r min−1, Plan B arrives at the optimum speed, and the maximum output voltage is 166.2 V, which significantly increases from 97.2 V of Plan A. The maximum output power is 0.966 W under the load resistance of 10 kΩ. The maximum voltage is 157.7 V under the load resistance of 120 kΩ. Nevertheless, the maximum voltage and maximum power of Plan A are 92.62 V and 0.52 W. Besides, the prototype has fewer materials and nearly 1.5 times the energy conversion rate compares to Plan A. It can light up 42 LEDs easily and can adapt to environmental vibration frequency changes, so it has an intensely adaptable and outstanding performance in practical applications.

DC to DC Boost Converter Optimized Speed Control for BLDC Motor

BLDC motors have the capacity to replace induction motors, but the major setback is the implementation of its drive system which should be efficient as well as low cost and can be implemented easily. Here we try to implement a improved speed control system for a BLDC motor using sensors and easily available controllers which can be utilized for operating under various conditions directly for many applications which will help in the replacement of induction motors with BLDC motor systems. The response of the system has been studied for various speed and load conditions using MATLAB simulation tools.

Enhancing speed recovery rapidity in bipedal walking with limited foot area using DCM predictions

The research on bipedal robots with limited foot area is gaining increasing attention. To tackle the challenge of dealing with unknown disturbances in the environment, the adjustment of footstep placement plays a vital role in maintaining stable motion during bipedal walking. This paper introduces an innovative approach based on the relationship between the Divergent Component of Motion (DCM) and footstep. It utilizes a DCM prediction model to optimize the optimal speed for recovering the foothold position. The goal is to enable quick and relevant footstep selection for bipedal robots, thereby facilitating the swift recovery of robot speed. The paper provides insights into the process of designing the desired DCM for achieving an optimal average walking speed without relying on predefined footstep sequences. By establishing a state equation between the DCM and footstep placement, this approach enables the prediction of multiple footstep positions within a fixed walking cycle, thereby facilitating the desired average motion speed. Extensive numerical simulations are conducted to compare the proposed method with various conventional footstep adjustment algorithms. The results emphasize our method’s ability to converge more rapidly to the target speed, even with minor step adjustments. To validate the feasibility and robustness of the algorithm, we conduct experiments on the bipedal robot BHR-B2. These experiments further confirm the algorithm’s effectiveness. Given its promising performance, this algorithm holds potential for applications in legged robots with point feet.

Boolean interpretation, matching, and ranking of natural language queries in product selection systems

E-commerce is a massive sector in the US economy, generating $767.7 billion in revenue in 2021. E-commerce sites maximize their revenue by helping customers find, examine, and purchase products. To help users easily find the most relevant products in the database for their individual needs, e-commerce sites are equipped with a product retrieval system. Many of these modern retrieval systems parse user-specified constraints or keywords embedded in a simple natural language query, which is generally easier and faster for the customer to specify their needs than navigating a product specification form, and does not require the seller to design or develop such a form. These natural language product retrieval systems, however, suffer from low relevance in retrieved products, especially for complex constraints specified on products. The reduced accuracy is in part due to under-utilizing the rich semantics of natural language, specifically queries that include Boolean operators, and lacking of the ranking on partially-matched relevant results that could be of interest to the customers. This undesirable effect costs e-commerce vendors to lose sales on their merchandise. In solving this problem, we propose a novel product retrieval system, called QuePR\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${\ extit{QuePR}}$$\\end{document}, that parses arbitrarily simple and complex natural language queries with(out) Boolean operators, utilizes combinatorial numeric and content-based matching to extract relevant products from a database, and ranks retrieved resultant products by relevance before presenting them to the end-user. The advantages of QuePR\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${\ extit{QuePR}}$$\\end{document} are its ability to process explicit and implicit Boolean operators in queries, handle natural language queries using similarity measures on partially-matched records, and perform best guess or match on ambiguous or incomplete queries. QuePR\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${\ extit{QuePR}}$$\\end{document} is unique, easy to use, and scalable to all product categories. To verify the accuracy of QuePR\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${\ extit{QuePR}}$$\\end{document} in retrieving relevant products on different product domains, we have conducted different performance analyses and compared QuePR\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${\ extit{QuePR}}$$\\end{document} with other ranking and retrieval systems. The empirical results verify that QuePR\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${\ extit{QuePR}}$$\\end{document} outperforms others while maintaining an optimal runtime speed.

Multiobjective optimization of laser welding parameters for P92 steel based on MFO/MOEAD-Kriging

P92 steel is used in steam equipment of thermal power plants and nuclear power plants. It needs to withstand high temperature and high pressure during service, so high-quality welds are required. Laser welding can easily obtain good weld quality and reduce the probability of coarse grains and welding defects in the heat-affected zone. Post-weld deformation, residual stress, and weld hardness are important indexes for evaluating weld quality, which largely depends on the combination of laser welding process parameters. The multiobjective optimization methods of moth-flame optimization (MFO) and multiobjective evolutionary algorithm based on decomposition under the Kriging model were compared in this research. It was found that the MFO algorithm has a faster optimization speed and better overall effect. The Optimal Latin hypercube sampling method was used to design the simulation test. The welding seam-forming process was simulated by the SYSWELD software. The Kriging model was used to make the nonlinear relationship between the process parameters and the weld quality indexes significant. A new data set was designed to evaluate the accuracy of the model. On this basis, the efficiency of the combination of process parameters optimized by the two algorithms was compared. The simulation and experimental results were in good agreement with the multiobjective optimization results of the MFO. In the experimental verification, the optimized weld quality was analyzed from the aspects of microstructure and mechanical properties. The results showed that the multiobjective optimization method quickly and effectively reduces the probability of weld quality defects in actual welding, thereby improving weld quality. This multiobjective optimization method provides valuable research ideas for engineering research and development to effectively shorten its development cycle.

Research on Multi-objective Optimization Model of Power Storage Materials Based on NSGA-II Algorithm

Aiming at the problems of slow convergence speed and low precision probability of multi-objective optimization of energy storage materials, a multi-objective optimization model of energy storage materials based on NSGA-II algorithm was proposed. The association rule set of storage materials in the joint supply chain operation performance management system is extracted, and the rough vector feature distribution set multi-objective optimization method is used to decompose and optimize the characteristics of storage materials in the joint supply chain operation performance management system. Using NSGA-II optimization analysis method, this paper summarizes the power storage materials under the joint supply chain operation performance management system, and summarizes three kinds of inventory control: periodic inventory, inventory coding, and computerized inventory. Combined with the positive regression learning method of organizational operational performance, the multi-objective optimization decision of electric storage materials under the joint supply chain operational performance management system is realized. The simulation results show that under the joint supply chain operation performance management system, the proposed method reaches the optimal convergence after 65 iterations, the convergence speed is fast, and the accuracy probability reaches 1.000 after 80 iterations, which solves the problems of slow convergence speed and low accuracy probability, and has a good scheduling ability of energy storage materials.

Performance Assessment of Two-Wheeler Electric Vehicle Batteries Using Multi-Mode Drive Cycles

This article presents a model-based approach to assess the battery performance of a two-wheeler EV drive train system for various user driving patterns using the selected urban drive cycles. The battery pack is one of the most expensive parts of an EV, and its life is heavily dependent on its usage pattern. The impact of the user’s driving behaviour on the performance parameters of the EV battery pack needs to be investigated. Thus, a two-wheeler EV drive train model was developed in MATLAB with a 5 kW motor, a 4.32 kWh battery, vehicle dynamics, and the power train control algorithms for in-depth analysis of battery performance. The validity of the developed model was tested against various state-of-the-art drive cycles for a duration of 3600 s. Numerous user driving behaviours, such as aggressive, moderate, and slow driving behaviours, were modelled with modified drive cycles, which were used to assess the two-wheeler battery pack performance. An optimum speed range, which ranges from 21 km/h to 34 km/h for different drive cycles, was identified, and these speed ranges minimised the battery energy consumption for the selected drive cycles with the modified drive cycle models.

Design and verification of a new non-contact piezoelectric energy harvester based on a sinusoidal exciting mechanism.

In this paper, a new non-contact rotary piezoelectric energy harvester based on a sinusoidal exciting mechanism has been proposed. The energy transformation is realized in a non-contact form. The sinusoidal orbital rotor can act as a sinusoidal excitation to the contacts, and it can avoid damage to piezoelectric ceramics from direct strikes while bending piezoelectric cantilever beams. After a series of experiments, the prototype demonstrated an excellent output performance. Having explored the influence of the rotation speed on the output voltage, it reaches the peak when the rotation speed is 180rpm and the maximum voltage is 18.6V. The relationship between power and voltage was validated with the rise of resistance at the optimum speed. When the resistance is 10 kΩ, the power that arrives at the peak is 1.35 mW, and the maximum voltage is 12.1V when the resistance is 200 kΩ. Some application experiments have been designed and verify the feasibility of the prototype; it can light up 18 LEDs and power some microelectronic equipment.

Study on Resin-based 3D Printed Product for Spin Casting Mold Making

Spin casting (centrifugal rubber mold casting) is well known process to produce castings by utilizing inertia. A vulcanized rubber mold is typically used by spinning it at an optimum speed along its central axis. There are many factors influence the quality of casting product, including the quality of mold, the temperature of molten material, the speed and duration of rotation, as well as the pressure upon the mold. This research aims to investigate the quality of casting product when a resin-based 3D printing is utilized to make a model for vulcanized rubber mold. The model is designed by using CAD software and printed layer by layer by using Selective Laser Sintering (SLS) method. Curing process is employed in order to harden the model before placed in the disc-shape silicone rubber. The rubber is then vulcanized so that the mold is formed. Standard edge gate and straight runner are then made to let the molten material flow during casting process. Zinc alloy is used as raw material in the casting process with five variations of rotation speed. Based on the experiment results it is observed that the resin-based 3D printed product can be used as a master model in rubber mold making with some limitations. The resin could not maintain its shape due to the high temperature and pressure during vulcanization process. However, for simple design without any need of very small and detail features, the resin-based 3D printed product still can be used as an alternative.

Effects of Confinement on Opposed-Flow Flame Spread over Cellulose and Polymeric Solids in Microgravity

Opposed-flow flame spread over solid materials has been investigated in the past few decades owing to its importance in fundamental understanding of fires. These studies provided insights on the behavior of opposed-flow flames in different environmental conditions (e.g., flow speed, oxygen concentration). However, the effect of confinement on opposed-flow flames remains under-explored. It is known that confinement plays a critical role in concurrent-flow flame spread in normal and microgravity conditions. Hence, for a complete understanding it becomes important to understand the effects of confinement for opposed-flow flames. In this study, microgravity experiments are conducted aboard the International Space Station (ISS) to investigate opposed-flow flame spread in different confined conditions. Two materials, cotton-fiberglass blended textile fabric (SIBAL) and 1 mm thick polymethyl methacrylate (PMMA) slab are burned between a pair of parallel flow baffles in a small flow duct. By varying the sample-baffle distance, various levels of confinement are achieved (H = 1–2 cm). Three types of baffles, transparent, black, and reflective, are used to create different radiative boundary conditions. The purely forced flow speed is also varied (between 2.6 and 10.5 cm/s) to investigate its interplay with the confinement level. For both sample materials, it is observed that the flame spread rate decreases when the confinement level increases (i.e., when H decreases). In addition, flame spread rate is shown to have a positive correlation with flow speed, up to an optimal value. The results also indicate that the optimal flow speed for flame spread can decrease in highly confined conditions. Surface radiation on the confinement boundary is shown to play a key role. For SIBAL fabric, stronger flames are observed when using black baffles compared to transparent. For PMMA, reflective baffles yield stronger flames compared to black baffles. When comparing the results to the concurrent-flow case, it is also noticed that opposed-flow flames spread slower and blow off at larger flow speeds but are not as sensitive to the flow speed. This work provides unique long-duration microgravity experimental data that can inform the design of future opposed-flow experiments in microgravity and the development of theory and numerical models.