Unit Time Research Articles

A novel dynamic predictive maintenance framework for gearboxes utilizing nonlinear Wiener process

Abstract In the context of advancing industrial automation, gearboxes, as pivotal components in power transmission systems, have a direct bearing on the operational efficiency and safety of the entire machinery. This study introduces a novel dynamic predictive maintenance framework for gearboxes using a nonlinear Wiener process. Comprehensive experiments validate the framework, demonstrating significant reductions in maintenance costs and improvements in reliability. First, a full-life degradation experiment was executed on the gearbox, leveraging the root mean square (RMS) value of the vibration signal as an indicator of system degradation. Subsequently, the signals from four vibration sensors were synthesized and normalized through Kernel Principal Component Analysis (KPCA), thereby enabling a more nuanced representation of the gearbox's degradation profile. The degradation trajectory was then modeled using a nonlinear Wiener process framework. The Wiener process's parameters and state variables were iteratively refined utilizing an online filtering algorithm grounded in Bayesian inference. This facilitated the derivation of the probability density function for the remaining useful life (RUL), thereby enabling a robust prediction of the gearbox's RUL. Finally, to minimize maintenance costs per unit of time, an optimization model for dynamic maintenance decision-making was formulated. The optimal maintenance timing was ascertained by solving this model. The empirical findings of this investigation demonstrate the efficacy of the proposed approach in executing dynamic predictive maintenance for gearboxes. This research endeavors to furnish novel theoretical underpinnings and pragmatic directives for the field of predictive maintenance in the context of gearboxes.

Organizational Commitment as an Intervening Variable in the Effect of Empowerment, Incentives and Self Efficacy on Employee Performance

Badan Kepegawaian Negara (BKN) are an organization of government agencies operating in the field of public service with a unique context as well as regulations and policies on public service management. The high performance of officials is crucial to government agencies. Staff performance is a qualitative and quantitative measure of work performance and work outcomes based on the standard of work achieved by employees per unit of time in performing work in accordance with the assigned responsibilities. The study aims to look at the impact of empowerment, incentives, self-effectiveness, and organizational commitments on staff performance, the influence of empowering, incentive, and self effectiveness in relation to organization commitments, as well as the effect of empoverment, motivation, and Self-effictiveness on employee performance with the intervening variable of organizational engagement. This is a quantitative descriptive study, with respondents of 90 BKN employees, using questionnaires as a basis for data assumption as well as data analysis using SmartPLS software. The results of the analysis show that empowerment, incentives, self efficacy, and commitment of the organization have a positive and significant impact on the performance of the staff, the influence of empowering, incentive, and self effecacy have a significant and positive effect on the organization's commitment, and empowerement, the incentive has an impact on staff's performance with the commitment organiassi as a variable part mediation, and the self efficiency on staff performance with organizational commitmentasi as a full mediation variable.

An EOQ Model for Delayed Deteriorating Items: Analysis of Three-Stage Consumption, Dual Storage Facilities, and Variable Partial Backlogging Rates

Study’s Excerpt An Economic Order Quantity (EOQ) model that accounts for items with delayed deterioration by integrating three distinct stages of consumption rates, dual storage facilities, and partially backlogged shortages was proposed. The model can minimize overall variable costs and establish the conditions for the existence and uniqueness of the optimal solution by optimizing cycle length, order quantity, and depletion timing. The model could help in inventory management of perishable goods, particularly in scenarios involving multiple storage options and consumption phases. Full Abstract This paper presents an Economic Order Quantity (EOQ) model for items with delayed deterioration, incorporating three distinct stages of consumption rates, dual storage facilities, and shortages. The model assumes that the average consumption rates before and after the onset of deterioration, as well as during shortages, are constant but differ from one another. The shortages are partially backlogged and vary according to the waiting time until the next replenishment. The model aims to minimize the overall variable cost per unit of time by optimizing the cycle length, order quantity, and the timing at which the inventory in the owned warehouse is depleted. The paper establishes the necessary and sufficient conditions to ensure the existence and uniqueness of the optimal solution. A sensitivity analysis is conducted, and the findings are used to derive managerial insights.

Three-Dimensional Moran Walk with Resets

In this current paper, we propose to study a three-dimensional Moran model (Xn(1),Xn(2),Xn(3)), where each random walk (Xn(i))∈{1,2,3} increases by one unit or is reset to zero at each unit of time. We analyze the joint law of its final altitude Xn=max(Xn(1),Xn(2),Xn(3)) via the moment generating tools. Furthermore, we show that the limit distribution of each random walk follows a shifted geometric distribution with parameter 1−qi, and we analyze the maximum of these three walks, also giving explicit expressions for the mean and variance.

Trends in satellite-based ocean parameters through integrated time series decomposition and spectral analysis: 1. chlorophyll, sea surface temperature, and sea level anomaly

Abstract This study investigated trends in satellite-based chlorophyll-a (Chl-a; 1998–2022), sea surface temperature (SST; 1982–2022), and sea level anomaly (SLA; 1993–2021) from the European Space Agency’s Climate Change Initiative records, integrating time series decomposition and spectral analysis. Trends in parameters signify prolonged increases, decreases, or no changes over time. These are time series in the same space as original parameters, excluding seasonalities and noise, and can exhibit nonlinearity. Trend rates approximate the pace of change per time unit. We quantified trends using conventional linear-fit and three incrementally advancing methods for time series decomposition: simple moving average (SMA), seasonal-trend decomposition using locally estimated scatterplot smoothing (STL), and multiple STL (MSTL), across the global ocean, the Bay of Bengal, and the Chesapeake Bay. Challenges in decomposition include specifying accurate seasonal periods that are derived here by combining Fourier and Wavelet Transforms. Globally, SST and SLA trend upwards, and Chl-a has no significant change, yet regional variations are notable. We highlight the advantage of extracting multiple periods with MSTL and, more broadly, decomposition’s role in disentangling time-series components (seasonality, trend, noise) without resorting to monotonic functions, thereby preventing overlooking episodic events. Illustrations include extreme events temporarily counteracting background trends, e.g., the 2010–2011 SLA drop due to La Niña-induced rainfall over land. The continuous analysis clarifies the warming hiatus debate, affirming sustained warming. Decadal trend rates per grid cell are also mapped. These are ubiquitously significant for SST and SLA, whereas Chl-a trend rates are globally low but extreme across coasts and boundary currents.

Event as the central construal of psychological time in humans.

Time is a fundamental dimension of our perception and mental construction of reality. It enables resolving changes in our environment without a direct sensory representation of elapsed time. Therefore, the concept of time is inferential by nature, but the units of subjective time that provide meaningful segmentation of the influx of sensory input remain to be determined. In this review, we posit that events are the construal instances of time perception as they provide a reproducible and consistent segmentation of the content. In that light, we discuss the implications of this proposal by looking at "events" and their role in subjective time experience from cultural anthropological and ontogenetic perspectives, as well as their relevance for episodic memory. Furthermore, we discuss the significance of "events" for the two critical aspects of subjective time-duration and order. Because segmentation involves parsing event streams according to causal sequences, we also consider the role of causality in developing the concept of directionality of mental timelines. We offer a fresh perspective on representing past and future events before age 5 by an egocentric bi-directional timeline model before acquiring the allocentric concept of absolute time. Finally, we illustrate how the relationship between events and durations can resolve contradictory experimental results. Although "time" warrants a comprehensive interdisciplinary approach, we focus this review on "time perception", the experience of time, without attempting to provide an all encompassing overview of the rich philosophical, physical, psychological, cognitive, linguistic, and neurophysiological context.

Supplying Cash-Constrained Retailers: Understanding Shopkeeper Behavior at the Bottom of the Pyramid

Problem definition: As the largest retail channel in the world, nanostores are the source of income for millions of shopkeepers in developing countries. Being cash constrained, nanostore shopkeepers face the challenge of deciding how much of their available cash to retain to support their families and how much to invest in acquiring products from their suppliers, who visit the nanostore with different frequencies and offer products with different margins. We study how shopkeepers deal with this challenge while seeking to make their nanostores financially sustainable. Methodology/results: We conduct an empirical study to explain how suppliers’ visit frequency influences shopkeepers’ ordering behavior. The study integrates more than 29 million orders placed with a multinational manufacturer. Results show that visit frequency adjustments by the supplier cause nanostores to experience a significant discontinuity in their orders such that reduced order frequency leads to significantly lower orders per unit of time. Building on this insight, we ran two experimental studies that make use of theoretical benchmarks to explore how shopkeepers’ ordering decisions are influenced by supplier visit frequency, product margin, and shopkeepers’ cash constraints. We analyze how and when shopkeepers deviate from the theoretical benchmarks and show how the available budget moderates the relationship between suppliers’ visit frequencies and shopkeepers’ order volumes, normalized by unit of time. Our results give support to the theory of diversification bias and show how this bias might bring additional benefits to suppliers offering low-margin products. Finally, we propose a behavioral model that explains how shopkeepers’ decisions might be driven by their preference toward inventory (i.e., leftover versus shortage aversion) and toward cash (i.e., liquidity preferences). Managerial implications: Our findings suggest that suppliers should deploy high-frequency visits to cash-constrained nanostores regardless of the margin that their products provide for shopkeepers. For low-margin suppliers, this mitigates shopkeepers’ tendencies to reduce the order volume per unit of time following a visit frequency decrease. For high-margin suppliers, high-frequency visits mitigate shopkeepers’ overdiversification bias and liquidity preference. Financial and digital schemes that reduce the need for high-frequency visits may lead to substantial efficiency gains. Supplemental Material: The online appendix is available at https://doi.org/10.1287/msom.2022.0362 .

Application of water cycle algorithm with demand follows green level and nonlinear power pattern of the product for an inventory system

It is commonly known that a number of variables, including price, supply levels, time, and green level, affect how quickly certain things are in demand. Furthermore, the inventory carrying cost is considered to be a nonlinear representation of time and is subject to variation throughout time. More precisely, it rises with time since longer storage times necessitate more costly warehouse space. This study presents a fully backlogged situation inventory system for a single commodity where the product’s selling price, green level, and time are used to simultaneously compute the demand rate in accordance with a power pattern. Purchase price is determined by the product’s nonlinear green level. Complete backorders are available for shortages. The impact of the product’s selling price, green level and time power function are combined to determine the product’s demand. Moreover, the holding cost also rises as the product is stored for a longer period of time. The primary goal is to determine the best inventory policy to maximise total profit per unit of time. Though the problem is highly nonlinear in nature. Hence, we cannot solve it analytically. To overcome these difficulties, we have applied several well-known popular metaheuristic algorithms (Water Cycle Algorithm (WCA), Artificial Electric Field Algorithm (AEFA), Teaching Learning Based Optimization Algorithm (TLBOA), Grey Wolf Optimizer Algorithm (GWOA), Sparrow Search Algorithm (SSA), Whale Optimizer Algorithm (WOA), Prairie Dog Optimization Algorithm (PDOA), Gazelle Optimization Algorithm (GOA), A Sinh Cosh Optimizer Algorithm (SCHOA) and White Sherk Optimizer Algorithm (WSOA), Archimedes Optimization Paradigm Algorithm (AOPA), Marine Predator Optimization Algorithm (MPOA), Geyser Inspired Algorithm (GIA), Runge Kutta Optimization Algorithm (RKOA), Lungs Performance-based Optimization Algorithm (LPOA) and Dwarf Mongoose Optimization Algorithm (DMOA)). It is observed that WCA perform better than other algorithms with respect to the convergence rate. A numerical example is taken in order to validate the proposed model. Finally, a post optimality analysis is performed in order to make a fruitful conclusion.

One respiratory cycle as a minimum time unit for making behavioral decisions in the mammalian olfactory system.

Voluntary behaviors such as sniffing, moving, and eating require decision-making accompanied by intentional respiration. Based on the study of respiration-coherent activity of rodent olfactory networks, we infer that during the inhalation phase of respiration, olfactory cortical areas process environmental odor information and transmit it to the higher multisensory cognitive areas via feedforward pathways to comprehensively evaluate the surrounding situation. We also infer that during the exhalation phase, the higher multisensory areas generate cognitive-signals and transmit them not only to the behavioral output system but also back to the olfactory cortical areas. We presume that the cortical mechanism couples the intentional respiration with the voluntary behaviors. Thus, in one respiratory cycle, the mammalian brain may transmit and process sensory information to cognize and evaluate the multisensory image of the external world, leading to one behavioral decision and one emotional expression. In this perspective article, we propose that one respiratory cycle provides a minimum time unit for decision making during wakefulness.

The Quantization Monte Carlo method for solving radiative transport equations

We introduce the Quantization Monte Carlo method to solve thermal radiative transport equations with possibly several collision regimes, ranging from few collisions to massive number of collisions per time unit. For each particle in a given simulation cell, the proposed method advances the time by replacing many collisions with sampling directly from the escape distribution of the particle. In order to perform the sampling, for each triplet of parameters (opacity, remaining time, initial position in the cell) on a parameter grid, the escape distribution is precomputed offline and only the quantiles are retained. The online computation samples only from this quantized (i.e., discrete) version by choosing a parameter triplet on the grid (close to actual particle’s parameters) and returning at random one quantile from the precomputed set of quantiles for that parameter. We first check numerically that the escape laws depend smoothly on the parameters and then implement the procedure on a benchmark with good results.

Application Of Microbubble Technology To Increase Oxygen Content In The Aquaculture Of Tambaqui (Colossoma Macropomum)

The oxygen consumed is used to oxidize food substances to produce energy. Therefore, the metabolic rate is usually indicated by the rate of oxygen consumption per time unit. In most intensive aquaculture systems, oxygen content is widely used due to the high stocking density and maximum feeding rates. This study aimed to evaluate the growth performance of freshwater pomfret (Colossoma macropomum) treated with increased oxygen in two different systems. The study was conducted in a laboratory scale by applying T-test data analysis. The difference of treatment level applied in the comparison of aeration and microbubble consists of two treatments and three replications. The researcher used commercial floating pellets PF0 with 25% protein content, the feeding rate was 5% of the biomass. The feed was given daily during the culture for two times a day. Data of the study were analyzed using T-test on the Statistical Product and Service Solutions (SPSS) software Version 17.0. Results of the study showed that aeration system using microbubble resulted in dissolved oxygen (DO) of 6.5 ± 0.17, 100% pomfret fish survival rate (SR), Survival Growth Rate (SGR) of 1.83 ± 0.24, Feed Conversion Ratio (FCR) of 1.37 ± 0.17, Protein Efficiency Ratio (PER) of 2.79 ± 0.37, Feed Utilization Efficiency (FUE) of 0.67 ± 0.09, Absolute Weight Growth of 52.02 ± 1.60; it was better than using aeration. This study has developed a model of freshwater pomfret fish aquaculture to provide growth value.

Simulasi Metode Monte Carlo untuk Mengatur Sistem Antrian Truk

This research focuses on optimizing pozzolan truck queue management at PT. Danas Putra Mandiri through the application of the Monte Carlo method. The main objective of this research is to develop and implement a simulation application that is able to predict and calculate the average availability of trucks in a certain time unit, with the ultimate goal of increasing the company's operational efficiency. In industries that rely on the transportation of raw materials such as pozzolan, effective truck queue management is key to avoiding distribution delays and reducing operational costs. A queue is a service from one or more services that is caused by the need for services exceeding the capacity of the service or service facilities, so that customers who arrive cannot immediately receive service due to busyness in the service. The method used in this research is the Monte Carlo method. Monte Carlo is an experiment of various elements of probability using random samples. The Monte Carlo method is useful for solving quantitative problems with real or physical processing. This method has the ability to simulate and manage queues that occur in companies. PT Danas Putra Mandiri is one of the companies operating in the mining sector which supplies pozzolan to PT Semen Padang. The pozzolan delivery process uses trucks. The delivery process using trucks can affect the availability of the number of trucks in the company. The data used in this research is data from January 2023 - December 2023 with a total of 1619 data. Data taken through the admin of PT. DANAS PUTRA MANDIRI. Based on simulation predictions of queues on trucks, results were obtained with an average accuracy of 80.6%. The queuing simulation results show that the application of the Monte Carlo method can effectively reduce truck waiting times and increase the availability of trucks for rental, which ultimately contributes to increasing the company's operational efficiency.

Determination of Demand for LNG in Poland

This study was aimed at improving the energy efficiency of the distribution of liquefied natural gas (LNG) as shipping fuel in the southern Baltic Sea. The objective of this study was to determine the demand for LNG for maritime shipping by analyzing the distribution of the resource from the water side (ship-to-ship). LNG was chosen due to the location of the LNG terminal in Świnoujście within the analyzed water area, where a problem has arisen in the southern part of the Baltic Sea regarding fuel supply for vessels due to the lack of developed infrastructure along the coast. An analysis was conducted to optimize the size of the LNG fleet and infrastructure facilities. Seeking compliance with Annex VI to the MARPOL 73/78 Convention, adopted by the International Maritime Organization (IMO), shipowners see potential in the switch from conventional fuels to LNG. As one of the alternative solutions, it will contribute to reducing harmful emissions. Determination of the LNG distribution volume requires the identification of LNG storage facility locations, specifying the number of LNG-powered ships (broken down by type) and the number of LNG bunkering ships. The first part of this study contains a detailed analysis of the number of sea-going ships that provide services in the southern part of the Baltic Sea and the world’s number of LNG bunkering ships. The database contains a set of the characteristics required to determine the optimal demand for LNG, where LNG bunkering vessels are capable of supplying fuel within the shortest possible time and covering the shortest possible distance to LNG-powered ships. The characteristics include the type of ship, requested LNG volume, the speed of LNG bunkering ships, the distance between LNG facilities, and the loading rate (the volume of fuel received per time unit). Based on the collected data, the volume of LNG distribution was determined using MATLAB R2019a software. The remainder of this study contains a description of the conducted research and results of an analysis of the traffic density in the Baltic Sea. The results were obtained on the basis of data from the Statistical Yearbook of Maritime Economy and IALA IWRAP Mk2 2020 software. The number of LNG-powered ships and number of LNG bunkering ships were specified, and the demand for LNG for the area under analysis was determined.

Technical Note: Dynamic Duopolistic Competition with Sticky Prices

Steven Heston of the R.H. Smith School of Business at the University of Maryland, College Park, and Bo Hu of the Costello College of Business, George Mason University, resolve a puzzle about industrial competition in “Dynamic Duopolistic Competition with Sticky Prices.” Previous papers analyze the effect of frictions on competition in continuous time, such as sticky prices or transactions costs. Those papers conclude that miniscule frictions have a large impact on market prices and quantities. However, Heston and Hu show that these conclusions are an artifact of the continuous-time math. Heston and Hu use discrete-time models to analyze the effect of frictions that are small per unit of time. These models show the effect of small frictions is small.

Estimation of the difference between peritoneal microenvironment and core body temperature during laparoscopic surgery – a prospective observational study

Maintaining patients’ temperature during surgery is beneficial since hypothermia has been linked with perioperative complications. Laparoscopic surgery involves the insufflation of carbon dioxide (CO2) into the peritoneal cavity and has become the standard in many surgical indications since it is associated with better and faster recovery. However, the use of cold and dry CO2 insufflation can lead to perioperative hypothermia. We aimed to assess the difference between intraperitoneal and core temperatures during laparoscopic surgery and evaluate the influence of duration and CO2 insufflation volume by fitting a mixed generalized additive model. In this prospective observational single-center cohort trial, we included patients aged over 17 with American Society of Anesthesiology risk scores I to III undergoing laparoscopic surgery. Anesthesia, ventilation, and analgesia followed standard protocols, while patients received active warming using blankets and warmed fluids. Temperature data, CO2 ventilation parameters, and intraabdominal pressure were collected. We recruited 51 patients. The core temperature was maintained above 36 °C and progressively raised toward 37 °C as pneumoperitoneum time passed. In contrast, the intraperitoneal temperature decreased, thus creating a widening difference from 0.4 [25th–75th percentile: 0.2–0.8] °C at the beginning to 2.3 [2.1–2.3] °C after 240 min. Pneumoperitoneum duration and CO2 insufflation volume significantly increased this temperature difference (P < 0.001 for both parameters). Core vs. intraperitoneal temperature difference increased linearly by 0.01 T °C per minute of pneumoperitoneum time up to 120 min and then 0.05 T °C per minute. Each insufflated liter per unit of time, i.e. every 10 min, increased the temperature difference by approximately 0.009 T °C. Our findings highlight the impact of pneumoperitoneum duration and CO2 insufflation volume on the difference between core and intraperitoneal temperatures. Implementing adequate external warming during laparoscopic surgery effectively maintains core temperature despite the use of dry and unwarmed CO2 gases, but peritoneal hypothermia remains a concern, suggesting the importance of further research into regional effects.Trial registration: Clinicaltrials.gov: NCT04294758.

Optimization approaches to Wolbachia-based biocontrol

This paper proposes two realistic and biologically viable methods for Wolbachia-based biocontrol of Aedes aegypti mosquitoes, assuming imperfect maternal transmission of the Wolbachia bacterium, incomplete cytoplasmic incompatibility, and direct loss of Wolbachia infection caused by thermal stress. Both methods are based on optimization approaches and allow for the stable persistence of Wolbachia-infected mosquitoes in the wild Ae. aegypti populations in a minimum time and using the smallest quantity of Wolbachia-carrying insects to release. The first method stems from the continuous-time optimal release strategy, which is further transformed into a sequence of suboptimal impulses mimicking instantaneous releases of Wolbachia-carrying insects. The second method constitutes a novel alternative to all existing techniques aimed at the design of release strategies. It is developed using metaheuristics (ϵ-constraint method combined with the genetic algorithm) and directly produces a discrete sequence of decisions, where each element represents the quantity of Wolbachia-carrying mosquitoes to be released instantaneously and only once per a specified time unit. It turns out that a direct discrete-time optimization (second method) renders better quantifiable results compared to transforming a continuous-time optimal release function into a sequence of suboptimal impulses (first method). As an illustration, we provide examples of daily, weekly, and fortnightly release strategies designed by both methods for two Wolbachia strains, wMel and wMelPop.

Discontinuous hyperpolarization schemes in liquid-state overhauser dynamic nuclear polarization experiments

Liquid-state Overhauser Dynamic Nuclear Polarization (ODNP) is an emerging technique, aimed at shortening NMR experiment times. It achieves this by increasing the otherwise poor nuclear polarization at room temperature, by polarization transfer from excited electron spins. The present work explores two ideas, aimed at achieving the optimal signal-to-noise per time unit for a given system, and quantitation of spectra showing a large disparity in ODNP enhancements at high magnetic fields (≥ 9.4 T). Both of these ideas are predicated on, perhaps counterintuitively, not allowing full dynamic nuclear polarization to build up, either by rapid rf pulsing, or gating of the microwave irradiation.

Cycle-frequency content EEG analysis improves the assessment of respiratory-related cortical activity

Objective. Time-frequency (T-F) analysis of electroencephalographic (EEG) is a common technique to characterise spectral changes in neural activity. This study explores the limitations of utilizing conventional spectral techniques in examining cyclic event-related cortical activities due to challenges, including high inter-trial variability.Approach. Introducing the cycle-frequency (C-F) analysis, we aim to enhance the evaluation of cycle-locked respiratory events. For synthetic EEG that mimicked cycle-locked pre-motor activity, C-F had more accurate frequency and time localization compared to conventional T-F analysis, even for a significantly reduced number of trials and a variability of breathing rhythm.Main results. Preliminary validations using real EEG data during both unloaded breathing and loaded breathing (that evokes pre-motor activity) suggest potential benefits of using the C-F method, particularly in normalizing time units to cyclic activity phases and refining baseline placement and duration.Significance. The proposed approach could provide new insights for the study of rhythmic neural activities, complementing T-F analysis.

Capacity planning in logistics corridors: Deep reinforcement learning for the dynamic stochastic temporal bin packing problem

This paper addresses the challenge of managing uncertainty in the daily capacity planning of a terminal in a corridor-based logistics system. Corridor-based logistics systems facilitate the exchange of freight between two distinct regions, usually involving industrial and logistics clusters. In this context, we introduce the dynamic stochastic temporal bin packing problem. It models the assignment of individual containers to carriers’ trucks over discrete time units in real-time. We formulate it as a Markov decision process (MDP). Two distinguishing characteristics of our problem are the stochastic nature of the time-dependent availability of containers, i.e., container delays, and the continuous-time, or dynamic, aspect of the planning, where a container announcement may occur at any time moment during the planning horizon. We introduce an innovative real-time planning algorithm based on Proximal Policy Optimization (PPO), a Deep Reinforcement Learning (DRL) method, to allocate individual containers to eligible carriers in real-time. In addition, we propose some practical heuristics and two novel rolling-horizon batch-planning methods based on (stochastic) mixed-integer programming (MIP), which can be interpreted as computational information relaxation bounds because they delay decision making. The results show that our proposed DRL method outperforms the practical heuristics and effectively scales to larger-sized problems as opposed to the stochastic MIP-based approach, making our DRL method a practically appealing solution.

Evaluation of an experimental prototype in an aerodynamic tunnel with characteristics of a small wind turbine with two blades

The experimental consideration of wind turbines in an aerodynamic tunnel is a powerful alternative to determine the performance of these equipment, both to assimilate their behavior in operation, and to identify constructive and aerodynamic aspects, aiming to convert these characteristics to the real turbine. In the understanding described in this work, the aerodynamic characteristics of the prototype were evaluated, the static torque for the different flow speeds and the power coefficient was estimated based on the concept of variation in the amplitude of movement of the drying when crossing the turbine at different speeds of the flow. The results are compared with the values achieved analytically. The research theory predicted the aerodynamic and structural event, investing numerical and experimental tools. The present work constituted the first experimental event to evaluate the aerodynamic performance of the research project. At this stage, the experimental assessment of static torque was carried out, analyzing the aerodynamic wake of the prototype and determining the RPM curves for the attached wind speeds. The perspective is that it will be possible to describe a new process to, based on the correlation coefficients, determine a curve of the amount of energy granted by a source in a reliable unit of time through simulation or prototyping before starting the survey of real module equipment.