Start Location Research Articles

Periodic morphing of a NACA6409 aerofoil in ground effect, its wake mechanisms and thrust generation

Abstract The camber morphing of an aerofoil in ground effect was investigated using the FishBAC method and Detached Eddy Simulations with the k-omega SST turbulence model at a Reynolds number of 320,000. The aerofoil was periodically morphed at a start location of 25% chord from the leading edge with a trailing edge deflection range of 0.1% to 3% and morphing frequencies between a Strouhal number of 0.45 to 4 at a constant ground clearance of 10%. Periodically morphing the aerofoil using a sinusoidal function showed that lift and drag increased on the downstroke and decreased on the upstroke in the cycle, resulting in periodic values of lift and drag throughout the cycle. The amplitude of lift and drag increased as the morphing frequency and/or trailing edge deflection increased. It was found that the wake characteristics varied as a function of trailing edge deflection and morphing frequency. For small trailing edge deflections below 0.4% and frequencies below a 2.2 Strouhal number, Kelvin Helmholtz shedding was observed, and above this the wake became chaotic. Large trailing edge deflections showed Von-Karman shedding, where the interaction between the lower counter-clockwise vortex and the ground plane resulted in a jet-like flow that caused forward thrust. For the maximum deflection and morphing frequency tested in this study, reversed Von-Karman shedding was observed, which caused forward thrust from the interaction of the two-shedding counter-rotating vortices. Von-Karman or reversed Von-Karman shedding shows positive thrust generation, however, chaotic shedding should be avoided due to large drag gains. Varying the Reynolds number caused the Strouhal number to change as they depend on the same variables. It was found that the Strouhal number variation had a large effect on the wake, however, the Reynolds number had a minimal effect.

Effect of Trailing Edge and Span Morphing on the Performance of an Optimised NACA6409 Wing in Ground Effect

Abstract A CFD investigation was carried out on a three-dimensional NACA6409 wing in ground effect at a Reynolds number of 320,000. Using a Multi-Objective-SHERPA algorithm, a root angle of 4 degrees angle of attack, a tip angle of 6 degrees, a forward sweep and a tip chord of 20% of the root chord produced an optimum aerodynamic efficiency across all ground clearances. Optimisation showed a gain in aerodynamic efficiency of 41% at h/c = 0.05 ground clearance and a 31% gain at h/c = 0.2 compared to a rectangular planform wing. Next FishBAC camber morphing was applied to the optimised wing varying the morphing start locations along the chord at both the tip and root. A later start location produced the highest aerodynamic efficiency but increased manufacturing complexity. Extendable span morphing was also tested and was found increasing the span from 1c to 1.5c increased the aerodynamic efficiency of the optimised wing by 27.4% at h/c = 0.1. Varying the span from 0.8c to 1.2c in ground effect had a small effect on the drag for small ground clearances, for large ground clearances the total drag decreased as the span increased. Smaller gains were seen when the span morphing was applied to the rectangular wing. The FishBAC morphing was applied in the spanwise direction to morph the wing tip, sealing the flow beneath the wing. Also, the proportion of the morphing wing tip caused the trailing edge to be closer to the ground further enhancing ground effect.

Cognitive Mapping of Reachable Space in Humans

Reinforcement learning illustrates how people represent and learn large-scale environments for spatial navigation. However, it is unclear how “reachable” spaces are represented when performing manual tasks like chopping vegetables. To analyze the mechanisms underlying learning reachable space, we used inspiration from a recent study (de Cothi et al., 2022) to develop a haptic maze task where human participants reached to a target while avoiding invisible haptic obstacles in the environment. Participants reached with a robotic handle, generating contact forces to simulate the maze boundary, obstacle walls, and the floor, which supported the hand. Participants were blind to their hand position, while the target and maze boundary remained visible. Two conditions were implemented for each experiment variation; maze obstacles were invisible in the first condition, then became visible in the second. Audio feedback was given upon finding the target. We tested participants in 25 unique mazes, performing 10 trials within each maze. Two experimental procedures were employed: one with a fixed target and varying starting locations (18 participants), while the other had a fixed starting location and variable targets (10 participants). We simulated and compared the likelihoods of 3 different reinforcement learning models: model-based (MB), model-free (MF) and successor representation (SR). MB-simulated agents learned a map of the maze, planning the shortest route to the target. MF-simulated agents cached and updated the expected total future reward for each action using experience, pursuing the highest-reward actions. SR-simulated agents generated a “cognitive” (predictive) map between grids (states), integrating it with the target to plan actions. Results suggested that humans use a combination of MB and MF to learn reachable spaces. MB agents had higher likelihoods (better represented human behaviour) than MF agents for earlier trials, but MF had greater likelihoods for later trials. Meanwhile, SR learning was significantly worse at predicting participant behaviour.

An Efficient Approach for Shortest Path Planning in Automotive Navigation System

Emerging automotive engineering solutions extensively rely on navigation tools. Shortest path evaluation is core competency of optimal path planning, one of the most critical component of any navigation application. Fibonacci heap variant of state-of-the-art Dijkstra’s algorithm solve single source shortest path problems in asymptotic O(E + V log V) worst-case time. In spite Fibonacci heap is theoretically dominant in time efficiency, it might not work much better empirically with real-world data, because although large and intricate, the real-world road network graphs are sparse and the graph topology greatly affects the performance of the algorithms. SGO algorithm, a more efficient approch to address single source shortest path problem, is applied to solve optimal path planning problem in automotive navigation applications. Each road segment of the road graph leads to a particular location thus will hold unique distance values all the time. The SGO calculates the total distance from start location to the end of the each road segment and evaluates shortest path based on these calculations. The SGO algorithm and the Fibonacci and Binary heap variants of Dijkstra’s algorithm were tested on real-world intercity and intra-city road network graphs to evaluate running time efficiency of the algorithms in automotive navigation systems. The experimental results show that the SGO algorithm outperformed both the variants. The empirical superiority of the SGO algorithm suggests its usefulness in automotive navigation systems.

Recording Neural Reward Signals in a Naturalistic Operant Task Using Mobile-EEG and Augmented Reality.

The electrophysiological response to rewards recorded during laboratory tasks has been well documented, yet little is known about the neural response patterns in a more naturalistic setting. Here, we combined a mobile-EEG system with an augmented reality headset to record event-related brain potentials (ERPs) while participants engaged in a naturalistic operant task to find rewards. Twenty-five participants were asked to navigate toward a west or east goal location marked by floating orbs, and once participants reached the goal location, the orb would then signify a reward (5 cents) or no-reward (0 cents) outcome. Following the outcome, participants returned to a start location marked by floating purple rings, and once standing in the middle, a 3 s counter signaled the next trial, for a total of 200 trials. Consistent with previous research, reward feedback evoked the reward positivity, an ERP component believed to index the sensitivity of the anterior cingulate cortex to reward prediction error signals. The reward positivity peaked ∼230 ms with a maximal at channel FCz (M = -0.695 μV, ±0.23) and was significantly different than zero (p < 0.01). Participants took ∼3.38 s to reach the goal location and exhibited a general lose-shift (68.3% ±3.5) response strategy and posterror slowing. Overall, these novel findings provide support for the idea that combining mobile-EEG with augmented reality technology is a feasible solution to enhance the ecological validity of human electrophysiological studies of goal-directed behavior and a step toward a new era of human cognitive neuroscience research that blurs the line between laboratory and reality.

Distance and grid-like codes support the navigation of abstract social space in the human brain.

People form impressions about others during daily social encounters and infer personality traits from others' behaviors. Such trait inference is thought to rely on two universal dimensions: competence and warmth. These two dimensions can be used to construct a 'social cognitive map' organizing massive information obtained from social encounters efficiently. Originating from spatial cognition, the neural codes supporting the representation and navigation of spatial cognitive maps have been widely studied. Recent studies suggest similar neural mechanism subserves the map-like architecture in social cognition as well. Here we investigated how spatial codes operate beyond the physical environment and support the representation and navigation of social cognitive map. We designed a social value space defined by two dimensions of competence and warmth. Behaviorally, participants were able to navigate to a learned location from random starting locations in this abstract social space. At the neural level, we identified the representation of distance in the precuneus, fusiform gyrus, and middle occipital gyrus. We also found partial evidence of grid-like representation patterns in the medial prefrontal cortex and entorhinal cortex. Moreover, the intensity of grid-like response scaled with the performance of navigating in social space and social avoidance trait scores. Our findings suggest a neurocognitive mechanism by which social information can be organized into a structured representation, namely cognitive map and its relevance to social well-being.

Recent shortening of the mature tropical cyclone stage over the western North Pacific

AbstractThis study investigates long‐term trends related to the mature stage duration of tropical cyclones (TCs) over the western North Pacific (WNP) during June–November between 1982 and 2021. There is a significant shortening in the mean duration of the WNP mature TC stage, which is defined as the period when a TC is within 5 kt of its lifetime maximum intensity. This shortening is induced by a significant (weak) poleward migration in the starting (ending) location of the WNP mature TC stage, which can be further explained by changes in environmental conditions. From 1982 to 2021, there have been significant increases in maximum potential intensity and 700–500‐hPa relative humidity over most of the WNP, which has broadly expanded the TC‐favorable area poleward. Consequently, WNP TCs can start their mature stages and reach their lifetime maximum intensities at higher latitudes. By contrast, there are only weak changes in 850‐hPa relative vorticity and 850–200‐hPa vertical wind shear (VWS). Given the dominant role of VWS in modulating the TC weakening rate, the TC‐suppressive area over the subtropical WNP has shown lesser changes, thus leading to insignificant changes in the ending location of the mature TC stage.

A Fault Diagnosis Method for Ultrasonic Flow Meters Based on KPCA-CLSSA-SVM

To enhance the fault diagnosis capability for ultrasonic liquid flow meters and refine the fault diagnosis accuracy of support vector machines, we employ Levy flight to augment the global search proficiency. By utilizing circle chaotic mapping to establish the starting locations of sparrows and refining the sparrow position with the highest fitness value, we propose an enhanced sparrow search algorithm termed CLSSA. Subsequently, we optimize the parameters of support vector machines using this algorithm. A support vector machine classifier based on CLSSA has been constructed. Given the intricate data collected from ultrasonic liquid flow meters for diagnostic purposes, the approach of employing KPCA to decrease data dimensionality is implemented, and a KPCA-CLSSA-SVM algorithm is proposed to achieve fault diagnosis in ultrasonic flow meters. By using UCI datasets, the findings indicate that KPCA-CLSSA-SVM achieves fault diagnosis accuracies of 94.12%, 100.00%, 97.30%, and 100% in the four flow meters, respectively. Compared with the Bayesian classifier diagnostic algorithm, this has been increased by 4.18%. And compared with support vector machine diagnostic algorithms improved by the SSA, it has increased by 2.28%.

Selection of the Shortest Path of Gamalama Eruption Evacuation Using the Astar Method

Natural disasters have no time limit and can happen anywhere at any moment. They put human life at risk and can result in both property loss and casualties. Like those at Gamalama volcano, volcanic eruption disasters are constant and incur enormous losses. There are numerous evacuation sites for self-rescue against volcanic eruptions, and knowing the distance to the evacuation sites is necessary to expedite the self-rescue process to reduce the number of victims. Naturally, the areas surrounding the disaster center must be kept clear. To solve this issue, a mechanism for choosing the shortest path via the astar method must be implemented. As part of the astar method, the shortest path is chosen by first figuring out the starting and destination locations, initializing intersections by turning each intersection into a node, adding the node to the open list, calculating it with weights, adding it to the closed list, figuring out potential nodes that lead to the destination, and saving the results. As a consequence of the master method's application, the shortest route—a distance of 3,560 meters—connects the starting point (the Tubo village head office) with the gathering point (the Salerno field). Bypassing every pre-planned path, which is as follows: No. 1 (Jl. Ake Tubo, Tubo) - Node 2 (Ake et al.) - Node 7 (Jl. Batu Angus No.1, Dufa Dufa) - Node 8 (North Sangaji) - Node 9 (Toboleu, Kec. Kota Ternate Utara) - Assembly Point (Salero Field). Keywords: Astar, Volcanic Disaster, and Geographic Information System.

Assessing Flash Characteristics in Lightning-Initiated Wildfire Events between 1995 and 2020 within the Contiguous United States

Abstract Twenty-six years of lightning data were paired with over 68 000 lightning-initiated wildfire (LIW) reports to understand lightning flash characteristics responsible for ignition in between 1995 and 2020. Results indicate that 92% of LIW were started by negative cloud-to-ground (CG) lightning flashes and 57% were single stroke flashes. Moreover, 62% of LIW reports did not have a positive CG within 10 km of the start location, contrary to the science literature’s suggestion that positive CG flashes are a dominant fire-starting mechanism. Nearly ⅓ of wildfire events were holdovers, meaning 1 or more days elapsed between lightning occurrence and fire report. However, fires that were reported less than a day after lightning occurrence statistically burned more acreage. Peak current was not found to be a statistically significant delineator between fire starters and non–fire starters for negative CGs but was for positive CGs. Results highlighted the need for reassessing the role of positive CG lightning and subsequently long-continuing current in wildfire ignition started by lightning. One potential outcome of this study’s results is the development of real-time tools to identify ignition potential during lightning events to aid in fire mitigation efforts.

Maze runners: monkeys show restricted Arabic numeral summation during computerized two-arm maze performance

Mazes have been used in many forms to provide compelling results showcasing nonhuman animals’ capacities for spatial navigation, planning, and numerical competence. The current study presented computerized two-arm mazes to four rhesus macaques. Using these mazes, we assessed whether the monkeys could maximize rewards by overcoming mild delays in gratification and sum the values of Arabic numerals. Across four test phases, monkeys used a joystick controller to choose one of two maze arms on the screen. Each maze arm contained zero, one or two Arabic numerals, and any numerals in the chosen maze arm provided the monkeys with rewards equivalent to the value of those numerals. When deciding which arm to enter, monkeys had to consider distance to numerals and numeral value. In some tests, gaining the maximum reward required summing the value of two numerals within a given arm. All four monkeys successfully maximized reward when comparing single numerals and when comparing arms that each contained two numerals. However, some biases occurred that were suboptimal: the largest single numeral and the delay of reward (by placing numerals farther into an arm from the start location) sometimes interfered with the monkeys’ abilities to optimize. These results indicate that monkeys experience difficulties with inhibition toward single, high valence stimuli in tasks where those stimuli must be considered in relation to overall value when represented by symbolic stimuli such as numerals.

Interplay of wayfinding strategies in route repetition and route retracing.

This study explores the interplay of navigation strategies in route repetition (repeating a recently travelled route) and route retracing (returning to the start location of a recently travelled route). Specifically, we investigated how sequence knowledge contributes to route repetition and retracing. In the learning phase, participants passively transported along a route. In the test phase, they were then asked to repeat or retrace the route. Decision points were either presented in an order coherent with the learning phase (from start to destination in route repetition, or from destination to start in route retracing), or in a randomised order. As expected, participants performed better in route repetition than in route retracing. Performance declined when intersections were presented in a randomised order indicating that sequence knowledge contributed to route repetition and route retracing. Presenting intersections in an order coherent with learning boosted performance specifically on the first part of the route during route repetition. This effect was not observed during route retracing. These results show that sequence knowledge is utilised differently during route repetition and retracing. We argue that participants use a "sequence of turns" strategy alongside associating landmarks with direction changes during route repetition, and that it is unlikely that route retracing relies on the same type of sequence knowledge. Instead, we believe route retracing utilises knowledge about the sequence in which decision points are encountered. Overall, the findings highlight a complex interplay of different strategies in route repetition and retracing, shedding light on how navigators utilise sequence knowledge for effective navigation.

Route retracing: way pointing and multiple vector memories in trail-following ants.

Maintaining positional estimates of goal locations is a fundamental task for navigating animals. Diverse animal groups, including both vertebrates and invertebrates, can accomplish this through path integration. During path integration, navigators integrate movement changes, tracking both distance and direction, to generate a spatial estimate of their start location, or global vector, allowing efficient direct return travel without retracing the outbound route. In ants, path integration is accomplished through the coupling of pedometer and celestial compass estimates. Within path integration, it has been theorized navigators may use multiple vector memories for way pointing. However, in many instances, these navigators may instead be homing via view alignment. Here, we present evidence that trail-following ants can attend to segments of their global vector to retrace their non-straight pheromone trails, without the confound of familiar views. Veromessor pergandei foragers navigate to directionally distinct intermediate sites via path integration by orienting along separate legs of their inbound route at unfamiliar locations, indicating these changes are not triggered by familiar external cues, but by vector state. These findings contrast with path integration as a singular memory estimate in ants and underscore the system's ability to way point to intermediate goals along the inbound route via multiple vector memories, akin to trapline foraging in bees visiting multiple flower patches. We discuss how reliance on non-straight pheromone-marked trails may support attending to separate vectors to remain on the pheromone rather than attempting straight-line shortcuts back to the nest.

An Efficient Image Encryption Method Based on Enhanced Josephus Problem and a Non-Invertible Economic Map

As an increasing number of digital images are created and transmitted over the internet, there is growing concern over their unauthorized use, which has a big impact on both security and privacy concerns. In this research, we provide a fast and secure image encryption scheme by using an enhanced Josephus problem and two-dimensional non-invertible economic chaotic map (2D-ECM) to safely and covertly protect digital image information throughout public channel transmission. First, the initial values of the 2D-ECM map are generated based on Secure Hash Algorithm (SHA-256) and the input secret key. Then, the Josephus problem is enhanced by substituting the extract operation with location exchange and dynamic start location and step size is employed to scramble the image pixels. In order to integrate the confusion process and diffusion process, the enhanced Josephus problem is utilized indirectly to choose two random columns from the scrambled image and random image to XOR them with the current column. The evaluation results prove that the proposed image cryptosystem is more efficient compared to existing cryptosystems.

Automatic peak detection algorithm based on continuous wavelet transform for complex chromatograms from multi-detector micro-scale gas chromatographs

Peak detection for chromatograms, including the detection of peak retention times, peak start locations, and peak end locations, is an important processing step for extracting peak information that is used for chemical recognition. Compared to benchtop gas chromatographs, the chromatograms generated by microscale gas chromatographs (µGCs) often contain higher noise levels, peak overlap, peak asymmetry, and both positive and negative chromatographic peaks, increasing the challenges for peak detection. This paper reports an automatic peak detection algorithm based on continuous wavelet transform (CWT) for chromatograms generated by multi-detector µGCs. The relationship between chemical retention time and peak width is leveraged to differentiate chromatographic peaks from noise and baseline drift. Special features in the CWT coefficients are leveraged to detect peak overlap and asymmetry. For certain detectors that may generate positive and negative chromatographic peaks, the peaks cannot be independently detected reliably, but the peak information can be well extracted using peak information generated by other in-line single-polarity detectors. The implemented algorithm provided a true positive rate of 97.2 % and false discovery rate of 7.8 % for chromatograms generated by a µGC with three integrated detectors, two capacitive and one photoionization. The chromatograms included complex scenarios with positive and negative chromatographic peaks, up to five consecutive overlapping peaks, peak asymmetry factor up to 24, and signal-to-noise ratios spanning 9-2800.

Tugboat Scheduling with Multiple Berthing Bases under Uncertainty

This study proposes a novel fuzzy programming optimization model for tugboat scheduling, directly considering multiple berthing bases, time windows, and operational uncertainties. The uncertainties in the required number of tugboats, the earliest start time, the latest start time, the processing time, and the start and end locations of each task are directly captured in the proposed fuzzy optimization model. The objective of the presented formulation is to minimize the total cost of fuel and delays. According to the characteristics of the problem, a Grey Wolf Optimization algorithm based on random probability encoding and custom genetic operators is proposed. The proposed algorithm, LINGO, the canonical Grey Wolf Optimization algorithm, and particle swarm optimization were used to compare and analyze the results of several examples. The results validate the efficiency of the proposed algorithm against the alternative exact and metaheuristics methods. Moreover, the findings from the conducted sensitivity analysis show the applicability of the developed fuzzy programming model for different confidence interval levels.

Toward Online Mobile Facility Location on General Metrics

We introduce an online variant of mobile facility location (MFL) (introduced by Demaine et al. (SODA 258–267 2007)). We call this new problem online mobile facility location (OMFL). In the OMFL problem, initially, we are given a set of k mobile facilities with their starting locations. One by one, requests are added. After each request arrives, one can make some changes to the facility locations before the subsequent request arrives. Each request is always assigned to the nearest facility. The cost of this assignment is the distance from the request to the facility. The objective is to minimize the total cost, which consists of the relocation cost of facilities and the distance cost of requests to their nearest facilities. We provide a lower bound for the OMFL problem that even holds on uniform metrics. A natural approach to solve the OMFL problem for general metric spaces is to utilize hierarchically well-separated trees (HSTs) and directly solve the OMFL problem on HSTs. In this paper, we provide the first step in this direction by solving a generalized variant of the OMFL problem on uniform metrics that we call G-OMFL. We devise a simple deterministic online algorithm and provide a tight analysis for the algorithm. The second step remains an open question. Inspired by the k-server problem, we introduce a new variant of the OMFL problem that focuses solely on minimizing movement cost. We refer to this variant as M-OMFL. Additionally, we provide a lower bound for M-OMFL that is applicable even on uniform metrics.

On the meeting of random walks on random DFA

We consider two random walks evolving synchronously on a random out-regular graph of n vertices with bounded out-degree r≥2, also known as a random Deterministic Finite Automaton (DFA). We show that, with high probability with respect to the generation of the graph, the meeting time of the two walks is stochastically dominated by a geometric random variable of rate (1+o(1))n−1, uniformly over their starting locations. Further, we prove that this upper bound is typically tight, i.e., it is also a lower bound when the locations of the two walks are selected uniformly at random. Our work takes inspiration from a recent conjecture by Fish and Reyzin (2017) in the context of computational learning, the connection with which is discussed.

Activity‐based model based on multi‐day cellular data: Considering the lack of personal attributes and activity type

AbstractCellular data is a sequence of base station‐interaction data that records user ID, timestamp, location area code (LAC), and cell identity (CID). With long observation periods, the data allows traffic planners to analyze coarse‐granularity user travel behaviours at low costs. However, utilizing cellular data for urban planning is not an easy task as the data lacks user socioeconomic attributes due to privacy issues. The data is also challenging to recognize user activity types. This paper proposed an activity‐based model (ABM) with skeleton schedule constraints for multi‐day cellular data. The model first infers the activity pattern and home location. Then it predicts start time, duration, and locations separately for primary and secondary activities. Next, the model infers the travel mode and path considering user multi‐day travel behaviour, path non‐linear coefficient, and transfers. Finally, a time adjustment module is proposed to avoid time conflicts in consecutive activities. The proposed activity‐based model is validated at activity, travel, and path levels. Results show that the proposed model can effectively predict activities and has much higher stability than existing ABMs based on travel surveys.

Dynamic CFD modeling of calcination in a rotary lime kiln with an external dryer

Mid-kiln ring formation is a problem in lime kilns that may be related to fluctuations in the start location of calcination. To calculate fluctuations in bed and gas temperature profiles within a lime kiln with an external dryer, a dynamic two-dimensional (2D) axisymmetric computational fluid dynamics (CFD) gas model with a methane burner implemented in ANSYS Fluent, coupled by mass and heat balances to a one-dimensional (1D) bed model, was developed. The dynamic model was used to calculate changes in the location where calcination starts with fluctuations in operational conditions using pulp mill data. This model simulates radiative, convective, and conductive heat transfer between the gas, wall, and bed to determine the axial bed temperature in the kiln. The calcination reaction is described using a shrinking core model that allows for the prediction of the location at which calcination begins and the degree of calcination achieved. The solid motion within the kiln is modeled using Kramer’s equation modified for transient response. Steady-state and dynamic simulation results were compared to data from an industrial dry lime kiln, and good agreement was found. A sensitivity analysis was also performed to provide insight on how operating conditions and model variables impact the calcination location and degree of calcination. Of the variables examined, the fuel rate and the feed temperature had the largest impact on both the calcination location and degree of calcination in the kiln. Model predictions of a period of ring formation in the industrial kiln showed that the start location of calcination fluctuated by more than 2 m on either side of the mean of regular operation, warranting further investigation of the importance of these fluctuations on mid-kiln ring formation.