193 research outputs found
Preliminary noise assessment of aircraft with distributed electric propulsion
No full textElectric and hybrid-electric propulsion technologies are an increasingly attractive option for aviation stakeholders, providing more reliable and efficient power plants than traditional internal combustion engines, while reducing the dependency on fossil fuels, such as oil, whose value is volatile and availability uncertain. Combined with distributed electric propulsion (DEP), these propulsion technologies have shown significant potential in reducing civil aircraft noise emissions and are therefore viable candidates for delivering the strict mid-to-longterm environmental goals set by aviation organisations worldwide, such as ACARE and NASA. This paper examines the noise emission of a concept tube and wing aircraft that falls in the A320 category and features DEP systems using two different power supply units (turboshaft engines or batteries) and a varying number of propulsors. The transition of conventional propulsory systems to electric and hybrid systems is discussed, with Noise-Power-Distance (NPD) curves and noise exposure contour maps computed for several DEP systems and propulsor number configurations. Noise benefits of DEP especially at takeoff are demonstrated, whereas it is shown that based on predicted year 2035 entry into service technology, All Electric aircraft exhibit a larger noise footprint than aircraft using hybrid electric propulsion systems. Finally, our analysis indicates that the number of propulsors is a key parameter that may be used to optimise the environmental performance and noise benefits of DEP aircraft.</p
Framework for predicting Noise-Power-Distance curves for novel aircraft designs
No full textAlong with flight profiles, Noise-Power-Distance (NPD) curves are the key input variable for computing noise exposure contour maps around airports. With the development of novel aircraft designs (incorporating noise reduction technologies) and new noise abatement procedures, NPD datasets will be required for assessing their potential benefit in terms of noise reduction around airports. NPD curves are derived from aircraft flyover noise measurements taken for a range of aircraft configurations and engine power settings. Clearly then, empirical NPD curves will be unavailable for novel aircraft designs and novel operations. This paper presents a generic framework for computationally generating NPD curves for novel aircraft and situations. The new framework derives computationally the NPD noise levels that are normally derived experimentally, by estimating noise level variations arising from technological and operational changes with respect to a baseline scenario, where the noise levels are known, or otherwise estimated. The framework is independent of specific prediction methods and can use any potential new model for existing or new noise sources. The paper demonstrates the methodology of the framework, discusses its benefits and illustrates its applicability by deriving NPD curves for an unconventional approach operation and for a future concept blended-wing-body (BWB) aircraft
Towards estimating noise-power-distance curves for propeller powered zero emission hydrogen aircraft
No full textAs part of the UK Research and Innovation project New Aviation, Propulsion, Knowledge and Innovation Network (NAPKIN), a high-level framework was developed for the assessment of the noise impact of the proposed regional-sized hydrogen-powered aircraft. This study consists of the methodology used to generate the industry-standard noise–power–distance (NPD) curves from individual component noise analysis, specifically propeller tonal noise. The model is based on an asymptotic analysis of a frequency domain propeller tonal noise model combined with a linear approximation, taking advantage of the logarithmic nature of noise. An error analysis on the linear approximation assumption proves that the relative error between predicted and actual values of the noise remains below 10% for appropriately chosen baseline points. Verification of the framework was achieved through a bench-marking procedure that compared predictions of departure NPD curves for current technology regional aircraft against published ones over a range of operational power settings. Finally, departure and approach NPD predictions for three of the NAPKIN hydrogen concept aircraft are presented. Concepts featuring a larger, slower-rotating propeller with an increased number of blades relative to the reference aircraft showed benefits over the reference aircraft, despite, in some cases, increases in maximum takeoff weight
Closed-form analytical approach for calculating noise contours of directive aircraft noise sources
No full textTechnological, economic, and environmental prospects of all-electric aircraft
No full textEver since the Wright Brothers’ first powered flight in 1903, commercial aircraft have relied on liquid hydrocarbon fuels. However, the need for greenhouse gas emission reductions along with recent progress in battery technology for automobiles has generated strong interest in electric propulsion in aviation. This work provides a first-order assessment of the energy, economic, and environmental implications of all-electric aircraft. We show that batteries with significantly higher specific energy and lower cost, coupled with further reductions of costs and CO2 intensity of electricity, are necessary for exploiting the full range of economic and environmental benefits provided by all-electric aircraft. A global fleet of all-electric aircraft serving all flights up to a 400-600 nmi (741-1,111 km) distance would demand an equivalent of 0.6-1.7% of worldwide electricity consumption in 2015. Whereas lifecycle CO2 emissions of all-electric aircraft depend on the power generation mix, all direct combustion emissions and thus direct air pollutants and direct non-CO2 warming impacts would be eliminated
Parallel pulsed jets for precise underwater propulsion
No full textThesis: S.M., Massachusetts Institute of Technology, Department of Mechanical Engineering, 2016.This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.Cataloged from student-submitted PDF version of thesis.Includes bibliographical references (pages 95-98).A significant limitation for underwater robots is their ability to maneuver in tight spaces or for complex tracking tasks. Next generation vehicles require thrusters that can overcome this problem and efficiently provide high maneuverability at low speeds. Recently, thruster design has begun to draw inspiration from nature's swimmers, applying the principles of pulsed jet propulsion to robotic thrusters. Although most developments have focused on single jet actuators, nature provides some indications that multi-jet systems can provide propulsive benefits -- marine invertebrates called sales connect into chains of individual animals that each eject short jets to collaboratively move the entire chain efficiently around the ocean. However, despite the promise of multi-jet propulsion, there are no existing models or empirical data that explain the physics of multi-jet propulsion. As a result, there are no physically motivated rules to guide the design of man-made multi-jet thrusters. In this thesis, I experimentally investigate how interactions between neighboring jets in a multi-jet thruster will affect the system's propulsive performance. I use high-speed fluorescence imaging to investigate the mutual influence of two pulsed jets under conditions relevant to low-speed maneuvering in a vehicle (Re ~ 350). Using a new force estimation technique developed in this thesis, I analyze the video data to evaluate how thrust and efficiency are affected by the jet spacing. This analysis reveals that, compared to non-interacting jets, the efficiency and thrust generated by the pair of interacting jets can fall by nearly 10% as the jets are brought into close proximity. Based on this data, I develop a model of vortex interactions to explain the thrust and efficiency drop. The data and model described in this thesis contribute new insights to understand vortex formation in pulsed jets, and these results can be used to guide the design of multi-jet underwater propulsion systeby Athanasios G. Athanassiadis.S.M
Optical breakdown acoustics : transduction and sensing underwater
No full textThis electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.Thesis: Ph. D., Massachusetts Institute of Technology, Department of Mechanical Engineering, 2019Cataloged from student-submitted PDF version of thesis.Includes bibliographical references (pages 191-199).In the sea, infrastructures such as ships, pipelines, and wind turbines are exposed to harsh conditions that can wear down the structures through wave loading and corrosion. Because of these wear mechanisms, maritime structures require regular inspections to identify early signs of damage or fatigue. Currently, inspections are performed visually or with contact acoustic transducers, often by a human diver. However, these methods are slow and costly, and can be hindered by surface irregularities like biofouling. Therefore, new sensing techniques are needed to meet the rising demand for offshore infrastructure monitoring. In this thesis, I develop optical breakdown as an acoustic source for non-contact measurements of underwater structures. Optical breakdown occurs when a high-power laser is focused to a small spot, causing nonlinear interactions between the light and water. A compact plasma forms at the focus and expands explosively, radiating a loud, broadband pressure wave.Since this source is compact, laser-controlled and broadband, it provides unique sensing capabilities that overcome challenges faced by traditional transducers. First, I demonstrate how the breakdown source can be used to remotely measure the internal properties of submerged plates. The source is used to excite leaky Lamb waves in the plates, and broadband elastic dispersion spectra are measured using hydrophones in the water. The dispersion spectra are used to calculate the thicknesses and sound speeds in aluminum, steel, bronze and glass plates of varying thickness. Second, I characterize how the source can be controlled and scaled up by combining acoustic measurements with high-speed images of the breakdown plasma. In general, breakdown produces a loud (>100kPa at 10cm), ultra-broadband (5kHz-5MHz) source, whose characteristics depend on measurement orientation and laser properties.This transduction behavior is explained by modeling the breakdown plasma as an array of laser-driven explosions. When the laser is tightly focused, the plasma is compact, producing a loud and omnidirectional signal. However, for weak focusing and high energies, the plasma lengthens and becomes erratic, producing a weaker signal with less consistent behavior. These results reveal design challenges, tradeoffs and opportunities when adapting the breakdown source for dierent applications.by Athanasios G. Athanassiadis.Ph. D.Ph.D. Massachusetts Institute of Technology, Department of Mechanical Engineerin
Numerical study of sorption of asphalt binders on minerals
No full textDuring the production of asphalt mixes, specific functional groups of asphalt binder interact chemically with certain reactive sites on the surface of minerals forming compounds that enhance the material resistance to environmental effects. The thermodynamics of surface phenomena between various combinations of functional groups of minerals and asphalt binders has been studied for quite a long time but it remains extremely difficult to control the desired material properties in practice. In this study, the chemical thermodynamics that determine the sorption phenomena and subsequently the relative affinity of asphalt binders onto mineral particles were analysed numerically and discussed. A two-step sorption configuration is studied in a multi-physics tool including reaction-driven mass transport of free species (i.e., carboxylic acid diluted in binder) onto a reactive surface (i.e., calcium functionalized mineral). Based on this configuration, the mechanism of asphalt-mineral interaction was determined at different surface temperatures and reactivity characteristics (i.e., activation energy and reaction kinetics of adsorption). The sorption model is applicable for various scenarios of asphalt-mineral interactions, especially for functionalized surfaces, in which the reaction-driven distribution of concentrations of asphalt adsorbates on minerals can provide useful information once the energetic parameters are known.Pavement Engineerin
Chemo-mechanics of epoxy-asphalt
No full textPavements of enhanced longevity would be expected to withstand long-term traffic as well as varying environmental conditions reducing in this way the major maintenance needs. Considering also the adoption of long-term contracts by road authorities, long-life pavements have started to attract the interest of road contractors worldwide. New and relatively new binders specially designed to produce long-life pavements have been proposed to minimize the regular maintenance and reconstruction operations. Among others, one promising technology to reach this goal is the epoxy modified asphalt binder, or epoxy-asphalt. Nevertheless, the addition of epoxy resins to asphalt binders may result in materials of inferior properties. Thermosetting epoxy resins may not mix homogeneously in asphalt binders leading to immiscible or partially miscible binders, which are mostly phase-separated materials. The phase-separated epoxy-asphalt binders can become brittle and thus more prone to cracking, leading the pavements to fail. Only a few epoxy products are applicable in asphalt binders, and the knowledge of incorporating chemistry to develop miscible epoxy binders remains unknown. For this to be the case, this thesis aims to provide a fundamental approach to elucidate the chemical and physical processes that determine the phase behavior of these binders. A vital role in implementing the thermosetting epoxy-asphalt binders also plays the curing. To obtain fundamental insights into the material curing, multi-physics models and experimental methods are considered in this research to identify and assess the curing-induced changes of epoxy-asphalt. The development of rheological properties that reflect the material workability is determined by laboratory experiments and used as input to multi-physics simulations. As the ultimate scope of implementing the epoxy-asphalt is to increase the longevity of pavements, the oxidation-induced changes of epoxy-asphalt materials as a function of time are evaluated as well in this research to prove their high aging resistance for wearing courses. Within the same framework, emphasis is also given to the effect of epoxy-asphalt on the durability and mechanical performance characteristics of an asphalt concrete mix. In conclusion, this thesis contributes to elucidating the factors that determine the curing- and oxidation-induced changes of epoxy-asphalt and understanding what bears miscible binders. The modeling and experimental programs discussed throughout the thesis can help to provide the fundamental knowledge to design and develop new binders and binding systems of the desired properties and characteristics.Pavement Engineerin
Goat-CNN: A Lightweight Convolutional Neural Network for Pose-Independent Body Condition Score Estimation in Goats
No full text<p>Here we introduce the dataset utilized in our published paper entitled "<a href="https://www.sciencedirect.com/science/article/pii/S2666154324002114">Goat-CNN: A Lightweight Convolutional Neural Network for Pose-Independent Body Condition Score Estimation in Goats</a>".</p>
<p>Contained within the "bcs" folder are all the videos collected for this study. Each video file is named with a format denoting its respective details. The first number signifies the sequence of collection, the second denotes the ear tag, and the final figure represents the body condition score (BCS) value.</p>
<p>For example: "1_158734_2.50" indicates the first sampling of an animal with the ear tag "158734" and a BCS value of "2.50".</p>
<p>Additionally, we provide two Python scripts in this repository. The first script, "Video2Frame.py", facilitates the splitting of videos into individual frames. The second script, "Frames2npy.py", converts these frames into two numpy-friendly files with the extension ".npy". These files contain both the images ("X_train_bcs300.npy") and their corresponding labels ("Y_train_bcs300.npy").</p>
<p>Furthermore, for the convenience of swift experimentation, we have included the desired .npy files within the repository.</p>
<p>To load these files into your Python environment, you can use the following code snippet:</p>
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<div>th4figs = '/content/drive/MyDrive/compag_2023/'</div>
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<div>path4images = "/content/drive/MyDrive/CodeRefarm/datasets/BCS/X_train_bcs300.npy"</div>
<div>Xtrain = np.load(path4images)</div>
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<div>path4labels = "/content/drive/MyDrive/CodeRefarm/datasets/BCS/Y_train_bcs300.npy"</div>
<div>Ytrain = np.load(path4labels).astype(float)</div>
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<div>print("X train : ", Xtrain.shape)</div>
<div>print("Y train : ", Ytrain.shape)</div>
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<pre>X train : (5332, 300, 300, 3)
Y train : (5332,)<br>
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</div><p>If you want to cite this work you can use the following text:</p>
<p>@article{TEMENOS2024101174,<br>title = {Goat-CNN: A lightweight convolutional neural network for pose-independent body condition score estimation in goats},<br>journal = {Journal of Agriculture and Food Research},<br>volume = {16},<br>pages = {101174},<br>year = {2024},<br>issn = {2666-1543},<br>doi = {https://doi.org/10.1016/j.jafr.2024.101174},<br>url = {https://www.sciencedirect.com/science/article/pii/S2666154324002114},<br>author = {Anastasios Temenos and Athanasios Voulodimos and Vera Korelidou and Athanasios Gelasakis and Dimitrios Kalogeras and Anastasios Doulamis and Nikolaos Doulamis},<br>keywords = {Body condition score, Artificial intelligence, Convolutional neural network, Precision livestock farming, Goat, Animal, Signal processing, Computer vision},<br>abstract = {Modern livestock farming systems face the challenge of meeting the growing demand for dairy and meat products while ensuring the well-being of animals. Body Condition Scoring serves as a vital process for assessing the body reserves in animals, impacting their health, welfare, and productivity. However, traditional body condition score (BCS) evaluation methods via observation and palpation of specific anatomical regions are labor-intensive and subjective, hindering their widespread adoption. To address this issue, Precision Livestock Farming (PLF) techniques, particularly those involving Internet of Things (IoT) devices and artificial intelligence (AI), have emerged as promising solutions. In this work, we explore the use of AI, specifically Convolutional Neural Networks (CNNs), to automate the assessment of BCS in goats utilizing imagery data. Our model was trained on 5000 images illustrating the dorsal view of the backside of goats achieving an overall accuracy of 97.94 % which was the highest compared to other popular deep learning architectures from literature (e.g. VGG16, ResNet34, ResNet50, DenseNet, GoogleNet). The proposed custom CNN model for goat-specific BCS estimation overcomes the limitations of manual sketching, providing automatic region identification for BCS assessment. Moreover, it is a lightweight model specifically designed for seamless integration with IoT devices, allowing for efficient on-board processing via cameras. The model's pose-independent nature and adaptability to environmental constraints make it a valuable tool for efficient and sustainable goat farming. This research advances the application of AI as a precision livestock farming tool, contributing to the reinforcement of the animal welfare and productivity, and supporting evidence-based decision-making processes to increase farms' resilience.}<br>}</p>
<p>Anastasios Temenos, Athanasios Voulodimos, Vera Korelidou, Athanasios Gelasakis, Dimitrios Kalogeras, Anastasios Doulamis, Nikolaos Doulamis,<br>Goat-CNN: A lightweight convolutional neural network for pose-independent body condition score estimation in goats,<br>Journal of Agriculture and Food Research,<br>Volume 16,<br>2024,<br>101174,<br>ISSN 2666-1543,<br>https://doi.org/10.1016/j.jafr.2024.101174.<br>(https://www.sciencedirect.com/science/article/pii/S2666154324002114)<br>Abstract: Modern livestock farming systems face the challenge of meeting the growing demand for dairy and meat products while ensuring the well-being of animals. Body Condition Scoring serves as a vital process for assessing the body reserves in animals, impacting their health, welfare, and productivity. However, traditional body condition score (BCS) evaluation methods via observation and palpation of specific anatomical regions are labor-intensive and subjective, hindering their widespread adoption. To address this issue, Precision Livestock Farming (PLF) techniques, particularly those involving Internet of Things (IoT) devices and artificial intelligence (AI), have emerged as promising solutions. In this work, we explore the use of AI, specifically Convolutional Neural Networks (CNNs), to automate the assessment of BCS in goats utilizing imagery data. Our model was trained on 5000 images illustrating the dorsal view of the backside of goats achieving an overall accuracy of 97.94 % which was the highest compared to other popular deep learning architectures from literature (e.g. VGG16, ResNet34, ResNet50, DenseNet, GoogleNet). The proposed custom CNN model for goat-specific BCS estimation overcomes the limitations of manual sketching, providing automatic region identification for BCS assessment. Moreover, it is a lightweight model specifically designed for seamless integration with IoT devices, allowing for efficient on-board processing via cameras. The model's pose-independent nature and adaptability to environmental constraints make it a valuable tool for efficient and sustainable goat farming. This research advances the application of AI as a precision livestock farming tool, contributing to the reinforcement of the animal welfare and productivity, and supporting evidence-based decision-making processes to increase farms' resilience.<br>Keywords: Body condition score; Artificial intelligence; Convolutional neural network; Precision livestock farming; Goat; Animal; Signal processing; Computer vision</p>
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