Search results for: Velimir Monty Vesselinov

Authors: Velimir Monty Vesselinov, Trais Kliplhuis, Hope Jasperson

Abstract:

Artificial intelligence (AI) is a powerful tool in the geothermal energy sector, aiding in both exploration and utilization. Identifying promising geothermal sites can be challenging due to limited surface indicators and the need for expensive drilling to confirm subsurface resources. Geothermal reservoirs can be located deep underground and exhibit complex geological structures, making traditional exploration methods time-consuming and imprecise. AI algorithms can analyze vast datasets of geological, geophysical, and remote sensing data, including satellite imagery, seismic surveys, geochemistry, geology, etc. Machine learning algorithms can identify subtle patterns and relationships within this data, potentially revealing hidden geothermal potential in areas previously overlooked. To address these challenges, a SIML (Science-Informed Machine Learning) technology has been developed. SIML methods are different from traditional ML techniques. In both cases, the ML models are trained to predict the spatial distribution of an output (e.g., pressure, temperature, heat flux) based on a series of inputs (e.g., permeability, porosity, etc.). The traditional ML (a) relies on deep and wide neural networks (NNs) based on simple algebraic mappings to represent complex processes. In contrast, the SIML neurons incorporate complex mappings (including constitutive relationships and physics/chemistry models). This results in ML models that have a physical meaning and satisfy physics laws and constraints. The prototype of the developed software, called GeoTGO, is accessible through the cloud. Our software prototype demonstrates how different data sources can be made available for processing, executed demonstrative SIML analyses, and presents the results in a table and graphic form.

Keywords: science-informed machine learning, artificial inteligence, exploration, utilization, hidden geothermal

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Authors: Monty Vacura

Abstract:

Hamilton’s Rule is a universal law of biology expressed in protists, plants and animals. When applied to human populations, this model explains: 1) Origin of religion in society as a biopsychological need selected to increase population size; 2) Instincts of racism expressed through intergroup competition; 3) Simultaneous selection for human cooperation and conflict, love and hate; 4) Connection between sporting events and instinctive social messaging for stimulating offensive and defensive responses; 5) Pathway to reduce human sacrifice. This chapter discusses the deep psychological influences of Hamilton’s Rule. Suggestions are provided to reduce human deaths via our instinctive sacrificial behavior, by consciously monitoring Hamilton’s Rule variables highlighted throughout our media outlets.

Keywords: psychology, Hamilton’s rule, evolution, human instincts

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Authors: Monty Vacura

Abstract:

Hamilton’s Rule is a universal law of biology expressed in protists, plants and animals. When applied to human populations, this model explains: 1) Origin of religion in society as a biopsychological need naturally selected to increase population size; 2) Instincts of racism expressed through intergroup competition; 3) Simultaneous selection for human cooperation and conflict, love and hate; 4) Places Dawkins’s selfish gene as the r, relationship variable; 5) Flipping the equation variable themes (close relationship to distant relationship, and benefit to threat) the new equation can now be used to identify the offensive and defensive sides of conflict; 6) Connection between sporting events and instinctive social messaging for stimulating offensive and defensive responses; 6) Pathway to reduce human sacrifice through manipulation of variables. This paper discusses the deep psychological influences of Hamilton’s Rule. Suggestions are provided to reduce human deaths via our instinctive sacrificial behavior, by consciously monitoring Hamilton’s Rule variables highlighted throughout our media outlets.

Keywords: psychology, Hamilton’s rule, evolution, human instincts

Procedia PDF Downloads 62

Authors: Jason Monty, Nicholas Hutchins, Jelle Will, Isnain Aliman, I. Ketut Utama, Bagus Nugroho

Abstract:

Hydrodynamic resistance (drag) on a ship accounts for up to 90% of the vessel’s energy consumption. For most common ship geometries the resistance is primarily caused by skin friction drag resulting from the formation of a turbulent boundary layer owing to the relative motion of the ship hull and the water. This drag is increased by biofouling that inevitably grows on the hull during operation. Biofouling is known to increase drag by up to 80%, meaning significant excess fuel is used by fouled vessels, leading to excessive harmful greenhouse gas emissions. The International Maritime Organisation estimates the wasted energy at 7% on average across all ships in operation, taking into account the varying stages of fouling growth on the world’s fleet. This investigation aims to directly measure the impact of fouling on skin friction drag,for the first time, on a ship in operation. Through collaboration with our partners Samudera Indonesia and ITS Surabaya, we have instrumented an operating tanker in Indonesia with a laser Doppler anemometer measuring through a glass window in the flat bottom of the hull. This permits us to make measurements of the turbulent boundary layer to estimate drag accurately. At the same time, we have built a custom photogrammetry rig with unprecedented resolution to acquire a 3D topographical maps of the state of the hull. Therefore, we are able to correlate the measured skin friction drag with the hull state such that predictions can be made for lost fuel due to biofouling and other sources of roughness. Such a predictive capability would allow ship operators to make more informed decisions on hull cleaning and to better understand fuel use patterns. Our preliminary results show that even a freshly cleaned and recoated vessel coming from dry-dock can have a drag penalty exceeding 20% compared to an assumed hydrodynamically smooth hull, already significantly higher than the IMO estimate. Ongoing measurements over multiple years demonstrate the evolution of the hull state and the ship drag change with the onset of biofouling. This paper will present these data, along with the first ever high fidelity boundary layer measurements on an operating vessel. The methodology and challenges of conducting boundary layer measurements at sea and measuring biofouling accurately will also be discussed.

Keywords: boundary layers, ship performance, biofouling, ship drag

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