Abstract. Existing process-based models for simulating coastal foredune evolution largely use the same analytical approach estimating wind-induced surface shear stress distributions over spatially variable topography. Originally developed smooth, low-sloping hills, these face significant limitations when topography of interest exhibits large height-to-length ratios and/or steep, localized features. In this work, we utilize computational fluid dynamics (CFD) to examine error trends a commonly...

In this study, the Bragg–Hawthorne equation (BHE) is extended in context of a steady, inviscid and compressible fluid, thus leading to an assortment partial differential equations that must be solved simultaneously. A solution pursued by implementing Rayleigh–Janzen expansion square reference Mach number. The corresponding formulation subsequently used derive approximation for Trkalian model bidirectional vortex. approximate compared representative computational fluid dynamics simulation...

The goal of this study is to leverage emerging machine learning (ML) techniques develop a framework for the global reconstruction system variables from potentially scarce and noisy observations explore epistemic uncertainty these models. This work demonstrates utility exploiting stochasticity dropout batch normalization schemes infer estimates super-resolved field sparse sensor measurements. A Voronoi tessellation strategy used obtain structured-grid representation observations, thus...

In this work, an exact inviscid solution is introduced for the incompressible Euler equation in context of a bidirectional, cyclonic vortex right-cylindrical chamber with hollow core. The presence gaseous core restricts flow domain to annular region that extends into toroid three dimensional space. procedure we follow based on Bragg-Hawthorne framework, which used conjunction unique assortment boundary conditions mirror large part those entailed derivation comparably complex-lamellar mean...

Abstract. Existing process-based models for simulating coastal foredune evolution largely use the same analytical approach estimating wind induced surface shear stress distributions over spatially variable topography. Originally developed smooth, low-sloping hills, these face significant limitations when topography of interest exhibits large height-to-length ratios and/or steep, localized features. In this work, we utilize computational fluid dynamics (CFD) to examine error trends a commonly...

The increasing deployment of AI in critical sectors necessitates advancements explainable (XAI) to ensure transparency and trustworthiness decisions. This paper introduces a novel methodology that leverages the Virtual Environmental Simulation for Physics-based Analysis (VESPA) framework conjunction with Randomized Input Sampling Explanation (RISE) provide enhanced explainability models, particularly complex simulated environments. VESPA, known its high-fidelity, physics-based simulations...

Model reduction for fluid flow simulation continues to be of great interest across a number scientific and engineering fields. In previous work [1], we explored the use Neural Ordinary Differential Equations (NODE) as non-intrusive method propagating latent-space dynamics in reduced order models. Here, investigate employing deep autoencoders discovering basis representation, which are then approximated by NODE. The ability represent is compared traditional proper orthogonal decomposition...

<h2>Abstract</h2> Modern reduced order models (ROMs) have widespread applicability in computational science and engineering as they allow accurate simulation of complex, nonlinear problems with minimal cost. In this paper, we introduce a Python-based implementation suite data-driven ROM techniques for dynamical systems governed by time-dependent, partial differential equations (PDEs). The versatility accuracy the presented frameworks been demonstrated various numerical experiments multiple...

This work considers the wall-injected swirling motions evolving inside a right-cylindrical solid rocket motor (SRM) with and without headwall injection and, alternatively, hybrid engine (HRE) an axisymmetric oxidizer showerhead at its forward closure. The bulk gaseous motion is modeled as non-reactive, inviscid flow swirl velocity component that increases linearly along axis of chamber. Our approach initiated from compressible Bragg-Hawthorne equation, which systematically solved using...

In this work, two mathematical procedures are used to derive the incompressible mean flow profile in a simulated solid rocket motor that is modeled as spinning right-cylindrical porous tube. The first approach starts with Navier–Stokes equations and leads large wall-injection Reynolds number approximation. second begins inviscid Bragg–Hawthorne equation, where variation of stagnation head taken reproduce classical Taylor–Culick approximation for nonspinning rockets. To permit swirl develop,...

This paper introduces an innovative quadrupole vortex flowfield in the context of a simulated, right-cylindrical, hybrid rocket engine. By analogy to diamond-impregnated tricone roller bits used oil-extraction industry, counter-rotating set vortices produces highly efficient fluid drill that leads substantial increases grain erosion at propellant surface. Other effects include increased mixing between hot combustion products core region and turbulent boundary layer flame surface, improved...

Autonomous vehicles (AVs) employ a wide range of sensing modalities including LiDAR, radar, RGB cameras, and more recently infrared (IR) sensors. IR sensors are becoming an increasingly common component AVs' sensor packages to provide redundancy enhanced capabilities in conditions that adverse for other types For example, while cameras sensitive lighting LiDAR performance is degraded inclement weather such as rain, unaffected by can contribute additional meaningful information weather. The...

Training object detection algorithms to operate in complex geo-environments remains a significant challenge, necessitating large and diverse datasets (i.e., unique backgrounds conditions) that are not always readily available. Physically generating requisite data can also be both cost time prohibitive depending on the object(s) area(s) of interest, especially case multi-spectral hyper-spectral imagery. Thus, there is increasing interest use synthetic supplement existing physical datasets. To...

In this work, an exact inviscid solution is developed for the incompressible Euler equations in context of a bidirectional, cyclonic flowfield right-cylindrical chamber with hollow core. The presence core confines flow domain to annular swirling region that extends into toroid three-dimensional space. procedure we follow based on Bragg–Hawthorne framework and judicious assortment boundary conditions correspond wall-bounded motion cylindrical At outset, self-similar stream function obtained...

This work considers a uniquely configured swirling motion that develops inside porous tube due to sidewall injection. The bulk fluid is modeled as steady inviscid Trkalian flow field with swirl-velocity component increases linearly along the axis of chamber. underlying procedure consists solving compressible Bragg–Hawthorne equation using Rayleigh–Janzen expansion produces closed-form approximation for stream function. Based on latter, most remaining attributes may be readily inferred....

The United State Army Corp of Engineers (USACE) Engineering Research and Development Center (ERDC) has developed a suite computational tools called the Computational Test Bed (CTB) for advanced high-fidelity physics-based autonomous vehicle sensor environment simulations. These provide insights into onboard navigation, image processing, fusion techniques, rapid data generation artificial intelligence machine learning techniques across full spectrum (visible, NIR, MWIR, LWIR) various...

Model reduction for fluid flow simulation continues to be of great interest across a number scientific and engineering fields. In previous work [arXiv:2104.13962], we explored the use Neural Ordinary Differential Equations (NODE) as non-intrusive method propagating latent-space dynamics in reduced order models. Here, investigate employing deep autoencoders discovering basis representation, which are then approximated by NODE. The ability represent is compared traditional proper orthogonal...