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At least 883 records · Page 49

Automatic numerical generation of body-fitted curvilinear coordinate system for field containing any number of arbitrary two-dimensional bodies

A method for automatic numerical generation of a general curvilinear coordinate system with coordinate lines coincident with all boundaries of a general multi-connected region containing any number of arbitrarily shaped bodies is presented. With this procedure the numerical solution of a partial differential system may be done on a fixed rectangular field with a square mesh with no interpolation required regardless of the shape of the physical boundaries, regardless of the spacing of the curvilinear coordinate lines in the physical field, and regardless of the movement of the coordinate system. Numerical solutions for the lifting and nonlifting potential flow about Joukowski and Karman-Trefftz airfoils using this coordinate system generation show excellent comparison with the analytic solutions. The application to fields with multiple bodies is illustrated by a potential flow solution for multiple airfoils.

Thompson, J. F.↗

Algebraic Generation Of Two-Dimensional Grids

Two-Boundary Grid Generation (TBGG) computer program applies interactive algebraic grid-generation technique in two dimensions. Incorporates algebraic equations that relate uniform rectangular computational domain to arbitrary physical domain. Hermite cubic interpolation performed between fixed, nonintersecting boundaries. Applied to variety of problems in which discrete techniques used to solve such partial differential equations as governing equations of fluid flow. Written in FORTRAN 77.

Smith, Robert E.↗

Measurement of a surface heat flux and temperature

The Heat Flux Microsensor is a new sensor which was recently patented by Virginia Tech and is just starting to be marketed by Vatell Corp. The sensor is made using the thin-film microfabrication techniques directly on the material that is to be measured. It consists of several thin-film layers forming a differential thermopile across a thermal resistance layer. The measured heat flux q is proportional to the temperature difference across the resistance layer q= k(sub g)/delta(sub g) x (t(sub 1) - T(sub 2)), where k(sub g) is the thermal conductivity and delta (sub g) is the thickness of the thermal resistance layer. Because the gages are sputter coated directly onto the surface, their total thickness is less than 2 micrometers, which is two orders of magnitude thinner than previous gages. The resulting temperature difference across the thermal resistance layer (delta is less than 1 micrometer) is very small even at high heat fluxes. To generate a measurable signal many thermocouple pairs are put in series to form a differential thermopile. The combination of series thermocouple junctions and thin-film design creates a gage with very attractive characteristics. It is not only physically non-intrusive to the flow, but also causes minimal disruption of the surface temperature. Because it is so thin, the response time is less than 20 microsec. Consequently, the frequency response is flat from 0 to over 50 kHz. Moreover, the signal of the Heat Flux Microsensor is directly proportional to the heat flux. Therefore, it can easily be used in both steady and transient flows, and it measures both the steady and unsteady components of the surface heat flux. A version of the Heat Flux Microsensor has been developed to meet the harsh demands of combustion environments. These gages use platinum and platinum-10 percent rhodium as the thermoelectric materials. The thermal resistance layer is silicon monoxide and a protective coating of Al2O3 is deposited on top of the sensor. The superimposed thin-film pattern of all six layers is presented. The large pads are for connection with pins used to bring the signal out the back of the ceramic. In addition to the heat flux measurement, the surface temperature is measured with a platinum resistance layer (RTS). The resistance of this layer increases with increasing temperature. Therefore, these gages simultaneously measure the surface temperature and heat flux. The demonstrated applications include rocket nozzles, SCRAM jet engines, gas turbine engines, boiling heat transfer, flame experiments, basic fluid heat transfer, hypersonic flight, and shock tube testing. The laboratory involves using one of these sensors in a small combustion flame. The sensor is made on a 2.5 cm diameter piece of aluminum nitride ceramic.

Davis, R. M.↗

Cell Culture in Microgravity: Opening the Door to Space Cell Biology

Adaptational response of human cell populations to microgravity is investigated using simulation, short-term Shuttle experiments, and long-term microgravity. Simulation consists of a clinostatically-rotated cell culture system. The system is a horizontally-rotated cylinder completely filled with culture medium. Low speed rotation results in continuous-fall of the cells through the fluid medium. In this setting, cells: 1) aggregate, 2) propagate in three dimensions, 3) synthesize matrix, 4) differentiate, and 5) form sinusoids that facilitate mass transfer. Space cell culture is conducted in flight bioreactors and in static incubators. Cells grown in microgravity are: bovine cartilage, promyelocytic leukemia, kidney proximal tubule cells, adrenal medulla, breast and colon cancer, and endothelium. Cells were cultured in space to test specific hypotheses. Cartilage cells were used to determine structural differences in cartilage grown in space compared to ground-based bioreactors. Results from a 130-day experiment on Mir revealed that cartilage grown in space was substantially more compressible due to insufficient glycosaminoglycan in the matrix. Interestingly, earth-grown cartilage conformed better to the dimensions of the scaffolding material, while the Mir specimens were spherical. The other cell populations are currently being analyzed for cell surface properties, gene expression, and differentiation. Results suggest that some cells spontaneously differentiate in microgravity. Additionally, vast changes in gene expression may occur in response to microgravity. In conclusion, the transition to microgravity may constitute a physical perturbation in cells resulting in unique gene expressions, the consequences of which may be useful in tissue engineering, disease modeling, and space cell biology.

Pellis, Neal R.↗

Manufacture of porous biodegradable polymer conduits by an extrusion process for guided tissue regeneration

We have fabricated porous, biodegradable tubular conduits for guided tissue regeneration using a combined solvent casting and extrusion technique. The biodegradable polymers used in this study were poly(DL-lactic-co-glycolic acid) (PLGA) and poly(L-lactic acid) (PLLA). A polymer/salt composite was first prepared by a solvent casting process. After drying, the composite was extruded to form a tubular construct. The salt particles in the construct were then leached out leaving a conduit with an open-pore structure. PLGA was studied as a model polymer to analyze the effects of salt weight fraction, salt particle size, and processing temperature on porosity and pore size of the extruded conduits. The porosity and pore size were found to increase with increasing salt weight fraction. Increasing the salt particle size increased the pore diameter but did not affect the porosity. High extrusion temperatures decreased the pore diameter without altering the porosity. Greater decrease in molecular weight was observed for conduits manufactured at higher temperatures. The mechanical properties of both PLGA and PLLA conduits were tested after degradation in vitro for up to 8 weeks. The modulus and failure strength of PLLA conduits were approximately 10 times higher than those of PLGA conduits. Failure strain was similar for both conduits. After degradation for 8 weeks, the molecular weights of the PLGA and PLLA conduits decreased to 38% and 43% of the initial values, respectively. However, both conduits maintained their shape and did not collapse. The PLGA also remained amorphous throughout the time course, while the crystallinity of PLLA increased from 5.2% to 11.5%. The potential of seeding the conduits with cells for transplantation or with biodegradable polymer microparticles for drug delivery was also tested with dyed microspheres. These porous tubular structures hold great promise for the regeneration of tissues which require tubular scaffolds such as peripheral nerve, long bone, intestine, or blood vessel.

NASA Discipline Cell Biology↗

Efficacy of compression of different capacitance beds in the amelioration of orthostatic hypotension

Orthostatic hypotension (OH) is the most disabling and serious manifestation of adrenergic failure, occurring in the autonomic neuropathies, pure autonomic failure (PAF) and multiple system atrophy (MSA). No specific treatment is currently available for most etiologies of OH. A reduction in venous capacity, secondary to some physical counter maneuvers (e.g., squatting or leg crossing), or the use of compressive garments, can ameliorate OH. However, there is little information on the differential efficacy, or the mechanisms of improvement, engendered by compression of specific capacitance beds. We therefore evaluated the efficacy of compression of specific compartments (calves, thighs, low abdomen, calves and thighs, and all compartments combined), using a modified antigravity suit, on the end-points of orthostatic blood pressure, and symptoms of orthostatic intolerance. Fourteen patients (PAF, n = 9; MSA, n = 3; diabetic autonomic neuropathy, n = 2; five males and nine females) with clinical OH were studied. The mean age was 62 years (range 31-78). The mean +/- SEM orthostatic systolic blood pressure when all compartments were compressed was 115.9 +/- 7.4 mmHg, significantly improved (p < 0.001) over the head-up tilt value without compression of 89.6 +/- 7.0 mmHg. The abdomen was the only single compartment whose compression significantly reduced OH (p < 0.005). There was a significant increase of peripheral resistance index (PRI) with compression of abdomen (p < 0.001) or all compartments (p < 0.001); end-diastolic index and cardiac index did not change. We conclude that denervation increases vascular capacity, and that venous compression improves OH by reducing this capacity and increasing PRI. Compression of all compartments is the most efficacious, followed by abdominal compression, whereas leg compression alone was less effective, presumably reflecting the large capacity of the abdomen relative to the legs.

Non-NASA Center↗

The Effect of Basis Selection on Static and Random Acoustic Response Prediction Using a Nonlinear Modal Simulation

An investigation of the effect of basis selection on geometric nonlinear response prediction using a reduced-order nonlinear modal simulation is presented. The accuracy is dictated by the selection of the basis used to determine the nonlinear modal stiffness. This study considers a suite of available bases including bending modes only, bending and membrane modes, coupled bending and companion modes, and uncoupled bending and companion modes. The nonlinear modal simulation presented is broadly applicable and is demonstrated for nonlinear quasi-static and random acoustic response of flat beam and plate structures with isotropic material properties. Reduced-order analysis predictions are compared with those made using a numerical simulation in physical degrees-of-freedom to quantify the error associated with the selected modal bases. Bending and membrane responses are separately presented to help differentiate the bases.

Rizzi, Stephen A.↗

When and why PINNs fail to train: A neural tangent kernel perspective

Physics-informed neural networks (PINNs) have lately received great attention thanks to their flexibility in tackling a wide range of forward and inverse problems involving partial differential equations. However, despite their noticeable empirical success, little is known about how such constrained neural networks behave during their training via gradient descent. More importantly, even less is known about why such models sometimes fail to train at all. Here in this work, we aim to investigate these questions through the lens of the Neural Tangent Kernel (NTK); a kernel that captures the behavior of fully-connected neural networks in the infinite width limit during training via gradient descent. Specifically, we derive the NTK of PINNs and prove that, under appropriate conditions, it converges to a deterministic kernel that stays constant during training in the infinite-width limit. This allows us to analyze the training dynamics of PINNs through the lens of their limiting NTK and find a remarkable discrepancy in the convergence rate of the different loss components contributing to the total training error. To address this fundamental pathology, we propose a novel gradient descent algorithm that utilizes the eigenvalues of the NTK to adaptively calibrate the convergence rate of the total training error. Finally, we perform a series of numerical experiments to verify the correctness of our theory and the practical effectiveness of the proposed algorithms.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

Abundance and properties of dark radiation from the cosmic microwave background

We study the cosmological signatures of new light relics that are collisionless like standard neutrinos or are strongly interacting. We provide a simple and succinct rephrasing of their physical effects in the cosmic microwave background, as well as the resulting parameter degeneracies with other cosmological parameters, in terms of the total radiation abundance and the fraction thereof that freely streams. In these more general terms, interacting and noninteracting light relics are differentiated by their respective decrease and increase of the free-streaming fraction, and, moreover, the scale-dependent interplay thereof with a common, correlated reduction of the fraction of matter in baryons. We then derive updated constraints on various dark-radiation scenarios with the latest cosmological observations, employing this language to identify the physical origin of the impact of each dataset. The “PR4” reanalyses of Planck CMB data prefer a larger primordial helium yield and therefore also slightly more radiation than the 2018 analysis; we investigate the differences between the two releases that drives these shifts. Smaller free-streaming fractions are disfavored by the excess lensing of the CMB measured in lensing reconstruction data from Planck and the Atacama Cosmology Telescope. On the other hand, baryon acoustic oscillation measurements from the Dark Energy Spectroscopic Instrument drive marginal detections of new, strongly interacting light relics due to that data's preference for lower matter fractions. Finally, we forecast measurements from the CMB-S4 experiment.

cosmological parameters from CMBR↗

Physics-Informed Sparse Gaussian Process for Probabilistic Stability Analysis of Large-Scale Power System with Dynamic PVs and Loads

This work proposes a physics-informed sparse Gaussian process (SGP) for probabilistic stability assessment of large-scale power systems in the presence of uncertain dynamic PVs and loads. The differential and algebraic equations considering uncertainties from dynamic PVs and loads are reformulated to a nonlinear mapping relationship that allows the application of SGP. Thanks to the nonparametric characteristic of Gaussian process, the proposed framework does not require distributions of uncertain inputs and this distinguishes it from existing approaches. As the original Gaussian process is not scalable to large-scale systems with high dimensional uncertain inputs, this paper develops the SGP with a stochastic variational inference technique. It leads to approximately two orders of complex reduction. A data pre-processing step is also introduced to tackle the coexistence of stable and unstable cases by sample clustering and constructing separate SGPs. The probabilistic transient stability index is analyzed to assess system stability under different uncertain dynamics loads and PVs. Comparisons are performed with the sampling-based, the polynomial chaos expansion-based, and traditional Gaussian process-based methods on the modified IEEE 118-bus and Texas 2000-bus systems under various scenarios, including different levels of uncertainties and the existence of nonlinear correlations among dynamic PVs. The impacts of data quality and quantity issues are also investigated. It is shown that the proposed SGP achieves significantly improved computational efficiency while maintaining high accuracy with a limited number of data.

24 POWER TRANSMISSION AND DISTRIBUTION↗

Radiative classical gravitational observables at $ \mathcal{O} $(G 3 ) from scattering amplitudes

We compute classical gravitational observables for the scattering of two spinless black holes in general relativity and N =8 supergravity in the formalism of Kosower, Maybee, and O’Connell (KMOC). We focus on the gravitational impulse with radiation reaction and the radiated momentum in black hole scattering at $ \mathcal{O} $(G 3 ) to all orders in the velocity. These classical observables require the construction and evaluation of certain loop-level quantities which are greatly simplified by harnessing recent advances from scattering amplitudes and collider physics. In particular, we make use of generalized unitarity to construct the relevant loop integrands, employ reverse unitarity, the method of regions, integration-by-parts (IBP), and (canonical) differential equations to simplify and evaluate all loop and phase-space integrals to obtain the classical gravitational observables of interest to two-loop order. The KMOC formalism naturally incorporates radiation effects which enables us to explore these classical quantities beyond the conservative two-body dynamics. From the impulse and the radiated momentum, we extract the scattering angle and the radiated energy. Finally, we discuss universality of the impulse in the high-energy limit and the relation to the eikonal phase.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

Deep energy-pressure regression for a thermodynamically consistent EOS model

Abstract In this paper, we aim to explore novel machine learning (ML) techniques to facilitate and accelerate the construction of universal equation-Of-State (EOS) models with a high accuracy while ensuring important thermodynamic consistency. When applying ML to fit a universal EOS model, there are two key requirements: (1) a high prediction accuracy to ensure precise estimation of relevant physics properties and (2) physical interpretability to support important physics-related downstream applications. We first identify a set of fundamental challenges from the accuracy perspective, including an extremely wide range of input/output space and highly sparse training data. We demonstrate that while a neural network (NN) model may fit the EOS data well, the black-box nature makes it difficult to provide physically interpretable results, leading to weak accountability of prediction results outside the training range and lack of guarantee to meet important thermodynamic consistency constraints. To this end, we propose a principled deep regression model that can be trained following a meta-learning style to predict the desired quantities with a high accuracy using scarce training data. We further introduce a uniquely designed kernel-based regularizer for accurate uncertainty quantification. An ensemble technique is leveraged to battle model overfitting with improved prediction stability. Auto-differentiation is conducted to verify that necessary thermodynamic consistency conditions are maintained. Our evaluation results show an excellent fit of the EOS table and the predicted values are ready to use for important physics-related tasks.

97 MATHEMATICS AND COMPUTING↗

Generalized fiducial inference on differentiable manifolds

We introduce a novel approach to inference on parameters that take values in a Riemannian manifold embedded in a Euclidean space. Parameter spaces of this form are ubiquitous across many fields, including chemistry, physics, computer graphics, and geology. Here, this new approach uses generalized fiducial inference (GFI) to obtain a posterior-like distribution on the manifold, without needing to know local parameterizations that map to the constrained space from an unconstrained Euclidean space. Using mathematical tools from Riemannian geometry, we construct a constrained generalized fiducial distribution (CGFD). A Bernstein-von Mises-type result for the CGFD, which provides intuition for how the desirable asymptotic qualities of the unconstrained generalized fiducial distribution are inherited by the CGFD, is provided. To illustrate the practical use of the CGFD, we provide a proof-of-concept example in the context of a linear logspline density estimation problem, and demonstrate that CGFD-based confidence sets exhibit desirable coverage properties via simulation. As an application, we fit a CGFD to COVID-19 case count data from North Carolina, USA.

97 MATHEMATICS AND COMPUTING↗

Engineering diamond interfaces free of dark spins

Nitrogen-vacancy (NV) centers in diamond are utilized extensively as quantum sensors for imaging fields at the nanoscale. The ultrahigh sensitivity of NV magnetometers has enabled the detection and spectroscopy of individual electron spins, with potentially far-reaching applications in condensed matter physics, spintronics, and molecular biology. However, the surfaces of these diamond sensors naturally contain electron spins, which create a background signal that can be hard to differentiate from the signal of the target spins. In this study, we develop a surface modification approach that eliminates the unwanted signal of these so-called dark electron spins. Our surface passivation technique, based on coating diamond surfaces with a thin titanium oxide (Ti⁢O 2 ) layer, reduces the dark spin density. The observed reduction in dark spin density aligns with our findings on the electronic structure of the diamond-Ti⁢O 2 interface. The reduction, from a typical value of 2000 μm −2 to a value below that set by the detection limit of our NV sensors (200 μm −2 ), results in a twofold increase in Hahn-echo coherence time of near surface NV centers. Furthermore, we derive a comprehensive spin model that connects dark spin relaxation with NV coherence, providing additional insights into the mechanisms behind the observed spin dynamics. Our findings are directly transferable to other quantum platforms, including nanoscale solid-state qubits and superconducting qubits.

74 ATOMIC AND MOLECULAR PHYSICS↗

Nonstandard finite difference schemes

The major research activities of this proposal center on the construction and analysis of nonstandard finite-difference schemes for ordinary and partial differential equations. In particular, we investigate schemes that either have zero truncation errors (exact schemes) or possess other significant features of importance for numerical integration. Our eventual goal is to bring these methods to bear on problems that arise in the modeling of various physical, engineering, and technological systems. At present, these efforts are extended in the direction of understanding the exact nature of these nonstandard procedures and extending their use to more complicated model equations. Our presentation will give a listing (obtained to date) of the nonstandard rules, their application to a number of linear and nonlinear, ordinary and partial differential equations. In certain cases, numerical results will be presented.

Mickens, Ronald E.↗

Bioelectric Control of a 757 Class High Fidelity Aircraft Simulation

This paper presents results of a recent experiment in fine grain Electromyographic (EMG) signal recognition, We demonstrate bioelectric flight control of 757 class simulation aircraft landing at San Francisco International Airport. The physical instrumentality of a pilot control stick is not used. A pilot closes a fist in empty air and performs control movements which are captured by a dry electrode array on the arm, analyzed and routed through a flight director permitting full pilot outer loop control of the simulation. A Vision Dome immersive display is used to create a VR world for the aircraft body mechanics and flight changes to pilot movements. Inner loop surfaces and differential aircraft thrust is controlled using a hybrid neural network architecture that combines a damage adaptive controller (Jorgensen 1998, Totah 1998) with a propulsion only based control system (Bull & Kaneshige 1997). Thus the 757 aircraft is not only being flown bioelectrically at the pilot level but also demonstrates damage adaptive neural network control permitting adaptation to severe changes in the physical flight characteristics of the aircraft at the inner loop level. To compensate for accident scenarios, the aircraft uses remaining control surface authority and differential thrust from the engines. To the best of our knowledge this is the first time real time bioelectric fine-grained control, differential thrust based control, and neural network damage adaptive control have been integrated into a single flight demonstration. The paper describes the EMG pattern recognition system and the bioelectric pattern recognition methodology.

Jorgensen, Charles↗

Autoregressive Neural Network for Simulating Open Quantum Systems via a Probabilistic Formulation

The theory of open quantum systems lays the foundation for a substantial part of modern research in quantum science and engineering. Rooted in the dimensionality of their extended Hilbert spaces, the high computational complexity of simulating open quantum systems calls for the development of strategies to approximate their dynamics. In this Letter, we present an approach for tackling open quantum system dynamics. Using an exact probabilistic formulation of quantum physics based on positive operator-valued measure, we compactly represent quantum states with autoregressive neural networks; such networks bring significant algorithmic flexibility due to efficient exact sampling and tractable density. We further introduce the concept of string states to partially restore the symmetry of the autoregressive neural network and improve the description of local correlations. Efficient algorithms have been developed to simulate the dynamics of the Liouvillian superoperator using a forward-backward trapezoid method and find the steady state via a variational formulation. Our approach is benchmarked on prototypical one-dimensional and two-dimensional systems, finding results which closely track the exact solution and achieve higher accuracy than alternative approaches based on using Markov chain Monte Carlo method to sample restricted Boltzmann machines. Our Letter provides general methods for understanding quantum dynamics in various contexts, as well as techniques for solving high-dimensional probabilistic differential equations in classical setups.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

The cosmological bootstrap: inflationary correlators from symmetries and singularities

Scattering amplitudes at weak coupling are highly constrained by Lorentz invariance, locality and unitarity, and depend on model details only through coupling constants and the particle content of the theory. For example, four-particle amplitudes are analytic for contact interactions and have simple poles with appropriately positive residues for tree-level exchange. In this paper, we develop an understanding of inflationary correlators which parallels that of flat-space scattering amplitudes. Specifically, we study slow-roll inflation with weak couplings to extra massive particles, for which all correlation functions are controlled by an approximate conformal symmetry on the boundary of the spacetime. After systematically classifying all possible contact terms in de Sitter space, we derive an analytic expression for the four-point function of conformally coupled scalars mediated by the tree-level exchange of massive scalars. Conformal symmetry implies that the correlator satisfies a pair of differential equations with respect to spatial momenta, encoding bulk time evolution in purely boundary terms. The absence of unphysical singularities (and the correct normalization of physical ones) completely fixes this correlator. Moreover, a “spin-raising” operator relates it to the correlators associated with the exchange of particles with spin, while “weight-shifting” operators map it to the four-point function of massless scalars. We explain how these de Sitter four-point functions can be perturbed to obtain inflationary three-point functions. Using our formalism, we reproduce many classic results in the literature, such as the three-point function of slow-roll inflation, and provide a complete classification of all inflationary three- and four-point functions arising from weakly broken conformal symmetry. Remarkably, the inflationary bispectrum associated with the exchange of particles with arbitrary spin is completely characterized by the soft limit of the simplest scalar-exchange four-point function of conformally coupled scalars and a series of contact terms. Finally, we demonstrate that the inflationary correlators contain flat-space scattering amplitudes via a suitable analytic continuation of the external momenta, which can also be directly connected with the signals for particle production seen in the squeezed limit.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗