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At least 955 records · Page 53

Predicting interface structure using the minima hopping method

Here, we adapt the minima hopping method (MHM) to the problem of interfacial structure prediction and apply it to study a canonical problem, the tilt grain boundaries in SrTiO 3 . Our method employs a hybrid approach by first exploring the potential energy surface (PES) of different grain boundary samplings with an empirical force field, among which the fifteen candidates with lower energies are then refined using ab initio density functional theory (DFT) calculations. During the exploratory stage, we bias the search using a local order parameter to primarily sample various reconstructions in the vicinity of the interface, while preserving the crystallinity of the bulk regions. We further enhance the search by incorporating initial structures with rigid body displacements to account for translational variations between bulk phases, enabling the MHM to effectively generate both stoichiometric and nonstoichiometric SrTiO 3 Σ⁢3(111)[110] and Σ⁢3(112)[110] grain boundaries. From an algorithmic standpoint, MHM outperforms earlier studies based on genetic algorithms (GA) by identifying more stable interfacial structures of several SrTiO 3 grain boundaries. The performance of the present implementation of the MHM approach is primarily limited by exploring an approximate description of the PES with a rather simple Buckingham potential. This limitation leads to variations in performance when compared to approaches utilizing more advanced surrogate PES models, such as direct DFT-PES sampling or GA with the embedded atom method (EAM). Despite the present limitations, the MHM approach is able to yield interfacial structures with comparable or lower interfacial energies in specific cases, such as Σ⁢3(111)[110] Γ=1, ±0.5 and Σ⁢3(112)[110] Γ= ±1, −2, underscoring the robustness of the MHM approach even with a simple approximation of the DFT PES. The MHM interfacial structure prediction method thus offers an efficient approach to understanding the grain boundaries and heterointerfaces at the atomic scale, providing an important prerequisite for effective materials design.

density functional theory↗

Controllability of Free-piston Stirling Engine/linear Alternator Driving a Dynamic Load

This paper presents the dynamic behavior of a Free-Piston Stirling Engine/linear alternator (FPSE/LA) driving a single-phase fractional horse-power induction motor. The controllability and dynamic stability of the system are discussed by means of sensitivity effects of variations in system parameters, engine controller, operating conditions, and mechanical loading on the induction motor. The approach used expands on a combined mechanical and thermodynamic formulation employed in a previous paper. The application of state-space technique and frequency domain analysis enhances understanding of the dynamic interactions. Engine-alternator parametric sensitivity studies, similar to those of the previous paper, are summarized. Detailed discussions are provided for parametric variations which relate to the engine controller and system operating conditions. The results suggest that the controllability of a FPSE-based power system is enhanced by proper operating conditions and built-in controls.

Kankam, M. David↗

A probabilistic approach to the evaluation of fatigue damage in a space propulsion system injector element

The fatigue damage of a space propulsion system component is computed using probabilistic structural analysis methods. The analysis takes into account the variations in static and dynamic loads, the uncertainty in structural damping, and the scatter in material fatigue resistance. The key elements of the probabilistic approach include: (1) a numerical engine model for describing the global component interface loads consistent with engine balance, (2) models for computing the local mode boundary conditions on the component, (3) a static structural analysis model that captures the strains at a damage critical location as a function of engine performance variables, (4) a finite element model for assessment of the random amplitude stress response due to random base and pressure excitations with uncertain power and correlation length, and (5) advanced first order reliability methods for computing the probabilities associated with the fatigue damage.

Rajagopal, K. R.↗

Flight-test verification of a pictorial display for general aviation instrument approach

Results of flight tests of the pictorial "follow me" box display results of the simulator study. The most important item of agreement was the frequency of the vertical and lateral models of motion of the pilot-aircraft-display system, which was 0.4 rad/sec in each study. Successful short, curved, descending approaches, such as are often suggested for use with microwave landing systems, can be executed with the "follow me" box display. Variations of the value of distance from the aircraft to the box were also examined using values of 736 m, 368 m, and 184 m. The results show that successful approaches can be made with all of these values. A sharper final turn and greater precision of position control are obtained with the shorter distance. Deletion of distance measuring equipment from the system was also studies. It was shown that successful approaches can be made with no distance measures included in the system, but the values of distance from the aircraft to the box that can be used are restricted.

Adams, J. J.↗

Data driven discovery and quantification of hyperspectral leaf reflectance phenotypes across a maize diversity panel

Abstract Estimates of plant traits derived from hyperspectral reflectance data have the potential to efficiently substitute for traits, which are time or labor intensive to manually score. Typical workflows for estimating plant traits from hyperspectral reflectance data employ supervised classification models that can require substantial ground truth datasets for training. We explore the potential of an unsupervised approach, autoencoders, to extract meaningful traits from plant hyperspectral reflectance data using measurements of the reflectance of 2151 individual wavelengths of light from the leaves of maize ( Zea mays ) plants harvested from 1658 field plots in a replicated field trial. A subset of autoencoder‐derived variables exhibited significant repeatability, indicating that a substantial proportion of the total variance in these variables was explained by difference between maize genotypes, while other autoencoder variables appear to capture variation resulting from changes in leaf reflectance between different batches of data collection. Several of the repeatable latent variables were significantly correlated with other traits scored from the same maize field experiment, including one autoencoder‐derived latent variable (LV8) that predicted plant chlorophyll content modestly better than a supervised model trained on the same data. In at least one case, genome‐wide association study hits for variation in autoencoder‐derived variables were proximal to genes with known or plausible links to leaf phenotypes expected to alter hyperspectral reflectance. In aggregate, these results suggest that an unsupervised, autoencoder‐based approach can identify meaningful and genetically controlled variation in high‐dimensional, high‐throughput phenotyping data and link identified variables back to known plant traits of interest.

Tross, Michael C.↗

The Orbiting Carbon Observatory: Mission Overview

OCO will acquire the space-based data needed to identify CO2 sources and sinks and quantify their variability over the seasonal cycle. Approach: a) Collect spatially resolved, high resolution spectroscopic observations of CO2 and O2 absorption in reflected sunlight; b) Use these data to resolve spatial and temporal variations in the column averaged CO2 dry air mole fraction, X cub CO2) over the sunlit hemisphere; and c) Employ independent calibration and validation approaches to produce X(sub CO2) estimates with random errors and biases no larger than 1-2 ppm (0.3-0.5%) on regional scales at monthly intervals.

CO2↗

Ensemble‐Based Spatially Distributed CLM5 Hydrological Parameter Estimation for the Continental United States

Abstract One of the major challenges in large‐domain hydrological modeling efforts lies in the estimation of spatially distributed hydrological parameters while simultaneously accounting for their associated uncertainties. Addressing this challenge is particularly difficult in ungauged locations. With growing societal demands for large‐scale streamflow projections to inform water resource management and long‐term planning, evaluating and constraining hydrological parameter uncertainty is increasingly vital. This study introduces a hybrid regionalization approach to enhance hydrological predictions of the Community Land Model version 5 (CLM5) across the Continental United States (CONUS), with a total of 50,629 1/8° grid cells. This hybrid method combines the strengths of two existing techniques: parameter regionalization and streamflow signature regionalization. It identifies ensemble behavioral parameters for each 1/8° grid cell across the CONUS domain, tailored to three distinct streamflow signatures focused on low flows, high flows, and annual water balance. Evaluating this hybrid method for 464 CAMELS (Catchment Attributes and Meteorology for Large‐sample Studies) basins demonstrates a significant improvement in CLM5 hydrological predictions, even in challenging arid regions. In CONUS applications, the derived spatially distributed parameter sets capture both spatial continuity and variation of parameters, highlighting their heterogeneous nature within specific regions. Overall, this hybrid regionalization approach offers a promising solution to the complex task of improving hydrological modeling over large domains for important hydrological applications.

CLM5↗

Classification of Orbits in Poincare Maps Using Machine Learning

Poincare plots, also called Poincare maps, are used by plasma physicists to understand the behavior of magnetically confined plasma in numerical simulations of a tokamak. These plots are created by the intersection of field lines with a two-dimensional poloidal plane that is perpendicular to the axis of the torus representing the tokamak. A plot is composed of multiple orbits, each created by a different field line as it goes around the torus. Each orbit can have one of four distinct shapes, or classes, that indicate changes in the topology of the magnetic fields confining the plasma. Given the (x, y) coordinates of the points that form an orbit, the analysis task is to assign a class to the orbit, a task that appears ideally suited for a machine learning approach. In this paper, we describe how we overcame two major challenges in solving this problem - creating a high-quality training set, with few mislabeled orbits, and converting the coordinates of the points into features that are discriminating, despite the variation within the orbits of a class and the apparent similarities between orbits of different classes. Our automated approach is not only more objective and accurate than visual classification, but is also less tedious, making it easier for plasma physicists to analyze the topology of magnetic fields from numerical simulations of the tokamak.

97 MATHEMATICS AND COMPUTING↗

Thermodynamics and kinetics of solution combustion synthesis: Ni(NO 3 ) 2 + fuels systems

Solution combustion synthesis (SCS) utilizes exothermic self-propagating reactions to prepare nanoscale materials that can be used widely in energy, electronics, and biomedical technologies and other applications. SCS is a specific variety of a more general combustion synthesis (CS) method. Investigations of the thermodynamics, kinetics, and the mechanisms of SCS reactions, are not as well studied as the other CS processes. This work reports on a systematic study of the thermodynamics and kinetics of SCS reactions involving Ni(NO 3 ) 2 , an oxidizer, and either glycine (C 2 H 5 NO 2 ) or hexamethylenetetramine (HMT, C 6 H 12 N 4 ) as fuels. A thermodynamic modeling approach, based on the Gibbs free energy minimization principle, is applied to the simultaneous calculations of the adiabatic temperatures and compositions of the equilibrium products. Our calculations reveal the influence of fuel-to-oxidizer ratio, amount of water, and the oxygen in air on the combustion temperature under adiabatic conditions and the composition of the resulting products. We have, in turn, measured the combustion temperature and phase composition of products and compared them with the calculations. Variations of drying times for the solutions yield precursor gels with varying water contents. This approach enables the manipulation of combustion parameters and confirms the use of calculated activation energies for reactions using the Merzhanov-Khaikin method. Here, the results show that SCS reactions in fuel-lean solutions producing NiO have higher activation energy in contrast to reactions with fuel-rich solutions that form Ni. Reduction of activation energies due to the increase in the fuel-to-oxidizer ratio could be related to the observed change of the rate-limiting stages of the endothermic decomposition of the individual reactants to the exothermic decomposition of coordinate compounds formed between the reactants.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Electron-Molecule Col1isions: Quantitative Approaches, and the Legacy of Aaron Temkin

This article, on electron-molecule collisions, is dedicated to the legacy of my good friend and sometime collaborator, Aaron Temkin on his retirement from the NASA-Goddard Space Flight Center after many years of work at the highest intellectual level in the theoretical treatment of electron-atom and electron-molecule scattering. Aaron's contributions to the manner in which we think about electron-molecule collisions is clear to all of us who have worked in this field. I doubt that the great progress that has occurred in the computational treatment of such complex collision problems could have happened without these contributions. For a brief historical account, see the discussion of Temkin's contribution to electron-molecule scattering in the first article of this volume by Dr. A. K. Bhatia. In this article, I will concentrate on the application of the so called, non-adiabatic R-matrix theory, to vibrational excitation and dissociative attachment, although I will also present some results applying the Linear Algebraic and Kohn-Variational methods to vibrational excitation. As a starting point for almost all computationally effective approaches to electron-molecule collisions, is the fixed nuclei approximation. That is, one recognizes, just as one does with molecular bound states, that there is a separation of electronic(fast) and nuclear(s1ow) degrees of freedom. This separation makes it possible to "freeze" the nuclei in space, calculate the collision parameters for the frozen molecule and then, somehow to add back the vibrations and rotations. The manner in which this is done, depends on the details of the collision problem. It is the work of Aaron and a number of other researchers that has provided the guidance necessary to resolve these issues.

Schneider, B.I.↗

Review of acousto-ultrasonic NDE for composites

Acousto-ultrasonics utilizes simulated stress waves to detect and quantify defect states, damage conditions, and variations of mechanical properties in fiber reinforced composites. The term acousto-ultrasonics denotes a combination of aspects of acoustic emission methodology with ultrasonic materials characterization. The acousto-ultrasonic approach was developed to deal primarily with evaluation of the integrated effect of minor flaws and diffuse flaw populations of subcritical flaws in composite and bonded structures. These factors singly and collectively also influence acousto-ultrasonic measurements that, in turn, correlate with dynamic response and mechanical property variations. Since it was first introduced, the acousto-ultrasonic approach was successfully applied to a variety of materials, including polymeric, metallic, and ceramic matrix composites; adhesively bonded materials; paper and wood products; cable and rope; and also human bone. Examples of applications and limitations of the approach are reviewed. Basic methods and guidelines are discussed. The underlying hypothesis and theory development needs are indicated.

Vary, Alex↗

Lithosphere-Atmosphere-Ionosphere Coupling (LAIC) Model - An Unified Concept for Earthquake Precursors Validation

The paper presents a conception of complex multidisciplinary approach to the problem of clarification the nature of short-term earthquake precursors observed in atmosphere, atmospheric electricity and in ionosphere and magnetosphere. Our approach is based on the most fundamental principles of tectonics giving understanding that earthquake is an ultimate result of relative movement of tectonic plates and blocks of different sizes. Different kind of gases: methane, helium, hydrogen, and carbon dioxide leaking from the crust can serve as carrier gases for radon including underwater seismically active faults. Radon action on atmospheric gases is similar to the cosmic rays effects in upper layers of atmosphere: it is the air ionization and formation by ions the nucleus of water condensation. Condensation of water vapor is accompanied by the latent heat exhalation is the main cause for observing atmospheric thermal anomalies. Formation of large ion clusters changes the conductivity of boundary layer of atmosphere and parameters of the global electric circuit over the active tectonic faults. Variations of atmospheric electricity are the main source of ionospheric anomalies over seismically active areas. Lithosphere-Atmosphere-Ionosphere Coupling (LAIC) model can explain most of these events as a synergy between different ground surface, atmosphere and ionosphere processes and anomalous variations which are usually named as short-term earthquake precursors. A newly developed approach of Interdisciplinary Space-Terrestrial Framework (ISTF) can provide also a verification of these precursory processes in seismically active regions. The main outcome of this paper is the unified concept for systematic validation of different types of earthquake precursors united by physical basis in one common theory.

Pulinets, S.↗

Reexamining the variational two-particle reduced density matrix for nuclear systems

We report that in most nuclear many-body methods, observables are calculated using many-body wave functions explicitly. The variational two-particle reduced density matrix method is one of the few exceptions to the rule. Ground-state energies of both closed-shell and open-shell nuclear systems can indeed be evaluated by minimizing a constrained linear functional of the two-particle reduced density matrix. However, it has virtually never been used in nuclear theory, because nuclear ground states were found to be well overbound, contrary to those of atoms and molecules. Consequently, we introduced new constraints in the nuclear variational two-particle reduced density matrix method, developed recently for atomic and molecular systems. Our calculations then show that this approach can provide a proper description of nuclear systems where only valence neutrons are included. For the nuclear systems where both neutrons and protons are active, however, the energies obtained with the variational two-particle reduced density matrix method are still overbound. The possible reasons for the noticed discrepancies and solutions to this problem will be discussed.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

Convolutional Variational Autoencoder-based Unsupervised Learning for Power Systems Faults

Classification of power system event data is a growing need, particularly where non-protective relaying-based sensors are used to monitor grid performance. Given the high burden of obtaining event data with appropriate labeling, an unsupervised approach is highly valuable. This approach enables using event data without labeling, which is far easier to obtain. This paper presents an unsupervised learning method to classify and label transients observed in the distribution grid. A Convolutional Variational Autoencoder (CVAE) was developed for this purpose. We demonstrate the efficacy of our approach using the transient data generated from the simulations. The simulation data is used to train the CVAE that identifies different faults as different clusters in the latent space. The clusters are then used as the foundation model to categorize the real-world data.

Alam, Maksudul↗

Motion Cueing Algorithm Development: Piloted Performance Testing of the Cueing Algorithms

The relative effectiveness in simulating aircraft maneuvers with both current and newly developed motion cueing algorithms was assessed with an eleven-subject piloted performance evaluation conducted on the NASA Langley Visual Motion Simulator (VMS). In addition to the current NASA adaptive algorithm, two new cueing algorithms were evaluated: the optimal algorithm and the nonlinear algorithm. The test maneuvers included a straight-in approach with a rotating wind vector, an offset approach with severe turbulence and an on/off lateral gust that occurs as the aircraft approaches the runway threshold, and a takeoff both with and without engine failure after liftoff. The maneuvers were executed with each cueing algorithm with added visual display delay conditions ranging from zero to 200 msec. Two methods, the quasi-objective NASA Task Load Index (TLX), and power spectral density analysis of pilot control, were used to assess pilot workload. Piloted performance parameters for the approach maneuvers, the vertical velocity upon touchdown and the runway touchdown position, were also analyzed but did not show any noticeable difference among the cueing algorithms. TLX analysis reveals, in most cases, less workload and variation among pilots with the nonlinear algorithm. Control input analysis shows pilot-induced oscillations on a straight-in approach were less prevalent compared to the optimal algorithm. The augmented turbulence cues increased workload on an offset approach that the pilots deemed more realistic compared to the NASA adaptive algorithm. The takeoff with engine failure showed the least roll activity for the nonlinear algorithm, with the least rudder pedal activity for the optimal algorithm.

Houck, Jacob A.↗

Quantum-Compatible Variational Segmentation for Image-to-Image Wildfire Detection Using Satellite Data

Wildfire occurrences have been increasing for the past decade, leaving devastating traces across the world. In the recent efforts, remote sensing and airborne missions have been utilized to better understand and manage wildfires. This has resulted in an exponential increase in volume of remote sensing data, which has pushed the need for intelligent automation of data extraction for wildfire studies. Machine learning offers accurate automation in detecting such natural anomalies and enable decision-makers to take actions in a timely manner. Recent advances in machine learning algorithms, namely probabilistic generative methods, allow researchers and decisionmakers to step beyond detection and study “what-if” scenarios for wildfire occurrences. Additionally, they offer better imitations to the stochastic behavior of nature, and wildfire events. However, optimizing the performance of these probabilistic generative models is a computationally expensive process, specially using digital computers. On the other hand, quantum computers have recently shown a promise to reduce computationally costly training of such models and provide performance improvements. There is a body of research investigating the potential for improved machine learning methods in which key operations are performed on a quantum computer. In this study, we propose a probabilistic image-toimage segmentation approach combining a very well-known segmentation method, U-NET, with a Conditional Variational Auto-Encoder (CVAE) to not only detect wildfires but also describe the stochasticity of the phenomenon and be capable of running “what-if” scenarios. Our proposed model is compatible with training on quantum computers, which results in a quantum-assisted image-to-image segmentation approach and can be used to benchmark the potential benefit of quantum computing over the classical one.

quantum↗

On the Power of Abstract Interpretation

Increasingly sophisticated applications of static analysis place increased burden on the reliability of the analysis techniques. Often, the failure of the analysis technique to detect some information my mean that the time or space complexity of the generated code would be altered. Thus, it is important to precisely characterize the power of static analysis techniques. We follow the approach of Selur et. al. who studied the power of strictness analysis techniques. Their result can be summarized by saying 'strictness analysis is perfect up to variations in constants.' In other words, strictness analysis is as good as it could be, short of actually distinguishing between concrete values. We use this approach to characterize a broad class of analysis techniques based on abstract interpretation including, but not limited to, strictness analysis. For the first-order case, we consider abstract interpretations where the abstract domain for data values is totally ordered. This condition is satisfied by Mycroft's strictness analysis that of Sekar et. al. and Wadler's analysis of list-strictness. For such abstract interpretations, we show that the analysis is complete in the sense that, short of actually distinguishing between concrete values with the same abstraction, it gives the best possible information. We further generalize these results to typed lambda calculus with pairs and higher-order functions. Note that products and function spaces over totally ordered domains are not totally ordered. In fact, the notion of completeness used in the first-order case fails if product domains or function spaces are added. We formulate a weaker notion of completeness based on observability of values. Two values (including pairs and functions) are considered indistinguishable if their observable components are indistinguishable. We show that abstract interpretation of typed lambda calculus programs is complete up to this notion of indistinguishability. We use denotationally-oriented arguments instead of the detailed operational arguments used by Selur et. al.. Hence, our proofs are much simpler. They should be useful for further future improvements.

Reddy, Uday S.↗