Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “missing information”

Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 109 records · Page 6
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Visibility-enhanced model-free deep reinforcement learning algorithm for voltage control in realistic distribution systems using smart inverters

Increasing integration of distributed solar photovoltaic (PV) into distribution networks could result in adverse effects on grid operation. Traditional model-based control algorithms require accurate model information that is difficult to acquire and thus are challenging to implement in practice. Here, this paper proposes a surrogate model-enabled grid visibility scheme to empower deep reinforcement learning (DRL) approach for distribution network voltage regulation using PV inverters with minimal system knowledge. In contrast to existing DRL methods, this paper presents and corroborates the adverse impact of missing load information on DRL performance and, based on this finding, proposes a surrogate model methodology to impute load information utilizing observable data. Additionally, a multi-fidelity neural network is utilized to construct the DRL training environment, chosen for its efficient data utilization and enhanced robustness to data uncertainty. The feasibility and effectiveness of the proposed algorithm are assessed by considering DRL testing across varying degrees of observable load information and diverse training environments on a realistic power system.

14 SOLAR ENERGY↗
DOE OSTI2019

Organic and Organometallic Chemistry at the Single-Molecule, -Particle, and -Molecular-Catalyst-Turnover Level by Fluorescence Microscopy

Mechanistic studies have historically played a key role in the discovery and optimization of reactions in organic and organometallic chemistry. However, even apparently simple organic and organometallic transformations may have surprisingly complicated multistep mechanisms, increasing the difficulty of extracting this mechanistic information. The resulting reaction intermediates often constitute a small fraction of the total reaction mixture, for example, creating a long-term analytical challenge of detection. This challenge is particularly pronounced in cases where the positions of intermediates on the reaction energy surface mean that they do not “build up” to the quantities needed for observation by traditional ensemble analytical tools. Thus, their existence and single-step elementary reactivity cannot be studied directly. New approaches for obtaining this otherwise-missing mechanistic information are therefore needed. Single-turnover, single-molecule, single-particle, and other subensemble fluorescence microscopy techniques are ideally suited for this role because of their sensitivity and spatiotemporal resolution. Inspired by the robust development of single-molecule fluorescence microscopy tools for studying enzyme catalysis, our laboratory has developed analogous fluorescence microscopy techniques to overcome mechanistic challenges in synthetic chemistry, with sensitivity as high as the single-complex, single-turnover, and single-molecule level. These techniques free the experimenter from the previous restriction that intermediates must “build up” to quantities needed for detection by ensemble analytical tools and are suited to systems where synchronization through flash photolysis or stopped flow would be inconvenient or inaccessible. In this process, the techniques transform certain previously “unobservable” intermediates and their elementary single-step reactivities into “observable” ones through sensitive and selective spectral handles. Our program has focused on imaging reactions in small-molecule, organic, and polymer synthetic chemistry with an accent on the reactivity of molecular transition metal complexes and catalysts. Our laboratory initiated studies in this area in 2008 with the imaging of individual palladium complexes that were tagged with spectator fluorophores. To enable imaging, we started with fluorophore selection and development, overcame challenges with imaging in organic solvents, and developed strategies compatible with air-sensitive chemistry and concentrations of reagents generally used in small-molecule synthesis. These studies grew to include characterization of previously unknown organometallic intermediates in the synthesis of organozinc reagents and the direct study of their elementary-step reactivity. The ability to directly observe this behavior generated predictive power for selecting salts that accelerated organozinc reagent formation in synthesis, including salts that had not yet been reported synthetically. In 2017 we also developed the first single-turnover imaging of molecular (chemo)catalysts, which through the technique’s spatiotemporal resolution revealed abruptly time-variable polymerization kinetics wherein molecular ruthenium ring-opening metathesis polymerization (ROMP) catalysts changed rates independently from other catalysts less than 1 μm away. Individual catalytic turnovers, each corresponding to one single-chain-elongation reaction arising from insertion of single ROMP or enyne monomers at individual Grubbs II molecular ruthenium catalysts, were spatiotemporally resolved as green flashes in growing polymers. In this work, we discuss the development of this technique from idea to application, including challenges overcome and strategies created to image synthetic organic and organometallic molecular chemistry at the highest levels of detection sensitivity. We also describe challenges not yet solved and provide an outlook for this growing field at the intersection of microscopy and synthetic/molecular chemistry.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
NASA NTRS2005

Fluctuations in Conjunction Miss Distance Projections as Time Approaches Time of Closest Approach

A responsibility of the Trajectory Operations Officer is to ensure that the International Space Station (ISS) avoids colliding with debris. United States Space Command (USSPACECOM) tracks and catalogs a portion of the debris in Earth orbit, but only objects with a perigee less than 600 km and a radar cross section (RCS) greater than 10 cm-objects that, in fact, represent only a small fraction of the objects in Earth orbit. To accommodate for this, the ISS uses shielding to protect against collisions with smaller objects. This study provides a better understanding of how quickly, and to what degree, USSPACECOM projections tend to converge to the final, true miss distance. The information included is formulated to better predict the behavior of miss distance data during real-time operations. It was determined that the driving components, in order of impact on miss distance fluctuations, are energy dissipation rate (EDR), RCS, and inclination. Data used in this analysis, calculations made, and conclusions drawn are stored in Microsoft Excel log sheets. A separate log sheet, created for each conjunction, contains information such as predicted miss distances, apogee and perigee of debris orbit, EDR, RCS, inclination, tracks and observations, statistical data, and other evaluation/orbital parameters.

Christian, John A., III↗
DOE OSTI2023

Automated reaction kinetics and network exploration (Arkane): A statistical mechanics, thermodynamics, transition state theory, and master equation software

The open-source statistical mechanics software described here, Arkane–Automated Reaction Kinetics and Network Exploration–facilitates computations of thermodynamic properties of chemical species, high-pressure limit reaction rate coefficients, and pressure-dependent rate coefficient over multi-well molecular potential energy surfaces (PES) including the effects of collisional energy transfer on phenomenological kinetics. Arkane can use estimates to fill in information for molecules or reactions where quantum chemistry information is missing. The software solves the internal energy master equation for complex unimolecular reaction systems. Inputs to the software include converged electronic structure computations performed by the user using a variety of supported software packages (Gaussian, Molpro, Orca, TeraChem, Q-Chem, Psi4). The software outputs high-pressure limit rate coefficients and pressure-dependent phenomenological rate coefficients, as well as computed thermodynamic properties (enthalpy, entropy, and constant pressure heat capacity) with added energy corrections. Some of the key features of Arkane include treatment of 1D, 2D or ND hindered internal rotation modes, treatment of free internal rotation modes, quantum tunneling effect consideration, transition state theory (TST) and Rice-Ramsperger-Kassel-Marcus (RRKM) rate coefficient computations, master equation solution with four implemented methods, inverse-Laplace transform of high-pressure limit rate coefficients into the energy domain, energy corrections based on bond-additivity or isodesmic reactions, automated and efficient PES exploration, and PES sensitivity analysis. The present work describes the design of Arkane, how it should be used, and refers to the theory that it employs. Arkane is distributed via the RMG-Py software suite (https://github.com/ReactionMechanismGenerator/RMG-Py).

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2014

Data free inference with processed data products

Here, we consider the context of probabilistic inference of model parameters given error bars or confidence intervals on model output values, when the data is unavailable. We introduce a class of algorithms in a Bayesian framework, relying on maximum entropy arguments and approximate Bayesian computation methods, to generate consistent data with the given summary statistics. Once we obtain consistent data sets, we pool the respective posteriors, to arrive at a single, averaged density on the parameters. This approach allows us to perform accurate forward uncertainty propagation consistent with the reported statistics.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2018

A Deep Look Into the Future of Quantitative Imaging in Oncology: A Statement of Working Principles and Proposal for Change

The adoption of enterprise digital imaging, along with the development of quantitative imaging methods and the re-emergence of statistical learning, has opened the opportunity for more personalized cancer treatments through transformative data science research. In the last 5 years, accumulating evidence has indicated that noninvasive advanced imaging analytics (i.e., radiomics) can reveal key components of tumor phenotype for multiple lesions at multiple time points over the course of treatment. Many groups using homegrown software have extracted engineered and deep quantitative features on 3-dimensional medical images for better spatial and longitudinal understanding of tumor biology and for the prediction of diverse outcomes. These developments could augment patient stratification and prognostication, buttressing emerging targeted therapeutic approaches. Unfortunately, the rapid growth in popularity of this immature scientific discipline has resulted in many early publications that miss key information or use underpowered patient data sets, without production of generalizable results. Quantitative imaging research is complex, and key principles should be followed to realize its full potential. The fields of quantitative imaging and radiomics in particular require a renewed focus on optimal study design and reporting practices, standardization, interpretability, data sharing, and clinical trials. Standardization of image acquisition, feature calculation, and statistical analysis (i.e., machine learning) are required for the field to move forward. A new data-sharing paradigm enacted among open and diverse participants (medical institutions, vendors and associations) should be embraced for faster development and comprehensive clinical validation of imaging biomarkers. In this review and critique of the field, we propose working principles and fundamental changes to the current scientific approach, with the goal of high-impact research and development of actionable prediction models that will yield more meaningful applications of precision cancer medicine.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2020

Indirect methods in nuclear astrophysics with relativistic radioactive beams

Reactions with radioactive nuclear beams at relativistic energies have opened new doors to clarify the mechanisms of stellar evolution and cataclysmic events involving stars and during the big bang epoch. Numerous nuclear reactions of astrophysical interest cannot be assessed directly in laboratory experiments. Ironically, some of the information needed to describe such reactions, at extremely low energies (e.g., keVs), can only be studied on Earth by using relativistic collisions between heavy ions at GeV energies. In this contribution, we make a short review of experiments with relativistic radioactive beams and of the theoretical methods needed to understand the physics of stars, adding to the knowledge inferred from astronomical observations. We continue by introducing a more detailed description of how the use of relativistic radioactive beams can help to solve astrophysical puzzles and several successful experimental methods. State-of-the-art theories are discussed at some length with the purpose of helping us understand the experimental results reported. The review is not complete and we have focused most of it to traditional methods aiming at the determination of the equation of state of symmetric and asymmetric nuclear matter and the role of the symmetry energy. Whenever possible, under the limitations of our present understanding of experimental data and theory, we try to pinpoint the information still missing to further understand how stars evolve, explode, and how their internal structure might be. We try to convey the idea that in order to improve microscopic theories for many-body calculations, nuclear structure, nuclear reactions, and astrophysics, and in order to constrain and allow for convergence of our understanding of stars, we still need considerable improvements in terms of accuracy of experiments and the development of new and dedicated nuclear facilities to study relativistic reactions with radioactive beams.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2019

Atomistic Defect Makes a Phase Plate for the Generation and High-Angular Splitting of Electron Vortex Beams

Topological defects in solid-state materials by breaking the translational symmetry offer emerging properties that are not present in their parental phases. For example, edge dislocations—the 2π phase-winding topological defects—in antiferromagnetic NiO crystals can exhibit ferromagnetic behaviors. We study how these defects could give rise to topological orders when they interact with a high-energy electron beam. To probe this interaction, we formed a coherent electron nanobeam in a scanning transmission electron microscope and recorded the far-field transmitted patterns as the beam steps through the edge dislocation core in [001] NiO. Surprisingly, we found the amplitude patterns of the <020> Bragg disks evolve in a similar manner to the evolution of an annular solar eclipse. Using the ptychographic technique, we recovered the missing phase information in the diffraction plane and revealed the topological phase vortices in the diffracted beams. Through atomic topological defects, the wave function of electrons can be converted from plane wave to electron vortex. Technologically, this approach provides a feasible route for the fabrication of phase plates that can generate electron vortex beams with an angular separation that is three orders of magnitude larger than what traditional nanofabrication technology can offer. This advance will enable the collection of magnetic circular dichroism spectra with high spatial resolution and high efficiency, boosting the understanding of the relationship between symmetry breaking and magnetic property of individual topological defect at the atomic scale.

36 MATERIALS SCIENCE↗
DOE OSTI2025

PhaseT3M: 3D imaging at 1.6 Å resolution via electron cryo-tomography with nonlinear phase retrieval

Electron cryo-tomography (cryo-ET) enables 3D imaging of complex, radiation-sensitive structures with molecular detail. However, image contrast from the interference of scattered electrons is nonlinear with atomic density and multiple scattering further complicates interpretation. These effects degrade resolution, particularly in conventional reconstruction algorithms, which assume linearity. Particle averaging can reduce such issues but is unsuitable for heterogeneous or dynamic samples ubiquitous in biology, chemistry, and materials sciences. Here, we develop a phase retrieval-based cryo-ET method, PhaseT3M. We experimentally demonstrate its application to an approximately 7 nm Co3O4 nanoparticle on an approximately 30 nm carbon substrate, achieving a maximum resolution of 1.6 Å, surpassing conventional limits using standard cryo-TEM equipment. PhaseT3M uses a multislice model for multiple scattering and Bayesian optimization for alignment and computational aberration correction, with a positivity constraint to recover ‘missing wedge’ information. Applied directly to biological particles, it enhances reconstruction quality and reduces artifacts, establishing a standard for routine 3D imaging with phase contrast.

Biophysics↗
DOE OSTI2017

Detection of magnetic circular dichroism in amorphous materials utilizing a single-crystalline overlayer

Physicists are fascinated with topological defects in solid-state materials, because by breaking the translational symmetry they offer emerging properties that are not present in their parental phases. For example, edge dislocations—the 2π phase-winding topological defects—in antiferromagnetic NiO crystals can exhibit ferromagnetic behaviors. Herein, we study how these defects could give rise to exotic topological orders when they interact with a high energy electron beam. To probe this interaction, we formed a coherent electron nanobeam in a scanning transmission electron microscope and recorded the far-field transmitted patterns as the beam steps through the edge dislocation core in [001] NiO. Surprisingly, we found the amplitude patterns of the <020> Bragg disks evolve in a similar manner to the evolution of an annular solar eclipse. Using the ptychographic technique, we recovered the missing phase information in the diffraction plane and revealed the topological phase vortices in the diffracted beams. Through atomic topological defects, the wave function of electrons can be converted from plane wave to electron vortex. This approach provides a new perspective for boosting the collection efficiency of magnetic circular dichroism spectra with high spatial resolution and understanding the relationship between symmetry breaking and exotic property of individual topological defect at atomic level.

36 MATERIALS SCIENCE↗
DOE OSTI2024

AlphaFold -assisted structure determination of a bacterial protein of unknown function using X-ray and electron crystallography

Macromolecular crystallography generally requires the recovery of missing phase information from diffraction data to reconstruct an electron-density map of the crystallized molecule. Most recent structures have been solved using molecular replacement as a phasing method, requiring an a priori structure that is closely related to the target protein to serve as a search model; when no such search model exists, molecular replacement is not possible. New advances in computational machine-learning methods, however, have resulted in major advances in protein structure predictions from sequence information. Methods that generate predicted structural models of sufficient accuracy provide a powerful approach to molecular replacement. Taking advantage of these advances, AlphaFold predictions were applied to enable structure determination of a bacterial protein of unknown function (UniProtKB Q63NT7, NCBI locus BPSS0212) based on diffraction data that had evaded phasing attempts using MIR and anomalous scattering methods. Using both X-ray and micro-electron (microED) diffraction data, it was possible to solve the structure of the main fragment of the protein using a predicted model of that domain as a starting point. The use of predicted structural models importantly expands the promise of electron diffraction, where structure determination relies critically on molecular replacement.

molecular replacement↗
DOE OSTI2021

Advanced Performance Metrics and their Application to the Sensitivity Analysis for Model Validation and Calibration

High-quality generator dynamic models are critical to reliable and accurate power systems studies and planning. With the availability of PMUs, measurement-based approach for model validation has gained significant prominence. In this approach, the quality of a model is analyzed by visually comparing measured generator response with the model-based simulated response for large system disturbances. This paper proposes a new set of performance metrics to assess the model validation results to facilitate automation of the model validation process. In the proposed methodology, first, the slow governor response and comparatively faster oscillatory response are separated, and then a separate set of performance metrics is calculated for each of these two components. These proposed metrics quantify the mismatch between the actual and model-based response in a comprehensive manner without missing any information enabling automation of the process. Furthermore, in this paper, we are also proposing that the sensitivity analysis for model calibration be performed with respect to the proposed metrics for the systematic identification of key parameters. In this work, results obtained using both simulated and real-world case-studies validate the effectiveness of the proposed performance metrics for model validation and their application to the sensitivity analysis for model calibration.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2021

Spin Berry points as crucial for ultrafast demagnetization

Laser-induced ultrafast demagnetization has puzzled researchers around the world for over two decades. Intrinsic complexity in electronic, magnetic and phononic subsystems is difficult to understand microscopically. So far, it is not possible to explain demagnetization using a single mechanism, which suggests a crucial piece of information still missing. In this paper, we return to a fundamental aspect of physics: spin and its change within each band in the entire Brillouin zone. We employ face-centered cubic (fcc) Ni as an example and use an extremely dense k mesh to map out spin changes for every band close to the Fermi level along all the high symmetry lines. To our surprise, spin angular momentum at some special k points abruptly changes from [Formula: see text] to [Formula: see text] simply by moving from one crystal momentum point to the next. This explains why intraband transitions, which the spin superdiffusion model is based upon, can induce a sharp spin moment reduction, and why electric current can change spin orientation in spintronics. These special k points, which are called spin Berry points [M. V. Berry, Proc. R. Soc. Lond. A 393 (1984) 45], are not random and appear when several bands are close to each other, so the Berry potential of spin majority states is different from that of spin minority states. Although within a single band, spin Berry points jump, when we group several neighboring bands together, they form distinctive smooth spin Berry lines. It is the band structure that disrupts those lines. Spin Berry points are crucial to laser-induced ultrafast demagnetization and spintronics.

Physics↗
DOE OSTI2020

GET-Solar (Generation forecasting and parameter Estimation Tool for Solar) [SWR-20-115]

GET-Solar (Generation forecasting and parameter Estimation Tool for Solar) can be used to estimate behind the meter (BTM) solar generation from limited datasets. Using distributed sets of diverse solar sites, but with close spatial proximity, and/or limited historical PV generation information can be used to generate of solar profiles. This is useful for applications where partial solar generation data is available, and on-site irradiance data is not available. GET-Solar calculates PV technical data parameters and calculates output by modeling shading and cloud cover impacts. Clear sky irradiance profiles are used to calculate base generation profiles on which the local shading and cloud cover layers are added. In order to arrive at clear sky generation data, PV mounting information like panel tilt and and angle is needed. For cases where PV mounting information is missing, panel tilt and azimuth angle can be estimated using a non-linear optimization algorithm. GET-Solar can also be used for forecasting applications where historical PV data is available to estimate local shading at various solar positions. This overall framework can be used for estimation of solar parameters, and assisting in analyzing and modeling diverse sets of DER where solar generation is also present in utility meter data. GET-Solar has been developed in Python and uses the Pyomo optimization language.

Abraham, Sherin Ann↗
DOE OSTI2023

Generalized Bayesian Framework for Evaluation of Integral Benchmark Experiments

A recently published generalized Bayesian optimization framework has provided a way to retract any or all of the three common assumptions underlying the conventional Generalized Linear Least Squares (GLLS) optimization method based on the concepts introduced in Ref. [2]. These assumptions are: 1. Perfection: The model used for data evaluation and the prior probability distribution function (PDF) of generalized data are perfect. 2. Normality: The prior and posterior PDF are normal. 3. Linearity: The model is linear. In this work we outline how the framework in [1] could be directly adopted for improved evaluation of nuclear criticality integral benchmark experiments (IBEs) by: 1. Removing the first assumption alone by utilizing the concept of imperfections introduced in [1] to enable evaluation in the presence of discrepancies between the data and model or of missing covariance information by a GLLS method that will be seen as a generalization of the conventional GLLS method employed by the TSURFER code, and by 2. Removing the remaining two assumptions by implementing a Markov Chain Monte Carlo method for computation of the posterior PDF in the SAMPLER code, where TSURFER and SAMPLER are the uncertainty quantification (UQ) codes for IBEs in the SCALE code system based on the GLLS and the stochastic method, respectively. The graphic in Figure 1 categorizes the methods discussed in terms of the assumptions that they employ to determine posterior PDFs.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗