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All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

A large-scale comparison of Artificial Intelligence and Data Mining (AI&DM) techniques in simulating reservoir releases over the Upper Colorado Region

In recent years, the Artificial Intelligence and Data Mining (AI&DM) models have become popular tools in assisting various aspects of reservoir operation. However, the practical uses are still rarely reported. Comparison experiment of many AI&DM models over a large number of reservoir cases is particularly valuable to help reservoir operators first examine the usefulness and transferability of different AI&DM models, and then identify the most stable and reliable AI&DM model in assist of various decision-making processes. In this study, a total of 12 AI&DM models with different parameterizations and simulation scenarios are comprehensively tested out and compared in simulating the controlled reservoir outflows of 33 reservoir cases over the Upper Colorado Region, United States. Results show that the Random Forecast and the Long-Short-Term-Memory model could consistently derive the best statistical performance than other models under the baseline simulation scenario. The employed AI&DM models could obtain satisfactory statistical interquartile ranges (25–75%) between [0.6–0.9], [0.3–0.8], and [0.2–0.8], for CORR, NSE, and KGE measurements, respectively, and [1.5–6.5], [–15 to 20], and [0.5–8.5] for the normalized RMSE, PBIAS and RSR measurements, respectively. Results also show Multi-Layer Perceptron model and Extreme Gradient Boosting Tree Algorithm produced more stable and superior performance than other models under more complex input scenarios. We also found that the performance of different AI&DM models are closely relevant to the reservoir elevations, sizes, and functionalities. Discussions were made about the sensitivity of AI&DM models’ parameterizations and the key advantages of AI&DM models over the rule-based reservoir models. We further identify that the main advantage of AI&DM models is the flexibility in designing input structures, whereas the rule-based simulation model is rather limited. Future studies were suggested regarding the best way reservoir operators and researchers could use, select, and apply different AI&DM models in simulating reservoir releases under different natural and modeling environments. Finally, this comparison study also serves as a reference and a piece of groundwork for further promoting the practical uses of AI&DM models in assisting reservoir operation.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2018

First-Principles and Thermodynamic Simulation of Elastiс Stress Effect on Energy of Hydrogen Dissolution in Alpha Iron

Mobile hydrogen, when dissolving in metals, redistributes due to the density gradients and elastic stresses, and enables destruction processes or phase transformations in local volumes of a solvent metal. It is rather important in solid state physics to investigate these interactions. The first-principle calculations performed in terms of the density functional theory, are used for thermodynamic simulation of the elastic stress effect on the energy of hydrogen dissolution in α-Fe crystal lattice. The paper presents investigations of the total energy of Fe–H system depending on the lattice parameter. As a result, the relation is obtained between the hydrogen dissolution energy and stress. A good agreement is shown between the existing data and simulation results. The extended equation is suggested for the chemical potential of hydrogen atom in iron within the local stress field. Two parameters affecting the hydrogen distribution are compared, namely local stress and phase transformations.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2015

Accessing defect dynamics using intense, nanosecond pulsed ion beams

Gaining in-situ access to relaxation dynamics of radiation induced defects will lead to a better understanding of materials and is important for the verification of theoretical models and simulations. We show preliminary results from experiments at the new Neutralized Drift Compression Experiment (NDCX-II) at Lawrence Berkeley National Laboratory that will enable in-situ access to defect dynamics through pump-probe experiments. Here, the unique capabilities of the NDCX-II accelerator to generate intense, nanosecond pulsed ion beams are utilized. Preliminary data of channeling experiments using lithium and potassium ions and silicon membranes are shown. We compare these data to simulation results using Crystal Trim. Furthermore, we discuss the improvements to the accelerator to higher performance levels and the new diagnostics tools that are being incorporated.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2015

BioNetFit: a fitting tool compatible with BioNetGen, NFsim and distributed computing environments

Rule-based models are analyzed with specialized simulators, such as those provided by the BioNetGen and NFsim open-source software packages. Here in this paper, we present BioNetFit, a general-purpose fitting tool that is compatible with BioNetGen and NFsim. BioNetFit is designed to take advantage of distributed computing resources. This feature facilitates fitting (i.e. optimization of parameter values for consistency with data) when simulations are computationally expensive.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2026

Dark Energy Survey Year 6 Results: Weak Lensing and Galaxy Clustering Cosmological Analysis Framework

We present the methodology for the weak lensing and galaxy clustering analyses of the Dark Energy Survey (DES) Year 6 data set. In this work, we design and validate the analysis pipeline for the cosmic shear, galaxy clustering plus galaxy$-$galaxy lensing ($2 \times 2$pt), and the joint analysis in the $3 \times 2$pt. Our framework accounts for key theoretical uncertainties, such as baryonic feedback and galaxy bias, incorporating both linear and non-linear models. We apply scale cuts in regimes where theoretical modeling becomes unreliable. The robustness of the pipeline is validated using mock data and simulations, confirming unbiased cosmological constraints and highlighting the importance of posterior projection effects in the validation process. As a result, we deliver robust and validated analysis pipelines for cosmic shear, $2 \times 2$pt, and $3 \times 2$pt in $Λ$CDM and $w$CDM scenarios, including a well-defined set of scales suitable for real data analysis, a robust prescription for theoretical systematics, and the theoretical covariance of the signal. This comprehensive methodology also lays the groundwork for future galaxy surveys such as the Vera C. Rubin Observatory Legacy Survey of Space and Time.

Sanchez-Cid, D. [Zurich U.; Madrid, CIEMAT; Madrid↗
DOE OSTI2020

A method to generate heating and cooling schedules based on data from connected thermostats

Internet-connected thermostats are a promising new source of temperature and operational data in homes because they record inside temperatures, setpoints, and HVAC runtimes every five minutes. Over 20 million Internet-connected thermostats have already been installed in American homes. Data from about 20,000 connected thermostats were collected and organized by climate zone, number of occupants, floor area, and day type. A method was developed to create up to 40 representative temperature schedules which, together, can more accurately capture the diversity of heating and cooling behaviors. These results are suitable for input into schedules for building energy simulation models. This information enables more realistic simulations of American heating and cooling behavior, leading to more accurate estimates of energy consumption and savings.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2021

Active anomaly detection for time-domain discoveries

Aims. We present the first evidence that adaptive learning techniques can boost the discovery of unusual objects within astronomical light curve data sets. Methods. Our method follows an active learning strategy where the learning algorithm chooses objects which can potentially improve the learner if additional information about them is provided. This new information is subsequently used to update the machine learning model, allowing its accuracy to evolve with each new information. For the case of anomaly detection, the algorithm aims to maximize the number of scientifically interesting anomalies presented to the expert by slightly modifying the weights of a traditional Isolation Forest (IF) at each iteration. In order to demonstrate the potential of such techniques, we apply the Active Anomaly Discovery (AAD) algorithm to 2 data sets: simulated light curves from the Photometric LSST Astronomical Time-Series Classification Challenge (PLAsTiCC) and real light curves from the Open Supernova Catalog. We compare the AAD results to those of a static IF. For both methods, we performed a detailed analysis for all objects with the ~2% highest anomaly scores. Results. We show that, in the real data scenario , AAD was able to identify ~80% more true anomalies than the IF. This result is the first evidence that AAD algorithms can play a central role in the search for new physics in the era of large scale sky surveys.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2015

Performance Evaluation of an Intelligent Sensor Platform for Radiation Monitoring Applications

Accurate, rugged, and reliable radiation detection systems are important for area and environmental monitoring applications. The desire for spectroscopic capability has increased in monitoring aspects of the nuclear fuel cycle to provide fast characterization of the radiation profile of a situation, such as the planned or unplanned release of material. The reduction or elimination of having to conduct sampling for laboratory analysis can result in significant cost savings for an industry, government agency, or regulatory body. A new system, comprised of a NaI:Tl scintillator and a G-M tube, has been designed and tested, taking into account the following end-user requirements: ease-of-use, capability to network and supervise multiple units, compact form factor, low power consumption, versatility, and stability. The detector sizes were selected to accommodate a dose rate up to 1 Sv/hr. Several algorithms and analysis routines have been developed to incorporate these key needs without sacrificing on accuracy, dynamic range, nuclide identification, and sensitivity. This presentation will introduce the major hardware and software components of the platform, as well as the user interface and data analysis workflow. Key features of the hardware include an environmentally robust housing, low power signal processing electronics, patented LED-based gain stabilization, and an embedded processor for unattended instrument management and data analysis. New and improved algorithms for determining scintillator gamma dose rates, total integrated dose, and nuclide identification will be introduced. The two detector elements were modeled with MCNP and validated experimentally. The results of the radiological testing shall be presented including energy resolution, throughput, dose response, and minimum detectable activities. The dose response has been evaluated in simulations and with measurements to ensure accurate response with respect to energy spectrum of the dose field and direction of the incident radiation. Spectroscopy evaluations, such as nuclide detection probability and false alarm rates, were performed and comparisons to existing algorithms shall also be presented. (authors)

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2014

Accurate spectroscopic characterization of protonated oxirane: a potential prebiotic species in Titan's atmosphere

An accurate spectroscopic characterization of protonated oxirane has been carried out by means of state-of-the-art computational methods and approaches. The calculated spectroscopic parameters from our recent computational investigation of oxirane together with the corresponding experimental data available were used to assess the accuracy of our predicted rotational and IR spectra of protonated oxirane. We found an accuracy of about 10 cm{sup –1} for vibrational transitions (fundamentals as well as overtones and combination bands) and, in relative terms, of 0.1% for rotational transitions. We are therefore confident that the spectroscopic data provided herein are a valuable support for the detection of protonated oxirane not only in Titan's atmosphere but also in the interstellar medium.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2019

A Parameter Study for Improving the Performance of the Production Target for the Muon G-2 Experiment

The target station of the Muon g-2 Experiment is one of the central pieces for the production of secondary pions which eventually will decay to the desired muons. In this paper, we report adjustments made to optimize its perfor-mance. For instance, in the simulation we vary the size of the primary incoming beam and examine its impact on the downstream production. We then compare this with the ac-tual measured beam size upstream of the target. In addition, we examine the sensitivity in performance with the strength of the lithium lens and the distance between lens and target. We compare measured data with simulation results.

43 PARTICLE ACCELERATORS↗
DOE OSTI2021

Nuclear Materials Process Modeling at the Y-12 National Security Complex

The Y-12 National Security Complex (Y-12) has implemented process modeling for various accountable nuclear materials operations that are performed throughout the plant. Using a discrete, event-based dynamic simulation program, key nuclear material streams are modeled, allowing Y-12 to effectively manage numerous points of interest within the plant’s production operations. Integration of the various material processes into a single, interdependent supply and demand model is one of the ongoing focuses within Y-12’s process modeling effort. The primary purpose of using dynamic simulation modeling is to allow for analysis of the nuclear materials inventories and forecasted supplies based on future demands. Analysis of these inventories includes capacity evaluation, bottleneck mitigation, and assessments of individual pieces of equipment to inform future facility investment decisions and associated project schedules. Modeling of the nuclear materials processes throughout the complex also allows for incorporation of changes relevant to production capabilities such as the upcoming transition of specific operations to the new Uranium Processing Facility. Prior to implementation of process modeling, Y-12 forecasted supply and demand of accountable nuclear materials streams using Microsoft Excel. With deterministic models such as Microsoft Excel, the annual forecasts, generated within data input condition parameters, can only provide a fixed point of data. Fixed data cannot simulate integrated material streams and account for the possibility of occurrences and other changes that dynamic simulations take into consideration. Y-12’s dynamic process modeling allows integrated simulations of multiple accountable nuclear materials processes, including supply and demand forecasting and analysis, and is a coordinated effort involving many steps of verification and validation (V&V), site briefings, testing, reporting, data mining, planning, and documentation that spans various programs throughout the Y-12 complex.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2025

Forward modeling fluctuations in the DESI LRGs target sample using image simulations

We use the forward modeling pipeline, Obiwan, to study the imaging systematics of the Luminous Red Galaxies (LRGs) targeted by the Dark Energy Spectroscopic Instrument (DESI). Imaging systematics refers to the false fluctuation of galaxy densities due to varying observing conditions and astrophysical foregrounds corresponding to the imaging surveys from which DESI LRG target galaxies are selected. We update the Obiwan pipeline, which we previously developed to simulate the optical images used to target DESI data, to further simulate WISE images in the infrared. This addition allows simulating the DESI LRGs sample, which utilizes WISE data in the target selection. Deep DESI imaging data combined with a method to account for biases in their shapes is used to define a truth sample of potential LRG targets. We inject these data evenly throughout the DESI Legacy Imaging Survey footprint at declinations between -30 and 32.375 degrees. We simulate a total of 15 million galaxies to obtain a simulated LRG sample (Obiwan LRGs) that predicts the variations in target density due to imaging properties. We find that the simulations predict the trends with depth observed in the data, including how they depend on the intrinsic brightness of the galaxies. We observe that faint LRGs are the main contributing source of the imaging systematics trend induced by depth. We also find significant trends in the data against Galactic extinction that are not predicted by Obiwan. These trends depend strongly on the particular map of Galactic extinction chosen to test against, implying systematic contamination in the Galactic extinction maps is a likely root cause (e.g., Cosmic-Infrared Background, dust temperature correction). We additionally observe a morphological change of the DESI LRGs population evidenced by a correlation between OII emission line average intensity and the size of the z-band PSF. This effect most likely results from uncertainties in background subtraction. The detailed findings we present should be used to guide any observational systematics mitigation treatment for the clustering of the DESI LRGs sample.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2016

Dragonfly : an implementation of the expand–maximize–compress algorithm for single-particle imaging

Single-particle imaging (SPI) with X-ray free-electron lasers has the potential to change fundamentally how biomacromolecules are imaged. The structure would be derived from millions of diffraction patterns, each from a different copy of the macromolecule before it is torn apart by radiation damage. The challenges posed by the resultant data stream are staggering: millions of incomplete, noisy and un-oriented patterns have to be computationally assembled into a three-dimensional intensity map and then phase reconstructed. In this paper, the Dragonfly software package is described, based on a parallel implementation of the expand–maximize–compress reconstruction algorithm that is well suited for this task. Auxiliary modules to simulate SPI data streams are also included to assess the feasibility of proposed SPI experiments at the Linac Coherent Light Source, Stanford, California, USA.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2019

Electron Beam Dynamics Simulation for Electron Lenses

A test stand is under construction at CERN to study high perveance electron guns, electron beam dynamics, and electron beam diagnostics for electron lenses. It will be used to test electron guns for the Hollow Electron Lenses under consideration for beam halo control for High Luminosity LHC (CERN), and for the Space Charge Compensation at SIS18 (GSI) in the frame of the EU funded ARIES project. In order to prepare for this test stand, simulations will be presented and compared with experiments undertaken at the Fermilab (FNAL) electron lens test stand. These were conducted using a hollow electron gun, with the magnetic field configuration and beam current varied to study their effect. The impact of imperfections on the beam dynamics and overall quality of the electron beam will be discussed. A method for comparing experimental data with simulation is also presented to allow bench-marking of the computer models and simulation tools that will later be applied to the analysis of measurements performed at CERN.

43 PARTICLE ACCELERATORS↗
DOE OSTI2024

Augmenting a Simulation Campaign for Hybrid Computer Model and Field Data Experiments

The Kennedy and O’Hagan (KOH) calibration framework uses coupled Gaussian processes (GPs) to meta-model an expensive simulator (first GP), tune its “knobs” (calibration inputs) to best match observations from a real physical/field experiment and correct for any modeling bias (second GP) when predicting under new field conditions (design inputs). There are well-established methods for placement of design inputs for data-efficient planning of a simulation campaign in isolation, that is, without field data: space-filling, or via criterion like minimum integrated mean-squared prediction error (IMSPE). Analogues within the coupled GP KOH framework are mostly absent from the literature. Here, in this study, we derive a closed form IMSPE criterion for sequentially acquiring new simulator data for KOH. We illustrate how acquisitions space-fill in design space, but concentrate in calibration space. Closed form IMSPE precipitates a closed-form gradient for efficient numerical optimization. We demonstrate that our KOH-IMSPE strategy leads to a more efficient simulation campaign on benchmark problems, and conclude with a showcase on an application to equilibrium concentrations of rare earth elements for a liquid–liquid extraction reaction.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2019

Reflector Selection for the Indexing of Electron Backscatter Diffraction Patterns

Abstract We propose a new methodology for ranking the reflectors used in traditional Hough-based indexing of electron backscatter diffraction (EBSD) patterns. Instead of kinematic X-ray or electron structure factors ( F hkl ) currently utilized, we propose the integrated Kikuchi band intensity parameter ( β hkl ) based on integrated dynamical electron backscatter intensities. The proposed parameter is compared with the traditional kinematical intensity, $I_{hkl}^{{\rm kin}} $ , as well as the average Hough transform peak intensity, $I_{hkl}^{{\rm HSP}} $ and used to index EBSD patterns for a number of different material systems of varying unit cell complexities including nickel, silicon, rutile, and forsterite. For elemental structures, β hkl closely follows the kinematical ranking. However, significant ranking differences arise for more complex unit cells, with the β hkl parameter showing a better correlation with the integrated Hough intensities. Lastly, Hough-based indexing of a simulated forsterite data set showed an appreciable improvement in the median confidence index (0.15 to 0.35) when β hkl is used instead of $I_{hkl}^{{\rm kin}} $ for ranking the reflectors.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Spatiotemporal Pattern Recognition in the PMU Signals in the WECC system

Phasor measurement unit (PMU) data has been used by multiple power system applications, including state estimation, post event analysis, oscillation detection, model validation, and many others. Still, due to its big data nature and availability to general research institutions, comprehensive understanding of the spatiotemporal patterns and underlying mechanisms are incomplete. This study applies a set of signal processing and machine learning approaches aiming at deciphering the characteristic behaviors of multiple phasor measurement units (PMUs) attributes (e.g., voltage, frequency, rate of change of frequency, phase angle), including their auto-correlation, cross-dependence, similarities and discrepancies across units and temporal scales, and distributions of anomalies and their linkages to potential external factors such as weather events. Data analytics are applied to PMUs from the U.S. Western Electricity Coordinating Council (WECC) system. The PMU measurements, recorded events, outages, and weather extremes are all from real world datasets. The findings from the study and mechanistic understanding of the PMU dynamics help provide guidance on system control or preventing blackouts. The derived metrics can be directly used for adjusting or filtering simulated PMU data used for advanced algorithm development.

Hou, Zhangshuan↗
DOE OSTI2015

New near-infrared period–luminosity–metallicity relations for RR Lyrae stars and the outlook for GAIA

We present results of the analysis of 70 RR Lyrae stars located in the bar of the Large Magellanic Cloud (LMC). Combining the spectroscopically determined metallicity of these stars from the literature with precise periods from the OGLE III catalog and multi-epoch K{sub s} photometry from the VISTA survey of the Magellanic Clouds system, we derive a new near-infrared period–luminosity–metallicity (PL{sub K{sub s}}Z) relation for RR Lyrae variables. In order to fit the relation we use a fitting method developed specifically for this study. The zero-point of the relation is estimated two different ways: by assuming the value of the distance to the LMC and by using Hubble Space Telescope parallaxes of five RR Lyrae stars in the Milky Way (MW). The difference in distance moduli derived by applying these two approaches is ∼0.2 mag. To investigate this point further we derive the PL{sub K{sub s}}Z relation based on 23 MW RR Lyrae stars that had been analyzed in Baade–Wesselink studies. We compared the derived PL{sub K{sub s}}Z relations for RR Lyrae stars in the MW and LMC. Slopes and zero-points are different, but still consistent within the errors. The shallow slope of the metallicity term is confirmed by both LMC and MW variables. The astrometric space mission Gaia is expected to provide a huge contribution to the determination of the RR Lyrae PL{sub K{sub s}}Z relation; however, calculating an absolute magnitude from the trigonometric parallax of each star and fitting a PL{sub K{sub s}}Z relation directly to period and absolute magnitude leads to biased results. We present a tool to achieve an unbiased solution by modeling the data and inferring the slope and zero-point of the relation via statistical methods.

79 ASTRONOMY AND ASTROPHYSICS↗