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At least 1,063 records · Page 59

All sources NASA NTRS DOE OSTI

DOE OSTI2022

Prediction of grain structure after thermomechanical processing of U-10Mo alloy using sensitivity analysis and machine learning surrogate model

Abstract Hot rolling and annealing are critical intermediate steps for controlling microstructures and thickness variations when fabricating uranium alloyed with 10% molybdenum (U-10Mo), which is highly relevant to worldwide nuclear non-proliferation efforts. This work proposes a machine-learning surrogate model combined with sensitivity analysis to identify and predict U-10Mo microstructure development during thermomechanical processing. Over 200 simulations were collected using physics-based microstructure models covering a wide range of thermomechanical processing routes and initial alloy grain features. Based on the sensitivity analysis, we determined that an increase in rolling reduction percentage at each processing pass has the strongest effect in reducing the grain size. Multi-pass rolling and annealing can significantly improve recrystallization regardless of the reduction percentage. With a volume fraction below 2%, uranium carbide particles were found to have marginal effects on the average grain size and distribution. The proposed stratified stacking ensemble surrogate predicts the U-10Mo grain size with a mean square error four times smaller than a standard single deep neural network. At the same time, with a significant speedup (1000×) compared to the physics-based model, the machine learning surrogate shows good potential for U-10Mo fabrication process optimization.

36 MATERIALS SCIENCE↗

DOE OSTI2014

Comparison of x-ray absorption spectra between water and ice: New ice data with low pre-edge absorption cross-section

The effect of crystal growth conditions on the O K-edge x-ray absorption spectra of ice is investigated through detailed analysis of the spectral features. The amount of ice defects is found to be minimized on hydrophobic surfaces, such as BaF{sub 2}(111), with low concentration of nucleation centers. This is manifested through a reduction of the absorption cross-section at 535 eV, which is associated with distorted hydrogen bonds. Furthermore, a connection is made between the observed increase in spectral intensity between 544 and 548 eV and high-symmetry points in the electronic band structure, suggesting a more extended hydrogen-bond network as compared to ices prepared differently. The spectral differences for various ice preparations are compared to the temperature dependence of spectra of liquid water upon supercooling. A double-peak feature in the absorption cross-section between 540 and 543 eV is identified as a characteristic of the crystalline phase. The connection to the interpretation of the liquid phase O K-edge x-ray absorption spectrum is extensively discussed.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2023

Distributed Software-Defined Network Architecture for Smart Grid Resilience to Denial-of-Service Attacks

An important challenge for smart grid security is designing a secure and robust smart grid communications architecture to protect against cyber-threats, such as Denial-of-Service (DoS) attacks, that can adversely impact the operation of the power grid. Researchers have proposed using Software Defined Network frameworks to enhance cybersecurity of the smart grid, but there is a lack of benchmarking and comparative analyses among the many techniques. In this work, a distributed three-controller software-defined networking (D3-SDN) architecture, benchmarking, and comparative analysis with other techniques is presented. The selected distributed flat SDN architecture divides the network horizontally into multiple areas or clusters, where each cluster is handled by a single Open Network Operating System (ONOS) controller. A case study using the IEEE 118-bus system is provided to compare the performance of the presented ONOS-managed D3-SDN, against the POX controller. In addition, the proposed architecture outperforms a single SDN controller framework by a tenfold increase in throughput; a reduction in latency of > 20%; and an increase in throughput of approximately 11% during the DoS attack scenarios.

Agnew Jr., Dennis↗

DOE OSTI2020

Electric Power Grid and Natural Gas Network Operations and Coordination

In this report, we present findings from three studies related to the coordination of natural gas and electricity system operations. We first propose and demonstrate a modeling platform for examining the interdependence of natural gas and electricity networks based on a direct current unit-commitment and economic dispatch model for the power system and a transient hydraulic gas model for the gas system. We use this platform to analyze the value of day-ahead coordination of power and natural gas network operations and to show the importance of considering gas system constraints when analyzing power systems operation with high penetration of gas generators and variable renewable energy sources. In the second study, we utilize our modeling platform to consider the U.S. Federal Energy Regulatory Commission (FERC) Order 809, issued in 2015 to improve day-ahead and intraday coordination of power and gas systems. Finally, in the third study we expand our modeling platform to focus on market-based coordination of electricity and natural gas system operations for a real system, namely a subset of the power and gas networks in the Front Range region of Colorado. We use real system data to evaluate the benefits of coordination operations under different conditions, including different levels of renewable penetration and the use of time-variant, shaped flow nominations. Our results indicate that coordination at various timescales can contribute to a reduction in curtailed gas in high-stress periods (such as those with large ramps in gas offtakes) and a reduction in energy consumption of gas compressor stations. We find that intraday coordination can reduce total power system production costs and natural gas deliverability constraints, yielding cost and reliability benefits. We observe these benefits for the test system as well as in the Colorado case study, where we find that coordination and shaped flows may provide additional value for systems with high penetration of variable renewable energy. Together, these three studies demonstrate a pathway for integrated gas and electricity grid modeling and for studying the benefits of coordinated operations of these increasingly interdependent energy systems.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

NASA NTRS1986

Airport-Noise Levels and Annoyance Model (ALAMO) user's guide

A guide for the use of the Airport-Noise Level and Annoyance MOdel (ALAMO) at the Langley Research Center computer complex is provided. This document is divided into 5 primary sections, the introduction, the purpose of the model, and an in-depth description of the following subsystems: baseline, noise reduction simulation and track analysis. For each subsystem, the user is provided with a description of architecture, an explanation of subsystem use, sample results, and a case runner's check list. It is assumed that the user is familiar with the operations at the Langley Research Center (LaRC) computer complex, the Network Operating System (NOS 1.4) and CYBER Control Language. Incorporated within the ALAMO model is a census database system called SITE II.

Deloach, R.↗

NASA NTRS1997

Hierarchical Simulation to Assess Hardware and Software Dependability

This thesis presents a method for conducting hierarchical simulations to assess system hardware and software dependability. The method is intended to model embedded microprocessor systems. A key contribution of the thesis is the idea of using fault dictionaries to propagate fault effects upward from the level of abstraction where a fault model is assumed to the system level where the ultimate impact of the fault is observed. A second important contribution is the analysis of the software behavior under faults as well as the hardware behavior. The simulation method is demonstrated and validated in four case studies analyzing Myrinet, a commercial, high-speed networking system. One key result from the case studies shows that the simulation method predicts the same fault impact 87.5% of the time as is obtained by similar fault injections into a real Myrinet system. Reasons for the remaining discrepancy are examined in the thesis. A second key result shows the reduction in the number of simulations needed due to the fault dictionary method. In one case study, 500 faults were injected at the chip level, but only 255 propagated to the system level. Of these 255 faults, 110 shared identical fault dictionary entries at the system level and so did not need to be resimulated. The necessary number of system-level simulations was therefore reduced from 500 to 145. Finally, the case studies show how the simulation method can be used to improve the dependability of the target system. The simulation analysis was used to add recovery to the target software for the most common fault propagation mechanisms that would cause the software to hang. After the modification, the number of hangs was reduced by 60% for fault injections into the real system.

Ries, Gregory Lawrence↗

NASA NTRSDate not supplied

A New Spectrum Management Concept for Future NAS Communications

With the growth of aviation operations and the emergence of new entrants into the National Airspace System, there has been an increasing demand for aeronautical communications. However, the existing VHF/UHF frequency allocations for NAS operations have become increasingly scarce and unable to support the projected growth in demand. While traditional approaches to address the spectrum scarcity issue (such as reduction of channel bandwidth) have provided some relief, there is a need for a more scalable and sustainable solution. Consequently, NASA is investigating a new approach to spectrum management within the NAS using Artificial Intelligence and other advanced technologies. The concept considers both air-ground and air-air communication networks with the anticipated outcome that the proposed concept will meet the spectrum needs of future NAS communications.

Rafael Daniel Apaza↗

NASA NTRSDate not supplied

A New Spectrum Management Concept for Future NAS Communications

Rafael D. Apaza↗

DOE OSTI2023

A Machine Learning Correction Model of the Winter Clear-Sky Temperature Bias over the Arctic Sea Ice in Atmospheric Reanalyses

Atmospheric reanalyses are widely used to estimate the past atmospheric near-surface state over sea ice. They provide boundary conditions for sea ice and ocean numerical simulations and relevant information for studying polar variability and anthropogenic climate change. Previous research revealed the existence of large near-surface temperature biases (mostly warm) over the Arctic sea ice in the current generation of atmospheric reanalyses, which is linked to a poor representation of the snow over the sea ice and the stably stratified boundary layer in the forecast models used to produce the reanalyses. These errors can compromise the employment of reanalysis products in support of polar research. Here, we train a fully connected neural network that learns from remote sensing infrared temperature observations to correct the existing generation of uncoupled atmospheric reanalyses (ERA5, JRA-55) based on a set of sea ice and atmospheric predictors, which are themselves reanalysis products. The advantages of the proposed correction scheme over previous calibration attempts are the consideration of the synoptic weather and cloud state, compatibility of the predictors with the mechanism responsible for the bias, and a self-emerging seasonality and multidecadal trend consistent with the declining sea ice state in the Arctic. The correction leads on average to a 27% temperature bias reduction for ERA5 and 7% for JRA-55 if compared to independent in situ observations from the MOSAiC campaign (respectively, 32% and 10% under clear-sky conditions). These improvements can be beneficial for forced sea ice and ocean simulations, which rely on reanalyses surface fields as boundary conditions.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2022

Machine Learning Derived Dynamic Operating Reserve Requirements in High-Renewable Power Systems

Accurately forecasting wind and solar power output poses challenges for deeply decarbonized electricity systems. Grid operators must commit resources to provide reserves to ensure reliable operations in the face of forecast errors, a process which can increase fuel consumption and emissions. We apply neural network-based machine learning to expand the usefulness of median point forecast data by creating probabilistic distributions of short-term uncertainty in demand, wind, and solar forecasts that adapt to prevailing grid conditions. Machine learning derived estimates of forecast errors compare favorably to estimates based on incumbent methods. Reserves derived from machine learning are usually smaller than values derived using incumbent methods, which enables fuel savings during most hours. Machine learning reserves are generally larger than incumbent reserves during times of higher forecast error, potentially improving system reliability. Performance is tested using multi-stage production simulation modeling of the California Independent System Operator (CAISO) system. Machine learning reserves provide production cost and greenhouse gas (GHG) emission reductions of approximately 0.3% relative to historical 2019 requirements. Savings in the 2030 timeframe are highly dependent on battery storage capacity. At lower levels of battery capacity, savings of 0.4% from machine learning reserves are shown. Significant quantities of battery storage are expected to be added to meet California's resource adequacy needs and GHG reduction targets. Addition of these batteries saturate reserve needs and results in minimal within-hour balancing costs in 2030.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2013

Spin-neurons: A possible path to energy-efficient neuromorphic computers

Recent years have witnessed growing interest in the field of brain-inspired computing based on neural-network architectures. In order to translate the related algorithmic models into powerful, yet energy-efficient cognitive-computing hardware, computing-devices beyond CMOS may need to be explored. The suitability of such devices to this field of computing would strongly depend upon how closely their physical characteristics match with the essential computing primitives employed in such models. In this work, we discuss the rationale of applying emerging spin-torque devices for bio-inspired computing. Recent spin-torque experiments have shown the path to low-current, low-voltage, and high-speed magnetization switching in nano-scale magnetic devices. Such magneto-metallic, current-mode spin-torque switches can mimic the analog summing and “thresholding” operation of an artificial neuron with high energy-efficiency. Comparison with CMOS-based analog circuit-model of a neuron shows that “spin-neurons” (spin based circuit model of neurons) can achieve more than two orders of magnitude lower energy and beyond three orders of magnitude reduction in energy-delay product. The application of spin-neurons can therefore be an attractive option for neuromorphic computers of future.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

DOE OSTI2015

Accelerated Sensitivity Analysis in High-Dimensional Stochastic Reaction Networks

Existing sensitivity analysis approaches are not able to handle efficiently stochastic reaction networks with a large number of parameters and species, which are typical in the modeling and simulation of complex biochemical phenomena. In this paper, a two-step strategy for parametric sensitivity analysis for such systems is proposed, exploiting advantages and synergies between two recently proposed sensitivity analysis methodologies for stochastic dynamics. The first method performs sensitivity analysis of the stochastic dynamics by means of the Fisher Information Matrix on the underlying distribution of the trajectories; the second method is a reduced-variance, finite-difference, gradient-type sensitivity approach relying on stochastic coupling techniques for variance reduction. Here in this paper we demonstrate that these two methods can be combined and deployed together by means of a new sensitivity bound which incorporates the variance of the quantity of interest as well as the Fisher Information Matrix estimated from the first method. The first step of the proposed strategy labels sensitivities using the bound and screens out the insensitive parameters in a controlled manner. In the second step of the proposed strategy, a finite-difference method is applied only for the sensitivity estimation of the (potentially) sensitive parameters that have not been screened out in the first step. Results on an epidermal growth factor network with fifty parameters and on a protein homeostasis with eighty parameters demonstrate that the proposed strategy is able to quickly discover and discard the insensitive parameters and in the remaining potentially sensitive parameters it accurately estimates the sensitivities. The new sensitivity strategy can be several times faster than current state-of-the-art approaches that test all parameters, especially in “sloppy” systems. In particular, the computational acceleration is quantified by the ratio between the total number of parameters over the number of the sensitive parameters.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2021

Neural network-based order parameter for phase transitions and its applications in high-entropy alloys

Phase transition is one of the most important phenomena in nature and plays a central role in materials design. All phase transitions are characterized by suitable order parameters, including the order–disorder phase transition. However, finding a representative order parameter for complex systems is non-trivial, such as for high-entropy alloys. Given the strength of dimensionality reduction of a variational autoencoder (VAE), we introduce a VAE-based order parameter. Here, we propose that the Manhattan distance in the VAE latent space can serve as a generic order parameter for order–disorder phase transitions. The physical properties of our order parameter are quantitatively interpreted and demonstrated by multiple refractory high-entropy alloys. Using this order parameter, a generally applicable alloy design concept is proposed by mimicking the natural mixing process of elements. Our physically interpretable VAE-based order parameter provides a computational technique for understanding chemical ordering in alloys, which can facilitate the development of rational alloy design strategies.

36 MATERIALS SCIENCE↗

DOE OSTI2015

Strategic supply system design – a holistic evaluation of operational and production cost for a biorefinery supply chain

Abstract Pioneer cellulosic biorefineries across the United States rely on a conventional feedstock supply system based on one‐year contracts with local growers, who harvest, locally store, and deliver feedstock in low‐density format to the conversion facility. While the conventional system is designed for high biomass yield areas, pilot scale operations have experienced feedstock supply shortages and price volatilities due to reduced harvests and competition from other industries. Regional supply dependency and the inability to actively manage feedstock stability and quality, provide operational risks to the biorefinery, which translate into higher investment risk. The advanced feedstock supply system based on a network of depots can mitigate many of these risks and enable wider supply system benefits. This paper compares the two concepts from a system‐level perspective beyond mere logistic costs. It shows that while processing operations at the depot increase feedstock supply costs initially, they enable wider system benefits including supply risk reduction (leading to lower interest rates on loans), industry scale‐up, conversion yield improvements, and reduced handling equipment and storage costs at the biorefinery. When translating these benefits into cost reductions per liter of gasoline equivalent ( LGE ), we find that total cost reductions between –$0.46 to –$0.21 per LGE for biochemical and –$0.32 to –$0.12 per LGE for thermochemical conversion pathways are possible. Naturally, these system level benefits will differ between individual actors along the feedstock supply chain. Further research is required with respect to depot sizing, location, and ownership structures. Published 2015. This article is a U.S. Government work and is in the public domain in the USA. Biofuels, Bioproducts and Biorefining published by Society of Industrial Chemistry and John Wiley & Sons Ltd.

09 BIOMASS FUELS↗

DOE OSTI2013

Relationship between Eu{sup 3+} reduction and glass polymeric structure in Al{sub 2}O{sub 3}-modified borate glasses under air atmosphere

The reduction of Eu{sup 3+} to Eu{sup 2+} is realized efficiently in Eu{sub 2}O{sub 3}-doped borate glasses prepared under air condition by melting-quenching method. Luminescent spectra show an increasing tendency of Eu{sup 2+} emission with increasing Al{sub 2}O{sub 3} concentration in B{sub 2}O{sub 3}–Na{sub 2}O glasses. It is interesting that significant enhancement appeared of Eu{sup 2+} luminescence in the Al{sub 2}O{sub 3}-rich sample comparing to the samples of Al{sub 2}O{sub 3} less than 6 mol%. FTIR and Raman scattering measurements indicated that some new vibration modes assigned to the low-polymerized structure groups decomposed from the slight Al{sub 2}O{sub 3} dopant samples. These results demonstrated that the polymerization of the glass structure decreased with increasing incorporation of Al{sub 2}O{sub 3} into the borate glasses, linking to the efficiency of Eu{sup 3+} self-reduction in air at high temperature. - graphical abstract: A novel europium valence reduction phenomenon occurred in Al{sub 2}O{sub 3} modified borate glasses, FTIR and Raman measurements revealed that high polymeric groups were destroyed to low polymery structures with Al{sub 2}O{sub 3} addition. - Highlights: • The efficient reduction of Eu{sup 3+} to Eu{sup 2+} is observed in the B{sub 2}O{sub 3}–Na{sub 2}O glasses. • Eu{sup 2+} luminescence is significant enhanced in the Al{sub 2}O{sub 3}-rich glasses. • The introduction of Al{sub 2}O{sub 3} changed the network structure of the borate glasses. • High polymeric borate groups in the glass matrix may be destroyed to the lower ones.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2019

Sulfate promotion of selective catalytic reduction of nitric oxide by ammonia on ceria

Selective catalytic reduction of nitric oxide by ammonia (NH 3 -SCR) is a promising technology for NO x emission control. In this work, the recently discovered sulfate promotion effect in this reaction was thoroughly investigated on ceria. Sulfates derived from different organic S precursors all exhibit a promotion effect. Mechanistic studies generate a reaction network including both Langmuir–Hinshelwood and Eley–Rideal mechanisms. Sulfates were shown to be versatile promoters with multiple functions: they tune the adsorption of reactants towards a more balanced intermediate coverage by creating strong Lewis acid sites, facilitate *NH 2 formation, and suppress ammonia oxidation through surface oxygen deactivation. The comparison between sulfates and the classic inorganic promoter FeO x revealed that sulfates are more effective, especially at high temperature. This is mainly attributed to the high coverages of Eley–Rideal intermediates and low ammonia oxidation activity on sulfated ceria. Overall, this work provides fundamental understanding of the role of surface sulfates in NH 3 -SCR, which are essential under actual operation conditions. This understanding presents an exciting opportunity for future catalyst design with high durability and low cost.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2021

Nickel(ii) complexes based on dithiolate–polyamine binary ligand systems: crystal structures, hirshfeld surface analysis, theoretical study, and catalytic activity study on photocatalytic hydrogen generation

To ascertain the influence of binary ligand systems [1,1-dicyanoethylene-2,2-dithiolate (i-mnt -2 ) and polyamine {tetraen = tris(2-aminoethyl)amine, tren = diethylene triamine and opda = o-phenylenediamine}] on the coordination modes of the Ni(II) metal center and resulting supramolecular architectures, a series of nickel(II) thiolate complexes [Ni(tetraen)(i-mnt)](DMSO) (1), [Ni 2 (tren) 2 (i-mnt) 2 ] (2), and [Ni 2 (i-mnt) 2 (opda) 2 ] n (3) have been synthesized in high yield in one step in water and structurally characterized by single crystal X-ray crystallography and spectroscopic techniques. X-ray diffraction studies disclose the diverse i-mnt -2 coordination to the Ni +2 center in the presence of active polyamine ligands, forming a slightly distorted octahedral geometry (NiN 4 S 2 ) in 1, square planar (NiS 4 ) and distorted octahedral geometries (NiN 6 ) in the bimetallic co-crystallized aggregate of cationic [Ni(tren) 2 ] +2 and anionic [Ni(i-mnt) 2 ] -2 in 2, and a one dimensional (1D) polymeric chain along the [100] axis in 3, having consecutive square planar (NiS 4 ) and octahedral (NiN 6 ) coordination kernels. The N–H···O, N–H···S, N–H···N, N–H···S, N–H···N, and N–H···O type hydrogen bonds stabilize the supramolecular assemblies in 1, 2, and 3 respectively imparting interesting graph-set-motifs. The molecular Hirshfeld surface analyses (HS) and 2D fingerprint plots were utilized for decoding all types of non-covalent contacts in the crystal networks. Atomic HS analysis of the Ni +2 centers reveals significant Ni–N metal–ligand interactions compared to Ni–S interactions. We have also studied the unorthodox interactions observed in the solid state structures of 1–3 by QTAIM and NBO analyses. Moreover, all the complexes proved to be highly active water reduction co-catalysts (WRC) in a photo-catalytic hydrogen evolution process involving iridium photosensitizers, wherein 2 and 3 having a square planar arrangement around the nickel center(s) – were found to be the most active ones, achieving 1000 and 1119 turnover numbers (TON), respectively.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2015

Experimental studies in magnetically induced transverse force-frequency effect in thin quartz microresonators

In this work, the transverse force-frequency sensitivity of magnetostrictive Metglas{sup ®} (Fe{sub 85}B{sub 5}Si{sub 10}) thin film coated AT-cut thickness shear mode quartz thin plate microresonator (500 μm × 500 μm × 19 μm) is experimentally measured and modeled in Lagrangian formulation by coupling magnetostrictive deformation equations with the basic plate equations from the theory of small deformation. The quartz plate resonator is fabricated by micromachining techniques and released into fixed-free structure using focused ion beam milling. Application of a magnetic field results in the out-of-plane bending of the structure due to elastic coupling between the magnetostrictive Metglas{sup ®} and quartz resonator layers. As a result of the transverse loading and out-of-plane bending, the admittance characteristics of the resonator shifts, and these shifts are recorded in real time utilizing a network analyzer. The sensitivity is experimentally measured to be 162.3 mdeg/Oe for phase, corresponding to a frequency sensitivity of Δf/H = 11 Hz/Oe. The equivalent force-frequency sensitivity can then be calculated as 2.36 μN/Hz using the developed model. The coupled domain analysis fits well with the experimental data. Further reduction of quartz thickness and optimization of the thickness ratio of the magnetostrictive to quartz layers offers the possibility of exploiting the stress sensitivity of plate microresonators as sensitive magnetic field sensors capable of low nanoTesla to picoTesla level magnetic flux densities.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗