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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.

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At least 1,009 records · Page 56
All sourcesNASA NTRSDOE OSTI
NASA NTRS2024

Comparison of Multisine Peak Factor Minimization Algorithms for Aircraft System Identification

Two phase-optimized multisine peak factor minimization algorithms are presented and evaluated. The first algorithm minimizes peak factor by iteratively clipping the peaks of generated multisine signals. The second algorithm optimizes peak factor indirectly through minimization of an approximation of the infinity norm of the multisine. Algorithm performance was evaluated as a function of different signal properties, including the number of harmonics, harmonic spacing, and number of snow harmonics (extra harmonics included for further reduction of the peak factor). The two algorithms are compared against results obtained by minimizing peak factor directly using a simplex algorithm, which has been a common approach when designing phase-optimized multisines for system identification flight tests. Sample results show that the clipping and infinity norm algorithms produced multisine signals with comparable peak factors that were lower than that of the simplex algorithm. However, the clipping algorithm runs an order of magnitude faster than the other two algorithms, which also makes it practical to repeat the algorithm multiple times to achieve even lower peak factors.

system identification↗
NASA NTRSDate not supplied

Comparison of Multisine Peak Factor Minimization Algorithms for Aircraft System Identification(Presentation)

Two phase-optimized multisine peak factor minimization algorithms are presented and evaluated. The first algorithm minimizes peak factor by iteratively clipping the peaks of generated multisine signals. The second algorithm optimizes peak factor indirectly through minimization of an approximation of the infinity norm of the multisine. Algorithm performance was evaluated as a function of different signal properties, including the number of harmonics, harmonic spacing, and number of snow harmonics (extra harmonics included for further reduction of the peak factor). The two algorithms are compared against results obtained by minimizing peak factor directly using a simplex algorithm, which has been a common approach when designing phase-optimized multisines for system identification flight tests. Sample results show that the clipping and infinity norm algorithms produced multisine signals with comparable peak factors that were lower than that of the simplex algorithm. However, the clipping algorithm runs an order of magnitude faster than the other two algorithms, which also makes it practical to repeat the algorithm multiple times to achieve even lower peak factors.

flight test↗
DOE OSTI2022

Advanced Sensor Deployment for Distribution System State Estimation and Fault Identification: Preprint

Distribution systems are currently facing steep operational challenges as a result of the rapidly increasing integration of renewables and other distributed energy resources (DERs)at both the primary and secondary circuit levels. Distribution utilities and system operators have traditionally had some visibility of their primary circuits using low-frequency supervisory control and data acquisition systems, and they have had very poor if not zero visibility of the secondary circuits where the presence of DERs is constantly increasing. Therefore, this paper presents simulation studies to demonstrate the benefits of an advanced, high-fidelity sensor technology, called as the Meta-Alert System (MAS), developed by Electrical Grid Monitoring Ltd. (EGM), on the distribution grid. First, a reliable model of the EGM sensors is developed, and then two use cases - distribution system state estimation (DSSE) and fault identification - are simulated to evaluate the performance of the MAS technology. Simulation results on the Electric Power Research Institute J1feeder demonstrate that the MAS can effectively participate in system-level DSSE programs and can detect and locate faults faster than traditional distribution protection schemes.

distribution system monitoring↗
DOE OSTI2023

3D Virtual Simulation for Radiation Safety and Hazards Identification Training

This presentation will be presented at CAES codebreaker session on May 4th. The slides include two projects that demonstrate how virtual training can benefit from visualization technology. One is a collaboration project between the Applied Visualization Laboratory and College of Eastern Idaho for radiation safety and survey training. The other one is Idaho National Laboratory's hazards identification online training that utilized LiDAR technology.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2017

Characterization of LIBS emission lines for the identification of chlorides, carbonates, and sulfates in salt/basalt mixtures for the application to MSL ChemCam data: LIBS OF CL, C, S IN SALT-BASALT MIXTURES

Ancient environmental conditions on Mars can be probed through the identification of minerals on its surface, including water-deposited salts and cements dispersed in the pore space of sedimentary rocks. Laser-induced breakdown spectroscopy (LIBS) analyses by the Martian rover Curiosity's ChemCam instrument can indicate salts, and ChemCam surveys aid in identifying and selecting sites for further, detailed in situ analyses. Here, we performed laboratory LIBS experiments under simulated Mars conditions with a ChemCam-like instrument on a series of mixtures containing increasing concentrations of salt in a basaltic background to investigate the potential for identifying and quantifying chloride, carbonate, and sulfate salts found only in small amounts, dispersed in bulk rock with ChemCam, rather than concentrated in veins. The data then indicate that the presence of emission lines from the basalt matrix limited the number of Cl, C, and S emission lines found to be useful for quantitative analysis; nevertheless, several lines with intensities sensitive to salt concentration were identified. Detection limits for the elements based on individual emission lines ranged from ~20 wt % carbonate (2 wt % C), ~5–30 wt % sulfate (1–8 wt % S), and ~5–10 wt % chloride (3–6 wt % Cl) depending on the basaltic matrix and/or salt cation. Absolute quantification of Cl, C, and S in the samples via univariate analysis depends on the cation-anion pairing in the salt but appears relatively independent of matrices tested, following normalization. Our results are promising for tracking relative changes in the salt content of bulk rock on the Martian surface with ChemCam.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2016

An adaptive Gaussian process-based method for efficient Bayesian experimental design in groundwater contaminant source identification problems: ADAPTIVE GAUSSIAN PROCESS-BASED INVERSION

Surrogate models are commonly used in Bayesian approaches such as Markov Chain Monte Carlo (MCMC) to avoid repetitive CPU-demanding model evaluations. However, the approximation error of a surrogate may lead to biased estimations of the posterior distribution. This bias can be corrected by constructing a very accurate surrogate or implementing MCMC in a two-stage manner. Since the two-stage MCMC requires extra original model evaluations, the computational cost is still high. If the information of measurement is incorporated, a locally accurate approximation of the original model can be adaptively constructed with low computational cost. Based on this idea, we propose a Gaussian process (GP) surrogate-based Bayesian experimental design and parameter estimation approach for groundwater contaminant source identification problems. A major advantage of the GP surrogate is that it provides a convenient estimation of the approximation error, which can be incorporated in the Bayesian formula to avoid over-confident estimation of the posterior distribution. The proposed approach is tested with a numerical case study. Without sacrificing the estimation accuracy, the new approach achieves about 200 times of speed-up compared to our previous work using two-stage MCMC.

Zhang, Jiangjiang↗
DOE OSTI2021

Disentangling Ferroelectric Wall Dynamics and Identification of Pinning Mechanisms via Deep Learning

Field-induced domain-wall dynamics in ferroelectric materials underpins multiple applications ranging from actuators to information technology devices and necessitates a quantitative description of the associated mechanisms including giant electromechanical couplings, controlled nonlinearities, or low coercive voltages. While the advances in dynamic piezoresponse force microscopy measurements over the last two decades have rendered visualization of polarization dynamics relatively straightforward, the associated insights into the local mechanisms have been elusive. This work explores the domain dynamics in model polycrystalline materials using a workflow combining deep-learning-based segmentation of the domain structures with nonlinear dimensionality reduction using multilayer rotationally invariant autoencoders (rVAE). The former allows unambiguous identification and classification of the ferroelectric and ferroelastic domain walls. Here, the rVAE discovers the latent representations of the domain wall geometries and their dynamics, consequently providing insight into the intrinsic mechanisms of polarization switching, that can further be compared to simple physical models. The rVAE disentangles the factors affecting the pinning efficiency of ferroelectric walls, offering insights into the correlation of ferroelastic wall distribution and ferroelectric wall pinning.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Quantitative Identification of Dopant Occupation in Li‐Rich Cathodes

Elemental doping is widely used to improve the performance of cathode materials in lithium‐ion batteries. However, macroscopic/statistical investigation on how doping sites are distributed in the material lattice, despite being a key prerequisite for understanding and manipulating the doping effect, has not been effectively established. Herein, to solve this predicament, a universal strategy is proposed to quantitatively identify the locations of Al and Mg dopants in lithium‐rich layered oxides (LLOs). Solid evidence confirms that Al prefers to occupy the transition metal (TM) layer, while Mg evenly occupies both TM and Li layers. As a result, Mg significantly reduces the thickness of LiO 2 slabs at room temperature, which will increase the energy barrier of oxygen activation and enhance the structure stability of LLOs. The suppressed oxygen activity in Mg‐doped LLO can be kinetically unlocked at 55 °C. The different characteristics of Al and Mg enlighten an Al/Mg co‐doping strategy to optimize LLOs, which significantly improves the cycle performance while lifting the capacity. In conclusion, these insights from the quantitative identification of doping sites shed light on the manipulation of doping effects toward better cathodes.

25 ENERGY STORAGE↗
DOE OSTI2022

Computational Identification and Experimental Demonstration of High‐Performance Methane Sorbents

Abstract Remarkable methane uptake is demonstrated experimentally in three metal‐organic frameworks (MOFs) identified by computational screening: UTSA‐76, UMCM‐152 and DUT‐23‐Cu. These MOFs outperform the benchmark sorbent, HKUST‐1, both volumetrically and gravimetrically, under a pressure swing of 80 to 5 bar at 298 K. Although high uptake at elevated pressure is critical for achieving this performance, a low density of high‐affinity sites (coordinatively unsaturated metal centers) also contributes to a more complete release of stored gas at low pressure. The identification of these MOFs facilitates the efficient storage of natural gas via adsorption and provides further evidence of the utility of computational screening in identifying overlooked sorbents.

Nath, Karabi↗
DOE OSTI2020

A Glycan Array-Based Assay for the Identification and Characterization of Plant Glycosyltransferases

Growing plants with modified cell wall compositions is a promising strategy to improve resistance to pathogens, increase biomass digestibility, and tune other important properties. In order to alter biomass architecture, a detailed knowledge of cell wall structure and biosynthesis is a prerequisite. We report here a glycan array-based assay for the high-throughput identification and characterization of plant cell wall biosynthetic glycosyltransferases (GTs). We demonstrate that different heterologously expressed galactosyl-, fucosyl-, and xylosyltransferases can transfer azido-functionalized sugar nucleotide donors to selected synthetic plant cell wall oligosaccharides on the array and that the transferred monosaccharides can be visualized “on chip” by a 1,3-dipolar cycloaddition reaction with an alkynyl-modified dye. The opportunity to simultaneously screen thousands of combinations of putative GTs, nucleotide sugar donors, and oligosaccharide acceptors will dramatically accelerate plant cell wall biosynthesis research.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Computational Identification and Experimental Demonstration of High‐Performance Methane Sorbents

Abstract Remarkable methane uptake is demonstrated experimentally in three metal‐organic frameworks (MOFs) identified by computational screening: UTSA‐76, UMCM‐152 and DUT‐23‐Cu. These MOFs outperform the benchmark sorbent, HKUST‐1, both volumetrically and gravimetrically, under a pressure swing of 80 to 5 bar at 298 K. Although high uptake at elevated pressure is critical for achieving this performance, a low density of high‐affinity sites (coordinatively unsaturated metal centers) also contributes to a more complete release of stored gas at low pressure. The identification of these MOFs facilitates the efficient storage of natural gas via adsorption and provides further evidence of the utility of computational screening in identifying overlooked sorbents.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2018

A framework for the identification of promising bio-based chemicals

Recent progress in metabolic engineering and synthetic biology enables the use of microorganisms for the production of chemicals (“bio-based chemicals”). Yet, it is still unclear which chemicals have the highest economic prospect. Because of this, we develop a framework for the identification of such promising ones. Specifically, we first develop a genome-scale constraintbased metabolic modeling approach, which is used to identify a candidate pool of 209 chemicals (together with the estimated yield, productivity and residence time for each) from the intersection of the High-Production-Volume (HPV) chemicals and the KEGG and MetaCyc databases. Second, we design three screening criteria based on a chemical’s profit margin, market volume and market size. The total process cost, including the downstream separation cost, is systematically incorporated into the evaluation. Third, given the three aforementioned criteria, we identify 32 products as economically promising if the maximum yields can be achieved, and 22 products if the maximum productivities can be achieved. The breakeven titer that renders zero profit margin for each product is also presented. Comparisons between extracellular and intracellular production, as well as E. coli and S. cerevisiae systems are also discussed. The proposed framework provides important guidance for future studies in the production of bio-based chemicals. It is also flexible in that the databases, yield estimations, and criteria can be modified to customize the screening.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2017

High‐Energy‐Resolution X‐ray Absorption Spectroscopy for Identification of Reactive Surface Species on Supported Single‐Site Iridium Catalysts

Abstract We report high‐energy‐resolution X‐ray absorption spectroscopy detection of ethylene and CO ligands adsorbed on catalytically active iridium centers isolated on zeolite HY and on MgO supports. The data are supported by density functional theory and FEFF X‐ray absorption near‐edge modelling, together with infrared (IR) spectra. The results demonstrate that high‐energy‐resolution X‐ray absorption spectra near the iridium L III (2p 3/2 ) edge provide clearly ascribable, distinctive signatures of the ethylene and CO ligands and illustrate effects of supports and other ligands. This X‐ray absorption technique is markedly more sensitive than conventional IR spectroscopy for characterizing surface intermediates, and it is applicable to samples having low metal loadings and in reactive atmospheres and is expected to have an increasing role in catalysis research by facilitating the determination of mechanisms of solid‐catalyzed reactions through identification of reaction intermediates in working catalysts.

Hoffman, Adam S.↗
DOE OSTI2016

Identification and Optimization of Anthranilic Acid Based Inhibitors of Replication Protein A

Replication protein A (RPA) is an essential single-stranded DNA (ssDNA)-binding protein that initiates the DNA damage response pathway through protein–protein interactions (PPIs) mediated by its 70N domain. The identification and use of chemical probes that can specifically disrupt these interactions is important for validating RPA as a cancer target. A high-throughput screen (HTS) to identify new chemical entities was conducted, and 90 hit compounds were identified. From these initial hits, an anthranilic acid based series was optimized by using a structure-guided iterative medicinal chemistry approach to yield a cell-penetrant compound that binds to RPA70N with an affinity of 812 nm. This compound, 2-(3- (N-(3,4-dichlorophenyl)sulfamoyl)-4-methylbenzamido)benzoic acid (20 c), is capable of inhibiting PPIs mediated by this domain.

Patrone, James D.↗
DOE OSTI2022

Energy-resolved mass spectrometry as a tool for identification of lignin depolymerization products

Lignin is the largest source of bio-based aromatic compounds in nature, and its valorization is essential to the sustainability of lignocellulosic biorefining. Characterizing lignin-derived compounds remains challenging due to the heterogeneity of this biopolymer. Tandem mass spectrometry is a promising tool for lignin structural analytics, as fragmentation patterns of model compounds can be extrapolated to identify characteristic moieties in complex samples. This work extends previous resonance excitation type collision-induced dissociation (CID) methods that identified lignin oligomers containing β-Ο-4, β-5, and β-β bonds, to also identify characteristics of 5-5, β-1, and 4-Ο-5 dimers, enabled by quadrupole time-of-flight (QTOF) CID with energy-resolved mass spectrometry (ERMS). Overall, beam type ERMS offers in-depth structural information and could ultimately contribute to tools for high-throughput lignin dimer identification.

09 BIOMASS FUELS↗
DOE OSTI2018

Probabilistic N - k failure-identification for power systems

This work considers a probabilistic generalization of the N - k failure-identification problem in power transmission networks, where the probability of failure of each component in the network is known a priori and the goal of the problem is to find a set of k components that maximizes disruption to the system loads weighted by the probability of simultaneous failure of the k components. The resulting problem is formulated as a bilevel mixed-integer nonlinear program. Convex relaxations, linear approximations, and heuristics are developed to obtain feasible solutions that are close to the optimum. A general cutting-plane algorithm is proposed to solve the convex relaxation and linear approximations of the N - k problem. Extensive numerical results corroborate the effectiveness of the proposed algorithms on small-, medium-, and large-scale test instances; the test instances include the IEEE 14-bus system, the IEEE single-area and three-area RTS96 systems, the IEEE 118-bus system, the WECC 240-bus test system, the 1354-bus PEGASE system, and the 2383-bus Polish winter-peak test system.

24 POWER TRANSMISSION AND DISTRIBUTION↗