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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 73 records · Page 4
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Incorporating historical data and past analyses for improved tensile property prediction of 9% Cr steel

Data-driven analytical clustering and visualization techniques were applied to the dataset of 9% Cr experimental alloy data generated through the eXtremeMAT project. Techniques and results were compared with the resulting clusters obtained through similar analytical techniques on previous and reduced versions of the dataset. The principal components were generated in order to reduce the dimensionality of the complex dataset and to visualize the underlying trends in the data. Partitioning around medoids was performed on the resulting principal components to determine relevant clusters. Domain knowledge labels were further applied to the principal components to compare the labels with the trends identified through the clustering methods. The resulting clusters show the addition of the new data into the dataset, and show where alignment between the domain knowledge labels and partitioning around medoids (PAM) clusters show where the labels are appropriate. The clusters can be used to compare the tensile properties of the alloys, and to reduce the variation in the dataset.

Alloy design, Mechanical Properties, data analysis↗
DOE OSTI2023

Coupled-cluster theory for strong entanglement in nuclei

Atomic nuclei can exhibit shape coexistence and multireference physics that enters in their ground states, and accurately capturing the ensuing correlations and entanglement is challenging. Here, we address this problem by applying single-reference coupled-cluster theory based on spherical and deformed reference states and the tailored coupled-cluster method. The latter combines configuration interactions to capture static correlations with coupled-cluster theory for dynamic correlations. We compute the atomic nuclei 12 C, 28 Si, and 56 Ni and find that the tailored coupled-cluster method and the single-reference approach based on a deformed Hartree-Fock state yield the most accurate results.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
NASA NTRS1992

Ab initio multireference study of the BN molecule

The lowest 1Sigma(+) and 3Pi states of the BN molecule are studied using multireference configuration interaction (MRCI) and averaged coupled-pair functional (ACPF) methods and large atomic natural orbital (ANO) basis sets, as well as several coupled cluster methods. Our calculations strongly support a 3Pi ground state, but the a1Sigma(+) state lies only 381 +/- 100/cm higher. The a1Sigma(+) state wave function exhibits strong multireference character and, consequently, the predictions of the perturbationally-based single-reference CCSD(T) coupled cluster method are not as reliable in this case as the multireference results. The theoretical predictions for the spectroscopic constants of BN are in good agreement with experiment for the Chi3Pi state, but strongly suggest a misassignment of the fundamental vibrational frequency for the a1Sigma(+) state.

Martin, J. M. L.↗
DOE OSTI2022

Intertwined spin, charge, and pair correlations in the two-dimensional Hubbard model in the thermodynamic limit

Significance The high-temperature superconducting cuprates are governed by intertwined striped magnetic and charge orders, in addition to superconductivity. Remarkably similar behavior has also been seen in numerical calculations for the Hubbard model describing the copper–oxygen layers in these materials. Finite-cluster methods typically find that spin- and charge-stripe order dominates, while embedded quantum-cluster methods, which access the thermodynamic limit, often conclude that superconductivity does. Here, we report the observation of fluctuating spin and charge stripes in an embedded cluster calculation for the Hubbard model. This discovery demonstrates that striped states survive in the thermodynamic limit and allows us to study their influence on the model’s superconducting properties, where we find evidence for pair-density-wave correlations intertwined with the stripe correlations.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2021

Multi-channel Imager Algorithm (MIA): A novel cloud-top phase classification algorithm

The current Geostationary Operational Environmental Satellites (GOES-16 and 17) cloud-top phase classification algorithm is based primarily on empirical thresholds at multiple wavelengths that have varying absorption capabilities for water and ice. The performance of current GOES-16 cloud-top phase product largely depends on the accuracy of the selection of reflectance ratios. Here this study aims at presenting a novel cloud-top phase classification algorithm (the Multi-channel Imager Algorithm, MIA) that provides a more judicious selection of relationships between channels using a supervised K-mean clustering method on multi-channel Red-Green-Blue images. The K-mean clustering method works analogously to how human eyes separate different colors in a microphysical color rendering set of satellite images, which differentiates water, ice and unclassified thin clouds. For water phase, cloud-top temperature information is used to further distinguish supercooled water. To evaluate the performance of the MIA, an extensive comparison with Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP), Moderate Resolution Imaging Spectroradiometer, and current GOES-16 cloud-top phase products is conducted, using CALIOP as the benchmark. Compared to the current GOES-16 cloud-top phase product, MIA demonstrates a substantial improvement in phase classification, where hit rate increases from 69% to 76% over the Continental United States and 58% to 66% over the full disk domain.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS1993

A possibilistic approach to clustering

Fuzzy clustering has been shown to be advantageous over crisp (or traditional) clustering methods in that total commitment of a vector to a given class is not required at each image pattern recognition iteration. Recently fuzzy clustering methods have shown spectacular ability to detect not only hypervolume clusters, but also clusters which are actually 'thin shells', i.e., curves and surfaces. Most analytic fuzzy clustering approaches are derived from the 'Fuzzy C-Means' (FCM) algorithm. The FCM uses the probabilistic constraint that the memberships of a data point across classes sum to one. This constraint was used to generate the membership update equations for an iterative algorithm. Recently, we cast the clustering problem into the framework of possibility theory using an approach in which the resulting partition of the data can be interpreted as a possibilistic partition, and the membership values may be interpreted as degrees of possibility of the points belonging to the classes. We show the ability of this approach to detect linear and quartic curves in the presence of considerable noise.

Krishnapuram, Raghu↗
NASA NTRS2005

Constrained spectral clustering under a local proximity structure assumption

This work focuses on incorporating pairwise constraints into a spectral clustering algorithm. A new constrained spectral clustering method is proposed, as well as an active constraint acquisition technique and a heuristic for parameter selection. We demonstrate that our constrained spectral clustering method, CSC, works well when the data exhibits what we term local proximity structure.

domain knowledge↗
DOE OSTI2022

Ultrafast infrared transient absorption spectroscopy of gas-phase Ni(CO) 4 photodissociation at 261 nm

Here, we employ ultrafast mid-infrared transient absorption spectroscopy to probe the rapid loss of carbonyl ligands from gas-phase nickel tetracarbonyl following ultraviolet photoexcitation at 261 nm. Here, nickel tetracarbonyl undergoes prompt dissociation to produce nickel tricarbonyl in a singlet excited state; this electronically excited tricarbonyl loses another CO group over tens of picoseconds. Our results also suggest the presence of a parallel, concerted dissociation mechanism to produce nickel dicarbonyl in a triplet excited state, which likely dissociates to nickel monocarbonyl. Mechanisms for the formation of these photoproducts in multiple electronic excited states are theoretically predicted with one-dimensional cuts through the potential energy surfaces and computation of spin–orbit coupling constants using equation of motion coupled cluster methods (EOM-CC) and coupled cluster theory with single and double excitations (CCSD). Bond dissociation energies are calculated with CCSD, and anharmonic frequencies of ground and excited state species are computed using density functional theory (DFT) and time-dependent density functional theory (TD-DFT).

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
NASA NTRS1985

A cluster analysis method for identification of subpopulations of cells in flow cytometric list-mode arrays

A specialized program was developed for flow cytometric list-mode data using an heirarchical tree method for identifying and enumerating individual subpopulations, the method of principal components for a two-dimensional display of 6-parameter data array, and a standard sorting algorithm for characterizing subpopulations. The program was tested against a published data set subjected to cluster analysis and experimental data sets from controlled flow cytometry experiments using a Coulter Electronics EPICS V Cell Sorter. A version of the program in compiled BASIC is usable on a 16-bit microcomputer with the MS-DOS operating system. It is specialized for 6 parameters and up to 20,000 cells. Its two-dimensional display of Euclidean distances reveals clusters clearly, as does its 1-dimensional display. The identified subpopulations can, in suitable experiments, be related to functional subpopulations of cells.

Li, Z. K.↗
DOE OSTI2024

Energetic and Spectroscopic Properties of Astrophysically Relevant MgC 4 H Radicals Using High-Level Ab Initio Calculations

Considering the importance of magnesium-bearing hydrocarbon molecules (MgC n H; n = 2, 4, and 6) in the carbon-rich circumstellar envelopes (e.g., IRC+10216), a total of 28 constitutional isomers of MgC 4 H have been theoretically investigated using density functional theory (DFT) and coupled-cluster methods. The zero-point vibrational energy corrected relative energies at the ROCCSD(T)/cc-pCVTZ level of theory reveal that the linear isomer, 1-magnesapent-2,4-diyn-1-yl (1, 2 Σ + ), is the global minimum geometry on the MgC 4 H potential energy surface. The latter has been detected both in the laboratory and in the evolved carbon star, IRC+10216. The calculated spectroscopic data for 1 match well with the experimental observations (error ~ 0.78%) which validates our theoretical methodology. Plausible isomerization processes happening among different isomers are examined using DFT and coupled-cluster methods. CASPT2 calculations have been performed for a few isomers exhibiting multireference characteristics. The second most stable isomer, 1-ethynyl-1λ 3 -magnesacycloprop-2-ene-2,3-diyl (2, 2 A 1 , μ = 2.54 D), is 146 kJ mol –1 higher in energy than 1 and possibly the next promising candidate to be detected in the laboratory or in the interstellar medium in future.

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

The Effect of Approximating Some Molecular Integrals in Coupled-Cluster Calculations: Fundamental Frequencies and Rovibrational Spectroscopic Constants of Cyclopropenylidene

The singles and doubles coupled-cluster method that includes a perturbational estimate of connected triple excitations, denoted CCSD(T), has been used, in conjunction with approximate integral techniques, to compute highly accurate rovibrational spectroscopic constants of cyclopropenylidene, C3H2. The approximate integral technique was proposed in 1994 by Rendell and Lee in order to avoid disk storage and input/output bottlenecks, and today it will also significantly aid in the development of algorithms for distributed memory, massively parallel computer architectures. It is shown in this study that use of approximate integrals does not impact the accuracy of CCSD(T) calculations. In addition, the most accurate spectroscopic data yet for C3H2 is presented based on a CCSD(T)/cc-pVQZ quartic force field that is modified to include the effects of core-valence electron correlation. Cyclopropenylidene is of great astronomical and astrobiological interest because it is the smallest aromatic ringed compound to be positively identified in the interstellar medium, and is thus involved in the prebiotic processing of carbon and hydrogen. The singles and doubles coupled-cluster method that includes a perturbational estimate of

Lee, Timothy J.↗
DOE OSTI2023

Estimating the electric vehicle charging demand of multi-unit dwelling residents in the United States

Abstract Early battery electric vehicle (EV) adopters can access home chargers for reliable charging. As the EV market grows, residents of multi-unit dwellings (MUDs) may face barriers in owning EVs and charging them without garage or parking availability. To investigate the mechanisms that can bridge existing disparities in home charging and station deployment, we characterized the travel behavior of MUD residents and estimated their EV residential charging demand. This study classifies the travel patterns of MUD residents by fusing trip diary data from the National Household Travel Survey and housing features from the American Housing Survey. A hierarchical agglomerative clustering method was used to cluster apartment complex residents’ travel profiles, considering attributes such as dwell time, daily vehicle miles traveled (VMT), income, and their residences’ US census division. We propose a charging decision model to determine the charging station placement demand in MUDs and the charging energy volume expected to be consumed, assuming that MUD drivers universally operate EVs in urban communities. Numerical experiments were conducted to gain insight into the charging demand of MUD residents in the US. We found that charging availability is indispensable for households that set out to meet 80% state of charge by the end of the day. When maintaining a 20% comfortable state of charge the entire day, the higher the VMT are, the greater the share of charging demand and the greater the energy use in MUD chargers. The upper-income group requires a greater share of MUD charging and greater daily kWh charged because of more VMT.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2020

On the Performance of Detecting Injection of Fabricated Messages into the CAN Bus

There have been several public demonstrations of attacks on connected vehicles showing the ability of an attacker to take control of a targeted vehicle by injecting messages into their Controller Area Network (CAN) bus. In this article, using injected speed reading and Revolutions Per Minute (RPM) reading messages in in-motion vehicle, we examine the ability of the Pearson correlation and the unsupervised learning methods k-means clustering and Hidden Markov Model (HMM) to differentiate ’no-attack’ and ’under-attack’ states of the given vehicle. We found that the Pearson correlation distinguishes the two states, the k-means clustering method has an acceptable accuracy but high false positive rate and HMM detects attacks with acceptable detection rate but has a high false positive in detecting attacks from speed readings when there is no attack. The accuracy of these unsupervised learning methods are comparable to the ones of the supervised learning methods used by CAN bus Intrusion Detection System (IDS) suppliers. In addition, the article shows that studying CAN anomaly detection techniques using off-vehicle test facilities may not properly evaluate the performance of the detection techniques. Finally, the results suggest using other features besides the data content of the CAN messages and integrate knowledge about how the Electronic Control Units (ECUs) collaborate in building effective techniques for the detection of injection of fabricated message attacks.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

An automated cluster surface scanning method for exploring reaction paths on metal-cluster surfaces

Metal-cluster surfaces present a wide variety of unique coordination environments. This complexity makes it difficult to manually probe the surface reactivity of such clusters. Here, we present a simple and automated method to systematically discover reaction pathways on cluster surfaces, based on the automated cluster surface scanning (ACSS) technique for mapping out potential energy surfaces. We showcase our method on 55-atom icosahedral Cu and Ag clusters, where we determine the activation energies of four elementary steps common in heterogeneous catalysis – hydrogen recombination (H* + H* → H 2 * + *), oxygen recombination (O* + O* → O 2 * + *), water formation (OH* + H* → H 2 O* + *), and CO oxidation (CO* + O* → CO 2 * + *) – with density functional theory calculations (DFT-PBE + D3). We show that the ACSS method requires significantly less human effort than the established manually performed climbing-image nudged elastic band (MP + CI-NEB) technique and locates transition states with comparable accuracy (root-mean-squared error of 0.10 eV) and similar computational cost. Rigorous sampling of the potential energy surface with the ACSS method allows one to locate all lowest-energy reaction pathways obtained via the MP+CI-NEB approach, as well as alternative pathways that one may have missed with the MP+CI-NEB approach due to the many possible pathways available on these clusters. The accuracy and efficiency afforded by the ACSS method could enable high-throughput exploration of the diverse reactivity of metal clusters.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Deconvolute individual genomes from metagenome sequences through short read clustering

Metagenome assembly from short next-generation sequencing data is a challenging process due to its large scale and computational complexity. Clustering short reads by species before assembly offers a unique opportunity for parallel downstream assembly of genomes with individualized optimization. However, current read clustering methods suffer either false negative (under-clustering) or false positive (over-clustering) problems. Here we extended our previous read clustering software, SpaRC, by exploiting statistics derived from multiple samples in a dataset to reduce the under-clustering problem. Using synthetic and real-world datasets we demonstrated that this method has the potential to cluster almost all of the short reads from genomes with sufficient sequencing coverage. The improved read clustering in turn leads to improved downstream genome assembly quality.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

A Transferable Force Field for Predicting Adsorption and Diffusion of Water in Cationic Zeolites with Coupled Cluster Accuracy

We present a transferable force field for water in proton-exchanged, alkali (Li, Na, K, Rb, and Cs) metal-exchanged, and alkaline-earth (Mg, Ca, Sr, and Ba) metal-exchanged zeolites. The fitting methodology is based on adsorbate–adsorbent interaction energies obtained from periodic density functional theory calculations and corrected using the coupled-cluster method applied to small model clusters. To ensure an accurate prediction of both adsorption and diffusion properties of water, sets of configurations that sample both adsorption sites and intracrystalline hopping transition states were used in the fitting. The quality of the force field is assessed for a wide range of zeolites with different topologies and chemical compositions, demonstrating good agreement between theoretical predictions and experimental measurements of water adsorption and diffusion.

Adsorption↗
DOE OSTI2023

A parallel, distributed memory implementation of the adaptive sampling configuration interaction method

The many-body simulation of quantum systems is an active field of research that involves several different methods targeting various computing platforms. Many methods commonly employed, particularly coupled cluster methods, have been adapted to leverage the latest advances in modern high-performance computing. Selected configuration interaction (sCI) methods have seen extensive usage and development in recent years. However, the development of sCI methods targeting massively parallel resources has been explored only in a few research works. Here, we present a parallel, distributed memory implementation of the adaptive sampling configuration interaction approach (ASCI) for sCI. In particular, we will address the key concerns pertaining to the parallelization of the determinant search and selection, Hamiltonian formation, and the variational eigenvalue calculation for the ASCI method. Load balancing in the search step is achieved through the application of memory-efficient determinant constraints originally developed for the ASCI-PT2 method. The presented benchmarks demonstrate near optimal speedup for ASCI calculations of Cr 2 (24e, 30o) with 10 6 , 10 7 , and 3 × 10 8 variational determinants on up to 16 384 CPUs. Importantly, to the best of the authors’ knowledge, this is the largest variational ASCI calculation to date.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

Five-day track forecast skills of WRF model for the western North Pacific tropical cyclones

In this study, the characteristics of simulated tropical cyclones (TCs) over the western North Pacific by a regional model (the WRF Model) are verified. We utilize 12-km horizontal grid spacing, and simulations are integrated for 5 days from model initialization. A total of 125 forecasts are divided into five clusters through the k-means clustering method. The TCs in the cluster 1 and 2 (group 1), which includes many TCs moving northward in the subtropical region, generally have larger track errors than for TCs in cluster 3 and 4 (group 2). The optimal steering vector is used to examine the difference in the track forecast skill between these two groups. The bias in the steering vector between the model and analysis data is found to be more substantial for group 1 TCs than group 2 TCs. The larger steering vector difference for group 1 TCs indicates that environmental fields tend to be poorly simulated in group 1 TC cases. Furthermore, the residual terms, including the storm-scale process, asymmetric convection distribution, or beta-related effect, are also larger for group 1 TCs than group 2 TCs. Therefore, it is probable that the large track forecast error for group 1 TCs is a result of unreasonable simulations of environmental wind fields and residual processes in the midlatitudes.

54 ENVIRONMENTAL SCIENCES↗