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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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Inferring radiation-induced microstructural evolution in single-crystal niobium through changes in thermal transport
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Multi-model polynomial chaos surrogate dictionary for Bayesian inference in elasticity problems
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Inferring the Energetics of CO 2 –Aniline Adduct Formation from Vibrational Spectroscopy
Control of atmospheric CO 2 is an important contemporary scientific and engineering challenge. Towards this goal, the reaction of CO 2 with amines to form carbamate bonds is an established method for CO 2 capture. However, controllable reversal of this reaction remains difficult and requires tuning the energetics of the carbamate bond. Through IR spectroscopy, we show that a characteristic frequency observed upon carbamate formation varies as a function of the substituent’s Hammett parameter for a family of para- substituted anilines. We present computational evidence that the vibrational frequency of the adducted CO 2 serves as a predictor of the energy of formation of the carbamate. Electron donating groups typically enhance the driving force of carbamate formation by transferring more charge to the adducted CO 2 and thus increasing the occupancy of the anti-bonding orbital in the carbon-oxygen bonds. Increased occupancy of the anti-bonding orbital within adducted CO 2 indicates a weaker bond, leading to a red shift in the characteristic carbamate frequency. As a result, our work serves the large field of CO 2 capture research where spectroscopic observables, such as IR frequencies, are more easily obtainable and can stand in as a descriptor of driving forces.
Bayesian inference of multi-messenger astrophysical data: Joint and coherent inference of gravitational waves and kilonovae
Multi-messenger observations of binary neutron star mergers can provide information on the neutron star’s equation of state (EOS) above the nuclear saturation density by directly constraining the mass-radius diagram. We present a Bayesian framework for joint and coherent analyses of multi-messenger binary neutron star signals. As a first application, we analyze the gravitational-wave GW170817 and the kilonova (kN) AT2017gfo data. These results are then combined with the most recent X-ray pulsar analyses of PSR J0030+0451 and PSR J0740+6620 to obtain new EOS constraints.We extend the bajes infrastructure with a joint likelihood for multiple datasets, support for various semi-analytical kN models, and numerical-relativity (NR)-informed relations for the mass ejecta, as well as a technique to include and marginalize over modeling uncertainties. The analysis of GW170817 used the TEOBResumS effective-one-body waveform template to model the gravitational-wave signal. The analysis of AT2017gfo used a baseline multicomponent spherically symmetric model for the kN light curves. Various constraints on the mass-radius diagram and neutron star properties were then obtained by resampling over a set of ten million parameterized EOSs, which was built under minimal assumptions (general relativity and causality).
Average neutron time-of-flight instrument response function inferred from single D-T neutron events within a plastic scintillator
The apparent ion temperature and neutron-reaction history are important characteristics of a fusion plasma. Extracting these quantities from a measured neutron-time-of-flight signal requires accurate knowledge of the instrument response function (IRF). This paper describes a novel method for obtaining the IRF directly for single DT neutron interactions by utilizing n-alpha coincidence. The t(d,α)n nuclear reaction was produced at Sandia National Laboratories’ Ion Beam Laboratory using a 300 keV Cockcroft-Walton generator to accelerate a 2.5 μA beam of 175 keV D + ions into a stationary ErT 2 target. The average neutron IRF was calculated by taking a time-corrected average of individual neutron events within an EJ-228 plastic scintillator. The scintillator was coupled to two independent photo-multiplier tubes operated in the current mode: a Hamamatsu 5946 mod-5 and a Photek PMT240. Finally, the experimental setup and results will be discussed.
Model independent inference of the expansion history and implications for the growth of structure
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Commutative Data Reordering: A New Technique to Reduce Data Movement Energy on Sparse Inference Workloads.
Abstract not provided.
Large scale maximum average power multiple inference on time-course count data with application to RNA-seq analysis
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Semi-supervised learning and inference in domain-wall magnetic tunnel junction (DW-MTJ) neural networks.
Abstract not provided.
Low-Power Deep Learning Inference using the SpiNNaker Neuromorphic Platform.
Abstract not provided.
Identifying strengths and weaknesses of methods for computational network inference from single-cell RNA-seq data
Single-cell RNA-sequencing (scRNA-seq) offers unparalleled insight into the transcriptional programs of different cellular states by measuring the transcriptome of thousands of individual cells. An emerging problem in the analysis of scRNA-seq is the inference of transcriptional gene regulatory networks and a number of methods with different learning frameworks have been developed to address this problem. Here, we present an expanded benchmarking study of eleven recent network inference methods on seven published scRNA-seq datasets in human, mouse, and yeast considering different types of gold standard networks and evaluation metrics. We evaluate methods based on their computing requirements as well as on their ability to recover the network structure. We find that, while most methods have a modest recovery of experimentally derived interactions based on global metrics such as Area Under the Precision Recall curve, methods are able to capture targets of regulators that are relevant to the system under study. Among the top performing methods that use only expression were SCENIC, PIDC, MERLIN or Correlation. Addition of prior biological knowledge and the estimation of transcription factor activities resulted in the best overall performance with the Inferelator and MERLIN methods that use prior knowledge outperforming methods that use expression alone. We found that imputation for network inference did not improve network inference accuracy and could be detrimental. Comparisons of inferred networks for comparable bulk conditions showed that the networks inferred from scRNA-seq datasets are often better or at par with the networks inferred from bulk datasets. Our analysis should be beneficial in selecting methods for network inference. At the same time, this highlights the need for improved methods and better gold standards for regulatory network inference from scRNAseq datasets.
Three-dimensional modeling of the neutron spectrum to infer plasma conditions in cryogenic inertial confinement fusion implosions
Neutron-based diagnostics are typically used to infer compressed core conditions such as areal density and ion temperature in deuterium–tritium (D–T) inertial confinement fusion (ICF) implosions. Asymmetries in the observed neutron-related quantities are important to understanding failure modes in these implosions. Neutrons from fusion reactions and their subsequent interactions including elastic scattering and neutron-induced deuteron breakup reactions are tracked to create spectra. Here, it is shown that background subtraction is important for inferring areal density from backscattered neutrons and is less important for the forward-scattered neutrons. A three-dimensional hydrodynamic simulation of a cryogenic implosion on the OMEGA Laser System [T. R. Boehly et al., Opt. Commun. 133, 495 (1997)] using the hydrodynamic code HYDRA [M. M. Marinak et al., Phys. Plasmas 8, 2275 (2001)] is post-processed using the tracking code IRIS3D. It is shown that different parts of the neutron spectrum from the view can be mapped into different regions of the implosion, enabling an inference of an areal-density map. It is also shown that the average areal-density and an areal-density map of the compressed target can be reconstructed with a finite number of detectors placed around the target chamber. Ion temperatures are inferred from the width of the D–D and D–T fusion neutron spectra. Backgrounds can significantly alter the inferred ion temperatures from the D–D reaction, whereas they insignificantly influence the inferred D–T ion temperatures for the areal densities typical of OMEGA implosions. Asymmetries resulting in fluid flow in the core are shown to influence the absolute inferred ion temperatures from both reactions, although relative inferred values continue to reflect the underlying asymmetry pattern. The work presented here is part of the wide range of the first set of studies performed with IRIS3D. Finally, this code will continue to be used for post-processing detailed hydrodynamic simulations and interpreting observed neutron spectra in ICF implosions.
Neural network approximation of Bayesian models for the inference of ion and electron temperature profiles at W7-X
Here, we describe a method for training a neural network (NN) to approximate the full model Bayesian inference of plasma profiles from x-ray imaging diagnostic measurements. The modeling is carried out within the Minerva Bayesian modeling framework where models are defined as a set of assumptions, prior beliefs on parameter values and physics knowledge. The goal is to use NNs for fast ion and electron temperature profile inversion from measured image data. The NN is trained solely on artificial data generated by sampling from the joint distribution of the free parameters and model predictions. The training is carried out in such a way that the mapping learned by the network constitutes an approximation of the full model Bayesian inference. The analysis is carried out on images constituted of 20 × 195 pixels corresponding to binned lines of sight and spectral channels, respectively. Through the full model inference, it is possible to infer electron and ion temperature profiles as well as impurity density profiles. When the network is utilized for the inference of the temperature profiles, the analysis time can be reduced down to a few tens of microseconds for a single time point, which is a drastic improvement if compared to the ≈4 h long Bayesian inference. The procedure developed for the generation of the training set does not rely on diagnostic-specific features, and therefore it is in principle applicable to any other model developed within the Minerva framework. The trained NN has been tested on data collected during the first operational campaign at W7-X, and compared to the full model Bayesian inference results.