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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 235 records · Page 13
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Ytterbium ion trap quantum computing: The current state-of-the-art

In this work, we present an overview of contemporary quantum computing with ytterbium ion traps, placing the emphasis on industry implementations. We provide brief, concrete descriptions of various key features, such as trap loading, electronic structure, qubit function, gates, error analysis, and benchmarking. We focus on the underlying science and current technologies to provide readers with a holistic picture of available techniques for using ytterbium in contemporary ion trap designs.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2014

Generic method for automatic bladder segmentation on cone beam CT using a patient-specific bladder shape model

Purpose: The aim of this study is to develop and validate a generic method for automatic bladder segmentation on cone beam computed tomography (CBCT), independent of gender and treatment position (prone or supine), using only pretreatment imaging data. Methods: Data of 20 patients, treated for tumors in the pelvic region with the entire bladder visible on CT and CBCT, were divided into four equally sized groups based on gender and treatment position. The full and empty bladder contour, that can be acquired with pretreatment CT imaging, were used to generate a patient-specific bladder shape model. This model was used to guide the segmentation process on CBCT. To obtain the bladder segmentation, the reference bladder contour was deformed iteratively by maximizing the cross-correlation between directional grey value gradients over the reference and CBCT bladder edge. To overcome incorrect segmentations caused by CBCT image artifacts, automatic adaptations were implemented. Moreover, locally incorrect segmentations could be adapted manually. After each adapted segmentation, the bladder shape model was expanded and new shape patterns were calculated for following segmentations. All available CBCTs were used to validate the segmentation algorithm. The bladder segmentations were validated by comparison with the manual delineations and the segmentation performance was quantified using the Dice similarity coefficient (DSC), surface distance error (SDE) and SD of contour-to-contour distances. Also, bladder volumes obtained by manual delineations and segmentations were compared using a Bland-Altman error analysis. Results: The mean DSC, mean SDE, and mean SD of contour-to-contour distances between segmentations and manual delineations were 0.87, 0.27 cm and 0.22 cm (female, prone), 0.85, 0.28 cm and 0.22 cm (female, supine), 0.89, 0.21 cm and 0.17 cm (male, supine) and 0.88, 0.23 cm and 0.17 cm (male, prone), respectively. Manual local adaptations improved the segmentation results significantly (p < 0.01) based on DSC (6.72%) and SD of contour-to-contour distances (0.08 cm) and decreased the 95% confidence intervals of the bladder volume differences. Moreover, expanding the shape model improved the segmentation results significantly (p < 0.01) based on DSC and SD of contour-to-contour distances. Conclusions: This patient-specific shape model based automatic bladder segmentation method on CBCT is accurate and generic. Our segmentation method only needs two pretreatment imaging data sets as prior knowledge, is independent of patient gender and patient treatment position and has the possibility to manually adapt the segmentation locally.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2014

SU-E-T-189: First Experimental Verification of the Accuracy of Absolute Dose Reconstruction From PET-CT Imaging of Yttrium 90 Microspheres

Purpose: In the past few years there have been numerous proposals for 3D dose reconstruction from the PET-CT imaging of patients undergoing radioembolization treatment of the liver with yttrium-90 microspheres. One of the most promising techniques uses convolution of the measured PET activity distribution with a pre-calculated Monte Carlo dose deposition kernel. The goal of the present study is to experimentally verify the accuracy of this method and to analyze the significance of various error sources. Methods: Optically stimulated luminescence detectors (OSLD) were used (NanoDot, Landauer) in this experiment. Two detectors were mounted on the central axis of a cylinder filled with water solution of yttrium-90 chloride. The total initial activity was 90mCi. The cylinder was inserted in a larger water phantom and scanned on a Siemens Biograph 16 Truepoint PET-CT scanner. Scans were performed daily over a period of 20 days to build a calibration curve for the measured absolute activity spanning 7 yttrium-90 half-lives. The OSLDs were mounted in the phantom for a predetermined period of time in order to record 2Gy dose. The measured dose was then compared to the dose reconstructed from the activity density at the location of each dosimeter. Results: Thorough error analysis of the dose reconstruction algorithm takes into account the uncertainties in the absolute PET activity, branching ratios, and nonlinearity of the calibration curve. The measured dose for 105-minute exposure on day 10 of the experiment was 219(11)cGy, while the reconstructed dose at the location of the detector was 215(47)cGy. Conclusion: We present the first experimental verification of the accuracy of the convolution algorithm for absolute dose reconstruction of yttrium-90 microspheres. The excellent agreement between the measured and calculated point doses will encourage the broad clinical adoption of the convolution-based dose reconstruction algorithm, making future quantitative dose/outcome studies possible.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2020

SNS: A Solution-Based Nonlinear Subspace Method for Time-Dependent Model Order Reduction

Several reduced order models have been successfully developed for nonlinear dynamical systems. To achieve a considerable speed-up, a hyper-reduction step is needed to reduce the computational complexity due to nonlinear terms. Many hyper-reduction techniques require the construction of nonlinear term basis, which introduces a computationally expensive offline phase. A novel way of constructing nonlinear term basis within the hyper-reduction process is introduced. In contrast to the traditional hyper-reduction techniques where the collection of nonlinear term snapshots is required, the SNS method avoids collecting the nonlinear term snapshots. Instead, it uses the solution snapshots that are used for building a solution basis, which enables avoiding an extra data compression of nonlinear term snapshots. As a result, the SNS method provides a more efficient offline strategy than the traditional model order reduction techniques, such as the DEIM, GNAT, and ST-GNAT methods. The SNS method is theoretically justified by the conforming subspace condition and the subspace inclusion relation. It is useful for model order reduction of large-scale nonlinear dynamical problems to reduce the offline cost. It is especially useful for ST-GNAT that has shown promising results, such as a good accuracy with a considerable online speed-up for hyperbolic problems in a recent paper by Choi and Carlberg [SIAM J. Sci. Comput., 41 (2019), pp. A26--A58], because ST-GNAT involves an expensive offline cost related to collecting nonlinear term snapshots. Error analysis for the SNS method is presented. Numerical results support that the accuracy of the solution from the SNS method is comparable to the traditional methods and a considerable speed-up (i.e., a factor of two to a hundred) is achieved in the offline phase.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

A Fast Time-Stepping Strategy for Dynamical Systems Equipped with a Surrogate Model

Simulation of complex dynamical systems arising in many applications is computationally challenging due to their size and complexity. Model order reduction, machine learning, and other types of surrogate modeling techniques offer cheaper and simpler ways to describe the dynamics of these systems but are inexact and introduce additional approximation errors. In order to overcome the computational difficulties of the full complex models, on one hand, and the limitations of surrogate models, on the other, this work proposes a new accelerated time-stepping strategy that combines information from both. This approach is based on the multirate infinitesimal general-structure additive Runge--Kutta framework. The inexpensive surrogate model is integrated with a small time step to guide the solution trajectory, and the full model is treated with a large time step to occasionally correct for the surrogate model error and ensure convergence. Here, we provide a theoretical error analysis, and several numerical experiments, to show that this approach can be significantly more efficient than using only the full or only the surrogate model for the integration.

Surrogate models↗
DOE OSTI2024

Parallel Randomized Tucker Decomposition Algorithms

The Tucker tensor decomposition is a natural extension of the singular value decomposition (SVD) to multiway data. Here, we propose to accelerate Tucker tensor decomposition algorithms by using randomization and parallelization. We present two algorithms that scale to large data and many processors, significantly reduce both computation and communication cost compared to previous deterministic and randomized approaches, and obtain nearly the same approximation errors. The key idea in our algorithms is to perform randomized sketches with Kronecker-structured random matrices, which reduces computation compared to unstructured matrices and can be implemented using a fundamental tensor computational kernel. We provide probabilistic error analysis of our algorithms and implement a new parallel algorithm for the structured randomized sketch. Our experimental results demonstrate that our combination of randomization and parallelization achieves accurate Tucker decompositions much faster than alternative approaches. We observe up to a 16X speedup over the fastest deterministic parallel implementation on 3D simulation data.

Tucker decompositions↗
DOE OSTI2019

Considerations on the design of a rare isotope beam facility based on superconducting heavy ion linacs

To establish an accelerator facility based on superconducting linear accelerator (linac) for nuclear and applied sciences in Korea, the rare isotope science project started in December 2011 under the auspices of the Institute for Basic Science (IBS). The layout of the facility has been mostly frozen since 2013 and civil construction began in 2016. On the other hand, an alternative linac design option was recently investigated in collaboration with the linac development group of Argonne National Lab in search of a further optimized configuration. A new linac lattice was developed and evaluated against realistic machine errors using TRACK. The beam optics simulations and error analysis then proved the soundness of this alternative design. In addition, beam optics of the injector was studied for simultaneous acceleration of both stable and rare isotope beams with the use of an electron beam ion source (EBIS). Additionally considered are alternative options of high-power cyclotrons as ISOL driver in order to enhance the capability of rare isotope beam production and fully exploit the superconducting linac facility built at high cost.

07 ISOTOPE AND RADIATION SOURCES↗
DOE OSTI2014

2HOT: an improved parallel hashed oct-tree n-body algorithm for cosmological simulation

We report on improvements made over the past two decades to our adaptive treecode N-body method (HOT). A mathematical and computational approach to the cosmological N-body problem is described, with performance and scalability measured up to 256k (2 18 ) processors. We present error analysis and scientific application results from a series of more than ten 69 billion (4096 3 ) particle cosmological simulations, accounting for 4 × 10 20 floating point operations. These results include the first simulations using the new constraints on the standard model of cosmology from the Planck satellite. Our simulations set a new standard for accuracy and scientific throughput, while meeting or exceeding the computational efficiency of the latest generation of hybrid TreePM N-body methods.

Computer Science↗
DOE OSTI2021

Evaluation of KDP Estimation Algorithm Performance in Rain Using a Known-Truth Framework

Accurate estimation of specific differential phase ( K DP ) is necessary for rain rate estimation, attenuation correction, and hydrometeor classification algorithms. There are numerous published methods to process polarimetric radar observations of propagation differential phase shift (Φ DP ) and estimate K DP , but the corresponding K DP estimate uncertainty is unquantified. This study provides guidance on how commonly used K DP estimation algorithms perform in various environments. Here, we create numerous synthetic (“true”) K DP profiles, integrate over them to obtain “smoothed” Φ DP , and then add noise typical of S-band operational weather radar measurements. Each algorithm is applied to our noisy Φ DP profiles and compared to the true K DP profile such that the errors and uncertainty are quantified. The synthetic K DP profiles are Gaussian in shape, which allows systematic variations in their magnitude and width to determine how each algorithm performs in smooth, slowly changing K DP profiles, as well as steep profiles. Results demonstrate that algorithm performance is dependent on the Φ DP field received. These results are further supported by an error analysis of each algorithm for two more complicated synthetic K DP profiles. Some K DP algorithms allow users to change various tuning parameters; a subset of these tuning parameters is tested to provide guidance on how changing these parameters impacts algorithm performance. We then provide evidence that our known-truth framework provides insight into algorithm performance in observed data through two case studies.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Dataset for "Evaluating Deep Learning Approaches for Predictions in Unmonitored Basins with Continental-scale Stream Temperature Models" Willard et al. (2024)

This data release provides all data and code used in the paper " "Evaluating Deep Learning Approaches for Predictions in Unmonitored Basins with Continental-scale Stream Temperature Models" Willard et al. (2024)" to model stream temperature, evaluate, and assess results. The associated manuscript explores current open questions in prediction in ungauged and unmonitored basins concerning top-down versus bottom-up approaches, tradeoffs between data available and input requirements, and the appropriate representation of catchment attributes as inputs to deep learning models. Modeling was done primarily with long short-term memory (LSTM) models, and stream site coverage spans 1362 locations across the conterminous United States. The data is organized into these items items:Code repository and data for the paper " "Evaluating Deep Learning Approaches for Predictions in Unmonitored Basins with Continental-scale Stream Temperature Models" Willard et al. (2024)".Code: stream_temp_ml_regionalization.zip contains the code repositoryData to run the code: - data_dir.zip -- contains all files that should be moved to the "DATA_DIR" variable defined in the "set_env_vars.sh" script in the code repository- metadata_dir.zip -- contains all files that should be moved to the "METADATA_DIR" variable defined in the "set_env_vars.sh" script in the code repository- error_analysis_attribute_and_groundwater_dir.zip - workflows for the extended error analysis by stream attribute and groundwater influenceData produced by the code and used in the paper:- outputs_dir.zip - contains model output and results (outputs_dir/results), model weights (outputs_dir/models), and all other outputs used for the paper including feature importances.To cite this code, please use the following BibTeX or MLA entries:bibtex:@misc{willard2024streamdata, author = {Jared Willard and Fabio Ciulla and Helen Weierbach and Vipin Kumar and Charuleka Varadharajan}, title = {Dataset for "Evaluating Deep Learning Approaches for Predictions in Unmonitored Basins with Continental-scale Stream Temperature Models"}, year = {2024}, doi = {10.15485/2448016}, publisher = {ESS-DIVE Repository}, url = {https://doi.org/10.15485/2448016}}MLA: Willard, Jared, et al. Dataset for "Evaluating Deep Learning Approaches for Predictions in Unmonitored Basins with Continental-scale Stream Temperature Models". 2024. ESS-DIVE Repository, doi:10.15485/2448016.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2018

Persuasive Influence Detection: The Role of Argument Sequencing

Automatic detection of persuasion in online discussion is key to understanding how social media is used. Predicting persuasiveness is difficult, however, due to the need to model world knowledge, dialogue, and sequential reasoning. Here, we focus on modeling the sequence of arguments in social media posts using neural models with embeddings for words, discourse relations, and semantic frames. We demonstrate significant improvement over prior work in detecting successful arguments. We also present an error analysis assessing novice human performance at predicting persuasiveness.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2021

CrossCheck: Rapid, Reproducible, and Interpretable Model Evaluation

Evaluation beyond aggregate performance metrics, e.g. F1-score, is crucial to both establish an appropriate level of trust in machine learning models and identify future model improvements. In this paper we demonstrate CrossCheck, an interactive visualization tool for rapid crossmodel comparison and reproducible error analysis. We describe the tool and discuss design and implementation details. We then present three use cases (named entity recognition, reading comprehension, and clickbait detection) that show the benefits of using the tool for model evaluation. CrossCheck allows data scientists to make informed decisions to choose between multiple models, identify when the models are correct and for which examples, investigate whether the models are making the same mistakes as humans, evaluate models’ generalizability and highlight models’ limitations, strengths and weaknesses. Furthermore, CrossCheck is implemented as a Jupyter widget, which allows rapid and convenient integration into data scientists’ model development workflows.

Arendt, Dustin L.↗
DOE OSTI2014

A biofilm microreactor system for simultaneous electrochemical and nuclear magnetic resonance techniques

In order to fully understand electrochemically active biofilms and the limitations to their scale-up in industrial biofilm reactors, a complete picture of the microenvironments inside the biofilm is needed. Nuclear magnetic resonance (NMR) techniques are ideally suited for the study of biofilms and for probing their microenvironments because these techniques allow for non-invasive interrogation and in situ monitoring with high resolution. By combining NMR with simultaneous electrochemical techniques, it is possible to sustain and study live electrochemically active biofilms. Here, we introduce a novel biofilm microreactor system that allows for simultaneous electrochemical and NMR techniques (EC-NMR) at the microscale. Microreactors were designed with custom radiofrequency resonator coils, which allowed for NMR measurements of biofilms growing on polarized gold electrodes. For an example application of this system, we grew Geobacter sulfurreducens biofilms. NMR was used to investigate growth media flow velocities, which were compared to simulated laminar flow, and electron donor concentrations inside the biofilms. We use Monte Carlo error analysis to estimate standard deviations of the electron donor concentration measurements within the biofilm. The EC-NMR biofilm microreactor system can ultimately be used to correlate extracellular electron transfer rates with metabolic reactions and explore extracellular electron transfer mechanisms.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2014

A Method for the Quantification of Model Form Error Associated with Physical Systems

In the process of model validation, models are often declared valid when the differences between model predictions and experimental data sets are satisfactorily small. However, little consideration is given to the effectiveness of a model using parameters that deviate slightly from those that were fitted to data, such as a higher load level. Furthermore, few means exist to compare and choose between two or more models that reproduce data equally well. These issues can be addressed by analyzing model form error, which is the error associated with the differences between the physical phenomena captured by models and that of the real system. This report presents a new quantitative method for model form error analysis and applies it to data taken from experiments on tape joint bending vibrations. Two models for the tape joint system are compared, and suggestions for future improvements to the method are given. As the available data set is too small to draw any statistical conclusions, the focus of this paper is the development of a methodology that can be applied to general problems.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2014

Susceptibility Study of Audio Recording Devices to Electromagnetic Stimulations

Little research has been performed to study how intentional electromagnetic signals may couple into recording devices. An electromagnetic susceptibility study was performed on an analog tape recorder, a digital video camera, a wired computer microphone, and a wireless microphone system to electromagnetic interference. Devices were subjected to electromagnetic stimulations in the frequency range of 1-990 MHz and field strengths up to 4.9 V/m. Carrier and message frequencies of the stimulation signals were swept, and the impacts of device orientation and antenna polarization were explored. Message signals coupled into all devices only when amplitude modulated signals were used as stimulation signals. Test conditions that produced maximum sensitivity were highly specific to each device. Only narrow carrier frequency ranges could be used for most devices to couple messages into recordings. A basic detection technique using cross-correlation demonstrated the need for messages to be as long as possible to maximize message detection and minimize detection error. Analysis suggests that detectable signals could be coupled to these recording devices under realistic ambient conditions.

42 ENGINEERING↗
DOE OSTI2016

Arbitrary Dimension Convection-Diffusion Schemes for Space-Time Discretizations

This note proposes embedding a time dependent PDE into a convection-diffusion type PDE (in one space dimension higher) with singularity, for which two discretization schemes, the classical streamline-diffusion and the EAFE (edge average finite element) one, are investigated in terms of stability and error analysis. The EAFE scheme, in particular, is extended to be arbitrary order which is of interest on its own. Numerical results, in combined space-time domain demonstrate the feasibility of the proposed approach.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2017

Centralized Activities in Support of the Radiative Forcing Model Intercomparison Project

A new paradigm in benchmark absorption-scattering radiative transfer is presented that enables both the globally-averaged and spatially-resolved testing of climate model radiation parameterizations using a novel benchmark computational strategy to uncover persistent sources of biases in the aerosol Instantaneous Radiative Effect (IRE). A proof-of-concept is demonstrated with the GFDL AM4 and CESM 1.2.2 climate models. Instead of prescribing atmospheric conditions and aerosols, as in prior intercomparisons, snapshots of the atmospheric state and aerosol optical properties from the participating models are used as inputs to a highly accurate radiation solver to uncover relevant biases unique to each climate model. These diagnostic results show that the models’ aerosol IRE bias is of the same magnitude as the persistent intermodel spread (~1 W/m2), and varies spatially and with intrinsic aerosol optical properties. The findings underscore the significance of native model error analysis and its ability to diagnose global biases, confirming its fundamental value for the Radiative Forcing Model Intercomparison Project (RFMIP).

58 GEOSCIENCES↗
DOE OSTI2019

Pressurizing Hagan and SAVY containers to 30-psig (air) to measure the release of analytical cerium oxide test powder

The need to pressure-test known nuclear material storage containers has been made apparent by the CSE (cognizant systems engineer) assigned to containers at TA-55. The main goal of the current study is to understand the behavior of the SAVY-4000 and Hagan containers during an internal pressurization (30 psig) event. The experiments incorporated a nominal mass of 100 g of CeO 2 (cerium oxide) powder to simulate MAR (material at risk) conditions. For the experimental results with mass losses of 0.0±0.1 g mass loss), using ASME error analysis, there is a total expanded uncertainty in a 95% confidence interval (±0.25 g) for the tested SAVY-4000 and Hagan containers. For purposes of conservatism, this has been rounded upward to yield a bounding value for the damage ratio DR = 0.003 = 0.3%.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗