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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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All sourcesNASA NTRSDOE OSTI
DOE OSTI2017

Rapid alignment of nanotomography data using joint iterative reconstruction and reprojection

As x-ray and electron tomography is pushed farther into the nanoscale, the limitations of rotation stages become more apparent leading to challenges in the alignment of the acquired projection images. Here we present an approach for rapid post-acquisition alignment of these projections to obtain high quality 3D images. Our approach is based on a joint estimation of alignment errors, and the object, using an iterative refinement procedure. We characterize convergence in simulations, and show its application in x-ray and electron nanotomography.

47 OTHER INSTRUMENTATION↗
DOE OSTI2024

First-principles calculation of lattice distortion, electronic structure, and bonding properties of GeTe-based and PbSe-based high-entropy chalcogenides

The massive amount of wasted heat energy from industry has pushed the development of thermoelectric (TE) materials that directly convert heat into electricity to a new level of concern. Recently, multicomponent alloys such as GeTe-based and PbSe-based high-entropy (HE) chalcogenides have attracted a great deal of attention due to their potential application as TE materials. The nature of the interatomic bonding, lattice distortion (LD), and the electronic structure in this class of materials is not fully understood. Herein, we report a comprehensive computational investigation of nine GeTe-based HE alloys with eight metallic elements (Ag, Pb, Sb, Bi, Cu, Cd, Mn, and Sn) with large supercells of 1080 atoms each; seven PbSe-based HE solid solutions: Pb0.99−ySb0.012SnySe1−2xTexSx (x = 0.1, 0.2, 0.25, 0.3, 0.35, 0.4, 0.45, with y = 0) with supercells of 1000 atoms each; and five Pb0.99−ySb0.012SnySe1−2xTexSx (y = 0.05, 0.1, 0.15, 0.2, 0.25 with x = 0.25) solid solutions with supercells of 1000 atoms each. All these HE models are theoretically investigated for the first time. The electronic structure, interatomic bonding, charge transfer, and lattice distortion (LD) are investigated by first-principles calculations based on density functional theory. Multicomponent HE alloys can cause a significant LD, which affects their mechanical, thermal, and TE properties. The calculations for the GeTe-based HE chalcogenides showed that they are semiconductors with a narrow bandgap, except for m8, which has a semi-metallic characteristic, and this makes them good candidates for TE applications. For most of these models, the Fermi level shifts upward and locates deeply in the conduction bands, resulting in the enhancement of the electrical conductivity (σ). The bonding properties showed that most bonds in m5 are more dispersed, indicating highest LD and lower lattice thermal conductivity. For PbSe-based HE solid solutions, the LD calculations showed that the models Pb0.99Sb0.012Se0.5Te0.25S0.25 and Pb0.89Sb0.012Sn0.1Se0.5Te0.25S0.25 have the higher LD, and thus a lower lattice thermal conductivity. Such investigations are in high demand since it enables us to design new HE chalcogenides for TE applications. We use the novel concept of total bond order density as a single quantum mechanical metric to characterize the internal cohesion of these HE alloys and correlate with calculated properties, especially the mechanical properties. This work provides a solid database for HE chalcogenides and a road map for many potential applications. Moreover, the computational procedure we developed can be used to design new HE chalcogenides for specific TE applications.

Physics↗
DOE OSTI2024

Calibration verification for stochastic agent-based disease spread models

Accurate disease spread modeling is crucial for identifying the severity of outbreaks and planning effective mitigation efforts. To be reliable when applied to new outbreaks, model calibration techniques must be robust. However, current methods frequently forgo calibration verification (a stand-alone process evaluating the calibration procedure) and instead use overall model validation (a process comparing calibrated model results to data) to check calibration processes, which may conceal errors in calibration. In this work, we develop a stochastic agent-based disease spread model to act as a testing environment as we test two calibration methods using simulation-based calibration, which is a synthetic data calibration verification method. The first calibration method is a Bayesian inference approach using an empirically-constructed likelihood and Markov chain Monte Carlo (MCMC) sampling, while the second method is a likelihood-free approach using approximate Bayesian computation (ABC). Simulation-based calibration suggests that there are challenges with the empirical likelihood calculation used in the first calibration method in this context. These issues are alleviated in the ABC approach. Despite these challenges, we note that the first calibration method performs well in a synthetic data model validation test similar to those common in disease spread modeling literature. We conclude that stand-alone calibration verification using synthetic data may benefit epidemiological researchers in identifying model calibration challenges that may be difficult to identify with other commonly used model validation techniques.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2016

DEVELOPMENT AND TESTING OF FAULT-DIAGNOSIS ALGORITHMS FOR REACTOR PLANT SYSTEMS

Argonne National Laboratory is further developing fault diagnosis algorithms for use by the operator of a nuclear plant to aid in improved monitoring of overall plant condition and performance. The objective is better management of plant upsets through more timely, informed decisions on control actions with the ultimate goal of improved plant safety, production, and cost management. Integration of these algorithms with visual aids for operators is taking place through a collaboration under the concept of an operator advisory system. This is a software entity whose purpose is to manage and distill the enormous amount of information an operator must process to understand the plant state, particularly in off-normal situations, and how the state trajectory will unfold in time. The fault diagnosis algorithms were exhaustively tested using computer simulations of twenty different faults introduced into the chemical and volume control system (CVCS) of a pressurized water reactor (PWR). The algorithms are unique in that each new application to a facility requires providing only the piping and instrumentation diagram (PID) and no other plant-specific information; a subject-matter expert is not needed to install and maintain each instance of an application. The testing approach followed accepted procedures for verifying and validating software. It was shown that the code satisfies its functional requirement which is to accept sensor information, identify process variable trends based on this sensor information, and then to return an accurate diagnosis based on chains of rules related to these trends. The validation and verification exercise made use of GPASS, a one-dimensional systems code, for simulating CVCS operation. Plant components were failed and the code generated the resulting plant response. Parametric studies with respect to the severity of the fault, the richness of the plant sensor set, and the accuracy of sensors were performed as part of the validation exercise. The background and overview of the software will be presented to give an overview of the approach. Following, the verification and validation effort using the GPASS code for simulation of plant transients including a sensitivity study on important parameters will be presented

Fault Diagnosis Algorithm↗
DOE OSTI2019

Virtual Guidance of Percutaneous Transthoracic Needle Biopsy with C-Arm Cone-Beam CT: Diagnostic Accuracy, Risk Factors and Effective Radiation Dose

Purpose: C-arm cone-beam computed tomography-guided transthoracic lung core needle biopsy (CBCT-CNB) is a safe and accurate procedure for the evaluation of patients with pulmonary nodules. The purpose of our study was to evaluate the diagnostic performance, complication rates and effective radiation dose of CBCT-CNB with virtual guidance. Materials and Methods: We retrospectively collected data regarding 375 CBCT-CNBs performed with virtual guidance (XperGuide—Philips Healthcare, Best, The Netherlands) from January 2010 to June 2015 on 355 patients (mean age, 68.1 years ± 11.8; age range, 31–88 years). Patients were divided into groups and compared based on the diagnostic failure and lesion size (15 mm cutoff). Diagnostic performance, complication rate and effective radiation dose were investigated. Variables influencing diagnostic performance and complications were assessed using Student’s T test and Pearson’s χ{sup 2} test. Results: The sensitivity, specificity, positive and negative predictive value and accuracy for patients subjected to CNBs were 96.8%, 100%, 100%, 100% and 97.2%, respectively. Considering risk factors for pneumothorax, no significant differences were found regarding patient and lesion characteristics. Perilesional hemorrhage occurred more frequently in older patients (p = 0.046) and in smaller lesions (p = 0.001). Hemoptysis was significantly more frequent in patients with perilesional hemorrhage (p = 0.01). Mean effective radiation dose in CBCT-CNB was 7.12 ± 8.78 mSv. Conclusions: CBCT-CNB combined with virtual guidance is a reliable and accurate technique that allows exact localization of pulmonary lesions, effective preprocedural planning and real-time fluoroscopy altogether.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2020

Mining a human transcriptome database for chemical modulators of NRF2

Nuclear factor erythroid-2 related factor 2 (NRF2) encoded by the NFE2L2 gene is a transcription factor critical for protecting cells from chemically-induced oxidative stress. We developed computational procedures to identify chemical modulators of NRF2 in a large database of human microarray data. A gene expression biomarker was built from statistically-filtered gene lists derived from microarray experiments in primary human hepatocytes and cancer cell lines exposed to NRF2-activating chemicals (oltipraz, sulforaphane, CDDOIm) or in which the NRF2 suppressor Keap1 was knocked down by siRNA. Directionally consistent biomarker genes were further filtered for those dependent on NRF2 using a microarray dataset from cells after NFE2L2 siRNA knockdown. The resulting 143-gene biomarker was evaluated as a predictive tool using the correlation-based Running Fisher algorithm. Using 59 gene expression comparisons from chemically-treated cells with known NRF2 activating potential, the biomarker gave a balanced accuracy of 93%. The biomarker was comprised of many well-known NRF2 target genes (AKR1B10, AKR1C1, NQO1, TXNRD1, SRXN1, GCLC, GCLM), 69% of which were found to be bound directly by NRF2 using ChIP-Seq. NRF2 activity was assessed across ~9840 microarray comparisons from ~1460 studies examining the effects of ~2260 chemicals in human cell lines. A total of 260 and 43 chemicals were found to activate or suppress NRF2, respectively, most of which have not been previously reported to modulate NRF2 activity. Using a NRF2-responsive reporter gene in HepG2 cells, we confirmed the activity of a set of chemicals predicted using the biomarker. The biomarker will be useful for future gene expression screening studies of environmentally-relevant chemicals.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2018

On One Numerical-Analytic Method for the Solution of One-Dimensional Quasistatic Problems of Thermoelasticity for Thermosensitive Bodies of Simple Geometry

We propose a procedure for the numerical-analytic solution of one-dimensional problems of thermoelasticity for bodies of simple geometry based on the representation of temperature dependences of the physicomechanical characteristics of materials in the form of piecewise constant functions of temperature and the application of the Kirchhoff substitution and the methods of generalized functions. This procedure enables us to study one-dimensional nonstationary thermal and quasistatic stress-strain states under the combined thermal and force action with controlled reliability.

36 MATERIALS SCIENCE↗
DOE OSTI2014

SU-D-BRF-06: A Brachytherapy Simulator with Realistic Haptic Force Feedback and Real-Time Ultrasounds Image Simulation for Training and Teaching

Purpose: Surgical procedures require dexterity, expertise and repetition to reach optimal patient outcomes. However, efficient training opportunities are usually limited. This work presents a simulator system with realistic haptic force-feedback and full, real-time ultrasounds image simulation. Methods: The simulator is composed of a custom-made Linear-DELTA force-feedback robotic platform. The needle tip is mounted on a force gauge at the end effector of the robot, which responds to needle insertion by providing reaction forces. 3D geometry of the tissue is using a tetrahedral finite element mesh (FEM) mimicking tissue properties. As the needle is inserted/retracted, tissue deformation is computed using a mass-tensor nonlinear visco-elastic FEM. The real-time deformation is fed to the L-DELTA to take into account the force imparted to the needle, providing feedback to the end-user when crossing tissue boundaries or needle bending. Real-time 2D US image is also generated synchronously showing anatomy, needle insertion and tissue deformation. The simulator is running on an Intel I7 6- core CPU at 3.26 MHz. 3D tissue rendering and ultrasound display are performed on a Windows 7 computer; the FEM computation and L-DELTA control are executed on a similar PC using the Neutrino real-time OS. Both machines communicate through an Ethernet link. Results: The system runs at 500 Hz for a 8333-tetrahedron tissue mesh and a 100-node angular spring needle model. This frame rate ensures a relatively smooth displacement of the needle when pushed or retracted (±20 N in all directions at speeds of up to 2 m/s). Unlike commercially-available haptic platforms, the oblong workspace of the L-DELTA robot complies with that required for brachytherapy needle displacements of 0.1m by 0.1m by 0.25m. Conclusion: We have demonstrated a real-life, realistic brachytherapy simulator developed for prostate implants (LDR/HDR). The platform could be adapted to other sites or training for other types of needle-based procedures.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2018

Ambient and building condition effects modeling of air-cooled natural circulation systems

Throughout the testing program at Natural convection Shutdown heat removal Test Facility (NSTF), strong influences of ambient conditions were observed in the experimental data when baseline tests were repeated under identical test procedures. Significant analysis efforts were conducted to gain understanding of these influences, and quantify the system response of the test facility. In this paper, an empirical model was developed based on theoretical considerations and using experimental data to correlate zero-power system flow rates with ambient meteorological conditions, including wind and temperatures. Coefficients in the model were determined based on best fitting of the experimental data set. The predictive capability of the empirical model was demonstrated by applying it to new sets of experimental data. Finally, after implementing the empirical model in both system-level thermal fluids and computational fluid dynamics modeling of the facility, simulations from both approaches resulted in accurate predictions in system natural circulation flow rates.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2019

Phase-change heterostructure enables ultralow noise and drift for memory operation

Artificial intelligence and other data-intensive applications have escalated the demand for data storage and processing. New computing devices, such as phase-change random access memory (PCRAM)–based neuro-inspired devices, are promising options for breaking the von Neumann barrier by unifying storage with computing in memory cells. However, current PCRAM devices have considerable noise and drift in electrical resistance that erodes the precision and consistency of these devices. We designed a phase-change heterostructure (PCH) that consists of alternately stacked phase-change and confinement nanolayers to suppress the noise and drift, allowing reliable iterative RESET and cumulative SET operations for high-performance neuro-inspired computing. Our PCH architecture is amenable to industrial production as an intrinsic materials solution, without complex manufacturing procedure or much increased fabrication cost.

Science & Technology - Other Topics↗
DOE OSTI2014

SU-E-J-90: MRI-Based Treatment Simulation and Patient Setup for Radiation Therapy of Brain Cancer

Purpose: Traditional radiation therapy of cancer is heavily dependent on CT. CT provides excellent depiction of the bones but lacks good soft tissue contrast, which makes contouring difficult. Often, MRIs are fused with CT to take advantage of its superior soft tissue contrast. Such an approach has drawbacks. It is desirable to perform treatment simulation entirely based on MRI. To achieve MR-based simulation for radiation therapy, bone imaging is an important challenge because of the low MR signal intensity from bone due to its ultra-short T2 and T1, which presents difficulty for both dose calculation and patient setup in terms of digitally reconstructed radiograph (DRR) generation. Current solutions will either require manual bone contouring or multiple MR scans. We present a technique to generate DRR using MRI with an Ultra Short Echo Time (UTE) sequence which is applicable to both OBI and ExacTrac 2D patient setup. Methods: Seven brain cancer patients were scanned at 1.5 Tesla using a radial UTE sequence. The sequence acquires two images at two different echo times. The two images were processed using in-house software. The resultant bone images were subsequently loaded into commercial systems to generate DRRs. Simulation and patient clinical on-board images were used to evaluate 2D patient setup with MRI-DRRs. Results: The majority bones are well visualized in all patients. The fused image of patient CT with the MR bone image demonstrates the accuracy of automatic bone identification using our technique. The generated DRR is of good quality. Accuracy of 2D patient setup by using MRI-DRR is comparable to CT-based 2D patient setup. Conclusion: This study shows the potential of DRR generation with single MR sequence. Further work will be needed on MR sequence development and post-processing procedure to achieve robust MR bone imaging for other human sites in addition to brain.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2021

Assembly and Analysis of Cell-Scale Membrane Envelopes

The march toward exascale computing will enable routine molecular simulation of larger and more complex systems, for example, simulation of entire viral particles, on the scale of approximately billions of atoms-a simulation size commensurate with a small bacterial cell. Anticipating the future hardware capabilities that will enable this type of research and paralleling advances in experimental structural biology, efforts are currently underway to develop software tools, procedures, and workflows for constructing cell-scale structures. Herein, we describe our efforts in developing and implementing an efficient and robust workflow for construction of cell-scale membrane envelopes and embedding membrane proteins into them. A new approach for construction of massive membrane structures that are stable during the simulations is built on implementing a subtractive assembly technique coupled with the development of a structure concatenation tool (fastmerge), which eliminates overlapping elements based on volumetric criteria rather than adding successive molecules to the simulation system. Using this approach, we have constructed two "protocells" consisting of MARTINI coarse-grained beads to represent cellular membranes, one the size of a cellular organelle and another the size of a small bacterial cell. The membrane envelopes constructed here remain whole during the molecular dynamics simulations performed and exhibit water flux only through specific proteins, demonstrating the success of our methodology in creating tight cell-like membrane compartments. Extended simulations of these cell-scale structures highlight the propensity for nonspecific interactions between adjacent membrane proteins leading to the formation of protein microclusters on the cell surface, an insight uniquely enabled by the scale of the simulations. We anticipate that the experiences and best practices presented here will form the basis for the next generation of cell-scale models, which will begin to address the addition of soluble proteins, nucleic acids, and small molecules essential to the function of a cell.

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

Simulations of multi-contrast x-ray imaging using near-field speckles

X-ray dark-field and phase-contrast imaging using near-field speckles is a novel technique that overcomes limitations inherent in conventional absorption x-ray imaging, i.e. poor contrast for features with similar density. Speckle-based imaging yields a wealth of information with a simple setup tolerant to polychromatic and divergent beams, and simple data acquisition and analysis procedures. Here, we present a simulation software used to model the image formation with the speckle-based technique, and we compare simulated results on a phantom sample with experimental synchrotron data. Thorough simulation of a speckle-based imaging experiment will help for better understanding and optimising the technique itself.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2012

Multilevel relaxation model for describing the Moessbauer spectra of nanoparticles in a magnetic field

A theory is developed for the Moessbauer absorption spectra of an ensemble of single-domain particles in a magnetic field. This theory is based on the generalization of a relaxation model with a quantummechanical description of the stationary states of a particle and on the formalism of Liouville operators for describing the hyperfine interaction for a hyperfine field changing in both the magnitude and direction for various stationary states. The general scheme of calculating relaxation Moessbauer spectra in terms of a standard stochastic approach is substantially optimized using operations with block matrices and a unique tridiagonalization of high-rank non-Hermitian matrices with a simple nonorthogonal transformation in the calculation procedure. The resulting model can easily be implemented on a personal computer. It considers the physical mechanisms of formation of a hyperfine structure in a spectrum of nanoparticles in a real situation and self-consistently describes the qualitative features of the nontrivial evolution of spectra with the temperature and the magnetic-field direction and strength, which has been detected in {sup 57}Fe nucleus experiments performed on magnetic nanoparticles for half a century.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2021

Adaptive resource allocation for surrogate modeling of systems comprised of multiple disciplines with varying fidelity

We present an adaptive algorithm for constructing surrogate models for integrated systems composed of a set of coupled components. With this goal we introduce ‘coupling’ variables with a priori unknown distributions that allow approximations of each component to be built independently. Once built, the surrogates of the components are combined and used to predict system-level quantities of interest (QoI) at a fraction of the cost of interrogating the full system model. We use a greedy experimental design procedure, based upon a modification of Multi-Index Stochastic Collocation (MISC), to minimize the error of the combined surrogate. This is achieved by refining each component surrogate in accordance with its relative contribution to error in the approximation of the system-level QoI. Our adaptation of MISC is a multi-fidelity procedure that can leverage ensembles of models of varying cost and accuracy, for one or more components, to produce estimates of system-level QoI. Several numerical examples demonstrate the efficacy of the proposed approach on systems involving feed-forward and feedback coupling. For a fixed computational budget, the proposed algorithm is able to produce approximations that are orders of magnitude more accurate than approximations that treat the integrated system as a black-box.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

A differentiable approach to the maximum independent set problem using dataless neural networks

The success of machine learning solutions for reasoning about discrete structures has brought attention to its adoption within combinatorial optimization algorithms. Such approaches generally rely on supervised learning by leveraging datasets of the combinatorial structures of interest drawn from some distribution of problem instances. Reinforcement learning has also been employed to find such structures. Here, in this paper, we propose a different approach in that no data is required for training the neural networks that produce the solution. In this sense, what we present is not a machine learning solution, but rather one that is dependent on neural networks and where backpropagation is applied to a loss function defined by the structure of the neural network architecture as opposed to a training dataset. In particular, we reduce the popular combinatorial optimization problem of finding a maximum independent set to a neural network and employ a dataless training scheme to refine the parameters of the network such that those parameters yield the structure of interest. Additionally, we propose a universal graph reduction procedure to handle large-scale graphs. The reduction exploits community detection for graph partitioning and is applicable to any graph type and/or density. Experimental results on both real and synthetic graphs demonstrate that our proposed method performs on par or outperforms state-of-the-art learning-based methods in terms of the size of the found set without requiring any training data.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2015

Identifying finite-time coherent sets from limited quantities of Lagrangian data

A data-driven procedure for identifying the dominant transport barriers in a time-varying flow from limited quantities of Lagrangian data is presented. Our approach partitions state space into coherent pairs, which are sets of initial conditions chosen to minimize the number of trajectories that “leak” from one set to the other under the influence of a stochastic flow field during a pre-specified interval in time. In practice, this partition is computed by solving an optimization problem to obtain a pair of functions whose signs determine set membership. From prior experience with synthetic, “data rich” test problems, and conceptually related methods based on approximations of the Perron-Frobenius operator, we observe that the functions of interest typically appear to be smooth. We exploit this property by using the basis sets associated with spectral or “mesh-free” methods, and as a result, our approach has the potential to more accurately approximate these functions given a fixed amount of data. In practice, this could enable better approximations of the coherent pairs in problems with relatively limited quantities of Lagrangian data, which is usually the case with experimental geophysical data. We apply this method to three examples of increasing complexity: The first is the double gyre, the second is the Bickley Jet, and the third is data from numerically simulated drifters in the Sulu Sea.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗