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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 847 records · Page 47
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Magnetic excitations of the hybrid multiferroic (ND 4 ) 2 FeCl 5 ·D 2 O

We report a comprehensive inelastic neutron scattering study of the hybrid molecule-based multiferroic compound ( ND 4 ) 2 FeCl 5 · D 2 O in the zero-field incommensurate cycloidal phase and the high-field quasicollinear phase. The spontaneous electric polarization changes its direction concurrently with the field-induced magnetic transition, from mostly aligned with the crystallographic a axis to the c axis. To account for such a change in polarization direction, the underlying multiferroic mechanism was proposed to switch from the spin-current model induced via the inverse Dzyaloshinskii-Moriya interaction to the p - d hybridization model. We perform a detailed analysis of the inelastic neutron data of ( ND 4 ) 2 FeCl 5 · D 2 O using linear spin-wave theory to quantify magnetic interaction strengths and investigate the possible impact of different multiferroic mechanisms on the magnetic couplings. Overall our result reveals that the spin dynamics of both multiferroic phases can be well described by a Heisenberg Hamiltonian with easy-plane anisotropy. We do not find notable differences between the optimal model parameters of the two phases. The hierarchy of exchange couplings and the balance among frustrated interactions remain the same between two phases, suggesting that magnetic interactions in ( ND 4 ) 2 FeCl 5 · D 2 O are much more robust than the electric polarization in response to delicate reorganizations of the electronic degrees of freedom in an applied magnetic field.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2018

Improving signal strength in serial crystallography with DIALS geometry refinement

The DIALS diffraction-modeling software package has been applied to serial crystallography data. Diffraction modeling is an exercise in determining the experimental parameters, such as incident beam wavelength, crystal unit cell and orientation, and detector geometry, that are most consistent with the observed positions of Bragg spots. These parameters can be refined by nonlinear least-squares fitting. In previous work, it has been challenging to refine both the positions of the sensors (metrology) on multipanel imaging detectors such as the CSPAD and the orientations of all of the crystals studied. Since the optimal models for metrology and crystal orientation are interdependent, alternate cycles of panel refinement and crystal refinement have been required. To simplify the process, a sparse linear algebra technique for solving the normal equations was implemented, allowing the detector panels to be refined simultaneously against the diffraction from thousands of crystals with excellent computational performance. Separately, it is shown how to refine the metrology of a second CSPAD detector, positioned at a distance of 2.5 m from the crystal, used for recording low-angle reflections. With the ability to jointly refine the detector position against the ensemble of all crystals used for structure determination, it is shown that ensemble refinement greatly reduces the apparent nonisomorphism that is often observed in the unit-cell distributions from still-shot serial crystallography. In addition, it is shown that batching the images by timestamp and re-refining the detector position can realistically model small, time-dependent variations in detector position relative to the sample, and thereby improve the integrated structure-factor intensity signal and heavy-atom anomalous peak heights.

36 MATERIALS SCIENCE↗
DOE OSTI2014

TH-A-9A-04: Incorporating Liver Functionality in Radiation Therapy Treatment Planning

Purpose: Liver SBRT patients have both variable pretreatment liver function (e.g., due to degree of cirrhosis and/or prior treatments) and sensitivity to radiation, leading to high variability in potential liver toxicity with similar doses. This work aims to explicitly incorporate liver perfusion into treatment planning to redistribute dose to preserve well-functioning areas without compromising target coverage. Methods: Voxel-based liver perfusion, a measure of functionality, was computed from dynamic contrast-enhanced MRI. Two optimization models with different cost functions subject to the same dose constraints (e.g., minimum target EUD and maximum critical structure EUDs) were compared. The cost functions minimized were EUD (standard model) and functionality-weighted EUD (functional model) to the liver. The resulting treatment plans delivering the same target EUD were compared with respect to their DVHs, their dose wash difference, the average dose delivered to voxels of a particular perfusion level, and change in number of high-/low-functioning voxels receiving a particular dose. Two-dimensional synthetic and three-dimensional clinical examples were studied. Results: The DVHs of all structures of plans from each model were comparable. In contrast, in plans obtained with the functional model, the average dose delivered to high-/low-functioning voxels was lower/higher than in plans obtained with its standard counterpart. The number of high-/low-functioning voxels receiving high/low dose was lower in the plans that considered perfusion in the cost function than in the plans that did not. Redistribution of dose can be observed in the dose wash differences. Conclusion: Liver perfusion can be used during treatment planning potentially to minimize the risk of toxicity during liver SBRT, resulting in better global liver function. The functional model redistributes dose in the standard model from higher to lower functioning voxels, while achieving the same target EUD and satisfying dose limits to critical structures. This project is funded by MCubed and grant R01-CA132834.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2023

Nuclear-Renewable-Storage Systems: Enhancing Planning and Operations of Integrated Energy Systems

Nuclear-renewable-storage integrated energy systems (IES) are multi-carrier energy systems that include not only electricity but also other forms of demands. Because individual IES components must observe their thermo-physical limits, including ramp rates, start-up, and shut-down time, we formulate operations of the IES as an optimization model by minimizing the total operations costs subject to physical limits of all constituent components. In addition, we develop a data-driven approach to improve the computational performance of the economic dispatch model by using reinforcement learning, where an agent is rewarded by meeting demands and penalized otherwise when shifting to the next state.

25 ENERGY STORAGE↗
DOE OSTI2016

An effective model for ergonomic optimization applied to a new automotive assembly line

An efficient ergonomic optimization can lead to a significant improvement in production performance and a considerable reduction of costs. In the present paper new model for ergonomic optimization is proposed. The new approach is based on the criteria defined by National Institute of Occupational Safety and Health and, adapted to Italian legislation. The proposed model provides an ergonomic optimization, by analyzing ergonomic relations between manual work in correct conditions. The model includes a schematic and systematic analysis method of the operations, and identifies all possible ergonomic aspects to be evaluated. The proposed approach has been applied to an automotive assembly line, where the operation repeatability makes the optimization fundamental. The proposed application clearly demonstrates the effectiveness of the new approach.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2019

Zigzag search for multi-objective optimization considering generation cost and emission

The zigzag search algorithm has been applied in engineering fields, such as oil well placement, with satisfactory results. Here, the zigzag search algorithm is introduced, modified with enhancement, and effectively applied to solve an economic emission dispatch problem and to demonstrate its practicability in power systems. The problem is formulated as a non-linear multi-objective optimization model taking energy constraints, generation limits, and transmission constraints into consideration. A set of non-dominant solutions can be obtained to form the Pareto front. Case studies are carried out with the IEEE 30-bus system and IEEE 118-bus system. The results indicate that the proposed zigzag search algorithms have the ability to deal with relevant power system problems. Comparisons are made with algorithms which have been widely used in literatures, such as the genetic algorithm (GA) and particle swarm optimization (PSO). Finally, this demonstrates that the zigzag search is easy to implement and is superior to other multi-objective (MO) techniques in both accuracy and efficiency.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS1993

Extended ecosystem signatures with application to Eos synergism requirements

The primary objective is to define the advantages of synergistically combining optical and microwave remote sensing measurements for the determination of biophysical properties important in ecosystem modeling. This objective was approached in a stepwise fashion starting with ground-based observations of controlled agricultural and orchard canopies and progressing to airborne observations of more natural forest ecosystems. This observational program is complemented by a parallel effort to model the visible reflectance and microwave scattering properties of composite vegetation canopies. The goals of the modeling studies are to verify our basic understanding of the sensor-scene interaction physics and to provide the basis for development of inverse models optimized for retrieval of key biophysical properties. These retrieval algorithms can then be used to simulate the expected performance of various aspects of Eos including the need for simultaneous SAR and HIRIS observations or justification for other (non-synchronous) relative timing constraints and the frequency, polarization, and angle of incidence requirements for accurate biophysical parameter extractions. This program completed a very successful series of truck-mounted experiments, made remarkable progress in development and validation of optical reflectance and microwave scattering models for vegetation, extended the scattering models to accommodate discontinuous and periodic canopies, developed inversion approaches for surface and canopy properties, and disseminated these results widely through symposia and journal publications. In addition, the third generation of the computer code for the microwave scattering models was provided to a number of other US, Canadian, Australian, and European investigators who are currently presenting and publishing results using the MIMICS research code.

Ulaby, Fawwaz T.↗
DOE OSTI2014

Exploring the effects of data quality, data worth, and redundancy of CO2 gas pressure and saturation data on reservoir characterization through PEST Inversion

This study examined the impacts of reservoir properties on CO2 migration after subsurface injection and evaluated the possibility of characterizing reservoir properties using CO2 monitoring data such as saturation distribution. The injection reservoir was assumed to be located 1400-1500 m below the ground surface such that CO2 remained in the supercritical state. The reservoir was assumed to contain layers with alternating conductive and resistive properties, which is analogous to actual geological formations such as the Mount Simon Sandstone unit. The CO2 injection simulation used a cylindrical grid setting in which the injection well was situated at the center of the domain, which extended up to 8000 m from the injection well. The CO2 migration was simulated using the PNNL-developed simulator STOMP-CO2e (the water-salt-CO2 module). We adopted a nonlinear parameter estimation and optimization modeling software package, PEST, for automated reservoir parameter estimation. We explored the effects of data quality, data worth, and data redundancy on the detectability of reservoir parameters using CO2 saturation monitoring data, by comparing PEST inversion results using data with different levels of noises, various numbers of monitoring wells and locations, and different data collection spacing and temporal sampling intervals. This study yielded insight into the use of CO2 saturation monitoring data for reservoir characterization and how to design the monitoring system to optimize data worth and reduce data redundancy.

Carbon sequestration↗
DOE OSTI2017

Fine-tuning the release of molecular guests from mesoporous silicas by controlling the orientation and mobility of surface phenyl substituents

Phenyl-functionalized mesoporous silica materials were used to explore the effect of non-covalent interactions on the release of Ibuprofen into simulated body fluid. Variations in orientation and conformational mobility of the surface phenyl groups were introduced by selecting different structural precursors: a rigid upright orientation was obtained using phenyl groups directly bound to surface Si atoms (Ph-MSN), mobile groups were produced by using ethylene linkers to connect phenyl groups to the surface (PhEt-MSN), and groups co-planar to the surface were obtained by synthesizing a phenylene-bridged periodic mesoporous organosilica (Ph-PMO). The Ibuprofen release profiles from these materials and non-functionalized mesoporous silica nanoparticles (MSN) were analyzed using an adsorption-diffusion model. The model provided kinetic and thermodynamic parameters that evidenced fundamental differences in drug-surface interactions between the materials. All phenyl-bearing materials show lower Ibuprofen initial release rates than bare MSN. The conformationally locked Ph-MSN and Ph-PMO have stronger interactions with the drug (negative ΔG of adsorption) than the flexible PhEt-MSN and bare MSN (positive ΔG of adsorption). These differences in strength of adsorption are consistent with differences between interaction geometries obtained from DFT calculations. B3LYP-D3-optimized models show that π-π interactions contribute more to drug adsorption than H-bonding with silanol groups. Here, the results suggest that the type and geometry of interactions control the kinetics and extent of drug release, and should therefore serve as a guide to design new drug delivery systems with precise release behaviors customized to any desired target.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

Rapid Identification of X-ray Diffraction Patterns Based on Very Limited Data by Interpretable Convolutional Neural Networks

Large volumes of data from material characterizations call for rapid and automatic data analysis to accelerate materials discovery. Herein, we report a convolutional neural network (CNN) that was trained based on theoretical data and very limited experimental data for fast identification of experimental X-ray diffraction (XRD) patterns of metal–organic frameworks (MOFs). To augment the data for training the model, noise was extracted from experimental data and shuffled; then it was merged with the main peaks that were extracted from theoretical spectra to synthesize new spectra. For the first time, one-to-one material identification was achieved. Theoretical MOFs patterns (1012) were augmented to a whole data set of 72 864 samples. It was then randomly shuffled and split into training (58 292 samples) and validation (14 572 samples) data sets at a ratio of 4:1. For the task of discriminating, the optimized model showed the highest identification accuracy of 96.7% for the top 5 ranking on a test data set of 30 hold-out samples. Neighborhood component analysis (NCA) on the experimental XRD samples shows that the samples from the same material are clustered in groups in the NCA map. Analysis on the class activation maps of the last CNN layer further discloses the mechanism by which the CNN model successfully identifies individual MOFs from the XRD patterns. Furthermore, this CNN model trained by the data augmentation technique would not only open numerous potential applications for identifying XRD patterns for different materials, but also pave avenues to autonomously analyze data by other characterization tools such as FTIR, Raman, and NMR spectroscopies.

36 MATERIALS SCIENCE↗
NASA NTRS1995

Analysis of the Hessian for aeroelastic optimization

The symbol of the Hessian for an aeroelastic optimization model problem is analyzed. The flow is modeled by the small-disturbance full potential equation and the structure is modeled by an isotropic (von Karman) plate equation. The cost function consists of both aerodynamic and structural terms. In the new analysis the symbol of the cost function Hessian near the minimum is computed. The result indicates that under some conditions, which are likely fulfilled in most applications, the system is decoupled for the non-smooth components. The result also shows that the structure part in the Hessian is well-conditioned while the aerodynamic part is ill-conditioned. Applications of the result to optimization strategies are discussed.

Arian, Eyal↗
DOE OSTI2016

Adsorption of iodine on hydrogen-reduced silver-exchanged mordenite: Experiments and modeling

The adsorption process of iodine, a major volatile radionuclide in the off-gas streams of spent nuclear fuel reprocessing, on hydrogen-reduced silver-exchanged mordenite (Ag 0 Z) was studied at the micro-scale. The gas-solid mass transfer and reaction involved in the adsorption process were investigated and evaluated with appropriate models. Optimal conditions for reducing the silver-exchanged mordenite (AgZ) in a hydrogen stream were determined. Kinetic and equilibrium data of iodine adsorption on Ag 0 Z were obtained by performing single-layer adsorption experiments with experimental systems of high precision at 373–473 K over various iodine concentrations. Results indicate approximately 91% to 97% of the iodine adsorption was through the silver-iodine reaction. The effect of temperature on the iodine loading capacity of Ag 0 Z was discussed. In conclusion, the Shrinking Core model describes the data well, and the primary rate controlling mechanisms were macro-pore diffusion and silver-iodine reaction. © 2016 American Institute of Chemical Engineers AIChE J, 2016

38 RADIATION CHEMISTRY, RADIOCHEMISTRY, AND NUCLEA↗
DOE OSTI2020

Quantitative Understanding and Implementation of Screen Printed p+ Poly-Si/Oxide Passivated Contact to Enhance the Efficiency of p-PERC Cells

This paper reports on the modeling, optimization and implementation of p-TOPCon (tunnel oxide passivated contacts) on the rear side of a PERC to enhance its efficiency. Local Al-BSF of a traditional PERC was replaced by p+ polySi/oxide passivated contact composed of ~15Å thick chemically grown tunnel oxide, capped with 120-250nm thick p+ poly-Si layer grown by LPCVD. Process optimization resulted in full area unmetallized saturated current density (Jo) of ~ 5fA/cm2 for planar surface, nearly independent of poly-Si thickness in the range. Metallized J0 showed an increase with decreased poly-Si thickness and was found to be 9.6 and ~25fA/cm2 for 250nm and 120 nm polySi, respectively, with 4.6% direct metal-Si contact fraction, suitable for bifacial cells. A 21.4% efficient baseline PERC cell with local BSF was fabricated and analyzed to extract the rear side saturation current density (J0b’) of 66fA/cm2. Model calculations showed that by replacing this local BSF with 250nm TopCon developed in the study showed some Voc enhancement of 7mV, consistent with the observed Voc increase of 10mV. Model calculations also reveal that a more advanced LBSF PERC with better bulk lifetime and emitter saturation current density can extends its potential gain up to 0.4% in cell efficiency from the integration of p-TOPCon.

14 SOLAR ENERGY↗
DOE OSTI2014

Modeling the irradiance and temperature dependence of photovoltaic modules in PVsyst

In order to reliably simulate the energy yield of photovoltaic (PV) systems, it is necessary to have an accurate model of how the PV modules perform with respect to irradiance and cell temperature. Building on previous work that addresses the irradiance dependence, two approaches to fit the temperature dependence of module power in PVsyst have been developed and are applied here to recent multi-irradiance and -temperature data for a standard Yingli Solar PV module type. The results demonstrate that it is possible to match the measured irradiance and temperature dependence of PV modules in PVsyst. As a result, improvements in energy yield prediction using the optimized models relative to the PVsyst standard model are considered significant for decisions about project financing.

14 SOLAR ENERGY↗
DOE OSTI2015

Patch-based generation of a pseudo CT from conventional MRI sequences for MRI-only radiotherapy of the brain

Purpose: In radiotherapy (RT) based on magnetic resonance imaging (MRI) as the only modality, the information on electron density must be derived from the MRI scan by creating a so-called pseudo computed tomography (pCT). This is a nontrivial task, since the voxel-intensities in an MRI scan are not uniquely related to electron density. To solve the task, voxel-based or atlas-based models have typically been used. The voxel-based models require a specialized dual ultrashort echo time MRI sequence for bone visualization and the atlas-based models require deformable registrations of conventional MRI scans. In this study, we investigate the potential of a patch-based method for creating a pCT based on conventional T{sub 1}-weighted MRI scans without using deformable registrations. We compare this method against two state-of-the-art methods within the voxel-based and atlas-based categories. Methods: The data consisted of CT and MRI scans of five cranial RT patients. To compare the performance of the different methods, a nested cross validation was done to find optimal model parameters for all the methods. Voxel-wise and geometric evaluations of the pCTs were done. Furthermore, a radiologic evaluation based on water equivalent path lengths was carried out, comparing the upper hemisphere of the head in the pCT and the real CT. Finally, the dosimetric accuracy was tested and compared for a photon treatment plan. Results: The pCTs produced with the patch-based method had the best voxel-wise, geometric, and radiologic agreement with the real CT, closely followed by the atlas-based method. In terms of the dosimetric accuracy, the patch-based method had average deviations of less than 0.5% in measures related to target coverage. Conclusions: We showed that a patch-based method could generate an accurate pCT based on conventional T{sub 1}-weighted MRI sequences and without deformable registrations. In our evaluations, the method performed better than existing voxel-based and atlas-based methods and showed a promising potential for RT of the brain based only on MRI.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2023

Accelerating crystal structure determination with iterative AlphaFold prediction

Experimental structure determination can be accelerated with artificial intelligence (AI)-based structure-prediction methods such as AlphaFold . Here, an automatic procedure requiring only sequence information and crystallographic data is presented that uses AlphaFold predictions to produce an electron-density map and a structural model. Iterating through cycles of structure prediction is a key element of this procedure: a predicted model rebuilt in one cycle is used as a template for prediction in the next cycle. This procedure was applied to X-ray data for 215 structures released by the Protein Data Bank in a recent six-month period. In 87% of cases our procedure yielded a model with at least 50% of C α atoms matching those in the deposited models within 2 Å. Predictions from the iterative template-guided prediction procedure were more accurate than those obtained without templates. It is concluded that AlphaFold predictions obtained based on sequence information alone are usually accurate enough to solve the crystallographic phase problem with molecular replacement, and a general strategy for macromolecular structure determination that includes AI-based prediction both as a starting point and as a method of model optimization is suggested.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Boosting Noise2Inverse via enhanced model selection for denoising computed tomography data

Synchrotron-based x-ray tomographic imaging enables the examination of the internal structure of materials at high spatial and temporal resolution. Experimental constraints can impose dose and time limits on the measurements, introducing a higher level of noise and artifacts in the reconstructed images. Deep learning has emerged as a powerful tool to remove noise from reconstructed images. Recently, the Noise2Inverse method was designed specifically for denoising reconstructed images without requiring paired noisy and clean images. This method creates multiple statistically independent reconstructions used to pair the data in which training involves transforming one reconstruction into the other, and vice versa. Originally designed to be used after a fixed number of epochs, we see in practice that this approach may not produce the optimal model and may unnecessarily waste computational resources. Therefore, we propose an alternative method of identifying the best model during training that aligns with the Noise2Inverse method. During validation, we compare the model output of the multiple reconstructions among each other. We hypothesize that the best model is the one that produces images with the highest similarity, implying a convergence in the predicted material properties and absorption values. To compare model outputs, we consider the absolute error, square error, structural similarity index (SSIM), peak signal-to-noise ratio (PSNR), and cosine similarity. We evaluate our method on two simulated tomography datasets and two, real-world, low-contrast, high-energy x-ray tomography datasets. We show our approach is more effective at determining the best model, up to an increase of 12.50% and 12.53% in SSIM and PSNR, respectively, while only requiring a fifth of the training time compared to the original approach.

CT↗
DOE OSTI2014

Phase 1 Development Report for the SESSA Toolkit.

The Site Exploitation System for Situational Awareness ( SESSA ) tool kit , developed by Sandia National Laboratories (SNL) , is a comprehensive de cision support system for crime scene data acquisition and Sensitive Site Exploitation (SSE). SESSA is an outgrowth of another SNL developed decision support system , the Building R estoration Operations Optimization Model (BROOM), a hardware/software solution for data acquisition, data management, and data analysis. SESSA was designed to meet forensic crime scene needs as defined by the DoD's Military Criminal Investigation Organiza tion (MCIO) . SESSA is a very comprehensive toolki t with a considerable amount of database information managed through a Microsoft SQL (Structured Query Language) database engine, a Geographical Information System (GIS) engine that provides comprehensive m apping capabilities, as well as a an intuitive Graphical User Interface (GUI) . An electronic sketch pad module is included. The system also has the ability to efficiently generate necessary forms for forensic crime scene investigations (e.g., evidence submittal, laboratory requests, and scene notes). SESSA allows the user to capture photos on site, and can read and generate ba rcode labels that limit transcription errors. SESSA runs on PC computers running Windows 7, but is optimized for touch - screen tablet computers running Windows for ease of use at crime scenes and on SSE deployments. A prototype system for 3 - dimensional (3 D) mapping and measur e ments was also developed to complement the SESSA software. The mapping system employs a visual/ depth sensor that captures data to create 3D visualizations of an interior space and to make distance measurements with centimeter - level a ccuracy. Output of this 3D Model Builder module provides a virtual 3D %22walk - through%22 of a crime scene. The 3D mapping system is much less expensive and easier to use than competitive systems. This document covers the basic installation and operation of th e SESSA tool kit in order to give the user enough information to start using the tool kit . SESSA is currently a prototype system and this documentation covers the initial release of the tool kit . Funding for SESSA was provided by the Department of Defense (D oD), Assistant Secretary of Defense for Research and Engineering (ASD(R&E)) Rapid Fielding (RF) organization. The project was managed by the Defense Forensic Science Center (DFSC) , formerly known as the U.S. Army Criminal Investigation Laboratory (USACIL) . ACKNOWLEDGEMENTS The authors wish to acknowledge the funding support for the development of the Site Exploitation System for Situational Awareness (SESSA) toolkit from the Department of Defense (DoD), Assistant Secretary of Defense for Research and Engineering (ASD(R&E)) Rapid Fielding (RF) organization. The project was managed by the Defense Forensic Science Center (DFSC) , formerly known as the U.S. Army Criminal Investigation Laboratory (USACIL). Special thanks to Mr. Garold Warner, of DFSC, who served as the Project Manager. Individuals that worked on the design, functional attributes, algorithm development, system arc hitecture, and software programming include: Robert Knowlton, Brad Melton, Robert Anderson, and Wendy Amai.

Knowlton, Robert G.↗