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Results for “optimization analysis”

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 1,423 records · Page 79
All sourcesNASA NTRSDOE OSTI
DOE OSTI2015

The mimetic finite difference method for elliptic and parabolic problems with a staggered discretization of diffusion coefficient

Numerical schemes for nonlinear parabolic equations based on the harmonic averaging of cell-centered diffusion coefficients break down when some of these coefficients go to zero or their ratio grows. To tackle this problem, we propose new mimetic finite difference schemes that use a staggered discretization of the diffusion coefficient. The primary mimetic operator approximates div (k•); the derived (dual) mimetic operator approximates - ∇(•). The new mimetic schemes preserve symmetry and positive-definiteness of the continuum problem which allows us to use algebraic solvers with optimal complexity. We perform detailed numerical analysis of the new schemes for linear elliptic problems and a specially designed linear parabolic problem that has solution dynamics typical for nonlinear problems. We also show that the new schemes are competitive with the state-of-the-art schemes for steady-state problems but provide much more accurate solution dynamics for the transient problem.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Structure-preserving neural networks for the regularized entropy-based closure of a linear, kinetic, radiative transport equation

The main challenge of large-scale numerical simulation of radiation transport is the high memory and computation time requirements of discretization methods for kinetic equations. In this work, we derive and investigate a neural network-based approximation to the entropy-based closure method to accurately compute the solution of the multi-dimensional moment system with a low memory footprint and competitive computational time. We extend methods developed for the standard entropy-based closure to the regularized entropy-based closures. The main idea is to interpret structure-preserving neural network approximations of the regularized entropy-based closure as a two-stage approximation to the original entropy-based closure. We conduct a numerical analysis of this approximation and investigate optimal parameter choices. Our numerical experiments demonstrate that the method has a much lower memory footprint than traditional methods with competitive computation times and simulation accuracy. The code and all trained networks are provided on GitHub.

entropy closure↗
DOE OSTI2019

Determining local thermal transport in a composite uranium-nitride/silicide nuclear fuel using square-pulse transient thermoreflectance technique

Laser-based techniques have become an appealing option for determining the thermal diffusivities (D) and conductivities (k) of nuclear materials that would otherwise prove too hazardous or difficult to measure otherwise. These techniques have enabled non-destructive and non-contact thermal transport measurements with micron-level spatial resolution. In this study, a new time-domain technique known as square-wave pulse thermoreflectance (SPTR) is described and used to determine D values of both uranium sesquisilicide (U 3 Si 2 ) and uranium nitride (UN) phases in a composite fuel. An extensive sensitivity analysis was conducted that identified the optimal measurement parameters of standard materials with a range of thermal transport properties comparable to those of various fuel types. The standards were measured using both SPTR as well as a spatial-domain thermoreflectance technique (SDTR). Several U 3 Si 2 and UN phase regions of polished UN/U 3 Si 2 (70/30 vol.%) samples were measured using both methods, and the resulting calculated D and k values are reported, with both techniques showing excellent agreement between samples. Finally, the ability to use the technique for local diffusivity mapping was demonstrated over a multiphase region, and could be used to determine thermal transport properties in precipitates, secondary phases, and irradiation-damaged regions of post-irradiated fuel samples.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2017

Photoacoustic flow cytometry for nanomaterial research

Conventional flow cytometry is a versatile tool for drug research and cell characterization. However, it is poorly suited for quantification of non-fluorescent proteins and artificial nanomaterials without the use of additional labeling. The rapid growth of biomedical applications for small non-fluorescent nanoparticles (NPs) for drug delivery, image contrast and therapy enhancement, as well as research focused on natural cell pigments and chromophores, demands high-throughput quantification methods for the non-fluorescent components. In this work, we present an advanced novel photoacoustic (PA) fluorescence flow cytometry (PAFFC) platform that integrates NP quantification though PA detection with conventional sample characterization using fluorescence labeling. PAFFC simplifies high-throughput analysis of cell-NP interactions, optimization of targeted nanodrugs, and NP toxicity assessment by providing a direct correlation between NP uptake and characterization of toxicity markers for every cell.

Acoustics↗
DOE OSTI2023

Design of Planarizing Growth Conditions on Unpolished and Faceted (100)-Oriented GaAs Substrates Using Hydride Vapor Phase Epitaxy

Here we performed a design of experiments (DoE) analysis to determine the effect of various growth parameters on in situ planarizing overgrowth of rough substrates using hydride vapor phase epitaxy (HVPE). We used two types of rough (100)-oriented GaAs substrates to compare the effect of the initial morphology on the epitaxial growth behavior: an irregular, as-cut surface resultant from cutting the wafer from a GaAs boule and a regularly faceted surface produced by controlled spalling. The DoE analysis identified trends in the overgrowth behavior with changing growth conditions, and we used these trends to design favorable planarization growth conditions for each surface type. These favorable conditions enabled full planarization of a spalled surface with >2.5 um facet height within 10.8 min of growth. The root mean squared (RMS) surface roughness of the resulting (100) surface was 24.5 nm over a 286 um x 215 um area. Our results show that planarization growth by HVPE is a promising technique to enable device growth directly on rough substrates, and the trends revealed through DoE analysis indicate a path toward further optimization of planarization growth conditions for as-cut and spalled surfaces.

14 SOLAR ENERGY↗
DOE OSTI2026

Analytic Nuclear Gradients Including Oriented External Electric Fields in a Molecule-Fixed Frame

Electric-field-assisted chemistry has attracted much attention in recent years, particularly in the context of oriented external electric fields for controlling molecular structure and reactivity. Such fields have been explored in a wide range of applications, including switching materials, nanoparticles, controllable catalysts, medicines, and clinical therapies. However, the determination of fixed fields in the laboratory frame becomes ineffective for flexible molecules, as conformational changes can significantly alter the relative orientation between the applied field and molecular structure. In this work, we propose two molecular reference frames─the principal axis frame and the local reference frame─to define oriented electric fields within the molecular framework. These coordinate systems powerfully eliminate ambiguities in the relative orientation between the applied field and the molecule. Analytic nuclear gradients in the presence of external electric fields are derived and implemented, with an initial application to field-dependent geometry optimizations of cis - and trans -formanilide. Analysis of the resulting field-induced equilibrium structures reveals distinct structural responses, validating the accuracy and robustness of the proposed formalism. The analytic gradient framework enables systematic investigations of molecular properties and reactivity under arbitrarily oriented electric fields, opening new opportunities for computational modeling and rational design in electric-field-controlled chemistry.

electric fields↗
DOE OSTI2020

Systematic Assessment of Retrieval Methods for Canopy Far-Red Solar-Induced Chlorophyll Fluorescence Using High-Frequency Automated Field Spectroscopy

Remote sensing of solar-induced chlorophyll fluorescence (SIF) offers potential to infer photosynthesis across scales and biomes. Many retrieval methods have been developed to estimate top-of-canopy SIF using ground-based spectroscopy. However, inconsistencies among methods may confound interpretation of SIF dynamics, eco-physiological/environmental drivers, and its relationship with photosynthesis. Using high temporal- and spectral resolution ground-based spectroscopy, we aimed to (1) evaluate performance of SIF retrieval methods under diverse sky conditions using continuous field measurements; (2) assess method sensitivity to fluctuating light, reflectance, and fluorescence emission spectra; and (3) inform users for optimal ground-based SIF retrieval. Analysis included field measurements from bi-hemispherical and hemispherical-conical systems and synthetic upwelling radiance constructed from measured downwelling radiance, simulated reflectance, and simulated fluorescence for benchmarking. Fraunhofer-based differential optical absorption spectroscopy (DOAS) and singular vector decomposition (SVD) retrievals exhibit convergent SIF-PAR relationships and diurnal consistency across different sky conditions, while O2A-based spectral fitting method (SFM), SVD, and modified Fraunhofer line discrimination (3FLD) exhibit divergent SIF-PAR relationships across sky conditions. We find that such behavior holds across system configurations, though hemispherical-conical systems diverge less across sky conditions. O 2 A retrieval accuracy, influenced by atmospheric distortion, improves with a narrower fitting window and when training SVD with temporally local spectra. This may impact SIF-photosynthesis relationships interpreted by previous studies using O 2 A-based retrievals with standard (759–767.76 nm) fitting windows. Fraunhofer-based retrievals resist atmospheric impacts but are noisier and more sensitive to assumed SIF spectral shape than O 2 A-based retrievals. We recommend SVD or SFM using reduced fitting window (759.5–761.5 nm) for robust far-red SIF retrievals across sky conditions.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2014

Structure and mechanism of action of the hydroxy-aryl-aldehyde class of IRE1 endoribonuclease inhibitors

Endoplasmic reticulum (ER) stress activates the unfolded protein response and its dysfunction is linked to multiple diseases. The stress transducer IRE1α is a transmembrane kinase endoribonuclease (RNase) that cleaves mRNA substrates to re-establish ER homeostasis. Aromatic ring systems containing hydroxy–aldehyde moieties, termed hydroxy–aryl–aldehydes (HAA), selectively inhibit IRE1α RNase and thus represent a novel chemical series for therapeutic development. We solved crystal structures of murine IRE1α in complex with three HAA inhibitors. HAA inhibitors engage a shallow pocket at the RNase-active site through pi-stacking interactions with His910 and Phe889, an essential Schiff base with Lys907 and a hydrogen bond with Tyr892. Structure–activity studies and mutational analysis of contact residues define the optimal chemical space of inhibitors and validate the inhibitor-binding site. These studies lay the foundation for understanding both the biochemical and cellular functions of IRE1α using small molecule inhibitors and suggest new avenues for inhibitor design.

Sanches, Mario↗
DOE OSTI2023

Grooved electrodes for high-power-density fuel cells

Proton exchange membrane fuel cells (PEMFCs) are leading candidates to decarbonize the transport sector, but widespread deployment will require improvements in lifetime, fuel economy and cost. Here we present the grooved electrode, an alternative electrode structure that enhances PEMFC performance and durability by coupling high ionomer (ion-conducting binder) content for improved H + transport with grooves for rapid O 2 transport. Grooved electrodes provide up to 50% higher performance than state-of-the-art conventional electrodes under standard operating conditions. Fuel cell diagnostics combined with multiphysics modelling demonstrate that grooved electrodes provide facile O 2 transport despite their high ionomer content, enabling improved reaction rate uniformity. Grooved electrodes also provide improved durability, with less performance loss after carbon corrosion compared with baseline electrodes. Machine learning analysis demonstrates the potential to further optimize grooved structures for next-generation PEMFCs with enhanced performance and durability, enabling smaller and cheaper fuel cell stacks with higher fuel efficiency.

25 ENERGY STORAGE↗
DOE OSTI2024

Efficient prediction of attosecond two-colour pulses from an X-ray free-electron laser with machine learning

Abstract X-ray free-electron lasers are sources of coherent, high-intensity X-rays with numerous applications in ultra-fast measurements and dynamic structural imaging. Due to the stochastic nature of the self-amplified spontaneous emission process and the difficulty in controlling injection of electrons, output pulses exhibit significant noise and limited temporal coherence. Standard measurement techniques used for characterizing two-coloured X-ray pulses are challenging, as they are either invasive or diagnostically expensive. In this work, we employ machine learning methods such as neural networks and decision trees to predict the central photon energies of pairs of attosecond fundamental and second harmonic pulses using parameters that are easily recorded at the high-repetition rate of a single shot. Using real experimental data, we apply a detailed feature analysis on the input parameters while optimizing the training time of the machine learning methods. Our predictive models are able to make predictions of central photon energy for one of the pulses without measuring the other pulse, thereby leveraging the use of the spectrometer without having to extend its detection window. We anticipate applications in X-ray spectroscopy using XFELs, such as in time-resolved X-ray absorption and photoemission spectroscopy, where improved measurement of input spectra will lead to better experimental outcomes.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2022

Machine learning reveals factors that control ion mobility in anti-perovskite solid electrolytes

Solid-state batteries are projected to exhibit improved energy densities and safety compared to liquid-electrolyte-based systems. Consequently, the development of solid electrolytes that can conduct cations at rates comparable to that of liquid electrolytes is an active area of research. To facilitate this search it is desirable to understand the chemical and structural features that control ion transport through a crystalline lattice. Here, machine learning (ML) is used to identify features that influence ion mobility in solids, quantify the relative importance of those features, and demonstrate how the variation of individual features alters ion mobility. Adopting the anti-perovskite lattice as a model system, ML algorithms were trained to predict migration barriers from a data set containing hundreds of barriers calculated using density functional theory. In this study, more than 10 6 feature sets of non-redundant descriptors were evaluated for their ability to predict migration barriers. The present analysis correctly reiterates the importance of several features that are known to influence mobility – for example, anion polarizability – but also identifies others whose importance is not widely recognized. Lattice properties such as hopping distance and channel width exhibit the greatest influence on cation mobility. Individual conditional expectation analyses performed on these features shows that barriers decrease as hopping distance decreases and as channel width increases. The defect formation energy – which is commonly associated with the concentration of defects, but not with their mobility – is identified as an important feature for interstitial migration. In sum, this analysis aids in the design of optimal solid electrolytes by simplifying the multi-dimensional design space to a sub-set of properties that are the most important.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Application-specific machine-learned interatomic potentials: exploring the trade-off between DFT convergence, MLIP expressivity, and computational cost

Machine-learned interatomic potentials (MLIPs) are revolutionizing computational materials science and chemistry by offering an efficient alternative to ab initio molecular dynamics (MD) simulations. However, fitting high-quality MLIPs remains a challenging, time-consuming, and computationally intensive task where numerous trade-offs have to be considered, e.g., How much and what kind of atomic configurations should be included in the training set? Which level of ab initio convergence should be used to generate the training set? Which loss function should be used for fitting the MLIP? Which machine learning architecture should be used to train the MLIP? The answers to these questions significantly impact both the computational cost of MLIP training and the accuracy and computational cost of subsequent MLIP MD simulations. In this study, we use a configurationally diverse beryllium dataset and quadratic spectral neighbor analysis potential. We demonstrate that joint optimization of energy versus force weights, training set selection strategies, and convergence settings of the ab initio reference simulations, as well as model complexity can lead to a significant reduction in the overall computational cost associated with training and evaluating MLIPs. This opens the door to computationally efficient generation of high-quality MLIPs for a range of applications which demand different accuracy versus training and evaluation cost trade-offs.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Assembly of small silica nanoparticles using lipid-tethered DNA ‘bonds’

Single-stranded DNA molecules modified with cholesterol functional groups are physically tethered to silica nanoparticles (diameter 25 nm) that are encapsulated in a lipid bilayer. Such tethering increases the azimuthal mobility of the DNA molecules across the nanoparticle surface and enables nonspecific bonding, eliminating the need for specialized surface chemistries (such as silane or thiol ligands). To induce assembly, double-stranded DNA ‘bridge’ molecules are then added with complementary nucleotides to the DNA ‘anchor’ molecules that are physically tethered to the lipids on the surface of the particles. Assembly is observed to occur at room temperature and without the need for temperature annealing. Using automated liquid handling tools, assemblies are created in high throughput and rapidly characterized using SAXS. It is determined that the relative concentration of DNA-to-silica and the ionic strength of the solution are important parameters that affect the resulting assembly. Analysis of SAXS data is performed using coarse-grained particle dynamics simulations. The results support the spontaneous formation of semi-crystalline particle assemblies by particle condensation, where the interparticle distance is tuned by the sequence of the DNA ‘bridge’ used to link the particles. Crystallinity analysis performed on the resulting simulations, optimized to match SAXS observations, suggest that particle clusters display increased crystallinity in the center of the clusters, but their maximum size remains relatively small (sub-micron) before settling occurs, which limits the extent of crystallization.

Chiang, Huat Thart [Univ. of Washington, Seattle, ↗
DOE OSTI2013

Revision of laser-induced damage threshold evaluation from damage probability data

In this study, the applicability of commonly used Damage Frequency Method (DFM) is addressed in the context of Laser-Induced Damage Threshold (LIDT) testing with pulsed lasers. A simplified computer model representing the statistical interaction between laser irradiation and randomly distributed damage precursors is applied for Monte Carlo experiments. The reproducibility of LIDT predicted from DFM is examined under both idealized and realistic laser irradiation conditions by performing numerical 1-on-1 tests. A widely accepted linear fitting resulted in systematic errors when estimating LIDT and its error bars. For the same purpose, a Bayesian approach was proposed. A novel concept of parametric regression based on varying kernel and maximum likelihood fitting technique is introduced and studied. Such approach exhibited clear advantages over conventional linear fitting and led to more reproducible LIDT evaluation. Furthermore, LIDT error bars are obtained as a natural outcome of parametric fitting which exhibit realistic values. The proposed technique has been validated on two conventionally polished fused silica samples (355 nm, 5.7 ns).

36 MATERIALS SCIENCE↗
DOE OSTI2015

Response of asymmetric carbon nanotube network devices to sub-terahertz and terahertz radiation

Demand for efficient terahertz radiation detectors resulted in intensive study of the asymmetric carbon nanostructures as a possible solution for that problem. It was maintained that photothermoelectric effect under certain conditions results in strong response of such devices to terahertz radiation even at room temperature. In this work, we investigate different mechanisms underlying the response of asymmetric carbon nanotube (CNT) based devices to sub-terahertz and terahertz radiation. Our structures are formed with CNT networks instead of individual CNTs so that effects probed are more generic and not caused by peculiarities of an individual nanoscale object. We conclude that the DC voltage response observed in our structures is not only thermal in origin. So called diode-type response caused by asymmetry of the device IV characteristic turns out to be dominant at room temperature. Quantitative analysis provides further routes for the optimization of the device configuration, which may result in appearance of novel terahertz radiation detectors.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2017

Turning a pyroelectric infrared motion sensor into a high-accuracy presence detector by using a narrow semi-transparent chopper

Pyroelectric infrared (PIR) sensors are the most popular presence detectors. However, their applications are limited to motion detection only, as the pyroelectric sensing element is only sensitive to radiated heat power fluctuation. Here, this letter reports a chopped PIR (C-PIR) presence sensor, capable of detecting both the stationary and the moving occupants at high accuracy by introducing a narrow semi-transparent optical chopper to shutter the incident radiation periodically. More importantly, even though the narrow chopper can efficiently shutter the unidirectional infrared (IR) power radiated by stationary occupants, it has near-zero influence on the omnidirectional power radiated from the ambient environment. Therefore, the voltage difference generated by the C-PIR sensor between occupied and unoccupied scenarios can be directly used for identifying the presence of stationary occupants with high-accuracy. For moving occupant detection, the optical chopper can barely affect the variation of the radiated power and thus lead to the same high-accuracy detection as the on-board PIR. An approximate theoretical model is created to illustrate the physics and dynamics of IR radiation shuttered by the optical chopper. Parametric studies, experimental characterization, and empirical analysis were performed to obtain the optimal setting of the chopper in terms of its thickness and chopping frequency. Experimental results reveal that the C-PIR sensor can detect stationary occupants for up to 4.0 m and moving occupants for up to 8.0 m, while maintaining an accuracy of 100%. The field of view of the C-PIR was found at 110° horizontally and 90° vertically, consistent with that of the on-board PIR sensor.

47 OTHER INSTRUMENTATION↗
DOE OSTI2020

Parallel high-frequency magnetic sensing with an array of flux transformers and multi-channel optically pumped magnetometer for hand MRI application

Here, we investigate an approach for parallel high-frequency magnetic sensing based on a multi-channel radio frequency (RF) optically pumped magnetometer (OPM) coupled to multiple flux transformers (FTs) with a focus on hand magnetic resonance imaging (MRI) application at ultra-low field (ULF). Multiple RF OPM sensing channels are realized by using a single large-area alkali-metal vapor cell and two laser beams for pumping and probing, shared for all the channels. This design leads to significant cost reduction when multi-channel sensing is desirable, as in the case of ULF MRI. The FT, composed of two connected coils, serves as a transmitter of a target magnetic field to the OPM, while decoupling the OPM from untargeted magnetic fields in the sensing area that can limit the OPM performance. For hand MRI application, theoretical and numerical analysis is performed to determine an optimal geometry for the FT array that could improve signal-to-noise ratio (SNR) and sufficiently reduce crosstalk between FTs. We estimate that the optimized multi-channel FT-OPM sensor can achieve a magnetic field sensitivity of the order of 1 fT / Hz 1 / 2 above 100 kHz, which would be sufficient for 1 mm resolution MRI. In general, the multi-channel capability enables simultaneous magnetic measurements, thus reducing the sensing time and improving the SNR, and we anticipate many applications of the multi-channel FT-OPM sensor beyond the targeted here hand MRI: anatomical parallel ULF MRI of the human brain and other parts of the body, airport security screening, magnetic material imaging, and many others.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2021

Inverse design of equilibrium cluster fluids applied to a physically informed model

Inverse design strategies have proven highly useful for the discovery of interaction potentials that prompt self-assembly of a variety of interesting structures. However, often the optimized particle interactions do not have a direct relationship to experimental systems. In this work, we show that Relative Entropy minimization is able to discover physically meaningful parameter sets for a model interaction built from depletion attraction and electrostatic repulsion that yield self-assembly of size-specific clusters. Here, we then explore the sensitivity of the optimized interaction potentials with respect to deviations in the underlying physical quantities, showing that clustering behavior is largely preserved even as the optimized parameters are perturbed.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗