SEARCH · Search NASA

Results for “Structural Optimization”

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.

SourceResearch connectionFrom yearThrough year

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 1,189 records · Page 66

All sources NASA NTRS DOE OSTI

DOE OSTI2020

Filtered Backprojection in Compton Imaging using a Spherical Harmonic Wiener Filter with Pixelated CdZnTe

Filtered backprojection is an image reconstruction technique for Compton imaging that provides reasonably high resolution at much lower computational costs when compared to iterative methods. Here, this work applies a Wiener filter that has been derived for spherical harmonics on Compton imaging using the OrionUM pixelated CdZnTe imaging-spectrometer. To regularize the filter, an investigation is made into the power spectral density of the signal and noise to develop an appropriate spectral signal-to-noise ratio model for the restoration process. Experimental measurements were conducted with two 228Th sources placed 30° apart. The resulting filtered image of the two sources have an average full-width-at-half-maximum (FWHM) of 9.8° or 7.5° when using a mean squared error and structural similarity optimization approach respectively; an improvement from the 29.0° FWHM image when using simple backprojection.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

DOE OSTI2019

Convolutional Neural Network Surrogate Models for the Mechanical Properties of Periodic Structures

The work in this paper describes neural network surrogate models for calculating the effective mechanical properties of a periodic composites. The models achieve good accuracy even when only provided with training data sampling a small portion of the design space. As an example, the surrogate models are applied to solving the inverse design problem of finding structures with optimal mechanical properties. The surrogate models are sufficiently accurate to recover optimal solutions in general agreement with established topology optimization methods. However, improvements will be required to develop robust, efficient neural network-based surrogate models and several directions for future research are highlighted here.

42 ENGINEERING↗

DOE OSTI2023

Digital quantum simulation of NMR experiments

Simulations of nuclear magnetic resonance (NMR) experiments can be an important tool for extracting information about molecular structure and optimizing experimental protocols but are often intractable on classical computers for large molecules such as proteins and for protocols such as zero-field NMR. We demonstrate the first quantum simulation of an NMR spectrum, computing the zero-field spectrum of the methyl group of acetonitrile using four qubits of a trapped-ion quantum computer. We reduce the sampling cost of the quantum simulation by an order of magnitude using compressed sensing techniques. We show how the intrinsic decoherence of NMR systems may enable the zero-field simulation of classically hard molecules on relatively near-term quantum hardware and discuss how the experimentally demonstrated quantum algorithm can be used to efficiently simulate scientifically and technologically relevant solid-state NMR experiments on more mature devices. Our work opens a practical application for quantum computation.

74 ATOMIC AND MOLECULAR PHYSICS↗

DOE OSTI2020

3C-like protease inhibitors block coronavirus replication in vitro and improve survival in MERS-CoV–infected mice

Pathogenic coronaviruses are a major threat to global public health, as exemplified by severe acute respiratory syndrome coronavirus (SARS-CoV), Middle East respiratory syndrome coronavirus (MERS-CoV), and the newly emerged SARS-CoV-2, the causative agent of coronavirus disease 2019 (COVID-19). We describe herein the structure-guided optimization of a series of inhibitors of the coronavirus 3C-like protease (3CLpro), an enzyme essential for viral replication. The optimized compounds were effective against several human coronaviruses including MERS-CoV, SARS-CoV, and SARS-CoV-2 in an enzyme assay and in cell-based assays using Huh-7 and Vero E6 cell lines. Two selected compounds showed antiviral effects against SARS-CoV-2 in cultured primary human airway epithelial cells. In a mouse model of MERS-CoV infection, administration of a lead compound 1 day after virus infection increased survival from 0 to 100% and reduced lung viral titers and lung histopathology. These results suggest that this series of compounds has the potential to be developed further as antiviral drugs against human coronaviruses.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2017

On the limit of the injection ability of silicon p{sup +}–n junctions as a result of fundamental physical effects

Analytical expressions describing the dependences of the p{sup +}–n-junction leakage current on the doping level of the p{sup +}-type region are derived by taking into account a whole set of nonlinear physical effects: electron–hole scattering, Auger recombination, band-gap narrowing, and nonlinear dependences of the charge-carrier lifetime and mobility on the doping level. It is shown that the dependence for the leakage current has a minimum at which the injection ability of the p{sup +}-type emitter is at a maximum. The dependence of the extremum position on the main parameters of the heavily doped p{sup +} layer is analyzed. The data obtained make it possible to optimize the structure of high-power silicon devices and to facilitate the adequacy of numerical calculations.

36 MATERIALS SCIENCE↗

DOE OSTI2022

Optimization of the first CUPID detector module

CUPID will be a next generation experiment searching for the neutrinoless double β decay, whose discovery would establish the Majorana nature of the neutrino. Based on the experience achieved with the CUORE experiment, presently taking data at LNGS, CUPID aims to reach a background free environment by means of scintillating Li 2 100 MoO 4 crystals coupled to light detectors. Indeed, the simultaneous heat and light detection allows us to reject the dominant background of α particles, as proven by the CUPID-0 and CUPID-Mo demonstrators. In this work we present the results of the first test of the CUPID baseline module. In particular, we propose a new optimized detector structure and light sensors design to enhance the engineering and the light collection, respectively. We characterized the heat detectors, achieving an energy resolution of (5.9 ± 0.2) keV FWHM at the Q-value of 100 Mo (about 3034 keV). We studied the light collection of the baseline CUPID design with respect to an alternative configuration which features gravity-assisted light detectors’ mounting. In both cases we obtained an improvement in the light collection with respect to past measures and we validated the particle identification capability of the detector, which ensures an α particle rejection higher than 99.9%, fully satisfying the requirements for CUPID.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2017

An Investigation of LSF-YSZ Conductive Scaffolds for Infiltrated SOFC Cathodes

Porous composites of Sr-doped LaFeO 3 (LSF) and yttria-stabilized zirconia (YSZ) were investigated as conductive scaffolds for infiltrated SOFC cathodes with the goal of producing scaffolds for which only a few perovskite infiltration steps are required to achieve sufficient conductivity. While no new phases form when LSF-YSZ composites are calcined to 1623 K, shifts in the lattice parameters indicate Zr can enter the perovskite phase. Measurements on dense, LSF-YSZ composites show that the level of Zr doping depends on the Sr:La ratio. Because conductivity of undoped LSF increases with Sr content while both the ionic and electronic conductivities of Zr-doped LSF decrease with the level of Zr in the perovskite phase, there is an optimum initial Sr content corresponding to La 0.9 Sr 0.1 FeO 3 (LSF91). Although scaffolds made with 100% LSF had a higher conductivity than scaffolds made with 50:50 LSF-YSZ mixtures, the 50:50 mixture provides the optimal interfacial structure with the electrolyte and sufficient conductivity, providing the best cathode performance upon infiltration of La 0.6 Sr 0.4 Co 0.2 Fe 0.8 O 3 (LSCF).

36 MATERIALS SCIENCE↗

DOE OSTI2017

SIRT-FILTER v1.0.0

Small Python package to compute tomographic reconstructions using a reconstruction method published in: Pelt, D.M., & De Andrade, V. (2017). Improved tomographic reconstruction of large-scale real-world data by filter optimization. Advanced Structural and Chemical Imaging 2: 17; and Pelt, D. M., & Batenburg, K. J. (2015). Accurately approximating algebraic tomographic reconstruction by filtered backprojection. In Proceedings of The 13th International Meeting on Fully Three-Dimensional Image Reconstruction in Radiology and Nuclear Medicine (pp. 158-161).

PELT, DANIEL↗

DOE OSTI2025

Massively parallel and universal approximation of nonlinear functions using diffractive processors

Nonlinear computation is essential for a wide range of information processing tasks, yet implementing nonlinear functions using optical systems remains a challenge due to the weak and power-intensive nature of optical nonlinearities. Overcoming this limitation without relying on nonlinear optical materials could unlock unprecedented opportunities for ultrafast and parallel optical computing systems. Here, we demonstrate that large-scale nonlinear computation can be performed using linear optics through optimized diffractive processors composed of passive phase-only surfaces. In this framework, the input variables of nonlinear functions are encoded into the phase of an optical wavefront—e.g., via a spatial light modulator (SLM)—and transformed by an optimized diffractive structure with spatially varying point-spread functions to yield output intensities that approximate a large set of unique nonlinear functions–all in parallel. We provide proof establishing that this architecture serves as a universal function approximator for an arbitrary set of bandlimited nonlinear functions, also covering wavelength-multiplexed nonlinear functions as well as multi-variate and complex-valued functions that are all-optically cascadable. Our analysis also indicates the successful approximation of typical nonlinear activation functions commonly used in neural networks, including the sigmoid, tanh, ReLU (rectified linear unit), and softplus. We numerically demonstrate the parallel computation of one million distinct nonlinear functions, accurately executed at wavelength-scale spatial density at the output of a diffractive optical processor. Furthermore, we experimentally validated this framework using in situ optical learning and approximated 35 unique nonlinear functions in a single shot using a compact setup consisting of an SLM and an image sensor. These results establish diffractive optical processors as a scalable platform for massively parallel universal nonlinear function approximation, paving the way for new capabilities in analog optical computing based on linear materials.

Rahman, Md Sadman Sakib [University of California,↗

DOE OSTI2019

Optimal Arrangements of Tetracene Molecule Pairs for Fast Singlet Fission

In search for a qualitative understanding of the effects of molecular packing on singlet fission (SF) rate, a simplified version of the frontier orbital model is described and illustrated on a pair of tetracene molecules. To identify all favorable physically accessible pair geometries, all significant local maxima of the square of the electronic matrix element for SF have been located within the six-dimensional space of possible arrangements of two rigid bodies, using a grid of over 4.7 × 10 8 pair geometries. Those at which the molecules interpenetrate were excluded using a hard-sphere model. The effects of intermolecular interaction on the SF energy balance and thus its rate constant k SF were approximated using Marcus theory at each of the maxima using the same simplified version of the frontier orbital model. Starting at these local maxima, the pair geometries were optimized for maximum k SF and the 21 best are reported along with their computed Davydov splitting and triplet biexciton binding energies. The optimal pair structures at the resulting maxima follow qualitative rules published previously and further elaborated here.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2023

Defects in MX2 Phases (DMX) Dataset: STEM Digital Twins

DFT is used to optimize defects in monolayer MX2 phases. We calculate ~600 optimized defect structures, then use multislice simulations in abTEM to generate STEM digital twins, which can be used to train experimental ML. The reference paper for procedures and demonstration of their utility is https://arxiv.org/abs/2305.02917, and the images here correspond with the discussed Messy dataset.

36 MATERIALS SCIENCE↗

DOE OSTI2023

Defects in MX2 (DMX) 2.0: STEM Digital Twins

36 MATERIALS SCIENCE↗

DOE OSTI2018

Improved quantitative circuit model of realistic patch-based nanoantenna-enabled detectors

Improving the sensitivity of infrared detectors is an essential step for future applications, including satellite- and terrestrial-based systems. We investigate nanoantenna-enabled detectors (NEDs) in the infrared, where the nanoantenna arrays play a fundamental role in enhancing the level of absorption within the active material of a photodetector. The design and optimization of nanoantenna-enabled detectors via full-wave simulations is a challenging task given the large parameter space to be explored. Here, we present a fast and accurate fully analytic circuit model of patch-based NEDs. This model allows for the inclusion of real metals, realistic patch thicknesses, non-absorbing spacer layers, the active detector layer, and absorption due to higher-order evanescent modes of the metallic array. We apply the circuit model to the design of NED devices based on Type II superlattice absorbers, and show that we can achieve absorption of ~70% of the incoming energy in subwavelength (~λ/5) absorber layers. In conclusion, the accuracy of the circuit model is verified against full-wave simulations, establishing this model as an efficient design tool to quickly and accurately optimize NED structures.

47 OTHER INSTRUMENTATION↗

DOE OSTI2022

A sensitive and specific genetically-encoded potassium ion biosensor for in vivo applications across the tree of life

Potassium ion (K + ) plays a critical role as an essential electrolyte in all biological systems. Genetically-encoded fluorescent K + biosensors are promising tools to further improve our understanding of K + -dependent processes under normal and pathological conditions. Here, we report the crystal structure of a previously reported genetically-encoded fluorescent K + biosensor, GINKO1, in the K + -bound state. Using structure-guided optimization and directed evolution, we have engineered an improved K + biosensor, designated GINKO2, with higher sensitivity and specificity. We have demonstrated the utility of GINKO2 for in vivo detection and imaging of K + dynamics in multiple model organisms, including bacteria, plants, and mice.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2017

Novel Transparent Phosphor Conversion Matrix with High Thermal Conductivity for Next Generation Phosphor-Converted LED-based Solid State Lighting

The low thermal conductivity of state-of-the-art polymer encapsulants (k ~ 0.15 Wm-1K-1) limits the efficiency and power density of current phosphor conversion light emitting diodes (pc-LEDs). The technical objective of this project was to demonstrate synthesis and processing schemes for the fabrication of polymer hybrid encapsulants with a thermal conductivity exceeding k = 0.4 Wm-1K-1 for LED applications. The ‘hybrid polymer’ approach encompasses the dispersion of high thermal conductivity particle fillers (zinc oxide, ZnO as well as the alpha-polymorph of alumina, Al2O3) within a polysiloxane matrix (poly(dimethylsiloxane), PDMS as well as poly(phenyl methyl siloxane), PPMS) to increase the thermal conductivity while maintaining optical transparency and photothermal stability at levels consistent with LED applications. To accomplish this goal, a novel synthesis method for the fabrication of nanosized ZnO particles was developed and a novel surface chemistry was established to modify the surface of zinc oxide particle fillers and thus to enable their dispersion in poly(dimethyl siloxane) (PDMS) matrix polymers. Molecular dynamics and Mie simulations were used to optimize ligand structure and to enable the concurrent mixing of particles in PDMS/PPMS embedding media while also minimizing the thermal boundary resistance as well as optical scattering of particle fillers. Using this approach the synthesis of PDMS/ZnO hybrid encapsulants exhibiting a thermal conductivity of 0.64 Wm-1K-1 and optical transparency > 0.7 mm-1 was demonstrated. A forming process based on micromolding was developed to demonstrate the forming of particle filled PDMS into film and lens shapes. Photothermal stability testing revealed stability of the materials for approximately 4000 min when exposed to blue light LED (450 nm, 30 W/cm2). One postgraduate and seven graduate students were supported by the project. The research performed within this project led to fifteen publications in peer-reviewed journals and one patent application. The grant stimulated a multi-investigator research collaborations among seven investigators at Carnegie Mellon University to address the challenge of encapsulants in pc-LED applications. The grant also catalyzed the future collaboration between researchers at Carnegie Mellon University and OSRAM Sylvania to address challenges associated with the use if polymers in LED applications.

36 MATERIALS SCIENCE↗

DOE OSTI2019

Data Architecture for Security Monitoring (Project Summary)

The research has 2 thrusts: 1. new data architectures and tech-transfer-ready prototype tools that use write optimized data structures (WODS) to track real-world events, provide value to analysts, and support our cyber missions; and 2. algorithm research to address infinite streams of data, including expiration and sustainability.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2020

New Approaches to Improved PEM Fuel Cell Catalyst Layers

Polymer-electrolyte membrane (PEM) fuel-cells are one of the most promising energy conversion technologies for renewable clean energy applications. A major challenge preventing their widespread commercialization is achieving high performance with lowloadings of platinum group metal (PGM) catalysts. One of the factors driving performance limitations in the cell is the mass transport losses within the cathode catalyst layers due to sluggish oxygen-reduction reactions occurring at the platinum-ionomer interface, which is believed to be linked to reduced transport of ions and oxygen. A viable solution to reduce the transport resistances in the catalyst layers is to create new ionomers that can provide good ion and oxygen transport needed to accomplish high-performing fuel cell catalysts. Characterization of transport properties of ionomers for various molecular architectures is the key step, in the effort to create and identify the optimized polymer structure with improved transport. Using this approach, Tetramer Technologies and LBNL propose improved fuel-cell catalyst ionomers based on Tetramers proprietary polymer chemistry, as highlighted under subtopic 17a Innovative Approaches Toward Discovery and Development of Improved Ionomers for Polymer Electrolyte Membrane Fuel Cell Catalyst Layer.

30 DIRECT ENERGY CONVERSION↗

DOE OSTI2025

Realizing the Materials-Designed-To-Environments Promise of Additive Manufacturing Through a Fundamentally Different Approach to Optimization of Nonlinear Solid Mechanics Structures

Additive Manufacturing (AM) is expected to play a large role in the labs-wide goals of accelerating innovation and leading in modern engineering. More specifically, AM is seen as a key enabling technology for increasing the agility of nuclear deterrence and other national security applications involving complex coupled environments. However, the impact of AM on these initiatives has not been as wide-ranging as hoped because – despite its unique qualities – the focus has mostly been on detailed qualification to force AM components into pre-existing performance envelopes. This paradigm fundamentally precludes the novel possibilities afforded by the geometric and material flexibility of AM. In particular, the engineering of small-scale features to undergo buckling and contact can cause large geometric and symmetry changes which provide responsiveness to different environments. Despite almost a decade of observing such behavior, there exists no way to systematically design for AM to exploit it. Our goal for this project was to connect material design to multi-environment component performance by reconceptualizing how to design for AM to exploit the buckling and contact of small-scale features.

36 MATERIALS SCIENCE↗