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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 397 records · Page 22
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Using ultrasonic attenuation in cortical bone to infer distributions on pore size

Here, in this work we infer the underlying distribution on pore radius in human cortical bone samples using ultrasonic attenuation data. We first discuss how to formulate polydisperse attenuation models using a probabilistic approach and the Waterman Truell model for scattering attenuation. We then compare the Independent Scattering Approximation and the higher-order Waterman Truell models’ forward predictions for total attenuation in polydisperse samples. Following this, we formulate an inverse problem under the Prohorov Metric Framework coupled with variational regularization to stabilize this inverse problem. We then use experimental attenuation data taken from human cadaver samples and solve inverse problems resulting in nonparametric estimates of the probability density function on pore radius. We compare these estimates to the “true” microstructure of the bone samples determined via microCT imaging. We find that our methodology allows us to reliably estimate the underlying microstructure of the bone from attenuation data.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Spherical time-encoded radiation imaging simulations

Radiation source localization is important for nuclear nonproliferation and can be obtained using time-encoded imaging systems with unsegmented detectors. A scintillation crystal can be used with a moving coded-aperture mask to vary the detected count rate produced from radiation sources in the far field. The modulation of observed counts over time can be used to reconstruct an image with the known coded-aperture mask pattern. Current time-encoded imaging systems incorporate cylindrical coded-aperture masks and have limits to their fully coded imaging field-of-view. This work focuses on expanding the field-of-view to 4π by using a novel spherical coded-aperture mask. A regular icosahedron is used to approximate a spherical mask. This icosahedron consists of 20 equilateral triangles; the faces of which are each subdivided into four equilateral triangle-shaped voxels which are then projected onto a spherical surface, creating an 80-voxel coded-aperture mask. Furthermore, these polygonal voxels can be made from high-Z materials for gamma-ray modulation and/or low-Z materials for neutron modulation. In this work, we present Monte Carlo N-Particle (MCNP) simulations and simple models programmed in Mathematica to explore image reconstruction capabilities of this 80-voxel coded-aperture mask.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2023

An in-silico NMR laboratory for nuclear magnetic shieldings computed via finite fields: Exploring nucleus-specific renormalizations of MP2 and MP3

Here we developed and implemented a method-independent, fully numerical, finite difference approach to calculating nuclear magnetic resonance shieldings, using gauge-including atomic orbitals. The resulting capability can be used to explore non-standard methods, given only the energy as a function of finite-applied magnetic fields and nuclear spins. For example, standard second-order Møller-Plesset theory (MP2) has well-known efficacy for 1H and 13C shieldings and known limitations for other nuclei such as 15N and 17O. It is, therefore, interesting to seek methods that offer good accuracy for 15N and 17O shieldings without greatly increased compute costs, as well as exploring whether such methods can further improve 1H and 13C shieldings. Using a small molecule test set of 28 species, we assessed two alternatives: κ regularized MP2 (κ-MP2), which provides energy-dependent damping of large amplitudes, and MP2.X, which includes a variable fraction, X, of third-order correlation (MP3). The aug-cc-pVTZ basis was used, and coupled cluster with singles and doubles and perturbative triples [CCSD(T)] results were taken as reference values. Our κ-MP2 results reveal significant improvements over MP2 for 13C and 15N, with the optimal κ value being element-specific. κ-MP2 with κ = 2 offers a 30% rms error reduction over MP2. For 15N, κ-MP2 with κ = 1.1 provides a 90% error reduction vs MP2 and a 60% error reduction vs CCSD. On the other hand, MP2.X with a scaling factor of 0.6 outperformed CCSD for all heavy nuclei. These results can be understood as providing renormalization of doubles amplitudes to partially account for neglected triple and higher substitutions and offer promising opportunities for future applications.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

A Consensus Equilibrium Approach for 3-D Land Seismic Shots Recovery

Physical and budget constraints often result in inadequate sampling for accurate subsurface imaging. Preprocessing approaches, such as missing trace interpolation, are typically employed to enhance seismic data in such cases. The compressed sensing (CS) framework has been applied for modeling missing seismic data, which is estimated by sparsity-based computational algorithms. While existing work mainly focuses on recovering missing traces resulting from receiver subsampling, source subsampling has greater economical advantages, as sources are more expensive than receivers. Moreover, stronger image models different from sparsity have not been explored for source recovery. This work presents a consensus equilibrium (CE) approach to recover missing seismic shots, which enables to incorporate various regularization operators modeling different data priors. Here, simulation results from a real 3-D land seismic dataset demonstrate that the CE approach provides more accurate estimations of the linear and hyperbolic events in the recovered shots, compared with pure sparsity-based reconstructions.

58 GEOSCIENCES↗
DOE OSTI2021

Error Estimates for the Optimal Control of a Parabolic Fractional PDE

In this work, we consider the integral definition of the fractional Laplacian and analyze a linear-quadratic optimal control problem for the so-called fractional heat equation; control constraints are also considered. We derive existence and uniqueness results, first order optimality conditions, and regularity estimates for the optimal variables. To discretize the state equation we propose a fully discrete scheme that relies on an implicit finite difference discretization in time combined with a piecewise linear finite element discretization in space. We derive stability results and a novel $L^2(0,T;L^2(\Omega))$ a priori error estimate. On the basis of the aforementioned solution technique, we propose a fully discrete scheme for our optimal control problem that discretizes the control variable with piecewise constant functions, and we derive a priori error estimates for it. We illustrate the theory with one- and two-dimensional numerical experiments.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

On a nonlocal Cahn–Hilliard model permitting sharp interfaces

A nonlocal Cahn–Hilliard model with a non-smooth potential of double-well obstacle type that promotes sharp interfaces in the solution is presented. To capture long-range interactions between particles, a nonlocal Ginzburg–Landau energy functional is defined which recovers the classical (local) model as the extent of nonlocal interactions vanish. In contrast to the local Cahn–Hilliard problem that always leads to diffuse interfaces, the proposed nonlocal model can lead to a strict separation into pure phases of the substance. In this work, the lack of smoothness of the potential is essential to guarantee the aforementioned sharp-interface property. Mathematically, this introduces additional inequality constraints that, in a weak formulation, lead to a coupled system of variational inequalities which at each time instance can be restated as a constrained optimization problem. We prove the well-posedness and regularity of the semi-discrete and continuous in time weak solutions, and derive the conditions under which pure phases are admitted. Moreover, we develop discretizations of the problem based on finite element methods and implicit–explicit time-stepping methods that can be realized efficiently. Finally, we illustrate our theoretical findings through several numerical experiments in one and two spatial dimensions that highlight the differences in features of local and nonlocal solutions and also the sharp interface properties of the nonlocal model.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

A nonsmooth nonconvex optimization algorithm for two-stage optimization problems

An optimization algorithm for a group of nonsmooth nonconvex problems inspired by two-stage stochastic programming problems is proposed. The main challenges for these problems include (1) the problems lack the popular lower-type properties such as prox-regularity assumed in many nonsmooth nonconvex optimization algorithms, (2) the objective can not be analytically expressed and (3) the evaluation of function values and subgradients are computationally expensive. To address these challenges, this report first examines the properties that exist in many two-stage problems, specifically upper-C 2 objectives. Then, we show that quadratic penalty method for securityconstrained alternating current optimal power flow (SCACOPF) contingency problems can make the contingency solution functions upper-C 2 . Based on these observations, a simplified bundle algorithm that bears similarity to sequential quadratic programming (SQP) method is proposed. It is more efficient in implementation and computation compared to conventional bundle methods. Global convergence analysis of the algorithm is presented under novel and reasonable assumptions. The proposed algorithm therefore fills the gap of theoretical convergence for smoothed SCACOPF problems. The inconsistency that might arise in our treatment of the constraints are addressed through a penalty algorithm whose convergence analysis is also provided. Finally, theoretical capabilities and numerical performance of the algorithm are demonstrated through numerical examples.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

BUTTER - Empirical Deep Learning Dataset

The BUTTER Empirical Deep Learning Dataset represents an empirical study of the deep learning phenomena on dense fully connected networks, scanning across thirteen datasets, eight network shapes, fourteen depths, twenty-three network sizes (number of trainable parameters), four learning rates, six minibatch sizes, four levels of label noise, and fourteen levels of L1 and L2 regularization each. Multiple repetitions (typically 30, sometimes 10) of each combination of hyperparameters were preformed, and statistics including training and test loss (using a 80% / 20% shuffled train-test split) are recorded at the end of each training epoch. In total, this dataset covers 178 thousand distinct hyperparameter settings ("experiments"), 3.55 million individual training runs (an average of 20 repetitions of each experiments), and a total of 13.3 billion training epochs (three thousand epochs were covered by most runs). Accumulating this dataset consumed 5,448.4 CPU core-years, 17.8 GPU-years, and 111.2 node-years.

Array↗
DOE OSTI2021

Ps and Qs: Quantization-Aware Pruning for Efficient Low Latency Neural Network Inference

Efficient machine learning implementations optimized for inference in hardware have wide-ranging benefits, depending on the application, from lower inference latency to higher data throughput and reduced energy consumption. Two popular techniques for reducing computation in neural networks are pruning, removing insignificant synapses, and quantization, reducing the precision of the calculations. In this work, we explore the interplay between pruning and quantization during the training of neural networks for ultra low latency applications targeting high energy physics use cases. Techniques developed for this study have potential applications across many other domains. We study various configurations of pruning during quantization-aware training, which we term quantization-aware pruning, and the effect of techniques like regularization, batch normalization, and different pruning schemes on performance, computational complexity, and information content metrics. We find that quantization-aware pruning yields more computationally efficient models than either pruning or quantization alone for our task. Further, quantization-aware pruning typically performs similar to or better in terms of computational efficiency compared to other neural architecture search techniques like Bayesian optimization. Surprisingly, while networks with different training configurations can have similar performance for the benchmark application, the information content in the network can vary significantly, affecting its generalizability.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2020

Autonomous Self-Healing Elastomers with Unprecedented Adhesion Force

Self-healable elastomers are extremely attractive due to their ability to prolong product lifetime. An additional function that could further expand their applications is strong adhesion force to clean and dusty surfaces. This study reports a series of autonomous self-healable and highly adhesive elastomers (ASHA-Elastomer) that are fabricated via a simple, efficient, and scalable process. The obtained elastomers exhibit outstanding mechanical properties with elongation at break up to 2102% and toughness (modulus of toughness) of 1.73 MJ m -3 . The damaged ASHA-Elastomer can autonomously self-heal with full recovery of functionalities, and the healing process is not affected by the presence of water. The elastomers are found to possess an ultrahigh adhesion force up to 3488 N m -1 , greatly outperforming previously reported self-healing adhesive elastomers. Furthermore, the adhesion force of the ASHA-Elastomer is negligibly affected by dust on the surface, in stark contrast with regular adhesive polymers that have adhesion strengths extremely sensitive to dust. The successful development of high-toughness, autonomous self-healable, and ultra-adhesive elastomers will enable a wide range of applications with enhanced longevity and versatility, including their use in sealants, adhesives, and stretchable devices.

36 MATERIALS SCIENCE↗
DOE OSTI2023

On the Importance of Chemical Precision in Organic Electronics: Fullerene Intercalation in Perfectly Alternating Conjugated Polymers

Abstract The true structure of alternating conjugated polymers—the state‐of‐the‐art materials for many organic electronics—often deviates from the idealized picture. Homocoupling defects are in fact inherent to the widely used cross‐coupling polymerization methods. Nevertheless, many polymers still perform excellently in the envisaged applications, which raises the question if one should really care about these imperfections. This article looks at the relevance of chemical precision (and lack thereof) in conjugated polymers covering the entire spectrum from the molecular scale, to the micro and mesostructure, up to the device level. The different types of polymerization errors for the alkoxylated variant of the benchmark (semi)crystalline polymer poly[2,5‐bis(3‐tetradecylthiophen‐2‐yl)thieno[3,2‐ b ]thiophene (PBTTT) are identified, visualized, and quantified and a general strategy to avoid homocoupling is introduced. Through a combination of experiments and supported by simulations, it is shown that these coupling defects hinder fullerene intercalation and limit device performance as compared to the homocoupling‐free analog. This clearly demonstrates that structural defects do matter and should be generally avoided, in particular when the geometrical regularity of the polymer is essential. These insights likely go beyond the specific PBTTT derivatives studied here and are of general relevance for the wider organic electronics field.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Tailoring Polymer Colloids Derived Porous Carbon Spheres Based on Specific Chemical Reactions

Porous carbon spheres derived from polymer colloids with regular geometry, monodispersed morphology, well-controlled contents and structures play important roles in many areas of application, such as energy storage/conversion, gas adsorption/separation, catalysis, and chemo-photothermal therapy. Suitable polymerization reaction and synthetic strategy are both critical for the obtainment of stable polymer colloids as carbon precursors. Basic polymerization reactions are the cornerstones of synthetic strategies, which directly provides the direct molecular-based design of functionalized polymer/ carbon spheres. Thus, this progress report mainly focuses on the summary of suitable polymerization reactions for colloidal polymer derived porous carbon spheres. Recent advances in the synthetic strategies and applications are also discussed, including their corresponding polymerization reactions. Finally, the perspectives for the development of polymer derived porous carbon spheres are provided based on the controlled synthesis of polymer colloids and optimization over the carbonization process to achieve highly functionalized carbon spheres for practical applications.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Operando Tailoring of Defects and Strains in Corrugated β-Ni(OH) 2 Nanosheets for Stable and High-Rate Energy Storage

Nickel hydroxide represents a technologically important material for energy storage, such as hybrid supercapacitors. It has two different crystallographic polymorphs, α- and β-Ni(OH) 2 , showing advantages in either theoretical capacity or cycling/rate performance, manifesting a trade-off trend that needs to be optimized for practical applications. In this work, the synergistic superiorities in both activity and stability of corrugated β-Ni(OH) 2 nanosheets are demonstrated through an electrochemical abuse approach. With ≈91% capacity retention after 10 000 cycles, the corrugated β-Ni(OH) 2 nanosheets can deliver a gravimetric capacity of 457 C g -1 at a high current density of 30 A g -1 , which is nearly two and four times that of the regular α- and β-Ni(OH) 2 , respectively. Operando spectroscopy and finite element analysis reveal that greatly enhanced chemical activity and structural robustness can be attributed to the in situ tailored lattice defects and the strain-induced highly curved micromorphology. This work demonstrates a multi-scale defect-and-strain co-design strategy, which is helpful for rational design and tuned fabrication of next-generation electrode materials for stable and high-rate energy storage.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Advances in SnO 2 for Efficient and Stable n-i-p Perovskite Solar Cells

Perovskite solar cells (PSCs) based on the regular n-i-p device architecture have reached above 25% certified efficiency with continuously reported improvements in recent years. A key common factor for these recent breakthroughs is the development of SnO 2 as an effective electron transport layer in these devices. In this review, we discuss the key advances in SnO 2 development, including various deposition approaches and surface treatment strategies, to enhance the bulk and interface properties of SnO 2 for highly efficient and stable n-i-p PSCs. We also discuss the general materials chemistry associated with SnO 2 along with the corresponding materials challenges and improvement strategies, focusing on defects, intrinsic properties, and impact on device characteristics. Finally, we highlight some SnO2 implementations related to scalable processes and flexible devices, and we also provide our perspective on the future development of efficient and stable large-scale perovskite solar modules.

14 SOLAR ENERGY↗
DOE OSTI2022

Cold-Starting All-Solid-State Batteries from Room Temperature by Thermally Modulated Current Collector in Sub-Minute

All-solid-state batteries (ASSBs) show great potential as high-energy and high-power energy storage devices but their attainable energy/power density at room temperature is severely reduced because of the sluggish kinetics of lithium-ion transport. Here we first reported a thermally modulated current collector (TMCC), which can rapidly cold-start ASSBs from room temperature to operating temperatures (70–90 °C) in less than one minute, and simultaneously enhance the transient peak power density by 15-fold compared to one without heating. This TMCC is prepared by integrating a uniform, ultrathin (~200 nm) nickel layer as a thermal modulator within an ultralight polymer-based current collector. By isolating the thermal modulator from the ion/electron pathway of ASSBs, it can provide fast, stable heat control yet does not interfere with regular battery operation. Moreover, this ultrathin (13.2 μm) TMCC effectively shortens the heat transfer pathway, minimizes heat losses, and mitigates the formation of local hot spots. The simulated heating energy consumption can be as low as ~3.94% of total battery energy. This TMCC design with good tunability opens new frontiers towards smart energy storage devices in the future from the current collector perspective.

25 ENERGY STORAGE↗
DOE OSTI2022

Tethered Small-Molecule Acceptors Simultaneously Enhance the Efficiency and Stability of Polymer Solar Cells

For polymer solar cells (PSCs), the mixture of polymer donors and small-molecule acceptors (SMAs) is fine-tuned to realize a favorable kinetically trapped morphology and thus a commercially viable device efficiency. However, the thermodynamic relaxation of the mixed domains within the blend raises concerns related to the long-term operational stability of the devices, especially in the record-holding Y-series SMAs. Here, a new class of dimeric Y6-based SMAs tethered with differential flexible spacers is reported to regulate their aggregation and relaxation behavior. In their polymer blends with PM6, it is found that they favor an improved structural order relative to that of Y6 counterpart. Most importantly, the tethered SMAs show large glass transition temperatures to suppress the thermodynamic relaxation in mixed domains. For the high-performing dimeric blend, an unprecedented open circuit voltage of 0.87 V is realized with a conversion efficiency of 17.85%, while those of regular Y6-base devices only reach 0.84 V and 16.93%, respectively. Most importantly, the dimer-based device possesses substantially reduced burn-in efficiency loss, retaining more than 80% of the initial efficiency after operating at the maximum power point under continuous illumination for 700 h. In conclusion, the tethering approach provides a new direction to develop PSCs with high efficiency and excellent operating stability.

14 SOLAR ENERGY↗
DOE OSTI2025

Stereochemical Control of Water Transport Properties in Thiol‐yne Polymers

Barrier polymers underpin almost every commercial sector, yet the needs of several emerging areas remain unmet by commercially‐available materials, including temporary orthopedic implants, transient health monitors, neural implants, and other long‐term implants. The ability to tune polymer composition independently of polymer structure positions thiol‐yne click chemistry as a promising platform to serve these emerging technologies. Here, this work describes the differences in the hierarchical structure of stoichiometrically identical materials which differ only in the proportion of the cis versus trans backbone alkene stereochemistry. Varying the isomer content in this way directs different temperature and rate dependent crystallization behavior, which affords control over micron‐scale structure. This investigation focuses on how these stereochemical features affect the water vapor permeation process by several methods and develops an understanding of how this unique structural regularity improves barrier performance relative to a commercially available water barrier polymer, poly(ethylene terephthalate).

77 NANOSCIENCE AND NANOTECHNOLOGY↗