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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 1,171 records · Page 65
All sourcesNASA NTRSDOE OSTI
NASA NTRS2019

The Updated GEO Population for ORDEM 3.1

The limited availability of data for satellite fragmentations and debris in the geosynchronous orbit (GEO) region creates challenges to building accurate models for the orbital debris environment at such altitudes. Updated methods to properly incorporate and extrapolate measurement data have become a cornerstone of the GEO component in the newest version of the NASA Orbital Debris Engineering Model (ORDEM), ORDEM 3.1. For the GEO region, the Space Surveillance Network (SSN) catalog provides coverage down to a limit of approximately 1 m. A more statistically complete representation of the GEO population for smaller objects, which can pose a high risk to operational spacecraft, is thus dependent on dedicated observations by instruments optimized to observe debris smaller than the SSN cataloging threshold. For ORDEM 3.1, optical data from the Michigan Orbital DEbris Survey Telescope (MODEST) provided the input for building the GEO population down to approximately 30 cm (converting absolute magnitude to size). For smaller sizes, the size distribution of debris in the MODEST dataset was extrapolated down to 10 cm, and orbital parameters were estimated based on the orbits of the larger objects. When compared to previous versions of the model, significant improvements were made to the process of building the GEO population in ORDEM 3.1, both in the assessment of fragmentation debris in the data and assignment of orbital elements within the model. A so-called “debris ring filter,” based on a range of angles between an orbit’s angular momentum vector and that of the stable Laplace plane, was applied to the data to reduce biases from non- GEO objects, such as objects in a GEO-transfer orbit. In addition, a new approach was implemented to assign noncircular mean motions and eccentricities to the fragmentation debris observed by MODEST because the short observation window (5 min) in GEO limits orbit resolution to a circular orbit assumption for assigning orbital parameters. For ORDEM 3.1, non-circular orbital elements were assigned using relationships that were identified between mean motion and the angle between the orbit plane and the stable Laplace plane, as well as between mean motion and eccentricity, based on breakup clouds modeled by the NASA Standard Breakup Model. This approach has yielded a high-fidelity GEO model that has been validated with data from more recent MODEST observation campaigns.

Manis, A.↗
DOE OSTI2024

Enhancing molecular design efficiency: Uniting language models and generative networks with genetic algorithms

This study examines the effectiveness of generative models in drug discovery, material science, and polymer science, aiming to overcome constraints associated with traditional inverse design methods relying on heuristic rules. Generative models generate synthetic data resembling real data, enabling deep learning model training without extensive labeled datasets. They prove valuable in creating virtual libraries of molecules for material science and facilitating drug discovery by generating molecules with specific properties. While generative adversarial networks (GANs) are explored for these purposes, mode collapse restricts their efficacy, limiting novel structure variability. To address this, we introduce a masked language model (LM) inspired by natural language processing. Although LMs alone can have inherent limitations, we propose a hybrid architecture combining LMs and GANs to efficiently generate new molecules, demonstrating superior performance over standalone masked LMs, particularly for smaller population sizes. This hybrid LM-GAN architecture enhances efficiency in optimizing properties and generating novel samples.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2019

Automated Computational Thermochemistry for Butane Oxidation: A Prelude to Predictive Automated Combustion Kinetics

arge-scale implementation of high level computational theoretical chemical kinetics offers the prospect for dramatically improving the fidelity of combustion chemical modeling. As a first step toward this goal, we developed a procedure for automatically generating the thermochemical data for combustion of an arbitrary fuel. The procedure begins by producing a list of combustion relevant species from a specification of the fuel and combustion conditions of interest. Then, for each element in the list of species, the procedure determines an internal coordinate z-matrix description of its structure, the optimal torsional configuration via Monte Carlo sampling, key rovibrational properties for that optimal geometry (including anharmonic corrections from torsional mappings and/or vibrational perturbation theory), and high level estimates of the electronic and zero-point energies via arbitrarily defined composite methods. This dataset is then converted first to partition functions, then to thermodynamic properties, and finally to NASA polynomial representations of the data. The end product is an automatically generated database of electronic structure results and thermochemical data including representations in a format appropriate for combustion simulations. The utility and functioning of this predictive automated computational thermochemistry (PACT) software package is illustrated through application to the automated generation of thermochemical data for the combustion of n-butane. Butane is chosen for this demonstration as its species list is of reasonably manageable size for debugging level computations, while still presenting most of the key challenges that need to be surmounted in the consideration of larger fuels. Furthermore, its low temperature chemistry is representative of that oc-curring with larger alkanes.

Keceli, Murat↗
DOE OSTI2023

Laser Scribing for Electrode Patterning of Perovskite Spectrometer-Grade CsPbBr 3 Gamma-ray Detectors

Making semiconductor radiation detectors that work at room temperature relies heavily on the deposition and pixelation of electrodes. Electrode patterning of perovskite solar cells widely implements laser scribing techniques, which is a convenient, scalable, and inexpensive technique. However, this method has not found its application in radiation detector patterning yet, and the question whether laser scribing can achieve high-quality patterns with minimum damage to a detector crystal and low interpixel cross-talk remains largely unanswered. To prove that laser scribing is a practical method for electrode patterning on perovskite CsPbBr 3 detectors, we use the material to create a variety of patterns. A very low lateral leakage current (60 nA at 10 V) and high mobility-lifetime product (9.7(3) x 10 -4 cm 2 /V) were observed between the pixel and the guard ring in tests of single-pixel devices with a separation of 200 or 100 μm between the central electrode and the guard ring. The 122 and 136 keV photopeaks in 57 Co gamma-ray spectra were very well resolved with an energy resolution of up to 6.1% at 122 keV. Finally, a further reduction in gap size to 50 mu m is conceivable, but more process optimization is needed.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2020

Quantum adiabatic machine learning by zooming into a region of the energy surface

Recent work has shown that quantum annealing for machine learning, referred to as QAML, can perform comparably to state-of-the-art machine learning methods with a specific application to Higgs boson classification. Here, we propose QAML-Z, an algorithm that iteratively zooms in on a region of the energy surface by mapping the problem to a continuous space and sequentially applying quantum annealing to an augmented set of weak classifiers. Results on a programmable quantum annealer show that QAML-Z matches classical deep neural network performance at small training set sizes and reduces the performance margin between QAML and classical deep neural networks by almost 50% at large training set sizes, as measured by area under the receiver operating characteristic curve. The significant improvement of quantum annealing algorithms for machine learning and the use of a discrete quantum algorithm on a continuous optimization problem both opens a class of problems that can be solved by quantum annealers and suggests the approach in performance of near-term quantum machine learning towards classical benchmarks.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2024

Scaling adaptive quantum simulation algorithms via operator pool tiling

Adaptive variational quantum simulation algorithms use information from a quantum computer to dynamically create optimal trial wave functions for a given problem Hamiltonian. A key ingredient in these algorithms is a predefined operator pool from which trial wave functions are constructed. Finding suitable pools is critical for the efficiency of the algorithm as the problem size increases. Here, we present a technique called operator pool tiling that facilitates the construction of problem-tailored pools for arbitrarily large problem instances. By first performing an Adaptive Derivative-Assembled Problem-Tailored Ansatz Variational Quantum Eigensolver (ADAPT-VQE) calculation on a smaller instance of the problem using a large, but computationally inefficient, operator pool, we extract the most relevant operators and use them to design more efficient pools for larger instances. We demonstrate the method here on strongly correlated quantum spin models in one and two dimensions, finding that ADAPT automatically finds a highly effective ansatz for these systems. Given that many problems, such as those arising in condensed matter physics, have a naturally repeating lattice structure, we expect the pool tiling method to be a widely applicable technique apt for such systems. Published by the American Physical Society 2024

Van Dyke, John S. (ORCID:0000000167815480)↗
DOE OSTI2020

Decentralized Schemes with Overlap for Solving Graph-Structured Optimization Problems

We present a new algorithmic paradigm for the decentralized solution of graph-structured optimization problems that arise in the estimation and control of network systems. A key and novel design concept of the proposed approach is that it uses overlapping subdomains to promote and accelerate convergence. We show that the algorithm converges if the size of the overlap is sufficiently large and that the convergence rate improves exponentially with the size of the overlap. The proposed approach provides a bridge between fully decentralized and centralized architectures and is flexible in that it enables the implementation of asynchronous schemes, handling of constraints, and balancing of computing, communication, and data privacy needs. The proposed scheme is tested in an estimation problem for a 9241-node power network and we show that it outperforms the alternating direction method of multipliers.

asynchronous↗
NASA NTRS2019

Multi-Objective Optimal Control of the 6-DoF Aeroservoelastic Common Research Model with Aspect Ratio 13.5 Wing

A new 6-DoF aeroservoelastic (ASE) Common Research Model (CRM) provided by The Boeing Company with aspect ratio 13.5 and 17 control surfaces per wing is utilized to demonstrate combined tracking and optimal multi-objective control. The multi-objective controller is derived on the closed loop tracking controller, and utilizes state and gust estimates provided by an extended state observer. Various methods of model reduction useful for control and estimation are presented. A computationally efficient MATLAB/Simulink simulation is presented which includes actuator dynamics, rate and deflection saturation limits, and gust disturbance inputs. The platform is used to demonstrate excellent 6-DoF tracking control performance coupled with the multi-objective controller, which is shown to effectively reduce structural mode movement, wing root bending moment, and drag. State and gust estimation is also shown to perform well, even when derived and/or implemented with significantly fewer states than the original full-sized model.

Drew, Michael C.↗
DOE OSTI2020

Optimal Spatial Separation of High-Order Harmonics from Infrared Driving Lasers with an Annular Beam in the Overdriven Regime

We study high-order harmonic generation (HHG) with an annularly shaped high-power, high-repetition-rate laser. In this work we investigate how the spatial separation (or divergence) of the incident laser and the generated high-order harmonics depends on the gas pressure when the laser intensity is in the overdriven regime. It is shown that, when the cone size of the incident annular beam is relatively large, the resulting harmonics are highly collimated along the propagation axis and are spatially separated from the incident beam. Under the overdriven condition, dynamic phase matching favors HHG emissions from short-trajectory electrons that are efficiently built up in the second half of the gas medium. We verify that using an annular beam is a simple and effective method for separating the intense incident infrared laser from the generated high harmonics. Such high-flux and low-divergence light pulses are expected to be powerful sources in the laboratory for ultrafast probing of electron dynamics.

47 OTHER INSTRUMENTATION↗
DOE OSTI2020

Latest Developments in PM Systems and Methods and their Application to WM Projects - 20364

How do you improve on an industry that is well established yet often misses the mark on key metrics demonstrating effective planning and execution of project scope, schedule and budget? With the increased complexity of DOE projects relative to either poorly kept records of what wastes are stored or the lack of specific knowledge relative to the waste concentrations/quantities, this is a question continually asked and investigated by many. In fact, in 2017, the GAO's report to Congress indicated that although the DOE has made some progress on its monitoring effectiveness and demonstrating progress criteria, these goals were not met and there needs to be more improvement and monitoring. This paper will offer some insights into the latest developments in project management methodologies and the possible application of these to waste management projects. Traditional data generated via a project schedule that utilizes critical path methodology (CPM) and additional earned value management (EVM) data needs to be accurate and enable the project manager to better understand the probability of project success. Although this data has readily been available on most large-scale projects, success as measured by various stakeholder groups including the client, project team, regulators, etc. is less than optimal. Therefore, an urgent need exists to explore how traditional project data (CPM and EVM) coupled with new methodologies allows for better planning and execution by the project team. Many new approaches/methodologies can even be used to predict the success of options prior to full implementation on the actual project. Three approaches/methodologies will be discussed: - Artificial Intelligence; - Change Management; - Blend of Traditional Management (Gantt chart) and Agile Methodologies. As market forces change, so will the direction of project management. The days of 'one size fits all' are no longer valid. A combination of predictive and adaptive approaches in the management of projects is essential to enable execution of project deliverables on time, under budget, and within the contractual scope. This paper is intended to provide the reader with insight to the close relationship of project management trends and business management trends, and to provoke critical thinking about what it means to align project management strategy with an environment of non-stop innovation. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2018

Flux growth and magneto-transport properties of cubic antiperovskite Ca3PbO single crystals

Highlights: • Antiperovskite Ca{sub 3}PbO is predicted to be a three-dimensional Dirac fermion system. • Cubic crystals, 1 mm in size, were obtained by a Ca-self-flux method. • The crucible and sealing method were critical factors to reduce carrier density. • Shubnikov-de Haas oscillations were clearly observed, indicating its high purity. - Abstract: We report the single crystal growth of cubic antiperovskite Ca{sub 3}PbO by a Ca-self-flux method. Cubic crystals, 1 mm in size, were successfully obtained through the use of iron crucibles sealed in stainless steel capsules. The crucible material and sealing method were found to be critical factors for achieving large crystals with low carrier density. The crystals were subsequently characterized by powder and single-crystal x-ray diffraction experiments, electron-probe microanalysis, and Hall effect measurements. Through the use of short stainless steel rods as tight caps for the crucible and fillers to occupy extra space in the capsule, the Ca evaporation loss of the flux, which contributes to Ca vacancy formation, was effectively reduced. Hence, the hole carrier density in the single crystal was reduced to 4.0 × 10{sup 19} cm{sup −3}. The crystal synthesized under optimized growth conditions showed clear Shubnikov-de Haas oscillations, indicating its high purity and homogeneity.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2022

Generalization in quantum machine learning from few training data

Modern quantum machine learning (QML) methods involve variationally optimizing a parameterized quantum circuit on a training data set, and subsequently making predictions on a testing data set (i.e., generalizing). In this work, we provide a comprehensive study of generalization performance in QML after training on a limited number N of training data points. We show that the generalization error of a quantum machine learning model with T trainable gates scales at worst as $\sqrt{T/N}$. When only K$\ll$T gates have undergone substantial change in the optimization process, we prove that the generalization error improves to $\sqrt{K/N}$. Our results imply that the compiling of unitaries into a polynomial number of native gates, a crucial application for the quantum computing industry that typically uses exponential-size training data, can be sped up significantly. We also show that classification of quantum states across a phase transition with a quantum convolutional neural network requires only a very small training data set. Other potential applications include learning quantum error correcting codes or quantum dynamical simulation. Our work injects new hope into the field of QML, as good generalization is guaranteed from few training data.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2019

Optimized illumination for high-throughput ptychography

As a scanning version of coherent diffraction imaging (CDI), X-ray ptychography has become a popular and very successful method for high-resolution quantitative imaging of extended specimens. The requirements of mostly coherent illumination and the scanning mechanism limit the throughput of ptychographic imaging. In this paper, we will introduce the methods we use at the Advanced Photon Source (APS) to achieve high-throughput ptychography by optimizing the parameters of the illumination beam. One work we have done is increasing the illumination flux by using a double-multilayer monochromator (DMM) optics with about 0.8% bandwidth. Compared with our double-crystal monochromator (DCM) optics with 0.01% bandwidth, this DMM optics provides around 20 times more flux. A multi-wavelength reconstruction method has been implemented to deal with the consequential degraded temporal coherence from such an illumination to ensure high-quality reconstruction. In the other work, we adopt a novel use of flat-top focusing optics to generate a flat-top beam with the diameter of about 1.5 µm on the focal plane. The better uniformity of the probe and the large beam size allow one to significantly increase the step size in ptychography scans and thereby the imaging efficiency.

Broad bandwidth↗
DOE OSTI2025

Processability and Material Behavior of NiTi Shape Memory Alloys Using Wire Laser-Directed Energy Deposition (WL-DED)

Utilizing additive manufacturing (AM) techniques with shape memory alloys (SMAs) like NiTi shows great promise for fabricating highly flexible and functionally superior 3D metallic structures. Compared to methods relying on powder feedstocks, wire-based additive manufacturing processes provide a viable alternative, addressing challenges such as chemical composition instability, material availability, higher feedstock costs, and limitations on part size while simplifying process development. This study presented a novel approach by thoroughly assessing the printability of Ni-rich Ni55.94Ti (Wt. %) SMA using the wire laser-directed energy deposition (WL-DED) technique, addressing the existing knowledge gap regarding the laser wire-feed metal additive manufacturing of NiTi alloys. For the first time, the impact of processing parameters—specifically laser power (400–1000 W) and transverse speed (300–900 mm/min)—on single-track fabrication using NiTi wires in the WL-DED process was examined. An optimal range of process parameters was determined to achieve high-quality prints with minimal defects, such as wire dripping, stubbing, and overfilling. Building upon these findings, we printed five distinct cubes, demonstrating the feasibility of producing nearly porosity-free specimens. Notably, this study investigated the effect of energy density on the printed part density, impurity pick-up, transformation temperature, and hardness of the manufactured NiTi cubes. The results from the cube study demonstrated that varying energy densities (46.66–70 J/mm3) significantly affected the quality of the deposits. Lower to intermediate energy densities achieved high relative densities (>99%) and favorable phase transformation temperatures. In contrast, higher energy densities led to instability in melt pool shape, increased porosity, and discrepancies in phase transformation temperatures. These findings highlighted the critical role of precise parameter control in achieving functional NiTi parts and offer valuable insights for advancing AM techniques in fabricating larger high-quality NiTi components. Additionally, our research highlighted important considerations for civil engineering applications, particularly in the development of seismic dampers for energy dissipation in structures, offering a promising solution for enhancing structural performance and energy management in critical infrastructure.

Dabbaghi, Hediyeh↗
DOE OSTI2024

Dyn$\mathrm{AMO}$: Multi-agent reinforcement learning for dynamic anticipatory mesh optimization with applications to hyperbolic conservation laws

Here we introduce DynAMO, a reinforcement learning paradigm for Dynamic Anticipatory Mesh Optimization. Adaptive mesh refinement is an effective tool for optimizing computational cost and solution accuracy in numerical methods for partial differential equations. However, traditional adaptive mesh refinement approaches for time-dependent problems typically rely only on instantaneous error indicators to guide adaptivity. As a result, standard strategies often require frequent remeshing to maintain accuracy. In the DynAMO approach, multi-agent reinforcement learning is used to discover new local refinement policies that can anticipate and respond to future solution states by producing meshes that deliver more accurate solutions for longer time intervals. By applying DynAMO to discontinuous Galerkin methods for the linear advection and compressible Euler equations in two dimensions, we demonstrate that this new mesh refinement paradigm can outperform conventional threshold-based strategies while also generalizing to different mesh sizes, remeshing and simulation times, and initial conditions.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Improved Productivity with Multilaser Rotary Powder Bed Fusion Additive Manufacturing

Laser powder bed fusion (LPBF) enables the fabrication of intricate, geometrically complex structures with a sufficiently fine surface finish for many engineering applications with a diversity of available feedstock metals. However, the production rate of LPBF systems is not well suited for mass production in comparison to traditional manufacturing methods. LPBF systems measure their deposition rates in 100's of grams per hour, while other processes measure in kilograms per hour or even in the case of processes such as forming, stamping, and casting, 100's of kilograms per hour. To be widely adopted in industry for mass production, LPBF requires a new scalable architecture that enables many orders of magnitude improvement in deposition rate, while maintaining the geometry freedom of additive manufacturing. This article explores concepts that could achieve as much as four orders of magnitude increase in the production rate through the application of (1) rotary table kinematic arrangements; (2) a dramatic number of simultaneously operating lasers; (3) reductions of laser optic size; (4) improved scanning techniques; and (5) an optimization of toroidal build plate size. To theoretically demonstrate the possibilities of production improvements, a productivity analysis is proposed for synchronous reluctance motors with relevance to the electric vehicle industry, given the recent increase in the diversity of printable soft magnetic alloys. The analysis provides insights into the impact of the architecture and process parameters necessary to optimize rotary powder bed fusion for mass production.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Spinel high-entropy oxides (FeNiCrMnZnX) 3 O 4 (X = Al, mg) as anode materials for high-performance lithium-ion batteries

To address the high cost, cobalt dependency, and resource constraints typical of conventional high-entropy oxide (HEO) anodes, this study reports the successful synthesis of two Co-free, six-component spinel-type HEOs(FeNiCrMnZnAl) 3 O 4 (HEO-Al) and (FeNiCrMnZnMg) 3 O 4 (HEO-Mg), via a sol-gel method. The distinct effects of Al 3+ and Mg 2+ incorporation on the electrochemical performance and lithium storage kinetics were systematically investigated. XRD, Raman, and TEM characterizations confirm that both materials possess a pure spinel phase, uniform particle size, and homogeneous elemental distribution. Notably, electrochemical evaluations reveal that HEO-Al delivers a superior reversible capacity of 480.7 mAh g −1 after 100 cycles at 0.1 A g −1 , and maintains 354.5 mAh g −1 after 1000 long-term cycles at 1 A g −1 , significantly outperforming HEO-Mg. Kinetic analysis indicates that HEO-Al exhibits lower charge transfer resistance, a higher Li + diffusion coefficient, and a pseudocapacitive contribution of up to 82%. Furthermore, these findings demonstrate that Al substitution effectively optimizes the structural stability and lithium storage kinetics of Co-free HEOs, providing a viable strategy for designing low-cost, highly stable HEO anode systems.

Anode materials↗
DOE OSTI2021

Evaluation of Force Fields for Molecular Dynamics Simulations of Platinum in Bulk and Nanoparticle Forms

Here, understanding the size- and shape-dependent properties of platinum nanoparticles is critical for enabling the design of nanoparticle-based applications with optimal and potentially tunable functionality. Toward this goal, we evaluated nine different empirical potentials with the purpose of accurately modeling faceted platinum nanoparticles using molecular dynamics simulation. First, the potentials were evaluated by computing bulk and surface properties - surface energy, lattice constant, stiffness constants, and the equation of state - and comparing these to prior experimental measurements and quantum mechanics calculations. Then, the potentials were assessed in terms of the stability of cubic and icosahedral nanoparticles with faces in the {100} and {111} planes, respectively. Although none of the force fields predicts all the evaluated properties with perfect accuracy, one potential - the embedded atom method formalism with a specific parameter set - was identified as best able to model platinum in both bulk and nanoparticle forms.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗