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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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All sourcesNASA NTRSDOE OSTI
DOE OSTI2012

Argonne National Laboratory Annual Report of Laboratory Directed Research and Development program activities FY 2011.

As a national laboratory Argonne concentrates on scientific and technological challenges that can only be addressed through a sustained, interdisciplinary focus at a national scale. Argonne's eight major initiatives, as enumerated in its strategic plan, are Hard X-ray Sciences, Leadership Computing, Materials and Molecular Design and Discovery, Energy Storage, Alternative Energy and Efficiency, Nuclear Energy, Biological and Environmental Systems, and National Security. The purposes of Argonne's Laboratory Directed Research and Development (LDRD) Program are to encourage the development of novel technical concepts, enhance the Laboratory's research and development (R and D) capabilities, and pursue its strategic goals. projects are selected from proposals for creative and innovative R and D studies that require advance exploration before they are considered to be sufficiently developed to obtain support through normal programmatic channels. Among the aims of the projects supported by the LDRD Program are the following: establishment of engineering proof of principle, assessment of design feasibility for prospective facilities, development of instrumentation or computational methods or systems, and discoveries in fundamental science and exploratory development.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2011

Validating Geant4 Versions 7.1 and 8.3 Against 6.1 for BaBar

Since 2005 and 2006, respectively, Geant4 versions 7.1 and 8.3 have been available, providing: improvements in modeling of multiple scattering; corrections to muon ionization and improved MIP signature; widening of the core of electromagnetic shower shape profiles; newer implementation of elastic scattering for hadronic processes; detailed implementation of Bertini cascade model for kaons and lambdas, and updated hadronic cross-sections from calorimeter beam tests. The effects of these changes in simulation are studied in terms of closer agreement of simulation using Geant4 versions 7.1 and 8.3 as compared to Geant4 version 6.1 with respect to data distributions of: the hit residuals of tracks in BABAR silicon vertex tracker; the photon and K{sub L}{sup 0} shower shapes in the electromagnetic calorimeter; the ratio of energy deposited in the electromagnetic calorimeter and the flux return of the magnet instrumented with a muon detection system composed of resistive plate chambers and limited-streamer tubes; and the muon identification efficiency in the muon detector system of the BABAR detector.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2020

Extreme ultraviolet microscope characterization using photomask surface roughness

We demonstrate a method for characterizing the field-dependent aberrations of a full-field synchrotron-based extreme ultraviolet microscope. The statistical uniformity of the inherent, atomic-scale roughness of readily-available photomask blanks enables a self-calibrating computational procedure using images acquired under standard operation. We characterize the aberrations across a 30-um field-of-view, demonstrating a minimum aberration magnitude of smaller than λ/21 rms averaged over the center 5-um area, with a measurement accuracy better than λ/180 rms. The measured field variation of aberrations is consistent with system geometry and agrees with prior characterizations of the same system. In certain cases, it may be possible to additionally recover the illumination wavefront from the same images. Our method is general and is easily applied to coherent imaging systems with steerable illumination without requiring invasive hardware or custom test objects; hence, it provides substantial benefits when characterizing microscopes and high-resolution imaging systems in situ.

47 OTHER INSTRUMENTATION↗
DOE OSTI2020

DECT-MULTRA: Dual-Energy CT Image Decomposition With Learned Mixed Material Models and Efficient Clustering

Dual-energy computed tomography (DECT) imaging plays an important role in advanced imaging applications due to its material decomposition capability. Image-domain decomposition operates directly on CT images using linear matrix inversion, but the decomposed material images can be severely degraded by noise and artifacts. This paper proposes a new method dubbed DECT-MULTRA for image-domain DECT material decomposition that combines conventional penalized weighted-least squares (PWLS) estimation with regularization based on a mixed union of learned transforms (MULTRA) model. Our proposed approach pre-learns a union of common-material sparsifying transforms from patches extracted from all the basis materials, and a union of cross-material sparsifying transforms from multi-material patches. The common-material transforms capture the common properties among different material images, while the cross-material transforms capture the cross-dependencies. The proposed PWLS formulation is optimized efficiently by alternating between an image update step and a sparse coding and clustering step, with both of these steps having closed-form solutions. The effectiveness of our method is validated with both XCAT phantom and clinical head data. The results demonstrate that our proposed method provides superior material image quality and decomposition accuracy compared to other competing methods.

47 OTHER INSTRUMENTATION↗
DOE OSTI2024

AI-Driven Crack Detection for Remanufacturing Cylinder Heads Using Deep Learning and Engineering-Informed Data Augmentation

Detecting cracks in cylinder heads traditionally relies on manual inspection, which is time-consuming and susceptible to human error. As an alternative, automated object detection utilizing computer vision and machine learning models has been explored. However, these methods often face challenges due to a lack of sufficiently annotated training data, limited image diversity, and the inherently small size of cracks. Addressing these constraints, this paper introduces a novel automated crack-detection method that enhances data availability through a synthetic data generation technique. Unlike general data augmentation practices, our method involves copying cracks from one location to another, guided by both random and informed engineering decisions about likely crack formations due to cyclic thermomechanical loads. The innovative aspect of our approach lies in the integration of domain-specific engineering knowledge into the synthetic generation process, which substantially improves detection accuracy. We evaluate our method’s effectiveness using two metrics: the F2 score, which emphasizes recall to prioritize detecting all potential cracks, and mean average precision (MAP), a standard measure in object detection. Experimental results demonstrate that, without engineering insights, our method increases the F2 score from 0.40 to 0.65, while maintaining a stable MAP. Incorporating detailed engineering knowledge further enhances the F2 score to 0.70 and improves MAP to 0.57, representing increases of 63% and 43%, respectively. These results confirm that our approach not only mitigates the limitations of traditional data augmentation but also significantly advances the reliability and precision of crack detection in industrial settings.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2017

Bridging the gap between peak and average loads on science networks

Backbone networks are typically overprovisioned in order to support peak loads. Research and education networks (RENs), for example, are often designed to operate at 20–30% of capacity. Thus, Internet2 upgrades its backbone interconnects when the weekly 95th-percentile load is reliably above 30% of link capacity, and analysis of ESnet traffic between major laboratories shows a substantial gap between peak and average utilization. As science data volumes increase exponentially, it is unclear whether this overprovisioning trend can continue into the future. Even if overprovisioning is possible, it may not be the most cost-effective (and desirable) approach going forward. Under the current mode of free access to RENs, traffic at peak load may include both flows that need to be transferred in near-real time–for example, for computation and instrument monitoring and steering–and flows that are less time-critical, for example, archival and storage replication operations. Thus, peak load does not necessarily indicate the capacity that is absolutely required at that moment. We thus examine how data transfers are impacted when the average network load is increased while the network capacity is kept at the current levels. We also classify data transfers into on-demand (time-critical) and best-effort (less time-critical) and study the impact on both classes for different proportions of both the number of on-demand transfers and amount of bandwidth allocated for on-demand transfers. For our study, we use real transfer logs from production GridFTP servers to do simulation-based experiments as well as real experiments on a testbed. We find that when the transfer load is doubled and the network capacity is fixed at the current level, the gap between peak and average throughput decreases by an average of 18% in the simulation experiments and 16% in the testbed experiments, and the average slowdown experienced by the data transfers is under 1.5×. Moreover, when transfers are classified as on-demand or best-effort, on-demand transfers experience almost no slowdown and the mean slowdown experienced by best-effort transfers is under 2× in the simulation experiments and under 1.2× in the testbed experiments.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

A Survey on Sustainable Software Ecosystems to Support Experimental and Observational Science at Oak Ridge National Laboratory

In the search for a sustainable approach for software ecosystems that supports experimental and observational science (EOS) across Oak Ridge National Laboratory (ORNL), we conducted a survey to understand the current and future landscape of EOS software and data. This paper describes the survey design we used to identify significant areas of interest, gaps, and potential opportunities, followed by a discussion on the obtained responses. The survey formulates questions about project demographics, technical approach, and skills required for the present and the next five years. The study was conducted among 38 ORNL participants between June and July of 2021 and followed the required guidelines for human subjects training. We plan to use the collected information to help guide a vision for sustainable, community-based, and reusable scientific software ecosystems that need to adapt effectively to: i) the evolving landscape of heterogeneous hardware in the next generation of instruments and computing (e.g. edge, distributed, accelerators), and ii) data management requirements for data-driven science using artificial intelligence.

Bernholdt, David↗
DOE OSTI2021

Mica filled polyetherketoneketones for material extrusion 3D printing

Polyetherketoneketone (PEKK) has superior physical properties to most available thermoplastics compatible with material extrusion-based 3D printing, including analogs in the polyaryletherketone (PAEK) family. To date, the performance of fused filament fabrication (FFF) compatible PEKK has been detailed primarily as a function of varying the isomer ratios composing the co-polymer structure. The strategy to form blends or composites with PEKK for FFF is attractive for further tailoring of performance in application, yet has received limited attention. Here, we report the integration of three grades of mica platelets into PEKK at 10% and 30% mass loadings to generate an array of filament feedstocks that were then used to print objects with a simple FFF machine. The effects of mica coating chemistry and surface treatment on the compatibility with PEKK and resulting properties are described. Mica fillers at both loadings have only subtle influence on the FFF relevant melt rheological properties inherent to PEKK. Pigment micas at high loadings can lower the melting temperature of PEKK (up to 19 ºC) without shifting its glass transition temperature and inhibit much of the undesirable crystallization occurring during processing with unfilled PEKK. The printed composites were effectively cold crystallized post-printing, affording crystalline fractions up to 90% relative to unfilled PEKK with increased dimensional stabilities. All micas, when used as fillers in low and high relative PEKK crystallinities, significantly increased the tensile modulus (as high as 126% or to 7.31 GPa) of parts in correlation to the loading. Furthermore, the underlying microstructural features of a printed composite were compared to unfilled PEKK by use of a high-resolution helical micro-computed tomography instrument. Practically, pigment micas can confer a wide range of rich colors to 3D-printed PEKK.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Experiential findings for sustainable software ecosystems to support experimental and observational science

In the search for a sustainable approach for software ecosystems that supports experimental and observational science (EOS) across Oak Ridge National Laboratory (ORNL), we conducted a survey to understand the current and future landscape of EOS software and data. This paper describes the survey design we used to identify significant areas of interest, gaps, and potential opportunities, followed by a discussion on the obtained responses. The survey formulates questions about project demographics, technical approach, and skills required for the present and the next five years. Here, the study was conducted among 38 ORNL participants between June and July of 2021 and followed the required guidelines for human subjects training. We plan to use the collected information to help guide a vision for sustainable, community-based, and reusable scientific software ecosystems that need to adapt effectively to: (i) the evolving landscape of heterogeneous hardware in the next generation of instruments and computing (e.g. edge, distributed, accelerators), and (ii) data management requirements for data-driven science using artificial intelligence.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2014

Extracting physics through deep data analysis

In recent decades humankind has become very apt at generating and recording enormous amounts of data, ranging from tweets and selfies on social networks, to financial transactions in banks and stores. The scientific community has not shunned this popular trend and now routinely produces hundreds of petabytes of data per year [1]. This is because materials and phenomena in the world around us exist in an interweaved, entangled form, which gives rise to the complexity of the Universe and determines the size and complexity of the data that describes it. Science and technology endeavor to unravel this convolution and extract pure components from the mixtures, be it in ore mining and metal smelting or separation of thermal conductivity into the electronic and phononic contributions. Decomposition of complex behavior is the key to understanding manifestations of Nature. However, tools to carry out this task are not readily available, and therefore, intricate systems often remain well-characterized experimentally, but still not well understood due to intricacy of the collected data. Lastly, in materials science, understanding and ultimately designing new materials with complex properties will require the ability to integrate and analyze data from multiple instruments, including computational models, designed to probe complementary ranges of space, time, and energy.

36 MATERIALS SCIENCE↗
DOE OSTI2011

The CDMS II data acquisition system

The Data Acquisition System for the CDMS II dark matter experiment was designed and built when the experiment moved to its new underground installation at the Soudan Lab. The combination of remote operation and increased data load necessitated a completely new design. Elements of the original LabView system remained as stand-alone diagnostic programs, but the main data processing moved to a VME-based system with custom electronics for signal conditioning, trigger formation and buffering. The data rate was increased 100-fold and the automated cryogenic system was linked to the data acquisition. A modular server framework with associated user interfaces was implemented in Java to allow control and monitoring of the entire experiment remotely.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2024

Role of Dynamic Polarization Interactions in the Electrical Double Layer at Calcite (104) Interfaces with Aqueous Solutions

Reactions at mineral interfaces with aqueous solutions control many geochemical and biogeochemical processes in the Earth’s critical zone. At the molecular level, insights into important properties such as the structure of the electrical double layer (EDL) at specific mineral interfaces continue to improve due to the increasing fidelity of laboratory instrumentation and computational approaches. However, molecular simulation approaches suffer from limited reach into relevant scales of time, length, and system complexity. To span this gap, a novel hybrid approach that couples first principle plane-wave density functional theory (DFT) with classical DFT (cDFT) is demonstrated and applied to calcite (104) interfaces with various electrolytes. In this approach, a region of interest described using DFT interacts with the surrounding medium described using cDFT to arrive at a self-consistent ground state. Benchmarking against experimental observations and entirely first principle DFT simulations demonstrates that this hybrid model efficiently encompasses the key short-range and collective interactions in the EDL. Simulations of calcite (104)/solution interfaces reveal the key static and dynamic polarization interactions that give rise to structuring of ions and water. Ion hydration interactions have the strongest effect on the depth of the first minimum in the density distribution of counterions at the surface, and the position and width of the first density peak is largely determined by the strength of ion-correlation forces. Finer details of ion distributions are controlled by mutual polarization of the calcite surface and interfacial electrolyte. Finally, this new ability to efficiently and rigorously predict EDL structure at mineral surfaces in contact with complex solutions paves the way to accurately modeling sorption, nucleation, dissolution, and growth in realistic systems.

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

In Situ Chemical Monitoring and Imaging of Contents within Microfluidic Devices Having a Porous Membrane Wall Using Liquid Microjunction Surface Sampling Probe Mass Spectrometry

The ability to observe dynamic chemical processes (e.g., signaling, transport, etc.) in vivo or in situ using nondestructive chemical imaging opens a new door to understanding the complex dynamics of developing biological systems. With the advent of “biology-on-a-chip” devices has come the ability to monitor dynamic chemical processes in a controlled environment, using these engineered habitats to capture key features of natural systems while allowing visual observation of system development. Having the capability to spatially and temporally map the chemical signals within these devices may yield new insights into the forces that drive biosystem development. Here, a porous membrane sealed microfluidic device was designed to allow normal microfluidic operation while enabling continuous, location specific sampling and chemical characterization by liquid microjunction surface sampling probe mass spectrometry (LMJ-SSP MS). LMJ-SSP was used to extract fluids with nL-to-μL/min flow rates directly from selected areas of the microfluidic device without negatively impacting the device function. These extracts were subsequently characterized using MS. This technique was used to acquire MS images of the entirety of several multi-input microfluidic devices having different degrees of fluid mixing. LMJ-SSP MS imaging visualized the spatial distribution of chemical components within the microfluidic channels and could visualize chemical reactions occurring in the device. These microfluidic devices with a porous membrane wall are wholly compatible with the construction of biology-on-a-chip devices. This ultimately would enable correlation of biosystem physical structure with an evolving chemical environment

47 OTHER INSTRUMENTATION↗
DOE OSTI2017

Three-dimensional nanomagnetism

Magnetic nanostructures are being developed for use in many aspects of our daily life, spanning areas such as data storage, sensing and biomedicine. Whereas patterned nanomagnets are traditionally two-dimensional planar structures, recent work is expanding nanomagnetism into three dimensions; a move triggered by the advance of unconventional synthesis methods and the discovery of new magnetic effects. In three-dimensional nanomagnets more complex magnetic configurations become possible, many with unprecedented properties. Here we review the creation of these structures and their implications for the emergence of new physics, the development of instrumentation and computational methods, and exploitation in numerous applications.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Emerging materials intelligence ecosystems propelled by machine learning

We report that the age of cognitive computing and artificial intelligence (AI) is just dawning. Inspired by its successes and promises, several AI ecosystems are blossoming, many of them within the domain of materials science and engineering. These materials intelligence ecosystems are being shaped by several independent developments. Machine learning (ML) algorithms and extant materials data are utilized to create surrogate models of materials properties and performance predictions. Materials data repositories, which fuel such surrogate model development, are mushrooming. Automated data and knowledge capture from the literature (to populate data repositories) using natural language processing approaches is being explored. The design of materials that meet target property requirements and of synthesis steps to create target materials appear to be within reach, either by closed-loop active-learning strategies or by inverting the prediction pipeline using advanced generative algorithms. AI and ML concepts are also transforming the computational and physical laboratory infrastructural landscapes used to create materials data in the first place. Surrogate models that can outstrip physics-based simulations (on which they are trained) by several orders of magnitude in speed while preserving accuracy are being actively developed. Automation, autonomy and guided high-throughput techniques are imparting enormous efficiencies and eliminating redundancies in materials synthesis and characterization. The integration of the various parts of the burgeoning ML landscape may lead to materials-savvy digital assistants and to a human-machine partnership that could enable dramatic efficiencies, accelerated discoveries and increased productivity. Here, we review these emergent materials intelligence ecosystems and discuss the imminent challenges and opportunities. The materials research landscape is being transformed by the infusion of approaches based on machine learning. This Review discusses the emerging materials intelligence ecosystems and the potential of human-machine partnerships for fast and efficient virtual materials screening, development and discovery.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Pausing ultrafast melting by timed multiple femtosecond-laser pulses

An intense femtosecond-laser excitation of a solid induces highly nonthermal conditions. In materials like silicon, laser-induced bond-softening leads to a highly incoherent ionic motion and eventually nonthermal melting. But is this outcome an inevitable consequence, or can it be controlled? Here, we performed ab initio molecular dynamics simulations of crystalline silicon after timed multiple femtosecond-laser pulse excitations with fluence above the nonthermal melting threshold. Our results demonstrate an excitation mechanism that pauses nonthermal melting and creates a metastable state instead, with an electronic structure similar to the ground state. This mechanism can be generalized to other materials, potentially enabling structural and/or electronic transitions to metastable phases in the high-excitation regime. In addition, our approach could be used to switch off nonthermal contributions in experiments, allowing reliable electron-phonon coupling constants to be obtained more easily.

47 OTHER INSTRUMENTATION↗
DOE OSTI2016

Synthesis of polycaprolactone-titanium oxide multilayer films by nanosecond laser pulses and electrospinning technique for better implant fabrication

In this study, a combination of electrospinning and laser texturing is introduced as a novel method for increasing the biocompatibility of metal implants. Besides having a rough laser treated surface, the implant benefits from the high porosity and better wettability of an electrospun fibrous structure, which is a more favorable environment for cell proliferation. Titanium samples were patterned using a nanosecond laser beam and were placed as collectors in an electrospinning machine. They were then soaked in simulated body fluid for four weeks. Energy Dispersive X-ray and X-Ray Diffraction results indicate significantly more hydroxyapatite formation on laser treated samples with nanoscale fibers deposited on their surface. This shows that having a laser treated surface underneath the fibrous layer can improve short-term biocompatibility even before degradation of fibers. The thermal conductivity of the electrospun layer, measured using a Hot Disk Transient Plane Source instrument and computer code, was shown to be considerably lower than that of titanium and very close to bone. The presence of this layer can therefore be beneficial in making the implant more compatible to a biological medium. In case of dental implants, it was shown that this layer can act as a thermal barrier while a hot beverage is consumed and it can decrease the temperature rise by about 60%, which avoids any possible damage to newly formed cells during the healing period.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2021

Performance of microchannel plate based detectors for <25 keV x rays: Monte Carlo simulations and comparisons with experimental results

We present the results of Monte Carlo simulations of the microchannel plate (MCP) response to x rays in the 250 eV to 25 keV energy range as a function of both x-ray energy and impact angle and their comparisons with the experimental results from the X8A beamline at the National Synchrotron Light Source at Brookhaven National Laboratory. Incoming x rays interact with the lead glass of the microchannel plate, producing photoelectrons. Transport of the photoelectrons is neglected in this model, and it is assumed that photoelectrons deposit all their energy at the point they are created. This deposition leads to the generation of many secondary electrons, some fraction of which diffuse to the MCP pore surface where they can initiate secondary electron cascades in the pore under an external voltage bias. X-ray penetration through multiple MCP pore walls is increasingly important above 5 keV, and the effect of this penetration on MCP performance is studied. In agreement with past measurements, we find that the dependence of MCP sensitivity with angle relative to the pore bias changes from a cotangent dependence to angular independence and then proceeds to a secant dependence as the x-ray energy increases. We also find that with the increasing x-ray energy, the MCP gain sensitivity as a function of bias voltage decreases. The simulations also demonstrate that for x rays incident normal to the MCP surface, spatial resolution shows little dependence on the x-ray energy but degrades with the increasing x-ray energy as the angle of incidence relative to the surface normal increases. This agrees with experimental measurements. Simulation studies have also been completed for MCPs gated with a subnanosecond voltage pulse. In this work, we find that the optical gate profile width increases as the x-ray energy is increased above 5 keV, a consequence of increased x-ray penetration at energies >5 keV. Simulations of the pulsed dynamic range show that the dynamic range varies between ~100 and 1000 depending on x-ray energy and peak voltage.

47 OTHER INSTRUMENTATION↗