Search NASA⌕ Search

SEARCH · Search NASA

Results for “Modern experimental methods”

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.

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

At least 19 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

The 2022 magneto-optics roadmap

Abstract Magneto-optical (MO) effects, viz. magnetically induced changes in light intensity or polarization upon reflection from or transmission through a magnetic sample, were discovered over a century and a half ago. Initially they played a crucially relevant role in unveiling the fundamentals of electromagnetism and quantum mechanics. A more broad-based relevance and wide-spread use of MO methods, however, remained quite limited until the 1960s due to a lack of suitable, reliable and easy-to-operate light sources. The advent of Laser technology and the availability of other novel light sources led to an enormous expansion of MO measurement techniques and applications that continues to this day (see section 1). The here-assembled roadmap article is intended to provide a meaningful survey over many of the most relevant recent developments, advances, and emerging research directions in a rather condensed form, so that readers can easily access a significant overview about this very dynamic research field. While light source technology and other experimental developments were crucial in the establishment of today’s magneto-optics, progress also relies on an ever-increasing theoretical understanding of MO effects from a quantum mechanical perspective (see section 2), as well as using electromagnetic theory and modelling approaches (see section 3) to enable quantitatively reliable predictions for ever more complex materials, metamaterials, and device geometries. The latest advances in established MO methodologies and especially the utilization of the MO Kerr effect (MOKE) are presented in sections 4 (MOKE spectroscopy), 5 (higher order MOKE effects), 6 (MOKE microscopy), 8 (high sensitivity MOKE), 9 (generalized MO ellipsometry), and 20 (Cotton–Mouton effect in two-dimensional materials). In addition, MO effects are now being investigated and utilized in spectral ranges, to which they originally seemed completely foreign, as those of synchrotron radiation x-rays (see section 14 on three-dimensional magnetic characterization and section 16 on light beams carrying orbital angular momentum) and, very recently, the terahertz (THz) regime (see section 18 on THz MOKE and section 19 on THz ellipsometry for electron paramagnetic resonance detection). Magneto-optics also demonstrates its strength in a unique way when combined with femtosecond laser pulses (see section 10 on ultrafast MOKE and section 15 on magneto-optics using x-ray free electron lasers), facilitating the very active field of time-resolved MO spectroscopy that enables investigations of phenomena like spin relaxation of non-equilibrium photoexcited carriers, transient modifications of ferromagnetic order, and photo-induced dynamic phase transitions, to name a few. Recent progress in nanoscience and nanotechnology, which is intimately linked to the achieved impressive ability to reliably fabricate materials and functional structures at the nanoscale, now enables the exploitation of strongly enhanced MO effects induced by light–matter interaction at the nanoscale (see section 12 on magnetoplasmonics and section 13 on MO metasurfaces). MO effects are also at the very heart of powerful magnetic characterization techniques like Brillouin light scattering and time-resolved pump-probe measurements for the study of spin waves (see section 7), their interactions with acoustic waves (see section 11), and ultra-sensitive magnetic field sensing applications based on nitrogen-vacancy centres in diamond (see section 17). Despite our best attempt to represent the field of magneto-optics accurately and do justice to all its novel developments and its diversity, the research area is so extensive and active that there remains great latitude in deciding what to include in an article of this sort, which in turn means that some areas might not be adequately represented here. However, we feel that the 20 sections that form this 2022 magneto-optics roadmap article, each written by experts in the field and addressing a specific subject on only two pages, provide an accurate snapshot of where this research field stands today. Correspondingly, it should act as a valuable reference point and guideline for emerging research directions in modern magneto-optics, as well as illustrate the directions this research field might take in the foreseeable future.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2023

Developing and applying quantifiable metrics for diagnostic and experiment design on Z

This project applies methods in Bayesian inference and modern statistical methods to quantify the value of new experimental data, in the form of new or modified diagnostic configurations and/or experiment designs. We demonstrate experiment design methods that can be used to identify the highest priority diagnostic improvements or experimental data to obtain in order to reduce uncertainties on critical inferred experimental quantities and select the best course of action to distinguish between competing physical models. Bayesian statistics and information theory provide the foundation for developing the necessary metrics, using two high impact experimental platforms on Z as exemplars to develop and illustrate the technique. We emphasize that the general methodology is extensible to new diagnostics (provided synthetic models are available), as well as additional platforms. We also discuss initial scoping of additional applications that began development in the last year of this LDRD.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Modeling Absolute Redox Potentials of Ferrocene in the Condensed Phase

Absolute thermodynamic quantities for critical chemical reactions are needed to determine the role of solvents and reactive environments in catalysis and electrocatalysis beyond the relative scales typically employed. In principle, theoretical methods can provide such quantification but are often hindered by the innate complexity of strong electron correlation and dynamic relaxation of solvent environments. Here, we present and validate a protocol for calculating the redox potentials of ferrocene/ferrocenium redox pair in the acetonitrile. Equation-of-motion ionization potential coupled-cluster single-double (EOM-IP-CCSD) and effective fragment potential (EFP) methods are used to characterize the adiabatic and vertical ionization potentials (IP) as well as the electron affinity processes. We benchmark molecular mechanics against the EFP model to show the differences in ferrocene electronic polarizability in two redox states. Our best estimate of the redox potential (4.94 eV) agrees well with the experimental value (4.93 eV). This demonstrated the ability of modern computational methods to predict absolute redox potentials quantitatively and, more critically, quantify the correlation of dynamic effects, which underlie their origin.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Exploring Quantum Materials with Resonant Inelastic X-Ray Scattering

Understanding quantum materials—solids in which interactions among constituent electrons yield a great variety of novel emergent quantum phenomena—is a forefront challenge in modern condensed matter physics. This goal has driven the invention and refinement of several experimental methods, which can spectroscopically determine the elementary excitations and correlation functions that determine material properties. Here we focus on the future experimental and theoretical trends of resonant inelastic x-ray scattering (RIXS), which is a remarkably versatile and rapidly growing technique for probing different charge, lattice, spin, and orbital excitations in quantum materials. We provide a forward-looking introduction to RIXS and outline how this technique is poised to deepen our insight into the nature of quantum materials and of their emergent electronic phenomena. Published by the American Physical Society 2024

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2024

Modern chemical graph theory

Abstract Graph theory has a long history in chemistry. Yet as the breadth and variety of chemical data is rapidly changing, so too do graph encoding methods and analyses that yield qualitative and quantitative insights. Using illustrative cases within a basic mathematical framework, we showcase modern chemical graph theory's utility in Chemists' analysis and model development toolkit. The encoding of both experimental and simulation data is discussed at various levels of granularity of information. This is followed by a discussion of the two major classes of graph theoretical analyses: identifying connectivity patterns and partitioning methods. Measures, metrics, descriptors, and topological indices are then introduced with an emphasis upon enhancing interpretability and incorporation into physical models. Challenging data cases are described that include strategies for studying time dependence. Throughout, we incorporate recent advancements in computer science and applied mathematics that are propelling chemical graph theory into new domains of chemical study. This article is categorized under: Molecular and Statistical Mechanics > Molecular Dynamics and Monte‐Carlo Methods Structure and Mechanism > Computational Materials Science Structure and Mechanism > Molecular Structures

Leite, Leonardo S. G.↗
DOE OSTI2024

Optimal experimental design: Formulations and computations

Questions of ‘how best to acquire data’ are essential to modelling and prediction in the natural and social sciences, engineering applications, and beyond. Optimal experimental design (OED) formalizes these questions and creates computational methods to answer them. This article presents a systematic survey of modern OED, from its foundations in classical design theory to current research involving OED for complex models. We begin by reviewing criteria used to formulate an OED problem and thus to encode the goal of performing an experiment. We emphasize the flexibility of the Bayesian and decision-theoretic approach, which encompasses information-based criteria that are well-suited to nonlinear and non-Gaussian statistical models. We then discuss methods for estimating or bounding the values of these design criteria; this endeavour can be quite challenging due to strong nonlinearities, high parameter dimension, large per-sample costs, or settings where the model is implicit. A complementary set of computational issues involves optimization methods used to find a design; we discuss such methods in the discrete (combinatorial) setting of observation selection and in settings where an exact design can be continuously parametrized. Finally we present emerging methods for sequential OED that build non-myopic design policies, rather than explicit designs; these methods naturally adapt to the outcomes of past experiments in proposing new experiments, while seeking coordination among all experiments to be performed. Throughout, we highlight important open questions and challenges.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Streaming Large-Scale Microscopy Data to a Supercomputing Facility

Data management is a critical component of modern experimental workflows. As data generation rates increase, transferring data from acquisition servers to processing servers via conventional file-based methods is becoming increasingly impractical. The 4D Camera at the National Center for Electron Microscopy generates data at a nominal rate of 480 Gbit s -1 (87,000 frames s -1 ⁠), producing a 700 GB dataset in 15 s. To address the challenges associated with storing and processing such quantities of data, we developed a streaming workflow that utilizes a high-speed network to connect the 4D Camera’s data acquisition system to supercomputing nodes at the National Energy Research Scientific Computing Center, bypassing intermediate file storage entirely. In this work, we demonstrate the effectiveness of our streaming pipeline in a production setting through an hour-long experiment that generated over 10 TB of raw data, yielding high-quality datasets suitable for advanced analyses. Additionally, we compare the efficacy of this streaming workflow against the conventional file-transfer workflow by conducting a postmortem analysis on historical data from experiments performed by real users. Our findings show that the streaming workflow significantly improves data turnaround time, enables real-time decision-making, and minimizes the potential for human error by eliminating manual user interactions.

4D-STEM↗
DOE OSTI2025

Reduced-basis method for few-body bound-state emulation

Recent advances in both theoretical and computational methods have enabled large-scale, precision calculations of the properties of atomic nuclei. With the growing complexity of modern nuclear theory, however, also comes the need for novel methods to perform systematic studies and quantify the uncertainties of models when confronted with experimental data. Here, this study presents an application of such an approach, the reduced basis method, to substantially lower computational costs by constructing a significantly smaller Hamiltonian subspace informed by previous solutions. Our method shows comparable efficiency and accuracy to other dimensionality reduction techniques on an artificial three-body bound system while providing a richer representation of physical information in its projection and training subspace. This methodological advancement can be applied in other contexts and has the potential to greatly improve our ability to systematically explore theoretical models and thus enhance our understanding of the fundamental properties of nuclear systems.

cluster models↗
DOE OSTI2023

Development of ML FPGA Filter for Particle Identification and Tracking in Real Time

Real-time data processing is a frontier field in experimental particle physics. Machine Learning methods are widely used and have proven to be very powerful in particle physics. The growing computational power of modern FPGA boards allows us to add more sophisticated algorithms for real time data processing. Many tasks could be solved using modern Machine Learning (ML) algorithms which are naturally suited for FPGA architectures. The FPGA-based machine learning algorithm provides an extremely low, sub-microsecond, latency decision and makes information-rich data sets for event selection. We report work has started to evaluate an FPGA based Machine Learning (ML) algorithm for a real-time particle identification and tracking with Transition Radiation Detector (TRD) and e/m calorimeter. The first target is the GlueX experiment, with a plan to build a TRD based on GEM technology. GlueX trigger latency is 3.3 μs.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2023

Ultrafast radiographic imaging and tracking: An overview of instruments, methods, data, and applications

Ultrafast radiographic imaging and tracking (U-RadIT) use state-of-the-art ionizing particle and light sources to experimentally study sub-nanosecond transients or dynamic processes in physics, chemistry, biology, geology, materials science and other fields. These processes are fundamental to modern technologies and applications, such as nuclear fusion energy, advanced manufacturing, communication, and green transportation, which often involve one mole or more atoms and elementary particles, and thus are challenging to compute by using the first principles of quantum physics or other forward models. One of the central problems in U-RadIT is to optimize information yield through, e.g. high-luminosity X-ray and particle sources, efficient imaging and tracking detectors, novel methods to collect data, and large-bandwidth online and offline data processing, regulated by the underlying physics, statistics, and computing power. We review and highlight recent progress in: (a.) Detectors such as high-speed complementary metal-oxide semiconductor (CMOS) cameras, hybrid pixelated array detectors integrated with Timepix4 and other application-specific integrated circuits (ASICs), and digital photon detectors; (b.) U-RadIT modalities such as dynamic phase contrast imaging, dynamic diffractive imaging, and four-dimensional (4D) particle tracking; (c.) U-RadIT data and algorithms such as neural networks and machine learning, and (d.) Applications in ultrafast dynamic material science using XFELs, synchrotrons and laser-driven sources. Hardware-centric approaches to U-RadIT optimization are constrained by detector material properties, low signal-to-noise ratio, high cost and long development cycles of critical hardware components such as ASICs. Interpretation of experimental data, including comparisons with forward models, is frequently hindered by sparse measurements, model and measurement uncertainties, and noise. Alternatively, U-RadIT make increasing use of data science and machine learning algorithms, including experimental implementations of compressed sensing. Machine learning and artificial intelligence approaches, refined by physics and materials information, may also contribute significantly to data interpretation, uncertainty quantification and U-RadIT optimization.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2025

MethodOpt: a Shiny-based graphical user interface for multivariate optimization of sampling and analytical instrumentation

Method optimization is an important step in producing useful data in various experimental settings involving the use of sampling and analytical instrumentation, such as gas-chromatography mass-spectrometry or other analytical techniques. However, traditional optimization techniques often lack the sophistication of more modern optimization techniques developed in areas of applied mathematics. A graphical user interface has been developed that implements a multivariate, multi-objective optimization technique for spectra-generating sampling and analytical instrumentation, which saves substantial time and resources compared to the more traditional approaches to method development.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2023

Scalable Incremental Checkpointing using GPU-Accelerated De-Duplication

Writing large amounts of data concurrently to stable storage is a typical I/O pattern of many HPC workflows. This pattern introduces high I/O overheads and results in increased storage space utilization especially for workflows that need to capture the evolution of data structures with high frequency as checkpoints. In this context, many applications, such as graph pattern matching, perform sparse updates to large data structures between checkpoints. For these applications, incremental checkpointing techniques that save only the differences from one checkpoint to another can dramatically reduce the checkpoint sizes, I/O bottlenecks, and storage space utilization. However, such techniques are not without challenges: it is non-trivial to transparently determine what data has changed since a previous checkpoint and assemble the differences in a compact fashion that does not result in excessive metadata. State-of-art data reduction techniques (e.g., compression and de-duplication) have significant limitations when applied to modern HPC applications that leverage GPUs: slow at detecting the differences, generate a large amount of metadata to keep track of the differences, and ignore crucial spatiotemporal checkpoint data redundancy. This paper addresses these challenges by proposing a Merkle tree-based incremental checkpointing method to exploit GPUs' high memory bandwidth and massive parallelism. Experimental results at scale show a significant reduction of the I/O overhead and space utilization of checkpointing compared with state-of-the-art incremental checkpointing and compression techniques.

Tan, Nigel↗
DOE OSTI2022

The Science of Scientific Software Development and Use

Increasingly powerful and affordable computing has revolutionized scientific and scholarly discovery across a broad range of fields. Computing relies on software, which has been rapidly growing in scope, diversity, and complexity. At the same time, the methods, processes, and tools used to produce and utilize this essential software are often ad hoc, and the study and improvement of them is often done without the benefit of direct funding or prioritization. Consequently, concerns are growing about the productivity of the developers and users of scientific software, its sustainability, and the trustworthiness of the results that it produces. The US Department of Energy Office of Science (DOE-SC) is at the forefront of modern software-enabled scientific discovery across numerous areas of computational, experimental, and observational science, including major investments in national user facilities that support these activities. In December 2021, the DOE-SC Office of Advanced Scientific Computing Research (ASCR) convened a workshop on basic research needs for the Science of Scientific Software Development and Use (SSSDU). Through keynote presentations, lightning talks, and breakout groups, participants discussed the current practice of software development, maintenance, evolution, and use, and considered how the scientific method could be used to examine these practices and develop more evidence-based approaches to enhance the impact of software and computing on all areas of science. Workshop participants identified three priority research directions (PRDs) and three important crosscutting themes that center on the following overarching insight: software has become an essential part of modern science that impacts new discovery, policy, and technological development. To have full confidence in science delivered via software, we must improve the processes and tools that help us create and use it, and this enhancement requires a deep understanding of the diverse array of teams and individuals doing the work. The full workshop report will be available at https://doi.org/10.2172/1846009.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2026

A comprehensive framework for validating simulation models of power system equipment using terminal measurements

Accurate simulation of power-plants is essential to the planning and operation of modern power grids. The current methods used to periodically check power-plant simulation models have many open questions about their limitations and accuracy. The research in this project explored using Monte-Carlo Experimentation (MCE) as a means for answering these important questions.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2024

Nuclear Charge Radii of Silicon Isotopes

The nuclear charge radius of 32 Si was determined using collinear laser spectroscopy. The experimental result was confronted with ab initio nuclear lattice effective field theory, valence-space in-medium similarity renormalization group, and mean field calculations, highlighting important achievements and challenges of modern many-body methods. The charge radius of 32 Si completes the radii of the mirror pair 32 Ar – 32 Si, whose difference was correlated to the slope L of the symmetry energy in the nuclear equation of state. Furthermore, our result suggests L ≤ 60 MeV, which agrees with complementary observables.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2025

Identifying Challenges in Safeguards for Metallic Fuel Fabrication Facilities

As new advanced reactors gain popularity, there is an increasing interest in metallic fuel fabrication for fast reactors. While metallic fuels themselves are not a new idea, as many of the first reactors employed metallic fuels, new designs, compositions, and fabrication methods are appearing throughout the nuclear community. As the interest grows and facilities are constructed, both domestic and international safeguards will need to be heavily involved to support safeguards-by-design (SBD) measures from the start. This work compiles a review of historical and modern fuel types and fabrication methods, fabrication processes, safeguards gaps, and potential safeguards solutions. Metallic nuclear fuel types have been around for many decades and were included in some of the first reactors including the Experimental Breeder Reactor (EBR)-I and -II, the Fermi 1 reactor, the Integral Fast Reactor (IFR), and the Dounreay Fast Reactor (DFR). These reactors used various compositions including pure uranium (U) metal, U-zirconium (Zr) alloys, plutonium (Pu)-aluminum (Al) alloys, U-fissium (Fs) alloys, U-Pu-Zr alloys, and U-molybdenum (Mo) alloys [1, 2, 3, 4, 5]. These small alloying additions are included to improve the material properties of the pure U metal. The alpha-phase U (stable below 661C) suffers elongation in one direction causing grain boundary cracking and increasing creep rate due to irradiation growth, thermal cycling, and preferential crystal orientation. It is ideal to utilize the gamma-phase U (typically stable above 769C) by adding small amounts of alloying elements such as Zr or Mo to stabilize this phase down to room temperature [3]. Additionally, some research has been focused on U with transuranic (TRU) elements present, typically coming from the used fuel recycling process. Including these elements in fast reactor fuel can aid in the reduction of nuclear waste by burning minor long-lived actinides. However, the additions of TRU elements can cause concerns to arise when trying to fabrication or safeguard metallic fuels. A typical metallic fuel element is shown in Figure 1. Sodium is added into the cladding to create a thermal bond between the fuel slug and cladding wall. The fuel slug is then inserted and the end plug is welded on to the top of the fuel element. A gas plenum is left to create a headspace for gaseous fission products to escape rather than continue to build in the fuel itself [1, 5]. Other fuel element geometries exist as well, such as the Lightbridge twisted cruciform geometry shown in Figure 2 [6]. This design allows for better cooling performance and provides room for fuel rod swelling without impacting the fuel rod diameter. There are many different fabrication methods for metallic fuels, which is one of the many benefits of these fuel types. Many of these fabrication methods are relatively easy and cost-efficient. The most popular fabrication method is injection casting, sometimes called vacuum induction melting (VIM), shown in Figure 3 [4, 8, 9, 7, 10]. This method was largely used for EBR-II fuel fabrication. The injection casting system is contained inside of a vessel consisting of a Y2O3-coated graphite crucible surrounded by an induction coil with ZrO2-coated quartz molds suspended above the crucible. The fuel feedstock is placed inside of the graphite crucible and melted using the induction furnace. The induction furnace utilizes a dual frequency with the high frequency melting the feedstock and the low frequency causing stirring of the melted feedstock to form a homogeneous mixture. The mixture is heated to approximately 1600C in an argon environment. The vessel is evacuated and then the quartz molds are lowered into the graphite crucible containing the molten metal and the vessel is repressurized to inject the metal fuel upwards into the molds. The molds are removed and then shattered to release the fuel slugs. This fabrication method was used to fabricate 39,000 metallic fuel pins for EBR-II. While injection casting has been the most common metallic fuel fabrication method throughout the decades, many other methods have been explored including low-pressure gravity casting, microwave casting, continuous casting, centrifugal casting, coextrusion, and many others [11, 12, 8, 13, 14, 15]. Some of these methods aim to mitigate challenges that arise with americium (Am) volatilization during the casting process for TRU-containing fuel feedstocks, an issue with injection casting. Coextrusion is one of the methods explored at the Idaho National Laboratory (INL) and has been utilized for the initial fabrication tests of Lightbridge's unique fuels, as well as other metallic fuels with cladding coextruded. In this process, large billets are formed and machined and then inserted into a molten salt bath for approximately 30 minutes. The billets are then loaded into the extrusion press and extruded. This process can be seen in Figure 4 [15].

11 - NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2025

Modern version of the uncited 1938 experiment that first observed DT fusion

Experiments are described, and results are provided, for the duplication of the first-ever triton-deuterium (colloquially referred to as DT) fusion experiment accidentally performed by A.J. Ruhlig in 1938, but forgotten in the published scientific literature. Here, we find that Ruhlig overestimated the ratio of the triton-deuterium over deuteron-deuterium neutron yields in his secondary reaction (Reaction-in-Flight) experiment compared to modern theoretical calculations and our duplication of his experiment using modern neutron detection methods. Nevertheless, Ruhlig’s observation provided the motivation for the application of DT fusion after World War II and its more recent importance in peaceful energy production at DT fusion facilities around the world. Additionally, the experimental technique used in the present work provides a novel approach for checking on low-energy triton stopping powers in deuterium containing compounds.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2022

Accomplishments of Thermal Neutron Scattering Research at North Carolina State University [Slides]

Thermal neutron scattering law (TSL) data evaluations have been completed and contributed to ENDF. New evaluations are underway. FLASSH is developing as a modern platform for thermal neutron data analysis featuring an enhanced user experience and low learning overhead. Advanced TSL techniques in combination with Doppler analysis have been developed, which were incorporated into FLASSH. ML/DL NeTS method development has been initiated. Experimental capabilities for validation have been developed at the PULSTAR reactor.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗