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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 OSTI2022

Design and Challenges of Edge Computing ASICs on Front-End Electronics

In situ or hardware-embedded data processing of raw signals, close to their source, in radiation detectors is expected to provide dramatic improvements in data quality and volumes. However, the implementation of artificial neural networks (ANNs) in the front-end electronics, and the design of custom integrated circuits (ASICs), comes with challenges. In addition, detectors have to operate with limited power budget and implement complex functionalities in a very dense space. They often are exposed to extreme conditions as they work in high-radiation environments and/or cryogenic temperatures. This paper presents examples of applications and design methodologies for in-situ ANNs, along with the challenges of retaining the fidelity of the trained networks. For illustration, we use the problem of estimating the energy deposited by the radiation from digitized waveforms. The proposed implementation starts with an ML algorithm trained in Qkeras and eventually leads to an equivalent ASIC implementation. The associated design challenges in realizing energy and area efficient implementations in CMOS processes are reviewed. Novel approaches that employ hybrid technologies (combination of CMOS with memristors), in-memory computing models, and bio-inspired spiking neural networks are also highlighted

47 OTHER INSTRUMENTATION↗
DOE OSTI2018

An algorithm to correct saturated mass spectrometry ion abundances for enhanced quantitation and mass accuracy in omic studies

The mass accuracy and peak intensity of ions detected by mass spectrometry (MS) measurements are essential to facilitate compound identification and quantitation. However, high concentration species can easily cause problems if their ion intensities reach beyond the limits of the detection system, leading to distorted and non-ideal detector response (e.g. saturation), and largely precluding the calculation of accurate m/z and intensity values. Here we present an open source computational method to correct peaks above a defined intensity (saturated) threshold determined by the MS instrumentation such as the analog-to-digital converters or time-to-digital converters used in conjunction with time-of-flight MS. In this method, the isotopic envelope for each observed ion above the saturation threshold is compared to its expected theoretical isotopic distribution. The most intense isotopic peak for which saturation does not occur is then utilized to re-calculate the precursor m/z and correct the intensity, resulting in both higher mass accuracy and greater dynamic range. The benefits of this approach were evaluated with proteomic and lipidomic datasets of varying complexities. After correcting the high concentration species, reduced mass errors and enhanced dynamic range were observed for both simple and complex omic samples. Specifically, the mass error dropped by more than 50% in most cases with highly saturated species and dynamic range increased by 1-2 orders of magnitude for peptides in a blood serum sample.

Environmental Molecular Sciences Laboratory↗
DOE OSTI2016

Preliminary results concerning the simulation of beam profiles from extracted ion current distributions for mini-STRIKE

The Radio Frequency (RF) negative hydrogen ion source prototype has been chosen for the ITER neutral beam injectors due to its optimal performances and easier maintenance demonstrated at Max-Planck-Institut für Plasmaphysik, Garching in hydrogen and deuterium. One of the key information to better understand the operating behavior of the RF ion sources is the extracted negative ion current density distribution. This distribution—influenced by several factors like source geometry, particle drifts inside the source, cesium distribution, and layout of cesium ovens—is not straightforward to be evaluated. The main outcome of the present contribution is the development of a minimization method to estimate the extracted current distribution using the footprint of the beam recorded with mini-STRIKE (Short-Time Retractable Instrumented Kalorimeter). To accomplish this, a series of four computational models have been set up, where the output of a model is the input of the following one. These models compute the optics of the ion beam, evaluate the distribution of the heat deposited on the mini-STRIKE diagnostic calorimeter, and finally give an estimate of the temperature distribution on the back of mini-STRIKE. Several iterations with different extracted current profiles are necessary to give an estimate of the profile most compatible with the experimental data. A first test of the application of the method to the BAvarian Test Machine for Negative ions beam is given.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2025

xGFabric: Coupling Sensor Networks and HPC Facilities with Private 5G Wireless Networks for Real-Time Digital Agriculture

Advanced scientific applications require coupling distributed sensor networks with centralized high-performance computing facilities. Citrus Under Protective Screening (CUPS) exemplifies this need in digital agriculture, where citrus research facilities are instrumented with numerous sensors monitoring environmental conditions and detecting protective screening damage. CUPS demands access to computational fluid dynamics codes for modeling environmental conditions and guiding real-time interventions like water application or robotic repairs. These computing domains have contrasting properties: sensor networks provide low-performance, limited-capacity, unreliable data access, while high-performance facilities offer enormous computing power through high-latency batch processing. Private 5G networks present novel capabilities addressing this challenge by providing low latency, high throughput, and reliability necessary for near-real-time coupling of edge sensor networks with HPC simulations. This work presents xGFabric, an end-to-end system coupling sensor networks with HPC facilities through Private 5G networks. The prototype connects remote sensors via 5G network slicing to HPC systems, enabling real-time digital agriculture simulation.

Digital Agriculture↗
DOE OSTI2015

Laboratory-based x-ray reflectometer for multilayer characterization in the 15–150 keV energy band

A laboratory-based X-ray reflectometer has been developed to measure the performance of hard X-ray multilayer coatings at their operational X-ray energies and incidence angles. The instrument uses a sealed-tube X-ray source with a tungsten anode that can operate up to 160 kV to provide usable radiation in the 15–150 keV energy band. Two sets of adjustable tungsten carbide slit assemblies, spaced 4.1 m apart, are used to produce a low-divergence white beam, typically set to 40 μm × 800 μm in size at the sample. Multilayer coatings under test are held flat using a vacuum chuck and are mounted at the center of a high-resolution goniometer used for precise angular positioning of the sample and detector; additionally, motorized linear stages provide both vertical and horizontal adjustments of the sample position relative to the incident beam. A CdTe energy-sensitive detector, located behind a third adjustable slit, is used in conjunction with pulse-shaping electronics and a multi-channel analyzer to capture both the incident and reflected spectra; the absolute reflectance of the coating under test is computed as the ratio of the two spectra. The instrument’s design, construction, and operation are described in detail, and example results are presented obtained with both periodic, narrow-band and depth-graded, wide-band hard X-ray multilayer coatings.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2017

Virtualized hardware environments for supporting digital I and C verification - 324

The technology of virtualized hardware testing platforms within the nuclear context in order to provide a framework for evaluating virtualized hardware/software approaches is discussed. To understand the applicability of virtualized hardware approaches, we have developed a virtual hardware model of an actual smart sensor using the Imperas product OVPsim development environment, as is seen in . An evaluation of the modeling effort required to create the digital I and C smart sensor is provided, followed by an analysis and findings on the various tools in the OVPsim environment for supporting testing, evidence collecting, and analysis. The analyzed tools include traditional input/output testing, model-based testing, mutation testing, and fault injection techniques. Finally, the goal of this research effort is to analyze virtual hardware platforms and present findings to the nuclear community on the efficacy and viability of this technology as a complementary means to actual hardware-in-the-loop testing Factory Acceptance Testing for Nuclear Power Plant (NPP) digital I and C systems. (authors)

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2017

Advances in rapid compression machine studies of low- and intermediate-temperature autoignition phenomena

Rapid compression machines (RCMs) are widely-used to acquire experimental insights into fuel autoignition and pollutant formation chemistry, especially at conditions relevant to current and future combustion technologies. RCM studies emphasize important experimental regimes, characterized by low- to intermediate-temperatures (600–1200 K) and moderate to high pressures (5–80 bar). At these conditions, which are directly relevant to modern combustion schemes including low temperature combustion (LTC) for internal combustion engines and dry low emissions (DLE) for gas turbine engines, combustion chemistry exhibits complex and experimentally challenging behaviors such as the chemistry attributed to cool flame behavior and the negative temperature coefficient regime. Challenges for studying this regime include that experimental observations can be more sensitive to coupled physical-chemical processes leading to phenomena such as mixed deflagrative/autoignitive combustion. Experimental strategies which leverage the strengths of RCMs have been developed in recent years to make RCMs particularly well suited for elucidating LTC and DLE chemistry, as well as convolved physical-chemical processes. Specifically, this work presents a review of experimental and computational efforts applying RCMs to study autoignition phenomena, and the insights gained through these efforts. A brief history of RCM development is presented towards the steady improvement in design, characterization, instrumentation and data analysis. Novel experimental approaches and measurement techniques, coordinated with computational methods are described which have expanded the utility of RCMs beyond empirical studies of explosion limits to increasingly detailed understanding of autoignition chemistry and the role of physical-chemical interactions. Fundamental insight into the autoignition chemistry of specific fuels is described, demonstrating the extent of knowledge of low-temperature chemistry derived from RCM studies, from simple hydrocarbons to multi-component blends and full-boiling range fuels. In conclusion, emerging needs and further opportunities are suggested, including investigations of under-explored fuels and the implementation of increasingly higher fidelity diagnostics.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2013

Spin-neurons: A possible path to energy-efficient neuromorphic computers

Recent years have witnessed growing interest in the field of brain-inspired computing based on neural-network architectures. In order to translate the related algorithmic models into powerful, yet energy-efficient cognitive-computing hardware, computing-devices beyond CMOS may need to be explored. The suitability of such devices to this field of computing would strongly depend upon how closely their physical characteristics match with the essential computing primitives employed in such models. In this work, we discuss the rationale of applying emerging spin-torque devices for bio-inspired computing. Recent spin-torque experiments have shown the path to low-current, low-voltage, and high-speed magnetization switching in nano-scale magnetic devices. Such magneto-metallic, current-mode spin-torque switches can mimic the analog summing and “thresholding” operation of an artificial neuron with high energy-efficiency. Comparison with CMOS-based analog circuit-model of a neuron shows that “spin-neurons” (spin based circuit model of neurons) can achieve more than two orders of magnitude lower energy and beyond three orders of magnitude reduction in energy-delay product. The application of spin-neurons can therefore be an attractive option for neuromorphic computers of future.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2018

Mapping the body’s battle with Ebola and Zika

The viruses that cause Ebola and Zika, daunting diseases that inspire concern at every outbreak, share a strong similarity in how they first infiltrate a host’s cells. Through the computer modeling capabilities of Los Alamos National Laboratory, the molecular calisthenics involved in invading a cell are visually documented, an essential step toward vaccine and therapeutic medicine development. “The two very different viruses both use a surface protein to initiate a process called ‘membrane fusion,’ which allows the viral entry into the host cell,” said Chang-Shung Tung, the leading and corresponding author of the recent work in the journal Biomolecules. “Before the membrane fusion started, these two proteins went through large conformational changes that was not fully understood, as the proteins would take on the physical shape that would allow them to fuse with the cell and begin the infection. In this work, we modeled the structures of the two proteins in the fusion-initiation state. Then we used the structures to explain how four different antibodies in our bodies can fight the infection and block the viral entry, based on how they bind to these two proteins,” Tung said. The aim of the team’s work was to understand the specific structure-function relationship of the Ebola glycoprotein (EBOV GP) and Zika envelope (ZIKA E) proteins, the ones that are instrumental in the fusion with the host cell. “This computer modeling approach allowed us to generate atomic-level models of EBOV GP and ZIKV E proteins in the fusion state for the first time,” said Anna Lappala, a postdoctoral fellow at the Center for Nonlinear Studies and the Theoretical Biology & Biophysics Group at Los Alamos. “These models, together with the knowledge of the envelope proteins in the pre-fusion state, allowed us to study the function of neutralizing antibodies as they bind to the viral envelope proteins.” Read the published scientific paper here: http://www.mdpi.com/2218-273X/8/2/25/pdf

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2015

Challenges at the Frontiers of Matter and Energy: Transformative Opportunities for Discovery Science

FIVE TRANSFORMATIVE OPPORTUNITIES FOR DISCOVERY SCIENCE As a result of this effort, it has become clear that the progress made to date on the five Grand Challenges has created a springboard for seizing five new Transformative Opportunities that have the potential to further transform key technologies involving matter and energy. These five new Transformative Opportunities and the evidence supporting them are discussed in this new report, “Challenges at the Frontiers of Matter and Energy: Transformative Opportunities for Discovery Science.” Mastering Hierarchical Architectures and Beyond-Equilibrium Matter Complex materials and chemical processes transmute matter and energy, for example from CO2 and water to chemical fuel in photosynthesis, from visible light to electricity in solar cells and from electricity to light in light emitting diodes (LEDs) Such functionality requires complex assemblies of heterogeneous materials in hierarchical architectures that display time-dependent away-from-equilibrium behaviors. Much of the foundation of our understanding of such transformations however, is based on monolithic single- phase materials operating at or near thermodynamic equilibrium. The emergent functionalities enabling next-generation disruptive energy technologies require mastering the design, synthesis, and control of complex hierarchical materials employing dynamic far-from-equilibrium behavior. A key guide in this pursuit is nature, for biological systems prove the power of hierarchical assembly and far- from-equilibrium behavior. The challenges here are many: a description of the functionality of hierarchical assemblies in terms of their constituent parts, a blueprint of atomic and molecular positions for each constituent part, and a synthesis strategy for (a) placing the atoms and molecules in the proper positions for the component parts and (b) arranging the component parts into the required hierarchical structure. Targeted functionality will open the door to significant advances in the harvesting, transforming (e.g., reducing CO2, splitting water, and fixing nitrogen), storing, and use of energy to create new materials, manufacturing processes, and technologies—the lifeblood of human societies and economic growth. Beyond Ideal Materials and Systems: Understanding the Critical Roles of Heterogeneity, Interfaces, and Disorder Real materials, both natural ones and those we engineer, are usually a complex mixture of compositional and structural heterogeneities, interfaces, and disorder across all spatial and temporal scales. It is the fluctuations and disorderly states of these heterogeneities and interfaces that often determine the system’s properties and functionality. Much of our fundamental scientific knowledge is based on “ideal” systems, meaning materials that are observed in “frozen” states or represented by spatially or temporally averaged states. Too often, this approach has yielded overly simplistic models that hide important nuances and do not capture the complex behaviors of materials under realistic conditions. These behaviors drive vital chemical transformations such as catalysis, which initiates most industrial manufacturing processes, and friction and corrosion, the parasitic effects of which cost the U.S. economy billions of dollars annually. Expanding our scientific knowledge from the relative simplicity of ideal, perfectly ordered, or structurally averaged materials to the true complexity of real-world heterogeneities, interfaces, and disorder should enable us to realize enormous benefits in the materials and chemical sciences, which translates to the energy sciences, including solar and nuclear power, hydraulic fracturing, power conversion, airframes, and batteries. Harnessing Coherence in Light and Matter Quantum coherence in light and matter is a measure of the extent to which a wave field vibrates in unison with itself at neighboring points in space and time. Although this phenomenon is expressed at the atomic and electronic scales, it can dominate the macroscopic properties of materials and chemical reactions such as superconductivity and efficient photosynthesis. In recent years, enormous progress has been made in recognizing, manipulating, and exploiting quantum coherence. This progress has already elucidated the role that symmetry plays in protecting coherence in key materials, taught us how to use light to manipulate atoms and molecules, and provided us with increasingly sophisticated techniques for controlling and probing the charges and spins of quantum coherent systems. With the arrival of new sources of coherent light and electron beams, thanks in large part to investments by the U.S. Department of Energy’s Office of Basic Energy Sciences (BES), there is now an opportunity to engineer coherence in heterostructures that incorporate multiple types of materials and to control complex, multistep chemical transformations. This approach will pave the way for quantum information processing and next-generation photovoltaic cells and sensors. Revolutionary Advances in Models, Mathematics, Algorithms, Data, and Computing Science today is benefiting from a convergence of theoretical, mathematical, computational, and experimental capabilities that put us on the brink of greatly accelerating our ability to predict, synthesize, and control new materials and chemical processes, and to understand the complexities of matter across a range of scales. Imagine being able to chart a path through a vast sea of possible new materials to find a select few with desired properties. Instead of the time-honored forward approach, in which materials with desired properties are found through either trial-and-error experiments or lucky accidents, we have the opportunity to inversely design and create new materials that possess the properties we desire. The traditional approach has allowed us to make only a tiny fraction of all the materials that are theoretically possible. The inverse design approach, through the harmonious convergence of theoretical, mathematical, computational, and experimental capabilities, could usher in a virtual cornucopia of new materials with functionalities far beyond what nature can provide. Similarly, enhanced mathematical and computational capabilities significantly enhance our ability to extract physical and chemical insights from vastly larger data streams gathered during multimodal and multidimensional experiments using advanced characterization facilities. Exploiting Transformative Advances in Imaging Capabilities across Multiple Scales Historically, improvements in imaging capabilities have always resulted in improved understanding of scientific phenomena. A prime challenge today is finding ways to reconstruct raw data, obtained by probing and mapping matter across multiple scales, into analyzable images. BES investments in new and improved imaging facilities, most notably synchrotron x-ray sources, free-electron lasers, electron microscopes, and neutron sources, have greatly advanced our powers of observation, as have substantial improvements in laboratory- scale technologies. Furthermore, BES is now planning or actively discussing exciting new capabilities. Taken together, these advances in imaging capabilities provide an opportunity to expand our ability to observe and study matter from the 3D spatial perspectives of today to true “4D” spatially and temporally resolved maps of dynamics that allow quantitative predictions of time-dependent material properties and chemical processes. The knowledge gained will impact data storage, catalyst design, drug delivery, structural materials, and medical implants, to name just a few key technologies. ENABLING SUCCESS Seizing each of these five Transformative Opportunities, as well as accelerating further progress on Grand Challenge research, will require specific, targeted investments from BES in the areas of synthesis, meaning the ability to make the materials and architectures that are envisioned; instrumentation and tools, a category that includes theory and computation; and human capital, the most important asset for advancing the Grand Challenges and Transformative Opportunities. While “Challenges at the Frontiers of Matter and Energy: Transformative Opportunities for Discovery Science” could be viewed as a sequel to the original Grand Challenges report, it breaks much new ground in its assessment of the scientific landscape today versus the scientific landscape just a few years ago. In the original Grand Challenges report, it was noted that if the five Grand Challenges were met, our ability to direct matter and energy would be measured only by the limits of human imagination. This new report shows that, prodded by those challenges, the scientific community is positioned today to seize new opportunities whose impacts promise to be transformative for science and society, as well as dramatically accelerate progress in the pursuit of the original Grand Challenges.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2010

High-power microwave LDMOS transistors for wireless data transmission technologies (Review)

The fields of the application, structure, fabrication, and packaging technology of high-power microwave LDMOS transistors and the main advantages of these devices were analyzed. Basic physical parameters and some technology factors were matched for optimum device operation. Solid-state microwave electronics has been actively developed for the last 10-15 years. Simultaneously with improvement of old devices, new devices and structures are actively being adopted and developed and new semiconductor materials are being commercialized. Microwave LDMOS technology is in demand in such fields as avionics, civil and military radars, repeaters, base stations of cellular communication systems, television and broadcasting transmitters, and transceivers for high-speed wireless computer networks (promising Wi-Fi and Wi-Max standards).

36 MATERIALS SCIENCE↗
DOE OSTI2017

Diversity and defense-in-depth analysis for I and C systems of research reactors: A case study on two research reactors - 376

This paper provides the analysis results of the diversity and defense-in-depth (D3) applied to two research reactors designed by the Korea Atomic Energy Research Institute. The D3 assessments for instrumentation and control (I and C) systems of two research reactors are performed according to the guidelines documented in NUREG/CR-6303. The I and C systems are classified into four echelons of defense: control echelon, reactor trip echelon, engineered safety feature (ESF) actuation echelon, and monitoring and indication echelon. The diversity between the echelons of defense is assessed to determine that a single software common cause failure (CCF) does not impair the functions of multiple echelons of defense. In addition, the effects of a software CCF of the reactor protection system on the design basis events of the research reactors are analyzed by assuming that the automated reactor trip and ESF actuation functions of the reactor protection system are inoperable due to the software CCF. From the D3 analyses for the research reactors, it is verified that (1) adequate diversity has been provided, (2) adequate defense-in-depth has been provided, and (3) the displays and manual controls for critical safety functions initiated by operator action are diverse from digital systems used in the automatic portion of the protection system. (authors)

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2022

Stealthy Cyber Anomaly Detection On Large Noisy Multi-material 3D Printer Datasets Using Probabilistic Models

As Additive Layer Manufacturing (ALM) becomes pervasive in industry, its applications in safety critical component manufacturing are being explored and adopted. However, ALM's reliance on embedded computing renders it vulnerable to tampering through cyber-attacks. Sensor instrumentation of ALM devices allows for rigorous process and security monitoring, but also results in a massive volume of noisy data for each run. As such, in-situ, near-real-time anomaly detection is very challenging. The ideal algorithm for this context is simple, computationally efficient, minimizes false positives, and is accurate enough to resolve small deviations. In this paper, we present a probabilistic-model-based approach to address this challenge. To test our approach, we analyze current measurements from a polymer composite 3D printer during emulated tampering attacks. Our results show that our approach can consistently and efficiently locate small changes in the presence of substantial operational noise.

Yoginath, Srikanth↗
DOE OSTI2014

Dynamical modeling of tidal streams

I present a new framework for modeling the dynamics of tidal streams. The framework consists of simple models for the initial action-angle distribution of tidal debris, which can be straightforwardly evolved forward in time. Taking advantage of the essentially one-dimensional nature of tidal streams, the transformation to position-velocity coordinates can be linearized and interpolated near a small number of points along the stream, thus allowing for efficient computations of a stream's properties in observable quantities. I illustrate how to calculate the stream's average location (its 'track') in different coordinate systems, how to quickly estimate the dispersion around its track, and how to draw mock stream data. As a generative model, this framework allows one to compute the full probability distribution function and marginalize over or condition it on certain phase-space dimensions as well as convolve it with observational uncertainties. This will be instrumental in proper data analysis of stream data. In addition to providing a computationally efficient practical tool for modeling the dynamics of tidal streams, the action-angle nature of the framework helps elucidate how the observed width of the stream relates to the velocity dispersion or mass of the progenitor, and how the progenitors of 'orphan' streams could be located. The practical usefulness of the proposed framework crucially depends on the ability to calculate action-angle variables for any orbit in any gravitational potential. A novel method for calculating actions, frequencies, and angles in any static potential using a single orbit integration is described in the Appendix.

79 ASTRONOMY AND ASTROPHYSICS↗