Search NASA⌕ Search

SEARCH · Search NASA

Results for “Computing, Instrumentation”

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 829 records · Page 46
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Spatiotemporal features of traffic help reduce automatic accident detection time

Quick and reliable automatic detection of traffic accidents is of paramount importance to save human lives in transportation systems. However, automatically detecting when accidents occur has proven challenging, and minimizing the time to detect accidents (TTDA) by using traditional features in machine learning (ML) classifiers has plateaued. We hypothesize that accidents affect traffic farther from the accident location than previously reported. Therefore, leveraging traffic signatures from neighboring sensors that are adjacent to accidents should help improve their detection. We confirm this hypothesis by using verified ground-truth accident data, traffic data from radar detection system sensors, and light and weather conditions and show that we can minimize the TTDA while maximizing classification performance by considering spatiotemporal features of traffic. Specifically, we compare the performance of different ML classifiers (i.e, logistic regression, random forest, and XGBoost) when controlling for different numbers of neighboring sensors and TTDA horizons. We use data from interstates 75 and 24 in the metropolitan area that surrounds Chattanooga, TN. Our results show that the XGBoost classifier produces the best results by detecting accidents as quickly as 1.0 min after their occurrence with an area under the receiver operating characteristic curve of up to 83% and an average precision of up to 49%. We describe limitations, open challenges, and how the proposed framework can be used for quicker operational accident detection.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2022

An arbitrary Lagrange-Eulerian investigation of HRAM shallow jet pre-spurt formation and time sensitivities to impact plate dynamics

With dry bay fires persisting as a significant contributor to aircraft vulnerability despite chronicled developments in survivability technologies, an accurate fire prediction capability remains paramount for credible vulnerability assessments. Physics based modeling of the hydrodynamic ram (HRAM) fluid deposition process is a key component of such capability, wherein capturing the first instance of fluid spurt, referred to herein as shallow jet spurts, is a core focus. Such pre-spurts as they have been formerly identified have only been witnessed sporadically in HRAM spurt experiments. In this work, ALE3D, a first-principles multi-physics code was employed to model the shallow jet spurt phenomenon with spherical projectiles impacting water-filled tanks faced with aluminum panels, such that the underlying physics and sensitivities could be explored. Development and verification of the 2D axisymmetric model is described relative to trends observed in a prior experimental campaign. Results from the verified model suggest shallow jet spurts have at least a quadratic sensitivity to the fundamental vibrational mode of the impact plate across impact velocities 610–1829 m/s (2000–6000 ft/s). It is further explained how shallow jet spurts are arrested for impacts at the two extremes of plate rigidity. This research constitutes the first fluid-structure modeling of shallow jet spurts which future three-dimensional analyses will expound upon.

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

Evaluating direct vessel injection accident-event progression of AP1000 and key figures of merit to support the design and development of water-cooled small modular reactors

The passive safety systems (PSSs) within water-cooled reactors are meticulously engineered to function autonomously, requiring no external power source or manual intervention. They depend exclusively on inherent natural forces and the fundamental principles of reactor physics, such as gravity, natural convection, and phase changes, to manage, alleviate, and avert the release of radioactive materials into the environment during accident scenarios like a loss-of-coolant accident (LOCA). PSSs are already integrated into such operating commercial reactors as the Advanced Pressurized Reactor-1000 MWe (AP1000) and the Water-Water Energetic Reactor-1200 MWe (WWER-1200) are adopted in most of the upcoming small modular reactor (SMR) designs. Examples of water-cooled SMR PSSs are the passive emergency core-cooling system (ECCS), passive containment cooling system (PCCS), and passive decay-heat removal system, the designs of which vary based on reactor system-design requirements. However, understanding the accident-event progression and phases of a LOCA is pivotal for adopting a specific PSS for a new SMR design. This study covers the accident-event progression for direct vessel injection (DVI) small-break loss-of-coolant accident (SB-LOCA), associated physics phenomena, knowledge gaps, and important figures of merit (FOMs) that may need to be evaluated and assessed to validate thermal-hydraulics models with an available experimental dataset to support new SMR design and development.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2021

A probability density function model describing height estimation uncertainty due to image pixel intensity noise in digital fringe projection measurements

Digital fringe projection is a surface-profiling technique used for highly accurate non-contact measurements. As with any measurement technique, a variety of sources degrade to the measurement accuracy of the method. Here, this paper presents an analytically-derived probability density function that explicitly models the surface height measurement error due to inevitable phase measurement error, and it includes the specific case of pixel noise inducing the phase measurement error that ultimately leads to the height estimation error. The accuracy of the model was validated through Monte-Carlo simulations of resultant height distributions subject to arbitrarily correlated pixel intensity noise and experimental digital fringe projection measurements where the pixel-by-pixel height uncertainty estimations were compared to the predictions of the derived model.

42 ENGINEERING↗
DOE OSTI2023

Accident event progression, gaps, and key performance indicators for steam generator tube rupture events in water-cooled SMRs: A review

According to historical records of reactor-related incidents, a steam generator tube rupture (SGTR) is one of the most common occurrences at operating pressurized water reactors (PWRs). Such design-basis accidents (DBAs) could lead to a direct path for radionuclides to be released to the atmosphere via the safety and relief valves, making assessments of radionuclide discharge from operating nuclear power plants (NPPs) into the environment crucial for ensuring safety. In such analyses, the primary focus has been on the extent of radioactive release, not the potential damage to the core. Moreover, significant and timely intervention by operators is needed during the initial stages of SGTRs in order to avert overfilling of the SGs, as well as to restrict the dissemination of radioactive materials. Determining the event sequence and phases that occur during an SGTR incident in an advanced passive (e.g., AP1000) water-cooled nuclear reactor is crucial for implementing effective passive safety systems (PSSs) for a specific water-cooled small modular reactor (SMR) design. Here this study provides a comprehensive review of the accident event progression, associated physical phenomena, knowledge gaps, and key performance indicators that must be evaluated and assessed in thermal-hydraulics models, based on relevant test data, respectively.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2017

Recovering fine details from under-resolved electron tomography data using higher order total variation ℓ 1 regularization

Over the last decade or so, reconstruction methods using ℓ 1 regularization, often categorized as compressed sensing (CS) algorithms, have significantly improved the capabilities of high fidelity imaging in electron tomography. The most popular ℓ 1 regularization approach within electron tomography has been total variation (TV) regularization. In addition to reducing unwanted noise, TV regularization encourages a piecewise constant solution with sparse boundary regions. In this paper we propose an alternative ℓ 1 regularization approach for electron tomography based on higher order total variation (HOTV). Like TV, the HOTV approach promotes solutions with sparse boundary regions. In smooth regions however, the solution is not limited to piecewise constant behavior. We demonstrate that this allows for more accurate reconstruction of a broader class of images – even those for which TV was designed for – particularly when dealing with pragmatic tomographic sampling patterns and very fine image features. In conclusion, we develop results for an electron tomography data set as well as a phantom example, and we also make comparisons with discrete tomography approaches.

47 OTHER INSTRUMENTATION↗
DOE OSTI2017

Rapid alignment of nanotomography data using joint iterative reconstruction and reprojection

As x-ray and electron tomography is pushed farther into the nanoscale, the limitations of rotation stages become more apparent leading to challenges in the alignment of the acquired projection images. Here we present an approach for rapid post-acquisition alignment of these projections to obtain high quality 3D images. Our approach is based on a joint estimation of alignment errors, and the object, using an iterative refinement procedure. We characterize convergence in simulations, and show its application in x-ray and electron nanotomography.

47 OTHER INSTRUMENTATION↗
DOE OSTI2022

Magnetic field mapping of inaccessible regions using physics-informed neural networks

A difficult problem concerns the determination of magnetic field components within an experimentally inaccessible region when direct field measurements are not feasible. In this paper, we propose a new method of accessing magnetic field components using non-disruptive magnetic field measurements on a surface enclosing the experimental region. Magnetic field components in the experimental region are predicted by solving a set of partial differential equations (Ampere’s law and Gauss’ law for magnetism) numerically with the aid of physics-informed neural networks (PINNs). Prediction errors due to noisy magnetic field measurements and small number of magnetic field measurements are regularized by the physics information term in the loss function. We benchmark our model by comparing it with an older method. The new method we present will be of broad interest to experiments requiring precise determination of magnetic field components, such as searches for the neutron electric dipole moment.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2020

Evolution of the Data Quality Monitoring and Prompt Processing System in the proto DUNE-SP experiment

The DUNE Collaboration currently operates an experimental program based at CERN which includes a beam test and an extended cosmic ray run of two large-scale prototypes of the massive Liquid Argon Time Projection Chamber (LArTPC) for the DUNE Far Detector. The volume of data collected by the single-phase prototype (protoDUNE-SP) amounts to 3PB and the sustained rate of data sent to mass storage is O(100) MB/s. Data Quality Monitoring was implemented by directing a fraction of the data stream to the protoDUNE prompt processing system (p3s) which is optimized for continuous low-latency calculation of the vital detector metrics and various graphics including event displays. It served a crucial role throughout the life cycle of the experiment. We present our experience in leveraging the CERN computing environment and operating the system over an extended period of time, while adapting to evolving requirements and computing platform.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2011

Study Regarding Electromagnetic Radiation Exposure Generated By Mobile Phone

Number of mobile phone users reached to 5 billion subscribers in 2010 [ABI Research, 2010]. A large number of studies illustrated the public concern about adverse effects of mobile phone radiation and possible health hazards. Position of mobile phone use in close proximity to the head leads the main radiation between the hand and the head. Many investigations studying the possible effects of mobile phone exposure, founded no measurable effects of short-term mobile phone radiation, and there was no evidence for the ability to perceive mobile phone EMF in the general population. In this study, field radiation measurements were performed on different brand and different models of mobile phones in active mode, using an EMF RF Radiation Field Strength Power Meter 1 MHz-8 GHz. The study was effectuated on both the 2G and 3G generations phones connected to the providers operating in the frequency range 450 MHz-1800 MHz. There were recorded values in outgoing call and SMS mode, incoming call and SMS mode. Results were compared with ICNIRP guidelines for exposure to general public.

61 RADIATION PROTECTION AND DOSIMETRY↗
DOE OSTI2017

Machine learning to analyze images of shocked materials for precise and accurate measurements

A supervised machine learning algorithm, called locally adaptive discriminant analysis (LADA), has been developed to locate boundaries between identifiable image features that have varying intensities. LADA is an adaptation of image segmentation, which includes techniques that find the positions of image features (classes) using statistical intensity distributions for each class in the image. In order to place a pixel in the proper class, LADA considers the intensity at that pixel and the distribution of intensities in local (nearby) pixels. This paper presents the use of LADA to provide, with statistical uncertainties, the positions and shapes of features within ultrafast images of shock waves. We demonstrate the ability to locate image features including crystals, density changes associated with shock waves, and material jetting caused by shock waves. This algorithm can analyze images that exhibit a wide range of physical phenomena because it does not rely on comparison to a model. LADA enables analysis of images from shock physics with statistical rigor independent of underlying models or simulations.

47 OTHER INSTRUMENTATION↗
DOE OSTI2019

Advanced Spectral Analysis Program

Advanced Spectral Analysis Program is a LabVIEW-based program intended for rapid and accurate analysis of large sets of spectral data. It can handle a range of different types of data including angle-resolved and energy-dispersive powder diffraction and Raman spectra. We present it here with a focus on high-temperature high-pressure powder diffraction. The program contains a novel graphical user interface that allows rapid manual fitting and indexing of peaks which require precise fitting ranges and includes tools for fitting any Bravais lattice and arbitrary user-defined multivariate equations of state. The program allows the user to simultaneously view and manipulate multiple data sets from an experiment. The user can save and load analysis progress at any point, allowing for repeatable calculations to be performed, and to allow the fast comparison of different analysis parameters.

47 OTHER INSTRUMENTATION↗
DOE OSTI2022

A scintillator attenuation spectrometer for intense gamma-rays

A new type of compact high-resolution high-sensitivity gamma-ray spectrometer for short-pulse intense gamma-rays (250 keV to 50 MeV) has been developed by combining the principles of scintillators and attenuation spectrometers. The first prototype of this scintillator attenuation spectrometer (SAS) was tested successfully in Trident laser experiments at LANL. Later versions have been used extensively in the Texas Petawatt laser experiments in Austin, TX, and more recently in OMEGA-EP laser experiments at LLE, Rochester, NY. The SAS is particularly useful for high-repetition-rate laser applications. Furthermore, we give a concise description of the design principles, capabilities, and sample preliminary results of the SAS.

47 OTHER INSTRUMENTATION↗
DOE OSTI2022

Monte Carlo N-Particle forward modeling for density reconstruction of double shell capsule radiographs

In the Double Shell Inertial Confinement Fusion concept, characterizing the shape asymmetry of imploding metal shells is vital for understanding energy-efficient compression and radiative losses of the thermonuclear fuel. The Monte Carlo N-Particle MCNP® code forward models radiography of Double Shell capsule implosions using the Advanced Radiographic Capability at the National Ignition Facility. A procedure is developed for using MCNP to reconstruct density profiles from the radiograph image intensity. For a given Double Shell imploding target geometry, MCNP radiographs predict image contrast, which can help guide experimental design. Finally, in future work, the calculated MCNP synthetic radiographs will be compared with experimental radiographs to determine the radial and azimuthal density profiles of the Double Shell capsules.

47 OTHER INSTRUMENTATION↗
DOE OSTI2023

Digital autofocusing of a coded-aperture Laue diffraction microscope

To provide optimal depth resolution with a coded-aperture Laue diffraction microscope, an accurate position of the coded-aperture and its scanning geometry need to be known. However, finding the geometry by trial and error is a time-consuming and often challenging process because of the large number of parameters involved. In this paper, we propose an optimization approach to automate the focusing process after data is collected. Here we demonstrate the robustness and efficiency of the proposed approach with experimental data taken at a synchrotron facility.

47 OTHER INSTRUMENTATION↗
DOE OSTI2026

Analog-to-digital converter based on voltage-controlled superconducting devices

The increasing demand for cryogenic electronics in superconducting and quantum computing systems calls for ultra-energy-efficient data conversion architectures that remain functional at deep cryogenic temperatures. Here, in this work, we present the first design of a voltage-controlled superconducting flash analog-to-digital converter (ADC) based on a voltage-controlled quantum-enhanced Josephson junction field-effect transistor (JJFET). Exploiting its strong gate tunability and transistor-like behavior, the JJFET offers a scalable alternative to conventional current-controlled superconducting devices while aligning naturally with CMOS-style design methodologies. Building on our previously developed Verilog-A compact model calibrated to experimental data, we design and simulate a three-bit JJFET-based flash ADC targeted for integration within cryogenic control and readout circuitry in quantum computing. The core comparator block is realized through careful bias current selection and augmented with a three-terminal nanocryotron to precisely define reference voltages. Cascaded JJFET comparators ensure robust voltage gain, cascadability, and logic-level restoration across stages. Simulation results demonstrate accurate quantization behavior with ultra-low power dissipation, underscoring the feasibility of voltage-driven superconducting mixed-signal circuits. This work establishes a critical step toward unifying superconducting logic and data conversion, paving the way for scalable cryogenic architectures in quantum–classical co-processors, low-power artificial intelligence accelerators, and next-generation energy-constrained computing platforms.

Analog-to-digital converter↗
DOE OSTI2026

Regression Analysis with the Directed Infusion of Data

Integrating artificial intelligence and machine learning tools into industry necessitates large-scale collaborative efforts that ensure the robust and accurate execution of downstream analytics such as time series prediction, uncertainty quantification, grid optimization, and condition monitoring. However, concerns related to data privacy pervade the nuclear industry due to the proprietary nature of its data and the possibility of data leakage. Legacy techniques such as encryption often require the explicit transmission of data to trustworthy parties, thereby inviting data leakage concerns. The ideal collaboration scenario avoids the explicit dissemination of data/code while maintaining experimental fidelity, which is currently accomplished using various techniques such as trusted execution environments, homomorphic encryption, differential privacy, and multimatrix masking. These techniques, however, often necessitate a trade-off between trust, efficiency, and utility. This article extends a previously proposed technique called the directed infusion of data (DIOD) that ensures data privacy, allows for scalable obfuscation, and combats the risk of data leakage without compromising utility. The experiments discussed in this article examine a regression-type scenario using DIOD with the goal of preserving the inferential link between two variables. Using the point-kinetics equations, regression experiments compare the performance of a model trained using the original data to that of a model trained using the obfuscated data, which produced identical results. Our claim is further strengthened by an information theoretic proof and experiment, which showed that the inferential content between variables remains the same after obfuscation, thereby avoiding the required communication of the proprietary data.

47 - OTHER INSTRUMENTATION↗