SEARCH · Engineering Papers
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.
The following information packages present the radiological data as well as physical properties of the four Flanged Tritium Waste Containers currently stored at TA-54, Area G. Also included are summaries of the contents of each container (including waste logs) as well as photographs of the inner and outer portions of each container.
The following information packages present the radiological data as well as physical properties of the four Flanged Tritium Waste Containers currently stored at TA-54, Area G. Also included are summaries of the contents of each container (including waste logs) as well as photographs of the inner and outer portions of each container.
The following information packages present the radiological data as well as physical properties of the four Flanged Tritium Waste Containers currently stored at TA-54, Area G. Also included are summaries of the contents of each container (including waste logs) as well as photographs of the inner and outer portions of each container.
The following information packages present the radiological data as well as physical properties of the four Flanged Tritium Waste Containers currently stored at TA-54, Area G. Also included are summaries of the contents of each container (including waste logs) as well as photographs of the inner and outer portions of each container.
In 1963, the U.S. Department of Energy (DOE) (formerly the Atomic Energy Commission [AEC]), implemented Operation Roller Coaster on the Tonopah Test Range (TTR) and an adjacent area of the Nevada Test and Training Range (NTTR) (formerly the Nellis Air Force Range). This operation resulted in radionuclide-contaminated soils at the Double Tracks site and Clean Slate I, II, and III sites. This report documents observations made during ongoing monitoring of radiological, meteorological, and dust conditions at stations installed adjacent to the Clean Slate sites and at the TTR Sandia National Laboratories (SNL) Range Operations Center (ROC). The main objectives of the monitoring effort are to support the DOE Environmental Management Nevada Program (EM NV) in the safe remediation of the environmental legacy of nuclear device development and testing by determining if wind blowing across the Clean Slate sites is transporting particles of radionuclide-contaminated soil beyond the physical and administrative boundaries of the sites, and providing information for designing long-term monitoring of the sites. The monitoring program in 2019 included five stations. Station 400, located within TTR Area 3 and near the ROC, monitors conditions near the local workforce center. Stations 401 and 403 are located on the northern and southeastern perimeter fence lines, respectively, of the Clean Slate III site. Stations 404 and 405 are along the northern and eastern boundary fence, respectively, at Clean Slate II. The stations are generally downwind of the contaminated areas during either northwesterly or southerly winds, which are the predominant wind directions.
In 1963, the U.S. Department of Energy (DOE) (formerly the Atomic Energy Commission [AEC]), implemented Operation Roller Coaster on the Tonopah Test Range (TTR) and an adjacent area of the Nevada Test and Training Range (NTTR) (formerly the Nellis Air Force Range). This operation resulted in radionuclide-contaminated soils at the Double Tracks site and Clean Slate I, II, and III sites. This report documents observations made during ongoing monitoring of radiological, meteorological, and dust conditions at stations installed adjacent to the Clean Slate sites and at the TTR Sandia National Laboratories (SNL) Range Operations Center (ROC). The monitoring effort supports the DOE Office of Legacy Management in its stewardship of the Clean Slate sites as part of their mission to protect human health and the environment at sites of former nuclear device development and testing. Specific monitoring objectives include determining if wind blowing across the Clean Slate sites is transporting particles of radionuclide-contaminated soil beyond the physical and administrative boundaries of the sites, and providing information for planning long-term stewardship of the sites.
Photonic Doppler Velocimetry (PDV) is a diagnostic commonly used in shock physics and dynamic compression experiments to reliably get velocity information from experiments. In PDV systems, a common method of reducing experimental cost is to use time and frequency multiplexing to increase the number of PDV probes. With time multiplexing, interference between probes is a frequent problem. In this report, we look at using a high-speed optical switch to reduce this interference, including measuring the amount of interference generated to determine if it has the potential to affect experiments and integrating a time multiplexing system into an experiment. We find that, when applied to PDV systems, there is approximately (-23.4 ± 0.9) dB of interference measured in the short time Fourier transform between switch inputs. When an optical switch based time multiplexing system was integrated into a dynamic compression experiment, the system was able to successfully combine the signals from four different PDV probes onto a single optical cable without unacceptable levels of interference in the spectrogram. An optical switch based time multiplexing system appears to be a promising method for reducing the cost of fielding larger numbers of PDV probes in an experiment.
This work aimed to apply Sandia’s expertise in metallurgy and modeling to enable the use of hybrid laser arc welding for building nuclear reactor containment structures, via a collaboration with Holtec International. Experimental observations were coupled with finite element analysis to resolve microstructure development, mechanical properties, distortion, and residual stress in welds relevant to the production of the Holtec SMR-160. High residual stresses were observed in welds that were not subjected to preheat. Meanwhile, the microstructure of the welds generally exhibited a narrow heat affected zone relative to conventional arc welds. FEA appeared to be effective in simulating the thermal/mechanical conditions that occur during hybrid laser arc welding of simplified and instrumented test welds. Subsequently, FEA was used to perform sensitivity analyses for various weld geometries that would be prohibitively costly to assess with physical experiments. Insights from the study were used to inform Holtec’s welding process, and successful production welds were performed in 2025.
The research grant addressed in this final report began its most recent 3-year segment June 1, 2022 and terminated May 31, 2025. The work funded by the grant supported our group at the Massachusetts Institute of Technology (MIT) in its collaboration on the nEDM@SNS (SNS refers to the Spallation Neutron Source) project at the Oak Ridge National Laboratory (ORNL). This project had as its goal the measurement of a non-zero value of the electric dipole moment of the neutron (nEDM). If indeed the neutron has a non-zero electric dipole moment, it will tell us very important information about the Standard Model of Nuclear and Particle Physics. The Standard Model is remarkably successful in describing much of the world we live in, but we know it is not complete. A non-zero nEDM would indicate the existence of strong CP violation, which would be a major addition to the Standard Model.
The Hadron Calorimeter (HCAL) in the Compact Muon Solenoid (CMS) experiment at the Large Hadron Collider (LHC) was recently upgraded for Run 3 to introduce depth segmentation and online timing measurements, expanding the physics capabilities. In particular, the augmentation of the calorimeter information at the hardware trigger level enables quick identification and recording of long-lived particle decays using lower thresholds on energy-based event quantities. The depth segmentation and online timing capabilities are utilized in novel HCAL-based hardware-level triggers to identify displaced and delayed long-lived particles (LLPs), either decaying inside the calorimeter volume or arriving at a delayed time. This increases the sensitivity to LLP decays occurring up to almost 6 m from the collision point. This two-pronged calorimeter trigger approach leverages the new capabilities of the CMS HCAL to expand the phase space accessible in ongoing LLP searches. These triggers were deployed for Run 3 of the LHC, beginning data-taking in 2022, and we review the trigger implementation and calibration.
Solar energetic particles (SEPs) are an essential source of space radiation, and are hazardous for humans in space, spacecraft, and technology in general. In this paper, we propose a deep-learning method, specifically a bidirectional long short-term memory (biLSTM) network, to predict if an active region (AR) would produce an SEP event given that (i) the AR will produce an M- or X-class flare and a coronal mass ejection (CME) associated with the flare, or (ii) the AR will produce an M- or X-class flare regardless of whether or not the flare is associated with a CME. The data samples used in this study are collected from the Geostationary Operational Environmental Satellite's X-ray flare catalogs provided by the National Centers for Environmental Information. We select M- and X-class flares with identified ARs in the catalogs for the period between 2010 and 2021, and find the associations of flares, CMEs, and SEPs in the Space Weather Database of Notifications, Knowledge, Information during the same period. Each data sample contains physical parameters collected from the Helioseismic and Magnetic Imager on board the Solar Dynamics Observatory. Experimental results based on different performance metrics demonstrate that the proposed biLSTM network is better than related machine-learning algorithms for the two SEP prediction tasks studied here. We also discuss extensions of our approach for probabilistic forecasting and calibration with empirical evaluation
Abstract The classical pathway (CP) is a potent mechanism for initiating complement activity and is a driver of pathology in many complement-mediated diseases. The CP is initiated via activation of complement component C1, which consists of the pattern recognition molecule C1q bound to a tetrameric assembly of proteases C1r and C1s. Enzymatically active C1s provides the catalytic basis for cleavage of the downstream CP components, C4 and C2, and is therefore an attractive target for therapeutic intervention in CP-driven diseases. Although an anti-C1s mAb has been Food and Drug Administration approved, identifying small-molecule C1s inhibitors remains a priority. In this study, we describe 6-(4-phenylpiperazin-1-yl)pyridine-3-carboximidamide (A1) as a selective, competitive inhibitor of C1s. A1 was identified through a virtual screen for small molecules that interact with the C1s substrate recognition site. Subsequent functional studies revealed that A1 dose-dependently inhibits CP activation by heparin-induced immune complexes, CP-driven lysis of Ab-sensitized sheep erythrocytes, CP activation in a pathway-specific ELISA, and cleavage of C2 by C1s. Biochemical experiments demonstrated that A1 binds directly to C1s with a K d of ∼9.8 μM and competitively inhibits its activity with an inhibition constant (K i) of ∼5.8 μM. A 1.8-Å-resolution crystal structure revealed the physical basis for C1s inhibition by A1 and provided information on the structure–activity relationship of the A1 scaffold, which was supported by evaluating a panel of A1 analogs. Taken together, our work identifies A1 as a new class of small-molecule C1s inhibitor and lays the foundation for development of increasingly potent and selective A1 analogs for both research and therapeutic purposes.
Dilute combustion with exhaust gas recirculation (EGR) in spark-ignition engines presents a cost-effective method for achieving higher levels of engine efficiency. At high levels of EGR, however, cycle-to-cycle variability (CCV) of the combustion process is exacerbated by sporadic occurrences of misfires and partial burns. Previous studies have shown that temporal deterministic patterns emerge at such conditions and certain combustion cycles have a significant influence over future events. Due to the complexity of the combustion process and the nature of CCV, harnessing all the deterministic information for control purposes has remained challenging even with physics based 0-D, 1-D, and high-fidelity computational fluid dynamics (CFD) models. In this study, we present a data-driven approach to optimize the combustion process by controlling CCV adjusting the cycle-to-cycle fuel injection quantity. Readily available data from in-cylinder pressure was used to train a spiking neural network (SNN) which learns the optimal way to manage fuel injection in order to reduce CCV while maintaining acceptable levels of fuel consumption. SNNs are particularly well suited for powertrain control applications due to their ability to be deployed on FPGA-based neuromorphic hardware which are small, inexpensive, and have a low power demand. The high-performance computing (HPC) resources of Oak Ridge National Laboratory were used to run an evolutionary-based training approach for choosing the best SNN configuration that minimizes the size of the network while achieving the desired goal. The neuromorphic hardware with the optimized SNN deployed was connected to the rapid prototyping engine control system for real-time control implementation and tested on a single cylinder version of a GM LNF 4-cylinder engine. The results show a significant reduction of CCV with a small percentage of additional fuel used to stabilize the charge.
The development of detectors that provide high resolution in four dimensions has attracted wide-spread interest in the scientific community for several applications in high-energy physics, nuclear physics, medical imaging, mass spectroscopy as well as quantum information. In addition to high time resolution and thanks to the AC-coupling of the electrodes, LGAD silicon sensors can provide high resolution in the measurement of spatial coordinates of an incident minimum ionizing particle. Such AC-coupled LGADs, also known as AC-LGADs, are therefore considered as candidates for future detectors to provide 4-dimensional measurements in a single sensing device with 100$\%$ fill factor. This article presents the first characterization of an AC-LGAD sensor with a proton beam of 120 GeV momentum at Fermilab. The sensor consists of strips with 80 $\mu$m width, fabricated at Brookhaven National Laboratory. The signal properties, efficiency, spatial, and time resolution are presented. The experimental results show that the time resolution of such an AC-LGAD is compatible to standard LGADs with similar gain, and that AC-LGADs can be segmented with fine pitches as standard strip or pixel detectors.
Abstract— Cyber-physical systems (CPS) are a key part of modern infrastructure. However, satellites are often excluded from discussions of CPS, despite their evident cyber-physical attributes, such as the ability to respond to information from its surroundings and adjust accordingly. Through a brief examination of relevant literature, we prove that satellites are CPSs, specifically through payload-bus interactions that occur on a satellite. By establishing satellites as a form of CPS, we hope to encourage its inclusion in discussions for bettering the security of such systems.
Nitrogen vacancy (NV) centers, optically active atomic defects in diamond, have attracted tremendous interest for quantum sensing, network, and computing applications due to their excellent quantum coherence and remarkable versatility in a real, ambient environment. One of the critical challenges to develop NV-based quantum operation platforms results from the difficulty in locally addressing the quantum spin states of individual NV spins in a scalable, energy-efficient manner. Here, we report electrical control of the coherent spin rotation rate of a single-spin qubit in NV-magnet based hybrid quantum systems. By utilizing electrically generated spin currents, we are able to achieve efficient tuning of magnetic damping and the amplitude of the dipole fields generated by a micrometer-sized resonant magnet, enabling electrical control of the Rabi oscillation frequency of NV spins. Our results highlight the potential of NV centers in designing functional hybrid solid-state systems for next-generation quantum-information technologies. The demonstrated coupling between the NV centers and the propagating spin waves harbored by a magnetic insulator further points to the possibility to establish macroscale entanglement between distant spin qubits.
Particle physics, with its reliance on shared instruments, projects spanning decades, highly international character, and research teams that range from a few individuals to thousands, has a special need for inclusive, coherent planning. In the United States in recent decades, the High Energy Physics Advisory Panel has recommended priorities to the Department of Energy and the National Science Foundation, informed in part by wide-ranging community-based studies.
In AdS/CFT, observables on the boundary are invariant under renormalization group (RG) flow in the bulk. In this paper, we study holographic entanglement entropy under bulk RG flow and find that it is indeed invariant. We focus on tree-level RG flow, where massive fields in a UV theory are integrated out to give the IR theory. We explicitly show that in several simple examples, holographic entanglement entropy calculated in the UV theory agrees with that calculated in the IR theory. Moreover, we give an argument for this agreement to hold for general tree-level RG flow. Along the way, we generalize the replica method of calculating holographic entanglement entropy to bulk theories that include matter fields with nonzero spin.