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The National Security Research Center (NSRC) is Los Alamos' classified library. It houses 75+ years of scientific and engineering research, designs, procedures, videos, photos, and other reports.
Heating, ventilation, and air conditioning (HVAC) consumes a significant portion of the energy used in buildings. Much of this is wasted energy, used when buildings are either not occupied at all, or occupied well under their maximum design conditions. This project has focused on residential occupancy detection to autonomously control HVAC systems and save energy. Limitations of existing occupancy sensors include one or more of the following: (i) they employ sensors or algorithms that are not able to detect stationary occupants; (ii) they cannot classify the source of the motion (such as a pet); (iii) depending on the camera resolution and employed algorithms, they do not allow for embedded or onboard computation, and require external or cloud-based processing; (iv) many algorithms developed for camera-based systems are sensitive to lighting changes, and thus prone to missed detections or false alarms; (v) Most existing systems depend on adjustment of settings for different scenarios, complicating self-commissioning; (vi) they cannot provide high enough accuracy; (vii) they are costly; (viii) they are not battery-powered, thus limiting ease of use and installation. In this project, Syracuse University and its partner SRI have developed a low-cost, high accuracy, standalone residential occupancy sensing platform, referred to as the MicroCam, to address all of the aforementioned challenges. MicroCam can operate on typical alkaline batteries without relying on the “cloud” or external computing resources, and consists of low-power, Artificial Intelligence (AI)-based, IoT platforms. Each platform has multi-modal sensors and can process motion, audio and video data, and send binary occupancy result to a lead platform. All sensor data is processed locally on platforms, and the only transmitted data is the binary occupancy state. In addition, preliminary work has been done on images wherein occupants are not discernable. Thus, MicroCam is a standalone solution preserving privacy of the occupants.
IRSN and NCSP collaboration has been very fruitful. Despite challenges presented by COVID-19, work has been performed regularly and successfully by video conference. This includes monthly basis meeting with LLNL and LANL and specifics meetings with ORNL and SNL when needed. After two years without any IRSN participation to experiments, ISRN is looking forward to fully participating in the three proposed integral experiments scheduled in 2022. IRSN is working on several new projects including TEX-MOX and TEX-low temperatures.
This document provides guidelines on how to run Moose-based applications from the NEAMS WorkbenchGUI on the HPC platform Sawtooth located at Idaho National Laboratory. The different steps are illustrated with a multi-app example modeling a sodium-cooled fast reactor (SFR) taken from the virtual test bed (VTB) website. The workflow consists of four steps to demonstrate the capabilities of the NEAMS Workbench GUI, that covers log into Sawtooth, editing and validating input files, submission of the job to the queue and visualization of the numerical solution and the geometry. Each step is illustrated with figures in the text, and a demonstration video is also available.
This is the final report on Phase II SBIR for the development of a collaborative VR data visualization tool. In the 15 months of the Phase II, we have met and exceeded most objectives that we outlined for ourselves in the proposal. COVID19 has caused us to do some thing differently, but in fact the outcome is better for the development - we were meant to have traveled to institutions in the UK and EU to show the capabilities in April, but instead we have got into an iterative cycle in-house of weekly testing of the new features and functions. This is working well, so we will soon have the ability to share the application with collaborators in remote locations, without the need to travel. We are also making short videos of the features to share with those afar, again mitigating the need to travel and demonstrate in-person our new capabilities. That said, we are behind on getting input from the Beta testers, but when we do get their input it will be to test features and functions that are working, and not failing due to some glitch, which in turn will more likely lead to the acceptance of the product. One of our goals is to have a near-term commercial product, and we are tantalizingly close to it with commercial interest from LANL, Survice and other entities.
One of the fielding requirements for Advanced and Small Modular Reactors (AR/SMR) is the ability to support remote and autonomous operations. Autonomous Control Systems (ACS) are found on platforms such as Autonomous Space Vehicles, Cruise Missiles, and advanced driver-assistance systems. Each of these ACS implementations depends upon a set of decision support subsystems responsible for supporting Autonomous Mission Managers (names vary based upon field and author preferences). These Autonomous Mission Managers receive inputs from system sensors (e.g., LIDAR collection from an automobile travelling down a street; transients from a nuclear reactor), and perform a set of classifications (e.g., Red Traffic Light; Small Pedestrian at 10m; Load Rejection; Single Coolant Pump Trip), and then use these classifications in combination with recommendation algorithms to achieve platform goals (e.g., Stop the Vehicle at the Traffic Light, Avoid the Small Pedestrian; Trip the Reactor to prevent a Safety Event). The design, implementation, and fielding of an ACS capability will alter the cyber-attack surface such that existing risk management plans will need to be updated to include how to protect and defend against data-science and decision-support-system attack classes. These attack classes would include protection of the design and training environments where algorithm selection and testing and training data would be obvious attack vectors. These attack classes would also require an informed set of detection and response procedures to identify anomalous behaviors and document best practices for anomaly assessment and vulnerability mitigation and remediation. Last year we published a Cyber Threat Assessment Methodology for Autonomous and Remote Operations for AR/SMRs along with a companion publication on Cyber Attack and Defense Use Cases. The focus of the methodology was on describing and enumerating ACS processes, components, and functions such that security engineers could: evaluate subversion options against the target; identify threat actor attributes and capabilities derived from each subversion option; and identify security controls and response countermeasures. The Use Cases document offered detailed methodology examples including an assessment of a Military Base SMR, an Autonomous System Decision Loop, and implementation of AR/SMR Machine Learning algorithms. Our proposal at the end of last year was to focus on implementation of subversion prototypes related to the last Use Case area: AR/SMR Machine Learning (ML) Algorithms. We included six attack scenarios in our Use Cases paper: a Poisoning Attack against ML functions implemented using an FPGA; a Trojaning Attack against ML classifiers exploiting the excitability of Nuclear Engineers; a Backdooring Attack against ML Training environments to ensure persistence of an attack vector; a False Positive Evasion Attack against multi-factor Access Control Systems using clever inputs; an Inference Attack against ML models by an Insider with access to the Operational environment; and an Adversarial Reprogramming Attack against a Material Access Control Video Surveillance System. At the beginning of this year these six attack scenarios were provided to our research teams at Georgia Tech and Idaho State University and each team successfully implemented a subversion attack against a ML implementation to include transient misclassifications. While this is a notable outcome from this type of research, this paper offers the reader insight into not only how to structure and execute these types of attacks, but into the thought process behind how the researcher investigated the problem space, performed initial algorithm implementation, and the trial-and-error behind arriving at the successful subversion prototypes. We include in this paper a set of associated Scenarios on how these subversion prototypes could be implemented and an initial set of guidance for AR/SMR architects, Nuclear Regulators, and Cyber Defenders to implement awareness and defense capabilities into their current operational portfolios.
Higher radiation-hardened video cameras are needed in the operation and remote handling of equipment in nuclear reactor inspection and refueling applications. Vega Wave Systems, Inc. has developed a radiation-hardened vision system for nuclear energy applications. This vision system has been developed under several small business innovative research programs (SBIRs) from the U.S. Department of Energy, and the previous GAIN voucher program at Argonne National Laboratory (ANL) was successful in demonstrating that a prototype of the camera is radiation-hard up to at least 525 kGy (5.25 × 106 rad, Si equivalent) at dose rates of 10 kGy/hr with no measurable degradation in image quality and no measurable radiation-induced noise (RIN). Vega Wave Systems has developed a new version of the camera with more than 3X the resolution using a new design and new components, and this new design requires radiation-hardness qualification for marketplace acceptance. The program described in this report provided high radiation-hardness testing of this redesigned high-resolution vision system using the Argonne Low-Energy Accelerator Facility (LEAF). This report presents the results of irradiation tests performed on Vega Wave System’s redesigned high resolution vision system at a dose rate of 9.3 kGy/hr and up to total doses of 1823 kGy. This was an accumulation of 197 hours of irradiation. The results were excellent, providing proof of the redesigned high-resolution vision system’s immunity to high levels of radiation.
EPOC uses the Deep Dive process to discuss and analyze current and planned science, research, or education activities and the anticipated data output of a particular use case, site, or project to help inform the strategic planning of a campus or regional networking environment. This includes understanding future needs related to network operations, network capacity upgrades, and other technological service investments. A Deep Dive comprehensively surveys major research stakeholders’ plans and processes in order to investigate data management requirements over the next 5–10 years. In October of 2022, staff members from the Engagement and Performance Operations Center (EPOC) met with researchers and staff from the National Institute of Standards and Technology (NIST) for the purpose of a Deep Dive into scientific and research drivers. The goal of this activity was to help characterize the requirements for a number of campus use cases, and to enable cyberinfrastructure support staff to better understand the needs of the researchers within the community. Material for this event included the written documentation from each of the profiled research areas, documentation about the current state of technology support, and a write-up of the discussion that took place via e-mail and video conferencing. The case studies highlighted the ongoing challenges and opportunities that NIST has in supporting a cross-section of established and emerging research use cases. Each case study mentioned unique challenges which were summarized into common needs.
In the coming decade, it is anticipated that deep-sea mining activities will commence throughout the deep ocean, at depths around 4000-6000m where vast deposits of baseball-sized rocks called polymetallic nodules lay on the seabed. These nodules contain resources such as cobalt, copper and nickel which are needed to produce batteries for electric vehicles. Collector machines driving on the seabed and gathering these nodules will stir up clouds of sediment, and there is concern about how the resulting concentration of sediment in the near-bottom ocean water will affect deep and mid-ocean biology. There is currently no technology capable of comprehensively monitoring the concentrations and properties of stirred up sediment in the deep. This project addressed this critical gap by developing a deep-sea particle measurement system (Real-Time Size and Settling Velocity, RTSSV) that uses a multi-camera high-resolution video imaging system, with accompanying image processing, to measure the concentration, size distribution and settling speed of sediment. Importantly, these measurements are made in-situ, so that delicate sediment aggregates will not be disturbed, as is the case when collecting samples at depth to be measured in the lab. The instrument can be mounted on a variety of established deep-sea platforms such as moorings, AUVs and ROVs, providing data in near real time, where the key measurements need to be made—from the source of the sediment disturbance and throughout the resulting plume—vital for determining if deep-sea mining can be done in an environmentally responsible manner. The developed system (RTSSV) will possess the only such capability worldwide, giving a US-based group a substantial competitive advantage as this newly evolving global industry develops. As added value, the technology will also have widespread application to the offshore oil and gas industry, dredging industry, and to sediment science research.
This is a chronicle file from the Telestream video digitization software that the NSRC Digital Collections Group has installed on the Red. We have not gotten this software to totally function and want to be able to do a workalong with a Telestream representative. One of their technician’s said this file might be helpful to replicate the problem we see in our digitization lab. (The main pointer to the red network that is in this file are file paths. They have been scrubbed so that “valid path to mxf” replaces the real file path.) (CML stands for controlled markup language.)
This is a chronicle file from the Telestream video digitization software that the NSRC Digital Collections Group has installed on the Red. We have not gotten this software to totally function and want to be able to do a workalong with a Telestream representative. One of their technician’s said this file might be helpful to replicate the problem we see in our digitization lab. (The main pointer to the red network that is in this file are file paths. They have been scrubbed so that “valid path to mxf” replaces the real file path.)
This video shows how to install the MCNP code, version 6.2, on a Microsoft Windows 10 computer. After installation, the Xming software is installed, a quick calculation is performed, and geometry is plotted. At this point, this setup is ready for use in an MCNP classroom setting and/or for performing calculations of the viewer’s own creation.
The Ice Cryo-Encapsulation Balloon (ICEBall) field campaign was designed to sample the ice crystals that compose high-altitude cirrus with a passive device. The campaign made use of a new instrument, ICEBall, which is a balloon-borne ice crystal sampling system. The ice crystal sounding system is capable of measuring ice crystal concentration, temperature, atmospheric pressure, ice crystal habit, aerosol particle morphology, and residual composition. The 3-kg instrument is carried upwards at 5 m s-1 by a high-altitude balloon. The instrument can be cut down from the balloon at any altitude up to 20 km, and the apparatus returns to the surface by parachute. Basic measurements such as temperature and pressure are recorded onboard, and high-frequency Global Positioning System (GPS) records altitude and latitude/longitude. Ice crystal concentrations are measured through the use of a high-resolution video camera mounted on the device. Ice crystals are collected through an open aperture leading to insulated collection chambers cooled with dry ice. Upon exiting the top of the cloud system, the chamber aperture is closed, and the ~1 mm3 sample cell is magnetically sealed and isolated at -78 °C, ensuring that ice particles do not sublimate or grow after collection. Once the crystals are returned to the surface, they are double-sealed and immersed at liquid nitrogen temperature in “dry-cryo shippers” before being transported back to the laboratory. Dr. Magee’s laboratory at The College of New Jersey contains a cryo-stage scanning electron microscope (SEM), which was used to interrogate the crystals and aerosol particles. The main purpose of this pilot field campaign was to provide an unprecedented level of detail on the crystal habits and ice surface complexity in mid-latitude cirrus, which may help resolve issues associated with habit identification and classification in cirrus. The ICEBall campaign was originally scheduled to run from March 28 to April 18 of 2021 at the U.S. Department of Energy Atmospheric Radiation Measurement (ARM) Southern Great Plains observatory. The COVID-19 pandemic intervened and caused us to shift the dates of the experiment to October 16-November 6 of 2021. This period is also climatologically favorable for cirrus. The approximately six-month gap between our original field campaign dates and the actual dates afforded us the opportunity to build two new ICEBall payload instruments. These instruments were tested during an August 2021 trip to The College of New Jersey. During this field testing phase, we decided to launch the ICEBall payload upstream from the ARM SGP site with the goal of landing in the vicinity of the site. Our goal was to sample the ice crystals before the cirrus were advected over the remote-sensing instruments at the SGP site. The team assembled for the field campaign consisted of the Principle Investigator (PI) and Co-Principle Investigator (Co-PI) (Drs. Harrington and Magee), The Pennsylvania State University research scientist Dr. Alfred Moyle, and two graduate students (Ms. Marley Majetic and Gwenore Pokrifka). The team operated out of a house rented in Enid, Oklahoma. We successfully sampled seven cirrus cloud systems during the three-week field campaign (October 21, 23-26, 31, and November 1). This was a much higher success rate than either of the PIs anticipated (our goal was closer to sampling three or four cases). The balloon was typically launched from oil pads or farm fields northwest of Enid and the payload was typically retrieved somewhat north of the SGP site. We never landed directly at the SGP site, and so did not need regular access to the SGP facilities. Our greatest concern going into the field campaign was the longer-term storage of crystals in the -196°C cryo dry-shipper dewars and the subsequent transport across the country. We had tested storage and transport prior to the field campaign, but we had never stored crystals for a few weeks nor had we transported the dewars over long distances. To our great relief, the storage and transport worked flawlessly and we were able to image a large number of crystals from six of the seven cases. Working with the staff at the ARM SGP office was excellent. They not only helped us find the sources we needed for helium, liquid nitrogen, and other materials, but also helped with contacts within the Federal Aviation Administration (FAA) and Vance Air Force Base. One goal of our field project was to tie the in situ measurements of ice crystal habits to the radar signatures derived from the Ka-band ARM Zenith-pointing Radar (KAZR). Unfortunately KAZR was down for the duration of our experiment. However, the Ka-band Scanning ARM Cloud Radar (KASACR) was put into vertically pointing mode during the ICEBall campaign and those data, along with Doppler lidar measurements, have proved very useful.
Lawrence Livermore National Laboratory (LLNL) has conservatively reduced the explosive safety standards suggested by the Small Quantities in Research Laboratories (SQRL) program testing by 60%. For this reason, further research into the detailed effects of small amounts of explosives in typically constructed rooms is needed in order to improve their factor of safety. The team was tasked with designing an experiment to investigate the effect of different variables on drywall under explosive blasts. In order to meet this objective, the team conducted a comprehensive literature review to gain an understanding of industry-standard construction practices and review previous tests conducted by the Army corp of engineers. Since the team used PBXN-5 rather than C4, as in SQRL, an initial shot was conducted to compare damage levels. From those results and the physical constraints of the testing chamber, the team redesigned multiple single-panel drywall frames to capture the entirety of the incurred damage. Proposed designs were narrowed down using a decision matrix. From the study of previous tests and literature review, variables were chosen that the team hypothesized to have an impact on drywall strength. The variables that were tested were chosen from the results of that work and specific variables the sponsor was interested in, and they were paint, humidity/moisture content, and explosive positioning relative to the studs. Detailed plans were made for each variable according to what conditions the team wanted to investigate. For humidity, this involved testing low, ambient, and high conditions by treating the panels in a chamber. Preliminary shots were performed to test the structural integrity of the frame and streamline the test diagnostics which involved a high-speed camera placed behind the drywall, outside of the chamber, and a pressure probe placed behind the drywall, inside the chamber. Once the instrumentation, diagnostics, and frame design were finalized, a quantitative damage criteria matrix was created to categorize the results of the main shot series. In conjunction with evidence from the high-speed video, the achieved damage levels indicate that high moisture content drywall is better able to withstand explosive blasts. Larger stud damage and lower drywall damage occurred when the explosive was located directly in front of a stud. Paint had no noticeable impact on strength. Ultimately, the team conducted a total of 17 tests, leaving the door open for future in-depth research into the impact of humidity.
PNNL staff performed an independent technical review (ITR) of the aluminum-clad spent nuclear fuel (ASNF) program Task 2. The objective of Task 2 is to determine G-values for the radiolytic production of gaseous molecular hydrogen (H 2 ) from the aluminum oxyhydroxide layers present on ASNF cladding. The G-values need to be defensible, bounding, but not overly conservative. As part of the ITR, the team reviewed program documents, Task 2 work plans, and results to date. In addition, the team held three separate video conferences with the program research staff to ask clarifying questions and obtain additional detail. The PNNL ITR team concludes that if the program follows the methodology and rigor outlined in the test matrices, the program will develop G-values for a defensible technical basis of extended storage of ASNF. Recommendations for improvement are given.
Blending petroleum fuels with oxygenates is a common approach to stem the depletion of crude oil while also mitigating the impact of their combustion on the environment. It has recently been considered that n-butyl acetate (BA, C6H12O2 , boiling point of 399K) could be a viable oxygenate additive to diesel fuel. In this application it is important to determine the influence of the fractional amount of BA on burning of the mixture. This presentation considers this problem from an experimental and computational approach using n-heptane (C7H14 , boiling point of 372K) as an essentially single component surrogate for diesel fuel. The burning configuration considered was ostensibly spherical symmetry as promoted by burning droplets under conditions where forced and natural convection effects were minimized. In this configuration the droplet and flame are concentric spheres and soot aggregates are trapped in a shell structure between the droplet and flame. Measurements were made of the droplet, flame and soot shell diameters through video imaging of the burning process. The experimental results were used to validate a detailed numerical model of the spherically symmetric droplet burning process that incorporated a consistent combustion kinetic mechanism for BA/heptane mixtures, comprised of 402 species and 16,872 reactions inclusive of soot chemistry. A model for soot formation was also included in the simulation to predict formation of the soot shell. The simulations agreed well with measured droplet and flame diameters. It was found both experimentally and computationally that adding BA to heptane had a minimal effect on the mixture burning rate, while the flame was positioned closer to the droplet surface and the simulated soot volume fraction decreased. The implications of these results are discussed for using BA as a potential additive to diesel fuel.
The Department of Energy (DOE) Water Power Technologies Office (WPTO) Triton Initiative supports the advancement of the marine energy (ME) industry through environmental monitoring research and technology development. This report presents the results and analysis of the Triton Initiative’s communications, outreach, and engagement (TCOE) efforts in fiscal year 2022. The primary TCOE goals were to: (1) educate and raise awareness of ME and the role of Triton's environmental monitoring research in supporting the industry; (2) build trust with audiences through transparent communications and outreach; and (3) evaluate and refine TCOE tactics based on feedback and metrics. To support these goals, the TCOE team used multiple platforms and approaches. Notable achievements include: (1) 12 newsletter issues sent to 185 subscribers with an average open rate of 56.4%. (2) 10 Triton Stories, resulting in 2,871 collective views contributing to 46% of all Triton website views. (3) 78 Triton-specific social media posts, which generated a total of 81,896 impressions, 1,037 post-clicks, and 7,814 video views. (4) 6,320 Triton website views with increased search engine optimization ranking in several categories. (5) 3 Triton researchers interviewed as guests on two different podcasts, Water Women and Big Deep: An Ocean Podcast. (6) A Triton special issue of the Journal of Marine Science and Engineering (JMSE) featuring 10 peer reviewed publications. All articles ranked in the top 25%, and two in the top 5%, for digital attention of all research outputs scored by Altmetric. (7) A seven-part webinar series called Triton Talks to share and discuss research and results from the research published in the JMSE special issue. Triton leveraged unique opportunities, particularly the JMSE special issue, to disseminate research results to end users, educate stakeholders, and gain valuable feedback. These concerted communication efforts increased exposure across platforms ultimately resulting in greater reach and access across audiences. Based on audience analyses of webinar attendees and newsletter subscribers, the TCOE team was able to successfully engage with general audiences, research partners, and ME stakeholders, including subject matter experts from government agencies, research organizations, and the regulatory community. The TCOE task established channels to gather input and create opportunities for two-way communication with people engaged with Triton’s outreach efforts. The feedback received will enable the TCOE team to support its goals for ongoing evaluation of outreach and engagement tools while continuing to build trust in the ME community and educate diverse audiences about the impactful research conducted by the Triton Initiative. This report is a Triton Initiative Fiscal Year 2022 Quarter 4 Milestone Deliverable due to the DOE WPTO Sponsor on 9/30/2022. An updated version to include data through 9/30/2022 will be delivered in October 2022.
In this project, we assembled a sensor suite that combines a scientific echosounder (sonar) system with video and acoustic cameras (secondary sensors). The sensor suite generates data that is amenable to automated target detection algorithms and can provide inputs to animal encounter models. We developed software (archiving software) to collect data simultaneously from all sensors in the suite, analyze the sonar data automatically in near real-time to identify time periods when targets of interest were present, and automatically archive data from the secondary sensors for these time periods. The product of the archiving software is a data set for the secondary sensors, containing data only for the times when targets of interest were determined to be present by the sonar. We performed controlled field testing to verify the operation of the sensor suite and the archiving software.