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.
Data Center Monitoring is the understanding of everything that happens in the Data Center. Our Ultimate (and probably unreachable) Goal is a holistic view of the Data Center and an understanding of how everything is correlated. We get to that Understanding by collecting logs and metrics, analyzing data, displaying information for customers, and staying alert on events.
The Interplanetary Network (IPN) is a detection, localization and alert system that utilizes the arrival time of transient signals in gamma-ray detectors on spacecraft separated by planetary baselines to geometrically locate the origin of these transients. Due to the changing astrophysical landscape and the new emphasis on time domain and multi messenger astrophysics (TDAMM) from the Pathways to Discovery in Astronomy and Astrophysics for the 2020s , this Gamma-ray Transient Network Science Analysis Group was tasked to understand the role of the IPN and high-energy monitors in this new era. The charge includes describing the science made possible with these facilities, tracing the corresponding requirements and capabilities, and highlighting where improved operations of existing instruments and the IPN would enhance TDAMM science. While this study considers the full multiwavelength and multimessenger context, the findings are specific to space-based high-energy monitors. These facilities are important both for full characterization of these transients as well as facilitating follow-up observations through discovery and localization. The full document reports the history of this field in Section 1, followed by our detailed analyses and findings in some 68 pages, providing a holistic overview of the role of the IPN and high-energy monitors in the coming decades.
Lawrence Livermore National Laboratory (LLNL) is developing a multiphysics sensor package for automatic live arms fire detection for use in schools, houses of worship, hospi tals, etc. The goal is to have a package the size of a common smoke or CO2 detector with a price point of $50 or less which may be deployed throughout the aforementioned facilities. The commercial device will detect live gun fire with low probability of false alarm and auto matically alert first responders. A prototype board with four accelerometers, two infrared (IR) and two microphones has been developed and deployed at two experiments at LLNL’s Small Firearms Training Facility (SFTF). The device successfully and consistently measured signals on the accelerometer and acoustic channels. IR responses were measured depending upon the environment and sound source.
Industrial equipment is a critical component of virtually all at-scale manufacturing and infrastructure. Broadly speaking, modern equipment is predominantly controlled using hardened digital controllers – computers designed to be able to operate continuously for sometimes extremely long periods of time, with little to no maintenance. Due to the nature of how these digital controllers have evolved, they are solely optimized to execute a single task, and do not have the capabilities or resources to monitor or analyze their internal state beyond simple execution of their program. As a result, for many “common sense” situations where individual data points can be easily determined to be out-of-normal, the controllers are unable to identify these incorrect operational modes unless a human has explicitly programmed in detection of this degradation. This project seeks to develop an AI/ML system which can identify incorrect or anomalous trends in industrial data streams, of exactly the kind that would be produced and seen by these digital controllers, with a minimal amount of computing resources. The benefits produced by developing this system would ultimately be self-monitoring and self-reporting infrastructure, capable of identifying and alerting humans to issues as soon as they happen, potentially long before they have the chance to impact the industrial process.
The Swift Burst Alert Telescope (BAT) is a coded-aperture gamma-ray instrument with a large field of view that primarily operates in survey mode when it is not triggering on transient events. The survey data consist of 80- channel detector plane histograms that accumulate photon counts over periods of at least 5 minutes. These histograms are processed on the ground and are used to produce the survey data set between 14 and 195 keV. Survey data comprise >90% of all BAT data by volume and allow for the tracking of long-term light curves and spectral properties of cataloged and uncataloged hard X-ray sources. Until now, the survey data set has not been used to its full potential due to the complexity associated with its analysis and the lack of easily usable pipelines. Here, we introduce the BatAnalysis Python package, a wrapper for HEASoftpy, which provides a modern, opensource pipeline to process and analyze BAT survey data. BatAnalysis allows members of the community to use BAT survey data in more advanced analyses of astrophysical sources, including pulsars, pulsar wind nebula, active galactic nuclei, and other known/unknown transient events that may be detected in the hard X-ray band. We outline the steps taken by the Python code and exemplify its usefulness and accuracy by analyzing survey data of the Crab Nebula, NGC 2992, and a previously uncataloged MAXI transient. The BatAnalysis package allows for ~18 yr of BAT survey data to be used in a systematic way to study a large variety of astrophysical sources.
In this project we are studying the application of programmable network hardware to provide a custom monitoring capability for the Mu2e Trigger and Data Acquisition System (TDAQ) system. The goal of the Mu2e experiment is to search for a charged-lepton flavor violating processes where a negative muon converts into an electron in the field of an aluminum nucleus. This experiment is intended to improve by four orders of magnitude the search sensitivity reached so far. We have a working prototype of a system that provides high-throughput, custom monitoring for the Mu2e TDAQ system. The custom Mu2e network packet header format is parsed as it crosses the network switch. Parsing extracts bits that convey information about error states at read-out controllers (ROCs). This information is periodically relayed to the switch controller, which in turn alerts experiment operators.
We experimentally and computationally investigate a proposed frequency-domain method for detecting and tracking cislunar spacecraft and near-earth asteroids using heliostat fields at night. Unlike imaging, which detects spacecraft and asteroids by their streak in sidereally-fixed long-exposure photographs, our proposed detection method oscillates the orientation of heliostats concentrating light from the stellar field and measures the light’s photocurrent power spectrum at sub-milliHertz resolution. If heliostat oscillation traces out an ellipse fixed in the galactic coordinate system, spacecraft or asteroids produce a peak in photocurrent power spectrum at a frequency slightly shifted from the starlight peak. The frequency shift is on the scale of milliHertz and proportional to apparent angular rate relative to sidereal. Relative phase corresponds to relative angular position, enabling tracking. A potential advantage of this frequency-domain method over imaging is that detectivity improves with apparent angular rate and number of heliostats. Since heliostats are inexpensive compared to an astronomical observatory and otherwise unused at night, the proposed method may cost-effectively augment observatory systems such as NASA’s Asteroid Terrestrial-impact Last Alert System (ATLAS).
Historically, cables are initially qualified for nuclear power plant use for 40 years. As plants extend their operating license to 60 and 80 years, continued use of these cables must shift to a performance-based approach since it is cost prohibitive to completely replace cables that are likely still capable of performing their design function. A variety of cable tests are available and are commonly applied during outages when the cables can be taken out of service. Frequency domain reflectometry (FDR) is one of these test methods that is being more broadly accepted and used because it not only detects anomalies along the cable with a low-voltage signal that does not stress the cable insulation, but the technique also locates the anomalies. This supports follow-up local inspection and local repair or partial replacement of a damaged cable segment. Currently, FDR testing is only applied to cables that are taken out of service since the test instrument would be damaged by operational voltages. A related technology that has found some acceptance in the aircraft and rail industry is spread spectrum time domain reflectometry (SSTDR). This technology has been implemented with a custom commercial instrument by LiveWire Innovation that is designed to operate on live cables up to 1000 volts and with a bandwidth of 48 MHz. Initial evaluation by the Pacific Northwest National Laboratory (PNNL) of the Live Wire system indicated that a broader bandwidth (BW) SSTDR may be better for many kinds of flaws. This led PNNL to develop an SSTDR laboratory instrument suitable for tests up to 500 MHz bandwidth. Testing on energized cables is also desirable for online monitoring systems so an inductive clamshell coupler was developed that allows energized cables to be tested up to at least 5 kV and likely higher voltage levels. Dielectric spectroscopy and tan delta testing plus various laboratory destructive tests were included in this data acquisition campaign directed to feed a machine learning (ML) study. With these kinds of developments, online energized cable tests may be possible with industrial adoption of such hardware advances but it will be completely impractical to have highly skilled data analysts continually examine these complex signals for indications of damage or compromised conditions. If online testing is to be implemented in new test hardware, it must be accompanied by software that can interpret the signals and alert plant operators of changing or degraded conditions. The thermally aged, shielded cable investigated here was separately treated for ML analysis. Visual analysis of electrical data showed generally increasing peaks where the cable entered and exited the oven. These peaks were not exactly aligned with expected locations, but these differences were attributed to velocity of propagation calibration errors. Only supervised ML was applied to the thermally aged data as this data was only available shortly before the committed publication date of this report. The supervised ML was structured to divide the 0 to 70-day responses as ‘normal’ from 0 to 35 days or ‘anomalous’ from 36 to 70 days, based on cable tensile elongation at break (EAB) insulation characterization. Using 80% of the data for training and 20% for testing, the supervised ML predicted normal versus anomalous was 70% accurate. Important conclusions include: • Accuracy to predict the presence of cable damage is improved from the 2023 effort by more training data. Weighted accuracies for comparisons among the instruments ranged from 67 to 89 % for unsupervised ML and 71 to 99% for supervised ML. • Based on the synthetic data tests, the unsupervised models are more generalizable to unseen anomalies. The Multi-Layer Perceptron classifier (MLP) model reported as high as 99.7% accuracy on the test data, but this dropped to 58.3% when tested on the synthetic data. In contrast, the unsupervised Pointwise model only achieved 89.7% accuracy on the experimental data but reported 78.3% accuracy on the synthetic data. • The best anomaly indicators are higher frequency (400 MHz BW) FDR data. Other tests may be interesting but for this study, this was the best predicter.
Molten salt reactor developers rely on thermal property data to design, license and operate the reactors. The Molten Salt Thermal Database-Thermophysical Properties (MSTDB-TP) was established under the DOE Nuclear Energy Advanced Modeling and Simulation (NEAMS) program and is managed by Oak Ridge National Laboratory to serve as a single source of thermophysical property values measured for a wide variety of molten salt systems for use by researchers, molten salt reactor developers, and regulators. These properties include density, viscosity and thermal diffusivity and conductivity. Published measurements of molten salt properties are lacking for many salts of interest and the data that are available are often inconsistent. This creates a challenge for MSR developers when determining which property values to use when designing their reactors. It is the purpose of this work to apply a consistent ranking system to all data entries that indicates the quality of property values listed in the database. These rankings will be the technical basis for down-selections by the database developers and alert users about the quality of the available property values. MSTDB-TP collects all available property data and indicates preferred data sets or correlations. However, all available data sets are included in the database. Quality assessments and rankings are being applied to data in MSTDB-TP to provide an indication of the quality of each data set independent of consistency with other data. Previous reports detailed the ranking system that was followed and assessments of unary fluoride data sets. Documentation of the quality of data in MSTDB-TP was continued by reviewing and assessing all available sources of density, viscosity and thermal diffusivity or conductivity values for unary chloride salts in MSTDB-TP V3.0 using the same criteria.
Tamper-indicating devices (TIDs), also known as seals, play a crucial role in various sectors including international nuclear safeguards, arms control, domestic security, and commercial products, by ensuring that monitored or high-value items are not accessed undetected. These devices do not block access but alert to unauthorized tampering. With adversaries' capabilities evolving, there's a pressing need for seals to advance in terms of effectiveness (e.g., better tamper indication and unique identification), and new technology can improve the efficiency of installation and verification. Passive loop seals, widely used in international nuclear safeguards to ensure that continuity of knowledge is maintained on declared items, face stringent International Atomic Energy Agency (IAEA) requirements that surpass those met by commercial products. The metal cup seal (Figure 1, left), a staple IAEA seal, is robust but requires significant resources for post-use verification – specifically, the seal’s unique identity can only be verified at IAEA headquarters after removal from facilities. Further, the seal has been in use for decades and seal types should periodically be replaced to counter adversarial efforts for defeating seals. In 2020, the IAEA outlined about 40 requirements for a new passive loop seal, aiming for in-situ verification, minimal external tool use, unique identification (UID), and clear tamper indication. In response, research and development efforts focused on creating a new passive loop seal that meets these criteria and in 2022 the IAEA announced the completion of the Field Verifiable Passive Loop Seal (FVPS) (Figure 1, right). Concurrently to the IAEA’s efforts, Sandia National Laboratories (SNL) and Oak Ridge National Laboratory (ORNL) designed, developed, and tested two seal versions – Puck and Puck/SAW, with Puck based on the IAEA’s requirements and including a novel visually-obvious tamper response, and Puck/SAW adding additional beneficial capabilities like the ability to receive a unique identifier from a standoff distance and monitoring the wire integrity. Puck/SAW was specifically designed and developed to address sealing applications in dry spent fuel storage facilities, where the number of sealed spent fuel containers results in heavy verification burden and inspector safety issues related to radiation exposure. These efforts are described in this Executive Summary.
First, we create a profile for each authorized driver based on their existing driving data. We then train a machine learning model on the driving data from this profile, yielding an individualized model for each driver. Finally during a drive, we pass the Controller Area Network (CAN) data to the model and authen ticate the driver’s identity in real-time. This verification or lack thereof could be used to alert supervisors of threats to their drivers or transported materials. Deviations from their normal driving behavior could indicate high-risk situations, medical events, or even insider threats.
One function of Continuous Air Monitor (CAM) samplers at LANL (Los Alamos National Laboratory) is to alert workers to the presence of airborne alpha-emitting (e.g.) plutonium particles.
griDNA is an advanced monitoring and alerting technology for protecting electrical grids and other critical infrastructure from disruptions caused by cyberattacks or system failures. griDNA provides the holistic cyber-physical situational awareness that operators require for modern infrastructure challenges.
Being a Liquid Argon Time Projection Chamber (LAr-TPC) detector, ICARUS exploits a series of three parallel wire planes to measure and collect the ionization charge coming from an event inside the active volume. The wires are read out by A2795 custom boards, arranged in crates located on top of the two liquid Argon vessels. Each board is provided with two temperature sensors that allow temperature monitoring while the experiment is running: this is key in order to ensure data reliability and maintain hardware in safe conditions. Moreover, temperature-related problems may force unplanned shutdowns that result in losing valuable time for the experiment to run. In this work we are discussing the characterization of the ICARUS TPC board temperatures and the system we implemented in order to monitor them effectively. Among the tools used, Grafana results in a valid application for both displaying temperature data and sending alerts to the shifters whenever a pathological situation occurs.
UX lessons from designing the Alarms app (a tool operators rely on to track accelerator subsystems and respond to alerts). The talk focuses on how observation, empathy, and iterative design helped us balance information, attention, and safety.
The Remote Access Attack on Oldsmar Water Treatment Facility 2021 Precursor Analysis Report leverages publicly available information about the Oldsmar cyber attack and catalogs anomalous observables for each technique employed in the attack. This analysis is based upon the methodology of the Cybersecurity for the Operational Technology Environment (CyOTE) program. On 5 February 2021, an adversary gained unauthorized remote access to Bruce T. Haddock Water Treatment Plant in Oldsmar, Florida, which provides treated water to 15,000 customers. The adversary accessed the facility’s Supervisory Control and Data Acquisition (SCADA) workstation and human machine interface (HMI) to change the chemical concentration of sodium hydroxide, commonly referred to as lye and used to regulate acidity levels, from 100 parts per million (PPM) to 11,100 PPM. The chemical was raised to lethal levels that if ingested could lead to serious soft tissue damage, burns, or even death. The facility, however, had redundancies and alarms in place to alert personnel of dangerous chemical levels, and facility officials stated it would have taken 24 to 36 hours for the chemical changes to affect the water supply. Researchers and analysts identified six unique techniques utilized during the attack with a total of 23 observables using MITRE ATT&CK® for Industrial Control Systems. The CyOTE program assesses observables accompanying techniques used prior to the triggering event to identify opportunities to detect malicious activity. If observables accompanying the attack techniques are perceived and investigated prior to the triggering event, earlier comprehension of malicious activity can take place. Four of the identified techniques used during the Oldsmar cyber attack were precursors to the triggering event. Analysis identified 21 observables associated with these precursor techniques, 20 of which were assessed to have an increased likelihood of being perceived in the minutes preceding the triggering event. The response and comprehension time could have been reduced if the observables had been identified earlier. The information gathered in this report contributes to a library of observables tied to a repository of artifacts, data sources, and technique detection references for practitioners and developers to support the comprehension of indicators of attack. Asset owners and operators can use these products if they experience similar observables or to prepare for comparable scenarios.
Time-Domain and Multi-Messenger Astrophysics (TDAMM) is entering a fundamentally new phase characterized by an unprecedented increase in the rate and diversity of astrophysical transient detections. The community is transitioning from a discovery-limited to a follow-up-limited era, driven by major investments across electromagnetic, gravitational-wave, and neutrino observatories. Upcoming facilities such as the Vera C. Rubin Observatory, the Nancy Grace Roman Space Telescope, and wide-field survey instruments will produce a deluge of time-domain alerts, reaching millions of events per night. Simultaneously, upgrades to the gravitational-wave network (LVK O5 and beyond) and neutrino observatories (IceCube Gen2) will significantly increase the detection rates of non-electromagnetic messengers. New high-energy missions and expansions of the InterPlanetary Network (IPN) will further enhance discovery capabilities across the gamma-ray and X-ray regimes. This convergence of capabilities represents a transformative opportunity: for the first time, the community will routinely detect rare and high-impact events across multiple messengers. However, the scientific return from these discoveries will depend critically on the ability to rapidly identify, prioritize, and coordinate follow-up observations across a heterogeneous and globally distributed set of facilities.
Background: Patient monitoring is vital in all stages of care. In particular, intensive care unit (ICU) patient monitoring has the potential to reduce complications and morbidity, and to increase the quality of care by enabling hospitals to deliver higher-quality, cost-effective patient care, and improve the quality of medical services in the ICU. Objective: We here report the development and validation of ICU length of stay and mortality prediction models. The models will be used in an intelligent ICU patient monitoring module of an Intelligent Remote Patient Monitoring (IRPM) framework that monitors the health status of patients, and generates timely alerts, maneuver guidance, or reports when adverse medical conditions are predicted. Methods: We utilized the publicly available Medical Information Mart for Intensive Care (MIMIC) database to extract ICU stay data for adult patients to build two prediction models: one for mortality prediction and another for ICU length of stay. For the mortality model, we applied six commonly used machine learning (ML) binary classification algorithms for predicting the discharge status (survived or not). For the length of stay model, we applied the same six ML algorithms for binary classification using the median patient population ICU stay of 2.64 days. For the regression-based classification, we used two ML algorithms for predicting the number of days. We built two variations of each prediction model: one using 12 baseline demographic and vital sign features, and the other based on our proposed quantiles approach, in which we use 21 extra features engineered from the baseline vital sign features, including their modified means, standard deviations, and quantile percentages. Results: We could perform predictive modeling with minimal features while maintaining reasonable performance using the quantiles approach. The best accuracy achieved in the mortality model was approximately 89% using the random forest algorithm. The highest accuracy achieved in the length of stay model, based on the population median ICU stay (2.64 days), was approximately 65% using the random forest algorithm. Conclusions: The novelty in our approach is that we built models to predict ICU length of stay and mortality with reasonable accuracy based on a combination of ML and the quantiles approach that utilizes only vital signs available from the patient’s profile without the need to use any external features. This approach is based on feature engineering of the vital signs by including their modified means, standard deviations, and quantile percentages of the original features, which provided a richer dataset to achieve better predictive power in our models.