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In recent years, the intervention of Deep Learning as a promising tool for science has simultaneously driven the demand for resources that aid in understanding and developing new learning models. Deep Learning has shown promise in being able to solve classification and regression problems in Astronomy and Physics
The Sandia National Laboratories' (SNL) Corporate Behavioral Health Program is a workplace- based program that: 1) provides Employee Assistance Program (EAP) services including early identification and resolution of personal concerns which may impact job performance, 2) assists managers and the organization in addressing productivity issues, and 3) supports the SNL commitment to provide a safe and healthful work environment. The program is offered to approximately 13,500 employees in New Mexico. The Behavioral Health Program is a corporate program combining services in NM and CA. It is integrated with other occupational health and clinical services including disability, disease management and preventive health programs. In addition, Sandia's Behavioral Health Program provides critical management consultation and psychological assessment services for external organizations including Human Resources, the Department of Energy and Security through the Clinical Evaluation (CE) process, Human Reliability Program (HRP), Protective Force Program, Workplace Violence/Threat Assessment Team (TAT), and Insider Threat Working Group programs. The program supports Sandia National Laboratories' mission to safeguard national security, the environment, and the public; it is a proactive approach to early identification, intervention and assessment. Importantly, it reduces barriers to accessing mental health services and assists with reducing health care costs attributed to illness or injuries related to unhealthy lifestyles and behaviors. The team is comprised of a professional staff including a licensed Clinical Psychologist, a licensed professional clinical counselor (LPCC) and a licensed Marriage and Family counselor (MFT) who is also a Certified Employee Assistance Professional and who holds a doctorate in counseling psychology.
Multi-site fatigue damage, hidden cracks in hard-to-reach locations, disbonded joints, erosion, impact, and corrosion are among the major flaws encountered in today's extensive fleet of aging aircraft and space vehicles. The use of in-situ sensors for real-time health monitoring of aircraft structures are a viable option to overcome inspection impediments stemming from accessibility limitations, complex geometries, and the location and depth of hidden damage. Reliable, structural health monitoring systems can automatically process data, assess structural condition, and signal the need for human intervention. Prevention of unexpected flaw growth and structural failure can be improved if on-board health monitoring systems are used to continuously assess structural integrity. Such systems are able to detect incipient damage before catastrophic failures occurs. Condition-based maintenance practices could be substituted for the current time-based maintenance approach. Other advantages of on-board distributed sensor systems are that they can eliminate costly, and potentially damaging, disassembly, improve sensitivity by producing optimum placement of sensors and decrease maintenance costs by eliminating more time- consuming manual inspections. This report presents a Sandia Labs-aviation industry effort to move SHM into routine use for aircraft maintenance. This program addressed formal SHM technology validation and certification issues so that the full spectrum of concerns, including design, deployment, performance and certification were appropriately considered. The Airworthiness Assurance NDI Validation Center (AANC) at Sandia Labs, in conjunction with Boeing, Delta Air Lines, Structural Monitoring Systems Ltd., Anodyne Electronics Manufacturing Corp. and the Federal Aviation Administration (FAA) carried out a certification program to formally introduce Comparative Vacuum Monitoring (CVM) as a structural health monitoring solution to a specific aircraft wing box application. Validation tasks were designed to address the SHM equipment, the health monitoring task, the resolution required, the sensor interrogation procedures, the conditions under which the monitoring will occur, the potential inspector population, adoption of CVM into an airline maintenance program and the document revisions necessary to allow for routine use of CVM as an alternate means of performing periodic structural inspects. To carry out the validation process, knowledge of aircraft maintenance practices was coupled with an unbiased, independent evaluation. Sandia Labs designed, implemented, and analyzed the results from a focused and statistically-relevant experimental effort to quantify the reliability of the CVM system applied to the Boeing 737 Wing Box fitting application. All factors that affect SHM sensitivity were included in this program: flaw size, shape, orientation and location relative to the sensors, as well as operational and environmental variables. Statistical methods were applied to performance data to derive Probability of Detection (POD) values for CVM sensors in a manner that agrees with current nondestructive inspection (NDI) validation requirements and also is acceptable to both the aviation industry and regulatory bodies. This report presents the use of several different statistical methods, some of them adapted from NDI performance assessments and some proposed to address the unique nature of damage detection via SHM systems, and discusses how they can converge to produce a confident quantification of SHM performance An important element in developing SHM validation processes is a clear understanding of the regulatory measures needed to adopt SHM solutions along with the knowledge of the structural and maintenance characteristics that may impact the operational performance of an SHM system. This report describes the major elements of an SHM validation approach and differentiates the SHM elements from those found in NDI validation. The activities conducted in this program demonstrated the feasibility of routine SHM usage in general and CVM in particular for the application selected. They also helped establish an optimum OEM-airline-regulator process and determined how to safely adopt SHM solutions. This formal SHM validation will allow aircraft manufacturers and airlines to confidently make informed decisions about the proper utilization of CVM technology. It will also streamline the regulatory actions and formal certification measures needed to assure the safe application of SHM solutions.
This short concept article discusses four specific ways to eradicate respiratory pandemics once and for all. These include: Protecting the nose, mouth, throat and lungs; New hygiene regimens; Clearing the air; and Biophysical interventions. Technical breakthoughs in all four of these areas would not only protect people from life-threatening pathogens, but also take the dread out of respiratory disease outbreaks.
This report documents the findings of an assessment of the Turbo FRMAC software's ability to implement International Atomic Energy Agency (IAEA) guidance for calculating Operational Intervention Levels (OIL) 1 & 2 for nuclear and radiological emergencies. The IAEA OIL and U.S. Federal Radiological Monitoring and Assessment Center (FRMAC) Derived Response Level methodology and implementation in respective tools were compared, as demonstrated through benchmarking activities for a nuclear power plant source term and potential radionuclides of concern for radiological dispersal devices. This comparison revealed some shortcomings in Turbo FRMACs ability to perform IAEA OIL calculations and resulted in recommended software modifications to be considered for future development.
This report is part of a series investigating the potential for utility-scale energy storage in South Asia. This Readiness Assessment Framework for Utility-Scale Energy Storage forms the basis for forthcoming country-specific evaluations of policy and regulatory environments for energy storage in the region, beginning with India. These evaluations provide insights on the opportunities and barriers for energy storage growth and deployment in each country. This report offers the overarching framework for these evaluations. It is designed to be allow policymakers and regulators to quickly gauge how well existing policy and regulatory frameworks support investments in energy storage and can be applied in any jurisdiction regardless of its governance, regulatory, or market structure. It is not designed to recommend specific policy or regulatory solutions and does not inform whether energy storage is the best solution, among a range of possible technical and non-technical interventions, to meet system needs.
Primary Focus Area: This proposal addresses focus area 2, “Predictive modeling through the use of AI techniques.” Science Challenge: Extreme climate events associated with severe weather, coastal and inland flooding, droughts, heat waves and wildfires are expected to increase in frequency and severity in the future. Due to the complexity and chaotic behavior of the climate system, accurately predicting and observing extreme climate events requires a tremendous amount of human intervention to run predictive climate simulations and deploy measurement systems. Extreme events often unfold very quickly, leaving little time to iterate on simulations or re-position instruments. Through reinforcement learning, autonomous AI agents can be designed to make real-time decisions to characterize extreme climate events more efficiently through adaptive models and targeted observations.
In many dynamic systems, interesting events occur locally in time and space. Examples of such systems include ignition events in combustion simulations, material fractures in mechanics simulations, and extreme weather events in climate simulations. Due to memory constraints and data I/O costs, current simulation workflows save data at regularly spaced time-steps, at a fixed rate determined before the start of the simulation. Often this mode of operation results in missed events of interest, necessitating a simulation restart from before an event occurred with more frequent data saves. This data saving workflow is grossly inefficient and is already a bottleneck in the computing process. We propose to develop machine learning algorithms that can detect when interesting dynamical events are occurring, triggering data saves. These machine learning algorithms will perform in situ anomaly detection to flag regions with different dynamical properties than those previously recorded. The adaptive data saves would be local in time and space to match the event of interest, thereby enabling a much more efficient workflow that will reduce data I/O costs and data storage memory requirements. The algorithms will be tested on two applications: auto-ignition simulations and climate simulations. A critical component of this project will be developing machine learning algorithms that can be deployed efficiently in situ on HPC platforms with out-of-the-box functionality. The development of in situ machine learning methods to detect anomalous events would enable a more efficient and effective workflow, in which all the relevant data are saved in a single simulation run, without re-starts or scientist intervention.
Existing evidence strongly suggests that viral infectious diseases can be transmitted via an airborne route across distances in indoor environments. Accordingly, the risk of airborne transmission within homes should be managed. The public health emergency associated with SARS-CoV-2 makes controlling airborne transmission of respired viruses in indoor environments critical, especially in poorly ventilated indoor environments. The effectiveness of engineering interventions requiring minor efforts that create a negative-pressure isolation zone (IZ) for a contagious person has yet to be tested for existing residential homes. To mitigate the risk of airborne virus transmission and maximize health protection for the population in existing single-family homes, the relative effectiveness of several control strategies are investigated in this report. While very high-efficiency MERV filtration, high ventilation rates, and other controls can help be effective, most occupants are not likely to have the time or means for advanced measures found in hospitals. This project focused on testing relatively simple efforts that utilize existing or easy to acquire materials and simple processes.
In early 2020, COVID-19 swept the globe. Governments attempted to “flatten the curve” through business shutdowns and stay-at-home orders, but the United States was hit hard. By the end of March, mere months after the virus first emerged in humans 7,000 miles away, the U.S. had recorded 192,300 cases and 5,300 deaths. While this unprecedented disaster sent shockwaves through every level of society and clouded an uncertain future, state and local governments turned to computational and mathematical epidemiology researchers to help formulate intervention strategies to limit the spread of the disease. Traditional forecasting models provided a reasonable understanding of how the near future was likely to look, but local policy makers and public health communities still struggled to understand how potential mitigations ought to be implemented. Decision makers needed a way to measure the impact of their policy choices—they needed better technology. EpiCast answered the call, bringing urgently needed answers to policymakers grappling with how to adjust school and business schedules. EpiCast is modeling software that generates a synthetic, representative population to simulate infectious disease transmission in the United States with extreme detail and granularity. The software models human behavior combined with community-specific information to provide a fine-grained preview of the effect of potential mitigation strategies for decision makers.
Gas Technology Institute (GTI) has completed a $1.815M (plus $500K cost share), 54-month Co-operative agreement with the Department of Energy’s National Energy Technology Laboratory to develop a Methane Mitigation Thermoelectric Generator (MMTEG) system for gas field applications. This novel system uses fugitive gas to produce electrical power and consists of Thermoelectric Generators (TEG) driven by a linear burner, an air compressor, an accumulator, valves, batteries, and power electronics. The electrical power generated by the system is used to compress air and, in turn, the air is used to operate the pneumatic valves at the well site instead of using natural gas (NG) which would then be vented. The team completed the project objectives which included (1) Design, fabricate and test an integrated 6We nominal MMTEG prototype system, (2) Design a “retrofit kit” MMTEG system capable of being field tested at a gas well production site, and (3) build and test the field MMTEG system in a laboratory environment. Multiple system options were developed prior to selecting a “Passive” system which meets cost, NG savings, and greenhouse gas (GHG) reduction targets although not as efficient as initially planned. The MMTEG system is built primarily from commercial off-the-shelf parts (in some cases re-purposed) including the heat exchanger, heat rejection, electronic components, and also the TEGs. The team developed and implemented a novel system including the control system developed by Morrison Applied Sciences (MAS). The team completed incremental demonstrations of the hardware prior to the MMTEG system demonstration. A one-year simulation of the air delivery and battery charging subsystems was completed prior to the integration into the MMTEG system. In addition, the team simulated two-years of thermal cycles for integrated Burner/TEG/heat rejection subsystem. Finally, the entire MMTEG system was assembled, and troubleshooting was completed over a two-week period. Next, the MMTEG system was tested to simulate over 15 weeks of entire system operation over approximately four weeks in an accelerated test fashion with minimal intervention (such as changing fuel tanks). The MMTEG system met the key goals of a unit cost of under $1500 while saving 97% of NG expended today (including leakage) on average by pneumatic systems venting to the atmosphere. The MMTEG system reduced GHG emissions by 1000X (using the methane intensification factor of 28 relative to CO2). GTI is currently pursuing a field test of the MMTEG system. Interfacing with producers has provided additional insight into system improvements. Planned improvements include additional weather protection and control system improvements including the implementation of a long- range radio capability to notify operators if there is a fault.
Following the 1986 disastrous accident at Chernobyl, gaining independence following the fall of the former Soviet Union, two revolutions, and on-going Russian intervention, Ukrainea is seeking to expand its use of commercial nuclear energy and to further reduce its dependence on Russia for energy. Nuclear energy in Ukraine has made a significant contribution toward achieving sustainable development and social goals. Ukraine is very vulnerable to the effects of climate change and nuclear is a cornerstone of the country’s effort to combat the effects. With broad support from the U.S. government, Ukraine is cooperating with the U.S. on fresh nuclear fuel supply, civilian nuclear security, outage optimization, spent fuel storage, and future nuclear technology. And is looking to tap U.S. expertise on plant management, and electric grid operations and expansion. NEIc hosted this virtual event to provide an update on nuclear energy projects and plans in Ukraine. Featured speakers included Yaroslav Demchenkov, Deputy Minister of Energy, Taras Kachka, Deputy Minister – Trade Representative, Petro Kotin, CEO, Energoatom,d and Ann K. Ganzer, Senior Bureau Official, Bureau of International Security and Nonproliferation, U.S. Dept. of State. Maria Korsnick, NEI CEO, moderated the discussion. About forty individuals attended representing the Ukraine and U.S. governments, and U.S. suppliers.
The costs associated with the increasing maintenance and surveillance needs of aging structures are rising at an unexpected rate. Multi-site fatigue damage, hidden cracks in hard-to-reach locations, disbonded joints, erosion, impact, and corrosion are among the major flaws encountered in today’s extensive fleet of aging aircraft and space vehicles. Aircraft maintenance and repairs represent about a quarter of a commercial fleet’s operating costs. The application of Structural Health Monitoring (SHM) systems using distributed sensor networks can reduce these costs by facilitating rapid and global assessments of structural integrity. The use of in-situ sensors for real-time health monitoring can overcome inspection impediments stemming from accessibility limitations, complex geometries, and the location and depth of hidden damage. Reliable, structural health monitoring systems can automatically process data, assess structural condition, and signal the need for human intervention. The ease of monitoring an entire on-board network of distributed sensors means that structural health assessments can occur more often, allowing operators to be even more vigilant with respect to flaw onset. SHM systems also allow for condition-based maintenance practices to be substituted for the current time-based or cycle-based maintenance approach thus optimizing maintenance labor. The Federal Aviation Administration has conducted a series of SHM validation and certification programs intended to comprehensively support the evolution and adoption of SHM practices into routine aircraft maintenance practices. This report presents one of those programs involving a Sandia Labs-aviation industry effort to move SHM into routine use for aircraft maintenance. The Airworthiness Assurance NDI Validation Center (AANC) at Sandia Labs, in conjunction with Sikorsky, Structural Monitoring Systems Ltd., Anodyne Electronics Manufacturing Corp., Acellent Technologies Inc., and the Federal Aviation Administration (FAA) carried out a trial validation and certification program to evaluate Comparative Vacuum Monitoring (CVM) and Piezoelectric Transducers (PZT) as a structural health monitoring solution to specific rotorcraft applications. Validation tasks were designed to address the SHM equipment, the health monitoring task, the resolution required, the sensor interrogation procedures, the conditions under which the monitoring will occur, the potential inspector population, adoption of CVM and PZT systems into rotorcraft maintenance programs and the document revisions necessary to allow for their routine use as an alternate means of performing periodic structural inspections. This program addressed formal SHM technology validation and certification issues so that the full spectrum of concerns, including design, deployment, performance and certification were appropriately considered. Sandia Labs designed, implemented, and analyzed the results from a focused and statistically relevant experimental effort to quantify the reliability of a CVM system applied to Sikorsky S-92 fuselage frame application and a PZT system applied to an S-92 main gearbox mount beam application. The applications included both local and global damage detection assessments. All factors that affect SHM sensitivity were included in this program: flaw size, shape, orientation and location relative to the sensors, as well as operational and environmental variables. Statistical methods were applied to performance data to derive Probability of Detection (POD) values for SHM sensors in a manner that agrees with current nondestructive inspection (NDI) validation requirements and is acceptable to both the aviation industry and regulatory bodies. The validation work completed in this program demonstrated the ability of both CVM and PZT SHM systems to detect cracks in rotorcraft components. It proved the ability to use final system response parameters to provide a Green Light/Red Light (“GO” – “NO GO”) decision on the presence of damage. In additional to quantifying the performance of each SHM system for the trial applications on the S-92 platform, this study also identified specific methods that can be used to optimize damage detection, guidance on deployment scenarios that can affect performance and considerations that must be made to properly apply CVM and PZT sensors. These results support the main goal of safely integrating SHM sensors into rotorcraft maintenance programs. Additional benefits from deploying rotorcraft Health and Usage Monitoring Systems (HUMS) may be realized when structural assessment data, collected by an SHM system, is also used to detect structural damage to compliment the operational environment monitoring. The use of in-situ sensors for health monitoring of rotorcraft structures can be a viable option for both flaw detection and maintenance planning activities. This formal SHM validation will allow aircraft manufacturers and airlines to confidently make informed decisions about the proper utilization of CVM and PZT technology. It will also streamline future regulatory actions and formal certification measures needed to assure the safe application of SHM solutions.
Sandia National Laboratories has developed a capability to estimate parameters of epidemiological models from case reporting data to support responses to the COVID-19 pandemic. A differentiating feature of this work is the ability to simultaneously estimate county-specific disease transmission parameters in a nation-wide model that considers mobility between counties. The approach is focused on estimating parameters in a stochastic SEIR model that considers mobility between model patches (i.e., counties) as well as additional infectious compartments. The inference engine developed by Sandia includes (1) reconstruction and (2) transmission parameter inference. Reconstruction involves estimating current population counts within each of the compartments in a modified SEIR model from reported case data. Reconstruction produces input for the inference formulations, and it provides initial conditions that can be used in other modeling and planning efforts. Inference involves the solution of a large-scale optimization problem to estimate the time profiles for the transmission parameters in each county. These provide quantification of changes in the transmission parameter over time (e.g., due to impact of intervention strategies). This capability has been implemented in a Python-based software package, epi_inference, that makes extensive use of Pyomo [5] and IPOPT [10] to formulate and solve the inference formulations.
The Oak Ridge National Laboratory (ORNL) research team in this project has focused on developing methods for improving overdose prevention by addressing three unresolved issues, divided into two groups, that could impact interventions: Understanding events with bad outcomes, and understanding events with better outcomes.
Modeling and simulation efforts frequently involve loosely coupled workflows. Automation of workflows, data exchanges, and simulation software execution is a productive and often desired feature or improvement for enhancing consistency of results and productivity of the analyst. It facilitates cross-collaboration of different analysts of multidisciplinary simulation by streamlining data exchange processes across local and high performance computing resources. This report proposes a high-level, extensible workflow module to enable consistent and efficient use of tools integrated into the Nuclear Energy Advanced Modeling and Simulation (NEAMS) Workbench with as little user intervention as possible.
Computer methods were developed and demonstrated that make it possible to predict the combustion and pyrolysis chemistry of proposed biofuels, even before the biofuels are ever manufactured or experimentally tested. The methods are based on first-principles calculations of reaction rates and equilibria. A large number of complicated calculations are required, so we developed a computer workflow that automates many of the steps. With the chemical structure of the biofuel and the system conditions (temperature, pressure, compositions, etc.) as the input, the computer methods can construct a detailed kinetic model with refined parameters without intense human intervention. The automation of model construction makes it practical for a single engineer to make the predictions in a reasonable time period. The accuracy of the computer predictions was tested by measuring the time-profiles of various reaction intermediates (carbon monoxide, carbon dioxide, water, formaldehyde, methane, and ethylene) formed as the biofuels pyrolyze or ignite using sophisticated laser shock tube methods. Several biofuels, including methyl propyl ether, cyclopentanone, butyl acetate isomers, etc., were carefully studied to demonstrate the validity of the proposed computational and experimental methodology. This research project opens up the possibility for assessing some proposed biofuels on the computer without expensive experimental campaigns. The new computer methods are open source and well-documented, allowing a knowledgeable modeler to easily access the developed methods and implement them in their work routine. Moreover, they are also convenient and powerful, promising for developing the fuel chemistry models needed for use in engine simulators, facilitating co-optimization of new engine designs with new fuels.
The United States has witnessed multiple attempts to improve fuel economy and reduce pollutant emissions in the transportation sector, guided chiefly via technological interventions by original equipment manufacturers (OEMs) and by specific policies of the U.S. Federal Government, like Corporate Average Fuel Economy (CAFE) norms (Burnham et al., 2006; U.S. DoT, 2013). These efforts have resulted in the growing adoption of alternatives to conventional materials, fuels, and vehicle propulsion technologies across various transportation modes, while also helping to lower global pollution (greenhouse gas, or GHG, emissions). Yet, a holistic evaluation of the ecofriendliness of these alternatives, particularly their energy use and emissions, merits detailed focus on their entire life-cycle, and thereby, on the life-cycle of associated transport modes. This is vital since alternative fuels and materials can differ vastly in energy sources and production methods employed for their processing vis-à-vis their existing counterparts — and this difference causes significant variation in their respective upstream emissions.