When very few samples of a random quantity are available from a source distribution of unknown shape, it is usually not possible to accurately infer the exact distribution from which the data samples come. Under-estimation of important quantities such as response variance and failure probabilities can result. For many engineering purposes, including design and risk analysis, we attempt to avoid under-estimation with a strategy to conservatively estimate (bound) these types of quantities -- without being overly conservative -- when only a few samples of a random quantity are available from model predictions or replicate experiments. This report examines a class of related sparse-data uncertainty representation and inference approaches that are relatively simple, inexpensive, and effective. Tradeoffs between the methods' conservatism, reliability, and risk versus number of data samples (cost) are quantified with multi-attribute metrics use d to assess method performance for conservative estimation of two representative quantities: central 95% of response; and 10-4 probability of exceeding a response threshold in a tail of the distribution. Each method's performance is characterized with 10,000 random trials on a large number of diverse and challenging distributions. The best method and number of samples to use in a given circumstance depends on the uncertainty quantity to be estimated, the PDF character, and the desired reliability of bounding the true value. On the basis of this large data base and study, a strategy is proposed for selecting the method and number of samples for attaining reasonable credibility levels in bounding these types of quantities when sparse samples of random variables or functions are available from experiments or simulations.
Within the EXAALT project, the SNAP [1] approach is being used to develop high accuracy potentials for use in large-scale long-time molecular dynamics simulations of materials behavior. In particular, we have developed a new SNAP potential that is suitable for describing the interplay between helium atoms and vacancies in high-temperature tungsten[2]. This model is now being used to study plasma-surface interactions in nuclear fusion reactors for energy production. The high-accuracy of SNAP potentials comes at the price of increased computational cost per atom and increased computational complexity. The increased cost is mitigated by improvements in strong scaling that can be achieved using advanced algorithms [3].
The digital electronics at the atomic limit (DEAL) project seeks to leverage Sandia's atomic-precision fabrication capability to realize the theorized orders-of-magnitude improvement in operating voltage for tunnel field effect transistors (TFETs) compared to CMOS. Not only are low-power digital circuits a critical element of many national security systems (e.g. satellites), TFETs can perform circuit functions inaccessible to CMOS (e.g. polymorphism).
Most national policy decisions are complex with a variety of stakeholders, disparate interests and the potential for unintended consequences. While a number of analytical tools exist to help decision makers sort through the mountains of data and myriad of options, decision support teams are increasingly turning to complexity science for improved analysis and better insight into the potential impact of policy decisions. While complexity science has great potential, it has only proven useful in limited case s and when properly applied. In advance of more widespread use, a national - level effort to refine complexity science and more rigorously establish its technical underpinnings is recommended.
Information is one of the most powerful tools available today. All advances in technology may be used, as David Sarnoff said, for the benefit or harm of society. Information can be used to shape the future by free people, or used to control people by less than benevolent governments, as has been demonstrated since the mid - 1930s, and with growing frequency over the past 50 years. What promised to once set people free and fuel an industrial revolution that might improve the standard of living over most of the world, has also been used to manipulate and enslave entire populations. The future of information is tied to the future of technologies that support the collection of data, processing those data into information and knowledge, and distribution. Technologies supporting the future of information must include technologies that help protect the integrity of data and information, and help to guarantee its discoverability and appropriate availability -- often to the whole of society. This Page Intentionally Left Blank
Two sodium spray fire experiments performed by Sandia National Laboratories (SNL) were used for a code - to - code comparison between CONTAIN - LMR and SPHINCS. Both computer codes are used for modeling sodium accidents in sodium fast reactors. The comparison between the two codes provides insights into the ability of both codes to model sodium spray fires. The SNL T3 and T4 experiments are 20 kg sodium spray fires with sodium spray temperature s of 200 deg C and 500 deg C, respe ctively. Given the relatively low sodium temperature in the SNL T3 experiment, the sodium spray experienced a period of non - combustion. The vessel in the SNL T4 experiment experienced a rapid pressurization that caused of the instrumentation ports to fail during the sodium spray. Despite these unforeseen difficulties, both codes were shown in good agreement with the experiment s . The subsequent pool fire that develops from the unburned sodium spray is a significant characteristic of the T3 experiment. SPHIN CS showed better long - term agreement with the SNL T3 experiment than CONTAIN - LMR. The unexpected port failure during the SNL T4 experiment presented modelling challenges. The time at which the port failure occurred is unknown, but is believed to have occur red at about 11 seconds into the sodium spray fire. The sensitivity analysis for the SNL T4 experiment shows that with a port failure, the sodium spray fire can still maintain elevated pressures during the spray.
In June 2017, dust and salt samples were collected from the surface of Spent Nuclear Fuel (SNF) dry storage canisters at the Calvert Cliffs Nuclear Power Plant. The samples were delivered to Sandia National laboratories for analysis. Two types of samples were collected: filter-backed Scotch-Brite TM pads were used to collect dry dust samples for characterization of salt and dust morphologies and distributions; and Saltsmart TM test strips were used to collect soluble salts for determining salt surface loadings per unit area. After collection, the samples were sealed into plastic sleeves for shipping. Condensation within the sleeves containing the Scotch-Brite TM samples remobilized the salts, rendering them ineffective for the intended purpose, and also led to mold growth, further compromising the samples; for these reasons, the samples were not analyzed. The SaltSmart TM samples were unaffected and were analyzed by ion chromatography for major anions and cations. The results of those analyses are presented here.
The objective of this project is to improve the fidelity of battery-scale simulations of abuse scenarios through the creation and application of microscale (particle-scale) electrode simulations.
Simulation Data Management (SDM), the ability to securely organize, archive, and share analysis models and the artifacts used to create them, is a fundamental requirement for modern engineering analysis based on computational simulation. We have worked separately to provide secure, network SDM services to engineers and scientists at our respective laboratories for over a decade. We propose to leverage our experience and lessons learned to help develop and deploy a next-generation SDM service as part of a multi-laboratory team. This service will be portable across multiple sites and platforms, and will be accessible via a range of command-line tools and well-documented APIs. In this document, we’ll review our high-level and low-level requirements for such a system, review one existing system, and briefly discuss our proposed implementation.
The Crude Oil Characterization Research Study is designed to evaluate whether crude oils currently transported in North America, including those produced from "tight" formations, exhibit physical or chemical properties that are distinct from conventional crudes, and how these properties associate with combustion hazards with may be realized during transportation and handling.
The Henkel technical data sheet (TDS) for Stycast 2651/Catalyst 11 lists three alternate cure schedules for the material, each of which would result in a different state of reaction and different material properties. Here, a cure schedule that attains full reaction of the material is defined. The use of this cure schedule will eliminate variance in material properties due to changes in the cure state of the material, and the cure schedule will serve as the method to make material prior to characterizing properties. The following recommendation was motivated by (1) a desire to cure at a single temperature for ease of manufacture and (2) a desire to keep the cure temperature low (to minimize residual stress build-up associated with the cooldown from the cure temperature to room temperature) without excessively limiting the cure reaction due to vitrification (i.e., material glass transition temperature, Tg, exceeding cure temperature).
The Emerson & Cuming technical data sheet (TDS) for Stycast 2651/Catalyst 9 lists three alternate cure schedules for the material, each of which would result in a different state of reaction and different material properties. Here, a cure schedule that attains full reaction of the material is defined. The use of this cure schedule will eliminate variance in material properties due to changes in the cure state of the material, and the cure schedule will serve as the method to make material prior to characterizing properties. The following recommendation uses one of the schedules within the TDS and adds a “post cure” to obtain full reaction.
This LDRD investigated plasma formation, field strength, and current loss in pulsed power diodes. In particular the Self-Magnetic Pinch (SMP) e-beam diode was studied on the RITS-6 accelerator. Magnetic fields of a few Tesla and electric fields of several MV/cm were measured using visible spectroscopy techniques. The magnetic field measurements were then used to determine the current distribution in the diode. This distribution showed that significant beam current extends radially beyond the few millimeter x-ray focal spot diameter. Additionally, shielding of the magnetic field due to dense electrode surface plasmas was observed, quantified, and found to be consistent with the calculated Spitzer resistivity. In addition to the work on RITS, measurements were also made on the Z-machine looking to quantify plasmas within the power flow regions. Measurements were taken in the post-hole convolute and final feed gap regions on Z. Dopants were applied to power flow surfaces and measured spectroscopically. These measurements gave species and density/temperature estimates. Preliminary B-field measurements in the load region were attempted as well. Finally, simulation work using the EMPHASIS, electromagnetic particle in cell code, was conducted using the Z MITL conditions. The purpose of these simulations was to investigate several surface plasma generations models under Z conditions for comparison with experimental data.
Rigorous characterization of the performance and generalization ability of cyber defense systems is extremely difficult, making it hard to gauge uncertainty, and thus, confidence. This difficulty largely stems from a lack of labeled attack data that fully explores the potential adversarial space. Currently, performance of cyber defense systems is typically evaluated in a qualitative manner by manually inspecting the results of the system on live data and adjusting as needed. Additionally, machine learning has shown promise in deriving models that automatically learn indicators of compromise that are more robust than analyst-derived detectors. However, to generate these models, most algorithms require large amounts of labeled data (i.e., examples of attacks). Algorithms that do not require annotated data to derive models are similarly at a disadvantage, because labeled data is still necessary when evaluating performance. In this work, we explore the use of temporal generative models to learn cyber attack graph representations and automatically generate data for experimentation and evaluation. Training and evaluating cyber systems and machine learning models requires significant, annotated data, which is typically collected and labeled by hand for one-off experiments. Automatically generating such data helps derive/evaluate detection models and ensures reproducibility of results. Experimentally, we demonstrate the efficacy of generative sequence analysis techniques on learning the structure of attack graphs, based on a realistic example. These derived models can then be used to generate more data. Additionally, we provide a roadmap for future research efforts in this area.
Advanced automotive gasoline engines that leverage a combination of reduced heat transfer, throttling, and mechanical losses; shorter combustion durations; and higher compression and mixture specific heat ratios are needed to meet aggressive DOE VTP fuel economy and pollutant emission targets. Central challenges include poor combustion stability at low-power conditions when large amounts of charge dilution are introduced and high sensitivity of conventional inductive coil ignition systems to elevated charge motion and density for boosted high-load operation. For conventional spark ignited operation, novel low-temperature plasma (LTP) or pre-chamber based ignition systems can improve dilution tolerances while maintaining good performance characteristics at elevated charge densities. Moreover, these igniters can improve the control of advanced compression ignition (ACI) strategies for gasoline at low to moderate loads. The overarching research objective of the Gasoline Combustion Fundamentals project is to investigate phenomenological aspects related to enhanced ignition. The objective is accomplished through targeted experiments performed in a single-cylinder optically accessible research engine or an in-house developed optically accessible spark calorimeter (OASC). In situ optical diagnostics and ex situ gas sampling measurements are performed to elucidate important details of ignition and combustion processes. Measurements are further used to develop and validate complementary high-fidelity ignition simulations. The primary project audience is automotive manufacturers, Tier 1 suppliers, and technology startups—close cooperation has resulted in the development and execution of project objectives that address crucial mid- to long-range research challenges.
Despite compliance issues in previous years, automakers have demonstrated that the newest generation of diesel power trains are capable of meeting all federal and state regulations (EPA, 2016). Diesels continue to be a cost-effective, efficient, powerful propulsion source for many light- and medium-duty vehicle applications (Martec, 2016). Even modest reductions in the fuel consumption of light- and medium duty diesel vehicles in the U.S. will eliminate millions of tons of CO2 emissions per year. Continued improvement of diesel combustion systems will play an important role in reducing fleet fuel consumption, but these improvements will require an unprecedented scientific understanding of how changes in engine design and calibration affect the mixture preparation, combustion, and pollutant formation processes that take place inside the cylinder. The focus of this year’s research is to provide insight into the physical mechanisms responsible for improved thermal efficiency observed with a stepped-lip piston. Understanding how piston design can influence efficiency will help engineers develop and optimize new diesel combustion systems.
Schenkman, Benjamin L.; Vandermeer, Jeremy B.; Mueller-Stoffels, Marc; Koplin, Clay; Benson, Cole
This report is a follow on to a previous study performed by Sandia National Labs and Alaska Center for Energy and Power which investigated the use of an energy storage system (ESS) providing spinning reserve within the Cordova Electric Cooperative (CEC) grid. The study provided the savings using the ESS as spinning reserve through reduced fossil fuel consumption and runtime on the diesel generators. In this report, the saving values are used from the previous study to determine the benefit-to-cost ratio for various ratings of ESS performing spinning reserve and quantifying other applications that are applicable to CEC.
Regulatory drivers and market demands for lower pollutant emissions, lower carbon dioxide emissions, and lower fuel consumption motivate the development of clean and fuel-efficient engine operating strategies. Most current production engines use a combination of both in-cylinder and exhaust emissions-control strategies to achieve these goals. The emissions and efficiency performance of in-cylinder strategies depend strongly on flow and mixing processes associated with fuel injection. Various diesel engine manufacturers have adopted close-coupled post-injection combustion strategies to both reduce pollutant emissions and to increase engine efficiency for heavy-duty applications, as well as for light- and medium-duty applications. Close-coupled post-injections are typically short injections that follow a larger main injection in the same cycle after a short dwell, such that the energy conversion efficiency of the post-injection is typical of diesel combustion. Of the various post-injection schedules that have been reported in the literature, effects on exhaust soot vary by roughly an order of magnitude in either direction of increasing or decreasing emissions relative to single injections (O’Connor et al., 2015). While several hypotheses have been offered in the literature to help explain these observations, no clear consensus has been established. For new engines to take full advantage of the benefits that post-injections can offer, the in-cylinder mechanisms that affect emissions and efficiency must be identified and described to provide guidance for engine design.
Many advanced combustion approaches have demonstrated potential for achieving diesel-like thermal efficiency but with much lower pollutant emissions of particulate matter (PM) and nitrogen oxides (NOx). RCCI is one advanced combustion concept, which makes use of in-cylinder blending of two fuels with differing reactivity for improved control of the combustion phasing and rate (Reitz et al., 2015). Previous research and development at ORNL has demonstrated successful implementation of RCCI on a light-duty multi-cylinder engine over a wide range of operating conditions (Curran et al., 2015). Several challenges were encountered when extending the research to practical applications, including limits to the operating range, both for high and low loads. Co-optimizing the engine and fuel aspects of the RCCI approach might allow these operating limits to be overcome. The in-cylinder mechanisms by which fuel properties interact with engine operating condition variables is not well understood, however, in part because RCCI is a new combustion concept that is still being developed, and limited data have been acquired to date, especially using in-cylinder optical/imaging diagnostics. The objective of this work is to use in-cylinder diagnostics in a heavy-duty single-cylinder optical engine at SNL to understand the interplay between fuel properties and engine hardware and operating conditions for RCCI in general, and in particular for the light-duty multi-cylinder all-metal RCCI engine experiments at ORNL.
This activity is being revised based on the November 2016 Project Plan and Installation Plan. These documents were developed and issued due to a change in the design responsibilities, assigned project personnel and the change in the installation of the RCS subsystems.
Batteries and hydrogen fuel cells provide zero emission power at the point of use. They are studied as an alternative powerplant for maritime vessels by considering 14 case studies of various ship sizes and routes varying from small passenger vessels to the largest cargo ships. The method used was to compare the mass and volume of the required zero emission solution to the available mass and volume on an existing vessel considering its current engine and fuel storage systems. The results show that it is practically feasible to consider these zero emission technologies for most vessels in the world's fleet. Hydrogen fuel cells proved to be the most capable while battery systems showed an advantage for high power, short duration missions. The results provide a guide to ship designers to determine the most suitable types of zero emission powerplants to fit a ship based on its size and energy requirements.
Wireless networking and mobile communications is increasing around the world and in all sectors of our lives. With increasing use, the density and complexity of the systems increase with more base stations and advanced protocols to enable higher data throughputs. The security of data transported over wireless networks must also evolve with the advances in technologies enabling more capable wireless networks. However, means for analysis of the effectiveness of security approaches and implementations used on wireless networks are lacking. More specifically a capability to analyze the lower-layer protocols (i.e., Link and Physical layers) is a major challenge. An analysis approach that incorporates protocol implementations without the need for RF emissions is necessary. In this research paper several emulation tools and custom extensions that enable an analysis platform to perform cyber security analysis of lower layer wireless networks is presented. A use case of a published exploit in the 802.11 (i.e., WiFi) protocol family is provided to demonstrate the effectiveness of the described emulation platform.
The Department of Energy, in response to requests from the U.S. Congress, wishes to maintain an up-to-date table documenting the number of available full drawdowns of each of the caverns owned by the Strategic Petroleum Reserve. This information is important for assessing the SPR's ability to deliver oil to domestic oil companies expeditiously if national or world events dictate a rapid sale and deployment of the oil reserves. The evaluation of drawdown risks require the consideration of several factors regarding cavern and wellbore integrity and stability, including stress states caused by cavern geometry and operations, salt damage caused by dilatant and tensile stresses, the effect on enhanced creep on wellbore integrity, the sympathetic stress effect of operations on neighboring caverns. Based on the work over the past several years, a consensus has been built regarding the assessment of drawdown capabilities and risks for the SPR caverns. This report draws upon the recently Bayou Choctaw model upgrade and analyses to reevaluate and update the available drawdowns for each of those caverns. BC-18, 19, 101 and 102 are predicted to have conditional five available drawdowns remaining BC-15 and 17 have only one remaining drawdowns due to their proximity.
We demonstrate a controlled phase gate approach to entangling neutral atoms using a tunable, Rydberg-dressed interaction. Previous approaches utilize a “dipole blockade” or a “spin-flip blockade” and thereby lack arbitrary phase control, and are less extensible to simultaneous entangling operations in many-atom systems. We systematically study major error mechanisms that limit the fidelity of the entangling interaction and find the dominant error mechanism to be phase noise in the coherent control of the single spins. Our work charts a definitive path to high fidelity entanglement using Rydberg-state-mediated interactions in neutral atoms.
As tradit ional numerical computing has faced challenges, researchers have turned towards alternative computing approaches to reduce power - per - computation metrics and improve algorithm performance. Here, we describe an approach towards non - conventional computing that strengthens the connection between machine learning and neuroscience concepts. The Hardware Acceleration of Adaptive Neural Algorithms (HAANA) project ha s develop ed neural machine learning algorithms and hardware for applications in image processing and cybersecurity. While machine learning methods are effective at extracting relevant features from many types of data, the effectiveness of these algorithms degrades when subjected to real - world conditions. Our team has generated novel neural - inspired approa ches to improve the resiliency and adaptability of machine learning algorithms. In addition, we have also designed and fabricated hardware architectures and microelectronic devices specifically tuned towards the training and inference operations of neural - inspired algorithms. Finally, our multi - scale simulation framework allows us to assess the impact of microelectronic device properties on algorithm performance.
Shale is characterized by the predominant presence of nanometer-scale (1-100 nm) pores. The behavior of fluids in those pores directly controls shale gas storage and release in shale matrix and ultimately the wellbore production in unconventional reservoirs. Recently, it has been recognized that a fluid confined in nanopores can behave dramatically differently from the corresponding bulk phase due to nanopore confinement (Wang, 2014). CO2 and H2O, either preexisting or introduced, are two major components that coexist with shale gas (predominately CH4) during hydrofracturing and gas extraction. Note that liquid or supercritical CO2 has been suggested as an alternative fluid for subsurface fracturing such that CO2 enhanced gas recovery can also serve as a CO2 sequestration process. Limited data indicate that CO2 may preferentially adsorb in nanopores (particularly those in kerogen) and therefore displace CH4 in shale. Similarly, the presence of water moisture seems able to displace or trap CH4 in shale matrix. Therefore, fundamental understanding of CH4-CO2-H2O behavior and their interactions in shale nanopores is of great importance for gas production and the related CO2 sequestration. This project focuses on the systematic study of CH4-CO2-H2O interactions in shale nanopores under high-pressure and high temperature reservoir conditions. The proposed work will help to develop new stimulation strategies to enable efficient resource recovery from fewer and less environmentally impactful wells.