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In August 2017, Sandia convened five workshops to explore the future of advanced technologies and global peace and security through the lenses of deterrence, information, innovation, nonproliferation, and population and Earth systems.
The Net Zero World Initiative, committed to accelerating decarbonization and fostering more inclusive, equitable, and resilient energy systems, is pleased to support the Government of Ukraine in developing decarbonization pathways for its energy sector. In a collaborative effort to promote the resilience and sustainability of the energy system as part of the country’s reconstruction, the leading US Department of Energy’s national laboratories and distinguished Ukrainian research institutes and think tanks worked on this study. Together, we have developed scenarios for achieving net zero emissions in the energy sector, which are in line with the goals set out in the Energy Strategy of Ukraine through 2050. The Ministry of Energy of Ukraine report will present this report at the Conference of the Parties (COP28).
This presentation focuses on approaches that could be used by a data computation center to identify attacks and ensure malicious code and backdoors are identified if planted in system. The goal is to identify actionable security information from the mountain of data that flows into and out of an organization. The approaches are applicable to big data computational center and some must also use big data techniques to extract the actionable security information from the mountain of data that flows into and out of a data computational center. The briefing covers the detection of malicious delivery sites and techniques for reducing the mountain of data so that intrusion detection information can be useful, and not hidden in a plethora of false alerts. It also looks at the identification of possible unauthorized data exfiltration.
Two of the challenges of current plant reliability approaches are the ability to integrate plant health data, and to support decision making. Condition based data and diagnostic/prognostic information are in fact not considered into plant reliability models to inform system engineers on the most critical components. Currently, the propagation of quantitative health data from the component to the system level is a challenge given the diverse nature/structure of the data. On the other hand, plant reliability methods (which are typically based on fault-trees or reliability block diagrams) can effectively propagate data from the component to the system level, but values of failure rates or failure probabilities are an approximated integral representation of the past industry-wide operational experience, and it neglects the present component health status (e.g., diagnostic and condition-based data) and health projection (when available from prognostic data). Our first claim is that system reliability models should propagate health information from the component to the system/plant level in order to provide a quantitative snapshot of system/plant health and identify the most critical components. Our second claim is that component health should be informed solely by that specific component current and historical performance data and should not be an approximated integral representation of the past industry-wide operational experience. This paper is directly supporting these two claims by proposing a different approach to perform reliability modeling which relies on available component diagnostic, prognostic and condition-based data to measure component health, and it propagates this information through fault tree models. The propagation of health data from the component to the system level is performed not in terms of probability, but in terms of margins where margin is defined as the “distance” between the present actual status and an undesired event (e.g., failure or unacceptable performance). Through a cause-effect lens, while classical reliability models target the effect associated to a component performance, a margin-based approach focuses on the cause of an undesired component performance (i.e., component health). Hence, thinking of reliability in terms of margins implies decision making based on causal reasoning. We will show how fault tree models can be solved using a margin language and how this process can effectively assist system engineers to identify the most critical components.
Current system reliability methods (typically based on fault trees or reliability block diagrams) can effectively propagate reliability data from the asset to the system level in order to identify system critical points. However, employed asset reliability data are an approximated integral representation of the past industrywide operational experience, and they neglect the present asset health status (available, for example, from online monitoring data and diagnostic assessments) and forecasted health projection (when available from prognostic models). Asset health should be informed solely by that specific asset’s current and historical performance data and should not be an approximated integral representation of the past industrywide operational experience (as currently performed by system reliability models through Bayesian updating processes). Sensor data, diagnostic assessments, and prognostic assessments are in fact not considered in plant reliability models used to inform system engineers on the most critical assets. In addition, the propagation of quantitative health data from the asset to the system level is a challenge given the diverse nature and structure of health data elements (e.g., vibration spectra, temperature readings, expected failure time). Ideally, in a predictive maintenance context, system reliability models should support decision making by propagating available health information from the asset to the system level in order to provide a quantitative snapshot of system health and identify the most critical assets. Here, this paper is directly addressing these two goals by proposing a different approach for reliability modeling that relies on asset diagnostic and prognostic assessments, along with monitoring data to measure asset health. The propagation of health data from the asset to the system level is performed through fault tree models not in probability terms, but in terms of margin where margin is the “distance” between the present status and an undesired event (e.g., failure or unacceptable performance). Through a cause-effect lens, while classical reliability models target the effect associated with asset performance, a margin-based approach focuses on the cause of an undesired asset performance (i.e., its health). Hence, thinking of reliability in terms of margins implies decision-making based on causal reasoning. We will show how fault tree models can be solved using a margin language and how this process can effectively assist system engineers to identify the most critical assets.
Broad classes of functional materials exhibit glass-like phenomena originating with the frustration of a soft phonon driven phase transition, including relaxor ferroelectrics and shape memory strain glasses. While the soft phonon mechanism is mostly understood, how this mechanism becomes frustrated in the presence of disorder remains intensely debated. A common structural feature of the frustrated state is nanoscale regions of local ferroic displacements that form well above the ordering temperature; these are called polar nanoregions (PNRs) in relaxor ferroelectrics and ferroelastic nanodomains (FND) in the strain glasses. The existence of these small regions provides a basis to explain glass-like slow relaxation phenomena, which can manifest in the lattice dynamics as phonon over damping. However, this does not explain why the long-range order becomes localized into PNRs or FNDs, or why this happens specifically at the nanoscale. Recent scattering experiments and theories suggest an exciting new way to think about these problems in terms of the physics of lattice vibrations in chemically disordered crystals. More generally, probing the lattice dynamics of these systems sheds new light on the microscopic origin of the nanoregions, glassy behavior, and enhanced functional properties.
The tons of organic material that come to the Earth every day on meteorites and dust particles probably helped to make the Earth habitable, and possibly played a role in the origin of life. These compounds."include amino acids, nucleo bases, vesicle forming fatty acids, and many others. Lab experiments suggest that low temperature radiation chemistry could account for many of these chemicals. If we find evidence of Life elsewhere in the Solar System it will probably be in form of chemical biomarkers, quintessentially biological molecules that indicate the presence of micro-organisms. While most people think of the molecules above as good biomarkers, they are not.
The tons of extraterrestrial organic material that come to the Earth every day probably helped to made the Earth habitable, and possibly played a role in the origin of life. At the astrochemistry lab (http://www.astrochem.orq) we investigate the formation and distribution of organic molecules in space and consider the impact such molecules may have on the habitability of planets and the search for life in the Solar System. The organic compounds in meteorites include amino acids, aromatics of various sorts including purine and pyrimidine bases, and fatty acids that form bi-layer vesicles. The origin of many of these species remains mysterious, but in recent years we and others have performed experiments that suggest low temperature radiation chemistry could account for the presence and deuterium enrichment of many of these molecules. . I will present our laboratory experiments that show the viability of low temperature radiation chemistry as a source of organic molecules such as;amino acids (Nature, 2002, 416, 401-403), amphiphiles (Astrobiology, 2003, 2, 371, Proc. Nat. Acad. Sci. 2001, 98, 815), quinones (Science, 1999, 283, 1135) and other functionalized aromatic compounds (Meteoritics, 2001, 36, 351 ; Astrophysical Journal., 2003, 582, L25), some of which were invoked as potential biomarkers in the Alan Hills 84001 Martian meteorite. Understanding how components of proteins and DNA could form in sterile space environments is also of relevance to our search for life elsewhere in the Solar System, the great task now ahead of NASA. If we find evidence of Life elsewhere in the Solar System it will probably be in form of chemical biomarkers, quintessentially biological molecules that indicate the presence of micro-organisms. While most people think of molecules such as amino acids, and nucleo-bases as good candidate biomarkers, these molecules are produced non-biotically in space and are expected to be present on the surface of other planets even in the absence of Life. Understanding the range of non-biological organic molecules which could act as false biomarkers in space is a prerequisite for any reasonable search for Life on other worlds.
Littoral Power Systems, Inc. (LPS) is writing a new story when it comes to turning the natural power of water into electricity. h-Modulor™ is a new way of designing and building hydropower plants that addresses the crucial issues of cost, environmental impact and regulatory time frames. For decades hydropower developers, owners and operators have struggled to build new or expanded facilities that didn’t cost so much, were friendlier to the environment and didn’t take so much time to license or build. The problem is particularly acute in smaller installations. The burdensome regulatory pathway for projects under 10MW in capacity have eased the licensing requirements for small hydro projects. But in small projects are still very hard, because capital costs are a much larger percentage of the generation revenues than they are in large facilities. There is an acute need for a cost effective solution. Understanding this need and opportunity, LPS conceived a new type of hydropower equipment – a kit of standard, pre-fabricated modular parts for dam safety, power generation, spill control and other hydropower facility functions to be used to build and operate a hydro plant at substantially lower costs than are currently achievable. This system of prefabricated parts also showed promise to reduce environmental impacts, reduce engineering time involved in the licensing process, and possibly provide a new way for regulators to think about licensing that could lead to reduced regulatory timeframes. Because of this invention’s compelling and intriguing promise the Department of Energy’s (DOE) Water Power Technology Office (WPTO) awarded funding to LPS through the Office of Energy Efficiency and Renewable Energy (EERE) to develop this new type of hydropower equipment to meet a levelized cost of energy (LCOE) goal and analyze it for dam safety. LPS teamed with professional engineers, PhDs, and geotechnical consultants from GZA GeoEnvironental, Inc. (GZA), Alden Research Laboratory (Alden), University of Massachusetts – Dartmouth (UMass-D), the National Renewable Energy Laboratory (NREL) and AECOM. In collaboration with the engineers and managers at the WPTO and EERE they matured the concepts through substantial design shifts. The project exceeded the original goals and delivered designs beyond the proof-of-concept stage backed up by thorough professional engineering analysis of stability, seepage, and structural integrity. A full-scale section with seals successfully retained water in full-head leakage tests. Construction plans and professional engineering cost estimates indicated an LCOE half of the original LCOE goal. A system of these modules is now on track to be used in a 4.5 MW commercial installation.
The D-Wave 2X is the third generation of quantum processing created by D-Wave. NASA (with Google and USRA) and Lockheed Martin (with USC), both own D-Wave systems. Los Alamos National Laboratory (LANL) purchased a D-Wave 2X in November 2015. The D-Wave 2X processor contains (nominally) 1152 quantum bits (or qubits) and is designed to specifically perform quantum annealing, which is a well-known method for finding a global minimum of an optimization problem. This methodology is based on direct execution of a quantum evolution in experimental quantum hardware. While this can be a powerful method for solving particular kinds of problems, it also means that the D-Wave 2X processor is not a general computing processor and cannot be programmed to perform a wide variety of tasks. It is a highly specialized processor, well beyond what NNSA currently thinks of as an “advanced architecture.”A D-Wave is best described as a quantum optimizer. That is, it uses quantum superposition to find the lowest energy state of a system by repeated doses of power and settling stages. The D-Wave produces multiple solutions to any suitably formulated problem, one of which is the lowest energy state solution (global minimum). Mapping problems onto the D-Wave requires defining an objective function to be minimized and then encoding that function in the Hamiltonian of the D-Wave system. The quantum annealing method is then used to find the lowest energy configuration of the Hamiltonian using the current D-Wave Two, two-level, quantum processor. This is not always an easy thing to do, and the D-Wave Two has significant limitations that restrict problem sizes that can be run and algorithmic choices that can be made. Furthermore, as more people are exploring this technology, it has become clear that it is very difficult to come up with general approaches to optimization that can both utilize the D-Wave and that can do better than highly developed algorithms on conventional computers for specific applications. These are all fundamental challenges that must be overcome for the D-Wave, or similar, quantum computing technology to be broadly applicable.
In the fast-paced field of biotechnology where innovation has such far-reaching impacts on human health and the environment, dealing with the implications of possible illicit activities, accidents or unintended research consequences with potential detrimental societal impacts tends to remain in the background. While controls may be inevitable for the biotech industry, workshop attendees agreed that the way in which controls are implemented will play a major role in the agility and innovation of the biotechnology industry. There is little desire to slow down the pace of the gains while dealing with the security issues that arise. As was seen from the brief examinations of the Nuclear, Chemical, and Information Technology sectors explored in this workshop, establishing a regulatory regime needs to be a partnership between the public, corporate interests, scientists, and the government. Regulation is often written to combat perceived risk rather than actual risk—the public’s perceptions (occasionally even fictional portrayals) can spur regulatory efforts. This leads to the need for a thorough and continuing assessment of the risks posed by modern biotechnology. Inadequate or minimal risk assessment might expedite development in the short term but has potential negative long-term security and economic consequences. Industry and the technical community also often have a large role in setting regulatory policy, especially when well-crafted incentives are incorporated into the regulations. Such incentives might actually lead to enhanced innovation while poorly designed incentives can actually reduce safety and security. Any regulations should be as agile and flexible as the technology they regulate and when applied to biotechnologies they will need a new framework for thinking and implementing. The new framework should consider biotechnology as a technology and not simply a science since it is an extremely complex and adaptive system. This suggests the need to invest in social and political systems, rather than exclusively in technology, as a part of maintaining not only robust, but agile regulation. Modeling and simulations to illustrate how the biotech ecosystem might respond to new policies may be a promising approach to crafting regulations.
The evolution of philosophy and computations in the International Data Center (IDC) related to aerosol samples have had profound impacts on the number of recorded detections in the network since routine operations began in 2000. Key decisions from policymakers have been the list of triggering radionuclides, the scheme for categorizing these into interest levels 1-5, and an algorithm for determining when an anthropogenic isotope is seen so often that it is no longer interesting, known as the Exponential Weighted Moving Average (EWMA). These are described in the Operations Manual of the IDC. Key parameters that are controlled by the IDC but for which the IDC receives occasional input from policymakers include the constants in EWMA and the peak significance threshold for individual gamma rays, the latter of which directly leads to determination of the presence or absence of a radionuclide in a sample. There are also changes in computations which the IDC makes and informs policy makers about, such as changes in how background is computed, which could also affect the ease of detecting a peak – real or false. Rather than focus on the quantitative changes due to computation changes, this work records some thinking on how isotopes and peak significance levels were chosen, and the resulting detections seen over 18 years during the buildup of the International Monitoring System (IMS). These detections are considered on a global scale to try to determine the relative impact on monitoring, and in some cases, the nature of their existence. Repeated detections of 131I and 133I are the most troublesome, but they are not so frequent to be a major problem for the Verification Regime. These detections could probably be handled adequately using scientific methods currently under development for xenon backgrounds. It is also somewhat problematic that top-level analysis of aerosol backgrounds has not been reported previous to this. The steep increase in the rate of detections after 2016 are a concern, either in the actual backgrounds or from changes in the calculations methods used to generate the Reviewed Radionuclide Report (RRR.) Final conclusions of the authors are that the computational stability of the RRR is very important. With computational stability, changes can be usefully analyzed as being due to changes in radioactivity in Earth’s atmosphere This report is a distillation into text of a talk given in the Radionuclide Experts Group (RNEG) in Vienna during Working Group B (WGB) in February of 2019. This report does not directly contain any IDC data, only summaries by year, or by isotope, or by location. No specific IDC detection by time, location, or isotope is included.
The objective of automatic programming is to improve the overall environment for describing the program. This improved environment is realized by a reduction in the amount of detail that the programmer needs to know and is exposed to. Furthermore, this improved environment is achieved by a specification language that is more natural to the user's problem domain and to the user's way of thinking and looking at the problem. The goal of this research is to apply the concepts of automatic programming (AP) to modeling discrete event simulation system. Specific emphasis is on the design and development of simulation tools to assist the modeler define or construct a model of the system and to then automatically write the corresponding simulation code in the target simulation language, GPSS/PC. A related goal is to evaluate the feasibility of various languages for constructing automatic programming simulation tools.
The assured mission support space architecture (AMSSA) study was conducted with the overall goal of developing a long-term requirements-driven integrated space architecture to provide responsive and sustained space support to the combatant commands. Although derivation of an architecture was the focus of the study, there are three significant products from the effort. The first is a philosophy that defines the necessary attributes for the development and operation of space systems to ensure an integrated, interoperable architecture that, by design, provides a high degree of combat utility. The second is the architecture itself; based on an interoperable system-of-systems strategy, it reflects a long-range goal for space that will evolve as user requirements adapt to a changing world environment. The third product is the framework of a process that, when fully developed, will provide essential information to key decision makers for space systems acquisition in order to achieve the AMSSA goal. It is a categorical imperative that military space planners develop space systems that will act as true force multipliers. AMSSA provides the philosophy, process, and architecture that, when integrated with the DOD requirements and acquisition procedures, can yield an assured mission support capability from space to the combatant commanders. An important feature of the AMSSA initiative is the participation by every organization that has a role or interest in space systems development and operation. With continued community involvement, the concept of the AMSSA will become a reality. In summary, AMSSA offers a better way to think about space (philosophy) that can lead to the effective utilization of limited resources (process) with an infrastructure designed to meet the future space needs (architecture) of our combat forces.
Customer adoption of distributed energy resources and public policies are driving changes in the uses of the distribution system. A system originally designed and built for one-way energy flows from central generating facilities to end-use customers is now experiencing injections of energy from customers anywhere on the grid and frequent reversals in the direction of energy flow. In response, regulators and utilities are re-thinking the design and operations of the grid to create more open and transactive electric networks. This evolution has the opportunity to unlock significant value for customers and utilities. Alternatively, failure to seize this potential may instead lead to an erosion of value if customers seek to defect and disconnect from the system. This paper will discuss how current grid modernization investments may be leveraged to create open networks that increase value through the interaction of intelligent devices on the grid and prosumerization of customers. Moreover, even greater value can be realized through the synergistic effects of convergence of multiple networks. This paper will highlight examples of the emerging nexus of non-electric networks with electricity.
It has been advocated that life cycle thinking (LCT) should be applied in impact assessment (IA) to a greater extent, since some development proposals pose a risk of significant impacts throughout the interconnected activities of product systems. Multiple authors have proposed the usage of life cycle assessment (LCA) for such analytical advancement, but little to no research on this tool application has been founded in IA practice so far. The aim of this article is to elaborate further on the gains assigned to application of LCA. The research builds on a review of 85 Danish IA reports, which were analysed for analytical appropriateness and application of LCT. Through a focus on the non-technical summary, the conclusion and the use of specific search words, passages containing LCT were searched for in each IA report. These passages were then analysed with a generic framework. The results reveal that LCT is appropriate for most of the IAs, but that LCA is rarely applied to provide such a perspective. Without LCA, the IAs show mixed performance in regard to LCT. Most IAs do consider the product provision of development proposals, but they rarely relate impacts to this function explicitly. Many IAs do consider downstream impacts, but assessments of upstream, distant impacts are generally absent. It is concluded that multiple analytical gains can be attributed to greater application of LCA in IA practice, though some level of LCT already exists. - Highlights: • Life cycle thinking is appropriate across the types and topics of impact assessment. • Yet, life cycle assessment is rarely used for adding such perspective. • Impact assessment practice does apply some degree of life cycle thinking. • However, application of life cycle assessment could bring analytical gains.
Highlights: • LCA was used for evaluating the performance of four provincial waste management systems. • Milano, Bergamo, Pavia and Mantova (Italy) are the provinces selected for the analysis. • Most of the data used to model the systems are primary. • Significant differences were found among the provinces located in the same Region. • LCA was used as a decision-supporting tool by Regione Lombardia. - Abstract: This paper reports some of the findings of the ‘GERLA’ project: GEstione Rifiuti in Lombardia – Analisi del ciclo di vita (Waste management in Lombardia – Life cycle assessment). The project was devoted to support Lombardia Region in the drafting of the new waste management plan by applying a life cycle thinking perspective. The present paper mainly focuses on four Provinces in the Region, which were selected based on their peculiarities. Life cycle assessment (LCA) was adopted as the methodology to assess the current performance of the integrated waste management systems, to discuss strengths and weaknesses of each of them and to design their perspective evolution as of year 2020. Results show that despite a usual business approach that is beneficial to all the provinces, the introduction of technological and management improvements to the system provides in general additional energy and environmental benefits for all four provinces. The same improvements can be easily extended to the whole Region, leading to increased environmental benefits from the waste management sector, in line with the targets set by the European Union for 2020.
This guide is for public utility commission staff, state energy office staff, and other stakeholders who review model results for utility integrated resource plans (IRPs). It builds on the "Beginner's Guide to Understanding Power System Model Results for Long-Term Resource Plans," and delves into more advanced topics that were not included or not fully covered in the beginner's guide. The topics included here are evolving and may or may not be important to the resource plan you are evaluating. Due to limitations in resources, it's also not possible to give the highest level of detail or attention to every topic within a plan. An important part of planning for an uncertain future involves deciding which topics deserve the most focus and resources. It's all about prioritizing efforts where they will have the most impact or be most beneficial despite uncertainties ahead. As with the beginner's guide, we intend for this guide to help improve decision-making in the electricity planning process by strengthening understanding and dialogue between electricity system planners and relevant stakeholders. The following information is designed to allow you to better engage in the planning process by evaluating results, asking questions, and thinking through what is most important. We allocate significant discussion in the guide to electricity demand evolution, demand-side resources, and resource adequacy. These topics have garnered increasing attention, hence their prominence in this guide. Other topics are also presented, but their incorporation into the planning process might vary based on interest and relevance. The absence of certain topics doesn't diminish their importance; rather, it reflects the reality that in planning, trade-offs necessitate prioritizing certain aspects over others.