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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.

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At least 19 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2015

Risk assessment for Industrial Control Systems quantifying availability using mean failure cost (MFC)

Industrial Control Systems (ICS) are commonly used in industries such as oil and natural gas, transportation, electric, water and wastewater, chemical, pharmaceutical, pulp and paper, food and beverage, as well as discrete manufacturing (e.g., automotive, aerospace, and durable goods.) SCADA systems are generally used to control dispersed assets using centralized data acquisition and supervisory control. Originally, ICS implementations were susceptible primarily to local threats because most of their components were located in physically secure areas (i.e., ICS components were not connected to IT networks or systems). The trend toward integrating ICS systems with IT networks (e.g., efficiency and the Internet of Things) provides significantly less isolation for ICS from the outside world thus creating greater risk due to external threats. Albeit, the availability of ICS/SCADA systems is critical to assuring safety, security and profitability. Such systems form the backbone of our national cyber-physical infrastructure. We extend the concept of mean failure cost (MFC) to address quantifying availability to harmonize well with ICS security risk assessment. This new measure is based on the classic formulation of Availability combined with Mean Failure Cost (MFC). The metric offers a computational basis to estimate the availability of a system in terms of the loss that each stakeholder stands to sustain as a result of security violations or breakdowns (e.g., deliberate malicious failures).

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2014

Quantifying Availability in SCADA Environments Using the Cyber Security Metric MFC

Supervisory Control and Data Acquisition (SCADA) systems are distributed networks dispersed over large geographic areas that aim to monitor and control industrial processes from remote areas and/or a centralized location. They are used in the management of critical infrastructures such as electric power generation, transmission and distribution, water and sewage, manufacturing/industrial manufacturing as well as oil and gas production. The availability of SCADA systems is tantamount to assuring safety, security and profitability. SCADA systems are the backbone of the national cyber-physical critical infrastructure. Herein, we explore the definition and quantification of an econometric measure of availability, as it applies to SCADA systems; our metric is a specialization of the generic measure of mean failure cost.

Availability↗
DOE OSTI2019

Microgrid Disaster Resiliency Analysis: Reducing Costs in Continuity of Operations (COOP) Planning

The electric grid serves a vital role in the supply chain of nearly all industrial and commercial organizations. A Microgrid infrastructure can provide this service and beneficial non-emergency services including a variety of generation/energy sources. To demonstrate the applicability of microgrids for energy resiliency, we present a microgrid resiliency case study for United Parcel Service’s (UPS) three separate shipping facilities. The goal, to enhance energy security, minimize cost and prevent cascading losses within other related business units. The impacts and consequences of which are quantified in this study using a Mean Failure Cost (MFC) risk assessment measure. MFC accounts for the potential loses to identified stakeholders that may result from a set of identified failures due to a set of identified threats. In this case, our study uses a method we call All Hazards Econometric System (AHES). AHES incorporates the cost of COOP using a strategy that considers the payback period of microgrid installation as compared to other energy delivery strategies.

Abercrombie, Robert K.↗
DOE OSTI2013

Failure Impact Analysis of Key Management in AMI Using Cybernomic Situational Assessment (CSA)

In earlier work, we presented a computational framework for quantifying the security of a system in terms of the average loss a stakeholder stands to sustain as a result of threats to the system. We named this system, the Cyberspace Security Econometrics System (CSES). In this paper, we refine the framework and apply it to cryptographic key management within the Advanced Metering Infrastructure (AMI) as an example. The stakeholders, requirements, components, and threats are determined. We then populate the matrices with justified values by addressing the AMI at a higher level, rather than trying to consider every piece of hardware and software involved. We accomplish this task by leveraging the recently established NISTR 7628 guideline for smart grid security. This allowed us to choose the stakeholders, requirements, components, and threats realistically. We reviewed the literature and selected an industry technical working group to select three representative threats from a collection of 29 threats. From this subset, we populate the stakes, dependency, and impact matrices, and the threat vector with realistic numbers. Each Stakeholder s Mean Failure Cost is then computed.

Algorithms↗
DOE OSTI2014

Quantifying the Impact of Unavailability in Cyber-Physical Environments

The Supervisory Control and Data Acquisition (SCADA) system discussed in this work manages a distributed control network for the Tunisian Electric & Gas Utility. The network is dispersed over a large geographic area that monitors and controls the flow of electricity/gas from both remote and centralized locations. The availability of the SCADA system in this context is critical to ensuring the uninterrupted delivery of energy, including safety, security, continuity of operations and revenue. Such SCADA systems are the backbone of national critical cyber-physical infrastructures. Herein, we propose adapting the Mean Failure Cost (MFC) metric for quantifying the cost of unavailability. This new metric combines the classic availability formulation with MFC. The resulting metric, so-called Econometric Availability (EA), offers a computational basis to evaluate a system in terms of the gain/loss ($/hour of operation) that affects each stakeholder due to unavailability.

Availability↗
DOE OSTI2014

Cryptographic Key Management and Critical Risk Assessment

The Department of Energy Office of Electricity Delivery and Energy Reliability (DOE-OE) CyberSecurity for Energy Delivery Systems (CSEDS) industry led program (DE-FOA-0000359) entitled "Innovation for Increasing CyberSecurity for Energy Delivery Systems (12CSEDS)," awarded a contract to Sypris Electronics LLC to develop a Cryptographic Key Management System for the smart grid (Scalable Key Management Solutions for Critical Infrastructure Protection). Oak Ridge National Laboratory (ORNL) and Sypris Electronics, LLC as a result of that award entered into a CRADA (NFE-11-03562) between ORNL and Sypris Electronics, LLC. ORNL provided its Cyber Security Econometrics System (CSES) as a tool to be modified and used as a metric to address risks and vulnerabilities in the management of cryptographic keys within the Advanced Metering Infrastructure (AMI) domain of the electric sector. ORNL concentrated our analysis on the AMI domain of which the National Electric Sector Cyber security Organization Resource (NESCOR) Working Group 1 (WG1) has documented 29 failure scenarios. The computational infrastructure of this metric involves system stakeholders, security requirements, system components and security threats. To compute this metric, we estimated the stakes that each stakeholder associates with each security requirement, as well as stochastic matrices that represent the probability of a threat to cause a component failure and the probability of a component failure to cause a security requirement violation. We applied this model to estimate the security of the AMI, by leveraging the recently established National Institute of Standards and Technology Interagency Report (NISTIR) 7628 guidelines for smart grid security and the International Electrotechnical Commission (IEC) 63351, Part 9 to identify the life cycle for cryptographic key management, resulting in a vector that assigned to each stakeholder an estimate of their average loss in terms of dollars per day of system operation. To further address probabilities of threats, information security analysis can be performed using game theory implemented in dynamic Agent Based Game Theoretic (ABGT) simulations. Such simulations can be verified with the results from game theory analysis and further used to explore larger scale, real world scenarios involving multiple attackers, defenders, and information assets. The strategy for the game was developed by analyzing five electric sector representative failure scenarios contained in the AMI functional domain from NESCOR WG1. From these five selected scenarios, we characterized them into three specific threat categories affecting confidentiality, integrity and availability (CIA). The analysis using our ABGT simulation demonstrated how to model the AMI functional domain using a set of rationalized game theoretic rules decomposed from the failure scenarios in terms of how those scenarios might impact the AMI network with respect to CIA.

Advanced Metering Infrastructure↗
DOE OSTI2014

Cyberspace Security Econometrics System (CSES) - U.S. Copyright TXu 1-901-039

Information security continues to evolve in response to disruptive changes with a persistent focus on information-centric controls and a healthy debate about balancing endpoint and network protection, with a goal of improved enterprise/business risk management. Economic uncertainty, intensively collaborative styles of work, virtualization, increased outsourcing and ongoing compliance pressures require careful consideration and adaptation. The Cyberspace Security Econometrics System (CSES) provides a measure (i.e., a quantitative indication) of reliability, performance, and/or safety of a system that accounts for the criticality of each requirement as a function of one or more stakeholders interests in that requirement. For a given stakeholder, CSES accounts for the variance that may exist among the stakes one attaches to meeting each requirement. The basis, objectives and capabilities for the CSES including inputs/outputs as well as the structural and mathematical underpinnings contained in this copyright.

Cyber Security Econometrics System↗
DOE OSTI2015

Remotely controlled manipulators using tele-robotic software provide enhanced efficiency and ergonomy for work in hot cells - 15101

Computer aid and electrical assistance have been applied to remote manipulation inside hostile environments. A heavy duty master/slave manipulator system, the MT200, has been updated with the addition of an electronic bay with specialized software, a motion module and a poly-articulated master arm. This is the result of an ambitious 20-year continuous improvement program undertaken by the French nuclear institutions, CEA LIST Interactive Robotics Unit and AREVA in order to improve ergonomics, safety and efficiency at the AREVA La Hague nuclear plant. The new manipulator system, named MT200 TAO, uses tele-robotics to improve ergonomics by literally lightening the workload transmitted to operators. The link between the master and slave arms is electrical rather than mechanical. The poly-articulated master arm uses haptic (force feedback) technology, so the operator effort is multiplied by a homothetic coefficient in the slave arm. Simplification of the offset function, now integrated into the master arm, means that the operator can be correctly positioned for each task. Some repetitive tasks can be carried out with great precision in playback (robotic mode). In addition, this technology contributes to better efficiency, higher availability rate, lower maintenance costs with increased MTBF (Mean Time Between Failures), reduced waste, improved performance and safer use (remote workstations are feasible). (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2012

Development of a Low-Cost Rotary Steerable Drilling System

The project had the goal to develop and commercialize a low-cost rotary steerable system (LCRSS) capable of operating downhole at conventional pressures and temperatures to reduce operating costs by a minimum of 50% and lost-in-hole charges by at least 50% over the currently offered systems. The LCRSS system developed under this project does reduce operating costs by 55% and lost-in-hole charges by at least 50%. The developed product is not commercializable in its current form. The overall objective was to develop and commercialize a low cost rotary steerable system (LCRSS) capable of operating downhole at conventional pressures and temperatures (20,000 psi/150 C) while reducing the operating costs by 50% and the lost-in-hole charges by 50% over the currently available systems. The proposed reduction in costs were to be realized through the significant reduction in tool complexity, a corresponding increase in tool reliability as expressed in the mean-time between failure (MTBF), and a reduction in the time and costs required to service tools after each field operation. Ultimately, the LCRSS system was to be capable of drilling 7 7/8 in. to 9 5/8 in. borehole diameters. The project was divided into three Phases, of which Phases I & II were previously completed and reported on, and are part of the case file. Therefore, the previously reported information is not repeated herein. Phase III included the fabrication of two field ready prototypes that were to be subjected to a series of drilling tests at GTI Catoosa, DOE RMOTC, and at customer partnering wells, if possible, as appropriate in the timing of the field test objectives to fully exercise all elements of the LCRSS. These tests were conducted in an iterative process based on a performance/reliability improvement cycle with the goal of demonstrating the system met all aspects required for commercial viability. These tests were conducted to achieve continuous runs of 100+ hours with well trajectories that fully exercised the tool's build/turn/drop/hold target capabilities and its higher end ratings for bit weight, torque and rotary speed. The tool teardowns were rigorously analyzed at the conclusion of each field run to assess component wear rates and to fully document any detrimental behavior(s) observed.

15 GEOTHERMAL ENERGY↗
DOE OSTI2017

Direct s-CO 2 Receiver Development

This project seeks to develop, characterize, and experimentally demonstrate a novel high-temperature receiver technology using s-CO 2 directly as the heat transfer fluid. To be considered successful the commercial technology must achieve the SunShot receiver targets of greater than 90% thermal efficiency while heating the CO 2 to 650°C. The commercial receiver will also be able to withstand 10,000 thermal cycles before mean-time-to- failure and have an expected commercial cost of less than 150 $/kWt. Several bench-scale experiments are executed with a goal of validating the performance model that will be used to develop the commercial receiver design. An important aspect of the success of this project is the characterization of a novel direct receiver concept through detailed performance modeling, the development of intellectual property from the designs, and the dissemination of design results using NREL’s commercialization pathways. This project also produces a modeling tool and methodologies that can be used by other parties to deploy the direct receiver technologies for s-CO 2 systems.

14 SOLAR ENERGY↗
DOE OSTI2021

A REDUCED ORDER MODELING APPROACH TO PROBABILISTIC CREEP-DAMAGE PREDICTIONS IN FINITE ELEMENT ANALYSIS

This paper introduces a computationally efficient Reduced Order Modeling (ROM) approach for the probabilistic prediction of creep-damage failure. Component-level probabilistic simulations are needed to assess the reliability and safety of high-temperature components. Full-scale probabilistic creep-damage modeling in finite element (FE) approach is computationally expensive requiring many hundreds of simulations to replicate the uncertainty of component failure. To that end, ROM is proposed to minimize the elevated computational cost while controlling the loss of accuracy. It is proposed that full-scale probabilistic simulations can be completed in 1D at a reduced cost, the extremum conditions extracted, and those conditions applied for lower-cost 2D/3D probabilistic simulations of components that capture the mean and uncertainty of failure. The probabilistic Sine-hyperbolic (Sinh) model is selected which in previous work was calibrated to alloy 304 stainless steel. The Sinh model includes probability density functions (pdfs) for test condition (stress and temperature), initial damage (i.e. microstructure), and material properties uncertainty. The Sinh model is programmed into ANSYS finite element software using the USERCREEP.F material subroutine. First, the Sinh model and FE code are subject to verification and validation to ensure the accuracy of the simulations. Numerous Monte Carlo simulations are executed in a 1D model to generate probabilistic creep deformation, damage, and rupture data. This data is analyzed and the probabilistic parameters corresponding to extreme creep response are extracted. The ROM concept is applied where only the extreme conditions are applied in the 2D probabilistic prediction of a component. The probabilistic predictions between the 1D and 2D geometry is compared to assess ROM for creep. The accuracy of the probabilistic prediction employing the ROM approach will potentially reduce the time and cost of simulating complex engineering systems. Future studies will introduce multi-stage Sinh, stochasticity, and spatial uncertainty for improved prediction.

36 MATERIALS SCIENCE↗
DOE OSTI2019

Moore vs. Murphy: Tradeoffs between complexity and reliability in distributed energy system scheduling using software-as-a-service

Software-based optimization of building control strategies, including scheduling, has the potential to improve the performance of existing complex heating, ventilation, and air conditioning (HVAC), storage, and other systems—especially if temporally variable energy production, such as solar thermal or photovoltaics, is included. If reductions in energy bills can be achieved using optimized control strategies that take advantage of cost-saving opportunities, such as time-of-use pricing, the additional bill savings can cover further efficiency investment costs. As computer processing becomes cheaper over time (Moore's Law), opportunities to perform complex control optimization become more abundant, and these can be performed remotely as software-as-a-service (SaaS). However, by “perfecting” our control strategies, we run an increased risk that when something unexpected happens (Murphy's Law), the consequences of failure are greater. This study used simulation to explore the potential benefits of HVAC schedule optimization, delivery, and implementation using a SaaS paradigm, at various levels of complexity. Implementing optimal schedules in a model of an efficient building's HVAC system, the study predicts energy cost savings of up to 10% compared to the naïve reference control strategy. Optimizing more system control variables increases the potential energy cost savings; however, these savings could be compromised by failures in communication inherent in delivering schedules via SaaS. The additional cost of energy resulting from the risk of increased demand charges generally increased with increased communication failure to a much larger extent than the risk of increased energy use charges. This work suggests that moderate improvements in performance, achieved at low cost by simple means, may be more effective than highly optimized schemes, which are more susceptible to failure due to their dependence on complex interactions between systems.

42 ENGINEERING↗
DOE OSTI2020

CoREC: Scalable and Resilient In-memory Data Staging for In-situ Workflows

The dramatic increase in the scale of current and planned high-end HPC systems is leading new challenges, such as the growing costs of data movement and IO, and the reduced mean time between failures (MTBF) of system components. In-situ workflows, i.e., executing the entire application workflows on the HPC system, have emerged as an attractive approach to address data-related challenges by moving computations closer to the data, and staging-based frameworks have been effectively used to support in-situ workflows at scale. However, the resilience of these staging-based solutions has not been addressed, and they remain susceptible to expensive data failures. Furthermore, naive use of data resilience techniques such as n-way replication and erasure codes can impact latency and/or result in significant storage overheads. In this article, we present CoREC, a scalable and resilient in-memory data staging runtime for large-scale in-situ workflows. CoREC uses a novel hybrid approach that combines dynamic replication with erasure coding based on data access patterns. It also leverages multiple levels of replications and erasure coding to support diverse data resiliency requirements. Furthermore, the article presents optimizations for load balancing and conflict-avoiding encoding, and a low overhead, lazy data recovery scheme. We have implemented the CoREC runtime and have deployed with the DataSpaces staging service on leadership class computing machines and present an experimental evaluation in the article. Here, the experiments demonstrate that CoREC can tolerate in-memory data failures while maintaining low latency and sustaining high overall storage efficiency at large scales.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Implications of pond reliability on the techno-economic and life cycle environmental impacts of algal biofuels

Despite extensive research on algal bioproducts, there is limited understanding of how pond contamination affects their economics and environmental impacts. This work compared the costs and environmental impacts of algal biofuels across different pond failure scenarios. Pond failure was simulated by a reliability model based on pond mean-time-to-failure (MTTF). The reliability model was integrated with a process model to analyze the impacts of pond failure on the operations of algal farms and biorefineries. Process model outputs were used for techno-economic analysis and life cycle assessment to determine the minimum fuel selling price (MFSP), global warming potential (GWP), and freshwater consumption impacts of algal biofuels for five MTTF scenarios of 20, 54, 80,120, and 350 days, assuming an average mean-time-to-reset of 7 days. Results show that higher MTTFs reduce the cost and environmental impact of algal biofuels, but with diminishing returns. The average MFSPs for the 20-day, 54-day, and 350-day MTTF scenarios were $\$3.52$, $\$2.54$, and $\$2.10$ per liter of gasoline equivalent, respectively. The GWP for the same scenarios were 131, 96, and 83 g CO 2eq MJ –1 , respectively. This study highlights the significant impact of larger seed trains, required under low MTTFs, on the costs and greenhouse gas emissions of algal biofuels. Moreover, the work shows that algal biofuels fail to be cost-competitive with conventional fuels, even when productivities are increased from 17 to 35 g m –2 d –1 . Furthermore, this work is the first to explore the implications of pond failure on the sustainability of algal biofuels and provides valuable insights to algae farmers on how to reduce the costs and financial risks of algal cultivation through process design and pond management strategies.

09 BIOMASS FUELS↗
DOE OSTI2014

Detailed Modeling and Evaluation of a Scalable Multilevel Checkpointing System

High-performance computing (HPC) systems are growing more powerful by utilizing more components. As the system mean time before failure correspondingly drops, applications must checkpoint frequently to make progress. But, at scale, the cost of checkpointing becomes prohibitive. A solution to this problem is multilevel checkpointing, which employs multiple types of checkpoints in a single run. Moreover, lightweight checkpoints can handle the most common failure modes, while more expensive checkpoints can handle severe failures. We designed a multilevel checkpointing library, the Scalable Checkpoint/Restart (SCR) library, that writes lightweight checkpoints to node-local storage in addition to the parallel file system. We present probabilistic Markov models of SCR's performance. We show that on future large-scale systems, SCR can lead to a gain in machine efficiency of up to 35 percent, and reduce the load on the parallel file system by a factor of two. In addition, we predict that checkpoint scavenging, or only writing checkpoints to the parallel file system on application termination, can reduce the load on the parallel file system by 20 × on today's systems and still maintain high application efficiency.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Holistic Measurement Driven Resilience: Combining Operational Fault and Failure Measurements and Fault Injection for Quantifying Fault Detection, Propagation and Impact. Final report

For HPC systems to date, application resilience to faults and failures has been accomplished by the brute- force method of checkpoint/restart, which allows an application to make forward progress in the face of system and application faults, errors, and failures independent of root cause or end result. It has remained the primary resilience mechanism because we lack a way to identify faults and anticipate consequences early enough to take meaningful mitigating action. However, checkpoint/restart implementations put a tremendous burden on system resources and on the applications themselves and is becoming less feasible at scale. Because we have not yet operated at scales at which checkpoint/restart fails to provide forward progress, despite increasing costs, vendors have had little motivation to provide the instrumentation necessary for early identification of faults and failures. However, as we move from petascale to exascale, component mean time to failure (MTTF) will render the existing techniques ineffectual and/or too expensive. Furthermore, fault recovery mechanisms such as failover and/or error correction introduce performance inconsistency. Instrumentation allowing early indication of problems and tools to enable use of such information by systems, operating systems, and applications offer an alternative, more scalable and less costly solution. In the HMDR project, we built on our experience and expertise developed and accumulated over years of research on design, monitoring, measurement, and assessment of resilient computing systems. Analysis of field data on the current and past generations of extreme-scale systems revealed several challenges that, if not addressed in increasingly larger and more complex systems, may hinder the effectiveness of future exascale computing systems. Specifically, i) file systems and interconnects in current-generation large-scale systems already operate at the margins of resiliency, including consistent performance, and may not scale to larger deployments; ii) automated, software-based failover mechanisms are frequently inadequate and can introduce wider failures, such that failures during recovery may lead to system/application failures, including system-wide outages; and iii) silent data corruption represents a critical fault mode and will require efficient detection mechanisms if next-generation applications are to take full advantage of exascale hardware. To address the above challenges, we assembled a team of world-renowned experts in resilient extreme- scale computing from the University of Illinois (Electrical and Computer Engineering, Computer Science, and NCSA), SNL, LANL, NERSC, and Cray. Our team includes representatives from centers that house many of the largest HPC resources in the world, both today and over the coming years. The team has a unique track record of research in i) system and application failure characterization based on the analysis of field data, ii) data-driven design of fault/error detection mechanisms, and iii) experimental characterization of system/application resiliency. The team includes system owners/operators who provide continuous data collection and access and ensure installation of appropriate analysis tools.

97 MATHEMATICS AND COMPUTING↗