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Software Supports Distributed Operations via the Internet
Multi-mission Encrypted Communication System (MECS) is a computer program that enables authorized, geographically dispersed users to gain secure access to a common set of data files via the Internet. MECS is compatible with legacy application programs and a variety of operating systems. The MECS architecture is centered around maintaining consistent replicas of data files cached on remote computers. MECS monitors these files and, whenever one is changed, the changed file is committed to a master database as soon as network connectivity makes it possible to do so. MECS provides subscriptions for remote users to automatically receive new data as they are generated. Remote users can be producers as well as consumers of data. Whereas a prior program that provides some of the same services treats disconnection of a user from the network of users as an error from which recovery must be effected, MECS treats disconnection as a nominal state of the network: This leads to a different design that is more efficient for serving many users, each of whom typically connects and disconnects frequently and wants only a small fraction of the data at any given time.
Cloud Computing Option for Modeling the Debris Environment
NASA’s Digital Transformation Initiative aims to promote the agency’s adoption of current and evolving digital technologies. Through agency-wide collaboration with other NASA teams, the Office of Safety and Mission Assurance (OSMA) has directed the Orbital Debris Program Office and the Meteoroid Environment Office to integrate cloud computing technologies in their publicly released software models: the Orbital Debris Engineering Model (ORDEM) and the Meteoroid Engineering Model (MEM). Decoupling the user interface from the backend processor was key for the software packages to run on a cloud computing framework. Benefits to this design include horizontal scaling of computing resources, user authentication and authorization, and automated deployment. Both models are hosted on a cloud computing platform supported by the NASA authorized IT security and compliance framework. This paper focuses on the new ORDEM web application, which includes the current features of the publicly released ORDEM software with an upgraded frontend design. The underlying ORDEM processor is run on a cloud container, allowing the user to run multiple spacecraft and telescope/radar mode simulations. Featuresexclusive to the ORDEM web application, such as importing multiple TLEs, auto-generated plotting, and the ability to check runtime progress are discussed. Comparisons between the current ORDEM software and the web application are summarized.
Cloud Computing Option for Modeling the Debris Environment
NASA’s Digital Transformation Initiative aims to promote the agency’s adoption of current and evolving digital technologies. Through agency-wide collaboration with other NASA teams, the Office of Safety and Mission Assurance (OSMA) has directed the Orbital Debris Program Office and the Meteoroid Environment Office to integrate cloud computing technologies into their publicly released software models: the Orbital Debris Engineering Model (ORDEM) and the Meteoroid Engineering Model (MEM). Decoupling the user interface from the backend processor was key for the software packages to run on a cloud computing framework. Benefits to this design include horizontal scaling of computing resources, user authentication and authorization, and automated deployment. Both models are hosted on a cloud computing platform supported by the NASA authorized IT security and compliance framework. This paper focuses on the new ORDEM web application, which includes the current features of the publicly released ORDEM software with an upgraded frontend design, although parallels between ORDEM and MEM are also discussed. The underlying ORDEM processor is run on a cloud container, allowing the user to run multiple spacecraft and telescope/radar mode simulations. Features exclusive to the ORDEM web application, such as importing multiple TLEs, auto-generated plotting, and the ability to check runtime progress are discussed. Comparisons between the current ORDEM software and the web application are summarized.
Exploiting GPUs for Scalable Network Intrusion Detection Phase I Final Report
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Cyber Resilient Design and Controls for Energy Systems
As the grid of today is evolving into a highly distributed and autonomous cyber-physical system with higher penetration of Distributed Energy Resources (DERs) and autonomous controls that are dependent on the cyber infrastructure, there are novel and increasing challenges to cyber-resilient operation of the grid. While the traditional mechanisms take a "bolt on" approach to cybersecurity and resiliency, this talk presents two novel technologies developed at NREL that "bake in" security and resilience into the design and control of the grid. An Adaptive Resilience Metrics framework is a novel mechanism for system owners and operators to understand the impact of cyber and physical system design and topology on the resilient operation, while also helping determine optimal policies in response to adverse cyber events. Along with that a zero-knowledge proof-based technique helps incorporate security into the controls layer by focusing on computational integrity rather than data integrity. By leveraging design and control properties unique to Operational Technology systems these technologies will not only help enhance cyber-resilience for the grid of today, but a highly distributed and autonomous grid of tomorrow.
Computational Algorithms for Unit Commitment with AC Power Flows (Final Report)
Security-constrained unit commitment (SCUC) is a key component in power system operations. When AC power flow constraints are considered in the SCUC model (AC-SCUC), the problem becomes extremely difficult due to its discrete and non-convex nature, as described in “Grid Optimization Competition Challenge 3 Problem Formulation (GOCC)”. There are four main challenges: (i) Discrete decisions regarding unit online/offline status and start-up/shut-down procedures for every single unit. The number of discrete decision variables increases considerably when a system integrates multiple generators; (ii) Configuration-based combined-cycle formulations, and multi-commodity models that include ramping products, spin/non-spin products, and regulation up/down products. The combined-cycle units introduce additional discrete decision variables and auxiliary service products further complicate the model by connecting multi-commodity products’ continuous and discrete variables; (iii) SCUC models with AC power flow constraints are far more complex due to massive bilinear terms in the large-scale nonlinear power balance equations. The nonlinear power balance equations are further complicated by the discrete step control variables of shunts; (iv) N − 1 contingency analysis. The size of the model increases linearly with the number of contingencies considered, greatly increasing the size of the optimization model. Accordingly, there is an emergent need to develop a robust algorithm capable of deriving a high-quality solution in a short time and passing through contingency tests simultaneously. In this project, we explore innovative techniques to address this challenging problem by integrating advanced polyhedral theory, approximation methods, relaxation strategies, decomposition techniques, and parallel computing. Each technique approaches the problem from a different perspective, leveraging its specific strengths to tackle distinct challenges. Each individual method has demonstrated its effectiveness in the PI’s previous research. Their integration is expected to significantly reduce the computational time required to solve the proposed complex problem. Successful completion of this project has the potential to transform the industry by enhancing optimization solvers capable of handling large-scale day-ahead energy market clearing models within strict time constraints, while incorporating AC power flow constraints. This advancement will lead to reduced overall generation costs and, consequently, increased social welfare.
Securing against errors in an error correcting code (ECC) implemented in an automotive system
In general, data is susceptible to errors caused by faults in hardware (i.e. permanent faults), such as faults in the functioning of memory and/or communication channels. To detect errors in data caused by hardware faults, the error correcting code (ECC) was introduced, which essentially provides a sort of redundancy to the data that can be used to validate that the data is free from errors caused by hardware faults. In some cases, the ECC can also be used to correct errors in the data caused by hardware faults. However, the ECC itself is also susceptible to errors, including specifically errors caused by faults in the ECC logic. A method, computer readable medium, and system are thus provided for securing against errors in an ECC.
Motion of the two-control airplane in rectilinear flight after initial disturbances with introduction of controls following an exponential law
An airplane in steady rectilinear flight was assumed to experience an initial disturbance in rolling or yawing velocity. The equations of motion were solved to see if it was possible to hasten recovery of a stable airplane or to secure recovery of an unstable airplane by the application of a single lateral control following an exponential law. The sample computations indicate that, for initial disturbances complex in character, it would be difficult to secure correlation with any type of exponential control. The possibility is visualized that the two-control operation may seriously impair the ability to hasten recovery or counteract instability.
Pathways to a Sustainable Aviation Ecosystem: Flight DNA: An Anonymized Aviation Data Tool and Repository
The National Renewable Energy Laboratory (NREL) has deep experience developing secure, national data repositories, which it augments with analysis, technology and market expertise, high-performance computing, and innovative data visualization. By adapting the architecture of existing mobility databases (i.e., Fleet DNA, the Transportation Secure Data Center, the National Fuel Cell Technology Evaluation Center), NREL can build a powerful aviation data clearinghouse - Flight DNA - that helps stakeholders navigate the web of pitfalls and possibilities generated by new aviation technologies.
Partially Collapsed Gibbs Samplers for Linear Inverse Problems and Applications to X-ray Imaging
Deblurring is one of the most common linear inverse problems in imaging, and most methods for deblurring an image assume that the convolution kernel, the so-called point spread function (PSF), is known or attempt to reconstruct the PSF simultaneously with the image. For many experimental imaging systems, like high energy X-ray radiography systems, the PSF must be computed from calibration imagery. The associated inverse problem is large-scale, so, in order to compute meaningful uncertainties with the reconstruction, it is common to use hierarchical Bayesian models and Markov chain Monte Carlo methods for sampling from the posterior. Classical Gibbs samplers can suffer from long autocorrelation lengths in the Markov chain when the reconstructed quantity in the inverse problem is finely discretized, resulting in poor computational efficiency of the Markov chain. In this work we present partially collapsed Gibbs samplers for linear inverse problems that greatly decrease correlations in the Markov chain, thus increasing the computational efficiency of the algorithms, and we present the results of the enhanced samplers on the problem of reconstructing the point spread function for an X-ray imaging system at the U.S. Department of Energy's Nevada National Security Site.
Development of a Cloud-based Application to Enable a Scalable Risk-informed Predictive Maintenance Strategy at Nuclear Power Plants
Light-water reactor operations and maintenance (O&M) costs are prohibitively high, thus contributing to the premature decommissioning of nuclear power plants (NPPs). This is partly due to how the equipment is monitored. In recent years, cloud computing has emerged as a dominant technology by virtue of its low costs, computing and storage adaptability, and ability to host applications over numerous types of virtual infrastructures. Cloud computing can be a cost-effective alternative to onsite storage and diagnostics. This paper conducts a techno-economic assessment of a provisional cloud deployment architecture for a NPP predictive monitoring (PdM) system. The cloud-based monitoring system would enable maintenance and diagnostics (M&D) analysts and other authorized plant users to remotely monitor equipment functionality so as to enable PdM practices and early detection of faults. The Microsoft Azure cloud platform is included in the proposed cloud architecture to provide data processing and storage, sensor device networking, and database management; however, this analysis could be extended to other cloud computing service providers as well. For the techno-economic assessment, technical feasibility is measured in terms of network performance metrics such as response time, latency, and throughput, whereas economic feasibility is measured in terms of operational costs and capital expenditures. Finally, this report covers certain regulatory and security aspects that may concern licensees looking to implement cloud computing. The report focuses on the integration of sensor database storage, the application of cloud resources to PdM, and the identification of technological and economic hurdles associated with moving to a cloud-computing-based architecture.
Control and Data Plane Security of High Performance Networks
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Technical and Regulatory Aspects of Integrating Safety and Security at Nuclear Power Plants
This paper provides an overview of lessons learned in applying a dynamic computational framework that links results from a commercially available FOF simulation tool, a commercially available thermal-hydraulic tool, and EMRALD to an operating commercial nuclear power plant. This process of including plant procedures and multiple analysis results is being called Modeling and Analysis for Safety Security using Dynamic EMRALD Framework. It describes how a user could integrate their plant-specific FOF models with safety mitigation actions in EMRALD, and with thermal-hydraulic tools, such as MAAP. The work performed in this paper is based on a generic EMRALD model with actual plant data used for the analysis. However, only the generic model and general results of the analysis are presented for dissemination. No plant’s sensitive information is included in this paper. The discussion shows examples of insights that can be obtained from the proposed methodology.
ASCR Cybersecurity for Scientific Computing Integrity
The Department of Energy (DOE) has the responsibility to address the energy, environmental, and nuclear security challenges that face our nation. Much of DOE’s enterprise involves distributed, collaborative teams; a significant fraction involves “open science,” which depends on multi-institutional, often international collaborations that must access or share significant amounts of information between institutions and over networks around the world. The mission of the Office of Science is the delivery of scientific discoveries and major scientific tools to transform our understanding of nature and to advance the energy, economic, and national security of the United States. The ability of DOE to execute its responsibilities depends critically on its ability to assure the integrity and availability of scientific facilities and computer systems, and of the scientific, engineering, and operational software and data that support its mission.
Containers and Security on Planet X
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Systemic Theoretic Process Analysis (STPA) Used for Cyber Security and Agile Software Development
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Advanced Simulation and Computing Business Plan
To maintain a credible nuclear weapons program, the National Nuclear Security Administration’s (NNSA’s) Office of Defense Programs (DP) needs to make certain that the capabilities, tools, and expert staff are in place and are able to deliver validated assessments. This requires a complete and robust simulation environment backed by an experimental program to test ASC Program models. This ASC Business Plan document encapsulates a complex set of elements, each of which is essential to the success of the simulation component of the Nuclear Security Enterprise. The ASC Business Plan addresses the hiring, mentoring, and retaining of programmatic technical staff responsible for building the simulation tools of the nuclear security complex. The ASC Business Plan describes how the ASC Program engages with industry partners—partners upon whom the ASC Program relies on for today’s and tomorrow’s high performance architectures. Each piece in this chain is essential to assure policymakers, who must make decisions based on the results of simulations, that they are receiving all the actionable information they need.