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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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DOE OSTI2015

Comprehensive, Multi-Source Cyber-Security Events Data Set

This data set represents 58 consecutive days of de-identified event data collected from five sources within Los Alamos National Laboratory’s corporate, internal computer network. The data sources include Windows-based authentication events from both individual computers and centralized Active Directory domain controller servers; process start and stop events from individual Windows computers; Domain Name Service (DNS) lookups as collected on internal DNS servers; network flow data as collected on at several key router locations; and a set of well-defined red teaming events that present bad behavior within the 58 days. In total, the data set is approximately 12 gigabytes compressed across the five data elements and presents 1,648,275,307 events in total for 12,425 users, 17,684 computers, and 62,974 processes. Specific users that are well known system related (SYSTEM, Local Service) were not de-identified though any well-known administrators account were still de-identified. In the network flow data, well-known ports (e.g. 80, 443, etc) were not de-identified. All other users, computers, process, ports, times, and other details were de-identified as a unified set across all the data elements (e.g. U1 is the same U1 in all of the data). The specific timeframe used is not disclosed for security purposes. In addition, no data that allows association outside of LANL’s network is included. All data starts with a time epoch of 1 using a time resolution of 1 second. In the authentication data, failed authentication events are only included for users that had a successful authentication event somewhere within the data set.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2011

Distributed Energy Resource Optimization Using a Software as Service (SaaS) Approach at the University of California, Davis Campus

Together with OSIsoft LLC as its private sector partner and matching sponsor, the Lawrence Berkeley National Laboratory (Berkeley Lab) won an FY09 Technology Commercialization Fund (TCF) grant from the U.S. Department of Energy. The goal of the project is to commercialize Berkeley Lab's optimizing program, the Distributed Energy Resources Customer Adoption Model (DER-CAM) using a software as a service (SaaS) model with OSIsoft as its first non-scientific user. OSIsoft could in turn provide optimization capability to its software clients. In this way, energy efficiency and/or carbon minimizing strategies could be made readily available to commercial and industrial facilities. Specialized versions of DER-CAM dedicated to solving OSIsoft's customer problems have been set up on a server at Berkeley Lab. The objective of DER-CAM is to minimize the cost of technology adoption and operation or carbon emissions, or combinations thereof. DER-CAM determines which technologies should be installed and operated based on specific site load, price information, and performance data for available equipment options. An established user of OSIsoft's PI software suite, the University of California, Davis (UCD), was selected as a demonstration site for this project. UCD's participation in the project is driven by its motivation to reduce its carbon emissions. The campus currently buys electricity economically through the Western Area Power Administration (WAPA). The campus does not therefore face compelling cost incentives to improve the efficiency of its operations, but is nonetheless motivated to lower the carbon footprint of its buildings. Berkeley Lab attempted to demonstrate a scenario wherein UCD is forced to purchase electricity on a standard time-of-use tariff from Pacific Gas and Electric (PG&E), which is a concern to Facilities staff. Additionally, DER-CAM has been set up to consider the variability of carbon emissions throughout the day and seasons. Two distinct analyses of value to UCD are possible using this approach. First, optimal investment choices for buildings under the two alternative objectives can be derived. Second, a week-ahead building operations forecaster has been written that executes DER-CAM to find an optimal operating schedule for buildings given their expected building energy services requirements, electricity prices, and local weather. As part of its matching contribution, OSIsoft provided a full implementation of PI and a server to install it on at Berkeley Lab. Using the PItoPI protocol, this gives Berkeley Lab researchers direct access to UCD's PI data base. However, this arrangement is in itself inadequate for performing optimizations. Additional data not included in UCD's PI database would be needed and the campus was not able to provide this information. This report details the process, results, and lessons learned of this commercialization project.

25 ENERGY STORAGE↗
DOE OSTI2011

RECOVERY ACT: DYNAMIC ENERGY CONSUMPTION MANAGEMENT OF ROUTING TELECOM AND DATA CENTERS THROUGH REAL-TIME OPTIMAL CONTROL (RTOC): Final Scientific/Technical Report

This final scientific report documents the Industrial Technology Program (ITP) Stage 2 Concept Development effort on Data Center Energy Reduction and Management Through Real-Time Optimal Control (RTOC). Society is becoming increasingly dependent on information technology systems, driving exponential growth in demand for data center processing and an insatiable appetite for energy. David Raths noted, 'A 50,000-square-foot data center uses approximately 4 megawatts of power, or the equivalent of 57 barrels of oil a day1.' The problem has become so severe that in some cases, users are giving up raw performance for a better balance between performance and energy efficiency. Historically, power systems for data centers were crudely sized to meet maximum demand. Since many servers operate at 60%-90% of maximum power while only utilizing an average of 5% to 15% of their capability, there are huge inefficiencies in the consumption and delivery of power in these data centers. The goal of the 'Recovery Act: Decreasing Data Center Energy Use through Network and Infrastructure Control' is to develop a state of the art approach for autonomously and intelligently reducing and managing data center power through real-time optimal control. Advances in microelectronics and software are enabling the opportunity to realize significant data center power savings through the implementation of autonomous power management control algorithms. The first step to realizing these savings was addressed in this study through the successful creation of a flexible and scalable mathematical model (equation) for data center behavior and the formulation of an acceptable low technical risk market introduction strategy leveraging commercial hardware and software familiar to the data center market. Follow-on Stage 3 Concept Development efforts include predictive modeling and simulation of algorithm performance, prototype demonstrations with representative data center equipment to verify requisite performance and continued commercial partnering agreement formation to ensure uninterrupted development, and deployment of the real-time optimal control algorithm. As a software implementable technique for reducing power consumption, the RTOC has two very desirable traits supporting rapid prototyping and ultimately widespread dissemination. First, very little capital is required for implementation. No major infrastructure modifications are required and there is no need to purchase expensive capital equipment. Second, the RTOC can be rolled out incrementally. Therefore, the effectiveness can be proven without a large scale initial roll out. Through the use of the Impact Projections Model provided by the DOE, monetary savings in excess of $100M in 2020 and billions by 2040 are predicted. In terms of energy savings, the model predicts a primary energy displacement of 260 trillion BTUs (33 trillion kWh), or a 50% reduction in server power consumption. The model also predicts a corresponding reduction of pollutants such as SO2 and NOx in excess of 100,000 metric tonnes assuming the RTOC is fully deployed. While additional development and prototyping is required to validate these predictions, the relative low cost and ease of implementation compared to large capital projects makes it an ideal candidate for further investigation.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2011

Power Minimization techniques for Networked Data Centers.

Our objective is to develop a mathematical model to optimize energy consumption at multiple levels in networked data centers, and develop abstract algorithms to optimize not only individual servers, but also coordinate the energy consumption of clusters of servers within a data center and across geographically distributed data centers to minimize the overall energy cost and consumption of brown energy of an enterprise. In this project, we have formulated a variety of optimization models, some stochastic others deterministic, and have obtained a variety of qualitative results on the structural properties, robustness, and scalability of the optimal policies. We have also systematically derived from these models decentralized algorithms to optimize energy efficiency, analyzed their optimality and stability properties. Finally, we have conducted preliminary numerical simulations to illustrate the behavior of these algorithms. We draw the following conclusion. First, there is a substantial opportunity to minimize both the amount and the cost of electricity consumption in a network of datacenters, by exploiting the fact that traffic load, electricity cost, and availability of renewable generation fluctuate over time and across geographical locations. Judiciously matching these stochastic processes can optimize the tradeoff between brown energy consumption, electricity cost, and response time. Second, given the stochastic nature of these three processes, real-time dynamic feedback should form the core of any optimization strategy. The key is to develop decentralized algorithms that can be implemented at different parts of the network as simple, local algorithms that coordinate through asynchronous message passing.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2011

Cyber Friendly Fire

Cyber friendly fire (FF) is a new concept that has been brought to the attention of Department of Defense (DoD) stakeholders through two workshops that were planned and conducted by the Air Force Research Laboratory (AFRL) and research conducted for AFRL by the Pacific Northwest National Laboratory. With this previous work in mind, we offer a definition of cyber FF as intentional offensive or defensive cyber/electronic actions intended to protect cyber systems against enemy forces or to attack enemy cyber systems, which unintentionally harms the mission effectiveness of friendly or neutral forces. Just as with combat friendly fire, a fundamental need in avoiding cyber FF is to maintain situation awareness (SA). We suggest that cyber SA concerns knowledge of a system's topology (connectedness and relationships of the nodes in a system), and critical knowledge elements such as the characteristics and vulnerabilities of the components that comprise the system (and that populate the nodes), the nature of the activities or work performed, and the available defensive (and offensive) countermeasures that may be applied to thwart network attacks. A training implication is to raise awareness and understanding of these critical knowledge units; an approach to decision aids and/or visualizations is to focus on supporting these critical knowledge units. To study cyber FF, we developed an unclassified security test range comprising a combination of virtual and physical devices that present a closed network for testing, simulation, and evaluation. This network offers services found on a production network without the associated costs of a real production network. Containing enough detail to appear realistic, this virtual and physical environment can be customized to represent different configurations. For our purposes, the test range was configured to appear as an Internet-connected Managed Service Provider (MSP) offering specialized web applications to the general public. The network is essentially divided into a production component that hosts the web and network services, and a user component that hosts thirty employee workstations and other end devices. The organization's network is separated from the Internet by a Cisco ASA network security device that both firewalls and detects intrusions. Business sensitive information is stored in various servers. This includes data comprising thousands of internal documents, such as finance and technical designs, email messages for the organization's employees including the CEO, CFO, and CIO, the organization's source code, and Personally Identifiable client data. Release of any of this information to unauthorized parties would have a significant, detrimental impact on the organization's reputation, which would harm earnings. The valuable information stored in these servers pose obvious points of interest for an adversary. We constructed several scenarios around this environment to support studies in cyber SA and cyber FF that may be run in the test range. We describe mitigation strategies to combat cyber FF including both training concepts and suggestions for decision aids and visualization approaches. Finally, we discuss possible future research directions.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2011

Collaborative Visualization for Large-Scale Accelerator Electromagnetic Modeling (Final Report)

This report contains the comprehensive summary of the work performed on the SBIR Phase II, Collaborative Visualization for Large-Scale Accelerator Electromagnetic Modeling at Kitware Inc. in collaboration with Stanford Linear Accelerator Center (SLAC). The goal of the work was to develop collaborative visualization tools for large-scale data as illustrated in the figure below. The solutions we proposed address the typical problems faced by geographicallyand organizationally-separated research and engineering teams, who produce large data (either through simulation or experimental measurement) and wish to work together to analyze and understand their data. Because the data is large, we expect that it cannot be easily transported to each team member's work site, and that the visualization server must reside near the data. Further, we also expect that each work site has heterogeneous resources: some with large computing clients, tiled (or large) displays and high bandwidth; others sites as simple as a team member on a laptop computer. Our solution is based on the open-source, widely used ParaView large-data visualization application. We extended this tool to support multiple collaborative clients who may locally visualize data, and then periodically rejoin and synchronize with the group to discuss their findings. Options for managing session control, adding annotation, and defining the visualization pipeline, among others, were incorporated. We also developed and deployed a Web visualization framework based on ParaView that enables the Web browser to act as a participating client in a collaborative session. The ParaView Web Visualization framework leverages various Web technologies including WebGL, JavaScript, Java and Flash to enable interactive 3D visualization over the web using ParaView as the visualization server. We steered the development of this technology by teaming with the SLAC National Accelerator Laboratory. SLAC has a computationally-intensive problem important to the nations scientific progress as described shortly. Further, SLAC researchers routinely generate massive amounts of data, and frequently collaborate with other researchers located around the world. Thus SLAC is an ideal teammate through which to develop, test and deploy this technology. The nature of the datasets generated by simulations performed at SLAC presented unique visualization challenges especially when dealing with higher-order elements that were addressed during this Phase II. During this Phase II, we have developed a strong platform for collaborative visualization based on ParaView. We have developed and deployed a ParaView Web Visualization framework that can be used for effective collaboration over the Web. Collaborating and visualizing over the Web presents the community with unique opportunities for sharing and accessing visualization and HPC resources that hitherto with either inaccessible or difficult to use. The technology we developed in here will alleviate both these issues as it becomes widely deployed and adopted.

43 PARTICLE ACCELERATORS↗
DOE OSTI2012

Using the Sirocco File System for high-bandwidth checkpoints.

The Sirocco File System, a file system for exascale under active development, is designed to allow the storage software to maximize quality of service through increased flexibility and local decision-making. By allowing the storage system to manage a range of storage targets that have varying speeds and capacities, the system can increase the speed and surety of storage to the application. We instrument CTH to use a group of RAM-based Sirocco storage servers allocated within the job as a high-performance storage tier to accept checkpoints, allowing computation to potentially continue asynchronously of checkpoint migration to slower, more permanent storage. The result is a 10-60x speedup in constructing and moving checkpoint data from the compute nodes. This demonstration of early Sirocco functionality shows a significant benefit for a real I/O workload, checkpointing, in a real application, CTH. By running Sirocco storage servers within a job as RAM-only stores, CTH was able to store checkpoints 10-60x faster than storing to PanFS, allowing the job to continue computing sooner. While this prototype did not include automatic data migration, the checkpoint was available to be pushed or pulled to disk-based storage as needed after the compute nodes continued computing. Future developments include the ability to dynamically spawn Sirocco nodes to absorb checkpoints, expanding this mechanism to other fast tiers of storage like flash memory, and sharing of dynamic Sirocco nodes between multiple jobs as needed.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2012

Hard Disk/Solid State Drive Synergy in Support of Data-Intensive Computing [Slides]

Data-intensive applications are becoming increasingly common in high-performance computing. Examples include combustion simulation, human genome analysis, and satellite image processing. Efficient access of data sets is critical to the performance of these applications. Because of the size of the data today's economically feasible approach is to store the data files on an array of hard disks or data servers equipped with hard disks and managed by a parallel file system such as PVFS or Lustre wherein the data is striped over a (large) number of disks for high aggregate I/O throughout. With file striping, a request for a segment of logically contiguous file space is decomposed into multiple sub-requests, each to a different server. While the data unit for this striping is usually reasonably large to benefit disk efficiency, the first and/or last sub-requests can be much smaller than the striping unit if the request does not align with the striping pattern, severely compromising hard disk efficiency and thus application performance. We propose to exploit solid state drives (SSD), whose efficiency is much less sensitive to small random accesses, to enable the alignment of requests to disk with the data striping pattern. In this scheme hard disks mainly serve large, aligned, sequential requests, with SSDs serving small or unaligned requests, thus respecting the relative cost, performance, and durability characteristics of the two media, and thereby achieving synergy in performance/cost. We will describe the design of the proposed scheme, its implementation on CCS-7's Darwin cluster, and performance results.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2012

Langston University - High Energy Physics (LU-HEP)

This final report is presented by Langston University (LU) for the project entitled "Langston University High Energy Physics" (LUHEP) under the direction of principal investigator (PI) and project director Professor Joel Snow. The project encompassed high energy physics research performed at hadron colliders. The PI is a collaborator on the DZero experiment at Fermi National Accelerator Laboratory in Batavia, IL, USA and the ATLAS experiment at CERN in Geneva, Switzerland and was during the entire project period from April 1, 1999 until May 14, 2012. Both experiments seek to understand the fundamental constituents of the physical universe and the forces that govern their interactions. In 1999 as member of the Online Systems group for Run 2 the PI developed a cross-platform Python-based, Graphical User Interface (GUI) application for monitoring and control of EPICS based devices for control room use. This served as a model for other developers to enhance and build on for further monitoring and control tasks written in Python. Subsequently the PI created and developed a cross-platform C++ GUI utilizing a networked client-server paradigm and based on ROOT, the object oriented analysis framework from CERN. The GUI served as a user interface to the Examine tasks running in the D\O\ control room which monitored the status and integrity of data taking for Run 2. The PI developed the histogram server/control interface to the GUI client for the EXAMINE processes. The histogram server was built from the ROOT framework and was integrated into the D\O\ framework used for online monitoring programs and offline analysis. The PI developed the first implementation of displaying histograms dynamically generated by ROOT in a Web Browser. The PI's work resulted in several talks and papers at international conferences and workshops. The PI established computing software infrastructure at LU and U. Oklahoma (OU) to do analysis of DZero production data and produce simulation data for the experiment. Eventually this included the FNAL SAM data grid system, the SAMGrid (SG) infrastructure, and the Open Science Grid software stacks for computing and storage elements. At the end of 2003 Snow took on the role of global Monte Carlo production coordinator for the DØ experiment. A role which continues til this day. In January of 2004 Snow started working with the SAMGrid development team to help debug, deploy, and integrate SAMGrid with DØ Monte Carlo production. Snow installed and configured SG execution and client sites at LUHEP and OUHEP, and a SG scheduler site at LUHEP. The PI developed a python based GUI (DAJ) that acts as a front end for job submission to SAMGrid. The GUI interfaces to the DZero Mone Carlo (MC) request system that uses SAM to manage MC requests by the physics analysis groups. DAJ significantly simplified SG job submission and was deployed in DZero in an effort to increase the user base of SG. The following year was the advent of SAMGrid job submission to the Open Science Grid (OSG) and LHC Computing Grid (LCG) through a forwarding mechanism. The PI oversaw the integration of these grids into the existing production infrastructure. The PI developed an automatic MC (Automc) request processing system capable of operating without user intervention (other than getting grid credentials), and able to submit to any number of sites on various grids. The system manages production at all but 2 sites. The system was deployed at Fermilab and remains operating there today. The PI's work in distributed computing resulted in several talks at international conferences. UTA, OU, and LU were chosen as the collaborating institutions that form the Southwest Tier 2 Center (SWT2) for ATLAS. During the project period the PI contributed to the online and offline software infrastructure through his work with the Run 2 online group, and played a major role in Monte Carlo production for DZero. During the part of the project period in which the PI served as MC production coordinator MC production increased very significantly. In the first year of the PI's tenure as production coordinator production was 159M events and 6.7~TB of data. During the last year of the project period production was 2,342~M events and 262~TB of data. That is a factor of 15 increase in events and 39 in data volume. The increase occurred with improvements in computer hardware and networks, through the use of grid technology on diverse resources, and through increased automation and efficiency of the production process. LU HEP developed and deployed the automatic MC request processing system in use at FNAL. The complementary strategies of automation and grid production served DZero well. Fermilab has recognized LU HEP's contribution to DZero by allowing the PI to devote full time to research activities by appointing him a guest scientist for the last six years of the project period.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2012

Economizer Based Data Center Liquid Cooling with Advanced Metal Interfaces. Final Technical Report

A new chiller-less data center liquid cooling system utilizing the outside air environment has been shown to achieve up to 90% reduction in cooling energy compared to traditional chiller based data center cooling systems. The system removes heat from Volume servers inside a Sealed Rack and transports the heat using a liquid loop to an Outdoor Heat Exchanger which rejects the heat to the outdoor ambient environment. The servers in the rack are cooled using a hybrid cooling system by removing the majority of the heat generated by the processors and memory by direct thermal conduction using coldplates and the heat generated by the remaining components using forced air convection to an air- to- liquid heat exchanger inside the Sealed Rack. The anticipated benefits of such energy-centric configurations are significant energy savings at the data center level. When compared to a traditional 10 MW data center, which typically uses 25% of its total data center energy consumption for cooling this technology could potentially enable a cost savings of up to $800,000-$2,200,000/year (assuming electricity costs of 4 to 11 cents per kilowatt-hour) through the reduction in electrical energy usage.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2012

Recovery Act: Data Center Transfer from “Always On” to “Always Available” to Reduce Power

The project encompasses the solution to the problem of underutilized IT resources in and across data centers, which is to develop an “On Demand” operational model. Within this context, by keeping the optimum number of servers online at any time to meet actual customer demand, data center operators are able to generate significant cost and energy savings. Servers that are not needed to meet demand are taken offline automatically. They are brought online dynamically as demand increases, thus matching customer demand with the computing supply needed to guarantee expected service levels. The swings between peak and non-peak usage become a cost reduction opportunity.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2014

Patriot Script User Guide (V.1.0.12)

Patriot Script is a lightweight desktop application that provides user access to the computationally intensive functions of the PATRIOT server. An analyst can set up attack scenarios, including the attack origin and destination locations, the attack device, and the GNDA options. Patriot Script calls on the PATRIOT server to generate attack routes and evaluate their reliabilities. Patriot Script results are output to a comma separated values file that can be used for statistical analysis. Routes generated by Patriot Script can be saved as .kml files for visualization in Google Earth or Patriot Viewer.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2014

Direct Liquid Cooling for Electronic Equipment

This report documents a demonstration of an electronic-equipment cooling system in the engineering prototype development stage that can be applied in data centers. The technology provides cooling by bringing a water-based cooling fluid into direct contact with high-heat-generating electronic components. This direct cooling system improves overall data center energy efficiency in three ways: 1) High-heat-generating electronic components are more efficiently cooled directly using water, capturing a large portion of the total electronic equipment heat generated. This captured heat reduces the load on the less-efficient air-based data center room cooling systems. The combination contributes to the overall savings. 2) The power consumption of the electronic equipment internal fans is significantly reduced when equipped with this cooling system. 3) The temperature of the cooling water supplied to the direct cooling system can be much higher than that commonly provided by facility chilled water loops, and therefore can be produced with lower cooling infrastructure energy consumption and possibly compressor-free cooling. Providing opportunities for heat reuse is an additional benefit of this technology. The cooling system can be controlled to produce high return water temperatures while providing adequate component cooling. The demonstration was conducted in a data center located at Lawrence Berkeley National Laboratory in Berkeley, California. Thirty-eight servers equipped with the liquid cooling system and instrumented for energy measurements were placed in a single rack. Two unmodified servers of the same configuration, located in an adjacent rack, were used to provide a baseline. The demonstration characterized the fraction of heat removed by the direct cooling technology, quantified the energy savings for a number of cooling infrastructure scenarios, and provided information that could be used to investigate heat reuse opportunities. Thermal measurement data were used with data center energy use modeling software to estimate overall site energy use. These estimates show that an overall data center energy savings of approximately 20 percent can be expected if a center is retrofitted as specified in the models used. Increasing the portion of heat captured by this technology is an area suggested for further development.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2014

Ultrascale Visualization Climate Data Analysis Tools (UV-CDAT) Final Report

A partnership across government, academic, and private sectors has created a novel system that enables climate researchers to solve current and emerging data analysis and visualization challenges. The Ultrascale Visualization Climate Data Analysis Tools (UV-CDAT) software project utilizes the Python application programming interface (API) combined with C/C++/Fortran implementations for performance-critical software that offers the best compromise between "scalability" and “ease-of-use.” The UV-CDAT system is highly extensible and customizable for high-performance interactive and batch visualization and analysis for climate science and other disciplines of geosciences. For complex, climate data-intensive computing, UV-CDAT’s inclusive framework supports Message Passing Interface (MPI) parallelism as well as taskfarming and other forms of parallelism. More specifically, the UV-CDAT framework supports the execution of Python scripts running in parallel using the MPI executable commands and leverages Department of Energy (DOE)-funded general-purpose, scalable parallel visualization tools such as ParaView and VisIt. This is the first system to be successfully designed in this way and with these features. The climate community leverages these tools and others, in support of a parallel client-server paradigm, allowing extreme-scale, server-side computing for maximum possible speed-up.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2014

Enabling a systems biology knowledgebase with gaggle and firegoose

The overall goal of this project was to extend the existing Gaggle and Firegoose systems to develop an open-source technology that runs over the web and links desktop applications with many databases and software applications. This technology would enable researchers to incorporate workflows for data analysis that can be executed from this interface to other online applications. The four specific aims were to (1) provide one-click mapping of genes, proteins, and complexes across databases and species; (2) enable multiple simultaneous workflows; (3) expand sophisticated data analysis for online resources; and enhance open-source development of the Gaggle-Firegoose infrastructure. Gaggle is an open-source Java software system that integrates existing bioinformatics programs and data sources into a user-friendly, extensible environment to allow interactive exploration, visualization, and analysis of systems biology data. Firegoose is an extension to the Mozilla Firefox web browser that enables data transfer between websites and desktop tools including Gaggle. In the last phase of this funding period, we have made substantial progress on development and application of the Gaggle integration framework. We implemented the workspace to the Network Portal. Users can capture data from Firegoose and save them to the workspace. Users can create workflows to start multiple software components programmatically and pass data between them. Results of analysis can be saved to the cloud so that they can be easily restored on any machine. We also developed the Gaggle Chrome Goose, a plugin for the Google Chrome browser in tandem with an opencpu server in the Amazon EC2 cloud. This allows users to interactively perform data analysis on a single web page using the R packages deployed on the opencpu server. The cloud-based framework facilitates collaboration between researchers from multiple organizations. We have made a number of enhancements to the cmonkey2 application to enable and improve the integration within different environments, and we have created a new tools pipeline for generating EGRIN2 models in a largely automated way.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2014

Implementation of a Bayesian Engine for Uncertainty Analysis

In probabilistic risk assessment, it is important to have an environment where analysts have access to a shared and secured high performance computing and a statistical analysis tool package. As part of the advanced small modular reactor probabilistic risk analysis framework implementation, we have identified the need for advanced Bayesian computations. However, in order to make this technology available to non-specialists, there is also a need of a simplified tool that allows users to author models and evaluate them within this framework. As a proof-of-concept, we have implemented an advanced open source Bayesian inference tool, OpenBUGS, within the browser-based cloud risk analysis framework that is under development at the Idaho National Laboratory. This development, the “OpenBUGS Scripter” has been implemented as a client side, visual web-based and integrated development environment for creating OpenBUGS language scripts. It depends on the shared server environment to execute the generated scripts and to transmit results back to the user. The visual models are in the form of linked diagrams, from which we automatically create the applicable OpenBUGS script that matches the diagram. These diagrams can be saved locally or stored on the server environment to be shared with other users.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2012

MIT - Mighty Steps toward Energy Sustainability

Massachusetts Institute of Technology (MIT) partnered with the U.S. Department of Energy (DOE) to develop and implement solutions to retrofit existing buildings to reduce energy consumption by at least 30% as part of DOE’s Commercial Building Partnerships (CBP) Program.1 Lawrence Berkeley National Laboratory (LBNL) provided technical expertise in support of this DOE program. MIT is one of the U.S.’s foremost higher education institutions, occupying a campus that is nearly 100 years old, with a building floor area totaling more than 12 million square feet. The CBP project focused on improving the energy performance of two campus buildings, the Ray and Maria Stata Center (RMSC) and the Building W91 (BW91) data center. A key goal of the project was to identify energy saving measures that could be applied to other buildings both within MIT’s portfolio and at other higher education institutions. The CBP retrofits at MIT are projected to reduce energy consumption by approximately 48%, including a reduction of around 72% in RMSC lighting energy and a reduction of approximately 55% in RMSC server room HVAC energy. The energy efficiency measure (EEM) package proposed for the BW91 data center is expected to reduce heating, ventilation, and air-conditioning (HVAC) energy use by 30% to 50%, depending on the final air intake temperature that is established for the server racks. The RMSC, an iconic building designed by Frank Gehry, houses the Computer Science and Artificial Intelligence Laboratory, the Laboratory for Information and Decision Systems, and the Department of Linguistics and Philosophy.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2014

Next Generation Environmentally-Friendly Driving Feedback Systems Research and Development

The objective of this project is to design, develop, and demonstrate a next-generation, federal safety- and emission-complaint driving feedback system that can be deployed across the existing vehicle fleet and improve fleet average fuel efficiency by at least 2%. The project objective was achieved with the driving feedback system that encourages fuel-efficient vehicle travel and operation through: 1) Eco-Routing Navigation module that suggests the most fuel-efficient route from one stop to the next, 2) Eco-Driving Feedback module that provides sensible information, recommendation, and warning regarding fuel-efficient vehicle operation, and 3) Eco-Score and Eco-Rank module that provides a means for driving performance tracking, self-evaluation, and peer comparison. The system also collects and stores vehicle travel and operation data, which are used by Algorithm Updating module to customize the other modules for specific vehicles and adapts them to specific drivers over time. The driving feedback system was designed and developed as an aftermarket technology that can be retrofitted to vehicles in the existing fleet. It consists of a mobile application for smart devices running Android operating system, a vehicle on-board diagnostics connector, and a data server. While the system receives and utilizes real-time vehicle and engine data from the vehicle’s controller area network bus through the vehicle’s on-board diagnostic connector, it does not modify or interfere with the vehicle’s controller area network bus, and thus, is in compliance with federal safety and emission regulations. The driving feedback system was demonstrated and then installed on 45 vehicles from three different fleets for field operational test. These include 15 private vehicles of the general public, 15 pickup trucks of the California Department of Transportation that are assigned to individual employees for business use, and 15 shuttle buses of the Riverside Transit Agency that are used for paratransit service. Detailed vehicle travel and operation data including route taken, driving speed, acceleration, braking, and the corresponding fuel consumption, were collected both before and during the test period. The data analysis results show that the fleet average fuel efficiency improvements for the three fleets with the use of the driving feedback system are in the range of 2% to 9%. The economic viability of the driving feedback system is high. A fully deployed system would require capital investment in smart device ($150-$350) and on-board diagnostics connector ($50-$100) as well as paying operating costs for wireless data plan and subscription fees ($20-$30 per month) for connecting to the data server and receiving various system services. For individual consumers who already own a smart device (such as smartphone) and commercial fleets that already use some kind of telematics services, the costs for deploying this driving feedback system would be much lower.

33 ADVANCED PROPULSION SYSTEMS↗