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All sourcesNASA NTRSDOE OSTI
DOE OSTI2019

Addendum to the REBUS Users Guide: REBUS-PC (V.1.5)

The Department of Energy (DOE) National Nuclear Security Administration (NNSA) Material Management and Minimization (M3) Reactor Conversion Program uses the REBUS-PC computer code to provide reactor physics and core design information such as neutron flux distributions in space, energy, and time, and to track isotopic changes in fuel and neutron absorbers with burnup. REBUS-PC has evolved from the original REBUS code, which was created starting in the 1960’s to study large liquid metal cooled fast reactors. In particular, additional outputs were added to improve user convenience and some inputs were modified to improve the functionality of the software. REBUS and REBUS-PC both model the external cycle, and are very general codes with 1D, 2D, and 3D neutronics capabilities, and with complete fuel shuffling capabilities. REBUS remains an extremely efficient reactor analysis tool for use in fast reactor systems, and so development has continued at Argonne National Laboratory (ANL) to this day for non-thermal reactor applications. While retaining the efficient computational engine of REBUS, REBUS-PC has evolved to its present status with continuous development since 1999. REBUS-PC has numerous changes and enhancements directed toward the needs of the thermal reactor analyst using Windows or Linux-based computational platforms.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2020

Extending the Operating Envelope for an Extremely Low NOx Axial Stage Combustion System

The overall goal of this program is to develop a low NOx combustion system which enables a gas turbine combined cycle efficiency of > 65%. The proposed combustion system is a combination of three advanced technologies under development by the Recipient: advanced transition design, the Distributed Combustion System (DCS), and advanced head end. Of note is that the DCS and advanced transition were initially developed with DOE funding under previous Cooperative Agreements. The continued development of these designs has resulted in a proof of concept design which was successfully tested in the Recipient’s state-of-the-art high-pressure rig. The initial rig testing indicated a substantial benefit with respect to high firing temperature operation as well as low NOx operation for the advanced transition and the DCS. Even lower NOx and higher firing temperatures are possible with the addition of the advanced head end to the design. Potential hurdles to the combustion design have been identified, such as high pressure drop and acoustics. The goal of this program is to develop variations of this design which address these hurdles and create an optimized design for implementation into the Recipient’s new high efficiency gas turbine engine.

03 NATURAL GAS↗
DOE OSTI2022

Artificial Intelligence and Machine Learning for Bioenergy Research: Opportunities and Challenges

The integration of artificial intelligence and machine learning (AI/ML) with automated experimentation, genomics, biosystems design, and bioprocessing technologies is poised to revolutionize scientific investigation and, particularly, bioenergy research. To identify the opportunities and challenges in this emerging research area, the U.S. Department of Energy’s (DOE) Biological and Environmental Research program (BER) and Bioenergy Technologies Office (BETO) held a joint virtual workshop on AI/ML for Bioenergy Research (AMBER) on August 23–25, 2022. These interests have since been amplified in a September 2022 Executive Order, “Advancing Biotechnology and Biomanufacturing Innovation for a Sustainable, Safe, and Secure U.S. Bioeconomy,” to promote a whole-of government approach to biotechnology development (White House 2022). Approximately 50 scientists with various backgrounds and expertise from academia, industry, and DOE national laboratories met to discuss the opportunities and challenges of AI/ML for bioenergy research. Workshop participants were tasked with assessing the potential for AI/ML and laboratory automation to advance biological understanding and engineering in general. They particularly examined how integrating AI/ML tools with laboratory automation could accelerate biosystems design and optimize biomanufacturing. Discussions included the data and computational infrastructure needed to augment biosystems design applications and the expertise and workforce development efforts urgently required to shift integrated systems toward bioenergy research more broadly. Participants discussed many existing and future applications of AI/ML for biosystems design ranging from enzymes to plants and microbes, microbiomes, and bioprocess development. They also identified three key categories of scientific and technical opportunities and challenges: high-quality data, AI/ML algorithms, and laboratory automation. Several main takeaways emerged from the workshop: 1. Numerous AI/ML and automated experimentation applications exist for a variety of DOE mission needs in energy and the environment; 2. Exemplary research grand challenges for which AI/ML could provide solutions include: building microbes and microbial communities to specifications, developing closed-loop autonomous design and control for biosystems design, and advancing scale-up and automation; 3. Lack of sufficient high-quality, annotated data hinders the development of AI/ML applications; 4. New and improved AI/ML tools are needed, particularly those meeting the specific needs of the BER and BETO research communities; 5. Trade-offs in performance, cost, and reliability exist between deploying commercially available versus building custom-developed instrumentation and software for automated or autonomous experimentation; translation of manual to automated or autonomous methods is often a nontrivial endeavor; 6. Training a new generation of young scientists who can develop and apply AI/ML tools is needed to solve long-standing scientific challenges in bioenergy research. The integration of AI/ML tools and automated experimentation represents a new data-driven research paradigm complementary to the traditional hypothesis-driven research paradigm. This paradigm accelerates design and optimization of biological systems and processes for a variety of DOE mission needs in energy and the environment. The AMBER workshop broadly explored the potential of this new paradigm for bioenergy research, of particular interest to BER and BETO, and identified key challenges and opportunities that DOE can address in the coming years by leveraging its unique capabilities and resources.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2019

In Situ Electrochemical Testing of Uranium Dioxide under Anoxic Conditions (FY19 Report)

Prediction of the corrosion behavior of spent uranium oxide (UO 2 ) fuel is needed for developing predictive performance assessment models for a geologic repository. Currently, the Fuel Matrix Dissolution Model (FMDM) is being used for modeling UO 2 chemistry in the Engineered Barrier System (EBS) and includes the effects of oxidants, oxygen and hydrogen peroxide generated from radiolysis of the fuel in contact with water as well as the role of hydrogen on limiting corrosion. The aim of this work is to investigate the corrosion of UO 2 in-situ in the Scanning Electron Microscope (SEM). The corrosion potential (E corr ) is measured using an electrochemical workstation and a unique microfluidic reactor containing three-electrodes and compatible for multiple analytical platforms, termed System for Analysis at the Vacuum Liquid Interface (SALVI) electrochemical cell (E-cell) which has been developed at PNNL. This approach aims to provide real-time and in operando monitoring of UO 2 electrode stability and morphological change and to study the UO 2 corrosion process at the microscale. The results will be utilized to support the Mixed Potential Model (MPM) that has been developed for the repository program.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2016

The 2015 Nonlinear Mechanics and Dynamics Research Institute

The Nonlinear Mechanics and Dynamics (NOMAD) Research Institute is a six week long collaborative research program for graduate students from across the world. The 2015 NOMAD Research Institute was hosted jointly by Sandia National Laboratories and the University of New Mexico, and featured 24 graduate students working on seven different research projects. These projects included: developing experimental strategies for studying the dynamics of nonlinear systems, a numerical round robin for predicting the response of a jointed system, quantification of uncertainty in a lap joint, assessment of experimental substructuring methods, a study of stress waves propagating through jointed interfaces, structural design optimization with joints, and the nonlinear system identification of MEMS devices. This report details both the technical research and the programmatic organization of the 2015 NOMAD Research Institute.

42 ENGINEERING↗
DOE OSTI2015

Distributed Fiber Optic Sensor for On-Line Monitoring of Coal Gasifier Refractory Health

This report summarizes technical progress on the program “Distributed Fiber Optic Sensor for On-Line Monitoring of Coal Gasifier Refractory Health,” funded by the National Energy Technology Laboratory of the U.S. Department of Energy, and performed by the Center for Photonics Technology of the Bradley Department of Electrical and Computer Engineering at Virginia Tech. The scope of work entails analyses of traveling grating generation technologies in an optical fiber, as well as the interrogation of the gratings to infer a distributed temperature along the fiber, for the purpose of developing a real-time refractory health condition monitoring technology for coal gasifiers. During the project period, which is from 2011-2015, three different sensing principles were studied, including four-wave mixing (FWM), coherent optical time-domain reflectometer (C-OTDR) and Brillouin optical time-domain analysis (BOTDA). By comparing the three methods, the BOTDA was selected for further development into a complete bench-top sensing system for the proposed high-temperature sensing application. Based on the input from Eastman Chemical, the industrial collaborator on this project, a cylindrical furnace was designed and constructed to simulate typical gasifier refractory temperature conditions in the laboratory, and verify the sensor’s capability to fully monitor refractory conditions on the back-side at temperatures up to 1000°C. In the later stages of the project, the sensing system was tested in the simulated environment for its sensing performance and high-temperature survivability. Through theoretical analyses and experimental research on the different factors affecting the sensor performance, a sensor field deployment strategy was proposed for possible future sensor field implementations.

01 COAL, LIGNITE, AND PEAT↗
DOE OSTI2016

Technical Advisor to the American Indian Research and Education Initiatives (AIREI).

The American Indian Research & Education Initiative (AIREI) is a pilot program that started in 2011 and is funded by the US Department of Energy (DOE) Economic Impact & Diversity and National Nuclear Security Administration in partnership with the American Indian Higher Education Consortium (AIHEC) and the American Indian Science and Engineering Society. AIREI brings science, technology, engineering, and mathematics (STEM) research and education funding to Tribal Colleges and Universities (TCU) and other US universities. AIREI has funded eight schools, including four pairs of tribal colleges and mainstream universities, in order for student and faculty research teams to bring energy projects to tribal lands. The research team from Southwest Indian Polytechnic Institute (SIPI) and Northern Arizona University (NAU) has performed a student-centric research and analysis feasibility study of a potential utility-scale solar power plant on the Jemez Pueblo reservation trust land. The research team from Navajo Technical University (NTU) and Arizona State University (ASU) has assessed the effectiveness of solar photovoltaic (PV) system designs in meeting the electricity demands of Navajo Tribal homes and public buildings in addition to the development of a solar technology curriculum that incorporates the outcomes of this study, helping to advance PV system design and installations on local Tribal lands. The Little Big Horn College (LBHC) and Montana State University-Bozeman (MSUB) team has developed fast growing strains of nitrogen-fixing cyanobacteria to help advance carbon capture and sequestration (CCS) technologies. The research supported the Crow Nation reservation as it evaluates opportunities for coal-to-liquid fuel and CCS projects. The Sinte Gleska University (SGU) and South Dakota School of Mines (SDSM) team developed computer modeling and simulation technologies to evaluate the feasibility of oil and gas development from the Niobrara Formation on the Rosebud Sioux reservation. Through this project, the students developed skills in applied energy-related research involving computer simulation, chemistry, geology, and petroleum engineering. AIREI supports collaboration between these universities and connects the teams with the technological expertise and mentorship opportunities provided through Sandia National Laboratories (Sandia). AIHEC consists of 37 American Indian tribally controlled colleges around the nation and provides technical assistance through professional development workshops, strategic planning meetings, and information sharing strategies.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2023

Digital Twin for Hydropower System Object Modeling: Alder Dam (FY2023)

Hydropower is the world's largest source of renewable electricity, and hydropower plants are distributed all over the world. Typical major components of a hydropower plant are the governor, excitation, generator, thrust bearing, hydraulic turbine, transformer, the main lead, metering and control, tailwater depression, and dissolved oxygen. For each component, various measures are taken. The measurements are acquired by various heterogeneous systems, including standalone sensors, programmable logic controllers (PLC), Supervisory control and data acquisition (SCADA), Internet of Things (IoT), and data acquisition and integration platforms such as OSI/PI. The measured data are often archived within the plant by a data management platform, and many institutions have cloud-based archive systems, such as Hydropower Research Institution (HRI), U.S. Army Corps of Engineers (USACE), and Columbia River Data Access in Real Time (DART). Object Modeling is a general framework for designing information systems. It focuses on objects, the actions they perform, and the messages they send to one another to cause those actions to be taken. The major differences among object modeling, network modeling, data modeling, and process modeling are that in the first we focus on the actions in response to information, objects which form the system, the actions they perform, and how they pass information to one another, while in the second we concentrate on where, when and how much information is moved, while in the third we focus on what information is moved and where it is moved, while in the last we focus on how it is moved and when it is moved. Object modeling was developed basically as a method to develop object-oriented systems and to support object-oriented programming. It describes the static structure of the system. The object Modeling Technique is easy to draw and use. That is why we choose object modeling to connect physical hydropower plants to Digital Twin. It recognizes the objects and the relationship between them. It identifies the attributes and functions of each class. Dynamic Modeling: It explains how objects respond to events. Functional Modeling indicates the processes executed in an object and how data changes when it moves to objects. It has been used in many applications like telecommunication, transportation, etc.

13 HYDRO ENERGY↗
DOE OSTI2014

Computation Annual Report (2013)

Computation provides the intellectual electricity that helps make Lawrence Livermore National Laboratory (LLNL) a thriving and innovative research and development (R&D) institution. Our hardware, software, and discipline experts are integrated into almost every field of research at LLNL. Our in-house programs, software applications, and support initiatives help achieve mission-critical national-security objectives for the U.S. Departments of Energy, Homeland Security, and Defense, as well as other federal and state agencies. We deliver comprehensive support to a wide set of scientific disciplines that include physical sciences (materials modeling, fluid dynamics), biology (genomic sequencing), mathematics (scalable algorithms), and computer sciences (parallel processing, real-time control systems). This year, we have expanded our outreach with high performance computing (HPC) collaborations and made great strides in our capabilities related to mathematical computation, control systems, and computation infrastructure, many of them in concert with partners in industry and academia. The Computation workforce remains highly motivated and passionate about melding computational expertise with cutting-edge science and technology to enable breakthroughs. 2013 has once again proven that Computation provides the foundation and support that are essential for productivity at LLNL. Every scientific discipline and program leverages the tools and expertise provided by our staff. We remain at the forefront of HPC moving towards exascale and have developed the infrastructure and proficiency to make this an achievable goal. Our researchers repeatedly deliver outstanding research, attracting collaborations with the biggest names in academia and industry, while joining forces with other national laboratories to further push the bounds of what is scientifically possible. Computation’s mission is echoed by the President of the United States in his assertion that “our future depends on reaffirming America’s role as the world’s engine of scientific discovery and technological innovation.” Driven by this lofty goal, we employ our state-of-the-art computational capabilities to ensure LLNL’s position as a global science and technology leader.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2026

A Coupled Theoretical and Experimental Approach to Elucidating the Mechanisms of Methyl Esters

The goal of this project was to probe fundamental kinetics questions regarding the gas phase reactive behavior of oxygenates. In order to succeed, our program necessitated both the development of new experimental tools and the development of theory based kinetic models to explain the chemistry of oxygenate fuels. By using advanced hybrid additive manufacturing/ traditional manufacturing techniques to create specialized ceramic micro-reactors, the team was able to simulate extreme high-temperature environments with much greater precision and durability than was previously possible. These tiny, high-tech tubes allow researchers to capture and identify "fleeting" chemical species—molecules that exist for only a fraction of a millisecond—using sophisticated light sources and mass spectrometry. Additionally, the team supported the development of a tabletop VUV laser system for isomer detection without a synchrotron. Through this work, we have successfully mapped out the specific chemical pathways of various oxygen-rich fuels, solving long-standing mysteries about how these substances break down kinetically. Ultimately, this research provides the fundamental knowledge needed to design next-generation engines and fuels that are better for the environment.

09 BIOMASS FUELS↗
DOE OSTI2013

Soil Segregation Methods for Reducing Transportation and Disposal Costs - 13544

At Formerly Utilized Sites Remedial Action Program (FUSRAP) sites where the selected alternative for contaminated soil is excavation and off-site disposal, the most significant budget items of the remedial action are the costs for transportation and disposal of soil at an off-site facility. At these sites, the objective is to excavate and dispose of only those soils that exceed derived concentration guideline levels. In situ soil segregation using gross gamma detectors to guide the excavation is often challenging at sites where the soil contamination is overlain by clean soil or where the contaminated soil is located in isolated, subsurface pockets. In addition, data gaps are often identified during the alternative evaluation and selection process, resulting in increased uncertainty in the extent of subsurface contamination. In response, the U.S. Army Corps of Engineers, Buffalo District is implementing ex situ soil segregation methods. At the remediated Painesville Site, soils were excavated and fed through a conveyor-belt system, which automatically segregated them into above- and below-cleanup criteria discharge piles utilizing gamma spectroscopy. At the Linde Site and the Shallow Land Disposal Area (SLDA) Site, which are both in the remediation phase, soils are initially segregated during the excavation process using gross gamma detectors and then transported to a pad for confirmatory manual surveying and sampling. At the Linde Site, the ex situ soils are analyzed on the basis of a site-specific method, to establish compliance with beneficial reuse criteria that were developed for the Linde remediation. At the SLDA Site, the ex situ soils are surveyed and sampled based on Multi-Agency Radiation Survey and Site Investigation Manual (MARSSIM) final status survey guidance to demonstrate compliance with the derived concentration guideline levels. At all three sites, the ex situ soils that meet the site- specific DCGLs are retained on-site and used as backfill material. This paper describes the ex situ soil segregation methods, the considerations of each method, and the estimated cost savings from minimizing the volume of soil requiring transportation and off-site disposal. (authors)

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

A uGNI-based Asynchronous Message-driven Runtime System for Cray Supercomputers with Gemini Interconnect

Gemini, the network for the new Cray XE/XK systems, features low latency, high bandwidth and strong scalability. Its hardware support for remote direct memory access enables efficient implementation of the global address space programming languages. Although the user Generic Network Interface (uGNI) provides a low-level interface for Gemini with support to the message-passing programming model (MPI), it remains challenging to port alternative programming models with scalable performance. CHARM++ is an object-oriented message-driven programming model. Its applications have been shown to scale up to the full Jaguar Cray XT machine. In this paper, we present an implementation of this programming model on uGNI for the Cray XE/XK systems. Several techniques are presented to exploit the uGNI capabilites by reducing memory copy and registration overhead, taking advantage of the persistent communication, and improving intra-node communication. Our microbenchmark results demonstrate that the uGNI-based runtime system outperforms the MPI-based implementation by up to 50% in terms of message latency. For communication intensive applications such as N-Queens, this implementation scales up to 15, 360 cores of a Cray XE6 machine and is 70% faster than the MPI-based implementation. In molecular dynamics application NAMD, the performance is also considerably improved by as much as 18%.

Computer Science↗
DOE OSTI2015

Scalable control program for multiprecursor flow-type atomic layer deposition system

The authors report the development and implementation of a scalable control program to control flow type atomic layer deposition (ALD) reactor with multiple precursor delivery lines. The program logic is written and tested in LABVIEW environment to control ALD reactor with four precursor delivery lines to deposit up to four layers of different materials in cyclic manner. The programming logic is conceived such that to facilitate scale up for depositing more layers with multiple precursors and scale down for using single layer with any one precursor in the ALD reactor. The program takes precursor and oxidizer exposure and purging times as input and controls the sequential opening and closing of the valves to facilitate the complex ALD process in cyclic manner. The program could be used to deposit materials from any single line or in tandem with other lines in any combination and in any sequence.

42 ENGINEERING↗
DOE OSTI2011

Y-12 Site Sustainability Plan

The accomplishments to date and the long-range planning of the Y-12 Energy Management and Sustainability and Stewardship programs support the DOE and the National Nuclear Security Administration (NNSA) vision for a commitment to energy efficiency and sustainability and to achievement of the Guiding Principles. Specifically, the Y-12 vision is to support the Environment, Safety and Health Policy and the DOE Strategic Sustainability Performance Plan (SSPP) while promoting overall sustainability and reduction of greenhouse gas (GHG) emissions. Table ES.2 gives a comprehensive overview of Y-12's performance status and planned actions. B&W Y-12's Energy Management mission is to incorporate renewable energy and energy efficient technologies site-wide and to position Y-12 to meet NNSA energy requirement needs through 2025 and beyond. During FY 2011, the site formed a sustainability team (Fig. ES.1). The sustainability team provides a coordinated approach to meeting the various sustainability requirements and serves as a forum for increased communication and consistent implementation of sustainability activities at Y-12. The sustainability team serves as an information exchange mechanism to promote general awareness of sustainability information, while providing a system to document progress and to identify resources. These resources are necessary to implement activities that support the overall goals of sustainability, including reducing the use of resources and conserving energy. Additionally, the team's objectives include: (1) Foster a Y-12-wide philosophy to conserve resources; (2) Reduce the impacts of production operations in a cost-effective manner; (3) Increase materials recycling; (4) Use a minimum amount of energy and fuel; (5) Create a minimum of waste and pollution in achieving Y-12-strategic objectives; (6) Develop and implement techniques, technologies, process modifications, and programs that support sustainable acquisition; (7) Minimize the impacts to resources, including energy/fuel, water, waste, pesticides, and pollution generation; (8) Incorporate sustainable design principles into the design and construction of facility upgrades, new facilities, and infrastructure; and (9) Comply with federal and state regulations, executive orders, and DOE requirements. Y-12 is working to communicate its sustainment vision through procedural, engineering, operational, and management practices. The site will make informed decisions based on the application of the five Guiding Principles for HPSBs to the maximum extent possible.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2019

Future SDN-HPON Control Plane Architecture and Protocol for On-Demand Terabit End-to-End Extreme-Scale Science Applications

The UC Davis team conducted architecture, algorithm and experimental studies for next generation ultra-high-bandwidth optical networks in support of extreme-scale science applications. In particular, over the course of this 3-year project, the UC Davis team achieved the following main accomplishments: Designed a network architecture for automated multi-domain service provisioning: Designed information exchange schemes between autonomous systems (domains) Designed application programming interfaces to orchestrate resource reservation in data centers, high performance computation facilities, and communication networks belonging to different autonomous systems Experimentally assessed the multi-domain network architecture in a distributed field trial set-up connecting premises in three continents Designed a broker-based orchestrated multi-domain optical networking framework supporting observe-analyze-act cycle based cognitive inter-domain service provisioning: Designed an inter-domain operation mechanism, e.g., topology abstraction, for the broker plane and domain managers to collaboratively accomplish inter-domain service provisioning while respecting the autonomy of each administrative domain Designed an alien wavelength monitoring scheme and an artificial neural network based optical signal-to-noise ratio estimator for quality-of-transmission aware inter-domain service provisioning Developed a deep neural network based multi-domain traffic predictor and a cognitive routing and spectrum assignment algorithm to realize knowledge-based autonomous traffic engineering in multi-domain networks Implemented an optical signal-to-noise ratio estimator and traffic predictor with Google’s Tensor flow and assessed their performance using experimental and synthetic data Demonstrated the benefits of the proposed cognitive schemes with experiments ona two-domain elastic optical network testbed and evaluations on a six-domain simulation framework Built a two-domain software-defined networking control plane testbed using the ONOS platform and demonstrated the workflow of broker and domain manager cooperation, including hierarchical performance monitoring, topology abstraction and cognitive resource allocation. Designed self-learning cognitive service provisioning schemes using advanced machine learning technologies: Designed a self-taught anomaly detection framework for optical networks, including an unsupervised data clustering module for pattern recognition, a supervised data regression and classification module for online anomaly detection, and the system operation principle Experimentally validated the effectiveness of the self-taught anomaly detection framework with various failure/anomaly scenarios. Designed a self-learning autonomic routing, modulation and spectrum assignment (RMSA) provisioning framework for elastic optical networks with deep reinforcement learning. Designed effective modeling and training mechanisms for the self-learning RMSA framework Conducted extensive numerical simulations with various traffic patterns and topologies to verify the advantage of the self-learning RMSA framework.

42 ENGINEERING↗
DOE OSTI2025

Methanol from Integrated Direct Air Capture and Ceramic Electrolysis (MIDACE)

The Methanol from Integrated Direct Air Capture and Ceramic Electrolysis (MIDACE) project advanced a novel system concept for integrating pressurized co-electrolysis of steam and crude carbon dioxide captured directly from ambient air using a sorbent technology. The co-electrolysis produced syngas provides feedstock for a gas-to-liquid methanol synthesis reactor. The system recovers waste heat from the reactor and electrolyzer into regenerating the direct air capture sorbent. The purpose of the project was to demonstrate critical integration elements of the design and perform a technical study illustrating how a large-scale installation could achieve the $800/ton program target for green methanol production. The concept addresses the program objective to consolidate operations by combining crude CO 2 cleanup, hydrogen production, and partial CO 2 reduction steps within a carbon tolerant high temperature electrolyzer. The design lowers costs by simplifying the methanol recovery cycle, reducing carbon losses from venting, reducing sensitivity to catalyst selectivity, and avoiding syngas compression.

10 SYNTHETIC FUELS↗
DOE OSTI2012

The Alternative Energy Future (Final Scientific/Technical Report)

The American Academy’s project on “The Alternative Energy Future” identified societal barriers to the widespread adoption of new energy technologies and assessed how these barriers might be better understood and managed through social science research. Recognizing that today’s energy system will require substantial change to maintain a sufficient energy supply in the face of environmental and geopolitical challenges, the Academy convened a wide range of experts to study the legal, social, and economic issues surrounding the energy future and to develop recommendations for future social science research. A May 2011 workshop in Washington, D.C., brought together social scientists, scientists and engineers, business leaders, and government officials to assess how individual, household, institutional, and community behaviors affect the adoption of new energy technologies and practices. Participants examined how social science research could help address this problem; identified priorities for future research; and developed five broad strategies for enhancing collaboration between social scientists and policy makers. The project generated a research agenda for future social science research on social and behavioral issues related to energy production and use. This agenda is already informing government, academic, and industry research on energy by providing a framework for new research programs and cross-disciplinary collaborations to advance knowledge of critical energy challenges. It is also the basis for a newly-established interdisciplinary forum on energy and social science based at the Academy. The findings and recommendations from this study were published in an Academy report, Beyond Technology: Strengthening Energy Policy through Social Science. The report was distributed widely to the Academy’s membership and other experts and is available to the public on the Academy’s web site (www.amacad.org/pdfs/AlternativeEnergy.pdf).

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2017

System and method of detecting cavitation in pumps

A system and method for detecting cavitation in pumps for fixed and variable supply frequency applications is disclosed. The system includes a controller having a processor programmed to repeatedly receive real-time operating current data from a motor driving a pump, generate a current frequency spectrum from the current data, and analyze current data within a pair of signature frequency bands of the current frequency spectrum. The processor is further programmed to repeatedly determine fault signatures as a function of the current data within the pair of signature frequency bands, repeatedly determine fault indices based on the fault signatures and a dynamic reference signature, compare the fault indices to a reference index, and identify a cavitation condition in a pump based on a comparison between the reference index and a current fault index.

42 ENGINEERING↗