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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 685 records · Page 38
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Fractionation of critical metals from authentic acid mine drainage using a multi-bed immobilized amine sorbent setup: A field site study

Here for the first time reported, a flow-through multi-fixed bed immobilized amine/silica sorbent strategy was employed to fractionate purified critical metals from authentic acid mine drainage (AMD). The U.S. Department of Energy’s (DOE) National Energy Technology Laboratory’s (NETL) patent-pending Multi-functional Sorbent Technology (MUST) sorbents were used to recover critical metals from AMD at the Pittsburgh Botanic Garden (Pittsburgh, PA USA). By adjusting the AMD/sorbent ratio, we recovered > 80% of pure adsorbed Mn (by adsorbed metal weight) and >90% pure adsorbed Al at lab-scale. Further optimizing the weight hourly space velocity (WHSV) enhanced the rate of adsorbed Al recovery by over five times, justifying a field site test. After treating >100 L of AMD at the field site, the optimized polyamine/epoxysilane/aminosilane sorbent recovered approximately 0.7 wt% adsorbed Al at > 90% purity. A tangible amount of purified aluminum hydroxide [Al(OH) 3 ] and aluminum sulfate [Al 2 (SO 4 ) 3 ] solids were then recovered after eluting and precipitating the previously adsorbed metals.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2019

LCLS-II Cryomodule and Cryogenic Distribution Control

The new superconducting Linear Coherent Light Source (LCLS-II) at the SLAC National Accelerator Laboratory will be an upgrade to LCLS, the world’s first hard X-ray free-electron laser. LCLS-II is in an advanced stage of construction with equipment for both Cryoplants as well as more than half of the 37 cryomodules onsite. Jefferson Lab (JLab) is a partner lab responsible for building half of the LCLS-II cryomodules. Hence the Low Energy Recirculation Facility (LERF) at JLab was used to stage and test LCLS-II cryomodules before shipping them to SLAC. LERF was set up to test two cryomodules at a time. LERF used LCLS-II cryogenic controls instrumentation racks, Programmable Logic Controllers (PLC) controls and Experimental Physics and Industrial Control System (EPICS) Input/Output Controllers (IOCs) with the intention to use the LERF setup to check-out and verify cryogenic controls for LCLS-II. The cryogenic controls first utilized at LERF would then be replicated for controlling all 37 cryomodules via an EPICS user interface. This paper discusses the cryogenic controls currently developed for implementation in the LCLS-II project.

43 PARTICLE ACCELERATORS↗
DOE OSTI2022

Improved Throughput and Analysis of Scratch Test Results via Automation and Machine Learning

A data analysis automation interface that incorporates machine learning (ML) has been developed to improve productivity, efficiency, and consistency in identifying and defining critical load values (or other values associated with optically identifiable characteristics) of a coating when a scratch test is performed. In this specific program, the machine learning component of the program has been trained to identify the Critical Load 2 (L C2 ) value by analyzing images of the scratch tracks created in each test. An optical examination of the scratch by a human operator is currently used to determine where this value occurs. However, the vagueness of the standard has led to varying interpretations and nonuniform usage by different operators at different laboratories where the test is implemented, resulting in multiple definitions of the desired parameter. Using a standard set of training and validation images to create the dataset, the critical load can be identified consistently amongst different laboratories using the automation interface without requiring the training of human operators. When the model was used in conjunction with an instrument manufacturer's scratch test software, the model produced accurate and repeatable results and defined L C2 values in as little as half of the time compared to a human operator. When combined with a program that automates other aspects of the scratch testing process usually conducted by a human operator, scratch testing and analysis can occur with little to no intervention from a human beyond initial setup and frees them to complete other work in the lab.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Accelerating Engineered Microbe Optimization through Machine Learning and Multi-Omics Datasets [Abstract]

The Agile Biofoundry (ABF) is a multi-national lab consortium funded by the DOE Bioenergy Technologies Office that has developed a biofoundry enabling the rapid deployment of bioproducts into the market. The ABF is a flexible platform that can adjust to the needs of numerous government, academic and industrial partners, thus enabling them to rapidly develop and optimize the production of a wide range of bioproducts. To demonstrate this capability, three ABF labs (NTESS, LBNL, and PNNL) have collaborated with the biomanufacturer Lygos, Inc. to use the ABF to demonstrate a high-throughput Design-Build-Test-Learn (DBTL) engineering cycle incorporating multi-omics analysis and machine learning with best in industry cycle times.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2023

Efficient Reinforcement Learning for Real-Time Hardware-Based Energy System Experiments: Preprint

In the context of urgent climate challenges and the pressing need for rapid technology development, Reinforcement Learning (RL) stands as a compelling data-driven method for controlling real-world physical systems. However, RL implementation often entails time-consuming and computationally intensive data collection and training processes, rendering them inefficient for real-time applications that lack non-real-time models. To address these limitations, real-time emulation techniques have emerged as valuable tools for the lab-scale rapid prototyping of intricate energy systems. While emulated systems offer a bridge between simulation and reality, they too face constraints, hindering comprehensive characterization, testing, and development. In this research, we construct a surrogate model using limited data from simulated systems, enabling an efficient and effective training process for a Double Deep Q-Network (DDQN) agent for future deployment. Our approach is illustrated through a hydropower application, demonstrating the practical impact of our approach on climate-related technology development.

deep Q-learning↗
DOE OSTI2022

Former Director Sig Hecker to present classified colloquium on the end of nuclear testing

Hecker is a professor emeritus (research) in the Department of Management Science and Engineering and a senior fellow emeritus at the Freeman Spogli Institute for International Studies (FSI) at Stanford University. He served as the Lab’s fifth director from 1986 to 1997 and is a Los Alamos National Laboratory Medal recipient. He is an internationally recognized expert in plutonium science, global threat reduction and nuclear security with wide research interests covering nuclear weapons policy, nuclear security, and the safe and secure expansion of nuclear energy.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2022

Development of a Configurable DERMS Test System in GridAPPS-D

Distributed Energy Resources and their increased penetration within the power grid are a suitable method to provide essential reliability services. In order to do so, they must operate in aggregate. Aggregation of a wide variety of systems with varying standards and protocols is a complex task, which could benefit from rules and standards to support interoperability. To this end, the Portland State University Power Lab has developed the Energy Grid of Things project, which includes a Distributed Energy Resource Management System to provide aggregation and demonstrate the usage of an Energy Services Interface: a set of rules governing the interface between resource owners and aggregator businesses. This thesis presents a Modeling Environment that provides a simulation and test platform reflecting the complex needs of an aggregator. A grid simulator is required that is able to reflect the effects of thousands of Distributed Energy Resources operating in tandem; furthermore, these effects must be communicated to external actors such as the Grid Operator, which provides grid service requests to the aggregator and requires feedback. The Modeling Environment provides all of this functionality by leveraging the GridAPPS-D platform to develop an interoperable class-based system that provides configurable simulations with modular input and output APIs. The Modeling Environment uses abstractions to allow any feasible type of input to be reflected within the model as a Distributed Energy Resource, and serves as a proof-of-concept for the development of aggregation simulation and testing within GridAPPS-D.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2022

Modeling a Bulb-Style Kaplan Unit Hydrogovernor and Turbine in Mathworks-Simulink and RTDS-RSCAD

Some run-of-river (ROR) hydropower plants use a horizontal bulb-style Kaplan turbine. This turbine and the corresponding governor are not represented well in standard high-fidelity power-system modeling platforms. This paper presents the characterization, development, and modeling of bulb-style Kaplan turbine and associated hydrogovernor equipment in two platforms: Simulink from the MathWorks, Inc., and RSCAD from RTDS Technologies, Inc. The developed models enable accurate assessment of the response of a bulb-style Kaplan hydropower unit to changes in electrical loading and water conditions. The resulting models enable accurate digital real-time simulation with hardware-in-the-loop testing of this class of turbine, which was not previously possible. This, in turn, improves the ability to innovate these technologies and hybridize them in a lab setting, accelerating the potential for innovation in this type of ROR hydropower plant.

13 HYDRO ENERGY↗
DOE OSTI2020

Ten Simple Rules to becoming a principal investigator

The biggest choke point in an academic career is going from postdoc to principal investigator (PI), moving from doing someone else’s research to getting other people to do yours. It is not an easy transition, draws on few of the skills you learn at the bench and the odds are clearly not ever in your favour. So calling this article ten simple rules is obviously a simplification: it is more accurate to call them ten tricky steps. In this article, we use Principal Investigator to mean anyone who runs their own research group, using funding that they have been awarded to answer their own questions. PI encompasses a number of different job titles depending on where the research is performed – fellow, lecturer, associate professor, senior scientist. The acid test is whether you can describe the people working for you as the X group, where X is your surname. The normal route from undergraduate to lab head involves a PhD, one or most postdoc positions and then PI. Given the diversity of ways to be a PI, the final step up from postdoc takes a number of forms – in the UK this tends to be either an individual fellowship or a lecturer position, in the US it generally starts with an independent position with associated funding – either as a startup package or funded grant. The aim of this article is to identify some of the broader skills (rules 1-4) and behaviours (rules 5-10) that can help with becoming a PI. They are meant as advice, not instruction. As you will see, most of them are soft/ leadership skills, which can as easily be applied to the world outside academia as within it.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2021

Status of PIP-II 650 MHz Prototype Dressed Cavity Qualification

Low-beta and high-beta sections of PIP-II linac will use nine low-beta cryomodules with four cavities each and four high-beta cryomodules with six cavities each. These cavities will be produced and qualified in collaboration between Fermilab and the international partner labs. Prior to their installation into prototype cryomodules, several dressed cavities, which include jacketed cavities, high power couplers, and tuners, will be qualified in STC horizontal test bed at Fermilab. After qualification of bare β = 0.9 cavities at Fermilab, several pre-production β = 0.92 and β = 0.61 cavities have been and are being fabricated and qualified. Procurements have also been started for high power couplers and tuners. In this contribution we present the current status of prototype dressed cavity qualification for PIP-II.

43 PARTICLE ACCELERATORS↗
DOE OSTI2018

Resistively Graded Insulation System for Next-Generation Converter-Fed Motors

The objective of this project was to demonstrate the feasibility of a new insulation material system capable of withstanding 10 kV/μs switching voltage ramp rate (dV/dt) with operating frequencies and temperatures targeted at converter-fed motors. If successful, with additional development, this insulation material can enable 5%-10% electric machine energy savings from advanced silicon carbide (SiC) drive implementation. To accomplish these goals, we propose to eliminate partial discharge as a degradation mechanism in high voltage, taped insulation. This can be accomplished through the use of a two-sided coating on a polymer film system that will envelop any residual defects on all sides, effectively creating a Faraday cage around the defect. This project focused on demonstrating the proof of concept for the coating. It was shown that concept of providing a stress grading coating around voids can prevent partial discharge. Simulation and lab scale experiments showed that the design can be implemented in a practical layered structure without causing overheating of the insulation due to conduction. Short term testing and calculations show there is a reasonable likelihood to improved insulation life. A path to full scale production was demonstrated, with gaps in process optimization identified as needing to be closed for full scale implementation. Longer term testing and integration onto a conductor is required to fully quantify life and show benefits at an application level.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2024

Wholly Sustainable, Cost-Effective Carbon Fiber-Nylon Compounds CRADA 592 (Final Report)

Carbon fiber composites have attracted considerable attention due to the potential for substantial mass savings, with many examples now implemented in the low-volume luxury car market. However, migration to higher volume applications has been hindered by: (a) high material cost, (b) high processing times, and (c) perception of low Sustainability. This project will address all three of these barriers: (a) carbon fiber material to replace aluminum in structural components at a cost penalty of no more than $5/Kg-saved (aka weight buy), (b) fitting into high-rate processes for automotive production like injection molding, and (c) end-to-end Sustainable material – based on post industrial waste carbon fiber and nylon 66 and ability to recycle end-of-life auto parts. The opportunity lies in combining DowAksa capabilities in carbon fiber manufacturing, resin chemistry intermediate production with the unique testing capabilities inherent within PNNL. The teams from PNNL and DowAksa held several meetings virtually and in-person in Michigan and at PNNL, including a lab tour at PNNL. Throughout, the teams discussed DowAksa material sources, commercially available recycled base materials, and preliminary material properties. The teams also engaged in multiple discussions and evaluations of potential automotive applications based on the ideas suggested by PNNL. The teams discussed several potential automotive applications in which recycled carbon fiber and recycled PA resin can be used. The PNNL team identified 38 cast aluminum components that can potentially be assessed for redesign using the DowAksa materials system. The PNNL team also identified 27 polyamide components. The teams discussed the lists and narrowed it down to a handful of applications that are exterior and interior to common vehicle architectures. The team also considered semi-structural and structural components and short-listed the highest potential candidates, such as cross-car-beam. The cross-car beam was considered to be highly suitable and potentially viable demonstration applications based on the properties of the materials as well as the weight savings potentials and the reduction in embodied energy by utilizing wholly sustainable materials, since both materials, carbon fiber and resin, were derived from recycled materials. The next step was to reach out to potential OEMs and/or Tier1s who were interested in exploring such technology for future applications. However, the project was terminated, and no further discussions or exchange of information took place. No new data were generated, including no IP and no publications.

36 MATERIALS SCIENCE↗
DOE OSTI2023

LLRF System for the Fermilab PIP-II Superconducting LINAC

PIP-II is an 800 MEV superconducting linac that is in the initial acceleration chain for the Fermilab accelerator complex. The RF system consists of a warm front-end with an ion source, RFQ and buncher cavities along with 25 superconducting cryo-modules comprised of five different acceleration \(\beta\). The LLRF system for the LINAC has to provide field and resonance control for a total of 125 RF cavities.The LLRF system design is in the final design review phase and will enter the production phase next year. The PIP-II project is an international collaboration with various partner labs contributing subsystems. The LLRF system design for the PIP-II Linac is presented and the specification requirements and system performance in various stages of testing are described in this paper.

43 PARTICLE ACCELERATORS↗
DOE OSTI2025

Thoroughly testing and integrating hundreds of Pull Requests per month: ROOT’s new Cost-efficient and Feature Rich GitHub-based CI

ROOT is an open source framework, freely available on GitHub, at the heart of data acquisition, processing and analysis of HE(N)P experiments, and beyond. It is developed collaboratively: contributions are not authored only by ROOT team members, but also by the user community at large: developers and scientists from universities, labs as well as the private sector. More than 1500 GitHub Pull Requests are merged on average per year. It is in this context that code integration acquires a primary role. The review of code contributions isn’t enough: not only they need to be thoroughly reviewed, they also need to be thoroughly tested through a powerful CI infrastructure on several different platforms to comply with the high code quality standards of the project. Since the end of 2023, ROOT moved its continuous integration system from Jenkins to GitHub Actions. In this contribution, we characterise the transition to the GitHub CI, focussing on our strategy, its implementation and the lessons learned, as well as the advantages the new system offers with respect to the previous one. Particular emphasis will be given to the evaluation of the cost-benefit ratio for Jenkins and GitHub Actions for the ROOT project. We also describe how we manage to run in less than one hour thousands of unit, integration, functional and end-to-end tests on different flavours of Windows, four versions of macOS, as well as about ten of the most used Linux distributions, taking advantage of the CERN computing infrastructure.

Piparo, Danilo [CERN]↗
DOE OSTI2021

Advanced extraction of the deuteron charge radius from electron-deuteron scattering data

To extract the charge radius of the proton, $r_{p}$, from the electron scattering data, the PRad collaboration at Jefferson Lab has developed a rigorous framework for finding the best functional forms - the fitters - for a robust extraction of $r_{p}$ from a wide variety of sample functions for the range and uncertainties of the PRad data. In this work we utilize and further develop this framework. Herein we discuss methods for searching for the best fitter candidates as well as a procedure for testing the robustness of extraction of the deuteron charge radius, $r_{d}$, from parameterizations based on elastic electron-deuteron scattering data. The ansatz proposed in this paper for the robust extraction of $r_{d}$, for the proposed low-$Q^{2}$ DRad experiment at Jefferson Lab, can be further improved once there are more data.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2023

Results from an International Interlaboratory Study on Light- and Elevated Temperature-Induced Degradation (LETID) in Solar Modules

This presentation reports recently submitted results from an international interlaboratory comparison study on light- and elevated temperature-induced degradation (LETID) on crystalline silicon photovoltaic (PV) modules. A large global network of PV module manufacturers and PV testing laboratories collaborated to design a protocol for LETID detection, and screen a large and diverse set of prototype modules for LETID. We summarize the range of observed degradation and provide initial conclusions on the reproducibility of results across labs. In LETID-sensitive modules, both open-circuit voltage (VOC) and short-circuit current (ISC) degrade by a roughly similar magnitude. We observe, as do previous studies, that LETID affects each cell in a module differently. An investigation of the potential mismatch losses caused by nonuniform LETID degradation found that mismatch loss is insignificant compared to the direct LETID-related losses. We summarize the conclusions drawn from the study, provide an update on a forthcoming standard technical specification for LETID testing of PV modules (IEC TS 63342 ED1), and discuss unresolved questions and potential future work on LETID in PV modules.

degradation↗
DOE OSTI2022

Self-powered Through-wall communication for dry cask storage monitoring

Many nuclear facilities, such as spent fuel storage dry casks and nuclear reactor pressure vessels, are entirely sealed by metal layers to prevent harmful radiation. For safety and security operations, the temperature, pressure, radiation, and humidity inside the vessel needs to be closely monitored. However, no practical technology is currently available to realize the through-wall data communication and monitoring for these vessels due to the inside harsh environment of high temperature and nuclear radiation. In this paper, an innovative self-powered wireless through-wall data communication system for the nuclear environment is presented, which demonstrates a successful solution to such challenges. The presented system is composed of four modules, i.e., energy harvester with power management circuits, ultrasound wireless communication using high-temperature piezoelectric transducers, electronic circuits for sensing and data transmission, and radiation shielding for electronics. Here, constitutive functions of each module were firstly designed and followed by the system integration. Experiments were conducted subsequently to validate the designed functions and evaluate the performance of the integrated system. Results showed that the average power of over 40 mW was harvested from the thermal flow inside the nuclear spent fuel canisters which could provide enough energy to operate the sensing and data communication systems. The gamma radiation test results showed that the thermoelectric energy harvester and ultrasound transceivers can withstand radiation dosing over 100 Mrad. Furthermore, temperature shock tests demonstrated that the entire system including the shielded electronics can survive and maintain their functionalities at temperatures as high as 195°C. Under the in-lab mocked-up high temperature conditions and radiation shielding, the proposed system is foreseen to survive and operate stably for fifty years inside a nuclear spent fuel canister, and send the frequency modulated data out of the canister for 3 s in every 10 min.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2019

DOE Data Day 2019 Report

The Department of Energy (DOE) has joined the larger scientific community in the promotion of data management as a means to higher quality, more efficient research and analysis, and as a critical component of data science. Tools and platforms to support data management and analysis are rapidly evolving and provide enormous opportunities. They also pose challenges that can be specific to DOE but are common across DOE mission areas and organizations. The DOE Data Day (D3) workshop was held on September 25–26, 2019, and organized by a team at Lawrence Livermore National Laboratory (LLNL) in an effort to gather the data management practitioners at the DOE labs together to share their work and results, facilitating knowledge transfers and best practices across project teams. Over 100 attendees participated from multiple national labs—LLNL, Sandia (SNL), Lawrence Berkeley (LBNL), Los Alamos (LANL), Oak Ridge (ORNL), Argonne (ANL), Pacific Northwest (PNNL), Stanford Linear Accelerator (SLAC)—along with Mission Support and Test Services (MSTS), National Energy Technology Laboratory (NETL), Pantex, National Security Complex (Y-12), National Nuclear Security Administration (NNSA), the DOE Office of Scientific and Technical Information (OSTI), and elsewhere in the DOE complex.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗