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At least 973 records · Page 54

A Specimen Size Effect on the Fatigue Crack Growth Rate Threshold of IN 718

Fatigue crack growth rate (FCGR) tests were conducted on IN 718 in the solution annealed and aged condition at room temperature in accordance with E647-87. As part of each test, the FCGR threshold was measured using the decreasing Delta K method. A new heat of material was being tested and some of this material was sent to a different laboratory which wanted to use a specimen with a 127 mm width. Threshold data previously had been established on specimens with a width of 50.8 mm. As a check of the laboratory, tests were conducted at room temperature and R equal to 0.1 for comparison with the earlier data. The results were a threshold significantly higher than previously observed. Interchanging of specimen sizes and laboratories showed that the results were not due to a heat-to-heat or lab-to-lab variation. The results to be presented here are those obtained at the original laboratory. Growth rates were measured using the electric potential drop technique at R values of 0.1, 0.7, and 0.9. Compact tension specimen sizes with planer dimensions of 25.4 mm, 50.8 mm, and 127 mm were used. Crack growth rates at threshold were generally below 2.5 X 10(exp -8) mm / cycle. Closure measurements were made on some of the specimens by a manual procedure using a clip gage. When the crack growth rate data for the specimens tested at R equal to 0.1 were plotted as a function of applied Delta K, the thresholds varied with specimen width. The larger the width, the higher the threshold. The thresholds varied from 6.5 MPa-m(exp 1/2) for the 25.4 mm specimen to 15.4 MPa-m(exp 1/2) for the 127 mm specimen. At R equal to 0.7, the 25.4 mm and 50.8 mm specimens had essentially the same threshold, about 2.9 MPa-m(exp 1/2)while the 127 mm specimen had a threshold of 4.5 MPa-m(exp 1/2). When plotted as a function of effective Delta K, the R equal to 0.1 data are essentially normalized. Various aspects of the test procedure will be discussed as well as the results of analysis of the data using some different closure models.

Garr, K. R.↗

Modeling of Reactor Design and Optimization for Scale-Up of the Catalyxx Process for Ethanol Conversion to Higher Alcohol Biofuels

This report summarizes the results of a collaborative efforts between Oak Ridge National Laboratory (ORNL) and Catalyxx Inc. to investigate scale-up of Catalyxx’s Ethanol upgrading to higher alcohols process. The study was funded by the U.S. Department of Energy (DOE) Bioenergy Technologies Office (BETO) under CRADA (Cooperative Research and Development Agreement) No: NFE-20-08396. The project is part of the Direct Funding Opportunity (DFO) for Computational Science to Enable Bioenergy program which utilized computational toolsets developed by the Consortium for Computational Physics and Chemistry, a multi-laboratory consortium in BETO. The report here summarizes a packed-bed reactor modeling effort spanning the range from lab to industrial scale (from 4 gram to 5-ton catalyst beds), and examining reactor design, process optimization strategies, and suggested design and operating conditions for Catalyxx’s ethanol upgrading plants. The results in this report have been shared in monthly steering meetings and presentations are available in the shared data house owned by Catalyxx. The modeling effort helped to define optimum operation conditions for maximum alcohol selectivity and yield: i.e., temperature control scenarios ranging from adiabatic to isothermal, feed rate, pressure, and inlet H 2 /Ethanol ratio. The modeling results were verified at lab-(4 gram) and pre-pilot (4 kg) scales and has been used to evaluate a 5-ton packed-bed reactor and identify operating conditions to maximize the butanol yield. Special focus was given to understanding mass-transfer effects in the pre-pilot and pilot-scale reactors, over the domain of flow rate, pressure, feed composition, pellet size, shape, porosity, bed voidage, and reactor dimensions (i.e., length/diameter). Modeling was also used to evaluate innovative reactor design concepts such as water removal to improve alcohol selectivity and yield, and a reactor with an additional side inlet to facilitate quenching. These concepts were thoroughly explored, and potential benefits were disclosed. The results in this report are summarized and described qualitatively to protect the IP rights of Catalyxx. The details have been shared with the Catalyxx team in the regular steering meetings. At the end of the project, Catalyxx Inc. announced a successful demonstration of pilot scale operation in Seville, Spain.

02 PETROLEUM↗

Experimental Validation of Model-Based Prognostics for Pneumatic Valves

Because valves control many critical operations, they are prime candidates for deployment of prognostic algorithms. But, similar to the situation with most other components, examples of failures experienced in the field are hard to come by. This lack of data impacts the ability to test and validate prognostic algorithms. A solution sometimes employed to overcome this shortcoming is to perform run-to-failure experiments in a lab. However, the mean time to failure of valves is typically very high (possibly lasting decades), preventing evaluation within a reasonable time frame. Therefore, a mechanism to observe development of fault signatures considerably faster is sought. Described here is a testbed that addresses these issues by allowing the physical injection of leakage faults (which are the most common fault mode) into pneumatic valves. What makes this testbed stand out is the ability to modulate the magnitude of the fault almost arbitrarily fast. With that, the performance of end-of-life estimation algorithms can be tested. Further, the testbed is mobile and can be connected to valves in the field. This mobility helps to bring the overall process of prognostic algorithm development for this valve a step closer to validation. The paper illustrates the development of a model-based prognostic approach that uses data from the testbed for partial validation.

Chetan S Kulkarni↗

Circularity Futures Workshop Series: Summary Report

The aim of this report is to synthesize key feedback received from the three-part Circularity Futures workshop series held in Spring 2024. The workshop series was conducted by the National Renewable Energy Laboratory (NREL) on behalf of U.S. Department of Energy, Office Energy Efficiency and Renewable Energy (EERE), and was broken into three workshops: Workshop 1 - Circularity Analysis Needs and Priorities; Workshop 2 - Circularity Metrics and Indicators; and Workshop 3 - Circularity Data. Together, the workshops focused on identifying the existing priorities and gaps in the circularity modeling space, understanding different stakeholders' use and interpretation of circularity metrics and indicators, identifying common data gaps and data quality challenges, and assessing the robustness of available solutions. The workshop series brought a diverse group of stakeholders - including representatives from U.S. government offices, national labs, nonprofit organizations, industry, and academia - to collect first-hand feedback on needs, priorities, challenges and opportunities in the circularity modeling and analysis space. The workshop discussions highlighted numerous common needs, priorities and challenges among the interviewed groups. Several topics were frequently discussed, including: 1) Circularity as a pathway for sustainable economic growth: While circularity is generally defined in terms of resource conservation and reducing wasteful disposal of materials, participants agreed that circular strategies should serve broader economic, environmental, and social goals. It is therefore crucial for circularity analysis to look beyond waste reduction and instead evaluate a variety of impact metrics such as cost savings, job creation, air quality, and pollutant emissions. Mutli-criteria decision-making frameworks may be useful for making sense of disparate metrics and evaluating tradeoffs between impact categories.; 2) Economic and social factors are not well understood: Underdevelopment of existing end-of-life (EOL) management infrastructure, inconsistent standardization codes and policy space in reusing recycled content, and suboptimal collection and sorting strategies collectively contribute to uncertainty about the economic potential of circular pathways. The latter observation is consistent among all technologies but more emphasized for renewable energy systems. Social impacts of circularity practices are less understood and less researched than other sustainability aspects.; 3) Inconsistent methods for assessing emerging technologies: LCA and TEA results vary widely depending on the assumptions made with regards to market adoption of new technologies. Emerging technologies suffer limited availability of data needed to conduct a robust circularity analysis. Yet, understanding projected impacts of proposed nascent technology is a key need for different stakeholder groups.; and 4) Lack of temporally and geospatially explicit data: There is a need for open data that represents variations in circularity technologies over time and location. The lack thereof leads to aggregated and potentially misrepresented results in circularity analysis. Sensitivity analyses should be included to verify whether options perceived as more sustainable align with real-world practices.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

NREL Stratus - Enabling Workflows to Fuse Data Streams, Modeling, Simulation, and Machine Learning

Integrating cloud services into advanced computing facilities provides significant new capabilities over focusing solely on traditional high performance computing (HPC) workloads. This brings complementary capabilities as well as enabling new focused roles for HPC. They are especially potent for workflows that fuse data streams, modeling and simulation ('modsim') and machine learning. A key challenge to adopting a hybrid edge-cloud-HPC model is to align optimal capability, data, and user intent on the right resources for each step in a workflow.?The NREL Stratus service provides a basis for this: Stratus layers capabilities needed to make?cloud services accessible to a lab-based scientific community on commercial offerings, and; currently supports upwards of 200 projects ranging from IOT integration to traditional modeling and simulation. This provides a real-world inventory of scientific workflow elements. A growing knowledge base enables placing these elements appropriately between the edge, cloud, and traditional HPC. This paper outlines a vision via reference architecture and the application of that architecture in a typical workflow highlighting multiple components: sensor data intake, cleaning and transforming (edge/cloud suitable); generation of synthetic data through modsim, computationally heavy ML training and hyperparameter optimization (HPC suitable), and; inference and deployment (cloud ideal). Every step in such a workflow involves a cost-benefit analysis regarding the data movement, computational efficiency, availability, latency, and resource capabilities. The reference architecture and examples outlined allow for understanding new opportunities in the context of emerging workflows that combine IOT, cloud, and HPC to bolster scientific productivity.

AI↗

28 NREL Stratus - Enabling Workflows to Fuse Data Streams, Modeling, Simulation, and Machine Learning: Preprint

Integrating cloud services into advanced computing facilities provides significant new capabilities over focusing solely on traditional high performance computing (HPC) workloads. This brings complementary capabilities as well as enabling new focused roles for HPC. They are especially potent for workflows that fuse data streams, modeling and simulation ('modsim') and machine learning. A key challenge to adopting a hybrid edge-cloud-HPC model is to align optimal capability, data, and user intent on the right resources for each step in a workflow.?The NREL Stratus service provides a basis for this: Stratus layers capabilities needed to make?cloud services accessible to a lab-based scientific community on commercial offerings, and; currently supports upwards of 200 projects ranging from IOT integration to traditional modeling and simulation. This provides a real-world inventory of scientific workflow elements. A growing knowledge base enables placing these elements appropriately between the edge, cloud, and traditional HPC. This paper outlines a vision via reference architecture and the application of that architecture in a typical workflow highlighting multiple components: sensor data intake, cleaning and transforming (edge/cloud suitable); generation of synthetic data through modsim, computationally heavy ML training and hyperparameter optimization (HPC suitable), and; inference and deployment (cloud ideal). Every step in such a workflow involves a cost-benefit analysis regarding the data movement, computational efficiency, availability, latency, and resource capabilities. The reference architecture and examples outlined allow for understanding new opportunities in the context of emerging workflows that combine IOT, cloud, and HPC to bolster scientific productivity.

AI↗

Lessons Learned from AskGDR: Usage and Impact Analysis of the Geothermal Data Repository's AI Research Assistant: Preprint

In October of 2024, the Department of Energy's (DOE) Geothermal Data Repository (GDR) team officially launched AskGDR, an AI research assistant resulting from the integration of a Large Language Model (LLM) with the metadata and supporting documents associated with GDR datasets. AskGDR allows GDR users to ask deeper questions about the origin of datasets, the methods used to collect them, and the findings they help support. Using Retrieval Augmented Generation (RAG), AskGDR can be used to summarize findings spread across dozens of papers and technical reports or to extract relevant information describing a single data field. However, generative AI is experimental. The National Renewable Energy Laboratory (NREL) has been collecting metrics on AskGDR and documenting lessons learned during its deployment. This paper will outline the efficacy and impact of AskGDR through analysis of its use, operating costs, number and types of questions asked, and the quality of answers provided.

15 GEOTHERMAL ENERGY↗

New Results at JLab Describing Operating Lifetime of GaAs Photo-guns

Po­lar­ized elec­trons from GaAs pho­to­cath­odes have been key to some of the high­est-im­pact re­sults of the Jef­fer­son Lab sci­ence pro­gram over the past 30 years. Dur­ing this time, var­i­ous stud­ies have given in­sight into im­prov­ing the op­er­a­tional life­time of these pho­to­cath­odes in DC high-volt­age photo-guns while using lasers with spa­tial Gauss­ian pro­files of typ­i­cally 0.5 mm to 1 mm FWHM, cath­ode volt­ages of 100 kV to 130 kV, and a wide range of beam cur­rents up to mul­ti­ple mA. In this con­tri­bu­tion, we show re­cent ex­per­i­men­tal data from a 100 kV to 180 kV setup and de­scribe our progress at pre­dict­ing the life­time based on the cal­cu­la­ble dy­nam­ics of ion­ized gas mol­e­cules in­side the gun. These new ex­per­i­men­tal stud­ies at Jef­fer­son Lab are specif­i­cally aimed at ex­plor­ing the ion dam­age of higher-volt­age guns being built for in­jec­tors.

Bruker, M. W.↗

New Results at JLab Describing Operating Lifetime of GaAs Photo-guns

Po­lar­ized elec­trons from GaAs pho­to­cath­odes have been key to some of the high­est-im­pact re­sults of the Jef­fer­son Lab sci­ence pro­gram over the past 30 years. Dur­ing this time, var­i­ous stud­ies have given in­sight into im­prov­ing the op­er­a­tional life­time of these pho­to­cath­odes in DC high-volt­age photo-guns while using lasers with spa­tial Gauss­ian pro­files of typ­i­cally 0.5 mm to 1 mm FWHM, cath­ode volt­ages of 100 kV to 130 kV, and a wide range of beam cur­rents up to mul­ti­ple mA. In this con­tri­bu­tion, we show re­cent ex­per­i­men­tal data from a 100 kV to 180 kV setup and de­scribe our progress at pre­dict­ing the life­time based on the cal­cu­la­ble dy­nam­ics of ion­ized gas mol­e­cules in­side the gun. These new ex­per­i­men­tal stud­ies at Jef­fer­son Lab are specif­i­cally aimed at ex­plor­ing the ion dam­age of higher-volt­age guns being built for in­jec­tors.

Bruker, M. W.↗

PACT Data Management Plan (V.1.0)

The Perovskite PV Accelerator for Commercial Technology (PACT) is an independent validation center for the evaluation of perovskite PV technologies and their bankability. The center is led by Sandia National Laboratories and the National Renewable Energy Laboratory (NREL) and includes as part of its team Los Alamos National Laboratory (LANL), CFV Labs, Black and Veatch (B&V), and the Electric Power Research Institute (EPRI). The goals of the center are to: Develop and improve indoor and outdoor performance characterization methods, Develop and validate accelerated qualification testing for early failures (5-10 years), Research degradation and failure modes, Validate outdoor performance, and Provide bankability services to US perovskite PV (PSC) industry. The importance of data and data management to the success and outcomes of the PACT center is paramount. This report describes how data will be managed and protected by PACT and identifies important data management principles that will guide our approach.

14 SOLAR ENERGY↗

Fruit Fly Lab - 01 Payload Overview

Presentation to POIWG meeting at MSFC to discuss planned operations for upcoming FFL-01 mission on SpaceX-5. Will show hardware suite used, on-orbit operations, training strategy, and data handling architecture.

Fruit Fly↗

Goniometric and Polarized Imaging Spectroscopic Lab Measurements ofSpacecraft Materials

To better characterize the spectral response of common spacecraft materials, the following laboratory measurements are presented to support the Space Situational Awareness community in the analysis of remotely sensed observational data. Of interest is classifying material reflective properties using spectral bidirectional reflectance distribution function (BRDF) data and spatially resolved polarized imaging spectroscopy, allowing laboratory data to be applicable to ground-based optical telescope observations. The team acquired a typical CubeSat solar panel and a sample of multi-layer insulation commonly used on spacecraft for initial measurements. The data were collected at the Goniometer of the Rochester Institute of Technology (GRIT) laboratory with a field and laboratory goniometer housing two Analytical Spectral Device (ASD) spectrometers and a Headwall micro-Hyperspec E-Series imaging spectrometer with an adjustable linear polarizer. The goniometer provides spectral reflectance over a broad spectral range from 350-2500 nm at 1 nm spacing with 3 nm spectral resolution in the visible and near infrared and 8 nm in the shortwave infrared. The Headwall imager covers a spectral range from 400-1000 nm with 1.6 nm spectral resolution. We present the results from these initial measurements that show highly reflective regions at various locations in the angular domain for both materials. In addition, the solar cell spectra exhibited strong interference effects typically observed with thin films. Our team is pursuing a variety of typical solar cells to assess variations in product type. The spatially resolved polarization ratio maps show variability across the materials due to surface structure and varying material composition. Based on these results, we outline a plan for simulating spectral radiance light curves of the materials in various orbital configurations as they would be measured from ground- based telescopes for a clear observing sky during twilight. The paper will also present a plan for expanding ours of interest to determine if the results presented are unique to these samples and to categorize the spectral response for different material classes.

Chris H. Lee↗

Considerations for Distributed Edge Data Centers and Use of Building Loads to Support Large Interconnections

The rapid expansion of artificial intelligence (AI) and machine learning is driving unprecedented electricity demand from data centers. It is predicted that by 2030, 90% of AI workloads will be inference-based, requiring interconnection of multiple low-latency edge data centers (<20 MW) sited closer to end users - often on already constrained distribution feeders. Although individually small, these loads can aggregate to large loads per feeder, straining infrastructure, creating multi-year interconnection delays, and driving up customer costs. This paper proposes a data center-focused grid-integration framework that combines feeder hosting capacity analysis with building energy efficiency, building load flexibility, and waste heat reuse to expand effective feeder and substation headroom. Such approaches can reduce interconnection delays, lower costs for ratepayers, and accelerate AI-ready infrastructure deployment.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

National Marine Renewable Energy Center Upgrades - LUPA

The data provided is part of a power take off damping optimization study. The power take off damping coefficient was swept from 0 to approximately 7000 N/m/s during a single regular wave test with a real time control of the motor/generator. The generated power from the LUPA (Lab Upgrade Point Absorber) wave energy converter is reported by the motor drive in watts. The csv files in this submission are the corresponding raw time series outputs for each mode of operation of LUPA (one body heave only, two body heave only, and two body six degrees of freedom). Data comes from testing in the Large WaveFlume (LWF) at the O.H. Hinsdale Wave Research Laboratory in Corvallis, OR.

16 TIDAL AND WAVE POWER↗

Toward an event-level analysis of hadron structure using differential programming

Reconstructing the internal properties of hadrons in terms of fundamental quark and gluon de- grees of freedom is a central goal in nuclear and particle physics. This effort lies at the core of major experimental programs, such as the Jefferson Lab 12 GeV program and the upcoming Electron-Ion Collider. A primary challenge is the inherent inverse problem: converting large-scale observational data from collision events into the fundamental QCD-defined densities that characterize the micro- scopic structure of hadronic systems. Recent advances in AI and machine learning have opened new avenues for addressing this challenge using deep learning techniques. A particularly promising direction is the integration of complex theoretical calculations and experimental simulations into a unified framework capable of reconstructing these densities directly from event-level information. In this document, we introduce a key algorithm called LOITS, which enables differentiable program- ming within such a framework, facilitating the use of AI/ML techniques to solve the inverse problem of QCF reconstruction at the event level.

Braga, Kevin [College of William and Mary, William↗

Machine Learning for Automating Analysis of Speckle Dynamics

Code used for generating results in the publication for automating analysis of non-equilibrium X-ray Photon Correlation Spectroscopy data, 'Machine Learning for analysis of speckle dynamics: quantification and outlier detection'.

Konstantinova, Tatiana [Brookhaven National Lab. (↗

STPPHAWKES

SAND2024-08541O STTPHAWKES software solution is a Bayesian estimation approach to a Hawkes process model that treats missing data as a latent variable in the estimation. The open-source software will be released to the R CRAN repository and Sandia National Labs’ Github. Sandia National Laboratories is a multimission laboratory managed and operated by National Technology & Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International Inc., for the U.S. Department of Energy’s National Nuclear Security Administration under contract DE-NA0003525.

Rowe, Stephen↗

Redesign of the Timeline Generator at Fermilab using a web-based Flutter application, GraphQL API and an IOC

The control system at Fermilab is undergoing an evolution with a shift towards web-based applications with connections to the EPICS infrastructure. The Timeline Generator (TLG) is an application that serves to coordinate events across the lab using different timing links. These links include the Tevatron clock (TCLK), a 10 MHz serial link with events encoded at 20Hz and Machine Data (MDAT), a communication link with states encoded at 720Hz. This paper covers the redesign of the major components of the TLG. This includes a web-based Flutter application for building timelines. A placement service is in use that has a GraphQL interface and uses a timeline input to compute a schedule of events and states. The Flutter application sends this computed schedule to the TLG IOC via a GraphQL interface to the Data Pool Manager (DPM). The TLG IOC runs on an Arria FPGA, the Accelerator Clock Generator (ACLK-GEN), which is responsible for writing the events and states on to the different timing links.

Carmichael, Linden [Fermilab]↗