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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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All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Nondestructive Assay Technical Infrastructure Program Mission and Vision

The Nondestructive Assay Program (NDAP) mission and vision is achieved by identifying and accomplishing a set of programmatic goals that correspond with eight broad technical program elements. The NDAP was created as a result of Defense Nuclear Facility Safety Board Recommendation 2007-1, entitled “Safety-Related In Situ Nondestructive Assay of Radioactive Materials,” which emphasized the need to improve in situ measurements of radioactive material at US Department of Energy (DOE) defense nuclear facilities. An NDAP five-year plan will define tasks to accomplish specific goals identified in the NDAP mission and vision. This mission and vision is applicable for eight technical program elements: Hardware/Software Development, Algorithm Development and Nuclear Data, Uncertainty Quantification, Nuclear Materials, Staffing, Personnel, And Training, Data Management, Requirement and Standards, and Information Preservation and Dissemination. An additional program element is technical support, which provides daily execution management support for the NDAP, site scope, and deliverable tracking via site task managers, along with technical advisement and support from the technical support group. The NDAP mission and vision provides attributes with specific goals for each program element, and some goals benefit multiple program elements. The goals and attributes defined herein are implemented via an NDAP five-year execution plan that defines site work scope, budget, and deliverables, all of which are updated annually. The NDAP is designed to benefit nondestructive assay (NDA) needs to support DOE nuclear criticality safety programs, ensuring that NDA technology is sufficiently capable of guaranteeing the safety of those who handle, store, process, or transport fissionable materials in the complex. Especially important for the NDAP is to maximize capabilities to identify, characterize, and manage in situ fissile material deposits in process equipment to ensure nuclear criticality safety at processing facilities.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2018

Synchrotron X-ray Microdiffraction and Fluorescence Imaging of Mineral and Rock Samples

In this paper, we describe a detailed procedure for acquiring and processing x-ray microfluorescence (μXRF), and Laue and powder microdiffraction two-dimensional (2D) maps at beamline 12.3.2 of the Advanced Light Source (ALS), Lawrence Berkeley National Laboratory. Measurements can be performed on any sample that is less than 10 cm x 10 cm x 5 cm, with a flat exposed surface. The experimental geometry is calibrated using standard materials (elemental standards for XRF, and crystalline samples such as Si, quartz, or Al 2 O 3 for diffraction). Samples are aligned to the focal point of the x-ray microbeam, and raster scans are performed, where each pixel of a map corresponds to one measurement, e.g. , one XRF spectrum or one diffraction pattern. The data are then processed using the in-house developed software XMAS, which outputs text files, where each row corresponds to a pixel position. Representative data from moissanite and an olive snail shell are presented to demonstrate data quality, collection, and analysis strategies.

36 MATERIALS SCIENCE↗
DOE OSTI2020

On Transfer Learning Of Neural Networks Using Bi-Fidelity Data For Uncertainty Propagation

Due to their high degree of expressiveness, neural networks have recently been used as surrogate models for mapping inputs of an engineering system to outputs of interest. Once trained, neural networks are computationally inexpensive to evaluate and remove the need for repeated evaluations of computationally expensive models in uncertainty quantification applications. However, given the highly parameterized construction of neural networks, especially deep neural networks, accurate training often requires large amounts of simulation data that may not be available in the case of computationally expensive systems. In this paper, to alleviate this issue for uncertainty propagation, we explore the application of transfer learning techniques using training data generated from both high- and low-fidelity models. We explore two strategies for coupling these two datasets during the training procedure, namely, the standard transfer learning and the bi-fidelity-weighted learning. In the former approach, a neural network model mapping the inputs to the outputs of interest is trained based on the low-fidelity data. The high-fidelity data are then used to adapt the parameters of the upper layer(s) of the low-fidelity network, or train a simpler neural network to map the output of the low-fidelity network to that of the high-fidelity model. In the latter approach, the entire low-fidelity network parameters are updated using data generated via a Gaussian process model trained with a small high-fidelity dataset. The parameter updates are performed via a variant of stochastic gradient descent with learning rates given by the Gaussian process model. Using three numerical examples, we illustrate the utility of these bi-fidelity transfer learning methods where we focus on accuracy improvement achieved by transfer learning over standard training approaches.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2018

County-level Estimates for Carbon Distribution in U.S. Croplands, 1990-2005

Net Primary Productivity (NPP) for croplands can be estimated using a statistical method that includes factors for dry weight, harvest indices, and root:shoot ratios multiplied by yield data from the National Agricultural Statistics Service (NASS). This method has been documented and published by Prince et al. (2001), Hicke and Lobell (2004), and Hicke et al. (2004). We expanded this method by including factors for more crops and by using an estimated carbon content of 0.45 for agricultural crops to estimate (a) total net carbon uptake, (b) carbon in aboveground biomass, (c) carbon in belowground biomass, (d) carbon harvested and transported off site, and (e) the amount of carbon remaining on the surface following harvest. These five variables are included with their respective Federal Information Processing Standards (FIPS) codes for all counties in the contiguous U.S. from 1990-2005. A mean harvest efficiency of 0.95 was assumed across all crops. Total cropland NPP for the U.S. ranges from 378-527 Tg C yr-1 within years 1990-2005, and total carbon harvested and removed ranges from 161-228 Tg C yr-1 within years 1990-2005.For access to the data files, click this link to the CDIAC data transition website: http://cdiac.ess-dive.lbl.gov/carbonmanagement/cropcarbon/

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2018

Industrial Scale Demonstration of Smart Manufacturing, Achieving Transformational Energy Productivity Gains

While many U.S. manufacturing operations utilize optimization for individual unit processes, smart manufacturing (SM) systems that integrate manufacturing intelligence in real time across an entire production operation are rare in large companies and virtually nonexistent in smaller organizations. One example of an area where SM systems can be applied is in the management of waste heat. A smart system that not only seeks to recover waste heat, but also to use energy more efficiently, is a more cost-effective approach because energy use, waste energy loss and product output are optimized together. The overall objective of this project was to develop an industry-accepted SM Platform that enables smart systems by accelerating and lowering the cost of development and implementation scales for use in diverse sets of manufacturing industry sectors, manufacturing operations and company sizes. Specifically, the project (1) designed and demonstrated the application of a prototype SM Platform for two diverse commercial test beds, (2) demonstrated at one and estimated for the other test bed the potential reduction in waste heat generation, and (3) worked with leading automation vendors to catalyze low-cost commercialization of the technology developed. The Smart Manufacturing Application and Data Platform (SM Platform) developed during this project is an innovative approach that marries IT virtualization technologies with operational data and modeling system requirements to construct a ready-to-go platform for enabling sensor – data – modeling – actuation systems, driven by real-time plant data and performance metrics. This SM Platform allows manufacturing organizations, regardless of their industry or size, to assemble new operational systems at a much lower cost, optimizing process knowledge and improving energy productivity by integrating with existing process control and automation systems. Also developed during this project is a methodology for developing energy productivity metrics that use real-time process information and provide comparisons for potential, practical, and actual performance, as well as examples and the methodology for provisioning of an Energy Dashboard that displays alternatives to optimize energy use within specific business contexts aimed at helping improve energy productivity and reduce emissions in U.S. manufacturing. The SM Platform designed utilizes current manufacturing, operational and IT industry standards. The platform can be used to expedite sensor – data – model – actuation system development and deployment in a cloud-based environment using a novel end-to-end workflow as a service capability that is already integrated with data connection, ingestion, and contextualization services. In addition, the methodology for development of energy productivity metrics and reusable application templates called Toolkits have been defined so that they can be tailored to a specific business situation. The project’s two test beds, one in a hydrogen production plant and the other in a forging, heat treatment, and machining operation, utilize sensor-driven modeling, measurement, and simulation systems. In these two test beds, the implementation of these as a comprehensive system has made it possible for energy productivity to be managed in real-time throughout the plant and enterprise using an energy dashboard. The development pathway to direct machine actuation was articulated. During the first year, the project team developed the most highly instrumented steam-methane reformer (SMR) in the U.S. using infrared cameras and thermocouples. Real-time data are streamed to high fidelity and reduced-order models for analysis. A high fidelity CFD model, parallelized in the SM Platform cloud environment, was demonstrated for operational use. This is the first real-time operational productivity system of a steam methane reformer using both high fidelity and data-driven models together in a real time situation to balance natural gas flow to the reformer tubes. An infrared camera system was developed and shown to outperform thermocouples in coverage, accuracy, reliability, and cost effectiveness for use in a high temperature, harsh environment. A forging, heat-treating, machining line operation test bed includes new instrumentation for data capture analysis, modeling, and simulation and will be integrated into SM Platform workflows so that business performance can be managed in real-time. Composable metrics were developed using the SM Platform to drive improvements in energy productivity, environmental performance, safety, asset management, costs, and overall operations. A major focus of the SM systems developed was optimization of energy productivity at the enterprise level, which resulted in reductions in waste heat generation for the two project test beds. Reductions in the waste heat generated will result in greater energy savings than will waste heat recovery due to inefficiencies in the heat transfer process. For the SMR test bed an efficiency improvement of 1 – 2% with a corresponding waste heat reduction of 5% were demonstrated. It should be noted that the test bed SMR unit had an efficiency of more than 90% as compared to 70 to 80% efficiencies of most SMR units. So implementation of similar SM systems at typical SMR units could yield more than 15 to 25% waste heat reductions. Similarly the forging, heat-treating, machining line operation test bed is a DOE AMO Platinum Level Superior Energy Performance plant with a much higher than typical overall operating efficiency. For this test bed heat integration using recuperators is estimated to result in fuel savings of 15.9% due to waste heat recovery compared to the base case without recuperators with negligible changes in part exit conditions. This results in an 18.9% energy efficiency improvement. The project team estimates that broad multi-sector adoption of the developed technology could reduce waste heat generation by an amount equivalent to 1.3% of total U.S. energy use. This waste heat reduction is associated with 69 million tons reduction in annual carbon dioxide emissions. Other potential benefits include reducing process times, solid and liquid wastes, environmental impacts, and water use. It is estimated that this investment has an anticipated payback period of less than one year in energy-intensive applications. The SM Platform could also reduce the costs of deploying SM systems by 50% in manufacturing operations. Commercialization of the SM Platform will require significant cross-industry collaboration and technology provider market changes. Steps taken toward commercialization included 1) analyzing savings from deployment of the SM Platform and 2) holding SM Platform workshops, webinars, and conferences with targeted individual industry segments, with a focus on small and medium-sized organizations. An upgraded community website has been established as a portal for accessing and distributing information. Most importantly, the SM platform enabling technology developed during this project and its commercialization will continue, evolve and expand under the Clean Energy Smart Manufacturing Innovation Institute (CESMII).

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2019

Histological Findings in Wild Rodents of the Great Basin

In the western United States, significant environmental change is expected through the combined effects of climate change, tree mortality, drought, and invasive species. Wild animals can be used as indicators of threats to populations and even entire communities by looking for histopathological changes in their tissues. This method may identify threats to wild populations quickly and more accurately than waiting to observe population declines; however, baseline data are needed for comparisons. In this work, rodents are surveyed at 5 sites in the Great Basin to record histopathological changes. Additionally, one site is next to a copper mine that was in operation for nearly 100 years. This site is compared to the other more pristine sites. Liver, lung, heart, and kidney tissues of 143 rodents from 10 species were processed and examined using standard histological methods. Thirty percent of rodents had histopathological changes. The most common histopathological changes were extramedullary hematopoiesis (11.9%), hyperplastic BALT (6.3%), and pyelitis (4.9%). Two pathogens were found: the nematode Capillaria hepatica (0.7%) and the fungus Emmonsia crescens (2.8%). The most common rodent captured, Peromyscus maniculatus (n = 116), was used to determine whether sex and mass predict histopathological changes. Body mass, not sex, was the best predictor, suggesting that heavier, older individuals should be selectively sampled if the goal is to assess potential threats to rodent populations. Data such as these can be compared to future data to assess populations and communities for effects of environmental change.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Soil organic carbon is not just for soil scientists: measurement recommendations for diverse practitioners

Soil organic carbon (SOC) regulates terrestrial ecosystem functioning, provides diverse energy sources for soil microorganisms, governs soil structure, and regulates the availability of organically bound nutrients. Investigators in increasingly diverse disciplines recognize how quantifying SOC attributes can provide insight about ecological states and processes. Today, multiple research networks collect and provide SOC data, and robust, new technologies are available for managing, sharing, and analyzing large data sets. Here, we advocate that the scientific community capitalize on these developments to augment SOC data sets via standardized protocols. We describe why such efforts are important and the breadth of disciplines for which it will be helpful, and outline a tiered approach for standardized sampling of SOC and ancillary variables that ranges from simple to more complex. We target scientists ranging from those with little to no background in soil science to those with more soil-related expertise, and offer examples of the ways in which the resulting data can be organized, shared, and discoverable.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Search for invisible Higgs-boson decays in events with vector-boson fusion signatures using 139 fb -1 of proton-proton data recorded by the ATLAS experiment

A direct search for Higgs bosons produced via vector-boson fusion and subsequently decaying into invisible particles is reported. The analysis uses 139 fb -1 of pp collision data at a centre-of-mass energy of $\sqrt{s}$ = 13 TeV recorded by the ATLAS detector at the LHC. The observed numbers of events are found to be in agreement with the background expectation from Standard Model processes. For a scalar Higgs boson with a mass of 125 GeV and a Standard Model production cross section, an observed upper limit of 0.145 is placed on the branching fraction of its decay into invisible particles at 95% confidence level, with an expected limit of 0.103. These results are interpreted in the context of models where the Higgs boson acts as a portal to dark matter, and limits are set on the scattering cross section of weakly interacting massive particles and nucleons. Invisible decays of additional scalar bosons with masses from 50 GeV to 2 TeV are also studied, and the derived upper limits on the cross section times branching fraction decrease with increasing mass from 1.0 pb for a scalar boson mass of 50 GeV to 0.1 pb at a mass of 2 TeV.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2024

Advancing electrochemical impedance analysis through innovations in the distribution of relaxation times method

Electrochemical impedance spectroscopy (EIS) is a key tool across various scientific disciplines, including energy sciences, chemistry, and biology, enabling the analysis of electrochemical systems. However, conventional methods for interpreting EIS data are often complex and model dependent. The distribution of relaxation times (DRT) offers a non-parametric approach that simplifies the interpretation process by providing a timescale interpretation of EIS data. This article provides a comprehensive review of current methods for DRT inversion. Additionally, a survey of practitioners highlights key challenges in the field. Here, the findings underscore the need for standardized DRT analysis and benchmarks, as well as the development of automated analysis tools. These advancements would improve the usability and interpretability of EIS data. Ultimately, implementing these improvements could not only propel the field forward but also expand the application of DRT in scientific research by making it accessible to a broader range of researchers, including those without specialized expertise in programming or statistics.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Physics-informed graph neural networks for predicting cetane number with systematic data quality analysis

Designing alternative fuels for advanced compression ignition engines necessitates a predictive model for cetane number (CN). In this study, the physics-informed graph neural networks are introduced for a reliable CN prediction by considering molecular features pertinent to the physical properties of molecules that affect CN. The reliability of measured data is another key factor to consider for improving the predictive model. Various experimental instruments for measuring CN exist, including standard and non-standard methods. In this regard, a systematic data quality analysis was carried out for the total 630 CNs collected from literature and new measurements in this study using Advanced Fuel Ignition Delay Analyzer (AFIDA). The results from this data curation process were reflected in the model by imposing lower sample weights on the data coming from less reliable measurement techniques. This approach effectively maximized the prediction accuracy while incorporating data from all available sources. Using the sample weights decreased the mean absolute error (MAE) up to 0.8 CN units. The accuracy was also improved by introducing the CN-related physical properties (the number of hydrogen bond donors and acceptors); the test set MAE is 5.74 and 7.01 for the model with and without such properties, respectively. Investigating molecular structural effects on CN was also carried out to gain chemical insights into factors used to design new fuel candidates. The dimensionality reduction analysis of feature vectors showed a clear clustering in terms of functional groups and CN and the structural effect derived from the model was consistent with the physicochemical insights. Finally, this physics-informed model and data curation would be helpful for accurate CN prediction and inform rational fuel design.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2018

Photometric Calibration for the Beijing–Arizona Sky Survey and Mayall z -band Legacy Survey

We present the photometric calibration of the Beijing–Arizona Sky Survey and Mayall z-band Legacy Survey, which are two of the three wide-field optical legacy imaging surveys to provide the baseline targeting data for the Dark Energy Spectroscopic Instrument project. The method of our photometric calibration is subdivided into the external and internal processes. The former utilizes the point-source objects of Pan-STARRS1 survey (PS1) as the reference standards to achieve the zero points of the absolute flux for individual exposures. And then the latter revise the zero points to make them consistent across the survey based on multiple tilings and large offset overlaps. Our process achieves a homogeneous photometric calibration over most of the sky with precision better than 10 mmag for g and r bands, 15 mmag for z band. The accuracy of the calibration is better than 1% at the bright end (16–18 mag) over most of the survey area.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2021

Customs: Inspect and Report Python Imports (customs) v1.0.0

We have developed a simple, minimally invasive monitoring framework that enables us to capture key metrics from Python user processes. This is part of a larger effort to collect data across our entire data-intensive science workload at NERSC. The approach for Python is inspired by a similar framework developed for Blue Waters at NCSA. Both frameworks leverage standard Python features, sitecustomize and atexit, to capture Python imports of interest and other job data. The customs package decomposes the information capture into separate inspection and reporting interfaces.

Thomas, Rollin↗
DOE OSTI2026

NEWTS Integrated Dataset (version 2.0)

The National Energy Water Treatment and Speciation (NEWTS) Integrated Dataset v2.0 provides water researchers, community leaders, regulators, and industry stakeholders with a unified and standardized energy-process wastewater chemistry database. This resource is derived from 39 state and federal entities, and scientific publications, and contains more than 700,000 sample records, many of which also provide geospatial information. The dataset includes chemistry data for several different energy-process wastewater types including produced water, other oil and gas wastewaters, mine drainage, coal ash leachate, power plant wastewater, and geothermal fluids. The NEWTS Integrated Dataset was built to support prudent decision-making, characterization of potential critical mineral sources, and modeling of treatment and valorization options. A subset of this novel resource is also featured on the NEWTS State-Level Database Dashboard. Additional data can be found in the NEWTS EDX Group and the NEWTS Federal Database Dashboard.

AMD↗
DOE OSTI2013

Search for $B_{s,d} \rightarrow \mu^+\mu^-$ Decays with 10/fb of $p\overline{p}$ Collisions

A search for the rare decays of B$^0$$_s$→ μ + μ - and B$^0$$_d$ → + μ + μ - is presented in p$\bar{p}$ collisions at √s=1.96 TeV corresponding to 10 fb -1 of integrated luminosity collected by the CDF II detector. The observed number of B$^0$$_d$ candidates is consistent with background-only expectations and yields an upper limit on the branching fraction B(B$^0$$_d$ → μ + μ - ) of 4.2 x 10 -9 at 95% confidence level. An excess is observed for the B0s signal region. The probability that background processes alone could produce such an excess or larger is 0.94%. The probability that the combination of background and the expected standard model rate B$^0$$_s$ → μ + μ - could produce such an excess or larger is 6.8%. These data are used to determine a central value of B(B$^0$$_s$ → μ + μ - ) = (1:3 +0.9 -0.7 ) 10 -8 as well as a two sided limit at 90% C.L. of 2:2 10 -9 < B(B$^0$$_s$ → μ + μ - ) < 3.0 x 10 -8 .

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2023

Release of ENDF/B-VIII.1β1-Based ACE Data Files

On March 1 st , 2023, the National Nuclear Data Center (NNDC) released the ENDF/B-VIII.1β1 nuclear data library, which contained neutron, spontaneous and neutron-induced fission yield, and alpha sublibraries. The library was released in the standard Evaluated Nuclear Data File (ENDF) format. The files can be accessed on Brookhaven National Laboratory’s GitLab. The thermal scattering law (TSL) sublibrary will be released at a later date. The files provided in the neutron sublibrary were processed into A Compact ENDF (ACE)-formatted files, verified, and validated by the XCP-5 Nuclear Data Team. The resulting collection of ACE files will be referred to as the “e81b1” library. This report details the processing of these files and the quality assurance approach taken. This is not intended to be a full validation effort; rather, this library is intended to simply reproduce the released files for further validation testing by the community. The e81b1 ACE library will be released to the NNDC for access through GitLab and hosted locally on LANL computing resources. This library is only intended for analysis to support the future ENDF/B-VIII.1 release and should not be used for production results.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2023

Dibosons and other EWK measurements

The latest electroweak results from CMS, based on pp collision data taken at 13 TeV during Run 2, are presented. Precise measurements of multi-boson processes are reviewed and compared with theory predictions, sometimes showing measurements many times more precise than previous results. All results agree with the Standard Model, and where appropriate, limits are set on anomalous triple and quartic gauge couplings.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2016

Search for Higgs boson off-shell production in proton-proton collisions at 7 and 8 TeV and derivation of constraints on its total decay width

A search is presented for the Higgs boson off-shell production in gluon fusion and vector boson fusion processes with the Higgs boson decaying into a WW pair and the W bosons decaying leptonically. The data observed in this analysis are used to constrain the Higgs boson total decay width. The analysis is based on the data collected by the CMS experiment at the LHC, corresponding to integrated luminosities of 4.9 inverse femtobarns at a centre-of-mass energy of 7 TeV and 19.4 inverse femtobarns at 8 TeV, respectively. An observed (expected) upper limit on the off-shell Higgs boson event yield normalised to the standard model prediction of 2.4 (6.2) is obtained at the 95% CL for the gluon fusion process and of 19.3 (34.4) for the vector boson fusion process. Observed and expected limits on the total width of 26 and 66 MeV are found, respectively, at the 95% confidence level (CL). These limits are combined with the previous result in the ZZ channel leading to observed and expected 95% CL upper limits on the width of 13 and 26 MeV, respectively.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2023

DEEPEN 3D PFA Weights for Exploration Datasets in Magmatic Environments

DEEPEN stands for DE-risking Exploration of geothermal Plays in magmatic ENvironments. As part of the development of the DEEPEN 3D play fairway analysis (PFA) methodology for magmatic plays (conventional hydrothermal, superhot EGS, and supercritical), weights needed to be developed for use in the weighted sum of the different favorability index models produced from geoscientific exploration datasets. This GDR submission includes those weights. The weighting was done using two different approaches: one based on expert opinions, and one based on statistical learning. The weights are intended to describe how useful a particular exploration method is for imaging each component of each play type. They may be adjusted based on the characteristics of the resource under investigation, knowledge of the quality of the dataset, or simply to reduce the impact a single dataset has on the resulting outputs. Within the DEEPEN PFA, separate sets of weights are produced for each component of each play type, since exploration methods hold different levels of importance for detecting each play component, within each play type. The weights for conventional hydrothermal systems were based on the average of the normalized weights used in the DOE-funded PFA projects that were focused on magmatic plays. This decision was made because conventional hydrothermal plays are already well-studied and understood, and therefore it is logical to use existing weights where possible. In contrast, a true PFA has never been applied to superhot EGS or supercritical plays, meaning that exploration methods have never been weighted in terms of their utility in imaging the components of these plays. To produce weights for superhot EGS and supercritical plays, two different approaches were used: one based on expert opinion and the analytical hierarchy process (AHP), and another using a statistical approach based on principal component analysis (PCA). The weights are intended to provide standardized sets of weights for each play type in all magmatic geothermal systems. Two different approaches were used to investigate whether a more data-centric approach might allow new insights into the datasets, and also to analyze how different weighting approaches impact the outcomes. The expert/AHP approach involved using an online tool (https://bpmsg.com/ahp/) with built-in forms to make pairwise comparisons which are used to rank exploration methods against one-another. The inputs are then combined in a quantitative way, ultimately producing a set of consensus-based weights. To minimize the burden on each individual participant, the forms were completed in group discussions. While the group setting means that there is potential for some opinions to outweigh others, it also provides a venue for conversation to take place, in theory leading the group to a more robust consensus then what can be achieved on an individual basis. This exercise was done with two separate groups: one consisting of U.S.-based experts, and one consisting of Iceland-based experts in magmatic geothermal systems. The two sets of weights were then averaged to produce what we will from here on refer to as the "expert opinion-based weights," or "expert weights" for short. While expert opinions allow us to include more nuanced information in the weights, expert opinions are subject to human bias. Data-centric or statistical approaches help to overcome these potential human biases by focusing on and drawing conclusions from the data alone. More information on this approach along with the dataset used to produce the statistical weights may be found in the linked dataset below.

15 GEOTHERMAL ENERGY↗