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All sourcesNASA NTRSDOE OSTI
DOE OSTI2012

Updated Greenhouse Gas and Criteria Air Pollutant Emission Factors and Their Probability Distribution Functions for Electricity Generating Units

Greenhouse gas (CO 2 , CH 4 and N 2 O, hereinafter GHG) and criteria air pollutant (CO, NO x , VOC, PM 10 , PM 2.5 and SO x , hereinafter CAP) emission factors for various types of power plants burning various fuels with different technologies are important upstream parameters for estimating life-cycle emissions associated with alternative vehicle/fuel systems in the transportation sector, especially electric vehicles. The emission factors are typically expressed in grams of GHG or CAP per kWh of electricity generated by a specific power generation technology. This document describes our approach for updating and expanding GHG and CAP emission factors in the GREET (Greenhouse Gases, Regulated Emissions, and Energy Use in Transportation) model developed at Argonne National Laboratory (see Wang 1999 and the GREET website at http://greet.es.anl.gov/main) for various power generation technologies. These GHG and CAP emissions are used to estimate the impact of electricity use by stationary and transportation applications on their fuel-cycle emissions. The electricity generation mixes and the fuel shares attributable to various combustion technologies at the national, regional and state levels are also updated in this document. The energy conversion efficiencies of electric generating units (EGUs) by fuel type and combustion technology are calculated on the basis of the lower heating values of each fuel, to be consistent with the basis used in GREET for transportation fuels. On the basis of the updated GHG and CAP emission factors and energy efficiencies of EGUs, the probability distribution functions (PDFs), which are functions that describe the relative likelihood for the emission factors and energy efficiencies as random variables to take on a given value by the integral of their own probability distributions, are updated using best-fit statistical curves to characterize the uncertainties associated with GHG and CAP emissions in life-cycle modeling with GREET.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2014

Impact of the High Flux Isotope Reactor HEU to LEU Fuel Conversion on Cold Source Nuclear Heat Generation Rates

Under the sponsorship of the US Department of Energy National Nuclear Security Administration, staff members at the Oak Ridge National Laboratory have been conducting studies to determine whether the High Flux Isotope Reactor (HFIR) can be converted from high enriched uranium (HEU) fuel to low enriched uranium (LEU) fuel. As part of these ongoing studies, an assessment of the impact that the HEU to LEU fuel conversion has on the nuclear heat generation rates in regions of the HFIR cold source system and its moderator vessel was performed and is documented in this report. Silicon production rates in the cold source aluminum regions and few-group neutron fluxes in the cold source moderator were also estimated. Neutronics calculations were performed with the Monte Carlo N-Particle code to determine the nuclear heat generation rates in regions of the HFIR cold source and its vessel for the HEU core operating at a full reactor power (FP) of 85 MW(t) and the reference LEU core operating at an FP of 100 MW(t). Calculations were performed with beginning-of-cycle (BOC) and end-of-cycle (EOC) conditions to bound typical irradiation conditions. Average specific BOC heat generation rates of 12.76 and 12.92 W/g, respectively, were calculated for the hemispherical region of the cold source liquid hydrogen (LH2) for the HEU and LEU cores, and EOC heat generation rates of 13.25 and 12.86 W/g, respectively, were calculated for the HEU and LEU cores. Thus, the greatest heat generation rates were calculated for the EOC HEU core, and it is concluded that the conversion from HEU to LEU fuel and the resulting increase of FP from 85 MW to 100 MW will not impact the ability of the heat removal equipment to remove the heat deposited in the cold source system. Silicon production rates in the cold source aluminum regions are estimated to be about 12.0% greater at BOC and 2.7% greater at EOC for the LEU core in comparison to the HEU core. Silicon is aluminum s major transmutation product and affects mechanical properties of aluminum including density, neutron irradiation hardening, swelling, and loss of ductility. Because slightly greater quantities of silicon will be produced in the cold source moderator vessel for the LEU core, these effects will be slightly greater for the LEU core than for the HEU core. Three-group (thermal, epithermal, and fast) neutron flux results tallied in the cold source LH2 hemisphere show greater values for the LEU core under both BOC and EOC conditions. The thermal neutron flux in the LH2 hemisphere for the LEU core is about 12.4% greater at BOC and 2.7% greater at EOC than for the HEU core. Therefore, cold neutron scattering will not be adversely affected and the 4 12 neutrons conveyed to the cold neutron guide hall for research applications will be enhanced.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2014

Impact of Generator Flexibility on Electric System Costs and Integration of Renewable Energy

Flexibility of traditional generators plays an important role in accommodating the increased variability and uncertainty of wind and solar on the electric power system. Increased flexibility can be achieved with changes to operational practices or upgrades to existing generation. One challenge is in understanding the value of increasing flexibility, and how this value may change given higher levels of variable generation. This study uses a commercial production cost model to measure the impact of generator flexibility on the integration of wind and solar generators. We use a system that is based on two balancing areas in the Western United States with a range of wind and solar penetrations between 15% and 60%, where instantaneous penetration of wind and solar is limited to 80%.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2016

WTP Waste Feed Qualification: Hydrogen Generation Rate Measurement Apparatus Testing Report

The generation rate of hydrogen gas in the Hanford tank waste will be measured during the qualification of the staged tank waste for processing in the Hanford Tank Waste Treatment and Immobilization Plant. Based on a review of past practices in measurement of the hydrogen generation, an apparatus to perform this measurement has been designed and tested for use during waste feed qualification. The hydrogen generation rate measurement apparatus (HGRMA) described in this document utilized a 100 milliliter sample in a continuously-purged, continuously-stirred vessel, with measurement of hydrogen concentration in the vent gas. The vessel and lid had a combined 220 milliliters of headspace. The vent gas system included a small condenser to prevent excessive evaporative losses from the sample during the test, as well as a demister and filter to prevent particle migration from the sample to the gas chromatography system. The gas chromatograph was an on line automated instrument with a large-volume sample-injection system to allow measurement of very low hydrogen concentrations. This instrument automatically sampled the vent gas from the hydrogen generation rate measurement apparatus every five minutes and performed data regression in real time. The fabrication of the hydrogen generation rate measurement apparatus was in accordance with twenty three (23) design requirements documented in the conceptual design package, as well as seven (7) required developmental activities documented in the task plan associated with this work scope. The HGRMA was initially tested for proof of concept with physical simulants, and a remote demonstration of the system was performed in the Savannah River National Laboratory Shielded Cells Mockup Facility. Final verification testing was performed using non-radioactive simulants of the Hanford tank waste. Three different simulants were tested to bound the expected rheological properties expected during waste feed qualification testing. These simulants were tested at different temperatures using purge gas spiked with varying amounts of hydrogen to provide verification that the system could accurately measure the hydrogen in the vent gas at steady state.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2016

Distributed Generation to Support Development-Focused Climate Action

This paper explores the role of distributed generation, with a high renewable energy contribution, in supporting low emission climate-resilient development. The paper presents potential impacts on development (via energy access), greenhouse gas emission mitigation, and climate resilience directly associated with distributed generation, as well as specific actions that may enhance or increase the likelihood of climate and development benefits. This paper also seeks to provide practical and timely insights to support distributed generation policymaking and planning within the context of common climate and development goals as the distributed generation landscape rapidly evolves globally. Country-specific distributed generation policy and program examples, as well as analytical tools that can inform efforts internationally, are also highlighted throughout the paper.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2017

3D-printed, integrated plug-and-play turbine-generator set for very-low-head hydro. Final Technical Report DOE-EMotors-15757-1

Hydro power has been used since the early days of electricity, but is generally limited to very large facilities. There remains a vast array of untapped locations, either rivers, tidal currents, or also man-made facilities where energy harvesting is technically feasible, but still too costly. E-Motors, in partnership with MTI, is developing an integrated design aimed at facilitating manufacturing, installation, and retrofitting. The integrated design includes a rim on the outside of the turbine runner to house the electrical generator rotor, a so-called rim drive. With this approach, a compact, in-line assembly can be produced and placed in straight pipes, without having to resort to elbows, shafts, and outside generators, thus improving efficiency and facilitating assembly. The long term goal of the project is to develop a realistic prototype for market development and start of manufacturing. During this Phase I grant, in collaboration with the University of Wisconsin-Milwaukee (UWM), the team pursued two objectives: First, design and test a small (3”) system in a laboratory at UWM, and second develop a prototype design at higher power between 50 and 100 kW. The tests showed the potential for integrating the generator rotor with the turbine runner. Output power is so far limited due to test conditions, but CFD calculations show good efficiency potential. With the larger system, a number of possibilities were explored, including: a generator design matching the turbine output, and capable of generating a peak of 86 kW from a flow of 136 cfs at 9’ of head; various intake tube geometries, showing that an elliptical shape is quite favorable; removing the hub, something made possible with a rim design, which can improve efficiency by about 10 percentage points.

13 HYDRO ENERGY↗
DOE OSTI2019

A Comparison of Fuel Choice for Backup Generators

The costs of a power outage to a business can be substantial, so there is a growing interest in generators for reliable backup power. Businesses are either considering installing backup generators or redundant backup systems for added resilience against grid outages. This report discusses the costs and benefits of backup generator configurations, and compares the relative merits of grid-connected backup systems that enable financial benefits when the grid is functioning, versus backup-only systems that only generate energy for critical services when the primary grid is down. This report discusses how to assign value to the reliability of each system and the revenue streams related to backup generators.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2019

Hydrogen Generation Rates for Saltstone Related Samples with Tank 50 Waste Grout and Glycoate Using Sealed Reactors

This report documents the results of sealed reactor tests to measure the hydrogen generation rate (HGR) from Tank 50 waste, which may be on the order of 3 x 10 -9 (ft 3 /h)/gal. Because the estimated HGR limit of detection (LOD) for an open, continuous flow system is approximately 6 x 10 -8 (ft 3 /h)/gal a more sensitive method was needed. By accumulating generated gases in a sealed system without a continuous purge gas, the resulting concentrations can increase the sensitivity of measurement, which is based on duration of heating. Prior sealed-reactor testing demonstrated measurements down to 7 x 10 -10 (ft 3 /h)/gal. In 2017, Savannah River Remediation, LLC (SRR) declared a Potential Inadequacy in the Safety Analysis (PISA) for the Saltstone Production Facility (SPF), among other facilities. The research for this report concerns waste from Tank 50 which is processed through SPF to produce saltstone. Previous studies examined the thermolysis of Tank 50 waste mixed with small individual amounts of saltstone grout premix additives, as well as glycolate. In contrast, the current study evaluated the impact of Tank 50 waste containing glycolate and stabilized within saltstone, on the generation of flammable gases, specifically hydrogen. The task included holding at target temperatures Tank 50 samples of salt solution in grout and spiked with 350 mg/L of glycolate to determine if the glycolate decomposes into H 2 . The measured hydrogen generation rates for radioactive saltstone monoliths over 28 days are: 2.0 x 10 -7 (ft 3 /h)/gal at 64-68°C, 1.4 x 10 -7 (ft 3 /h)/gal at 80-82°C, and 4.5 x 10 -7 (ft 3 /h)/gal at 112°C. Testing also included a non-radioactive salt simulant of Tank 50 waste, which was spiked with a concentration of glycolate expected in Savannah River Site (SRS) low-level waste, after the waste is solidified in grout. Included in the simulant tests was using grout made with two different surface areas, a monolith form with only the upper surface exposed and a pellet, or cube, form to increase the exposed surface area, to demonstrate the importance of exposed area in H 2 generation.

08 HYDROGEN↗
DOE OSTI2026

Region-Specific Merchant Hydrogen Market Assessment and Techno-Economic Assessment of Electrolytic Hydrogen Generation: Cooperative Research and Development Final Report, CRADA Number CRD-18-00751

Utilities need to recover value from baseload and some renewable assets even when the electricity is curtailed. The declining cost of renewables (particularly solar), the persistently low-cost of natural gas, and the declining electricity demand, have all worked to lower the price of electricity. Furthermore, the baseload assets have a high turndown cost, which is not reflected in the low market electricity prices. One option for utilizing the curtailed electricity is hydrogen generation. One important aspect of this project will be to provide a “go/no-go” recommendation on the economic feasibility of pursuing hydrogen generation vs. other markets that could offtake curtailed electricity or nuclear steam. This project will specifically assess the opportunities for hydrogen production and use in the service territories of Southern Company Services (SCS), Exelon SBC, and Xcel Energy. Each utility partner represents a different mix of generation fleets: Exelon has significant nuclear assets, Xcel is primarily renewables (wind) with some nuclear, and SCS represents both a renewables fleet and a heavily baseload fleet, which includes significant nuclear. The Contractors will conduct the study of hydrogen market opportunities by first considering the approximate electricity demand and price profiles for the year, and power generation capacity that can be used to produce hydrogen. Therefore, a profile for hydrogen generation will also be developed to better understand the quantity and timing of hydrogen production. Once the hydrogen production schedule is estimated, hydrogen storage requirements can be determined. This effort will provide sufficient information to make a go/no-go decision on moving ahead with a detailed regional case study for hydrogen production in any of the three utility areas.

08 HYDROGEN↗
DOE OSTI2024

Utilization of Synthetic Near-Infrared Spectra via Generative Adversarial Network to Improve Wood Stiffness Prediction

Near-infrared (NIR) spectroscopy is widely used as a nondestructive evaluation (NDE) tool for predicting wood properties. When deploying NIR models, one faces challenges in ensuring representative training data, which large datasets can mitigate but often at a significant cost. Machine learning and deep learning NIR models are at an even greater disadvantage because they typically require higher sample sizes for training. In this study, NIR spectra were collected to predict the modulus of elasticity (MOE) of southern pine lumber (training set = 573 samples, testing set = 145 samples). To account for the limited size of the training data, this study employed a generative adversarial network (GAN) to generate synthetic NIR spectra. The training dataset was fed into a GAN to generate 313, 573, and 1000 synthetic spectra. The original and enhanced datasets were used to train artificial neural networks (ANNs), convolutional neural networks (CNNs), and light gradient boosting machines (LGBMs) for MOE prediction. Overall, results showed that data augmentation using GAN improved the coefficient of determination (R 2 ) by up to 7.02% and reduced the error of predictions by up to 4.29%. ANNs and CNNs benefited more from synthetic spectra than LGBMs, which only yielded slight improvement. All models showed optimal performance when 313 synthetic spectra were added to the original training data; further additions did not improve model performance because the quality of the datapoints generated by GAN beyond a certain threshold is poor, and one of the main reasons for this can be the size of the initial training data fed into the GAN. LGBMs showed superior performances than ANNs and CNNs on both the original and enhanced training datasets, which highlights the significance of selecting an appropriate machine learning or deep learning model for NIR spectral-data analysis. The results highlighted the positive impact of GAN on the predictive performance of models utilizing NIR spectroscopy as an NDE technique and monitoring tool for wood mechanical-property evaluation. Further studies should investigate the impact of the initial size of training data, the optimal number of generated synthetic spectra, and machine learning or deep learning models that could benefit more from data augmentation using GANs.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2026

Dataset of Generative AI Workload Power Profiles

This dataset provides a collection of high-resolution (5/10 Hz or every 0.2/0.1 seconds) power consumption profiles for generative artificial intelligence (GenAI) workloads executed on NLR's High Performance Computing (HPC) platform Kestrel. The dataset also includes examples of representative whole-facility power profiles generated using a bottom-up, event-driven, data center energy model . This dataset is designed to support research in energy modeling, infrastructure planning, energy system integration, and sustainability analysis for AI-driven computing systems. The dataset captures time-resolved electrical power measurements across a diverse set of configurations, including variations in job type (inference vs. training), workload (LLM vs. image generation), datasets, and number of compute nodes. Power traces are provided in a standardized format and include both raw/instantaneous and aggregated files. Each profile is accompanied by metadata describing workload parameters, enabling reproducibility and cross-study comparison. The dataset is intended for use in applications such as data center infrastructure planning, energy modeling, demand response and grid impact studies, and development and validation of system-level simulation tools. By making these workload-specific power profiles publicly available, this dataset aims to address the current lack of open, empirical energy data for generative AI systems and to facilitate transparent, reproducible research on the energy and environmental impacts of large-scale AI deployment. If you use this dataset, please cite the associated publication: Vercellino et al., “Measurement of Generative AI Workload Power Profiles for Whole-Facility Data Center Infrastructure Planning,” arXiv:2604.07345 (2026).

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2017

A Methodology for Calculating EGS Electricity Generation Potential Based on the Gringarten Model for Heat Extraction From Fractured Rock

Existing methodologies for estimating the electricity generation potential of Enhanced Geothermal Systems (EGS) assume thermal recovery factors of 5% or less, resulting in relatively low volumetric electricity generation potentials for EGS reservoirs. This study proposes and develops a methodology for calculating EGS electricity generation potential based on the Gringarten conceptual model and analytical solution for heat extraction from fractured rock. The electricity generation potential of a cubic kilometer of rock as a function of temperature is calculated assuming limits on the allowed produced water temperature decline and reservoir lifetime based on surface power plant constraints. The resulting estimates of EGS electricity generation potential can be one to nearly two-orders of magnitude larger than those from existing methodologies. The flow per unit fracture surface area from the Gringarten solution is found to be a key term in describing the conceptual reservoir behavior. The methodology can be applied to aid in the design of EGS reservoirs by giving minimum reservoir volume, fracture spacing, number of fractures, and flow requirements for a target reservoir power output. Limitations of the idealized model compared to actual reservoir performance and the implications on reservoir design are discussed.

15 GEOTHERMAL ENERGY↗
DOE OSTI2020

Loosely Conditioned Emulation of Global Climate Models With Generative Adversarial Networks

Climate models encapsulate our best understanding of the Earth system, allowing research to be conducted on its future under alternative assumptions of how human-driven climate forces are going to evolve. An important application of climate models is to provide metrics of mean and extreme climate changes, particularly under these alternative future scenarios, as these quantities drive the impacts of climate on society and natural systems. Because of the need to explore a wide range of alternative scenarios and other sources of uncertainties in a computationally efficient manner, climate models can only take us so far, as they require significant computational resources, especially when attempting to characterize extreme events, which are rare and thus demand long and numerous simulations in order to accurately represent their changing statistics. Here we use deep learning in a proof of concept that lays the foundation for emulating global climate model output for different scenarios. We train two "loosely conditioned" Generative Adversarial Networks (GANs) that emulate daily precipitation output from a fully coupled Earth system model: one GAN modeling Fall-Winter behavior and the other Spring-Summer. Our GANs are trained to produce spatiotemporal samples: 32 days of precipitation over a 64x128 regular grid discretizing the globe. We evaluate the generator with a set of related performance metrics based upon KL divergence, and find the generated samples to be nearly as well matched to the test data as the validation data is to test. We also find the generated samples to accurately estimate the mean number of dry days and mean longest dry spell in the 32 day samples. Our trained GANs can rapidly generate numerous realizations at a vastly reduced computational expense, compared to large ensembles of climate models, which greatly aids in estimating the statistics of extreme events.

climate emulation, extreme climate, impacts, machi↗
DOE OSTI2026

State of Innovation 2025: Progress in Accelerating Next-Generation Cement and Concrete Technologies

The cement and concrete sectors are entering a decisive period as next-generation technologies advance from laboratory research to demonstration, early deployment, and first-of-a-kind commercial plants. Building on the 2024 State of Innovation report, the 2025 outlook highlights both the rapid acceleration of innovation and the urgent need for coordinated action across the value chain. Venture capital activity into the cement and concrete space stabilized following the record surge of 2022-2023, yet landmark financings, such as Sublime Systems' $200 million round and Terra CO2's $124 million Series B, signal continued investor confidence in companies approaching commercialization. Corporate procurement has become a powerful new catalyst, with Microsoft, Amazon, and CRH (Cement Roadstone Holdings) Ventures providing long-term commitments that underpin the first wave of next-generation cementitious products. The sector is shifting from early-stage experimentation toward the scaling of well-capitalized leaders capable of bridging the critical "capitalization gap." Early innovators continue to expand the toolkit through novel binders, electrochemical cements, biogenic limestone, and carbonate mineralization pathways. Going into 2026, cost competitiveness, durability validation, and scalability enabled by resilient supply chains remain the decisive factors for market adoption. At the 2025 Next Generation Cement and Concrete Critical Technologies Meeting, attendees emphasized dual-track funding strategies that integrate federal grants with private capital as key to enabling market breakthrough. State programs and corporate demand are sustaining momentum, while successful companies increasingly demonstrate both economic value and reduced dependence on imported materials. The National Concrete Pavement Technology Center and others underscored that broad integration of next-generation materials will hinge on standards compatibility, verified field performance, and workforce readiness. Colorado continues to serve as a proving ground through pilot programs that combine supplier training, phased implementation, and real-world data to de-risk innovation and provide replicable models for other regions. The 2025 Cement and Concrete Critical Technologies Workshop reinforced that scaling next-generation materials will require alignment among technology innovation, performance validation, and market demand. Stakeholders must move beyond siloed efforts toward collaborative frameworks that coordinate standards, funding, and infrastructure deployment. As a neutral convener and technical validator, the National Laboratory of the Rockies (NLR) plays a pivotal role in bridging innovation and market adoption through collaborative research, technology validation, and entrepreneurship programs. By uniting innovators, incumbents, policymakers, contractors, and investors, NLR and its partners are helping chart a credible pathway toward widespread commercialization in the decade ahead.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2024

InAs Terahertz Metalens Emitter for Focused Terahertz Beam Generation

Metasurfaces have opened doors to combining multiple photonic functionalities in a single compact device. In particular, the ability to generate short terahertz (THz) pulses with precise wavefront engineering in a single THz metasurface redefined the role metasurfaces can play in THz systems. Here, an InAs metalens emitter which generates and focuses a THz pulse beam is demonstrated using a 130 nm thick InAs metasurface designed as a binary‐phase Fresnel zone plate. The THz beam is focused to a spot of ≈430 μm at 1 THz with a short focal length of 5 mm and large numerical aperture of 0.5. Nanoscale InAs Mie resonators comprising the metasurface enable THz generation with an amplitude as high as 20 times compared to plasmonic THz emitters and several times compared to a 1 mm thick ZnTe crystal. This InAs metasurface emitter provides a new paradigm for designing THz imaging, spectroscopy, and communication systems, where THz beam generation and shaping are performed with a single device without compromising the generation efficiency, while eliminating losses and avoiding limitations of phase matching of conventional nonlinear optics approaches.

47 OTHER INSTRUMENTATION↗
DOE OSTI2017

Electricity generation by Pyrococcus furiosus in microbial fuel cells operated at 90°C

ABSTRACT Hyperthermophiles are microorganisms that thrive in extremely hot environments with temperatures near and even above 100°C. They are the most deeply rooted microorganisms on phylogenetic trees suggesting they may have evolved to survive in the early hostile earth. The simple respiratory systems of some of these hyperthermophiles make them potential candidates to develop microbial fuel cells (MFC) that can generate power at temperatures approaching the boiling point. We explored extracellular electron transfer in the hyperthermophilic archaeon Pyrococcus furiosus ( Pf ) by studying its ability to generate electricity in a two‐chamber MFC. Pf growing in defined medium functioned as an anolyte in a MFC operated at 90°C, generating a maximum current density of 2 A m −2 and a peak power density of 225 mW m −2 without the addition of any external redox mediator. Electron microscopy and electrochemical impedance spectroscopy of the anode with the attached Pf biofilm demonstrated bio‐electrochemical behavior that led to electricity generation in the MFC via direct electron transfer. This proof of concept study reveals for the first time that a hyperthermophile such as Pf can generate electricity in MFC at extreme temperatures. Biotechnol. Bioeng. 2017;114: 1419–1427. © 2017 Wiley Periodicals, Inc.

Sekar, Narendran↗
DOE OSTI2024

Report on the AAPM grand challenge on deep generative modeling for learning medical image statistics

Abstract Background The findings of the 2023 AAPM Grand Challenge on Deep Generative Modeling for Learning Medical Image Statistics are reported in this Special Report. Purpose The goal of this challenge was to promote the development of deep generative models for medical imaging and to emphasize the need for their domain‐relevant assessments via the analysis of relevant image statistics. Methods As part of this Grand Challenge, a common training dataset and an evaluation procedure was developed for benchmarking deep generative models for medical image synthesis. To create the training dataset, an established 3D virtual breast phantom was adapted. The resulting dataset comprised about 108 000 images of size 512 512. For the evaluation of submissions to the Challenge, an ensemble of 10 000 DGM‐generated images from each submission was employed. The evaluation procedure consisted of two stages. In the first stage, a preliminary check for memorization and image quality (via the Fréchet Inception Distance [FID]) was performed. Submissions that passed the first stage were then evaluated for the reproducibility of image statistics corresponding to several feature families including texture, morphology, image moments, fractal statistics, and skeleton statistics. A summary measure in this feature space was employed to rank the submissions. Additional analyses of submissions was performed to assess DGM performance specific to individual feature families, the four classes in the training data, and also to identify various artifacts. Results Fifty‐eight submissions from 12 unique users were received for this Challenge. Out of these 12 submissions, 9 submissions passed the first stage of evaluation and were eligible for ranking. The top‐ranked submission employed a conditional latent diffusion model, whereas the joint runners‐up employed a generative adversarial network, followed by another network for image superresolution. In general, we observed that the overall ranking of the top 9 submissions according to our evaluation method (i) did not match the FID‐based ranking, and (ii) differed with respect to individual feature families. Another important finding from our additional analyses was that different DGMs demonstrated similar kinds of artifacts. Conclusions This Grand Challenge highlighted the need for domain‐specific evaluation to further DGM design as well as deployment. It also demonstrated that the specification of a DGM may differ depending on its intended use.

Radiology, Nuclear Medicine & Medical Imaging↗
DOE OSTI2021

Floating platform effects on power generation in spar and semisubmersible wind turbines

The design and financing of commercial-scale floating offshore wind projects require a better understanding of how power generation differs between newer floating turbines and well-established fixed-bottom turbines. In floating turbines, platform mobility causes additional rotor motion that can change the time-averaged power generation. In this work, OpenFAST simulations examine the power generated by the National Renewable Energy Laboratory's 5-MW reference turbine mounted on the OC3-UMaine spar and OC4-DeepCWind semisubmersible floating platforms, subjected to extreme irregular waves and below-rated turbulent inflow wind from large-eddy simulations of a neutral atmospheric boundary layer. For these below-rated conditions, average power generation in floating turbines is most affected by two types of turbine displacements: an average rotor pitch angle that reduces power, caused by platform pitch; and rotor motion upwind-downwind that increases power, caused by platform surge and pitch. The relative balance between these two effects determines whether a floating platform causes power gains or losses compared to a fixed-bottom turbine; for example, the spar creates modest (3.1%–4.5%) power gains, whereas the semisubmersible creates insignificant (0.1%–0.2%) power gains for the simulated conditions. Furthermore, platform surge and pitch motions must be analyzed concurrently to fully capture power generation in floating turbines, which is not yet universal practice. Finally, a simple analytical model for predicting average power in floating turbines under below-rated wind speeds is proposed, incorporating effects from both the time-averaged pitch displacement and the dynamic upwind-downwind displacements.

17 WIND ENERGY↗