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All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Reducing Data Center Peak Cooling Demand and Energy Costs with Underground Thermal Energy Storage (UTES)

By recent estimates, data center energy demands are projected to consume between 6.7% and 12% of U.S. annual electricity generation by the year 2028, driven primarily by expanded demands from cloud services, big data analytics, and Artificial Intelligence (AI) (Shehabi et al., 2024). As much as 40% of data center total energy consumption are loads associated with the site infrastructure cooling systems, and these are often highly water consumptive (Aljbour et al., 2024). For energy system planners, this presents significant challenges to meeting and managing the anticipated loads, and especially the peak loads of projected data center deployments. Geothermal technologies offer two unique solutions to these challenges: 1) by serving loads through the deployment of new conventional and/or next-generation geothermal power technologies such as EGS and 2) through an often-overlooked opportunity to reduce data center peak cooling loads. The latter is the focus of this paper which explores Cold Underground Thermal Energy Storage ("Cold UTES") as an emerging industrial-scale geothermal cooling solution. This cooling solution is energy efficient, non-water-consumptive, and utilizes long duration energy storage (LDES) on both diurnal and seasonal time scales. Cold UTES has the potential to also function as a virtual power plant (VPP). The US Department of Energy's Geothermal Technologies Office is supporting R&D to understand the grid and system-wide value, costs, and impacts of deploying this emergent cooling solution at scale.

AI↗
DOE OSTI2022

Comparing and contrasting In-Vial and full-scale systems for sparging volatile analytes

Here, in-vial sparging was demonstrated as an effective, practical alternative to a full-scale sparging system for supporting the analysis of volatile constituents. Using elemental mercury (Hg 0 ) and toluene as representative purgeable analytes, the mass removal for various sparge configurations was measured and a reduced order model was developed and validated. In the primary experiments, Hg 0 in the sparge gas was trapped on activated carbon or gold, thermally desorbed, and quantified using atomic absorption or atomic fluorescence spectroscopy. Toluene experiments using the same in-vial sparge apparatus and sparge parameters were performed to demonstrate the applicability of the reduced order model to a broad range of compounds. Toluene removal was tracked by measuring the remaining toluene in sparged aliquots using Ultraviolet-visible (UV–Vis) spectroscopy. For the sparging, flow rates varied from 25 to 75 mL/min for periods from 0 to 30 min. Sparge performance, mass removal as a function of time, and sparge gas volume were measured for both in-vial and full-scale systems. A model based on dimensionless Henry's Law coefficient, normalized sparge gas volume, and fractional extent of equilibrium matched the experimental data for both compounds and provides a practical tool for future applications. For the conditions tested in this study, the calibrated model indicated that the sparge gas in the in-vial system reached approximately 33% of its equilibrium value before exiting the water surface, while a full-scale system reached approximately 100%. The tests validated the quality, reproducibility, and predictability of sparging performance for both full scale and in-vial sparge systems. Related factors such as waste generation, worker risk, and labor were also assessed. Full scale sparge systems provide the advantage of lower detection levels due to larger sample volume, while the in-vial sparge systems provide advantages for most other factors; including automatability, reducing secondary wastes, lessening the need to clean and check the sparge apparatus, and lowering labor and costs. The data and associated reduced order model support continued development and deployment of in-vial sparge platforms as a practical option for analysis of purgeable analytes such as volatile organic compounds and volatile metals/organometallics.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Unlocking Manufacturing Sustainability: Energy Efficiency Opportunities through the US Department of Energy’s Better Plants Program Energy Treasure Hunts (2023–2024)

The US manufacturing sector faces critical challenges: improving sustainability, reducing energy consumption, and reducing greenhouse gas emissions. Energy Treasure Hunt (ETH) training, a service provided by the US Department of Energy’s Better Plants program, offers a compelling solution. Although ETHs have traditionally focused on energy and cost savings, data indicate that ETHs can be used to identify opportunities to reduce emissions and water use and to support a sustainable and circular operation. These 3-day on-site events engage employees in a collaborative search for operational and maintenance efficiency improvement opportunities. The success of ETHs lies in a comprehensive methodology. Each phase in an ETH uses various tools and resources to empower employees to identify practical solutions. This study presents data from 13 ETHs conducted between 2023 and 2024 across diverse manufacturing subsectors in the United States and demonstrates that the events can help create a pragmatic decarbonization pathway. Through the events, a total of 234 energy and emissions reduction opportunities were identified, and the potential impact is significant. Implementing the recommendations could translate to annual savings of 497,299 MMBtu of energy, 64,374 kgal of water, and 4.85 million tCO 2 e of emissions. The fiscal savings from the proposed recommendations translate into nearly $\$$5 million annually. This study identifies the opportunities by energy system type and by the specific actions recommended, while also analyzing the identified opportunities, presenting the most established sustainability recommendations. Case studies from participating partners are presented to further demonstrate that ETHs provide a practical and impactful approach to reducing energy consumption, emissions, and operating costs and promote a more sustainable future for the industrial sector.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2017

Benefits of Integration of Aerojet Rocketdyne and RTI Advanced Gasification Technologies for Hydrogen-Rich Syngas Production

The key objective for this project was to evaluate the potential to achieve substantial reductions in the production cost of H 2 -rich syngas via coal gasification with near-zero emissions due to the cumulative and synergistic benefits realized when multiple advanced technologies are integrated into the overall conversion process. In this project, Aerojet Rocketdyne’s (AR’s) advanced gasification technology (currently being offered as R-GAS™) and RTI International’s (RTI’s) advanced warm syngas cleanup technologies were evaluated via a number of comparative techno-economic case studies. AR’s advanced gasification technology consists of a dry solids pump and a compact gasifier system. Based on the unique design of this gasifier, it has been shown to reduce the capital cost of the gasification block by between 40 and 50%. At the start of this project, actual experimental work had been demonstrated through pilot plant systems for both the gasifier and dry solids pump. RTI’s advanced warm syngas cleanup technologies consist primarily of RTI’s Warm Gas Desulfurization Process (WDP) technology, which effectively allows decoupling of the sulfur and CO 2 removal allowing for more flexibility in the selection of the CO 2 removal technology, plus associated advanced technologies for direct sulfur recovery and water gas shift (WGS). WDP has been demonstrated at pre-commercial scale using an activated amine carbon dioxide recovery process which would not have been possible if a majority of the sulfur had not been removed from the syngas by WDP. This pre-commercial demonstration of RTI’s advanced warm syngas cleanup system was conducted in parallel to the activities on this project. The technical data and cost information from this pre-commercial demonstration were extensively used in this project during the techno-economic analysis. With this project, both of RTI’s advanced WGS technologies were investigated. Because RT’s advanced fixed-bed WGS (AFWGS) process was successfully implemented in the WDP pre-commercial demonstration test mentioned above, this technology was used as part of RTI’s advanced warm syngas technology package for the techno-economic analyses for this project. RTI’s advanced transport-reactor-based WGS (ATWGS) process was still conceptual at the start of this project, but one of the tasks for this project was to evaluate the technical feasibility of this technology. In each of the three application-based comparison studies conducted as part of this project, the reference case was based on an existing Department of Energy National Energy Technology Laboratory (DOE/NETL) system study. Each of these references cases used existing commercial technology and the system resulted in > 90% carbon capture. In the comparison studies for the use of the hydrogen-rich syngas generated in either an Integrated Gasification Combined Cycle (IGCC) or a Coal-to-Methanol (CTM) plant, the comparison cases consisted of the reference case, a case with the integration of each individual advanced technology (either AR or RTI), and finally a case with the integration of all the advanced technologies (AR and RTI combined). In the Coal-to-Liquids (CTL) comparison study, the comparison study consisted of only three cases, which included a reference case, a case with just RTI’s advanced syngas cleaning technology, and a case with AR’s and RTI’s advanced technologies. The results from these comparison studies showed that the integration of the advanced technologies did result in substantial benefits, and by far the greatest benefits were achieved for cases integrating all the advanced technologies. For the IGCC study, the fully integrated case resulted in a 1.4% net efficiency improvement, an 18% reduction in capital cost per kW of capacity, a 12% reduction in the operating cost per kWh, and a 75–79% reduction in sulfur emissions. For the CTM case, the fully integrated plant resulted in a 22% reduction in capital cost, a 13% reduction in operating costs, a > 99% net reduction in sulfur emissions, and a reduction of 13–15% in CO 2 emissions. Because the capital cost represents over 60% of the methanol Required Selling Price (RSP), the significant reduction in the capital cost for the advanced technology case resulted in an 18% reduction in methanol RSP. For the CTL case, the fully integrated plant resulted in a 16% reduction in capital cost, which represented a 13% reduction in diesel RSP. Finally, the technical feasibility analysis of RTI’s ATWGS process demonstrated that a fluid-bed catalyst with sufficient attrition resistance and WGS activity could be made and that the process achieved about a 24% reduction in capital cost compared to a conventional fixed-bed commercial process.

08 HYDROGEN↗
DOE OSTI2015

Solar, Install, Mount, Production, Labor, Equipment Balance of Systems

The GTRI led project team in partnership with the DOE, universities, and numerous industry leaders, have advanced the mission of the DOE EERE, the Solar Energy Technologies Program, and the SunShot Initiative by accelerating the research, development, and demonstration of solar PV technologies that provide Extreme Balance of Systems Cost Reductions (BOS-X). The research produced 132 design concepts, resulting in 19 invention disclosures, five patent applications, four 90% pre-commercial designs, and three licensed technologies. Technology practice rights were obtained by an industry partner, and a new solar commercial start-up company was launched in Atlanta as a result of this project. Innovations in residential, commercial, and utility scale balance of systems technologies were realized through an unprecedented multi-disciplinary university/industry partnership with over 50 students and 24 faculty members that produced 18 technical publications, a PhD thesis, and two commercially deployed operating prototypes. The technical effectiveness and economic feasibility of the multidisciplinary systems based approach executed by the project team was realized through 1) a comprehensive evaluation of industry, regulatory, and public stakeholder requirements; 2) numerous industry/student/faculty engagements in design studios, technical conferences, and at solar PV installation sites; 3) time and motion studies with domain experts that provided technical data and costs for each phase and component of the solar PV installation processes; 4) extensive wind tunnel and systems engineering modeling; and 5) design, construction, and demonstration of the selected technologies in the field at high profile sites in Atlanta. The SIMPLE BOS project has benefitted the public in the following ways: • Workforce development: The launch of a start-up company to commercialize the DOE funded SIMPLE BoS designs has directly created 9 new jobs in the State of Georgia. As of November 2014, the Georgia solar industry employs 2,890 solar workers, representing a 12.8% growth in employment over 2013 (Solar Jobs Census, 2014). • Growth of the solar industry: The DOE SIMPLE BoS SunShot Award to GTARC accelerated the growth of the solar industry in Georgia, due to the national publicity of the award and the engagement of numerous solar PV manufacturers, designers, and installers on the SIMPLE BoS project. In 2011 less than 50 megawatts of solar PV capacity existed in Georgia; by 2016 Georgia may reach nearly 800 MW of total approved solar capacity with the 2012 Georgia Power Advanced Solar Initiative. • Technical outreach and publications: Georgia Tech has participated in numerous technical symposiums, technology demonstrations, campus solar PV tours, and produced 18 publications for the solar industry and general public. • Cost reductions for consumers: The SIMPLE BoS pre-commercial systems and discoveries enable cost reductions of 50% or more in labor and materials for residential, commercial and utility scale PV installations.

14 SOLAR ENERGY↗
DOE OSTI2015

Remote-sensing application for facilitating land resource assessment and monitoring for utility-scale solar energy development

A monitoring plan that incorporates regional datasets and integrates cost-effective data collection methods is necessary to sustain the long-term environmental monitoring of utility-scale solar energy development in expansive, environmentally sensitive desert environments. Using very high spatial resolution (VHSR; 15 cm) multispectral imagery collected in November 2012 and January 2014, an image processing routine was developed to characterize ephemeral streams, vegetation, and land surface in the southwestern United States where increased utility-scale solar development is anticipated. In addition to knowledge about desert landscapes, the methodology integrates existing spectral indices and transformation (e.g., visible atmospherically resistant index and principal components); a newly developed index, erosion resistance index (ERI); and digital terrain and surface models, all of which were derived from a common VHSR image. The methodology identified fine-scale ephemeral streams with greater detail than the National Hydrography Dataset and accurately estimated vegetation distribution and fractional cover of various surface types. The ERI classified surface types that have a range of erosive potentials. The remote-sensing methodology could ultimately reduce uncertainty and monitoring costs for all stakeholders by providing a cost-effective monitoring approach that accurately characterizes the land resources at potential development sites.

Desert regions↗
DOE OSTI2020

Southeast Regional CO 2 Utilization and Storage Acceleration Partnership (SECARB-USA): Data Quality Methodology (4.2.1)

SECARB-USA Deliverable 4.2.1 utilizes the outputs from the Needs Assessment (Subtask 2.1) to develop a data quality methodology. Many types of data are needed to evaluate a site for technical and financial viability. Several inventories of data types have been produced (for example NETL, 2010). The objective here is to organize the data types so that the needs met are specified. From this cross index (Table 1 in the Appendix), it will be possible in future tasks to (1) determine, on a site-specific basis, how much of each data type is required at each stage of a project to meet the need, and conversely (2) to further specify and define the data collection methods applied such that the data are tailored to fit that need. Derivative tables can then be developed to semi-quantitively evaluate the extent to which need is critical for early go/no decision points, or if it is more important than average, requiring faster or larger capitalization and spend to meet the need. In an additional step, the current availability of data for a site can be semi-quantitively assessed. From the table of data criticality and the table of data availability, site-specific cost for meeting the data needs can be determined, and allow a pre-permit spend estimated for a portfolio of projects. All of the data needs to evaluate a site are somewhat interconnected. We used criterion (2) above to determine if the data collection design would have to be modified to meet the need; if this was commonly true a linkage was shown. The application of this cross index to sites in the subsequent tasks will demonstrate that the demand for data types varies site-to-site and project-to-project. Examples of factors to be considered are the complexity of the geology, the injection goals such as rate and duration of the injection, and the types of risk and risk tolerance of key stakeholders. In future tasks the team will compare the demand for data with existing data availability. This will, in turn, determine when data needs to be acquired to support project development. For example, in a project area with complex structure, 3-D seismic data may be needed earlier and more urgently than in an area with simple rock body geometries. In some locations, a 3-D seismic survey has already been collected and can be purchased. In other locations the project developer will need to collect these data. For another example, a project near an urban area or near to a park may generate earlier and more substantive public concern than a site that is developed in mined lands. A calculation using the derivative from table 1 will show the different investment needs. Project cost will vary corresponding to data criticality and data availability.

42 ENGINEERING↗
NASA NTRS1981

FOCIS: A forest classification and inventory system using LANDSAT and digital terrain data

Accurate, cost-effective stratification of forest vegetation and timber inventory is the primary goal of a Forest Classification and Inventory System (FOCIS). Conventional timber stratification using photointerpretation can be time-consuming, costly, and inconsistent from analyst to analyst. FOCIS was designed to overcome these problems by using machine processing techniques to extract and process tonal, textural, and terrain information from registered LANDSAT multispectral and digital terrain data. Comparison of samples from timber strata identified by conventional procedures showed that both have about the same potential to reduce the variance of timber volume estimates over simple random sampling.

Strahler, A. H.↗
DOE OSTI2023

Cost of Fish Exclusion Technologies for Hydropower

This dataset compiles data on the costs of environmental mitigations related to fish exclusion for 35 hydropower projects based on documents obtained from the U.S Federal Energy Regulatory Commission (FERC). Data for all measures and their associated project-specific information, including installed and hydraulic capacities, were manually extracted from environmental impact statements and/or environmental assessment (EIS/EA) documents. All data were consolidated in a comma-separated (.csv) file, referred to as the Cost of Fish Exclusion Technologies for Hydropower Database.

13 HYDRO ENERGY↗
DOE OSTI2013

Surface characterization of nanomaterials and nanoparticles: Important needs and challenging opportunities

This review examines characterization challenges inherently associated with understanding nanomaterials and the roles surface and interface characterization methods can play in meeting some of the challenges. In parts of the research community, there is growing recognition that studies and published reports on the properties and behaviors of nanomaterials often have reported inadequate or incomplete characterization. As a consequence, the true value of the data in these reports is, at best, uncertain. With the increasing importance of nanomaterials in fundamental research and technological applications, it is desirable that researchers from the wide variety of disciplines involved recognize the nature of these often unexpected challenges associated with reproducible synthesis and characterization of nanomaterials, including the difficulties of maintaining desired materials properties during handling and processing due to their dynamic nature. It is equally valuable for researchers to understand how characterization approaches (surface and otherwise) can help to minimize synthesis surprises and to determine how (and how quickly) materials and properties change in different environments. Appropriate application of traditional surface sensitive analysis methods (including x-ray photoelectron and Auger electron spectroscopies, scanning probe microscopy, and secondary ion mass spectroscopy) can provide information that helps address several of the analysis needs. In many circumstances, extensions of traditional data analysis can provide considerably more information than normally obtained from the data collected. Less common or evolving methods with surface selectivity (e.g., some variations of nuclear magnetic resonance, sum frequency generation, and low and medium energy ion scattering) can provide information about surfaces or interfaces in working environments (operando or in situ) or information not provided by more traditional methods. Although these methods may require instrumentation or expertise not generally available, they can be particularly useful in addressing specific questions, and examples of their use in nanomaterial research are presented.

77 NANOSCIENCE AND NANOTECHNOLOGY↗
DOE OSTI2014

CT imaging during microwave ablation: Analysis of spatial and temporal tissue contraction

Purpose: To analyze the spatial distribution and temporal development of liver tissue contraction during high-temperature ablation by using intraprocedural computed tomography (CT) imaging. Methods: A total of 46 aluminum fiducial markers were positioned in a 60 × 45 mm grid, in a single plane, around a microwave ablation antenna in each of six ex vivo bovine liver samples. Ablations were performed for 10 min at 100 W. CT data of the liver sample were acquired every 30 s during ablation. Fiducial motion between acquisitions was tracked in postprocessing and used to calculate measures of tissue contraction and contraction rates. The spatial distribution and temporal evolution of contraction were analyzed. Results: Fiducial displacement indicated that the zone measured postablation was 8.2 ± 1.8 mm (∼20%) smaller in the radial direction and 7.1 ± 1.0 mm (∼10%) shorter in the longitudinal direction than the preablation tissue dimension. Therefore, the total ablation volume was reduced from its preablation value by approximately 45%. Very little longitudinal contraction was noted in the distal portion of the ablation zone. Central tissues contracted more than 60%, which was near an estimated limit of ∼70% based on initial water content. More peripheral tissues contracted only 15% in any direction. Contraction rates peaked during the first 60 s of heating with a roughly exponential decay over time. Conclusions: Ablation zones measured posttreatment are significantly smaller than the pretreatment tissue dimensions. Tissue contraction is spatially dependent, with the greatest effect occurring in the central ablation zone. Contraction rate peaks early and decays over time.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2014

Rapid estimation of 4DCT motion-artifact severity based on 1D breathing-surrogate periodicity

Purpose: Motion artifacts are common in patient four-dimensional computed tomography (4DCT) images, leading to an ill-defined tumor volume with large variations for radiotherapy treatment and a poor foundation with low imaging fidelity for studying respiratory motion. The authors developed a method to estimate 4DCT image quality by establishing a correlation between the severity of motion artifacts in 4DCT images and the periodicity of the corresponding 1D respiratory waveform (1DRW) used for phase binning in 4DCT reconstruction. Methods: Discrete Fourier transformation (DFT) was applied to analyze 1DRW periodicity. The breathing periodicity index (BPI) was defined as the sum of the largest five Fourier coefficients, ranging from 0 to 1. Distortional motion artifacts (excluding blurring) of cine-scan 4DCT at the junctions of adjacent couch positions around the diaphragm were classified in three categories: incomplete, overlapping, and duplicate anatomies. To quantify these artifacts, discontinuity of the diaphragm at the junctions was measured in distance and averaged along six directions in three orthogonal views. Artifacts per junction (APJ) across the entire diaphragm were calculated in each breathing phase and phase-averaged APJ{sup ¯}, defined as motion-artifact severity (MAS), was obtained for each patient. To make MAS independent of patient-specific motion amplitude, two new MAS quantities were defined: MAS{sup D} is normalized to the maximum diaphragmatic displacement and MAS{sup V} is normalized to the mean diaphragmatic velocity (the breathing period was obtained from DFT analysis of 1DRW). Twenty-six patients’ free-breathing 4DCT images and corresponding 1DRW data were studied. Results: Higher APJ values were found around midventilation and full inhalation while the lowest APJ values were around full exhalation. The distribution of MAS is close to Poisson distribution with a mean of 2.2 mm. The BPI among the 26 patients was calculated with a value ranging from 0.25 to 0.93. The DFT calculation was within 3 s per 1DRW. Correlations were found between 1DRW periodicity and 4DCT artifact severity: −0.71 for MAS{sup D} and −0.73 for MAS{sup V}. A BPI greater than 0.85 in a 1DRW suggests minimal motion artifacts in the corresponding 4DCT images. Conclusions: The breathing periodicity index and motion-artifact severity index are introduced to assess the relationship between 1DRW and 4DCT. A correlation between 1DRW periodicity and 4DCT artifact severity has been established. The 1DRW periodicity provides a rapid means to estimate 4DCT image quality. The rapid 1DRW analysis and the correlative relationship can be applied prospectively to identify irregular breathers as candidates for breath coaching prior to 4DCT scan and retrospectively to select high-quality 4DCT images for clinical motion-management research.

60 APPLIED LIFE SCIENCES↗
NASA NTRSDate not supplied

NASA GSFC Flood Mapping System (In beta testing)

The authors are developing the NASA GSFC Flood Mapping System, designed to provide near real time, on-demand satellite-based products to disaster management practitioners for requests provided through a website interface. The initial version of the system, which only provides products based on Planetscope data provided by Planet Labs to NASA through the NASA CSDA Program, is currently being beta tested in partnership with the disaster management community and interested research partners. Products include images, NDWI maps, NDVI maps, and water extent maps for flood scenes of interest, the same four products for a specified reference scene, and water extent change maps based on the flood scenes and reference scenes. The team’s vision for the system includes adding products based on all appropriate sources of data that are available at no cost to the project (Landsat, Sentinel-1, Sentinel-2), and obtaining funding for commercial SAR data to provide products in the case where none of the “no-cost” data meets end-user needs. We provide an overview of the system, and discuss the products offered, lessons learned from initial testing, and plans to evolve the system in accordance with our overall vision and the lessons learned.

F. Policelli↗
DOE OSTI2014

Interactive prostate segmentation using atlas-guided semi-supervised learning and adaptive feature selection

Purpose: Accurate prostate segmentation is necessary for maximizing the effectiveness of radiation therapy of prostate cancer. However, manual segmentation from 3D CT images is very time-consuming and often causes large intra- and interobserver variations across clinicians. Many segmentation methods have been proposed to automate this labor-intensive process, but tedious manual editing is still required due to the limited performance. In this paper, the authors propose a new interactive segmentation method that can (1) flexibly generate the editing result with a few scribbles or dots provided by a clinician, (2) fast deliver intermediate results to the clinician, and (3) sequentially correct the segmentations from any type of automatic or interactive segmentation methods. Methods: The authors formulate the editing problem as a semisupervised learning problem which can utilize a priori knowledge of training data and also the valuable information from user interactions. Specifically, from a region of interest near the given user interactions, the appropriate training labels, which are well matched with the user interactions, can be locally searched from a training set. With voting from the selected training labels, both confident prostate and background voxels, as well as unconfident voxels can be estimated. To reflect informative relationship between voxels, location-adaptive features are selected from the confident voxels by using regression forest and Fisher separation criterion. Then, the manifold configuration computed in the derived feature space is enforced into the semisupervised learning algorithm. The labels of unconfident voxels are then predicted by regularizing semisupervised learning algorithm. Results: The proposed interactive segmentation method was applied to correct automatic segmentation results of 30 challenging CT images. The correction was conducted three times with different user interactions performed at different time periods, in order to evaluate both the efficiency and the robustness. The automatic segmentation results with the original average Dice similarity coefficient of 0.78 were improved to 0.865–0.872 after conducting 55–59 interactions by using the proposed method, where each editing procedure took less than 3 s. In addition, the proposed method obtained the most consistent editing results with respect to different user interactions, compared to other methods. Conclusions: The proposed method obtains robust editing results with few interactions for various wrong segmentation cases, by selecting the location-adaptive features and further imposing the manifold regularization. The authors expect the proposed method to largely reduce the laborious burdens of manual editing, as well as both the intra- and interobserver variability across clinicians.

07 ISOTOPE AND RADIATION SOURCES↗
DOE OSTI2016

2016 Annual Technology Baseline (ATB)

Consistent cost and performance data for various electricity generation technologies can be difficult to find and may change frequently for certain technologies. With the Annual Technology Baseline (ATB), National Renewable Energy Laboratory provides an organized and centralized dataset that was reviewed by internal and external experts. It uses the best information from the Department of Energy laboratory's renewable energy analysts and Energy Information Administration information for conventional technologies. The ATB will be updated annually in order to provide an up-to-date repository of current and future cost and performance data. Going forward, we plan to revise and refine the values using best available information. The ATB includes both a presentation with notes (PDF) and an associated Excel Workbook. The ATB includes the following electricity generation technologies: land-based wind; offshore wind; utility-scale solar PV; concentrating solar power; geothermal power; hydropower plants (upgrades to existing facilities, powering non-powered dams, and new stream-reach development); conventional coal; coal with carbon capture and sequestration; integrated gasification combined cycle coal; natural gas combustion turbines; natural gas combined cycle; conventional biopower. Nuclear laboratory's renewable energy analysts and Energy Information Administration information for conventional technologies. The ATB will be updated annually in order to provide an up-to-date repository of current and future cost and performance data. Going forward, we plan to revise and refine the values using best available information.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2015

Radiofrequency Ablation for the Treatment of Hepatocellular Carcinoma in Patients with Transjugular Intrahepatic Portosystemic Shunts

PurposeTo assess radiofrequency (RF) ablation efficacy, as well as the patency of transjugular intrahepatic portosystemic shunts (TIPSs), in patients with hepatocellular carcinoma (HCC).Materials and MethodsRetrospective database review of patients with pre-existing TIPS undergoing RF ablation of HCC was conducted over a 159-month period ending in November 2013. TIPS patency pre- and post-RF ablation was assessed by ultrasound, angiography, and/or contrast-enhanced CT or MRI. Patient demographics and immediate post-RF ablation outcomes and complications were also reviewed.Results19 patients with 21 lesions undergoing 25 RF ablation sessions were included. Child-Pugh class A, B, and C scores were seen in 1, 13, and 5 patients, respectively. Eleven patients (58 %) ultimately underwent liver transplantation. Immediate technical success was seen in all ablation sessions without residual tumor enhancement (100 %). No patients (0 %) suffered liver failure within 1 month of ablation. Pre-ablation TIPS patency was demonstrated in 22/25 sessions (88 %). Of 22 cases with patent TIPS prior to ablation, post-ablation patency was demonstrated in 22/22 (100 %) at immediate post-ablation imaging and in 21/22 (95 %) at last follow-up (1 patient was incidentally noted to have occlusion 31 months later). No immediate complications were observed.ConclusionAblation efficacy was similar to the cited literature values for patients without TIPS. Furthermore, TIPS patency was preserved in the majority of cases. Patients with both portal hypertension and HCC are not uncommonly encountered, and a pre-existing TIPS does not appear to be a definite contraindication for RF ablation.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2021

A review of machine learning in building load prediction

The surge of machine learning in recent years has been empowering engineer modeling in various fields. The decreasing hardware cost, increasing data accessibility, and advances of building automation system (BAS) allow the collection and storage of a significant amount of building operation data. The two facts provide great opportunities of applying machine learning to building energy systems modeling and analysis. There are a great number of research papers on this topic but there lacks a comprehensive and general review to summarize the current development, limitations, gaps and future trend. In this review paper series, machine learning techniques in building energy system modeling and analysis are reviewed under the organization and logic of the machine learning definition by Tom M. Mitchell: a computer program is said to learn from experience E with respect to some class of tasks T and performance measure P if its performance at tasks in T, as measured by P, improves with experience E. This paper is the first part of the review paper series, which focuses on building load prediction. First, the applications of building load prediction model (task T) are reviewed. Then, the modeling algorithms improving machine learning performance and accuracy (performance P) are reviewed. At the same time, the literature on the data perspective for modeling (experience E), including data engineering from sensors level to data level, pre-processing, feature extraction and selection, is reviewed. Finally, what is well-studied and what is lacking but with great potential are concluded; the gaps between present and future utilization of machine learning techniques are identified; the future trend and development are also predicted. The target readers of this paper are not only researchers from the building side who can get exposed to cutting edge machine learning tools, but also those from machine learning side who can understand the potential and challenge to apply machine learning in buildings.

Liang, Zhang↗
DOE OSTI2025

Machine learning for domain transfer between simulated and experimental 2D X-ray diffraction patterns using generative adversarial networks

X-ray diffraction (XRD) is a well-established technique for analyzing materials at an atomic level. Dynamic compression experiments (DCE), in which materials are subject to extreme pressures, can provide fundamental understanding to pressure-induced phase transitions and compression of the crystal lattice. The analysis of XRD patterns from highly compressed samples is non-trivial given the sparsity of data, high experimental costs, and the fact that the data is often marred with X-ray background and other artifacts. While accurate computational frameworks exist, they solve the forward problem—from structures and orientations to XRD patterns. Solving the inverse problem for 2D experimental diffraction patterns is currently a complex manual process of matching and comparing experimentally observed patterns to computationally generated ones. Machine learning is a promising tool for automating the matching process but often requires data-intensive architectures. Here, in this study, we use a CycleGAN to translate the domain of limited experimental data to a domain in which there is readily available simulated data. This domain shift allows data-intensive machine learning models that have only been trained on simulated XRD patterns to be used in the analysis of experiments.

Brozak, Samantha Jean [Sandia National Laboratorie↗