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All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

On-target uniformity of the OMEGA 60-beam inertial confinement fusion laser

Successful direct-drive inertial confinement fusion (ICF) experiments require excellent on-target laser 9 irradiance uniformity maintained over the duration of the pulse. Achieving this symmetry relies on maintaining an 10 energy, power, and fluence balance among all beams in a multibeam laser system. As a result of the improvements 11 described in this paper, the OMEGA 60-beam laser performance has been assessed at approximately 2% on-target 12 irradiance nonuniformity using an updated performance assessment metric that accounts for the laser diagnostic noise 13 floor. The performance is measurement-limited, prompting the need for an improved diagnostic suite. This manuscript 14 provides a comprehensive first-order assessment of extant status of on-target energy, power, and fluence uniformity 15 and explores avenues for improvements.

Diagnostics↗
DOE OSTI2024

Performance Comparison of Machine Learning Models for Ultrasonic Nondestructive Evaluation of Alkali-Silica Reaction in Concrete

Alkali-silica reaction (ASR) causes concrete degradation, leading to cracking, rebar corrosion, and reduced structural integrity, which raises safety concerns. Ultrasonic nondestructive evaluation (NDE) effectively assesses concrete properties and monitors ASR progression. However, its deployment and analysis require specialized expertise and subjective interpretation. As computational power increases, artificial intelligence (AI) and machine learning (ML) algorithms are increasingly being used to automate NDE data analysis across various industries for AI-assisted automation. Regulatory agencies are adapting to this technological shift, prompting a need to evaluate current ML technologies’ capabilities and limitations in assessing concrete material properties and damage. This report presents a comparative analysis of four ML regression models for predicting concrete material damage induced by ASR expansion using long-term ultrasonic data monitoring. The models investigated include linear regression (LR), support vector regression (SVR), shallow neural networks (NN), and deep neural networks (DNN). LR, SVR, and shallow NN models use features extracted from ultrasonic signals, whereas the DNN model processes time-domain ultrasonic signals and frequency spectra directly. The study systematically compared the models’ performance from various perspectives, including model input, prediction performance, and generalization ability. The findings indicate significant variability in model performance, with some ML algorithms achieving very high or very low prediction accuracy depending on the preprocessing and feature engineering (extraction and selection) applied. Key insights include the observation that shallow ML models (LR, SVR, and shallow NNs) require meticulous preprocessing and feature extraction to achieve high accuracy. In contrast, the DNN model, although it bypasses the need for feature engineering, necessitates extensive preprocessing to mitigate noise and computational demands. The SVR model emerged as the top performer among the shallow models, and the DNN model exhibited superior performance on specific datasets but struggled with generalization across specimens from different batches. Additionally, the SVR model is sensitive to temperature variations, whereas the DNN model is robust in this regard. Using recurrent neural networks is recommended for future ASR expansion prediction studies. Recurrent neural networks’ inherent ability to capture temporal dependencies and long-term patterns makes them well suited for analyzing sequential ultrasonic monitoring data. Overall, the results and conclusions of this study could provide insights into the capabilities and effectiveness of ML when applied to ultrasonic NDE data and help identify best practices for using ML for ultrasonic NDE of concrete material properties.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Practices for Demonstrating Energy Savings from Commercial PACE Projects

Nearly three-fourths of U.S. states have authorized local governments to use voluntary special assessments on commercial properties to finance energy improvements that boost economic development, create jobs, increase property values and advance energy goals. Commercial Property Assessed Clean Energy (C-PACE) financing allows building owners to repay the borrowed capital — from private or public sources — over time using their property as security. Berkeley Lab is supporting the Department of Energy’s Commercial PACE Working Group by developing a series of C-PACE issue briefs. The second brief in this series, Practices for Demonstrating Energy Savings from Commercial PACE Projects, looks at common practices for demonstrating energy savings to support state and local governments that sponsor C-PACE programs and want to track their energy impacts. This brief reviews: -The value proposition and trade-offs of conducting energy impact assessments for C-PACE programs; -Methods to quantify energy savings impacts from energy efficiency building improvements; and -Available resources and tools to support energy impact assessments. C-PACE programs may benefit from energy impact assessments for many reasons, including: -Validating the public benefits of the programs -Demonstrating that C-PACE can deliver participant benefits -Illustrating program impacts on public policy goals -Generating data to help improve program performance Many C-PACE programs are collecting data on project energy savings impacts, and these data can be leveraged to further support decision making and program implementation. Potential drawbacks to energy impact assessments may include added cost and burdens on property owners (e.g., the need to collect building energy consumption data). Where these burdens are considerable, they might slow program uptake. State and local governments can balance the benefits of energy impact assessments with the range of costs and accuracy inherent to available assessment methodologies. Additionally, depending on the policy context in the state or local government, a C-PACE program may be able to leverage existing efforts (e.g., building energy benchmarking programs) to reduce impact assessment costs, align with building owner practices and expectations, and efficiently assess program impact.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2023

A perfused multi-well bioreactor platform to assess tumor organoid response to a chemotherapeutic gradient

There is an urgent need to develop new therapies for colorectal cancer that has metastasized to the liver and, more fundamentally, to develop improved preclinical platforms of colorectal cancer liver metastases (CRCLM) to screen therapies for efficacy. To this end, we developed a multi-well perfusable bioreactor capable of monitoring CRCLM patient-derived organoid response to a chemotherapeutic gradient. CRCLM patient-derived organoids were cultured in the multi-well bioreactor for 7 days and the subsequently established gradient in 5-fluorouracil (5-FU) concentration resulted in a lower IC 50 in the region near the perfusion channel versus the region far from the channel. We compared behaviour of organoids in this platform to two commonly used PDO culture models: organoids in media and organoids in a static (no perfusion) hydrogel. The bioreactor IC 50 values were significantly higher than IC 50 values for organoids cultured in media whereas only the IC 50 for organoids far from the channel were significantly different than organoids cultured in the static hydrogel condition. Using finite element simulations, we showed that the total dose delivered, calculated using area under the curve (AUC) was similar between platforms, however normalized viability was lower for the organoid in media condition than in the static gel and bioreactor. Our results highlight the utility of our multi-well bioreactor for studying organoid response to chemical gradients and demonstrate that comparing drug response across these different platforms is nontrivial.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Basin scale distributions of dissolved manganese, nickel, zinc and cadmium in the Mediterranean Sea

Samples for dissolved trace metal concentrations were collected during GEOTRACES expedition GA04-N in summer/spring in the Mediterranean Sea, starting in the Atlantic Ocean and sampling both deep basins of the Mediterranean Sea. Outflow of Mediterranean Outflow Water leads to elevated concentrations of Mn, Ni and Zn in the Atlantic Ocean, but a concentration minimum in the Atlantic distribution of Cd. Nevertheless, when comparing the in- and outflow, the Mediterranean is a net source of Cd to the Atlantic Ocean. Surface concentrations of Mn, Ni, Zn and Cd are elevated in the Mediterranean relative to the Atlantic Ocean where Ni and Cd gradually increased along the eastward surface water flow path, Zn reached a homogenous concentration in the order of 1 to 1.5 nM, and Mn displayed a patchy surface distribution. The observed differences are attributable to the different dynamics of their biogeochemical cycling, notably the partitioning between the dissolved and particulate phases due to biological uptake, scavenging and possibly organic complexation. The elevated surface concentrations of Mn, Ni, Zn and Cd in the Mediterranean are derived from atmospheric deposition, where most likely Zn and Cd are mainly sourced from anthropogenic origin, Mn mostly from lithogenic origin and Ni from both anthropogenic and lithogenic origin. Dissolved Zn and Cd, as well as phosphate and nitrate, display striking inter-basin fractionations with elevated concentrations in the deep water of the western basin compared to the deep eastern basin, without a coinciding increase in the apparent oxygen utilization. Given that physical circulation or contribution from biogenic particulate metals cannot explain the elevated dissolved concentrations, an external non-biological source is required. This source is most likely a vertical flux of metal laden particles dissolving through the water column of the western Mediterranean where these particles, most likely from anthropogenic origin, are derived from either atmospheric deposition or particulate material deposited on the continental shelves that makes its way into the deep basin. Furthermore, to confirm or detect trends in dissolved metal concentrations in the deep basin, regular basin wide assessments of the trace metal distributions in the Mediterranean are needed. The distributions of Mn, Ni, Zn and Cd in the Sea of Marmara illustrate that all of these metals can be affected by anthropogenic surface sources and highlight the different susceptibilities of the dissolved metal distributions to supply from remineralization, and to removal through scavenging. This study provides a first baseline to assess future changes and underlines that the Mediterranean marine environment is susceptible to anthropogenic disturbances, with varying effects for different metals due to differing source strengths and biogeochemical cycles.

58 GEOSCIENCES↗
DOE OSTI2020

Assessment of the Griffin Reactor Multiphysics Application Using the Empire Micro Reactor Design Concept

In late 2019, INL and ANL agreed to jointly develop the reactor physics code named Griffin based on the integration of the two code suites, MAMMOTH/Rattlesnake (INL) and MC2 - 3/PROTEUS (ANL). Griffin is being developed based on the MOOSE framework and MOOSE quality assurance procedures. This decision was made to be able to allow DOE-NE to efficiently invest funding to this area and to provide effective and timely support for existing and potential users; the latter includes industry and government organizations who are developing various types of advanced reactors in the near and long term. Since MAMMOTH/Rattlesnake has been developed based on the MOOSE framework, the INL/ANL Griffin development team agreed to build Griffin beginning with a merger of MAMMOTH and Rattlesnake into a single code and moving forward by implementing capabilities from the PROTEUS suite into Griffin. Moving forward, both ANL and INL efforts are equally invested in the Griffin project, with management support, to provide an advanced reactor multiphysics tool to assist in reactor design, optimization, and safety analysis. Much work remains in moving Griffin forward to migrate PROTEUS capabilities and to optimize performance to meet user needs. The main objective of this work is to assess the current status of Griffin capabilities in terms of performance and accuracy, to determine priorities for PROTEUS migration, and to identify capabilities and features to improve for supporting the code integration effort. For this assessment, the Empire micro reactor problem that was developed in the ARPA-E MEITNER program was selected as an advance reactor concept of interest to the technical community. The Empire reactor problem was expanded from its original incomplete specification to be a small heat-pipe-cooled micro reactor core with ~113 cm radius and 70 cm in height, composed of 18 fuel assemblies, 12 control drums, and beryllium radial and axial reflectors. In the current model, using 5 cm axial reflectors specified in the original Empire assembly model, more than 10% of neutrons leak axially and through the empty center safety hole, as well as through heat pipe channels in fuel assembly elements that extend through the top reflector region. Several calculation models of the core were defined for systematic assessment, including 2-D and 3-D fuel assemblies and whole cores with cylindrical boundaries. Cross sections were generated using Serpent 2, and meshes were produced using the Argonne mesh tool or the INL neutronics meshing tools combined with CUBIT. Cross sections and meshes were converted to the ISOXML and Exodus formats, respectively, so that Griffin and PROTEUS could use consistent data for solving the reactor problems. With the prepared cross sections and meshes, PROTEUS was run first to ensure that all input data were correctly generated and input options in terms of angle, mesh, and energy group were accurately determined. Comparisons against Serpent 2 solutions were made in terms of eigenvalue and pin power. The same calculations and comparisons were then conducted using Griffin. For the fuel assembly and whole core problems, the PROTEUS eigenvalues agreed well with reference Serpent 2 solutions within 100 and 30 pcm, respectively, and pin power differences relative to Serpent 2 were overall less than 2.2% and RMS 0.8% for the whole core models. This indicated that all input data were properly prepared. Using the same data, Griffin was run selecting the SAAF-CFEM SN solver with Legendre-Gaussian quadrature and NDA and DSA for acceleration. It was found that the SAAF-CFEM solver of Griffin required finer meshes to achieve eigenvalue and pin power solutions in good agreement with Serpent 2, consequently requiring more memory requirement and longer computation time. On the other hand, the SPH-Diffusion 2-D core calculations performed using Griffin were able to recover the exact eigenvalue from the reference Serpent 2 solutions, resulting in a pin-power distribution with an RMS of 0.6% and maximum absolute difference of less than 1.4%. The runtimes for SPH-Diffusion for the 2-D core were less than 3 minutes on 40 cores. During this evolution of this evaluation, many updates were made in Griffin by the Griffin development team of INL (focusing on software updates) and ANL (reviewing and supporting software updates) to complete this assessment. Observations from the code assessment are presented in the conclusion section of this report, followed by a discussion of recommendations for future work.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2024

A Comprehensive Assessment of the Viability of Small Modular Reactors in Africa: A Nuclear Security and Nonproliferation Perspective

In response to escalating global energy demands driven by industrialization and the pressing need for decarbonization, this paper explores the potential of Small Modular Reactors (SMRs) as a sustainable energy solution in Africa. Focusing on nuclear security and non-proliferation concerns, the study assesses Africa's energy landscape, emphasizing the need for diverse and reliable power sources. While highlighting the scalability and cost-effectiveness of SMRs, the analysis acknowledges potential challenges associated with their introduction, particularly concerning nuclear security and non-proliferation. Given the recommendation to use High Assay Low Enriched Uranium (HALEU) in some SMRs, it is important to critically consider the security implications of transporting nuclear materials, the proximity of the public to the plant, and the time taken to respond to planned assaults. The possibility of using HALEU makes the nuclear material more prone to adversaries such as sabotage, theft, and terrorism. Utilizing PESTLE analysis, this research seeks to outline the detailed political, economic, social, technological, environmental, and legal readiness of Africa to embrace the first-of-a-kind technology (SMR) while fulfilling its mandate to the Non-Proliferation Treaty (NPT). Examining regulatory frameworks, international cooperation, and safety protocols, the study underscores the importance of regional collaboration to prevent the misuse of nuclear technology for military and malicious intent. Drawing insights from successful case studies, the paper concludes by synthesizing key findings and proposing recommendations for policymakers and stakeholders. These recommendations encompass regulatory enhancement, capacity building, technology transfer, and diplomatic efforts to strengthen nuclear security, non-proliferation, and safeguards in Africa.

Prah, Christina↗
DOE OSTI2020

A systematic strategy for estimating hERG block potency and its implications in a new cardiac safety paradigm

Introduction: hERG block potency is widely used to calculate a drug's safety margin against its torsadogenic potential. Previous studies are confounded by use of different patch clamp electrophysiology protocols and a lack of statistical quantification of experimental variability. Since the new cardiac safety paradigm being discussed by the International Council for Harmonisation promotes a tighter integration of nonclinical and clinical data for torsadogenic risk assessment, a more systematic approach to estimate the hERG block potency and safety margin is needed. Methods: A cross-industry study was performed to collect hERG data on 28 drugs with known torsadogenic risk using a standardized experimental protocol. A Bayesian hierarchical modeling (BHM) approach was used to assess the hERG block potency of these drugs by quantifying both the inter-site and intra-site variability. A modeling and simulation study was also done to evaluate protocol-dependent changes in hERG potency estimates. Results: A systematic approach to estimate hERG block potency is established. The impact of choosing a safety margin threshold on torsadogenic risk evaluation is explored based on the posterior distributions of hERG potency estimated by this method. The modeling and simulation results suggest any potency estimate is specific to the protocol used. Discussion: This methodology can estimate hERG block potency specific to a given voltage protocol. The relationship between safety margin thresholds and torsadogenic risk predictivity suggests the threshold should be tailored to each specific context of use, and safety margin evaluation may need to be integrated with other information to form a more comprehensive risk assessment.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2024

On the compatibility of established methods with emerging artificial intelligence and machine learning methods for disaster risk analysis

Abstract There is growing interest in leveraging advanced analytics, including artificial intelligence (AI) and machine learning (ML), for disaster risk analysis (RA) applications. These emerging methods offer unprecedented abilities to assess risk in settings where threats can emerge and transform quickly by relying on “learning” through datasets. There is a need to understand these emerging methods in comparison to the more established set of risk assessment methods commonly used in practice. These existing methods are generally accepted by the risk community and are grounded in use across various risk application areas. The next frontier in RA with emerging methods is to develop insights for evaluating the compatibility of those risk methods with more recent advancements in AI/ML, particularly with consideration of usefulness, trust, explainability, and other factors. This article leverages inputs from RA and AI experts to investigate the compatibility of various risk assessment methods, including both established methods and an example of a commonly used AI‐based method for disaster RA applications. This article utilizes empirical evidence from expert perspectives to support key insights on those methods and the compatibility of those methods. This article will be of interest to researchers and practitioners in risk‐analytics disciplines who leverage AI/ML methods.

Mathematical Methods In Social Sciences↗
DOE OSTI2021

Medium and Heavy-Duty Electric Vehicle Infrastructure - Load Operations and Deployment (HEVI-LOAD) v1.0

In order to support the California state goals to reduce the carbon and pollution emissions (AB2127), The goal of this project (HEVI-Pro) is to project the charging infrastructure needs by future medium- and heavy- duty electric vehicles, and assess optimal deployment the infrastructure in California and provide grid impacts analysis. The MHDV projections will also consider transportation system and electric system interaction with light duty vehicles. The project consists of 2 approaches in general: top-down approach that takes aggregated MHDV adoptions as the inputs and provide the county-level projections of charging load profile and infrastructure need, and the bottom-up approach that that incorporates more granular (temporal, spatial and duty-cycle-specific) behaviors of a variety of MHDVs into activity simulations/optimizations for further analysis.

Wang, Bin↗
DOE OSTI2020

TRANSENSOR: Transformer Real-time Assessment INtelligent System with Embedded Network of Sensors and Optical Readout. Final Report

Utilities across the world are wrestling with evolving market dynamics, population growth and climate change. Distributed Energy Resources (DERs) such as solar photovoltaics, distributed generators and energy storage systems are becoming important parts of the U.S. energy mix. These factors are driving an increasing need for low-cost grid asset monitoring. Aside from being costly, traditional utility monitoring systems are not sufficiently robust and do not provide real-time visibility into the condition of grid assets such as transformers. Lack of accurate real-time measurements on performance has resulted in the use of lagging indicators, such as oil sample analysis. To address this need, an innovative embedded optical sensing technology, Transformer Real-time Assessment Intelligent System (TRANSENSOR) was developed, validated and demonstrated in this project. To date, TRANSENSOR has focused on transformers but is extendable to other grid assets. In addition to the technology being embedded into new transformers during manufacturing, a retrofit configuration that can be installed on existing transformers in the field was also developed. It is anticipated that the ability to retrofit existing transformers will help accelerate adoption of the underlying TRANSENSOR technology. Phase 1 of the project focused on laboratory development and qualification of the technology. Following iterations and exploration of relevant optical sensing modalities and multiplexed configurations of interest for the transformer environment, an effective candidate configuration was agreed upon, down-selected and custom-designed for embedding into General Electric (GE) network transformers. Two new GE 500 kilovolt ampere (kVA), 27 kilovolt (kV) distribution network transformers were built with embedded fiber-optic (FO) sensors and successfully qualified per industry standards at GE’s Shreveport, Louisiana facility during Phase 1 of the project. Following the successful completion of Phase 1, the team proceeded to Phase 2, which focused on a field demonstration of the technology. Over Phase 2, the first new GE transformer built with embedded fiber-optic sensors was installed in an above-ground cage and connected to the grid at Con Edison’s Astoria facility. A second transformer equipped with TRANSENSOR was installed in an underground vault. Additionally, an older (1982 year model) GE transformer in an above-ground cage was retrofitted with fiber- optic sensors and reconnected to the grid at ConEd’s facility. Analysis of data acquired from the sensors showed interesting correlations with transformer loading. Additionally, key events such as transformer low-voltage network connection, primary-side energizing, and a pressure loss event from an oil sampling were detected by the TRANSENSOR system. Online data processing/feature extraction algorithms for the second transformer in the underground vault detected key features and event alerts that were transmitted through a wireless 4G connection. The remote deployment concept showed promising results with data collection running for a total of 8 months across the three (3) GE transformers instrumented for the Phase 2 field demonstration. This sets the stage well for further development and commercialization.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2020

Discovering the nature of neutrinos: How nuclear theory will advance this grand endeavor. Final report

Why is there more matter than antimatter in the visible universe? Are neutrinos their own antiparticles? These are some of the most profound questions in science. The detection of a (0vββ) neutrinoless doublebeta decay and the measurement of its half-life is one of the most important experimental endeavors in science because it will answer these questions. Paramount to these measurements are accurate predictions with quantified uncertainties of the 0vββ-decay nuclear transitions, especially for the germanium-76 and xeon-136 nuclei, two candidate materials for building the next generation of DOE funded 0vββ-decay experiments. These predictions will guide the Office of Science’s down selection of experiments as well as aid in determining how much material is needed by these experiments. We studied a specific difficult to assess source of uncertainty that will impact coming microscopic predictions, uncertainty which arises from an incomplete yet systematically improvable model of interactions between nucleons and of nucleons with the other particles involved in decay process, referred to as the theory uncertainty. We employed a surrogate model of the germanium-76 nucleus, and its decay products, just detailed enough to adequately mimic the decaying nucleons need for a reasonable measure of this uncertainty and nothing more, avoiding the immense computational expense required in quantifying uncertainties of the full calculation. We found that microscopic calculations of this process can be expected to achieve relative theory uncertainties around 30%, including an unconstrained contribution that had the potential to spoil microscopic calculations. This results in a nearly order of magnitude on previously estimated uncertainties for this rare decay process.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2021

Repository-Scale Performance Assessment Incorporating Postclosure Criticality

A key objective of the United States Department of Energy’s (DOE) Office of Nuclear Energy’s Spent Fuel and Waste Science and Technology Campaign is to better understand the technical basis, risks, and uncertainty associated with the safe and secure disposition of spent nuclear fuel (SNF) and high-level radioactive waste. Commercial nuclear power generation in the United States has resulted in thousands of metric tons of SNF, the disposal of which is the responsibility of the DOE (Nuclear Waste Policy Act of 1982, as amended). Any repository licensed to dispose of SNF must meet requirements regarding the long-term performance of that repository. For an evaluation of the long-term performance of the repository, one of the events that may need to be considered is the SNF achieving a critical configuration during the postclosure period. Of particular interest is the potential behavior of SNF in dual-purpose canisters (DPCs), which are currently licensed and being used to store and transport SNF but were not designed for permanent geologic disposal. A study has been initiated to examine the potential consequences, with respect to long-term repository performance, of criticality events that might occur during the postclosure period in a hypothetical repository containing DPCs. The first phase (a scoping phase) consisted of developing an approach to creating the modeling tools and techniques that may eventually be needed to either include or exclude criticality from a performance assessment (PA) as appropriate; this scoping phase is documented in Price et al. (2019a). In the second phase, that modeling approach was implemented and future work was identified, as documented in Price et al. (2019b). This report gives the results of a repository-scale PA examining the potential consequences of postclosure criticality, as well as the information, modeling tools, and techniques needed to incorporate the effects of postclosure criticality in the PA.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2025

Standardising the “Gregory method” for calculating equilibrium climate sensitivity

The equilibrium climate sensitivity (ECS) – the equilibrium global mean temperature response to a doubling of atmospheric CO 2 – is a high-profile metric for quantifying the Earth system's response to human-induced climate change. A widely applied approach to estimating the ECS is the “Gregory method” (Gregory et al., 2004), which uses an ordinary least squares (OLS) regression between the net radiative flux, N, and surface air temperature anomalies, ΔT, from a 150 year experiment in which atmospheric CO 2 concentrations are quadrupled. The ECS is determined by extrapolating the linear fit to N=0, i.e. the ΔT-intercept, indicating the point at which the system is back in equilibrium. This method has been used to compare ECS estimates across the CMIP5 and CMIP6 ensembles and will likely be a key diagnostic for CMIP7. Despite its widespread application, there is little consistency or transparency between studies in how the climate model data is processed prior to the regression, leading to potential discrepancies in ECS estimates. We identify 32 alternative data processing pathways, varying by differences in global mean weighting, net radiative flux variable, anomaly calculation method, and linear regression fit. Using 44 CMIP6 models, we systematically assess the impact of these choices on ECS estimates and calculate uncertainty ranges using two bootstrap approaches. While the inter-model ECS range is insensitive to the data processing pathway, individual outlier models exhibit notable differences. Approximating a model's native grid cell area (if irregular) with cosine of the latitude can decrease the ECS by 11 %, the choice of N-variable can change the ECS by 6 %, and some anomaly calculation methods can introduce spurious temporal correlations in the processed data. Beyond data processing choices, we also evaluate an alternative linear regression method – total least squares (TLS) – which has a more statistically robust basis than OLS. However, for consistency with previous literature, and given TLS may reduce the ECS compared to OLS (by up to 24 %), thereby making a known bias in the Gregory method worse, we do not feel there is sufficient clarity to recommend a transition to TLS in all cases. To improve reproducibility and comparability in future studies, we recommend a standardised Gregory method: weighting the global mean by cell area, using the top of the atmosphere (as opposed to the top of model) N-variable, and calculating anomalies by first applying a rolling average to the preindustrial control timeseries then subtracting from the raw CO 2 quadrupling experiment. This approach accounts for model drift while reducing noise in the data to best meet the pre-conditions of the linear regression. While CMIP6 results of the multi-model mean ECS appear insensitive to these processing choices, similar assumptions may not hold for CMIP7, underscoring the need for standardised data preparation in future climate sensitivity assessments.

Geosciences↗
DOE OSTI2020

A Rapid Population Assessment Method for Wild Pigs Using Baited Cameras at 3 Study Sites

ABSTRACT Reliable and efficient population estimates are a critical need for effective management of invasive wild pigs ( Sus scrofa ). We evaluated the use of 10‐day camera grids for rapid population assessment (RPA) of wild pigs at 3 study sites that varied in vegetation communities and wild pig densities. Study areas included Buck Island Ranch, Florida; Tejon Ranch, California; and the Savannah River Site, South Carolina, USA, during 2016–2018. Rapid population assessments grids were composed of baited camera traps spaced approximately 500 or 750 m apart. Two RPA grids were deployed per study site and each grid was deployed twice (4–6 months apart) to assess changes in response to season or population control efforts. We assessed the ability of RPA grids to track population trends, how camera number influenced estimate precision, and how relative abundance indices related to density estimates. We detected changes in occupancy probability, detection probability, and N‐mixture estimates following removal operations and between seasons, but the ability of RPA grids to track population trends was dependent on the statistical method used and number of cameras traps. Increasing the number of cameras traps used in RPA grids increased precision, and these results can be used in determining survey design and estimate choice. We found that estimates of occupancy probability, detection probability, and N‐mixture estimates were positively correlated with spatially explicit capture–recapture density estimates. Thus, these less labor‐intensive estimates from RPA grids showed potential to index the relative abundance of wild pigs in some systems. Our evaluation of RPAs indicates that using study‐specific combinations of statistical method and number of cameras can provide a useful tool for monitoring wild pig presence, tracking population trends, and evaluating the effectiveness of management actions. © 2020 The Wildlife Society.

Schlichting, Peter E.↗
DOE OSTI2021

Supply Chain Sustainability Analysis of Renewable Hydrocarbon Fuels via Indirect Liquefaction, Ex Situ Catalytic Fast Pyrolysis, Hydrothermal Liquefaction, Combined Algal Processing, and Biochemical Conversion: Update of the 2020 State-of-Technology Cases

The Department of Energy’s (DOE) Bioenergy Technologies Office (BETO) aims to develop and deploy technologies to transform renewable biomass resources into commercially viable, high-performance biofuels, bioproducts, and biopower through public and private partnerships (U.S. Department of Energy, 2016). BETO and its national laboratory teams conduct in-depth techno-economic assessments (TEA) of biomass feedstock supply and logistics and conversion technologies to produce biofuels. There are two general types of TEAs: A design case outlines a target case (future projection) for a particular biofuel pathway. It enables identification of data gaps and research and development needs and provides goals and benchmarks against which technology progress is assessed. A state of technology (SOT) analysis assesses progress within and across relevant technology areas based on actual results at current experimental scales relative to technical targets and cost goals from design cases, and includes technical, economic, and environmental criteria as available. In addition to developing a TEA for a pathway of interest, BETO also performs a supply chain sustainability analysis (SCSA). The SCSA takes the life-cycle analysis approach that BETO has been supporting for about 20 years. It enables BETO to identify energy consumption, environmental, and sustainability issues that may be associated with biofuel production. Approaches to mitigate these issues can then be developed. Additionally, the SCSA allows for comparison of energy and environmental impacts across biofuel pathways in BETO’s research and development portfolio. This technical report describes the SCSAs for the production of renewable hydrocarbon transportation fuels via a range of conversion technologies in the 2020 SOTs: (1) renewable high octane gasoline (HOG) via indirect liquefaction (IDL) of woody lignocellulosic biomass (note that the IDL pathway in this SCSA represents the syngas conversion design [Harris et al. 2021]); (2) renewable gasoline (RG) and diesel (RD) blendstocks via ex situ catalytic fast pyrolysis of woody lignocellulosic biomass [Abhijit et al. 2021]; (3) RD via hydrothermal liquefaction (HTL) of wet sludge from a wastewater treatment plant; (4) renewable hydrocarbon fuels via biochemical conversion of herbaceous lignocellulosic biomass (Davis et al. 2021; Lin et al. 2021); (5) renewable diesel via HTL of a blend of algae (Davis and Klein, 2021) and woody biomass (Hartley et al. 2020); and (6) renewable diesel via combined algae processing (CAP) (Wiatrowski and Davis, 2021). This technical report focuses on the environmental performance of these six biofuel production pathways in their 2020 SOT cases. The results of these renewable hydrocarbon fuel pathways in these SCSA analyses update those for the respective 2019 SOT cases (Cai et al. 2020). They also provide an opportunity to examine the impact of technology improvements in both biomass feedstock production and biofuel production that have been achieved in 2020 SOTs on the sustainability performance of these renewable transportation fuels. The SCSA results also reflect updates to Argonne National Laboratory’s Greenhouse gases, Regulated Emissions, and Energy use in Technologies (GREET®) model, which was released in October 2020 (Wang et al. 2020). These GREET updates include the production of natural gas, electricity, and petroleum-based fuels that can influence biofuels’ supply chain greenhouse gas (GHG) (CO 2 , CH 4 , and N 2 O) emissions, water consumption, and air pollutant emissions. GHG emissions, water consumption, and nitrogen oxides (NO x ) emissions are the main sustainability metrics assessed in this analysis. In this analysis, we define water consumption as the amount of water withdrawn from a freshwater source that is not returned (or returnable) to a freshwater source at the same level of quality. Life-cycle fossil energy consumption and net energy balance, which is the life-cycle fossil energy consumption deducted from the renewable biofuel energy produced, are also assessed.

09 BIOMASS FUELS↗
DOE OSTI2020

Advanced Sensors and Instrumentation (ASI) Program Plan. Nuclear Energy Enabling Technologies (NEET)

The U.S. Department of Energy Office of Nuclear Energy (DOE-NE) sponsors a crosscutting technology development (CTD) program addressing common instrumentation & controls (I&C) needs in all DOE-NE sponsored programs. In 2012, DOE-NE initiated the Nuclear Energy Enabling Technology (NEET) Program to conduct research, development, and demonstration (RD&D) in crosscutting technologies that directly support and enable the development of existing, new and advanced reactor designs, and fuel cycle technologies. These technologies will advance the state of nuclear technology, improve its competitiveness, and promote continued contribution to meeting our Nation’s energy and environmental challenges. The NEET Program has spurred innovation in the field of sensors and instrumentation, advanced methods for manufacturing, and cyber security. The Advanced Sensors and Instrumentation (ASI) program is one element of the NEET CTD that has spurred innovation in the measurement science field by funding research on sensors, instrumentation and controls, communication, and advanced analytics that measure system parameters, gauge component control input, and provide power plant owners and operators critical awareness so they can plan and safely manage operational evolutions. The NEET ASI Program has carried out research and development (R&D) required to develop and deploy innovative and advanced sensors, and instrumentation and control capabilities for current and future nuclear energy systems, and to enable the advanced technologies essential to NE’s R&D efforts needed to realize mission goals. The concept of technology readiness assessment is helpful for the classification of instrumentation technology as part of the program strategy. A technology readiness assessment evaluates technology maturity using the Technology Readiness Level (TRL) scale and was pioneered by the National Aeronautics and Space Administration in the 1980s for space technology. It has recently been adopted to advance fuels and materials. The NEET ASI program objective is to mature instruments technology to TRL 6, which is “demonstration of the technology in relevant environment.” While there may be instances in which a different strategy is justified, the aim of the program is to respond to DOE program and stakeholder needs for nuclear instrumentation then hand off the technology after demonstration for integration in a more complex system, for example an irradiation test, experimental loop or a plant component such as the core, primary system or heat exchanger. A mechanism to continue some level of support from NEET ASI during and after the adoption (for example, to maintain fabrication and calibration capabilities) will be defined as the first example of technology hand off comes to fruition. In addition, several paths exist within DOE to complement NEET ASI activities to extend the maturity beyond TRL, for example through GAIN, technology commercialization funding, and small-business programs. Role of NEET ASI CINR projects in the TRL space, and integration with direct funded demonstration.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2023

Standardized perioperative management in acute abdominal surgery: Swedish SMASH controlled study

Abstract Background Acute high-risk abdominal surgery is common, as are the attendant risks of organ failure, need for intensive care, mortality, or long hospital stay. This study assessed the implementation of standardized management. Methods A prospective study of all adults undergoing emergency laparotomy over an interval of 42 months (2018–2021) was undertaken; outcomes were compared with those of a retrospective control group. A new standardized clinical protocol was activated for all patients including: prompt bedside physical assessment by the surgeon and anaesthetist, interprofessional communication regarding location of resuscitation, elimination of unnecessary factors that might delay surgery, improved operating theatre competence, regular epidural, enhanced recovery care, and frequent early warning scores. The primary endpoint was 30-day mortality. Secondary endpoints were duration of hospital stay, need for intensive care, and surgical complications. Results A total of 1344 patients were included, 663 in the control group and 681 in the intervention group. The use of antibiotics increased (81.4 versus 94.7 per cent), and the time from the decision to operate to the start of surgery was reduced (3.80 versus 3.22 h) with use of the new protocol. Fewer anastomoses were performed (22.5 versus 16.8 per cent). The 30-day mortality rate was 14.5 per cent in the historical control group and 10.7 per cent in the intervention group (P = 0.045). The mean duration of hospital (11.9 versus 10.2 days; P = 0.007) and ICU (5.40 versus 3.12 days; P = 0.007) stays was also reduced. The rate of serious surgical complications (grade IIIb–V) was lower (37.6 versus 27.3 per cent; P = <0.001). Conclusion Standardized management protocols improved outcomes after emergency laparotomy.

Timan, Terje J.↗