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DOE OSTI2022

Recovery of DNA from fired and unfired cartridge casings: comparison of two DNA collection methods

For over 10 years, various studies have attempted to increase the recovery of DNA from ammunition by modifying the DNA collection, extraction, purification, and amplification procedures, with varying levels of success. This study focused on the “soaking” method of Montpetit & O’Donnell [1] and the “rinse-and-swab" method of Bille et al. [2]. First, testing for the presence of exogenous DNA, 210 boxed cartridges (brass, steel, and nickel-plated) from nine manufacturers were swabbed and DNA was extracted, concentrated, and quantified. Extracts that quantified > 0 ng/µL (44 of 210) were amplified and genotyped with GlobalFiler™. Of those, only one extract yielded two alleles indicating that the manufacturing and packaging of ammunition was virtually DNA free. Next, to obtain a baseline comparison of two DNA collection methods on a non-metallic substrate and identify a suitable number of cells to spot on cartridges, different DNA input amounts of primary human adult epidermal keratinocytes (HEKa) were tested. Thereafter, 300 brass and 300 nickel-plated, cartridges were spotted with HEKa cells containing ~5 ng of DNA, fired or unfired, and processed with either method. Finally, five methods representing hybrids of the soaking and rinse-and-swab methods were tested to determine if variations of those methods could be used to increase DNA yield and recovery. The results show that the soaking method consistently yielded more DNA than the rinse-and-swab method from a non-metallic substrate. However, the comparison study demonstrated that both methods performed comparably for cartridges. On average, the soaking method recovered 0.25 ng of DNA (5.1% recovery) and the rinse-and-swab method recovered 0.28 ng (5.8% recovery). However, average recoveries were significantly different among three analysts and considerable variation in yields were observed, possibly due to storage time. Furthermore, consistent with prior reports, the DNA recovered from brass casings was only 16% of that recovered from nickel-plated casings and the average yield of DNA from fired casings was reduced to 67% of unfired casings. Moreover, DNA extracts from brass or nickel-plated casings did not appear to contain amplification inhibitors and only 30/596 appeared severely degraded. Finally, both the published rinse-and-swab and soaking methods yielded more DNA than all modifications of the two methods. Overall, both methods yielded equivalent DNA quantities. Additionally, recovery of DNA from any given cartridge casing may be dependent on storage time as well as the skill, proficiency, and experience of the analyst and may reflect stochastic effects, particularly for casings containing low copy and/or degraded DNA.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2026

Self-Healing Cements with Improved Toughness at Casing and Formation Interfaces for Subsurface Applications (CRADA 530)

Over the three-year CRADA, Pacific Northwest National Laboratory (PNNL) advanced a new class of Molecular Velcro™ self-healing wellbore cements designed to improve the long-term integrity of wells used for geothermal energy production and carbon storage. Conventional wellbore cement can crack due to repeated mechanical loading, temperature swings, and exposure to aggressive subsurface fluids. Once cracks form, they can create leakage pathways, drive expensive remediation (“workovers”), and in extreme cases shorten well life. This project addressed that problem by developing polymer-modified cement systems that can autonomously re-seal cracks and better tolerate the extreme conditions experienced in subsurface wells, while remaining compatible with practical cementing operations.

15 GEOTHERMAL ENERGY↗

DOE OSTI2020

Spectral solar irradiance on inclined surfaces: A fast Monte Carlo approach

Estimating spectral plane-of-array (POA) solar irradiance on inclined surfaces is an important step in the design and performance evaluation of both photovoltaic and concentrated solar plants. This work introduces a fast, line-by-line spectral, Monte Carlo (MC) radiative transfer model approach to simulate anisotropic distributions of shortwave radiation through the atmosphere as photon bundles impinge on inclined surfaces. This fast Monte Carlo approach reproduces the angular distribution of solar irradiance without the undesirable effects of spatial discretization and thus computes detailed POA irradiance values on surfaces at any orientation and also when surfaces are subjected to the anisotropic ground and atmospheric scattering. Polarization effects are also easily incorporated into this approach that can be considered as direct numerical simulation of the physics involved. Here, we compare our Monte Carlo radiative transfer model with the most widely used empirical transposition model, Perez4, under various conditions. The results show that the Perez4 model reproduces the more detailed Monte Carlo simulations with less than 10% deviation under clear skies for all relevant surface tilt and azimuth angles. When optically thin clouds are present, observed deviations are larger, especially when the receiving surface is strongly tilted. Deviations are also observed for large azimuth angle differences between the receiving surface and the solar position. When optically thick clouds are present, the two models agree within 15% deviation for nearly all surface orientation and tilt angles. The overall deviations are smaller when compared with cases for optically thin clouds. The Perez4 model performs very well (6.0% deviation) in comparison with the detailed MC simulations for all cases, thus validating its widespread use for practical solar applications. When detailed atmospheric profiles and cloud optical properties are available, the proposed fast Monte Carlo radiative model reproduces accurate spectral and angular POA irradiance levels for various atmospheric and cloud cover conditions, surface orientations, and different surface and ground properties.

42 ENGINEERING↗

DOE OSTI2020

Multiperspective Automotive Labeling

Supervised machine learning techniques inherently rely on datasets to be trained. With image datasets traditionally being annotated by humans, many advancements in image annotation tools have been made to ensure creation of rich datasets with accurate labels. Nevertheless, users still find it challenging to create and use their own datasets with labels that reflect their problem domain. We propose a streamlined labeling process that aligns multiperspective images and allows a transition from a labeled perspective to other perspectives. The main goal of this work is to reduce the human effort required for labeling vehicle images under favorable conditions where the image perspectives are correlated and one or more perspectives are known. A case study is described and analyzed to show the effectiveness of the process, as well as constraints and limitations when applied to other cases.

training dataset, multiperspective images, supervi↗

DOE OSTI2022

Uncertainty analysis for VERA problem 2 using the cell-code Condor v2.8.05

Condor is a cell-level neutronic calculation code that applies multi-group collision probabilities with heterogeneous response coupling method within generic geometry configurations. Under the Condor's code continuous development, the incorporation of up-to-date methodologies and state-of-the-art practices in reactor analysis represents a driving force. In this work, the capabilities of Condor v2.8.05 to develop an uncertainty analysis for realistic PWR-kind fuel assemblies are studied. The Total Monte Carlo approach is applied to quantify the impact of fabrication tolerances in the code's results for reactivity and power distributions, by means of randomly sampled input values using the VERA problem 2 as basis. The VERA problem 2 proposes a series of Westinghouse 2D 17 x 17-type fuel lattices, to be calculated reflected at beginning-of-life without Xe. The configurations correspond to a modern PWR. Selected neutronic parameters from Condor runs are thus analyzed in terms of the observed spread as well as the obtained distributions for the randomly perturbed cases, showing the capability of the code to handle the required input data, as well as its ability to provide valuable insights regarding uncertainty quantification.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗

DOE OSTI2020

Estimating rates and patterns of diversification with incomplete sampling: a case study in the rosids

Premise Recent advances in generating large‐scale phylogenies enable broad‐scale estimation of species diversification. These now common approaches typically are characterized by (1) incomplete species coverage without explicit sampling methodologies and/or (2) sparse backbone representation, and usually rely on presumed phylogenetic placements to account for species without molecular data. We used empirical examples to examine the effects of incomplete sampling on diversification estimation and provide constructive suggestions to ecologists and evolutionary biologists based on those results. Methods We used a supermatrix for rosids and one well‐sampled subclade (Cucurbitaceae) as empirical case studies. We compared results using these large phylogenies with those based on a previously inferred, smaller supermatrix and on a synthetic tree resource with complete taxonomic coverage. Finally, we simulated random and representative taxon sampling and explored the impact of sampling on three commonly used methods, both parametric (RPANDA and BAMM) and semiparametric (DR). Results We found that the impact of sampling on diversification estimates was idiosyncratic and often strong. Compared to full empirical sampling, representative and random sampling schemes either depressed or inflated speciation rates, depending on methods and sampling schemes. No method was entirely robust to poor sampling, but BAMM was least sensitive to moderate levels of missing taxa. Conclusions We suggest caution against uncritical modeling of missing taxa using taxonomic data for poorly sampled trees and in the use of summary backbone trees and other data sets with high representative bias, and we stress the importance of explicit sampling methodologies in macroevolutionary studies.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2021

Tropospheric and stratospheric wildfire smoke profiling with lidar: mass, surface area, CCN, and INP retrieval

We present retrievals of tropospheric and stratospheric height profiles of particle mass, volume, surface area, and number concentrations in the case of wildfire smoke layers as well as estimates of smoke-related cloud condensation nuclei (CCN) and ice-nucleating particle (INP) concentrations from backscatter lidar measurements on the ground and in space. Conversion factors used to convert the optical measurements into microphysical properties play a central role in the data analysis, in addition to estimates of the smoke extinction-to-backscatter ratios required to obtain smoke extinction coefficients. The set of needed conversion parameters for wildfire smoke is derived from AERONET observations of major smoke events, e.g., in western Canada in August 2017, California in September 2020, and southeastern Australia in January–February 2020 as well as from AERONET long-term observations of smoke in the Amazon region, southern Africa, and Southeast Asia. The new smoke analysis scheme is applied to CALIPSO observations of tropospheric smoke plumes over the United States in September 2020 and to ground-based lidar observation in Punta Arenas, in southern Chile, in aged Australian smoke layers in the stratosphere in January 2020. These case studies show the potential of spaceborne and ground-based lidars to document large-scale and long-lasting wildfire smoke events in detail and thus to provide valuable information for climate, cloud, and air chemistry modeling efforts performed to investigate the role of wildfire smoke in the atmospheric system.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2020

Benefits and risks of lead halide perovskite photovoltaics

Photovoltaics (PV) do not emit anything during operation, but, over their life-cycle, emissions are generated from the use of fossil-fuels in the extraction and production of materials and in the manufacturing and installation of PV components and systems. Nonetheless, these emissions are always very small compared to those from conventional combustion-based generators of electricity. The case for CO2 emissions has been well documented; over their lifetime, they range from 10 g CO2-eq / kWh for CdTe PV system installed in high-irradiation (2300 kWh/(m2-yr) locations to 80 g CO2-eq / kWh for Chinese single-crystalline silicon PV systems installed in low-irradiation (1000 kWh/(m2-yr) locations,1 compared to ~1000 g CO2-eq/kWh for electricity from coal. However, in the case of the rapidly emerging lead halide perovskite PV (LHP-PV) technology, concerns are raised regarding the potential of emissions of lead from the life-cycles of LHP-PV, including at their end of life. This warranted an evaluation of lead emissions from PV and comparisons with emissions in other power generation life-cycles.

Perovskites, PV, Lead, Risks↗

DOE OSTI2025

A flexible test facility for liquid xenon detector development

As liquid xenon time projection chambers scale to ever-larger sizes, so too do the engineering challenges they pose. Here, we describe a large, flexible, multipurpose test facility capable of supporting the development of a number of key aspects of liquid xenon detector systems. Example applications of this facility include characterization of large-area light and charge sensor arrays, tests of xenon purification techniques and materials compatibility, and investigations into high-voltage phenomena. This facility uses an automated and remotely monitored cryo-cooling system based on immersion of the test chamber in a liquid bath rather than conductive coupling, leading to advantages in temperature and pressure stability, as well as increasing required response times in the case of cooling-power loss. Design advantages, operational procedures, and performance of the facility are described, as well as five examples of liquid xenon test chambers that use the facility.

Dark Matter detectors↗

DOE OSTI2022

High CO2 Capture Rate Cost and Performance for F- and H-Class Natural Gas Combined Cycles (NGCC)

The National Energy Technology Laboratory (NETL) is updating its study titled “Cost and Performance Baselines for Fossil Energy Plants, Volume 1: Bituminous Coal and Natural Gas to Electricity.” Revision 4 was published in 2019. Pulverized coal (PC) and natural gas combined cycle (NGCC) cases with carbon capture in that study capture carbon dioxide (CO2) at 90% efficiency. Field-testing of post-combustion CO2 capture technology as well as vendor and industry feedback on projects currently in the planning stages (including FEED projects sponsored by DOE) indicate that capture rates as high as 95% are feasible for both coal- and natural gas-fueled electricity generating units. Technology suppliers (as reflected in vendor-supplied information provided to DOE for this study that included cost and performance estimates for both NGCC (97%) and PC (99%) study cases) as well as subject matter experts acknowledge and support that solvent-based post-combustion CO2 capture technologies are capable of achieving CO2 removal rates beyond 95% on low-purity streams representative of fossil-fueled combustion; however, the relatively limited experience with design and operation of capture systems that can routinely, reliably, and economically achieve very high removal rates (e.g., 97% for NGCC flue gas) requires further study. This presentation will address performance and cost estimates for 90%, 95%, and 97% capture for F- and H-class NGCC plants. This presentation was given at the 2022 Pittsburgh Coal Conference.

Fout, Timothy↗

DOE OSTI2026

The future of subsurface monitoring: AEC’s breakthroughs in CCS technology

Carbon capture and storage (CCS) has emerged as a key solution in the fight against climate change. However, for CCS to succeed, it is crucial to ensure that the sequestered CO2 stays safely trapped underground. The U.S. Department of Energy (DOE) has emphasized the need for advancements in subsurface monitoring, measurement, reporting, and verification. Aside from caprock integrity failure, the other primary failure points usually involve defective cement in the casing annulus of wellbores or plugged and abandoned wells. In addition, many energy producers (e.g., oil and gas, geothermal) and storage and disposal operators (e.g., H2 and water) must deal with the same issue. Poorly placed or degraded cement can create pathways for gas or fluid to escape from casing annuli and in plugged and abandoned or orphan wells, posing environmental risks. Yet, a reliable and cost-effective way to monitor cement and well integrity over multiple decades is still unavailable. Traditional geophysical methods like 4D seismic imaging and surface-based electromagnetic monitoring lack the resolution and accuracy for detecting these types of failures (Vasco et al., 2022; Fawad and Mondol, 2021). Wireline logging is expensive to run continuously and is obtrusive to the operation. While fiber optics can potentially be a solution, its bulkiness can significantly compromise the cement's integrity. To address these challenges, the Advanced Energy Consortium (AEC) at The University of Texas at Austin’s Bureau of Economic Geology (the Bureau) has been pioneering research in subsurface monitoring using its portfolio of distributed autonomous microfabricated sensors for harsh subsurface environments since 2008. A class of these microsensors [System on a Chip (SoC)] can be mixed in cement and permanently placed without compromising the cement column; the sensors would then communicate with each other or a data acquisition (DAQ) master node. Another class of the AEC microsensors can be fully autonomous, with rechargeable micro-batteries capable of exceeding 100°C, flash memory, and, currently, a pressure and temperature sensor. They are designed to circulate in mud, geothermal fluids, U-loops, or pipelines. They can log data into memory and are unobtrusive to operations. Our team has been working on a multi-year DOE-funded project (DE-FE0031856)—supported by $2.95M in federal funding and $0.75M in cost-matching from the AEC—to demonstrate SoC sensor utility for CO2 leakage monitoring in CCS applications. This multi-institutional collaboration developed a novel sensing architecture utilizing radiofrequency (RF) microsensors embedded within the cement sheath. These sensors detect CO2 migration and are interrogated via a Smart Casing Collar (SCC).

58 GEOSCIENCES↗

DOE OSTI2025

Developing a Prototype Methodology to Rank CO2-EOR Wells and Assess Their Reuse Potential for Geologic Carbon Sequestration

This study presents a prototype methodology for evaluating the reuse potential of Class II CO₂-enhanced oil recovery (CO₂-EOR) wells as Class VI wells for geologic carbon sequestration. The approach focuses on assessing wellbore construction materials—casing, cement, tubing, and packers—based on U.S. Environmental Protection Agency (EPA) Class VI well conversion guidelines. Utilizing Python scripts and JSON representations, the methodology automates checks on digitized Texas Railroad Commission (TRRC) data to rank wells based on regulatory and integrity criteria. Key factors include casing integrity, cementing techniques, tubing compatibility, and packer selection. Due to limitations in digitized data, manual verification is required for certain sections. Future enhancements include incorporating non-digitized data via web scraping and machine learning. This research provides a practical framework for well owners and regulators, supporting informed decision-making for sustainable CO₂ storage.

carbon sequestration↗

DOE OSTI2021

Integrating Machine Learning into a Methodology for Early Detection of Wellbore Failure [Slides]

Approximately 93% of US total energy supply is dependent on wellbores in some form. The industry will drill more wells in next ten years than in the last 100 years (King, 2014). Global well population is around 1.8 million of which approximately 35% has some signs of leakage (i.e. sustained casing pressure). Around 5% of offshore oil and gas wells “fail” early, more with age and most with maturity. 8.9% of “shale gas” wells in the Marcellus play have experienced failure (120 out of 1,346 wells drilled in 2012) (Ingraffea et al., 2014). Current methods for identifying wells that are at highest priority for increased monitoring and/or at highest risk for failure consists of “hand” analysis of multi-arm caliper (MAC) well logging data and geomechanical models. Machine learning (ML) methods are of interest to explore feasibility for increasing analysis efficiency and/or enhanced detection of precursors to failure (e.g. deformations). MAC datasets used to train ML algorithms and preliminary tests were run for “predicting” casing collar locations and performed above 90% in classification and identifying of casing collar locations.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2021

Integrating Machine Learning into a Methodology for Early Detection of Wellbore Failure [Slides]

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2023

Comprehensive Review of Multi-arm Caliper Data for the Big Hill SPR Site

The Big Hill SPR site has a rich data set consisting of multi-arm caliper (MAC) logs collected from the cavern wells. This data set provides insight into the on-going casing deformation at the Big Hill site. This report summarizes the MAC surveys for each well and presents well longevity estimates where possible. Included in the report is an examination of the well twins for each cavern and a discussion on what may or may not be responsible for the different levels of deformation between some of the well twins. The report also takes a systematic view of the MAC data presenting spatial patterns of casing deformation and deformation orientation in an effort to better understand the underlying causes. The conclusions present a hypothesis suggesting the small-scale variations in casing deformation are attributable to similar scale variations in the character of the salt-caprock interface. These variations do not appear directly related to shear zones or faults.

58 GEOSCIENCES↗

DOE OSTI2024

2024 Update of Comprehensive Review of Multi-arm Caliper Data for the Big Hill SPR Site

58 GEOSCIENCES↗

DOE OSTI2020

Effect of Computational Schemes on Coupled Flow and Geo-Mechanical Modeling of CO 2 Leakage through a Compromised Well

Carbon capture, utilization, and storage (CCUS) describes a set of technically viable processes to separate carbon dioxide (CO 2 ) from industrial byproduct streams and inject it into deep geologic formations for long-term storage. Legacy wells located within the spatial domain of new injection and production activities represent potential pathways for fluids (i.e., CO 2 and aqueous phase) to leak through compromised components (e.g., through fractures or micro-annulus pathways). The finite element (FE) method is a well-established numerical approach to simulate the coupling between multi-phase fluid flow and solid phase deformation interactions that occur in a compromised well system. We assumed the spatial domain consists of a three-phases system: a solid, liquid, and gas phase. For flow in the two fluids phases, we considered two sets of primary variables: the first considering capillary pressure and gas pressure (PP) scheme, and the second considering liquid pressure and gas saturation (PS) scheme. Fluid phases were coupled with the solid phase using the full coupling (i.e., monolithic coupling) and iterative coupling (i.e., sequential coupling) approaches. The challenge of achieving numerical stability in the coupled formulation in heterogeneous media was addressed using the mass lumping and the upwinding techniques. Numerical results were compared with three benchmark problems to assess the performance of coupled FE solutions: 1D Terzaghi’s consolidation, Liakopoulos experiments, and the Kueper and Frind experiments. We found good agreement between our results and the three benchmark problems. For the Kueper and Frind test, the PP scheme successfully captured the observed experimental response of the non-aqueous phase infiltration, in contrast to the PS scheme. These exercises demonstrate the importance of fluid phase primary variable selection for heterogeneous porous media. We then applied the developed model to the hypothetical case of leakage along a compromised well representing a heterogeneous media. Considering the mass lumping and the upwinding techniques, both the monotonic and the sequential coupling provided identical results, but mass lumping was needed to avoid numerical instabilities in the sequential coupling. Additionally, in the monolithic coupling, the magnitude of primary variables in the coupled solution without mass lumping and the upwinding is higher, which is essential for the risk-based analyses.

deformation flow↗

DOE OSTI2022

tapir: A tool for topologies, amplitudes, partial fraction decomposition and input for reductions

The demand for precision predictions in the field of high energy physics has dramatically increased over recent years. Experiments conducted at the LHC, as well as precision measurements at the intensity frontier such as Belle II require equally precise theoretical predictions to make full use of the acquired data. To match the experimental precision, second-, third- and, for certain quantities, even higher-order calculations in perturbative quantum field theory are required. To facilitate such calculations, computer software automating as many steps as possible is required. Yet, each calculation poses different challenges and thus, a high level of configurability is required. In this context we present tapir: a tool for identification, manipulation and minimization of Feynman integral families. It is designed to integrate in toolchains based on the computer algebra system FORM, the use of which is common practice in the field. tapir can be used to reduce the complexity of multi-loop problems with cut-filters, topology mapping, partial fraction decomposition and alike. Program Title:tapir CPC Library link to program files:https://doi.org/10.17632/ptc9t46xyn.1 Developer's repository link:https://gitlab.com/tapir-devs/tapir Licensing provisions: GPLv3 Programming language:python 3, C++ Nature of problem: Multi-loop computations require the automatization of a large number of different tasks related to Feynman integral topologies. Among them are the identification and minimization of integral topologies, partial fraction decomposition of topologies in the case of linearly dependent propagators as well as mapping scalar products of loop momenta to scalar functions. Solution method: The minimization of topologies is performed by comparison of their respective Nickel indices [1], even further minimization utilizes Pak's algorithm [2]. To efficiently map scalar products of loop momenta to scalar functions FORM [3] code is generated. Additional comments including restrictions and unusual features: Minimization based on Pak's algorithm slows down for many lines and scales. A coarser minimization using the Nickel indices, however, is still possible. [1]B. Nickel, D. Meiron, G.A.J. Baker, Compilation of 2-pt and 4-pt graphs for continuous spin model, Report, University of Guelph, 1977.[2]A. Pak, J. Phys. Conf. Ser. 368 (2012) 012049, https://doi.org/10.1088/1742-6596/368/1/012049, arXiv:1111.0868.[3]B. Ruijl, T. Ueda, J. Vermaseren, FORM version 4.2, arXiv:1707.06453, 7 2017.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗