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DOE OSTI2022

High Strength Steel-Aluminum Components by Vaporizing Foil Actuator Welding

This project aimed to address the challenge of effectively welding dissimilar materials—high-strength steel and high-strength aluminum for creating lightweight, multi-material automotive components. For automotive companies, reducing weight of a vehicle is critical task regulated by the government to solve the issue of greenhouse gas emissions. Production of lightweight cars and trucks can be achieved by substitution of current all-steel structures with multi-material lightweight structures that include high strength-to-weight-ration materials such as high-strength steels, aluminum alloys, magnesium alloys, titanium alloys, coupled with lightweight designs. This requires dissimilar metal welding, which is challenging for state of the art joining processes such as resistance spot welding. The cycle of melting-cooling-freezing during traditional welding that can easily ruin the designed outstanding properties of the advanced base metals, such as aluminum alloys, making the welded area much weaker than the base metals. To weld two different metals with great difference in melting points, such as aluminum and steel, it’s even more difficult or impossible because of the formation of brittle intermetallic compounds at the welded interface. In this project, a novel welding method, developed at OSU, was selected for validation and development. This novel technology enables welding by impact without melting and proves to be robust to join various dissimilar lightweight metals. Termed as vaporizing foil actuator welding or VFAW, the technology uses a thin aluminum foil that is rapidly vaporized by a high current pulse to produce an explosive-like pressure pulse to drive one metallic piece into another at the high speed required for impact welding. This project entailed development of the early-stage welding technology in terms of (a) the consumables, the welding apparatus and the power sources, (b) coupon scale screening of many material combinations including corrosion studies, (c) computational modeling and design of the welded interface as well as of the multi-material prototype component, and (d) mechanical testing for strength and durability at coupon scale and to a certain extent the prototype scale. The all-steel engine cradle of 2016 Chevrolet Cruze was chosen as the baseline prototype component. The target set for the project was to demonstrate a 20% weight reduction at a cost premium of less than $\$ $5/lb saved without compromising on baseline mechanical properties. At project completion, a 12% lighter prototype component was demonstrated with an estimated cost premium of $\$ $9.8/lb saved. Besides prototype level demonstration of the technology, this project also enabled elevation of the technology’s readiness level to where a hydraulically actuated welding head was developed and made ready for deployment at a research and development facility for Tier 1 automotive supplier.

36 MATERIALS SCIENCE↗
DOE OSTI2017

astroABC : An Approximate Bayesian Computation Sequential Monte Carlo sampler for cosmological parameter estimation

Given the complexity of modern cosmological parameter inference where we arefaced with non-Gaussian data and noise, correlated systematics and multi-probecorrelated data sets, the Approximate Bayesian Computation (ABC) method is apromising alternative to traditional Markov Chain Monte Carlo approaches in thecase where the Likelihood is intractable or unknown. The ABC method is called"Likelihood free" as it avoids explicit evaluation of the Likelihood by using aforward model simulation of the data which can include systematics. Weintroduce astroABC, an open source ABC Sequential Monte Carlo (SMC) sampler forparameter estimation. A key challenge in astrophysics is the efficient use oflarge multi-probe datasets to constrain high dimensional, possibly correlatedparameter spaces. With this in mind astroABC allows for massive parallelizationusing MPI, a framework that handles spawning of jobs across multiple nodes. Akey new feature of astroABC is the ability to create MPI groups with differentcommunicators, one for the sampler and several others for the forward modelsimulation, which speeds up sampling time considerably. For smaller jobs thePython multiprocessing option is also available. Other key features include: aSequential Monte Carlo sampler, a method for iteratively adapting tolerancelevels, local covariance estimate using scikit-learn's KDTree, modules forspecifying optimal covariance matrix for a component-wise or multivariatenormal perturbation kernel, output and restart files are backed up everyiteration, user defined metric and simulation methods, a module for specifyingheterogeneous parameter priors including non-standard prior PDFs, a module forspecifying a constant, linear, log or exponential tolerance level,well-documented examples and sample scripts. This code is hosted online athttps://github.com/EliseJ/astroABC

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2015

Characterization of a real-time tracer for isoprene epoxydiols-derived secondary organic aerosol (IEPOX-SOA) from aerosol mass spectrometer measurements

Substantial amounts of secondary organic aerosol (SOA) can be formed from isoprene epoxydiols (IEPOX), which are oxidation products of isoprene mainly under low-NO conditions. Total IEPOX-SOA, which may include SOA formed from other parallel isoprene oxidation pathways, was quantified by applying positive matrix factorization (PMF) to aerosol mass spectrometer (AMS) measurements. The IEPOX-SOA fractions of organic aerosol (OA) in multiple field studies across several continents are summarized here and show consistent patterns with the concentration of gas-phase IEPOX simulated by the GEOS-Chem chemical transport model. During the Southern Oxidant and Aerosol Study (SOAS), 78 % of PMF-resolved IEPOX-SOA is accounted by the measured IEPOX-SOA molecular tracers (2-methyltetrols, C5-Triols, and IEPOX-derived organosulfate and its dimers), making it the highest level of molecular identification of an ambient SOA component to our knowledge. An enhanced signal at C 5 H 6 O + (m/z 82) is found in PMF-resolved IEPOX-SOA spectra. To investigate the suitability of this ion as a tracer for IEPOX-SOA, we examine f C 5 H 6 O ( f C 5 H 6 O= C 5 H 6 O + /OA) across multiple field, chamber, and source data sets. A background of ~ 1.7 ± 0.1 ‰ (‰ = parts per thousand) is observed in studies strongly influenced by urban, biomass-burning, and other anthropogenic primary organic aerosol (POA). Higher background values of 3.1 ± 0.6 ‰ are found in studies strongly influenced by monoterpene emissions. The average laboratory monoterpene SOA value (5.5 ± 2.0 ‰) is 4 times lower than the average for IEPOX-SOA (22 ± 7 ‰), which leaves some room to separate both contributions to OA. Locations strongly influenced by isoprene emissions under low-NO levels had higher f C 5 H 6 O (~ 6.5 ± 2.2 ‰ on average) than other sites, consistent with the expected IEPOX-SOA formation in those studies.f C 5 H 6 O in IEPOX-SOA is always elevated (12–40 ‰) but varies substantially between locations, which is shown to reflect large variations in its detailed molecular composition. The low f C 5 H 6 O (< 3 ‰) reported in non-IEPOX-derived isoprene-SOA from chamber studies indicates that this tracer ion is specifically enhanced from IEPOX-SOA, and is not a tracer for all SOA from isoprene. We introduce a graphical diagnostic to study the presence and aging of IEPOX-SOA as a triangle plot of fCO2 vs. f C 5 H 6 O. Finally, we develop a simplified method to estimate ambient IEPOX-SOA mass concentrations, which is shown to perform well compared to the full PMF method. The uncertainty of the tracer method is up to a factor of ~ 2, if the f C 5 H 6 O of the local IEPOX-SOA is not available. When only unit mass-resolution data are available, as with the aerosol chemical speciation monitor (ACSM), all methods may perform less well because of increased interferences from other ions at m/z 82. This study clarifies the strengths and limitations of the different AMS methods for detection of IEPOX-SOA and will enable improved characterization of this OA component.

Hu, W. W.↗
DOE OSTI2015

SU-E-T-679: Retrospective Analysis of the Sensitivity of Planar Dose Measurements To Gamma Analysis Criteria

Purpose: IMRT QA using planar dose measurements is still a widely used method for checking the accuracy of treatment plans. A pass/fail judgment is made using gamma analysis based on a single endpoint. Using more stringent criteria is a way to increase the sensitivity to planning and delivery errors. Before such implementation, it is necessary to understand how the sensitivity to different gamma criteria settings affects gamma passing rates (GPR). Methods: 752 IMRT QA measurements were re-analyzed with varying distance to agreement (DTA) and dose difference (DD) percentages using a Matlab program. Other quantifying information such as the mean dose difference in the treatment target (defined as points that are greater than 80% of maximal dose) were stored in a relational database for retrospective analysis. Results: The average and standard deviation of GPR (%) fell from 99.84 ± (0.43) to 89.61 ± (6.08) when restricting DD from 5 − 1% respectively, as compared to a drop from 99.15 ± (1.19) to 95.00 ± (4.43), when restricting the DTA from 5 − 1 mm respectively. The mean dose difference (%) in the treatment target between measured and calculated dose was −1.96 ± (0. 83), −0.09 ± (0.98), and 1.44 ± (0. 86) for each of our institution’s three matched linear accelerators (LINAC 1, 2, and 3 respectively). For plans that are approximately 2.7 sigma below the mean GPR, an average of 78.4% of those plans were measured on LINAC 1 or 3, while only 48% of the total plans were run on those machines. Conclusion: The data demonstrates that when restricting gamma criterion, such as the DD, the greatest indicator of reduced GPR in our institution is which matched LINAC the plan was measured on. While small, these differences manifest themselves to levels comparable to other treatment related differences and possibly confound the gamma analysis.

61 RADIATION PROTECTION AND DOSIMETRY↗
DOE OSTI2015

TU-F-CAMPUS-J-01: A Formulation of 4D Treatment Planning for Tumour Tracking Volumetric Modulated Arc Therapy for Lung Cancer

Purpose: To develop a formulation for 4D treatment planning for a tumour tracking volumetric modulated arc therapy treatment (VMAT) plan for lung cancer. Methods: A VMAT plan was optimized based on a reference phase of the 4DCT of a lung cancer patient. The PTV was generated from the GTV of the reference phase. The collimator angle was set to 90 degrees such that the MLC travels along superior-inferior direction which is the main component of movement of a lung tumour. Then, each control point of the VMAT plan was assigned to a particular phase of the 4DCT in chronological order. The MLC positions of each control point were shifted according to the position of the tumour centroid of its assigned phase to form a tumour tracking VMAT plan. The control points of the same phase were grouped to form a pseudo VMAT plan for that particular phase. Dose calculation was performed for each pseudo VMAT plan on the corresponding phase of the 4DCT. The CTs of all phases were registered to the reference phase CT according to the displacement of the tumour centroid. The individual dose distributions of the pseudo VMAT plans were summed up and displayed on the reference phase of the 4DCT. A control VMAT plan was optimized based on a PTV generated from the ITV of all phases and compared with the tumour tracking VMAT plan. Results: Both plans achieved >95% volume coverage at the prescription dose level (96% for the tumour tracking plan and 97% for the control plan). But the normal lung volume irradiated at the prescription dose level was 39% less for the tumour tracking plan than the control plan. Conclusion: A formulation of 4D treatment planning for tumour tracking VMAT plans for lung cancer was developed.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2015

Accurate ab initio vibrational energies of methyl chloride

Two new nine-dimensional potential energy surfaces (PESs) have been generated using high-level ab initio theory for the two main isotopologues of methyl chloride, CH{sub 3}{sup 35}Cl and CH{sub 3}{sup 37}Cl. The respective PESs, CBS-35{sup HL}, and CBS-37{sup HL}, are based on explicitly correlated coupled cluster calculations with extrapolation to the complete basis set (CBS) limit, and incorporate a range of higher-level (HL) additive energy corrections to account for core-valence electron correlation, higher-order coupled cluster terms, scalar relativistic effects, and diagonal Born-Oppenheimer corrections. Variational calculations of the vibrational energy levels were performed using the computer program TROVE, whose functionality has been extended to handle molecules of the form XY {sub 3}Z. Fully converged energies were obtained by means of a complete vibrational basis set extrapolation. The CBS-35{sup HL} and CBS-37{sup HL} PESs reproduce the fundamental term values with root-mean-square errors of 0.75 and 1.00 cm{sup −1}, respectively. An analysis of the combined effect of the HL corrections and CBS extrapolation on the vibrational wavenumbers indicates that both are needed to compute accurate theoretical results for methyl chloride. We believe that it would be extremely challenging to go beyond the accuracy currently achieved for CH{sub 3}Cl without empirical refinement of the respective PESs.

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

Computer Science Research Needs for Parallel Discrete Event Simulation (PDES)

Historically, scientific computing efforts have demonstrated the clear need for, and effective use of, supercomputing with traditional time-stepped simulations. Nevertheless, there are several areas in the mission spaces of the U.S. Department of Energy and other agencies waiting to tap advanced computing research using a different, discrete event style of modeling, simulation, and analysis. These span a wide spectrum of applications including energy grid resilience, urban planning and policy, transportation science, building technologies, emergency response and planning, environmental impact analysis, computational epidemiology, Internet communications, cyber security, and cyber-physical systems, to name only a few. Even within traditional scientific applications, the role of discrete event modes of execution is increasing in the form of new event-based mathematical solvers such as quantized state integration methods and discrete-continuous hybrid system solvers. Co-design of advanced supercomputing hardware systems is another area that exploits discrete event simulation at its core for effective analyses. Complex systems, entity behaviors and interconnections play a significant role in all these applications, which are mapped to large-scale models with discrete event formulations. To make advancements in all the aforementioned scientific areas, many technical aspects need to be more thoroughly studied and deeply understood in parallel discrete event simulation (PDES). The unique dynamics inherent in a discrete event modeling approach, by their very nature, intersect and influence the entire stack of the computing system, including (a) the unique nature of the instruction sets exercised in PDES workloads without a predominance of high-precision floating point operations, (b) virtual time-constrained multi-threaded execution of many logical processes per processor, (c) extremely variable and difficult to predict network traffic characteristics, (d) interfaces and inter-dependencies with machine learning and artificial intelligence codes at higher software layers, and (e) highly challenging load balancing needs, especially in effectively accounting for accelerated/extremely heterogeneous computing in current and future high-performance computing systems. Efficient and accurate parallel execution of PDES workloads is also dominated by challenges in dealing with their asynchronous concurrency fundamentally present at the model level. Conservative synchronization, optimistic/speculative synchronization, and their hybrid schemes open new questions in fundamental computer science with respect to reversibility of computation and prediction (lookahead) of behaviors inherent within model codes. On the implementation front, there are relatively few scalable, general-purpose parallel discrete event simulators in the world, and even fewer have been studied on emerging hardware platforms. To enable scientific advances using PDES, the research needs in computer science must also be pursued and met in the intersection of the algorithmic and hardware-aware aspects of scalable PDES engines. This report is aimed at capturing a computer science-oriented view of this important area of research in PDES, presenting a sample of important applications with their inherent discrete event technology elements. Needs are outlined in core areas of parallel discrete event research as well as cross-cutting directions in computer science research that positively impact scientific advancements across several important application areas. A selection of priority research opportunities in advanced computing for PDES is identified to serve as reference for key research topics and their order of importance for scientific advancements.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Measurements of Argon-39 from locations near historic underground nuclear explosions

Measurement of radioactive gas seepage from an underground nuclear explosion is one of the primary methods to confirm whether an event was nuclear in nature. Radioactive noble gas indicators that are commonly targeted by such measurements (e.g. 133Xe, 37Ar) have half-lives of 35 days or less. Argon-39, an activation product similar to 37Ar, is produced by the interaction between neutrons and potassium in the surrounding geology and has a half-life of 269 years. Measurements taken at three sites near three historic underground nuclear test locations at the Nevada National Security Site have all shown highly elevated levels of 39Ar in subsurface air decades after the test events. Elevated levels of 39Ar (30-50 times background) were also detected in atmospheric air collected roughly 30 cm above ground level near two of these sites, and outside the entrance of the one tunnel site. These measurements demonstrate that 39Ar has the potential to be a long-term signature of an underground nuclear event which can be reliably detected at the surface or in the shallow subsurface. This radionuclide detection of an underground nuclear event decades after the event takes place is in contrast to the commonly held assumption that detecting underground nuclear events via radionuclides at the surface needs to be done in a matter of months. Depending upon what further studies show about the robustness of this signature in a variety of geological settings, it may in fact be easy to detect underground nuclear events at the surface for a very long time post-detonation.

Johnson, Christine M.↗
DOE OSTI2016

Association Between Travel Distance and Choice of Treatment for Prostate Cancer: Does Geography Reduce Patient Choice?

Objective: To determine whether the distance between a prostate cancer patient's home and treatment facility was related to the choice of treatment received among those opting for surgery or radiation. Methods and Materials: We identified 222,804 patients diagnosed with National Comprehensive Cancer Network low-, intermediate-, or high-risk N0M0 prostate cancer and treated with local therapy (surgery or radiation alone, with or without hormone therapy) using the National Cancer Database. We used multivariable logistic regression to determine whether the choice of radiation therapy vs radical prostatectomy varied by distance among patients living in rural and urban areas. Analyses were adjusted for geographic location within the United States, age, race, Charlson/Deyo comorbidity score, year of diagnosis, income quartile, education quartile, Gleason score, prostate-specific antigen level, and T stage. Results: Patients living in urban or rural areas were less likely to receive radiation compared with surgery if they lived farther from the treatment facility. Among urban patients living ≤5 miles from the treatment facility, 53.3% received radiation, compared with 47.0%, 43.6%, and 33.8% of those living 5 to 10, 10 to 15, or >15 miles away, respectively (P<.001 in all cases). Similarly, rural patients were less likely to receive radiation the farther they lived from the treatment facility (≤25 miles: 62.3%; 25-50 miles: 55.5%; 50-75 miles: 38.4%; >75 miles: 23.8%; P<.05 in all cases). These trends were also present when each risk group was analyzed separately. Conclusion: Patients with prostate cancer in both urban and rural settings were less likely to receive radiation therapy rather than surgery the farther away they lived from a treatment center. These findings raise the possibility that the geographic availability of radiation treatment centers may be an important determinant of whether patients are able to choose radiation rather than surgery for localized prostate cancer.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2016

Modulation of P-glycoprotein activity by novel synthetic curcumin derivatives in sensitive and multidrug-resistant T-cell acute lymphoblastic leukemia cell lines

Background: Multidrug resistance (MDR) and drug transporter P-glycoprotein (P-gp) represent major obstacles in cancer chemotherapy. We investigated 19 synthetic curcumin derivatives in drug-sensitive acute lymphoblastic CCRF–CEM leukemia cells and their multidrug-resistant P-gp-overexpressing subline, CEM/ADR5000. Material and methods: Cytotoxicity was tested by resazurin assays. Doxorubicin uptake was assessed by flow cytometry. Binding modes of compounds to P-gp were analyzed by molecular docking. Chemical features responsible for bioactivity were studied by quantitative structure activity relationship (QSAR) analyses. A 7-descriptor QSAR model was correlated with doxorubicin uptake values, IC{sub 50} values and binding energies. Results: The compounds displayed IC{sub 50} values between 0.7 ± 0.03 and 20.2 ± 0.25 μM. CEM/ADR5000 cells exhibited cross-resistance to 10 compounds, collateral sensitivity to three compounds and regular sensitivity to the remaining six curcumins. Molecular docking studies at the intra-channel transmembrane domain of human P-gp resulted in lowest binding energies ranging from − 9.00 ± 0.10 to − 6.20 ± 0.02 kcal/mol and pKi values from 0.24 ± 0.04 to 29.17 ± 0.88 μM. At the ATP-binding site of P-gp, lowest binding energies ranged from − 9.78 ± 0.17 to − 6.79 ± 0.01 kcal/mol and pKi values from 0.07 ± 0.02 to 0.03 ± 0.03 μM. CEM/ADR5000 cells accumulated approximately 4-fold less doxorubicin than CCRF–CEM cells. The control P-gp inhibitor, verapamil, partially increased doxorubicin uptake in CEM/ADR5000 cells. Six curcumins increased doxorubicin uptake in resistant cells or even exceeded uptake levels compared to sensitive one. QSAR yielded good activity prediction (R = 0.797 and R = 0.794 for training and test sets). Conclusion: Selected derivatives may serve to guide future design of novel P-gp inhibitors and collateral sensitive drugs to combat MDR. - Highlights: • Novel derivatives of curcumin in reversing multidrug resistance (MDR) • Biological and Insilco assays to assess effect on P-glycoprotein (P-gp) • Curcumin synthetic derivatives as possible lead compound against multidrug resistant cancer.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2019

GPLADD: Quantifying Trust in Government and Commercial Systems A Game-Theoretic Approach

Trust in a microelectronics-based system can be characterized as the level of confidence that a system is free of subversive alterations made during system development, or that the development process of a system has not been manipulated by a malicious adversary. Trust in systems has become an increasing concern over the past decade. This report introduces a novel game-theoretic framework, called GPLADD (Graph-based Probabilistic Learning Attacker and Dynamic Defender), for analyzing and quantifying system trustworthiness at the end of the development process, through the analysis of risk of development-time system manipulation. GPLADD represents attacks and attacker-defender contests over time. Here, time is an explicit constraint and allows incorporating the informational asymmetries between the attacker and defender into analysis. GPLADD includes an explicit representation of attack steps via multi-step attack graphs, attacker and defender strategies, and player actions at different times. GPLADD allows quantifying the attack success probability over time and the attacker and defender costs based on their capabilities and strategies. This ability to quantify different attacks provides an input for evaluation of trust in the development process. We demonstrate GPLADD on an example attack and its variants. We develop a method for representing success probability for arbitrary attacks and derive an explicit analytic characterization of success probability for a specific attack. We present a numeric Monte Carlo study of a small set of attacks, quantify attack success probabilities, attacker and defender costs, and illustrate the options the defender has for limiting the attack success and improving trust in the development process.

37 attack↗
DOE OSTI2022

Commercial, industrial, and institutional discount rate estimation for efficiency standards analysis: Sector-level data 1998–2021

Underlying each of the Department of Energy’s (DOE’s) federal appliance and equipment energy conservation standards are a set of complex analyses of the projected costs and benefits of regulation. Any new or amended standard must be designed to achieve significant additional energy conservation, provided that it is technologically feasible and economically justified (42 U.S.C. 6295(o)(2)(A)). DOE determines economic justification based on whether the benefits exceed the burdens, considering a variety of factors, including the economic impact of the standard on consumers of the product and the savings in lifetime operating cost compared to any increase in price or maintenance expenses (42 U.S.C. 6295(o)(2)(B)). As part of this determination, DOE conducts a Life-Cycle Cost (LCC) analysis, which models the combined impact of appliance first cost and operating cost changes on a representative commercial building sample in order to identify the fraction of customers achieving LCC savings or incurring net cost at the considered efficiency levels. Thus, the commercial discount rate value(s) used to calculate the present value of energy cost savings within the LCC model implicitly plays a role in estimating the economic impact of potential standard levels. This report provides an in-depth discussion of the commercial discount rate estimation process. It is an update to previous reports on estimating commercial discount rates from firm-level financial data (Fujita, 2016). Major topics covered in this report include: Discount rate estimation methods and rationale; -Data sources used and data limitations; -Discount rate distributions for use in standards analysis; -Discount rate estimation methods and distributions specific to the small business subgroup analysis. Going forward, this report will be updated as data allow and analyses necessitate.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2021

A Comparison of Machine Learning Methods to Forecast Tropospheric Ozone Levels in Delhi

Ground-level ozone is a pollutant that is harmful to urban populations, particularly in developing countries where it is present in significant quantities. It greatly increases the risk of heart and lung diseases and harms agricultural crops. This study hypothesized that, as a secondary pollutant, ground-level ozone is amenable to 24 h forecasting based on measurements of weather conditions and primary pollutants such as nitrogen oxides and volatile organic compounds. We developed software to analyze hourly records of 12 air pollutants and 5 weather variables over the course of one year in Delhi, India. To determine the best predictive model, eight machine learning algorithms were tuned, trained, tested, and compared using cross-validation with hourly data for a full year. The algorithms, ranked by R2 values, were XGBoost (0.61), Random Forest (0.61), K-Nearest Neighbor Regression (0.55), Support Vector Regression (0.48), Decision Trees (0.43), AdaBoost (0.39), and linear regression (0.39). When trained by separate seasons across five years, the predictive capabilities of all models increased, with a maximum R 2 of 0.75 during winter. Bidirectional Long Short-Term Memory was the least accurate model for annual training, but had some of the best predictions for seasonal training. Out of five air quality index categories, the XGBoost model was able to predict the correct category 24 h in advance 90% of the time when trained with full-year data. Separated by season, winter is considerably more predictable (97.3%), followed by post-monsoon (92.8%), monsoon (90.3%), and summer (88.9%). These results show the importance of training machine learning methods with season-specific data sets and comparing a large number of methods for specific applications.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2015

SOPPA and CCSD vibrational corrections to NMR indirect spin-spin coupling constants of small hydrocarbons

We present zero-point vibrational corrections to the indirect nuclear spin-spin coupling constants in ethyne, ethene, cyclopropene and allene. The calculations have been carried out both at the level of the second order polarization propagator approximation (SOPPA) employing a new implementation in the DALTON program, at the density functional theory level with the B3LYP functional employing also the Dalton program and at the level of coupled cluster singles and doubles (CCSD) theory employing the implementation in the CFOUR program. Specialized coupling constant basis sets, aug-cc-pVTZ-J, have been employed in the calculations. We find that on average the SOPPA results for both the equilibrium geometry values and the zero-point vibrational corrections are in better agreement with the CCSD results than the corresponding B3LYP results. Furthermore we observed that the vibrational corrections are in the order of 5 Hz for the one-bond carbon-hydrogen couplings and about 1 Hz or smaller for the other couplings apart from the one-bond carbon-carbon coupling (11 Hz) and the two-bond carbon-hydrogen coupling (4 Hz) in ethyne. However, not for all couplings lead the inclusion of zero-point vibrational corrections to better agreement with experiment.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2016

High E6 Gene Expression Predicts for Distant Metastasis and Poor Survival in Patients With HPV-Positive Oropharyngeal Squamous Cell Carcinoma

Purpose: Patients with human papillomavirus (HPV)–positive oropharyngeal squamous cell carcinoma (OPSCC) have a favorable prognosis. As a result, de-escalation clinical trials are under way. However, approximately 10% of patients will experience distant recurrence even with standard-of-care treatment. Here, we sought to identify novel biomarkers to better risk-stratify HPV-positive patients with OPSCC. Methods and Materials: Gene expression profiling by RNA sequencing (RNA-seq) and quantitative polymerase chain reaction was performed on HPV-positive OPSCC primary tumor specimens from patients with and without distant metastasis (DM). Results: RNA-seq analysis of 39 HPV-positive OPSCC specimens revealed that patients with DM had 2-fold higher E6 gene expression levels than did patients without DM (P=.029). This observation was confirmed in a validation cohort comprising 93 patients with HPV-positive OPSCC. The mean normalized E6 expression level in the 17 recurring primary specimens was 13 ± 2 compared with 8 ± 1 in the remaining 76 nonrecurring primaries (P=.001). Receiver operating characteristic analysis established an E6 expression level of 7.3 as a cutoff for worse recurrence-free survival (RFS). Patients from this cohort with high E6 gene expression (E6-high) (n=51, 55%) had more cancer-related deaths (23% vs 2%, P<.001) and DM (26% vs 5%, P<.001) than did patients with low E6 gene expression (E6-low) (n=42, 45%). Kaplan-Meier survival analysis revealed that E6-high had worse RFS (95% vs 69%, P=.004) and cancer-specific survival (97% vs 79%, P=.007). E6-high maintained statistical significance in multivariate regression models balancing surgery, chemotherapy, nodal stage, and smoking status. Gene set enrichment analysis demonstrated that tumors with high E6 expression were associated with P53, epidermal growth factor receptor, activating transcription factor-2, and transforming growth factor-β signaling pathways. Conclusion: High E6 gene expression level identifies HPV-positive OPSCC patients with 5-fold greater risk of distant disease recurrence and worse cancer-specific survival. Validation in a multi-institutional prospective clinical trial is required to assess the utility of E6 gene expression as a clinically useful prognostic biomarker.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2024

Multiphysics Design Optimization and Additive Manufacturing of Nuclear Components (Final CRADA Report - Executive Summary)

Westinghouse Electric Company (WEC) actively participated in the advancement of the nuclear fuel and reactor design space and requested the help of Oak Ridge National Laboratory (ORNL) in the creation of a new design tool set. This report details the creation of a collection of software tool sets that are linked together to collectively assist WEC design engineers in developing novel ideas outside the normal scope of traditional nuclear fuel and reactor design formulas. Specifically, Siemens HEEDS, a design space exploration and parametric optimization software, monitored and changed parameters in a collection of softwares to meet the team’s objective. The HEEDS parametric optimization method, SHERPA, was developed to control the Siemens NX CAD platform to adjust the native CAD of a hexahedral spacer grid. This new geometry can be used to execute a topological design optimization by the NX Topology software add-in. The resulting geometry is additively manufacturable. This topological optimization occurred twice—once on the spacer grid’s spring, and once on the dimple geometry. These new geometries were imported by Siemens’ STAR-CCM+, a multiphysics structural and fluid dynamic computational solver in which the spring geometry is deflected to match the rod insertion configuration. Along with the dimple geometry, this new deflected spring was used to complete a hydraulic assessment of a single-unit cell comprising one rod, one spring, and two dimples. The HEEDS SHERPA algorithm ranks the design based on the final mass of the unit cell and the hydraulic pressure drop performance. The ORNL team demonstrated the ability to use this software and provided engineering judgement to apply modern aerospace aerodynamic design. The effort has been focused on thinking outside the conventional design space and redesigning a spacer grid to perform beyond the WEC set objectives. Furthermore, the ORNL team also demonstrated that the HEEDS optimization routine can independently develop a design that meets the WEC design goals. Although these designs were at a low technology readiness level, their demonstration confirmed the team’s capability to create novel advanced nuclear concepts.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2018

Effective Field Theory Analysis and Active Neutron Veto Design for the Cryogenic Dark Matter Search

Astrophysical measurements and cosmological predictions suggest the exists of a large amount of matter in the universe that does not interact via electromagnetic forces. This non-luminescent matter, known dark matter, exists in halos that encompass and are within galaxies, including the Milky Way. Therefore, dark matter particles should should be directly detectable by experiments on Earth, such as the Super Cryogenic Dark Matter Search (SuperCDMS). Dark matter is assumed to be low mass (< 100 GeV/c2) and interact via the weak force using either a spin-independent or spin-dependent coupling. However, making incorrect assumptions about dark matter interactions can lead to misleading results. Because interactions with dark matter particles are rare, direct detection experiments must be able to shield for or reject backgrounds to very low levels. Low energy neutron backgrounds that make it to the detectors are especially dangerous, because they cannot be easily distinguished from the expected dark matter signal. Scintillator doped with a high neutron-capture cross-section material can be used to detect neutrons via their resulting gamma rays. Examples of such detectors using liquid scintillator have been successfully used in past high-energy physics (HEP) experiments. However, a liquid scintillator can leak and is not as amenable to modular or complex shapes as a solid scintillator. The light outputs and efficiencies of gadolinium-loaded polystyrene-based scintillators have been explored using a wide variety of gadolinium compounds with varying concentrations. Collection strategies using a wavelength shift- ing (WLS) fiber and silicon photomultipliers (SiPMs) were also evaluated as a possible neutron veto for an upgrade to SuperCDMS SNOLAB. The scattering of dark matter particles off nuclei in direct detection experiments can be described in terms of a multidimensional effective field theory (EFT). A new systematic analysis technique is developed using the EFT approach and Bayesian inference methods to exploit, when possible, the energy-dependent information of the detected events, experimental efficiencies, and backgrounds. Highly dimensional likelihoods are calculated over the mass of the weakly interacting massive particle (WIMP) and multiple EFT coupling coefficients, which can then be used to set limits on these parameters and choose models (EFT operators) that best fit the direct detection data. Expanding the parameter space beyond the standard spin-independent isoscalar cross section and WIMP mass reduces tensions between previously published experiments. Combining these experiments to form a single joint likelihood leads to stronger limits than when each experiment is considered on its own. Simulations using two nonstandard operators (O3 and O8) are used to test the proposed analysis technique in up to five dimensions and demonstrate the importance of using multiple likelihood projections when determining constraints on WIMP mass and EFT coupling coefficients. In particular, this shows that an explicit momentum dependence in dark matter scattering can be identified. CDMSlite Run 2 was a search for Weakly Interacting Massive Particles (WIMPs) with a cryogenic 600 g Germanium detector operated deep underground. It was operated in a mode optimizing sensitivity to WIMPs of relatively low mass, 2 - 20 GeV, while sacrificing background rejection. An EFT analysis of CDMSlite Run 2 data from SuperCDMS Soudan is presented here. A binned likelihood Bayesian analysis was performed on the data, optimizing over the parameters of EFT interactions and the recoil energy spectra due to the dominant Compton scattering and tritium backgrounds. Recoil energy regions within 5σ of known activation peaks were removed from the analysis. The Bayesian evidences of the resulting likelihoods show that CDMSlite Run 2 data is entirely consistent with the background models with no EFT interaction necessary. Upper limits on the WIMP mass and coupling coefficients amplitudes and phases are presented for each EFT operator.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2025

Bayesian model mixing with multireference energy density functional

Reliably predicting nuclear properties across the entire chart of isotopes is important for applications ranging from nuclear astrophysics to superheavy science to nuclear technology. To this day, however, all the theoretical models that can scale at the level of the chart of isotopes remain semiphenomenological. Because they are fitted locally, their predictive power can vary significantly; different versions of the same theory provide different predictions. Bayesian model mixing takes advantage of such imperfect models to build a local mixture of a set of models to make improved predictions. Earlier attempts to use Bayesian model mixing for mass table calculations relied on models treated at single-reference energy density functional level, which fail to capture some of the correlations caused by configuration mixing or the restoration of broken symmetries. In this study we have applied Bayesian model mixing techniques within a multireference energy density functional (MR-EDF) framework. We considered predictions of two-particle separation energies from particle number projection or angular momentum projection with four different energy density functionals—a total of eight different MR-EDF models. We used a hierarchical Bayesian stacking framework with a Dirichlet prior distribution over weights together with an inverse log-ratio transform to enable positive correlations between different models. We found that Bayesian model mixing provides significantly improved predictions compared to the participating models. Published by the American Physical Society 2025

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗