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At least 1,351 records · Page 75

Towards Thermomechanical Processing of Alloy 709: Progress in Defining High-Temperature Deformation-Recrystallization "Space"

The Advanced Reactor Technologies (ART) Program has established a multi-year plan to develop Alloy 709 advanced stainless steel, generate the data package and develop material-specific design parameters in qualifying it as a new structural material for Class A construction in the American Society of Mechanical Engineers (ASME) Boiler and Pressure Vessel Code, Section III, Division 5, High Temperature Reactors (ASME 2025). In collaboration with material vendors, the Advanced Materials Development activities under ART have successfully scaled the Alloy 709 plate form production from a laboratory heat of 500 pounds to commercial heats totaling 133,000 pounds of Alloy 709 plate fabricated from three heats. The goal of the overall Alloy 709 development program is to establish the necessary microstructural and mechanical properties relationship for Alloy 709 to ultimately develop fabrication parameters for other product forms such as bars, pipes, and forgings using the available ART Alloy 709 materials. This study will enable its deployment in the industry as an advanced construction material for high-temperature components. The objective of this Alloy 709 development work in FY 2025 is to experimentally determine the high-temperature deformation-recrystallization response of the Alloy 709 heats and to experimentally generate true stress-true strain data for Alloy 709 using the available commercial heat plate materials. Integral to this work is the previous characterization of the as-rolled materials and the determination of an effective solution-annealing process, which was reported in Y. Wang et al., 2023, and the evaluation of the effect of controlled cooling on the resultant precipitation in these commercial heats. This report summarizes and builds on the results of the previous reports to assess the high-temperature deformation behavior as a function of deformation temperature, strain and strain rate using commercial Alloy 709 heat 58776-3RB fabricated by G. O. Carlson and heat 529900-02 fabricated by Allegheny Technologies Incorporated (ATI) Specialty Rolled Products.

11 - NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

Lung cancer mortality among construction workers: implications for early detection

Objectives This study examined predictors of lung cancer mortality, beyond age and smoking, among construction workers employed at US Department of Energy (DOE) sites to better define eligibility for low-dose CT (LDCT) lung cancer screening. Methods Predictive models were based on 17 069 workers and 352 lung cancer deaths. Risk factors included age, gender, race/ethnicity, cigarette smoking, years of trade or DOE work, body mass index (BMI), chest X-ray results, spirometry results, respiratory symptoms, beryllium sensitisation and personal history of cancer. Competing risk Cox models were used to obtain HRs and to predict 5-year risks. Results Factors beyond age and smoking included in the final predictive model were chest X-ray changes, abnormal lung function, chronic obstructive pulmonary disease (COPD), respiratory symptoms, BMI, personal history of cancer and having worked 5 or more years at a DOE site or in construction. Risk-based LDCT eligibility demonstrated improved sensitivity, specificity and positive predictive value compared with current US Preventive Services Task Force guidelines. The risk of lung cancer death from 5 years of work in the construction industry or at a DOE site was comparable with the risk from a personal cancer history, a family history of cancer or a diagnosis of COPD. LDCT eligibility criteria used for DOE construction workers, which includes factors beyond age and smoking, identified 86% of participants who eventually would die from lung cancer compared with 51% based on age and smoking alone. Conclusions Results support inclusion of risk from occupational exposures and non-malignant respiratory clinical findings in LDCT clinical guidelines.

Public, Environmental & Occupational Health↗

A Census of Star Formation in the Outer Galaxy. II. The GLIMPSE360 Field

We have conducted a study of star formation in the outer Galaxy from 65°< l < 265° in the region observed by the GLIMPSE360 program. This Spitzer warm mission program mapped the plane of the outer Milky Way with IRAC at 3.6 and 4.5 μm. We combine the IRAC, Wide-field Infrared Survey Explorer (WISE), and Two Micron All Sky Survey catalogs and our previous results from another outer Galaxy survey and identify a total of 47,338 young stellar objects (YSOs) across the field spanning >180° in Galactic longitude. Using the DBSCAN method on the combined catalog, we identify 618 clusters or aggregations of YSOs having five or more members. We identify 10,476 class I, 29,604 class II, and 7325 anemic class II/class III YSOs. The ratio of YSOs identified as members of clusters was 25,528/47,338, or 54%. We found that 100 of the clusters identified have previously measured distances in the WISE H ii survey. We used these distances in our spectral energy distribution (SED) fitting of the YSOs in these clusters, of which 96 had YSOs with <3σ fits. We used the derived masses from the SED model fits to estimate the initial mass function (IMF) in the inner and outer Galaxy clusters; dividing the clusters by galactocentric distances, the slopes were Γ = 1.87 ± 0.31 above 3 M {sub ⊙} for R {sub Gal} < 11.5 kpc and Γ = 1.15 ± 0.24 above 3 M {sub ⊙} for R {sub Gal} > 11.5 kpc. The slope of the combined IMF was found to be Γ = 1.92 ± 0.42 above 3 M {sub ⊙}. These values are consistent with each other within the uncertainties and with literature values in the inner Galaxy high-mass star formation regions. The slopes are likely also consistent with a universal Salpeter IMF.

79 ASTRONOMY AND ASTROPHYSICS↗

Using Flory–Huggins-informed human-in-the-loop Bayesian optimization to map the phase diagram of polymer blends

Mapping the phase diagram of polymer blends is an essential step in controlling the structure–property relationship of polymer-based materials. However, traditional grid-based approaches are inefficient and rely on subjective judgements for terminating the experimental campaign. Artificial intelligence-guided experimentation offers a compelling alternative, especially when data-driven decision-making is interfaced with established polymer thermodynamics to improve efficiency and interpretability. Here, we introduce a physics-informed Bayesian optimization approach to guide the mapping of the phase diagram of a model blend containing poly(methyl methacrylate) and poly(styrene-ran-acrylonitrile). Physical information is derived from a Flory–Huggins representation of the spinodal curve, which is integrated into the Bayesian optimization process as a structured prior mean that acts as a soft constraint. Implemented as a human-in-the-loop workflow, the approach leverages optical imaging of film cloudiness with iterative Gaussian process surrogate modeling and a parameter selection decision policy to identify the composition-temperature conditions for sequential iterations. Convergence of kernel and Flory–Huggins-based hyperparameters provided a stopping criterion, ensuring an objective and interpretable termination of the experimental campaign. The framework recovered the known lower critical solution temperature (∼160 °C), while increasing material efficiency through targeted sampling. This work establishes a proof-of-concept for the application of Bayesian optimization workflows to study polymer blend miscibility.

36 MATERIALS SCIENCE↗

Effect of Confinement of SIA Cluster Diffusion by Impurities on Radiation Defect Accumulation due to 14MeV neutrons in Tungsten

Impurities acting as traps, reduce the self-interstitial atom (SIA) cluster mobility, thereby increasing recombination and in turn enhancing radiation-resistance of polycrystalline materials. However, at elevated temperatures, depending on SIA-trap (impurity) binding energy, SIA clusters may trap and detrap (detach) numerous times. In tungsten, SIA clusters of all sizes glide one-dimensionally (1D) along their Burgers vector direction. Hence, detrapped SIAs would retrace their path, confining their 1D-glide between traps. However, small SIA clusters can change the direction of their 1D-glide by overcoming a rotation barrier. In addition, impurities or solutes are known to reduce this barrier. A lower rotation barrier can result in detrapped SIA clusters that diffuse in a random 1D direction, effectively leading to 3D (net-3D) diffusion. Here we investigate the effect of two cases of SIA diffusion, namely confined-1D diffusion and net-3D diffusion, on the damage accumulation in polycrystalline tungsten under 14 MeV neutron irradiation using the object kinetic Monte Carlo method. Simulations were performed using cascade debris obtained from molecular dynamics simulations with primary knock-on atom (PKA) energies corresponding to 14 MeV neutrons. In the simulations with net-3D diffusion, SIA clusters of all sizes diffuse in a random 1D direction after the clusters detrap. While for the confined 1D diffusion case, SIA clusters larger than size 5 retain their original direction of 1D-glide. In both types of simulations, trapped vacancies are assumed to be permanently immobilized. Unexpectedly, the damage accumulation was lower with confined 1D diffusion than that with net-3D. We present a systematic comparison of the influence of the two cases of SIA diffusion on the radiation damage accumulation as a function of dose rate, detrapping barrier, and impurity concentration at 1025 K.

Accumulation and recovery of lattice damage, tungs↗

A geospatial risk analysis graphical user interface for identifying hazardous chemical emission sources

Background: Performing back trajectory and forward trajectory using the Hybrid Single-Particle Lagrangian Integrated Trajectory Model (HYSPLIT) is a reliable approach for assessing particle transport after release among mid-field atmospheric models. HYSPLIT has an externally facing online interface that allows non-expert users to run the model trajectories without requiring extensive training or programming. However, the existing HYSPLIT interface is limited if simulations have a large amount of meteorological data and timesteps that are not coincident. The objective of this study is to design and develop a more robust tool to rapidly evaluate hazard transport conditions and to perform risk analysis, while still maintaining an intuitive and user-friendly interface. Methods: HYSPLIT calculates forward and backward trajectories of particles based on wind speed, wind direction, and the corresponding location, timestamp, and Pasquill stability classes of the regions of the atmosphere in terms of the wind speed, the amount of solar radiation, and the fractional cloud cover. The computed particle transport trajectories, combined with the online Proton Transfer Reaction-Mass Spectrometry (PTR-MS) data (https://figshare.com/articles/dataset/ARL_Data_from_PROS_station_at_Hanford_site/19993964), can be used to identify and quantify the sources and affected area of the hazardous chemicals’ emission using the potential source distribution function (PSDF). PSDF is an improved statistical function based on the well-known potential source contribution function (PSCF) in establishing the air pollutant source and receptor relationship. Performing this analysis requires a range of meteorological and pollutant concentration measurements to be statistically meaningful. The existing HYSPLIT graphical user interface (GUI) does not easily permit computations of trajectories of a dataset of meteorological data in high temporal frequency. To improve the performance of HYSPLIT computations from a large dataset and enhance risk analysis of the accidental release of material at risk, a geospatial risk analysis tool (GRAT-GUI) is created to allow large data sets to be processed instantaneously and to provide ease of visualization. Results: The GRAT-GUI is a native desktop-based application and can be run in any Windows 10 system without any internet access requirements, thus providing a secure way to process large meteorological datasets even on a standalone computer. GRAT-GUI has features to import, integrate, and convert meteorological data with various formats for hazardous chemical emission source identification and risk analysis as a self-explanatory user interface. The tool is available at https://figshare.com/articles/software/GRAT/19426742.

97 MATHEMATICS AND COMPUTING↗

Collective optical Thomson scattering in pulsed-power driven high energy density physics experiments (invited)

Optical collective Thomson scattering is used to diagnose magnetized high energy density physics experiments at the Magpie pulsed-power generator at Imperial College London. The system uses an amplified pulse from the 2nd harmonic of a Nd:YAG laser (3 J, 8 ns, 532 nm) to probe a wide diversity of hightemperature plasma objects; with densities in the range of 10 17 -10 19 cm -3 and temperatures between 10 eV and a few keV. The scattered light is collected from 100 μm-scale volumes within the plasmas, which are imaged onto optical fiber arrays. Multiple collection systems observe these volumes from different directions, providing simultaneous probing with different scattering K-vectors (and different associated α-parameters, typically in the range 0.5 – 3) allowing independent measurements of separate velocity components of the bulk plasma flow. The fiber arrays are coupled to an imaging spectrometer with a gated ICCD. The spectrometer is configured to view the ion-acoustic waves (IAWs) of the collective Thomson scattered spectrum. Fits to the spectra with the theoretical spectral density function S(K,ω) yield measurements of the local plasma temperatures and velocities. Fitting is constrained by independent measurements of the electron density from laser interferometry, and the corresponding spectra for different scattering vectors. Furthermore, this TS diagnostic has been successfully implemented on a wide range of experiments, revealing temperature and flow velocity transitions across magnetized shocks, inside rotating plasma jets and imploding wire arrays, as well as providing direct measurements of drift velocities inside a magnetic reconnection current sheet.

47 OTHER INSTRUMENTATION↗

Advancing 3D surface imaging: single-axis structured light illumination plenoptic camera with machine learning integration

Structured light illumination (SLI) is a configurable 3D surface imaging modality that can function largely independently of surface texture. At the same time, machine learning (ML) approaches are providing new ways to capture relevant information from SLI patterns, avoiding the need to develop advanced computer vision algorithms. By projecting an optical pattern onto a surface and measuring the apparent distortion of that pattern, one can determine surface topography from a single image. Common realizations of SLI 3D imaging use off-axis SLI to allow for parallax-based determination of depth; however, in constrained geometries, the ability to make single-axis measurements can be of major benefit. While plenoptic imaging (PI) cameras have long been developed for the purpose of single-axis 3D imaging, they are generally reliant on the surface texture of the measured object, thus making them unreliable in certain experimental conditions. Therefore, we present a single-axis 3D SLI plenoptic camera, which combines the single-axis benefits of PI technology while using coaxial SLI to maintain indifference to surface conditions. We also present a study of the camera capabilities paired with the development of several algorithms, including traditional feature tracking methods as well as ML methods, which are found to enhance resolution and range. We report depth sensitivity down to 0.2% $\frac{dz}{z_0}$. The single-axis SLI 3D plenoptic camera demonstrates potential applicability for in-situ topographical measurements under a wide range of conditions including, but not limited to, objects without trackable surface texture, high temperatures, and constrained geometry environments.

Imaging systems↗

Mesh objective stochastic simulations of quasibrittle fracture

Continuum finite element (FE) modeling of damage and failure of quasibrittle structures suffers from the spurious mesh sensitivity due to strain localization. Here this issue has been addressed for deterministic analysis through the development of localization limiters. Here this study proposes a mechanism-based model to mitigate the mesh sensitivity in stochastic FE simulations of quasibrittle fracture. The interest is placed on the analysis of large-size structures, where the mesh size is conveniently chosen to be larger than the width of the fracture process zone as well as the correlation length of the random fields of constitutive properties. The present model is formulated within the framework of continuum damage mechanics. Two localization parameters are introduced to describe the evolution of the damage pattern of each finite element. These parameters are used to guide the energy regularization of the constitutive law, as well as to formulate the mesh-dependent probability distributions of constitutive properties. Depending on the prevailing damage pattern, different energy regularization schemes and mesh dependence of the probability distribution functions are used in the constitutive law. The model is applied to simulate the stochastic failure behavior of quasibrittle structures of different geometries featuring different failure processes including damage initiation, localization, and propagation. It is shown that using fixed probability distribution functions of constitutive properties could lead to strong mesh dependence of the prediction of the mean and variance of the structural load capacity. The probability distribution functions of constitutive properties must be linked to the damage pattern, which may evolve during the failure process. Such a mechanism-based modeling of the probability distributions of constitutive properties is essential for mitigating the spurious mesh sensitivity in stochastic FE analysis of quasibrittle fracture.

42 ENGINEERING↗

Challenges in integrating dissolved organic matter chemodiversity into kinetic models of soil respiration

The chemodiversity of dissolved organic matter (DOM) in soil has been proposed to influence the microbial metabolism and fate of belowground organic carbon (C). However, integrating DOM chemistry into soil C cycle models to improve predictions of C stocks and fluxes—beyond simply considering DOM pool size—remains a challenge. While recent research suggests that incorporating DOM chemodiversity into models can improve predictions of microbial respiration, there is still a lack of mechanistic understanding describing how DOM chemodiversity affects microbial metabolism and soil respiration. Here, we evaluated whether DOM chemodiversity was a determinant of soil respiration using paired measurements of high-resolution DOM chemistry, obtained from Fourier transform ion cyclotron resonance mass spectrometry (FTICR-MS), and potential soil respiration rates from across the United States (U.S.), all data provided by the Molecular Observation Network. Our objectives were to (1) assess statistical relationships between DOM chemodiversity and microbial respiration, and (2) evaluate the ability of kinetic models to leverage DOM chemistry to explain empirical relationships found in statistical models. Statistical regressions revealed that DOM chemodiversity (alpha diversity) was nonlinearly related to potential soil respiration rates, both independently and through its interactions with DOM and total C concentrations. In soils with relatively high DOM but low total C concentrations, potential soil respiration rates were negatively correlated with DOM alpha diversity, whereas in soils with relatively low DOM and high total C concentrations showed the opposite trend. However, when metabolic transition theory kinetic models were modified to include chemodiversity, their performance was comparable to traditional Monod kinetics approaches, which simulate respiration rates as a function of DOM concentration. The inability to account for nonlinearities in DOM chemodiversity–respiration relationships highlight an opportunity to advance substrate uptake kinetics by establishing causal links between DOM chemodiversity, microbial metabolism trade-offs, and potential interactions under varied environmental conditions.

Bioenergetic model↗

Annotated genome sequence of a fast-growing diploid clone of red alder ( Alnus rubra Bong.)

Abstract Red alder (Alnus rubra Bong.) is an ecologically significant and important fast-growing commercial tree species native to western coastal and riparian regions of North America, having highly desirable wood, pigment, and medicinal properties. We have sequenced the genome of a rapidly growing clone. The assembly is nearly complete, containing the full complement of expected genes. This supports our objectives of identifying and studying genes and pathways involved in nitrogen-fixing symbiosis and those related to secondary metabolites that underlie red alder's many interesting defense, pigmentation, and wood quality traits. We established that this clone is most likely diploid and identified a set of SNPs that will have utility in future breeding and selection endeavors, as well as in ongoing population studies. We have added a well-characterized genome to others from the order Fagales. In particular, it improves significantly upon the only other published alder genome sequence, that of Alnus glutinosa. Our work initiated a detailed comparative analysis of members of the order Fagales and established some similarities with previous reports in this clade, suggesting a biased retention of certain gene functions in the vestiges of an ancient genome duplication when compared with more recent tandem duplications.

59 BASIC BIOLOGICAL SCIENCES↗

Designing a Framework for Solving Multiobjective Simulation Optimization Problems

Multiobjective simulation optimization (MOSO) problems are optimization problems with multiple conflicting objectives, where evaluation of at least one of the objectives depends on a black-box numerical code or real-world experiment, which we refer to as a simulation. Whereas an extensive body of research is dedicated to developing new algorithms and methods for solving these and related problems, it is challenging and time-consuming to integrate these techniques into real-world production-ready solvers. This is partly because of the diversity and complexity of modern state-of-the-art MOSO algorithms and methods and partly because of the complexity and specificity of many real-world problems and their corresponding computing environments. The complexity of this problem is only compounded when introducing potentially complex and/or domain-specific surrogate-modeling techniques, problem formulations, design spaces, and data acquisition functions. Here, this paper carefully surveys the current state of the art in MOSO algorithms, techniques, and solvers, as well as problem types and computational environments where MOSO is commonly applied. We then present several key challenges in the design of a parallel multiobjective simulation optimization framework (ParMOO) and how they have been addressed. Finally, we provide two case studies demonstrating how customized ParMOO solvers can be quickly built and deployed to solve real-world MOSO problems.

engineering design optimization↗

EMI Mitigation of a &Cacute;uk-Based Power-Electronic System Using Switching-Sequence-Based Control

Switching-sequence-based control (SBC) laws when designed based on topological switching behavior can have positive effects on slow- and fast-scale dynamics of a power-electronic system (PES). The slow-scale control can encompass fast PES state regulation and tracking, based on predefined objective, while fastscale control can address differential-mode (DM) and commonmode (CM) spectral-peak energy associated with PES switching operation. Such control laws may offer enhanced programmability to conventional PES design where bulky electromagnetic interference (EMI) filters have been traditionally used to reduce EMI of switching power converters to meet EMI regulatory standards. An EMI filter is always a less programmable solution since it is usually designed for the worst-case EMI mitigation and usually overkill for a PES operating under reduced load condition. The control scheme outlined in this article offers EMI mitigation across wide operating regions without compromising PES regulation. Moreover, it does so by use of switching sequences that guarantee the reachability of the PES dynamics using an advanced Lyapunov-function-based approach. SBC is a powerful tool to generate control actions for a PES based on multivariate PES state constraints. Hence, contemporary EMI regulatory standards are used as constraints in the SBC formulation to operate the PES under wide operating regime while autonomously mitigating the EMI levels. The work may be of paramount importance for operating the ultra-fast-transition recent wide-bandgap semiconductor devices like GaN–FET and SiC MOSFET under higher power with increasing switching frequencies, which is usually desirable for increased power density and reduced switching losses. Here, a hardware Cuk–PES operated ´ with GaN–FETs is fabricated and is used for case illustration. It is shown by experimental results how SBC mitigate DM and CM EMI noise of the PES while maintaining regulation even for the higher order nonminimum phase PES, while reducing sensor requirements using state observer derived from the switching model of the PES.

42 ENGINEERING↗

Feasibility and preliminary efficacy for morning bright light therapy to improve sleep and plasma biomarkers in US Veterans with TBI. A prospective, open-label, single-arm trial

Mild traumatic brain injury (TBI) is associated with persistent sleep-wake dysfunction, including insomnia and circadian rhythm disruption, which can exacerbate functional outcomes including mood, pain, and quality of life. Present therapies to treat sleep-wake disturbances in those with TBI (e.g., cognitive behavioral therapy for insomnia) are limited by marginal efficacy, poor patient acceptability, and/or high patient/provider burden. Thus, this study aimed to assess the feasibility and preliminary efficacy of morning bright light therapy, to improve sleep in Veterans with TBI (NCT03578003). Thirty-three Veterans with history of TBI were prospectively enrolled in a single-arm, open-label intervention using a lightbox (~10,000 lux at the eye) for 60-minutes every morning for 4-weeks. Pre- and post-intervention outcomes included questionnaires related to sleep, mood, TBI, post-traumatic stress disorder (PTSD), and pain; wrist actigraphy as a proxy for objective sleep; and blood-based biomarkers related to TBI/sleep. The protocol was rated favorably by ~75% of participants, with adherence to the lightbox and actigraphy being ~87% and 97%, respectively. Post-intervention improvements were observed in self-reported symptoms related to insomnia, mood, and pain; actigraphy-derived measures of sleep; and blood-based biomarkers related to peripheral inflammatory balance. The severity of comorbid PTSD was a significant positive predictor of response to treatment. Morning bright light therapy is a feasible and acceptable intervention that shows preliminary efficacy to treat disrupted sleep in Veterans with TBI. A full-scale randomized, placebo-controlled study with longitudinal follow-up is warranted to assess the efficacy of morning bright light therapy to improve sleep, biomarkers, and other TBI related symptoms.

60 APPLIED LIFE SCIENCES↗

A Heterogeneous System for Eagle Detection, Deterrent, and Wildlife Collision Detection for Wind Turbines (Final Technical Report)

This report summarizes the design, implementation, and test of an integrated system for automated detection and deterrence of eagles, with included wind turbine blade strike detection and imaging functionality. A machine learning approach was used in conjunction with a 360° camera system for automated detection and classification of golden eagles. This was developed using footage obtained from trained golden eagles and other raptors, in collaboration with wildlife biologists and professional bird handlers. Oregon State University developed a visual deterrent system, which uses inflatable anthropomorphic sculptures with random, kinetic motion to deter eagles, and conducted limited field testing on live eagles; the deterrent can be triggered by the visual detection of eagles using the vision system. Finally, a multi-sensor module was developed that is mounted at the turbine blade root. This module measures vibration and other motions to detect blade strikes, and an integrated on-blade camera captures an image of any impacting objects. Long-term, this blade strike detection system is intended to support an automatic monitoring and certification system for the eagle detection and deterent system. Independent field testing of each system component is described. Testing of the integrated system on an operational wind turbine was conducted across three separate field tests. This includes multi-day fields tests on a General Electric 1.5MW wind turbine at the National Renewable Energy Laboratory (NREL) National Wind Technology Center (NWTC) in Boulder, CO in October 2018 and July 2019; installation procedures, test procedures, and a summary of collected data are presented. A third multi-day on-turbine field test is also presented, which was performed using a General Electric 1.5MW wind turbine at the North American Wind Research and Training Center (NAWRTC) at Mesalands Community College, Tucumcari, NM in April 2019. Across these field tests, the vision system was demonstrated using unmanned aerial vehicles (UAV), and the eagle classification algorithm was not tested; the visual deterrent system was demonstrated, including automatic, remote deployment following surrogate visual detections; and, multi-sensor on-blade data was recorded across multiple wind turbine operational conditions and through more than 100 surrogate blade strikes using soft projectiles, including the successful demonstration of automatic image capture of striking objects. This data set was also used for offline development and validation of enhanced collision detection algorithms. As summarized in this report, the development and field validation of an integrated detection, deterrent, and blade collision detection system represents a critical proof of concept for future technology development of related detection and deterrent technologies, where both deterrent as well as collision detection recording devices are needed for future siting, monitoring, and operation of wind turbine installations, both onshore and offshore.

17 WIND ENERGY↗

Building 438 Drainage Channel Soil Sampling and Analysis Plan (February 2023)

This Soil Sampling and Analysis Plan (SAP) was prepared by the Environmental Function Area (EFA)/Technical Services Department (TSD) of the Environment, Safety & Health (ES&H) Directorate for the Project Management Office (PMO) for the proposed Building 438 Drainage Channel Project (project) located near Building 438 (B438) in the southern portion, Building 433 (B433) in the middle portion, and Building 543 (B543) in the northern portion (project area as shown on Figure 1). The purpose of the SAP was to describe the procedures for collection and analysis of environmental samples and evaluation of analytical data (chemical and radiological) to determine management options of excavated soil during project construction in accordance with Lawrence Livermore National Laboratory’s (LLNL) Soils Screening and Management Plan (SSMP) (LLNL 2022), which is consistent with U.S. Environmental Protection Agency (EPA) guidance for developing Data Quality Objectives for environmental data (EPA 2006). The SAP was developed in accordance with the SSMP and based on preliminary design information provided to EFA by PMO.

54 ENVIRONMENTAL SCIENCES↗

Uniting Theory and Experiment to Deliver Flexible MOFS for Superior Methane (NG) Storage

The objective of the project was to use previous insights developed through synthesis and quantitative modeling of rigid metal–organic frameworks (MOFs) in an established synergistic theoretical/experimental team to create, modify, and evaluate flexible MOFs (FlexMOFs) for natural gas (NG) storage and release at practically useful pressures and transform the NG storage economy. The goal was reduced pressure absorbed natural gas (ANG) FlexMOF storage at operating pressures less than 100 bar with physisorption exploiting the favorable thermodynamics and kinetics of flexible porous material opening in response to adsorption. Hydrogen behavior in this context was also considered. The specific aim of the project was to design and develop a standard computational modeling methodology for, first, detailed atomistic retrodiction of FlexMOF gating behavior and ultimately prediction of the effects of functionalization and/or substitution on structural transitioning. The project was also geared towards the establishment of the interaction of methane with the framework and binding sites using modeling and the FlexMOF will be both internally and external validated as a SMART metric. This has value to the scientific community both from the obvious standpoint of providing a better understanding of the promising test case systems (CdIF-13 and the MIL-53(Al) series of MOFs), but also in providing an avenue of obtaining insight into these FlexMOF systems in general, which is of particular interest given the tendency of structure-function correlation to lag behind synthesis methodology (making the latter a hit or miss proposition for applications). This gap remains significant for FlexMOF systems whose gate opening behaviors complicate computational examination. The computational methodologies are relatively inexpensive in terms of both money and computational resources enhancing the general viability of these methodologies. The ultimate gain to the public will be in the application of these techniques to design systems for natural gas storage and use to cut down on green-house emissions.

03 NATURAL GAS↗