Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “component degradation”

Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 523 records · Page 29
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Machine Learning–Guided Boolean Matrix Inference for Real-Time O-RAN Conflict Detection

Open Radio Access Networks (O-RAN) are emerging, software-driven cellular architectures that promote flexibility by enabling components from different vendors to interoperate. Multiple control applications called xApps can independently adjust network parameters in near real time, often without awareness of each other's actions. This creates a system highly prone to unintended conflicts and performance degradation due to the inherent complexity of such openness. To model such systems and ultimately prevent or mitigate xApp conflicts, it is essential to understand the dynamic relationships between xApps (A), the control parameters they adjust (P), and the resulting KPI responses (K). While the mappings from A to P and from K to A can often be derived from xApp specifications, the relationship from P to K is typically hidden within the system’s dynamics and must be inferred from observed data. We propose a novel data-driven Boolean inference framework that uncovers the hidden P?K dependencies using machine learning and interpretable rule induction. Continuous parameters and KPIs are first binarized using decision tree classifiers, and a binary influence matrix L is then inferred by solving Boolean matrix equations over time. This compact representation improves interpretability and enables real-time tracking of dynamically evolving parameter-KPI dependencies. We demonstrate the effectiveness of our method in a realistic mobile handover scenario, where it accurately recovers the underlying logic and enables proactive conflict detection.

42 - ENGINEERING↗
DOE OSTI2021

Outlier classification using autoencoders: application for fluctuation driven flows in fusion plasmas

Understanding the statistics of fluctuation driven flows in the boundary layer of magnetically confined plasmas is desired to accurately model the lifetime of the vacuum vessel components. Mirror Langmuir probes (MLPs) are a novel diagnostic that uniquely allow us to sample the plasma parameters on a time scale shorter than the characteristic time scale of their fluctuations. Sudden large-amplitude fluctuations in the plasma degrade the precision and accuracy of the plasma parameters reported by MLPs for cases in which the probe bias range is of insufficient amplitude. While some data samples can readily be classified as valid and invalid, we find that such a classification may be ambiguous for up to 40% of data sampled for the plasma parameters and bias voltages considered in this study. In this contribution, we employ an autoencoder (AE) to learn a low-dimensional representation of valid data samples. By definition, the coordinates in this space are the features that mostly characterize valid data. Ambiguous data samples are classified in this space using standard classifiers for vectorial data. In this way, we avoid defining complicated threshold rules to identify outliers, which require strong assumptions and introduce biases in the analysis. By removing the outliers that are identified in the latent low-dimensional space of the AE, we find that the average conductive and convective radial heat fluxes are between approximately 5% and 15% lower as when removing outliers identified by threshold values. For contributions to the radial heat flux due to triple correlations, the difference is up to 40%.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2021

Information Leakage Analysis using a Co-design-Based Fault Injection Technique on a RISC-V Microprocessor

The RISC-V instruction set architecture open licensing policy has spawned a hive of development activity, making a range of implementations publicly available. The environments in which RISC-V operates have expanded correspondingly, driving the need for a generalized approach to evaluating the reliability of RISC-V implementations under adverse operating conditions or after normal wear-out periods. Fault injection (FI) refers to the process of changing the state of registers or wires, either permanently or momentarily, and then observing execution behavior. The analysis provides insight into the development of countermeasures that protect against the leakage or corruption of sensitive information which might occur because of unexpected execution behavior. In this paper, we develop a hardware-software co-design architecture that enables fast, configurable fault emulation and utilize it for information leakage and data corruption analysis. Modern System-on-chip FPGAs enable building an evaluation platform where control elements run on a processor(s) (PS) simultaneously with the target design running in the programmable logic (PL). Software components of the FI system introduce faults and report execution behavior. A pair of RISC-V FI-instrumented implementations are created and configured to execute the Advanced Encryption Standard and Twister algorithms. Key and plaintext information leakage and degraded pseudo-random sequences are both observed in the output for a subset of the emulated faults.

42 ENGINEERING↗
DOE OSTI2022

Analytics-at-scale of Sensor Data for Digital Monitoring in Nuclear Plants (3 rd Annual Report)

Nuclear power plants collect and store large volumes of heterogeneous data from various components and systems. With recent advances in machine learning (ML) techniques, these data can be leveraged to develop diagnostic and short-term forecasting models to better predict future equipment condition. Maintenance operations can then be planned in advance whenever degraded performance is predicted, thus resulting in fewer unplanned outages and the optimization of maintenance activities. This enables lower maintenance costs and improves the overall economics of nuclear power. This report primarily focuses on developing a short-term forecasting process that leverages a feature selection process to distill large volumes of heterogeneous data and predict specific equipment parameters. A variety of feature selection methods, including Shapley Additive Explanations (SHAP) and variance inflation factor (VIF), were used to select the optimal features as inputs for three ML methods: long short-term memory (LSTM) networks, support vector regression (SVR), and random forest (RF). Each combination of model and input features was used to predict a pump bearing temperature both 1 and 24 hours in advance, based on actual plant system data. The optimal inputs for the LSTM and SVR were selected using the SHAP values, while the optimal input for the RF consisted solely of the response variable itself. Each model produced similar 1-hour-ahead predictions, with root mean square errors (RMSEs) of roughly 0.006. For the 24-hour-ahead predictions, differences could be seen between LSTM, SVR, and RF, as reflected by model performances of 0.036 ± 0.014, 0.0026 ± 0, and 0.063 ± 0.004 RMSE, respectively. As big data and continuous online monitoring become more widely available, the proposed feature selection process can be used for many applications beyond the prediction of process parameters within nuclear infrastructure. This report summarizes the Fiscal Year 2021 research progress encompassing the (1) data cleaning and feature selection necessary for ML applications; (2) development of short-term forecasting models to predict future plant process parameters for both single and multiple time steps ahead; and (3) validation of the feature selection methods and short-term forecasting models given new data from different systems.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2026

A Comprehensive Comparison of Building Height Products at a Global Level

The built environment continues to expand due to increasing human populations with some projections indicating additional growth of 2.5 billion persons by 2050. Buildings are a key component of the built environment and information on building height and urban morphology in general can help to better understand and address a host of development and urban planning issues including energy consumption, urbanization, population dynamics, urban heat islands, environmental degradation, and others. To date, several approaches have been developed to address the need for mapping building height at scale. These approaches can be grouped into two main categories: 1) directly measure height from remote sensing products or 2) indirectly estimate height from footprint morphology features at the building-by-building level. Due to a number of these global height datasets now being available for consumption and use across domains, there is a critical need to understand the differences displayed between these products and how the use of these may influence the outcome when applied. In this analysis, we compare and evaluate 5 global building height datasets: Global Human Settlement, World Settlement Footprint 3D, Global Building Morphology dataset, three-dimensional Global Building Footprints, and an approach developed at Oak Ridge National Laboratory (ORNL). Our analysis shows that, across 5 cities at a global scale with high-quality building height reference data, the approach developed by ORNL of inferring height at the building-by-building level is more accurate and consistent. This was evidenced by the ORNL product improving upon the baseline in all 5 cities, the only product to do so. These findings will aid researchers in choosing a global building height product that best meets their needs and provides global availability, consistency, and accuracy.

42 ENGINEERING↗
DOE OSTI2022

Development of Short-Term Forecasting Models Using Plant Asset Data and Feature Selection

Nuclear power plants collect and store large volumes of heterogeneous data from various components and systems. With recent advances in machine learning (ML) techniques, these data can be leveraged to develop diagnostic and short-term forecasting models to better predict future equipment condition. Maintenance operations can then be planned in advance whenever degraded performance is predicted, thus resulting in fewer unplanned outages and the optimization of maintenance activities. This enables lower maintenance costs and improves the overall economics of nuclear power. This paper focuses on developing a short-term forecasting process that leverages a feature selection process to distill large volumes of heterogeneous data and predict specific equipment parameters. A variety of feature selection methods, including Shapley Additive Explanations (SHAP) and variance inflation factor (VIF), were used to select the optimal features as inputs for three ML methods: long short-term memory (LSTM) networks, support vector regression (SVR), and random forest (RF). Each combination of model and input features was used to predict a pump bearing temperature both 1 and 24 hours in advance, based on actual plant system data. The optimal inputs for the LSTM and SVR were selected using the SHAP values, while the optimal input for the RF consisted solely of the response variable itself. Each model produced similar 1-hour-ahead predictions, with root mean square errors (RMSEs) of roughly 0.006. For the 24-hour-ahead predictions, differences could be seen between LSTM, SVR, and RF, as reflected by model performances of 0.036 +- 0.014, 0.0026 +- 0, and 0.063 +- 0.004 RMSE, respectively. As big data and continuous online monitoring become more widely available, the proposed feature selection process can be used for many applications beyond the prediction of process parameters within nuclear infrastructure.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2024

Management of Risks Associated with Application of Novel Materials in Novel Operating Environments in Novel Reactor Designs

There is currently no widely agreed, detailed general method for licensing a novel plant incorporating novel materials (or materials being deployed in novel environments); in many such situations, there are no directly applicable engineering code cases for decision-makers (including regulators) to rely on. This paper discusses a framework for solving this problem that is based on the Reliability and Integrity Management (RIM) approach delineated in ASME BPVC Section XI Division 2. NRC Regulatory Guide 1.246, Rev. 0, endorses, with conditions, the subject portion of the ASME Code. The proposed framework is meant to support development of a licensing case by addressing certain technical challenges. The framework discussed here is compatible with the Licensing Modernization Project, but applying it in a specific case will call for advances in the state of practice, if not the state of the art. The RIM approach calls for applicants to (a) allocate reliability targets to plant structures, systems, and components (SSCs), (b) show that they are able to relate the currently observed physical condition of each SSC in the program to its failure probability well enough to determine whether the target reliability allocations are being satisfied, allowing for uncertainty related to the novelty of the materials/designs/operating environments, and (c) be able to demonstrate that the proposed program of surveillances will reliably detect unacceptable degradation of an SSC before SSC failure occurs. These challenges are discussed in the paper, and a potentially applicable modeling approach based on cumulative damage rather than failure rates is briefly illustrated.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2020

High-efficiency, air-stable manganese–iron oxide nanoparticle-pigmented solar selective absorber coatings toward concentrating solar power systems operating at 750 °C

Solar selective absorber coating with long-term thermal stability at high temperatures ≥750 °C in air is an important component to reduce the levelized cost of energy (LCOE) of concentrating solar power (CSP) systems toward 50% power efficiency and dispatchable solar electricity. Conventionally, solar spectral selectivity requires multilayer-interference coatings implemented by stringently controlled vacuum deposition, and these coatings degrade significantly at >700 °C in air. Herein, we established a quantitative design approach and demonstrated a proof-of-concept air-stable, manganese–iron oxide nanoparticle (NP)-pigmented solar selective coatings with a high solar absorptance of ~93%, a relatively low thermal emittance of ~52%, and an optical-to-thermal energy conversion efficiency >89% under 1,000× solar concentration at 750 °C toward Generation 3 CSP systems. These coatings demonstrate spectral selectivity using cost-effective spray coating approach, a notable improvement over conventional vacuum-deposited, multilayer solar selective coatings for low-cost, high-efficiency solar thermal receivers. In contrast to the thermal degradation of spectrally non-selective benchmark Pyromark 2500 coatings at 750 °C in air, the solar absorptance of the MnFe 2 O 4 -pigmented coatings on stainless steel 310 (SS 310) substrates is increased to ~92.9% and the optical-to-thermal energy conversion efficiency is improved to 89.7% after serving at 750 °C in air for 700 h. X-ray diffraction results reveal that this improvement is due to the transformation of MnFe 2 O 4 NPs into more thermodynamically stable, non-stoichiometric manganese-rich manganese ferrite and iron-rich manganese–iron oxide phases after 500 h aging at 750 °C. For >1,000 h-endurance testing at 750 °C in air and the subsequent 19 day-night thermal cycling between 750 °C (12 h/cycle) and 25 °C (12 h/cycle) on SS 310 substrates, the thermal degradation is mainly due to the CrO x microflake formation from SS 310 substrates rather than the coatings, which can be suppressed by preoxidizing the surface of SS 310. With lower emittance matrix material and further optimization of pigment NP stoichiometry, concentration, and coating thickness, it is promising to achieve an optimized thermal efficiency ≥92.5% with long-term thermal stability at 750 °C for Generation 3 CSP systems.

14 SOLAR ENERGY↗
DOE OSTI2025

A hybrid surrogate modeling framework for the Digital Twin of a Fluoride-salt-cooled High-temperature Reactor (FHR)

While nuclear energy is a non-greenhouse-gas emitting energy source, expensive operational costs due to the high-level of safety requirements decreases their competitiveness in the sustainable energy market. Advanced reactor concepts paired with Digital Twins aim to increase the commercialization gains of nuclear energy by reducing operational costs, increasing reactor reliability and enhancing power generation. To support Digital Twin tasks such as real-time autonomous control, proactive maintenance monitoring or optimizing power demand operations, a fast and accurate virtual representation of the Nuclear Power Plant (NPP) is required. The computational cost of high-fidelity, physics-based models are unsuitable for real-time analysis or scalability. Here, in this work, a hybrid surrogate modeling framework is developed fora Fluoride-salt-cooled High-temperature Reactor (FHR) that leverages physics-inspired models for key reactor components and uses data-driven methods for rapid system state space prediction. The Xenon reactivity feedback model is integrated to inform the surrogate model about the reactor core and the homologous pump theory model is the basis for representing pump degradation. Using a detailed, two dimensional thermal hydraulics model to generate data on the FHR, we train a network of Vectorized Autoregressive Moving-Average with eXogenous input (VARMAX) models to predict the remaining state values. The result is a surrogate model that provides a detailed reactor state representation of 41 system states and a pump degradation analysis. The framework is applied to Load Follows profiles, yielding high accuracy and a speedup that is more than 4000x faster compared to the higher- fidelity thermal hydraulics model, enabling real-time operational intelligence and applications in long horizon predictions. While the surrogate model framework is demonstrated for the particular case of FHR, the hybrid physical/data-driven modeling approach including the network of surrogates and the underlying modularity has the potential to be applied to other physical asset systems.

Digital Twins↗
DOE OSTI2025

Delineating the Factors Impacting the Electrochemical Behavior of Single-Crystal High-Nickel Layered Oxide Cathodes

High-nickel (Ni) (≥80%) single-crystal LiNi 1-x-y Mn x Co y O 2 (NMCs) have garnered recent interest as cathodes in lithium (Li)-ion batteries (LIBs). However, capacity fade at high voltages, particularly after the onset of the H2–H3 phase transition, hampers their viability. In this study, single-crystal LiNi 0.8 Mn x Co 0.2-x O 2 (x = 0.2, 0.1, 0) are synthesized and tested in LiPF 6 in ethyl methyl carbonate-based electrolytes, with and without monofluoroethylene carbonate and LiF 2 PO 2 additives, to clarify the effects of Co/Mn ratio and surface stabilization on high-voltage cycling degradation. By imposing a kinetic barrier to the accessible H2–H3 capacity, surface reconstruction is identified as the primary driver of high-voltage capacity loss, being greater in the Co-free cathode and in the absence of fluorinated electrolyte components. This is attributed to a synergy between increased mechanical stress due to worsened bulk and interfacial H2–H3 kinetics and decreased interfacial stability due to the poor passivating capability of the electrolyte. Here, the findings highlight the importance of limiting cathode impedance growth during high-voltage cycling, which can be achieved by tuning bulk dopants and electrolyte chemistry.

25 ENERGY STORAGE↗
DOE OSTI2023

A Hybrid Transmitter With Voltage-Mode SST Preemphasis and Current-Mode Transmitter Equalization Capable of Operating at 77 K in DUNE

The Deep Underground Neutrino Experiment (DUNE) requires that the front-end transmitters operate at cryogenic temperature and drive 25–35 m long twin-axial (twinax) cables. Here, to compensate the frequency-dependent channel loss over the long cables and alleviate the de-emphasizing of the low-frequency signal magnitude, a hybrid of a current-mode (CM) transmitter equalization (TXEQ) and a voltage-mode (VM) preemphasis is proposed. The TXEQ employs a finite-impulse response (FIR) filter to boost the high-frequency components while de-emphasizing the low-frequency signal magnitude, thereby flattening the overall channel frequency response and reducing the intersymbol interference (ISI). The VM preemphasis is proposed to further mitigate ISI by boosting the high-frequency portion without degrading the signal magnitude, allowing for high signal swing. The main driver utilizes VM source-series-terminated (SST) output stages, which offers higher signal swing and better power efficiency than the conventional CM logic (CML) drivers. To ensure the lifetime and reliability at cryogenic temperature, the transmitter is implemented in a 65-nm CMOS process operating at 1.1 V of supply voltage and employing transistors with larger than minimum lengths. Silicon measurement results at 77 K have validated the proposed approaches.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2021

Redundancy in Aromatic O -Demethylation and Ring-Opening Reactions in Novosphingobium aromaticivorans and Their Impact in the Metabolism of Plant-Derived Phenolics

ABSTRACT Lignin is a plant heteropolymer composed of phenolic subunits. Because of its heterogeneity and recalcitrance, the development of efficient methods for its valorization still remains an open challenge. One approach to utilize lignin is its chemical deconstruction into mixtures of monomeric phenolic compounds, followed by biological funneling into a single product. Novosphingobium aromaticivorans DSM 12444 has been previously engineered to produce 2-pyrone-4,6-dicarboxylic acid (PDC) from depolymerized lignin by simultaneously metabolizing multiple aromatics through convergent routes involving the intermediates 3-methoxygallic acid (3-MGA) and protocatechuic acid (PCA). We investigated enzymes predicted to be responsible for O -demethylation and oxidative aromatic ring opening, two critical reactions involved in the metabolism of phenolic compounds by N. aromaticivorans . The results showed the involvement of DesA in O -demethylation of syringic and vanillic acids, LigM in O- demethylation of vanillic acid and 3-MGA, and a new O- demethylase, DmtS, in the conversion of 3-MGA into gallic acid (GA). In addition, we found that LigAB was the main aromatic ring-opening dioxygenase involved in 3-MGA, PCA, and GA metabolism and that a previously uncharacterized dioxygenase, LigAB2, had high activity with GA. Our results indicate a metabolic route not previously identified in N. aromaticivorans that involves O -demethylation of 3-MGA to GA. We predict that this pathway channels ∼15% of the carbon flow from syringic acid, with the rest following ring opening of 3-MGA. The new knowledge obtained in this study allowed for the creation of an improved engineered strain for the funneling of aromatic compounds that exhibits stoichiometric conversion of syringic acid into PDC. IMPORTANCE For lignocellulosic biorefineries to effectively contribute to reduction of fossil fuel use, they need to become efficient at producing chemicals from all major components of plant biomass. Making products from lignin will require engineering microorganisms to funnel multiple phenolic compounds to the chemicals of interest, and N. aromaticivorans is a promising chassis for this technology. The ability of N. aromaticivorans to efficiently and simultaneously degrade many phenolic compounds may be linked to having functionally redundant aromatic degradation pathways and enzymes with broad substrate specificity. A detailed knowledge of aromatic degradation pathways is thus essential to identify genetic engineering targets to maximize product yields. Furthermore, knowledge of enzyme substrate specificity is critical to redirect flow of carbon to desired pathways. This study described an uncharacterized pathway in N. aromaticivorans and the enzymes that participate in this pathway, allowing the engineering of an improved strain for production of PDC from lignin.

Biotechnology & Applied Microbiology↗
DOE OSTI2020

Comprehensive Data Analysis and Analytic Method Development for PBX9501 Material (FY2019 Annual Report of Aging and Lifetimes Program)

In the polymer bonded explosive (PBX) 9501, a binder consisting of 2.5 wt% nitroplasticizer (NP) and 2.5 wt% Estane R 5703 (Estane) is combined with 94.9 wt% of the high-explosive HMX and 0.1 wt% stabilizer. Because of its flexibility and tensile strength, this binder lowers the sensitivity and improves the manufacturability of PBX 9501. However, like many plasticizers comprised of low molecular weight components, NP has a tendency to diffuse out of the PBX 9501 matrix and can decompose at moderate temperatures into reactive byproducts, such as NO, NO 2 , H 2 O, and HNO x . Through oxidation and hydrolysis, these molecules can further degrade NP and Estane, ultimately degrading the properties of PBX 9501. While gaseous molecules can readily diffuse out of the PBX 9501 charges, the intermediates with lower volatility are more likely trapped in the condensed phase inside the charges and, in a closed system, will co-exist with Estane for an extended period of time creating an ongoing reactive environment. Understanding the rates of formation for these volatiles and intermediates during the NP degradation is therefore a critical prerequisite for understanding the long-term stability of polymeric binders used in munition systems. To work toward this ultimate goal, in the past year, we have conducted comprehensive studies on the physical properties of NP and finished three-year long aging experiment to understand the aging behavior of NP under thermal treatment. Important results are summarized in this annual report. Although NP is widely used in the DOE complex, their physical properties are rather scattered and inconsistent in the open literature. For example, there are at least two widely different values, 14.5°C and -15°C, for the melting point of NP. Although it is known that NP is a eutectic 50:50 mixture of BDNPA and BDNPF, their eutectic phase diagram is not well documented. Furthermore, the effect of temperature on the miscibility between water and eutectic BDNPA/F mixture is rarely reported. To fill these knowledge gaps, a large set of BDNPA/F mixtures with BDNPA concentration ranging from 0 to 100 wt% was analyzed using DSC techniques. In addition to determining the eutectic melt point as -25°C, a phase diagram of the BDNPA/F system was constructed from -30°C to 45°C. With this phase diagram, the phase transition temperatures and composition can be readily found for the BDNPA/F mixtures with various mass ratios.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Understanding the dynamics of vacuole trafficking and secretion for enhanced plant storage reserves

Vacuoles are essential in plants and serve roles in both protein and organelle degradation for reutilization and storage of high energy molecules such as storage proteins which are critical for seed germination under normal and stress conditions and impact biomass production and nutrition. In addition, plants secrete many different molecules such as cell wall components, lipids, waxes and others that are important for plant protection, structure and biomass. Vacuole accumulation and secretion are closely linked and highly regulated pathways within the endomembrane trafficking network. We have discovered many drug-like synthetic small molecules that can suppress vacuole trafficking and enhance secretion; other chemicals can enhance vacuole trafficking and suppress secretion. The ability of our compounds to modulate these pathways indicates that these pathways are tightly coupled and highly regulated. Thus, to achieve the goals of enhanced energy reserves or enhanced secretion it is essential to understand how this dynamic is controlled. Bioactive chemicals have provided us unique insight into natural mechanisms controlling the dynamics of vacuole trafficking and secretion that was not possible previously. One such compound, ES2, negatively modulates secretion while enhancing vacuole trafficking. This is the first compound discovered that targets an essential secretory complex known as the exocyst and will reveal important regulatory domains controlling secretion and vacuole trafficking. During the current funding period, we determined the structure of the EXO70A1 in plants and identifying its binding site. To understand the dynamics of vacuole trafficking it is key to have compounds that inhibit vacuole trafficking as well as enhancing it (ES2). We have used sophisticated image analysis to identify another novel compound ES17 that enhances secretion while negatively modulating vacuole trafficking (opposite of ES2). We have already made rapid progress. Our preliminary data indicates that ES17 very specifically targets a late stage of vesicle fusion with the tonoplast which is highly regulated. Thus, ES17 is an ideal compound to dissect the regulation of vacuole targeting dynamics. In addition, we have now synthesized a biotinylated version of ES17 that is fully functional, permitting the use of chemical proteomics to identify the cognate target with a high probability of success. We propose to investigate the ES17 target which can negatively modulate vacuole trafficking and combine this with knowledge of the ES2 target which can promote vacuole trafficking. Dissecting the mechanisms controlling the dynamics of secretion vs vacuole accumulation and the use of synthetic modulators will permit us to understand and regulate trafficking to enhance secretion to the plasma membrane and cell wall or the accumulation of biological energy reserves for increased biomass, nutritional value, stress resistance, and the secretion of specialized plant products.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Space Charge Effects on Spin Polarization in High-Intensity Preinjector

This analytical study investigates the space charge impact on electron beam polarization within the low-energy range, spanning from the polarized electron gun up to the linac. The research study three sections: the gun-to-Wien filter interface, the Wien filter section, and the bunching section. We will comprehensively assess polarization degradation mechanisms in each region characterized by space charge effects. We are planning to simulate the spin tracking through the entire low-energy transmission system using Generalized Particle Tracer (GPT) to validate our analytical findings and includes the higher order components.

43 PARTICLE ACCELERATORS↗
DOE OSTI2021

Effect of temperature and aging duration on ethylene propylene diene monomer (EPDM) nonmetallic components used in caustic liquid waste transfer lines

Nonmetallic materials are used in waste transfer lines at the United States Department of Energy’s Hanford Site Tank Farm in Benton County, Washington, USA. During use, the ethylene propylene diene monomer (EPDM) inner hose of the hose-in-hose transfer lines (HIHTLs) are exposed to β and γ radiation, caustic solutions as well as high temperatures and high pressures. Aging behavior of specimens of EPDM HIHTLs and dog-bone shaped specimens were evaluated by exposing to a solution of 25% sodium hydroxide (NaOH) at 38 °C, 54 °C and 77 °C for 6 and 12-months as well as water only at 77 °C for 12-months. Tensile strength and burst pressure of the specimens were characterized and compared with the unexposed (baseline) specimens. Both the tensile strength of the EPDM material and the burst pressure of the HIHTLs significantly decreased with the higher temperatures and longer exposure times. Analyses using scanning electron microscopy with energy dispersive X-ray spectroscopy (SEM-EDS) of the HIHTLs and the dog-bone specimens were conducted. Our results show that the surface degradation increased with increasing exposure temperature. Penetration of NaOH into the EPDM material also increased with increasing temperature and exposure duration.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Creep Degradation of Austenitic Steels in CO2 Environments

Steels have a proven track record of safe operation in steam power plants for decades. Interest in developing supercritical CO2 power cycles as a more efficient and sustainable alternative to steam cycles has driven a need to understand steel performance in these new environments. In particular, the potential of the high temperature CO2 environment to influence the creep behavior of the steel must be determined. Prior research on this topic between the 1960s and 1980s found conflicting conclusions, but nevertheless raised the possibility that carburization during CO2 exposure may strongly affect the creep behavior. This raises concerns particularly for thin-sectioned components such as compact heat exchangers, where even small rates of carburization can become problematic over long operating lifetimes. To shed light on this issue, this research investigates the creep behavior of austenitic stainless steel 347H and 309H (a higher Cr alternative) at 650°C. Specimens of 0.5, 1.0, and 2.0 mm thickness were tested to further assess the effect of steel thickness. Both steels show a reduction in creep life in CO2 relative to air, with 309H showing slightly better performance than 374H. Analysis is ongoing to determine the reason for degraded creep properties.

Rozman, Kyle A.↗