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At least 919 records · Page 51
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Characterization of the fatigue threshold behavior of UHMWPE

Ultra-high-molecular-weight-polyethylene (UHMWPE) has been the material of choice for bearings in total joint replacements (TJRs) for decades as a result of its excellent wear resistance, chemical inertness, energetic toughness, low friction, and biocompatibility. Utilization of this polymer in orthopedic devices requires oxidation, wear, and fatigue resistance. Balancing these important properties by tailoring processing techniques and modulating microstructural features has been an ongoing endeavor in the field. Research into the clinical applications of UHMWPE has primarily focused on the challenges of wear and oxidation while studies into the realm of fatigue have been more limited. Literature gaps exist in fully understanding the fatigue crack initiation near notches or propagation of small existing flaws in UHMWPE used in TJRs. In particular, the characterization of the fatigue thresholds and near-threshold fatigue behavior of orthopedic grade UHMWPE has yet to be thoroughly explored. In this work, we characterized the fatigue crack arrest threshold of clinically-relevant UHMWPE formulations. Correlations between the fatigue thresholds and bulk mechanical properties as well as microstructural properties were examined across these medical resins. The important role played by crosslinking in influencing the fatigue performance of UHMWPE is highlighted in this study. In addition, it is established that J-integral fracture toughness is the best predictor of fatigue thresholds and could possibly be used as a stand-in metric for fatigue performance if thresholds cannot be directly ascertained. Finally, this study corroborates that the true constitutive parameters best describe the mechanical behavior of UHMWPE.

Cross-linking↗
DOE OSTI2024

Glass-bonded ceramic waste forms for immobilization of radioiodine from caustic scrubber wastes

Glass-bonded sodalite composite waste forms have been developed for the immobilization of liquid radioactive wastes resulting from off-gas treatment during aqueous reprocessing of used nuclear fuel, with a particular focus on 129I. The proposed composite waste form is comprised of aluminosilicate ceramic phases containing volatile radionuclides bonded with a glassy matrix. In this work, a suite of ten candidate low-temperature glass binders (ZnO-Bi2O3-based glasses and a Na2O-B2O3-SiO2 glass) were examined. Six glasses were mixed with caustic scrubber waste simulant previously converted into a sodalite-rich material (to provide glass fractions of 10 and 20 wt.%), uniaxially pressed into pellets, and sintered at 350 °C or 550 °C for 8 h in air. Iodine retention after heat treatment was assessed by neutron activation analysis, showing retention of 67-100 % of expected iodine. The aqueous durabilities of the resulting materials were then determined, following the ASTM C1308 standard test, showing iodine releases of 1 to 23 g m-2 after 4 d. The cumulative iodine release for the best performing system (a zinc-bismuth-borate glass binder) was <1 g m-2, and its iodine retention from processing was 67 %. The iodine releases compared favorably with other waste forms. In parallel, this best-performing composition was also consolidated via hot isostatic pressing (HIP) in a stainless-steel canister at 550 °C for 2 h under 100 MPa pressure. The HIPed sample was produced at the ~20 g scale and showed improved densification and minimal reaction with the canister.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2024

Determining reference standard strength for neutron-irradiated reduced activation ferritic/martensitic steel F82H by Bayesian method

The deterministic approach widely adopted in the design of structural components relies on systematically defined design limits using empirically determined safety factors. However, this approach is not always appropriate because structures are subjected to a variety of loads in the practical environment, which may result in excessively conservative design limits. In recent years, a more rigorous probabilistic approach that incorporates material strength distributions has become an important solution. In the probabilistic approach, the probability density functions of material strength properties underpin the design criteria. Here, the objective of this study is to identify the density distribution functions that best describe tensile properties of irradiated F82H to define a reference strength for DEMO design. Due to the limited number of existing data, this study specifically employs a Bayesian prediction method based on Monte Carlo simulations to determine a material reference value with statistical reliability and to investigate its effectiveness. For example, the dependence of tensile properties of 300 °C irradiated materials on irradiation damage and the range predicted by 95% Bayesian estimation was evaluated. As a statistical model for the dose dependence of statistical parameters, the normal distribution exhibited a better fit for 0.2% proof strength and tensile strength, whereas the distribution of total elongation data gave comparable reference values for both the normal and Weibull distribution models. Both models gave comparable criteria for the distribution of total elongation data. The Weibull model also gave better results for uniform elongation. The function best describing the model was a logarithmic law for both 0.2% proof strength and tensile strength, while a power law for both total and uniform elongation, which allowed for more comprehensive data prediction of irradiation data with statistical accuracy for DEMO reactor design.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Comprehensive review of photophysical parameters (ε, Φ f , τ s ) of tetraphenylporphyrin (H 2 TPP) and zinc tetraphenylporphyrin (ZnTPP) – Critical benchmark molecules in photochemistry and photosynthesis

Tetraphenylporphyrin (H 2 TPP) and zinc tetraphenylporphyrin (ZnTPP) are widely used benchmark molecules in diverse photochemical studies given facile synthetic access, rich visible-region spectra, and broad structural analogy to chlorophylls. Yet the literature values for each key photophysical parameter – the molar absorption coefficient (ε), fluorescence quantum yield (Φ f ), and also singlet excited-state lifetime (τ S ) – vary over an astonishing range. Here, a comprehensive literature review (~1940–September 2020) encompassing 871 publications is reported for these essential parameters. Each parameter is determined by measurement with distinct instrumentation and suffers idiosyncratic sources of error. The best values for H 2 TPP are ε = 460,000 cm -1 ·M -1 , Φ f = 0.090, and τ S = 12.8 ns in Ar- purged toluene (Φf = 0.070, τS = 9.9 ns in toluene in air); the best values for ZnTPP are ε = 560,000 cm -1 ·M -1 , Φf = 0.030, and τ S = 2.1 ns in Ar-purged toluene (Φ f = 0.029, τ S = 2.0 ns in toluene in air). The choice of values for such parameters has far-reaching consequences in photochemistry ranging from fluorescence (or Förster) resonance energy transfer (FRET) processes to assessments of molecular brightness.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

Layer-structured triple-conducting electrocatalyst for water-splitting in protonic ceramic electrolysis cells: Conductivities vs. activity

Electron, proton and oxygen-triple-conducting materials are becoming the dominant steam electrode candidate to break the rate limit on the water-splitting reaction that throttles the performance of protonic ceramic electrolysis cells (PCECs). In this study, based on Pr 2 NiO 4+δ Ruddlesden-Popper phase, we manipulate these conductivities by Pr-site Ba substitution to probe the correlation of each conductivity with the kinetics of the elementary reaction steps. It is found that the proton conductivity is vital to sustain an extended active surface area for faster adsorption of reactants and desorption of products. The effect of oxygen conductivity is surprisingly found insignificant in the water-splitting reaction. On the contrary, surface oxygen removal is discovered as the most rate-limiting process. The electronic conductivity is not a direct limiting factor. However, an electron transfer process between the current collector and the electrode junction could introduce extra resistance that is perceptible at a high operating temperature range. The best water-splitting activity is obtained on a proton conductivity/oxygen surface desorption capability well-balanced sample after Ba substitution. As a result, a water-splitting reaction resistance of 0.022 Ωcm 2 , a current density of 1.96 A/cm 2 at 700 °C is achieved on Pr 1.7 Ba 0.3 NiO 4+δ , one of the best performances for PCECs.

25 ENERGY STORAGE↗
DOE OSTI2025

Low Peripheral Blood Counts and Elevated Proinflammatory Cytokines Signal a Poor CD19 Chimeric Antigen Receptor T-cell Response in Acute Lymphoblastic Leukemia

CD19 chimeric antigen receptor T-cell (CAR-T) therapy has significantly improved outcomes for patients with relapsed/refractory B-cell acute lymphoblastic leukemia (R/R B-ALL). However, approximately 20% of patients fail to achieve a complete remission (CR), and some develop severe, life-threatening toxicities. Understanding the biological mechanisms underlying both dysfunctional responses and severe toxicity is essential for optimizing patient management and improving therapeutic efficacy. This study aimed to (1) characterize cytokine profiles associated with dysfunctional responses and severe toxicity following CAR-T infusion, (2) examine the timing and trajectory of cytokine changes in relation to treatment outcomes, and evaluate potential strategies for mitigating toxicity and treatment failure. We conducted a comprehensive analysis of serum cytokine profiles in 86 adult and pediatric patients undergoing autologous CD19 CAR-T therapy for B-ALL. Patients were categorized into three groups: (1) Dysfunctional response—Patients who failed to achieve a minimal residual disease-negative CR (MRD-CR) by Day 63 or who experienced recurrence of CD19+ disease in the setting ongoing CAR-T cell detection before Day 63. (2) Functional response with severe cytokine release syndrome (CRS) and/or neurotoxicity (NTX)—Patients with best response of MRD-CR by Day 63 who experienced grade 3 or higher CRS or NTX. (3) Functional response without severe CRS or NTX—Patients with best response of MRD-CR by Day 63 who did not experience grade =3 CRS or NTX. Cytokine levels were measured during the first-week postinfusion and correlated with treatment efficacy, toxicity outcomes, complete blood counts, and CAR-T expansion dynamics. This analysis aimed to better understand how cytokine profiles relate to patient outcomes and immune responses in CAR-T therapy. Patients with dysfunctional response exhibited decreased neutrophils, platelets, and levels of granulocytic cytokines (suggestive of low bone marrow reserve) alongside elevated pro-inflammatory cytokines by Day 1. Functional response with severe toxicity patients showed a progressive rise in proinflammatory cytokines, reaching similar levels to dysfunctional response patients by Day 7. We observed that high cytokines at both the Day 1 and Day 7 time points were associated with poor survival. These findings remained significant when adjusting for high disease burden, a known predictor of severe inflammatory toxicity and lack of response. Early post-CAR-T infusion inflammation is associated with both dysfunctional response and severe toxicity—even after adjusting for disease burden. This suggests that inflammation, in addition to disease burden, plays a role in determining patient outcome. Therefore, strategies aimed at reducing the pro-inflammatory state prior to or early after CAR-T cell infusion may improve outcomes for R/R B-ALL patients.

Serum cytokines↗
DOE OSTI2022

Hydrogen trapping and storage in the group IVB-VIB transition metal carbides

The transition metal carbides have been known to act as traps for hydrogen in steels as well as potential materials for hydrogen storage. Despite numerous experimental and a few theoretical studies, what impacts hydrogen trapping and storage in the transition metal carbides is not well understood. In this work, we use density functional theory to systematically investigate the bulk trapping and storage capabilities of the transition metal carbides. We specifically examine how trapping and storage changes with the transition metal, from the group IVB to group VIB, carbon concentration, and structure. Our results demonstrate a strong correlation between the trap energy and the number of valence electrons in the transition metal, suggesting that the group IVB transition metal carbides are the best carbides for trapping and storage. Hydrogen preferentially sits at octahedral interstices, e.g., the carbon vacancies, in the structure except in certain cases near the Me 2 C concentration when tetrahedral interstices, devoid of nearest neighbor carbon, can become more favorable. Our results further demonstrate that the lower the carbon concentration, the more hydrogen the transition metal carbide can store. These results demonstrate which carbides will act as the best traps for hydrogen.

08 HYDROGEN↗
DOE OSTI2020

PdAu/YSZ composite hydrogen separation membranes with enhanced stability in the presence of CO

The effects of gold composition on the tolerance of palladium-based composite membranes to CO exposure was studied under the presence of high CO concentrations. Membranes with PdAu compositions ranging up to 41% by mass were exposed to binary 50/50 H 2 /CO gas atmospheres and both hydrogen flux and purity were monitored over time. It was observed that the highest gold composition (41%) suffered no degradation in membrane performance in terms of permeate hydrogen purity and hydrogen permeation flux during the 48 h exposure. Furthermore, for lower gold compositions, X-ray diffraction patterns suggested that carbon entered the film and could be removed by exposure to a non-CO containing gas mixture over time. However, removal of the carbon adversely affected purity; interestingly, some of the purity decline could be reversed upon re-exposure to the CO gas mixture. It is hypothesized that carbon loading in the palladium films causes lattice expansion and may even fill pore defects, thus lowering leaks through the Pd films. Furthermore, this study suggests that a membrane exposed to a CO mixture should not be cycled between CO and non-CO environments, but rather run continuously for best performance. Additionally, the choice of Pd-alloy and metal composition should be considered in regard to its carbon solubility to best predict lifetime performance.

08 HYDROGEN↗
DOE OSTI2022

Effect of tool design and pass strategy on defect elimination and uniform, enhanced tensile properties of friction stir processed high-pressure die-cast A380 alloy

This work reports implementation of friction stir processing (FSP) to locally modify microstructure and create a defect-free processing zone with the aid of different tool designs and altering the passing strategy for two different process parameter combinations. Here, FSP is applied on high pressure die-cast (HPDC) A380 alloy, a widely used Al-Si alloy fabricated in the die casting sector for automotive and aerospace applications. However, the presence of gas and shrinkage porosity, brittle needle-shaped Fe-containing ß-FeSiAl 5 intermetallic compounds, Al-dendrites, coarse and acicular silicon particles, and large second-phase particulates have a detrimental influence on the uniformity of tensile properties across the thickness direction of an HPDC plate. FSP is able to eliminate porosity, refine microstructure and improve tensile properties. An FSP tool design and a passing strategy are identified for the best microstructure consolidation and uniform enhanced tensile properties. This study noted the importance of tool features such as scroll design and flats on tool pin and multiple pass orientation strategy to find best combination of defect-free refined microstructure and uniform tensile strength and ductility across the processing zone of HPDC Al alloys.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Bidentate Lewis bases are preferred for passivation of MAPbI 3 surfaces: A time-domain ab initio analysis

We report chemical passivation is a predominant approach to inhibit intrinsic defects responsible for electron–hole recombination in perovskite solar cells. Using time-domain density functional theory combined with nonadiabatic molecular dynamics, we demonstrate defect passivation by separation of electrons and holes in metal halide perovskites (MHPs), and show that bidentate ligands exhibit the best performance. Defects in traditional semiconductors create deep midgap states, and passivation eliminates these states. In contrast, common defects produce no deep midgap states in MHPs. Instead, defects localize electrons and holes around defect sites, enhancing electron–hole interaction. Defect passivation in MHPs acts to separate charges, decreasing electron–hole and charge–phonon interactions, and increasing charge lifetimes. Bidentate ligands work best, because they can passivate both unsaturated chemical bonds created due to vacancy defects. Bidentate ligands with spatially separated binding sites are preferred, since they provide better match to the sparse inorganic lattice of MHPs. Similar to the Lewis base ligands, water also acts as a ligand and extends charge lifetimes, with the oxygen atom donating its lone electron pair to the defect site. However, water accelerates chemical degradation of MHPs. The binding energy of most ligands is larger than that of water, and therefore, the ligands displace water, increase MHP stability and prolong carrier lifetimes. The established physical mechanism of defect passivation, and the specific principles guiding the choice of passivating molecules advance our understanding of the exceptional properties of MHPs and suggest routes for further improvement of MHP performance in solar energy and optoelectronic applications.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Efficient and mechanically-robust organic solar cells based on vertical stratification modulation through sequential blade-coating

Mechanically durable organic solar cells (OSCs) with high efficiency are deemed as the ideal candidate for the power source of the next generation wearable electronic devices. However, the brittle nature of small molecules in most high-efficiency OSCs consisting of polymer and small molecule encourages easy formation of cracks in the photoactive film under deformation. Here, the vertical composition distribution of the active layer has been well optimized through sequential blade coating to realize highly deformable while efficient OSC. The optimized morphology exhibits distinct donor-rich and homogenous region distributed along the vertical direction in the bulk. The donor-rich region provides sufficient chain entanglements and strong interfaces beneficial for mechanical robustness of film, while homogeneously mixed region offers continuous interpenetrating network to maintain high device through-put, resulting in superior efficiency of 14.4% with high crack-onset strain (COS) of 30.5%. This efficiency versus COS combination is much higher than the best combination reported in all polymer systems (COS of 15.9% and efficiency of 11.1%). To the best of our knowledge, it is the highest COS value achieved in polymer-small molecule systems. As a result, the rational control over vertical stratification demonstrated here would guide researchers in the development of innovative wearable electronics.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Dual atom catalysts for rapid electrochemical reduction of CO to ethylene

Strong CO adsorption and facile CO dimerization are the key challenges in electrochemical CO2 reduction towards multi-carbon (C2+) products. We recently showed that CoPc immobilized on a single-walled carbon nanotube can selectively reduce CO2 to methanol. This is enabled through molecular strain, which dramatically improves the CO adsorption energy to CoPc, which in turn facilitates methanol formation. We now examine the extended Phthalocyanine (PcEx) dual atom catalyst (DAC), which is intrinsically strained and contains two catalyst centers, making it a candidate for reducing CO to C2+ products. Using Quantum Mechanics (QM), we screened 20 elements embedded in the PcEx, seeking catalysts with weak hydrogen binding, strong CO binding, and facile CO dimerization. We identi>ied Fe, Ru, Co, and Ir as the best performers and subsequently evaluated the entire CO to C2H4 mechanism (9 steps) using each of these elements as catalysts. In terms of limiting potential and overall exergonicity, we identi>ied CoPcEx as the best catalyst, followed by IrPcEx. We then examined the full CO to C2H4 mechanism on the bimetallic IrCoPcEx catalyst using grand canonical QM to obtain the reaction energetics as a function of applied potential. We conclude that the bimetallic IrCoPcEx is most promising for ef>iciently converting CO to ethylene.

Musgrave, Charles B.↗
DOE OSTI2021

Benchmarking the Performance of Neuromorphic and Spiking Neural Network Simulators

Software simulators play a critical role in the development of new algorithms and system architectures in any field of engineering. Neuromorphic computing, which has shown potential in building brain-inspired energy-efficient hardware, suffers a slow-down in the development cycle due to a lack of flexible and easy-to-use simulators of either neuromorphic hardware itself or of spiking neural networks (SNNs), the type of neural network computation executed on most neuromorphic systems. While there are several openly available neuromorphic or SNN simulation packages developed by a variety of research groups, they have mostly targeted computational neuroscience simulations, and only a few have targeted small-scale machine learning tasks with SNNs. Evaluations or comparisons of these simulators have often targeted computational neuroscience-style workloads. In this work, we seek to evaluate the performance of several publicly available SNN simulators with respect to non-computational neuroscience workloads, in terms of speed, flexibility, and scalability. We evaluate the performance of the NEST, Brian2, Brian2GeNN, BindsNET and Nengo packages under a common front-end neuromorphic framework. Our evaluation tasks include a variety of different network architectures and workload types to mimic the computation common in different algorithms, including feed-forward network inference, genetic algorithms, and reservoir computing. We also study the scalability of each of these simulators when running on different computing hardware, from single core CPU workstations to multi-node supercomputers. Our results show that the BindsNET simulator has the best speed and scalability for most of the SNN workloads (sparse, dense, and layered SNN architectures) on a single core CPU. However, when comparing the simulators leveraging the GPU capabilities, Brian2GeNN outperforms the others for these workloads in terms of scalability. NEST performs the best for small sparse networks and is also the most flexible simulator in terms of reconfiguration capability NEST shows a speedup of at least 2x compared to the other packages when running evolutionary algorithms for SNNs. The multi-node and multi-thread capabilities of NEST show at least 2x speedup compared to the rest of the simulators (single core CPU or GPU based simulators) for large and sparse networks. We conclude our work by providing a set of recommendations on the suitability of employing these simulators for different tasks and scales of operations. We also present the characteristics for a future generic ideal SNN simulator for different neuromorphic computing workloads.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Investigation of a digitizer for the plastic scintillation detectors of time-of-flight mass measurements

A CAEN DT5742 digitizer has been investigated to process the fast signals from the photomultiplier tubes of the time-of-flight detectors for fast ion beams. A test setup consisting of two plastic scintillation detectors and a pulsed laser source provided signals which were recorded by the digitizer and systematically analyzed with different algorithms to derive the amplitude, rise time and arrival time of the detection signal. To obtain the best amplitude and time resolutions, various optimization techniques including peak fitting and signal smoothing, interpolation improvement, and time-walk correction by amplitude and rise time have been performed and compared in detail. Here we compared the time resolutions obtained by three digital algorithms of leading-edge, zero-crossing constant-fraction (ZC-CFD) and direct constant-fraction discriminations. Finally, we found that the best time-of-flight resolution between two PMTs can be achieved as 12 ps by the method using sample minimum for peak location and using the time-walk-resistant ZC-CFD with 4-point interpolation for timing.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2023

Neutron calorimeter development at fission and fusion pulsed neutron sources

A neutron calorimeter fielded in pulsed neutron irradiation experiments at the National Ignition Facility (NIF) and White Sands Missile Range Fast Burst Reactor (WSMR FBR) is described, and results are provided and discussed. The calorimeter is modular in design to allow fielding of various neutron absorber materials and thicknesses, as well as use at both fission and fusion sources, all of which has been done. Neutron calorimeters designed for fission sources have been fielded at fusion sources as well, but these instances are not documented in available literature—making this work novel. The functional calorimeter consists of a neutron absorber and two sheathed, grounded thermocouples secured within a subassembly made of thermally insulating materials. This subassembly is placed into a sealed vessel for material and facility safety considerations. The device was fielded at NIF in four polar-direct-drive exploding pusher shots and eleven WSMR FBR pulses. Calorimeter data from NIF experiments show a discernable thermal response accredited to neutron heating within the first 10 s, but the data are dominated by noise from ambient X-ray heating after time = 10 s post-pulse. This response is highly consistent between shots and linearly related to incident neutron fluence and neutron absorber thickness. A temperature rise of 1.1E-2 K/(10 13 neutrons/cm 2 ) per millimeter of absorber thickness was determined for both 0.4 mm and 1 mm thick depleted uranium absorbers with linear best fit R 2 of 0.97 and 0.98 for each thickness and y-axis offsets of 8.8E-3 and 1.3E-2, respectively. WSMR FBR experiment data show a temperature rise of 6.2E-2 K/(10 13 neutrons/cm 2 ) with linear best fit R 2 of 0.14 and y-axis offset of 7.6E-3 for a calorimeter with a 1 mm thick enriched boron carbide ( 10 B 4 C) absorber, though it must be noted that device-to-device variability is significant at both facilities. So these data are also affected by noise on a similar timescale as at NIF, though the noise is significantly lesser in magnitude but oscillatory.

42 ENGINEERING↗
DOE OSTI2022

Multiobjective optimization of nuclear microreactor reactivity control system operation with swarm and evolutionary algorithms

To improve the marketability of novel microreactor designs, there is a need for automated and optimal control of these reactors. This paper presents a methodology for performing multiobjective optimization of control drum operation for a microreactor under normal and off-nominal conditions. Here, two different case studies are used where the control drum configuration is optimized for the reactor to be critical with some desired power distribution that would satisfy peaking limits. A surrogate model for power distribution is developed based on a feedforward neural network. The process for determining weights for scalarization of the multiobjective optimization problem is also detailed. Six optimization algorithms: evolutionary strategies, differential evolution, grey wolf optimization, Harris hawks optimization, moth flame optimization and particle swarm optimization, are all applied to these cases and the results analyzed. Although all these algorithms will demonstrate optima-seeking behavior, for real-time control it is necessary to identify the best algorithm to efficiently provide reasonable optima without operator interference. The moth flame optimization algorithm was found to perform particularly well on both cases. Overall, it was found that the algorithms capable of supplying the best optima were also the most consistent. Finally, the found optima were verified with the original model used to train surrogates.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2022

BISON fuel performance modeling optimization for experiment X447 and X447A using axial swelling and cladding strain measurements

With the recent need to qualify new reactor designs such as the Versatile Test Reactor (VTR), fuel performance calculations need to be performed to determine safety criteria of the proposed designs. In order to validate the fuel performance results obtained by a fuel performance code, BISON, for new reactor designs, legacy fuel from EBR-II and FFTF MFF with Post -Irradiation Examination (PIE) data need to be used as validation cases to benchmark models. Here in this work, BISON has been paired with the Fuels Irradiation & Physics Database (FIPD) and IFR Materials Information System (IMIS) to supply PIE data for comparison with simulations of EBR-II experiments X447/X447A. X447/X447A were assessed by implementing models for Fuel Cladding Chemical Interaction (FCCI) within BISON and optimizing the friction coefficient between the fuel surface and the cladding, the anisotropic swelling factor, and the HT9 first thermal creep scalar (which scales the first term in the HT9 creep equation) to best match the PIE axial fuel swelling height and cladding profilometry for all pins in X447/X447A. The optimal values were found using a generic algorithm developed to select different values for the three parameters until end criteria was met and error couldn’t be reduced further. The BISON-simulated cladding profilometry was evaluated using Standard Error of the Estimate (SEE) to account for the profile shape of the cladding profilometry. Optimal values for the friction coefficient, anisotropic fuel swelling factor, and HT9 first thermal creep scalar were found to best fit the BISON simulation results to the PIE measurements found in IMIS and FIPD. Improvements to current models are suggested to account for the underprediction of fuel swelling at low burnups and the overprediction of fuel swelling at higher burnups observed for the axial fuel swelling height. Although two pins in EBR-II X447/X447A (DP70 and DP75) were known to fail due to FCCI, none of the pins simulated in BISON reached a cumulative damage fraction (CDF) above 0.008 with FCCI correlations coupled in the BISON simulations. The error estimate generated for all pins in X447/X447A using optimal values was 209 µm, which is deemed acceptable.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2022

Development and assessment of a reactor system prognosis model with physics-guided machine learning

Autonomous control systems provide recommendations to help operators in decision-making during plant operations ranging from normal operation to accident management. An important step of autonomous control is prognosis. In nuclear engineering domain, prognosis is the process of predicting future conditions of a system or equipment based on present signs and symptoms of a fault. The prognosis model allows predicting future reactor states for possible candidate control strategies so that the outcomes can be evaluated to determine the best control strategy. The prognosis model requires representing direct relationships between the symptoms and the predictions. In nuclear engineering, computational simulations are approximate representations of the operation of the real system. However, prognosis with computational simulations requires high computation power and time due to possible large number of scenarios. Necessary computation resources can be reduced with machine learning (ML) approach for fast predictions by building a surrogate function using the simulation data. A critical issue is, ML models are ignorant of physical knowledge, and these models approximate statistical relationships between the system variables. This ignorance can produce results that are inconsistent with physical laws, even if an optimal result is achieved from a mathematical point of view. Physics-guided machine learning (PGML) is an approach to tackle this issue. Here, this work formulates and illustrates a framework to guide development and assessment of the ML-based prognosis model for autonomous control systems. The development of the prognosis model considers the training of a ML model which consists of optimizing many aspects of the ML approach. The assessment of the prognosis model considers training data limitations and uncertainties of the ML approach. Prognosis models with standalone ML and PGML are developed and assessed on the loss-of-flow scenario of Experimental Breeder Reactor II. The results indicate that PGML based prognosis model has the best performance compared to other prognosis models.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗