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At least 1,621 records · Page 90
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Ion Exchange Processing of AW-105 Hanford Tank Waste through Crystalline Silicotitanate in a Staged 2- then 3-Column System

The Hanford Site stores an estimated 56 million gallons of mixed radioactive and chemically hazardous waste in large underground tanks. In support of the Direct Feed Low-Activity Waste (DFLAW) Program for expediting Hanford tank waste supernate treatment, laboratory-scale ion exchange processing using prototypic unit operations was conducted on AW-105 tank waste at the Pacific Northwest National Laboratory Radiochemical Processing Laboratory. This report describes the small-scale ion exchange testing with 9.2 L of diluted and filtered supernate from Tank 241-AW-105 (hereafter referred to as AW-105) at 16 °C (62 °F). One of the waste acceptance criteria (WAC) for the Waste Treatment Plant (WTP) Low-Activity Waste Facility is that the waste must contain less than 3.18×10 -5 Ci 137 Cs per mole of Na. For the AW-105 tank waste to meet this criterion, only 0.225% of the influent 137 Cs concentration may be delivered to the WTP; this requires a Cs decontamination factor of 445. Testing with AW-105 matched current Tank Side Cesium Removal (TSCR) facility prototypic operations where a lead-lag configuration was used until the lag column reached the WAC limit, then a polish column was brought online for continued processing in a lead-lag-polish column configuration. Feed was processed at 1.9 bed volumes (BVs) per hour; the flowrate, in terms of contact time with the crystalline silicotitanate (CST) bed, matched the expected flowrate at TSCR. The Cs-decontaminated product was retained for vitrification testing (to be reported separately). The lead column reached 83% Cs breakthrough after processing ~1500 BVs of feed; the 50% Cs breakthrough was interpolated from the breakthrough data and occurred at 1041 BVs. Despite the AW-105 having a significantly higher K concentration (0.55 M compared to 0.10 M), testing compared to previous AP-107 ion exchange column testing at 16 °C showed no difference in BVs processed to reach the WAC on the lead column and only an approximate ~20 BV decrease in volume processed to reach the WAC limit on the lag column. The negligible differences in capacity despite the 5x concentration differences in K was determined to be due to the significantly lower NO3 concentration in the AW-105 supernate compared to the AP-107 tank waste matrix. A comparison in breakthrough curves for the two tests also indicated slightly faster kinetic behavior in the AW-105, with the variations in feed matrices (lower NO3 concentration) likely responsible for the deviation. The Cs effluent from the lag column reached the WAC limit after processing 772 BVs. Anticipating this breakthrough point, the polish column was preemptively installed around 675 BVs. Cs breakthrough from the lag column began at 300 BVs, reaching 1.10×10 1 µCi/mL, or 14.13 % Cs breakthrough, after processing all 1500 BVs of feed. The polish column processed nominally 830 BVs and reached 2.10×10 -1 µCi/mL, or 0.27 % Cs breakthrough at the conclusion of the test. Table S.1 and Figure S.1 summarize the observed column performance and relevant Cs loading characteristics.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2024

HSQC spectra of lignin isolated from poplar stems

Here we present a curated dataset of two-dimensional heteronuclear single quantum coherence (HSQC) nuclear magnetic resonance (NMR) spectra of lignin isolated from stems of genetically engineered poplar through auxin signaling gene modification. The plants were grown in greenhouse with temperatures between 21 and 23 °C. Plants were harvested and the aboveground stems were cut off an approximately five-inch-long segment from the bottom end of the plant stem, debarked and air-dried for three weeks. The dried stem samples were Wiley milled (mesh size 20), Soxhlet-extracted with toluene/ethanol for 24 h to remove extractives. The extracted biomass was ball-milled in a Retsch PM100 planetary ball mill using a porcelain jar with ceramic balls at 600 rpm for 2 h (in 5 min on and 5 min off cycles to avoid excessive sample heating). The ball-milled materials were then subjected to enzymatic hydrolysis for 48 h followed by centrifugation and washing with deionized water. The solid residue was freeze-dried to recover the lignin. The dry stem lignin samples were dissolved in deuterated dimethyl sulfoxide (d6) and transferred into a 5 mm tube. 13C–1H HSQC experiments were performed in a Bruker Avance III HD 500 MHz NMR spectrometer operating at a frequency of 125.12 MHz for the 13C nucleus using a standard Bruker pulse sequence on a Prodigy platform cryoprobe. The NMR spectra were acquired under the following acquisition conditions: 230 ppm spectral width in F1 (13C) dimension with 256 data points and 12 ppm spectral width in F2 (1H) dimension with 2048 data points, a 90° pulse, a one bond C–H coupling constant of 145 Hz, a 1.0 s pulse delay, and 64 scans. Spectra were processed using the Bruker TopSpin 3.6 software. Additional meta data is embedded in the raw spectra figures.

HSQC, lignin, poplar, stems, CBI↗
DOE OSTI2024

An assessment of ocean alkalinity enhancement using aqueous hydroxides: kinetics, efficiency, and precipitation thresholds

Abstract. Ocean alkalinity enhancement (OAE) is a promising approach to marine carbon dioxide removal (mCDR) that leverages the large surface area and carbon storage capacity of the oceans to sequester atmospheric CO2 as dissolved bicarbonate (HCO3-). One OAE method involves the conversion of salt in seawater into aqueous alkalinity (NaOH), which is returned to the ocean. The resulting increase in seawater pH and alkalinity causes a shift in dissolved inorganic carbon (DIC) speciation toward carbonate and a decrease in the surface ocean pCO2. The shift in the pCO2 results in enhanced uptake of atmospheric CO2 by the seawater due to gas exchange. In this study, we systematically test the efficiency of CO2 uptake in seawater treated with NaOH at aquarium (15 L) and tank (6000 L) scales to establish operational boundaries for safety and efficiency in advance of scaling up to field experiments. CO2 equilibration occurred on the order of weeks to months, depending on circulation, air forcing, and air bubbling conditions within the test tanks. An increase of ∼0.7–0.9 mol DIC per mol added alkalinity (in the form of NaOH) was observed through analysis of seawater bottle samples and pH sensor data, consistent with the value expected given the values of the carbonate system equilibrium calculations for the range of salinities and temperatures tested. Mineral precipitation occurred when the bulk seawater pH exceeded 10.0 and Ωaragonite exceeded 30.0. This precipitation was dominated by Mg(OH)2 over hours to 1 d before shifting to CaCO3,aragonite precipitation. These data, combined with models of the dilution and advection of alkaline plumes, will allow the estimation of the amount of carbon dioxide removal expected from OAE pilot studies. Future experiments should better approximate field conditions including sediment interactions, biological activity, ocean circulation, air–sea gas exchange rates, and mixing zone dynamics.

Ringham, Mallory C. (ORCID:0000000348020185)↗
DOE OSTI2024

WEC-Sim Modeling of Laminar Scientific's Patented Seesaw Wave Energy Converter: Preprint

Laminar Scientific's patented seesaw wave energy converter was modeled in WEC-Sim to predict performance. The device operates by utilizing ocean surface waves to rotate a truss in pitch about a pivot. The pivot is located at the top of two pylons, which are embedded in the seafloor. The seesaw has a float on either end, and the buoyancy forces from each float cause the system to rise or fall with passing waves. Device performance relies upon seesaw length and ocean wavelength creating an antiphase effect. The seesaw truss has an adjustable length intended to achieve this effect. The operation method enforces a narrow band of wavelengths which induce the largest rotational motion from the device. The hydrodynamic analysis of the device was performed using Capytaine, and the results confirmed that the device operates best in a narrow frequency band. Four float-to-float spacing cases and three pylon radii were examined. The hydrodynamic results indicate a match between the model and the physical expectations for the device, and that varying the pylon radii by 0.1-m increments for three instances creates minimal changes in hydrodynamic properties. Power matrices for three float spacing cases of the device were simulated with Joint North Sea Wave Project spectra waves and optimal power take-off damping in WEC-Sim. The maximum average power production for the 15-m spacing case was 14.1 kW with a 5.0-s peak wave period and 4-m significant wave heights. Plots of capture-width ratios indicated that the device performance was linear and confirmed that the device is optimal in a narrow frequency band. The maximum percentage of the available wave power produced by the 15-m device was approximately 16%. Simulations of the device in regular waves were used to produce plots of average power compared to a ratio of float spacing to wavelength. These plots indicate that the power production is maximized at a ratio of 0.5, and further confirm that the device has a narrow frequency response. The device was simulated at an example field location, where the device produced an annual average power rating of 1.6 kW given an average omnidirectional wave climate of 10.3 kW m-1 and an optimal, linearized power take-off model. While the maximum predicted device performance is reliant upon a narrow band of wave frequencies, the conducted analysis provides an opportunity to improve device design prior to prototyping and testing. Modifying the design to respond to a broader frequency range would improve device performance.

numerical modeling↗
DOE OSTI2024

A Simplified Method for Predicting Shaker Voltage in IMMATs

Impedance Matched Multi-Axis Tests (IMMATs) can replicate in-service vibration induced stress more accurately than single axis shaker table tests as they can better match a part’s operational boundary conditions and excite it in multiple degrees of freedom simultaneously. Here, the shakers used in IMMATs are less powerful than shaker tables, so shaker force limits can be exceeded during tests if they are not placed adequately for the desired environment. The ability to predict shaker voltage and force before performing a test is, therefore, helpful in selecting shaker locations so that their limits are not exceeded. In this study, electrodynamic shakers were modeled as discrete electromechanical systems, and the shaker parameters were chosen to match experimentally obtained acceleration/voltage frequency response functions (FRFs). These models were coupled to a finite element model of the device under test (DUT) via dynamic substructuring, and the substructured model was demonstrated to accurately predict shaker voltage as well as the error in reproducing the environment at multiple accelerometer locations. A simple method called the FRF Multiplication method, in which the FRF of the substructured system is approximated as the product of two separate FRFs of the shaker and DUT respectively, was proposed and applied to the same system, yielding similar voltage and error predictions to those obtained using substructuring. Simple case studies were presented to explore the applicability of the proposed method, and it was demonstrated to have similar accuracy to the substructuring method in a range of cases. Additionally, we showed that while it was not possible to derive a unique model of the shakers from acceleration/voltage FRFs alone, the models that could be obtained were sufficient to predict test error almost perfectly and shaker voltage with less than 40 percent error.

42 ENGINEERING↗
DOE OSTI2024

Prognostic model and failure mechanisms of steam generators in Sodium-Cooled fast reactors

This paper presents a prognostic model for sodium-cooled fast reactor (SFR) steam generators (SGs). Here, the purpose of the model is to estimate the remaining useful life of SFR SGs and thus to support the decision-making of autonomous control. SFR SGs are of great interest for plant integrity due to their harsh operation environment. They operate at higher temperatures and higher coolant-to-steam pressure differences than those of current light water reactors (LWR). The severity of the SFR SG failure consequences, which include water-sodium contact, is another reason. Understanding its failure mechanisms is important for the development of its prognostic model. Based on our literature review and physics-based analysis, we concluded that creep would be a dominant degradation mode of SFR SGs due to SFR’s high-temperature and high-pressure environment. Thus, creep is the focus of the prognostic model. Various other failure modes were also investigated in this study. The mechanical fatigue due to flow-induced vibration is usually observed in early developmental phases and would not be an important issue during normal operation. The thermal fatigue due to thermal stripping is occasionally observed in other components in SFRs but does not affect SG integrity. Pure water stress corrosive crack, fretting, and so on are commonly observed in LWR SGs but are significantly less important in SFR SGs because of the high temperatures, high pressure differences, and chemical properties of liquid sodium. Based on these investigations, a prognostic model focusing on creep failures was developed. It estimates the failure probability profile by sampling the Larson-Miller parameter (LMP) and temperature and associated uncertainties through Monte Carlo methods. Two case studies were presented. The first one demonstrated the model’s capability to calculate the failure probability for a new specimen within a given time. If the tube is working under temperatures of 500C ± 3 and pressures of 15.2 MPa, which leads to LMP of 20,100 ± 50, the failure probabilities within 50, 70, and 90 years are approximately 0.1 %, 3.2 %, and 17.5 %. The second demonstrated how adjusting the workload can help to protect the integrity of the component. For an old tube reaching 99 % of its lifespan, continuing to run at temperatures of 500C ± 3 and LMP of 20,100 ± 50 leads to a failure probability of about 29.0 % within a year. If the temperature is reduced by 5C, the failure probability can be reduced to 2.5 %, and, if the pressure is also reduced such that the LMP is increased by 100, the failure probability can further be reduced to 0.12 %.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2021

Refueling Infrastructure Scoping and Feasibility Assessment for Hydrogen Rail Applications

The feasibility and component cost of hydrogen rail refueling infrastructure is examined. Example reference stations can inform future studies on components and systems specifically for hydrogen rail refueling facilities. All of the 5 designs considered assumed the bulk storage of liquid hydrogen on-site, from which either gaseous or liquid hydrogen would be dispensed. The first design was estimated to refuel 10 multiple unit trains per day, each train containing 260 kg of gaseous hydrogen at 350 bar on-board. The second base design targeted the refueling of 50 passenger locomotives, each with 400 kg of gaseous hydrogen on-board at 350 bar. Variations from this basic design were made to consider the effect of two different filling times, two different hydrogen compression methods, and two different station design approaches. For each design variation, components were sized, approximate costs were estimated for major components, and physical layouts were created. For both gaseous hydrogen-dispensing base designs, the design of direct-fill using a cryopump design was the lowest cost due to the high cost of the cascade storage system and gas compressor. The last three base designs all assumed that liquid hydrogen was dispensed into tender cars for freight locomotives that required 7,500 kg of liquid hydrogen, and the three different designs assumed that 5, 50, or 200 tender cars were refueled every day. The total component costs are very different for each design, because each design has a very different dispensing capacity. The total component cost for these three designs are driven by the cost of the liquid hydrogen tank; additionally, delivering that much liquid hydrogen to the refueling facility may not be practical. Many of the designs needed the use of multiple evaporators, compressors, and cryopumps operating in parallel to meet required flow rates. In the future, the components identified here can be improved and scaled-up to better fit the needs of heavy-duty refueling facilities. This study provides basic feasibility and first-order design guidance for hydrogen refueling facilities serving emerging rail applications.

08 HYDROGEN↗
DOE OSTI2024

Initial Demonstration of New Griffin Technologies for Simulating the Running-In Phase of Pebble Bed Reactors

Griffin is a reactor multiphysics modeling application based on MOOSE (Multiphysics Object-Oriented Simulation Environment) and specifically targeting transient modeling of advanced reactors. Griffin has been used recently to model pebble-bed reactors for the Nuclear Regulatory Commission (NRC) Office of Nuclear Regulatory Research and the Advanced Reactor Technology program. This modeling work has focused thus far on the direct calculation of equilibrium cores. This report documents an initial demonstration of a new running-in simulation capability. The new running-in capability is verified using the existing direct equilibrium core calculation capability. A simplified pebble-bed reactor model is then used to demonstrate the running-in simulation capability. This demonstration shows that Griffin is able to simulate years of operation during the running-in phase efficiently with each depletion step taking only several seconds. Two new technologies are also presented in this report which have been developed in Griffin that will be essential for improved accuracy both of the direct equilibrium core computation and the new running-in simulation capability. The first technology is an online cross section generation capability specifically targeted for pebble-bed reactors. This will improve the accuracy of the depletion calculation as the cross sections are generated at the exact core status. This also avoids the difficult step of pre-generating a separate standalone multigroup cross section set. Secondly, a newly implemented discretization for discontinuous finite element method (DFEM) SN transport in cylindrical (RZ) coordinates, which can be solved efficiently using the existing SN sweep solver, is discussed and some results are shown demonstrating the usefulness of the additional accuracy transport provides over a diffusion approximation.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2023

Irradiation creep measurement and microstructural analysis of chromium nitride–coated zirconium alloy using pressurized tubes

Environmental barrier coatings for Zr-based materials are currently under development to reduce oxidation and embrittlement in light-water reactors. Chromium nitride is one such candidate for this application, particularly as accident-tolerant fuel cladding. However, quantifying the impact of coatings on the irradiation-induced creep of zircaloy (Zry) is critical as this mechanism often exceeds thermal creep rates under light-water reactor operating conditions and can be a limiting design characteristic. Additionally, examining irradiation effects in the microstructure at the coating interface is key to understanding the compatibility of the material system. Here, to accelerate the experimental measurement of irradiation creep and microstructure evolution in CrN-Zry, compact, pressurized creep tubes were fabricated and irradiated in the High Flux Isotope Reactor (HFIR) at Oak Ridge National Laboratory. Miniature, thin-walled rodlets fabricated from annealed Zr-Sn barstock were coated with CrN using physical vapor deposition (PVD) to nominal thicknesses of 4 and 8 μm. Coated and uncoated rodlet specimens were internally pressurized and welded, generating nominal circumferential hoop stresses of 0, 90, or 180 MPa under 300°C irradiation conditions. Twelve specimens were measured diametrically prior to irradiation using a low-cost, automated, contactless laser profilometer developed for this work. Specimens were irradiated in sealed capsules for one 25-day HFIR cycle, accumulating approximately 1.8 $\times$ 10 21 n/cm 2 fast fluence (E n > 10 MeV). The irradiated samples were retrieved and remeasured using the same profilometry system in a shielded hot cell facility. Irradiation creep between specimens was compared using standard statistical tests and showed that both thicknesses of CrN coating had a negligible effect on the irradiation creep strain of the Zry material. Microstructure characterization of pre- and post-irradiated CrN-Zry specimens showed minimal changes due to irradiation but did show a substantial O-rich region at the Zry-CrN interface.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Formulation and solution approach for calibrating activity-based travel demand model-system via microsimulation

This study addresses the problem of calibrating utility-maximizing nested logit activity-based travel demand model-systems. After estimation, it is common practice to use aggregate measurements to calibrate the estimated model-system’s parameters prior to their application in transportation planning, policy making, and operations. However, calibration of activity-based model-systems has received much less attention. Existing calibration approaches are myopic heuristics in the sense that they do not consider the fundamental inter-dependencies among choice-models and do not have a systematic way to adjust model parameters. Also, other purely simulation-based approaches do not perform well in large-scale applications. In this study, we focus on utility-maximizing nested logit activity-based model-systems and calibrating aggregate statistics such as activity shares, mode shares, time-dependent & mode-specific OD flows, and time-dependent & mode-specific sensor counts. We formulate the calibration problem as a simulation-based optimization problem and propose a stochastic gradient-based solution procedure to solve it. The solution procedure relies on microsimulation to calculate expectations of the aggregate statistics of interest to the calibration problem. Additionally, we derive approximate analytical expressions for the gradient of the objective function —that are evaluated through microsimulation on mini-batches of the population. The proposed solution procedure is sensitive to the fundamental structure of the activity-based model-system and is non-myopic in considering the dependencies across its model components. The formulated optimization problem is non-convex, highly nonlinear, and potentially has multiple-minima. Lastly, we show —through a real-world application— that the proposed solution procedure outperforms other state-of-the-art purely simulation-based optimization approaches in terms of computational efficiency, stability, and convergence. We also compare various gradient-based solution algorithms to determine the best algorithm to update the parameters. This work has the potential to facilitate wider and easier application of activity-based model-systems.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Universal linear intensity transformations using spatially incoherent diffractive processors

Abstract Under spatially coherent light, a diffractive optical network composed of structured surfaces can be designed to perform any arbitrary complex-valued linear transformation between its input and output fields-of-view (FOVs) if the total number ( N ) of optimizable phase-only diffractive features is ≥~2 N i N o , where N i and N o refer to the number of useful pixels at the input and the output FOVs, respectively. Here we report the design of a spatially incoherent diffractive optical processor that can approximate any arbitrary linear transformation in time-averaged intensity between its input and output FOVs. Under spatially incoherent monochromatic light, the spatially varying intensity point spread function ( H ) of a diffractive network, corresponding to a given, arbitrarily-selected linear intensity transformation, can be written as H ( m , n ; m ′, n ′) = | h ( m , n ; m ′, n ′)| 2 , where h is the spatially coherent point spread function of the same diffractive network, and ( m , n ) and ( m ′, n ′) define the coordinates of the output and input FOVs, respectively. Using numerical simulations and deep learning, supervised through examples of input-output profiles, we demonstrate that a spatially incoherent diffractive network can be trained to all-optically perform any arbitrary linear intensity transformation between its input and output if N ≥ ~2 N i N o . We also report the design of spatially incoherent diffractive networks for linear processing of intensity information at multiple illumination wavelengths, operating simultaneously. Finally, we numerically demonstrate a diffractive network design that performs all-optical classification of handwritten digits under spatially incoherent illumination, achieving a test accuracy of >95%. Spatially incoherent diffractive networks will be broadly useful for designing all-optical visual processors that can work under natural light.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Spatiotemporal Super-Resolution with Generative Machine Learning for Creating Renewable Energy Resource Data Under Climate Change Scenarios

As we plan for a future with higher penetrations of renewables and increasing electrification, it becomes more important to understand how the electricity grid will operate under a variety of weather events. We must also consider that the weather our future grid will experience will be different and possibly more extreme than the historical weather that we have extensive data for. We can use data from global climate models (GCMs) to help understand how our climate may change over the next several decades, but there is often a significant gap between the low-resolution GCM data and the high-resolution weather data required to study power systems under specific weather events. Therefore, our objective in this work is to develop tools that can bridge this gap by using low-resolution GCM data to create realistic high-resolution weather datasets that can be used to study renewable energy generation and electricity demand. To accomplish this objective, we have developed a set of generative machine learning models that can rapidly downscale GCM daily average output data at an approximate grid resolution of 100km to hourly data at an approximate 4 km grid resolution. The models can be used to create high resolution data from nearly any GCM included in the Coupled Model Intercomparison Project (CMIP) Phase 5 or 6. Our methods include all datasets regularly used to study the integration of wind and solar power plants as well as changes in electricity demand due to heating and cooling loads. These models and datasets enable power systems modelers to study climate change-influenced weather events and their impact on the grid. We have downscaled and validated wind, solar, temperature, and humidity data with very promising results. The generative machine learning methods are computationally efficient and produce data that has similar statistical characteristics to current state-of-the-art historical datasets. We have trained initial generative models and produced an initial dataset collectively referred to as Sup3rCC: Super-Resolved Renewable Energy Resource Data with Climate Change Impacts. The data covers a (mostly) historical period from 2015-2025 and a future period from 2050-2059. We have also taken hypothetical high-electrification load data and scaled the heating and cooling loads with respect to the 2050-2059 high-resolution Sup3rCC meteorology. The results show how future levels of renewable energy generation and electrified load may be impacted by climate change, setting the stage for capacity expansion models to consider a dynamic climate through model years.

climate change↗
DOE OSTI2024

Wind Turbine Maintenance Costs: Assessing the Potential of Gear Oil Improvements

Wind turbine operations & maintenance (O&M) costs constitute a sizable portion of total energy cost for wind power. There are many components in a utility-scale wind turbine that need to be lubricated with either oil or grease. This study uses gearbox oil as an example and assesses how lubricant technology improvements may impact wind turbine power production and levelized cost of energy. Using the modeling tools (i.e., WOMBAT, reV, and SAM) developed at National Renewable Energy Laboratory and lubrication oil technology scenarios defined based on inputs provided by ExxonMobil and industry stakeholders, we quantify the potential for increasing energy production and reducing maintenance costs across the current and future U.S. fleet of wind turbines as a result of improvements in lubrication technologies. The modeled improvements reduce the median levelized cost of energy by 1-2% across the U.S. fleet. Cumulative saving from gear oil technology improvements in 2050 for U.S. fleet is estimated to be approximately $6 billion. Extending the lubricant replacement interval has a larger impact on total cost and energy production than reducing lubricant cost.

costs↗
DOE OSTI2024

A Data-Driven Methodology for Contextual Unit Commitment Using Regression Residuals

Day after day, system operators are faced with the challenge of taking unit commitment (UC) decisions under uncertain net load conditions. The standard operating procedure for taking UC decisions begins by leveraging auxiliary data on covariates (such as the day of the week or latest weather information) to generate a point prediction for net load, which is used in solving a deterministic UC problem. Such an approach, however, is known to deliver a notoriously poor out-of-sample (OOS) performance, as it completely disregards the stochastic nature of net load. While stochastic programming models explicitly represent uncertainty, they mostly do so using a generic set of scenarios that neglect covariate observations, squandering useful auxiliary data that could be harnessed to glean insights into uncertainty. In this article, we discuss a contextual stochastic optimization approach to UC, which effectively exploits covariate observations while explicitly assessing uncertainty so as to boost the OOS performance of UC decisions. The key thrust of our approach is to leverage regression models, along with their empirical residuals, to set up and solve sample average approximation problems. Not only do we prove that our approach satisfies the requisite conditions for asymptotic optimality and consistency laid out in (Kannan et al., 2022), but we also assess its performance on several case studies conducted using real-world data collected in California ISO and New York ISO grids. In conclusion, results show that the proposed approach can significantly improve OOS performance compared to alternative methods proposed in the literature under varying dataset sizes.

Yurdakul, Ogun↗
DOE OSTI2020

Neutronic Consideration of TREAT Facility Fuel SiC Recladding

The Transient Reactor Test (TREAT) Facility is an air-cooled reactor composed of graphite-urania fuel encapsulated in zirconium alloy canisters and surrounded by ~2 ft (~61 cm) of nuclear grade graphite. The primary role of TREAT is to provide in-pile nuclear-heated safety research via transient testing [1]. Benchmark efforts have demonstrated sensitivity of TREAT neutronics models to graphite cross section data and key neutron poisons found in the fuel assemblies [2]. During fabrication of TREAT fuel assemblies, a small concentration of boron was inadvertently introduced with variable concentration into the aggregate of fuel blocks prepared for TREAT use. The concentration of boron, and its respective uncertainty, represent the most sensitive component in a neutronics study; unfortunately, nothing can be done to reduce or otherwise mitigate its impact. Hydrogen in the fuel blocks reduces the mean free path of the core and its true quantity and uncertainty is approximated. The hydrogen remains from pitch binder used in the fuel fabrication process and was not completely released due to incomplete fuel graphitization. Destructive fuel testing is necessary to quantify the actual hydrogen content and to improve benchmark model quality. A third neutron poison of some significance is the hafnium impurity in the Zircaloy-3 (Zry-3) assembly cladding. The quantity of hafnium is expected to be within nominal manufacturing limits for nuclear grade zircaloy. Recladding the fuel assemblies with material that is less neutron absorbing is expected to benefit reactor neutron economy. Moreover, TREAT’s total transient energy is restricted based on the rate of Zry-3 oxidation in TREAT’s air coolant environment. Hence, cladding with more benign air reaction kinetics could dramatically uprate the facility’s experimental capability. Silicon carbide (SiC) has been proposed as a candidate material for recladding TREAT assemblies, as SiC/SiC demonstrate favorable properties within nuclear environments [3]. The current operational limits of TREAT are restricted by the oxidation of Zry-3. Change of the cladding to SiC could feasibly double the current temperature limitation. Recladding opportunities allow for previously damaged fuel assemblies to be returned to service, as Zry-3 is not commercially available. The initial effort required to consider proceeding with any recladding effort is to neutronically characterize key reactor parameters while replacing Zry-3 with SiC. A characterization summary, with respective results, is provided herein.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2023

SQMBox: Interfacing a semiempirical integral library to modular ab initio electronic structure enables new semiempirical methods

Ab initio and semiempirical electronic structure methods are usually implemented in separate software packages or use entirely different code paths. As a result, it can be time-consuming to transfer an established ab initio electronic structure scheme to a semiempirical Hamiltonian. Here we present an approach to unify ab initio and semiempirical electronic structure code paths based on a separation of the wavefunction ansatz and the needed matrix representations of operators. With this separation, the Hamiltonian can refer to either an ab initio or semiempirical treatment of the resulting integrals. We built a semiempirical integral library and interfaced it to the GPU-accelerated electronic structure code TeraChem. Equivalency between ab initio and semiempirical tight-binding Hamiltonian terms is assigned according to their dependence on the one-electron density matrix. The new library provides semiempirical equivalents of the Hamiltonian matrix and gradient intermediates, corresponding to those provided by the ab initio integral library. This enables the straightforward combination of semiempirical Hamiltonians with the full pre-existing ground and excited state functionality of the ab initio electronic structure code. We demonstrate the capability of this approach by combining the extended tight-binding method GFN1-xTB with both spin-restricted ensemble-referenced Kohn–Sham and complete active space methods. We also present a highly efficient GPU implementation of the semiempirical Mulliken-approximated Fock exchange. The additional computational cost for this term becomes negligible even on consumer-grade GPUs, enabling Mulliken-approximated exchange in tight-binding methods for essentially no additional cost.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Diagnosing and overcoming recombination and resistive losses in non-silicon solar cells using a silicon-inspired characterization platform

This project aimed to generate the characterization tools needed for accurate and systematic loss analysis in non-silicon photovoltaic solar cell technologies and, through the use of these tools and analysis techniques, contribute to the development of a novel class of hetero-contacts to II-VI absorbers, with the final goal of demonstrating record-breaking CdSeTe devices. CdSeTe solar cells provide a prime example of the potential impact of the techniques we proposed to develop and implement: record poly-CdSeTe cells have bandgap-voltage deficits (W oc ) of approximately 550 mV, as compared with below 400 mV for all other mature PV technologies. Similarly, these record CdSeTe devices have FFs below 80%, when other mature cells are near or above 85%. Frustratingly, a systematic identification of the origin of these sub-par performances—for example recombination or resistive losses—has been lacking, thus slowing down the development of these technologies. Similarly, it is often asserted that CdSeTe cells need a better back (hole) contact. Although most believe this is true, no one knew—at the start of this project—how high the V oc and FF could be for a given cell if it had a perfect back contact. Such characterization techniques and loss analysis methods exist and are routinely performed on c-Si solar cells (e.g. injection-dependent lifetime, Suns-V oc , transfer length method, etc). Over the years, they have been instrumental in the development of silicon devices that operate at 91% of their theoretical (Auger) limit. Lifetime testing, and the associated reconstruction of the implied-J-V curve, can moreover be performed at every cell-processing step, thus allowing a direct peek into the impact of that step on cell performance. Therefore, adapting these techniques and tools to non-Si devices would greatly improve their learning rate. In this project, we developed a Suns-ERE technique—the equipment, methodology, and know-how—to measure the implied-J-V curve, the pseudo-J-V curve, and the actual J-V curve of a thin-film solar cell, allowing an accurate assessment of the quality of the bulk material and its surface passivation, the selectivity of the contact, and its resistivity. We used this technique to show that the absorber of present CdSeTe solar cells is capable of achieving 1 V Voc,, that passivation layers exist (e.g., Al2O3) that can support such high voltages, and that the barrier is identifying contact layers that are both passivating and carrier-selective. The characterization platform created in this project and the understanding generated using it will accelerate the progress of non-silicon PV technologies. In particular, the project will contribute to CdSeTe solar cells with Voc > 1 V and cell efficiency > 24%. Such cells provide a pathway to module-level efficiencies >23%. As CdSeTe presently competes with silicon on module cost (in $\$ $/W) and yet has significantly more room for efficiency gains, the potential for LCOE reduction is particularly large. For example, CdTe modules with an efficiency of 21% would allow an LCOE below $\$ $0.04kWh -1 in average US climates.

14 SOLAR ENERGY↗
DOE OSTI2023

Development, Monitoring, and Control of Fracture Thermal Energy Storage in Crystalline Rock Formations (DEMO-FTES) [Abstract]

Approximately half of global energy consumption is used for heating and cooling. Because fossil fuels are used to meet most of this demand, heating and cooling of buildings produces a large portion of global greenhouse gas (GHG) emissions. The proposed project seeks to demonstrate the feasibility of a seasonal thermal energy storage concept called fracture thermal energy storage (FTES), which has the potential to dramatically lower energy requirements for heating and cooling and improve the resilience of building energy systems. FTES is a technique for building a highly efficient heat exchanger by creating a carefully designed set of fractures in the ground below a building. This heat exchanger allows thermal energy to be stored over seasonal timeframes—for example, hot thermal energy that is easily captured in the summer or energy from waste heat sources—to meet heating needs during colder months. FTES offers a solution with a relatively small footprint and lower cost compared to currently operating aquifer thermal energy storage (ATES) systems and could be used in the many areas without aquifers suitable for ATES. If commercialized, FTES systems could exceed the 2.5 TWh of energy storage per year from the more than 2,800 ATES systems in operation worldwide, which range from 0.1 MW to 30 MW. The savings in CO2 emissions per year are also expected to match or exceed individual ATES projects, with the largest, a 30 MW system used to provide heating and cooling to the University of Technology in Eindhoven in the Netherlands, estimated to save 13,000 tons of CO2 emissions per year. FTES utilizes mature drilling and fracturing technology and therefore has the potential to be rapidly commercialized once demonstrated. The ability to construct and establish flow through an FTES heat exchanger has been demonstrated by a previous project and the potential for efficient, large-capacity energy storage has been shown using numerical models. However, no experimental validation of these numerical estimates of thermal energy storage has been made. The first crucial need to advance FTES technology is to identify the sensitivity of key metrics such as thermal energy storage and production rates, capacities, and efficiencies to design parameters such as the number of fractures, depth/temperature of fractures, size of fractures, and circulation rates. The second crucial need is experimental testing of achievable thermal performance with optimized system design parameters. The proposed scope of work seeks to systematically address these two critical needs through a highly complementary international collaboration spanning theory, laboratory, and mesoscale field evaluation. The proposed work plan calls for using dimensional analysis and existing state-of-the-art numerical simulators to design carefully scaled laboratory and 10-meter-scale field tests of the thermal efficiency of FTES. The existing advanced laboratory and intermediate-scale field testbeds that will be used for this project will allow for detailed monitoring of the system performance during the test and of how the performance changes across time and length scales. These results will determine the feasibility of full- scale FTES systems. If the thermal performance is consistent with model predictions, the results will provide a strong economic justification for rapid commercialization of FTES technology in a wide range of geographical areas.

25 ENERGY STORAGE↗