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DOE OSTI2021

Radiation Effects in III-V Solar Cells Grown by Dynamic Hydride Vapor Phase Epitaxy

The recent development of hydride vapor phase epitaxy (HVPE) is potentially promising as a route to lower the cost of high-efficiency III-V solar cells for space applications. HVPE produces the same materials and device structures as the industry-standard organometallic vapor phase epitaxy (OMVPE) process, and although HVPE has shown promising device efficiencies, it lags the OMVPE growth technique in technical maturity. For example, there are no reports of the performance of HVPE-grown devices in radiation environments. There is an expectation that high-quality (that is, single crystal and low-defect) III-V materials will behave similarly regardless of the growth method. However, it is important to verify assumptions as facts. In this project, we produced materials and devices using both HVPE and OMVPE at NREL and irradiated them using 1 MeV electrons to ascertain the effect of radiation on the materials. We also performed initial work on producing more radiation-hard structures, including devices with the pn junction at the front of the device, and devices with a graded doping profile, both of which help with radiation tolerance. Measurements of the devices post radiation exposure showed that the open-circuit voltage (V OC ) of the HVPE-grown solar cells degraded least on a percent basis, although they also started from a lower baseline than the OMVPE-grown devices. All solar cells reached approximately the same V OC after irradiation, indicating that the dose used was sufficient to degrade all device equally. The short-circuit current in the HVPE devices did degrade more than the other solar cells, and this was attributed to a higher-than-expected doping density in the base layer of that cell. The results of these experiments, while by no means comprehensive, do not show any material difference in the radiation effects in OMVPE- and HVPE-grown materials and devices.

14 SOLAR ENERGY↗

DOE OSTI2022

Perovskite Sorbent Oxygen Separation Modeling with MFiX

This document chronicles the development and implementation of computational kinetic rate models that capture absorption and desorption characteristics of the National Energy Technology Laboratory (NETL) developed perovskite, Sr 1-x Ca x FeO 3-δ . Two paths to create accurate kinetic rates were followed: (1) an isothermal rate approach where thermogravimetric (TGA) data are recast as oxygen capacities through a pseudo-second order Lagergren equation (He et al., 2009); and (2) a more traditional Arrhenius approach where experimental data are fit with a power law model to derive associate activation energies (Bulfin et al., 2020a). For reference, the mathematics and associate experimental strategies that support these derivations are included in this report. In addition, computational fluid dynamics (CFD) models were developed to utilize both kinetic rate formulations and applied to simulate oxygen uptake and release in small scale scenarios. The program Multiphase Flow with interphase eXchanges (MFiX) was used to create: (1) discrete element method (DEM) simulations of a single tube of granular perovskite experiencing isothermal O 2 -absorption; and desorption and (2) two-fluid-model (TFM) non-isothermal simulations of perovskite O 2 -absorption and desorption tubes that share a wall. Conjugate heat transfer between steel walled tubes and the perovskite bed were managed with user-defined functions. As the project moves to simulating larger scale devices that will require more robust conjugate heat transfer methods, developed kinetic rates and associate methodologies have been recast for use in the ANSYS Fluent CFD program.

36 MATERIALS SCIENCE↗

DOE OSTI2022

Exponential integrators for non-linear diffusion

The goal of this project is to compare the performance of exponential time integrators with traditional methods such as diagonally implicit Runge-Kutta methods in the context of solving the system of reduced magnetohydrodynamics (RMHD). In this report, we present initial results of a proof of concept study that shows that exponential integrators can be an efficient alternative to traditional integration schemes.

42 ENGINEERING↗

DOE OSTI2022

Multi-group Examination of Nickel-Reflected HEU System [Slides]

Researchers noted an unusually large bias for 8-in. nickel reflected HEU sphere in HMF-003 between the 252- group library and the CE library in SCALE 6.2.4. The bias was investigated by reviewing reactions that $k_{eff}$ is sensitive to using TSUNAMI and collecting reaction rate data using tools within SCALE. For this system, bias is primarily due to the elastic scattering in nickel. Researchers compared new multigroup structures in SCALE 6.3. For this system, reduction in CE-to-MG bias seen in the 302-group and further improved in the 1597-group structure. When researchers compared ENDF/B-VIII.0 in SCALE 6.3, library showed improved results with new nickel evaluation. However, the MG-to-CE bias can still be large as large biases can occur in any system. This example highlights the importance of validating results with measured systems.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

DOE OSTI2024

ML-Based Rock Properties and Seismic Volume Enhancement

This project aims to improve field-scale Carbon Capture and Storage (CCS) assessments by enhancing petrophysical and geophysical log predictions through machine learning and neural networks. In our work during EY23, we applied Conditional Variational Autoencoders (CVAEs) to predict compressional velocity (Vp) and assess CO2 saturation levels in geological formations at the Illinois Basin Decatur Project (IBDP). In another task, we improved full-waveform inversion (FWI) methods with machine-learning approaches using lithological constraints. Full-waveform inversion (FWI) of seismic data estimates the elastic properties of subsurface rocks with high spatial resolution.

Nathanail, Athanasios↗

DOE OSTI2024

Zero-Emissions Roadmap for Oakland County

This document outlines a strategic pathway for Oakland County, Michigan to achieve zero emissions by 2050 under the Clean Energy to Communities (C2C) Program. The report encompasses analyses and high-level modeling to guide Oakland County in its emission reduction goals. Key methods include establishing an emissions inventory baseline with ongoing evaluation of future projects’ emissions impacts. Core elements include decommissioning old infrastructure, enhancing energy efficiency, deploying hybrid and ground-source heat pumps, and transitioning to electric fleets. It is proposed to structure the planning process into 5-year strategic plans to make the decarbonization process manageable. The document also emphasizes the need for reassessment of goals and periodic updates to remain adaptive to technological advancements and funding considerations. A matrixed approach for evaluating projects by cost and emissions savings is suggested to optimize decision-making given finite resources.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

DOE OSTI2023

Nanosecond Laser Drying in Wafer Foundries for Significant Energy Efficiency Improvement

Surface drying is a critical process in microelectronics wafer fabrication. In order to reduce and eliminate watermarks, many surface drying processes have been studied and used. Most of the wafer foundries currently use drying processes based on Isopropyl Alcohol (IPA), which causes serious safety (fire), health, environmental, and energy efficiency issues. The long-term goal of this project is to develop a fast, effective, chemical-free, and extremely energy efficient wafer drying technology that employs nanosecond laser-induced sub-surface evaporation and explosion. The objectives of this project are to 1) experimentally develop and verify the laser surface drying method, explore and identify how and to what extent various physical parameters influence the drying efficiency and effectiveness; and 2) understand and optimize the underlying thermodynamics in the proposed laser surface drying, including micro/nanoscale sub-surface superheating, evaporation, explosion, and photo/thermochemical bond breaking via atomistic modeling and experimental characterization. Toward these objectives, five tasks have been accomplished successfully, including 1) develop in-lab laser surface drying setup and study the effect of varied laser parameters, 2) use multiscale hybrid-modeling to study and understand the physics of water behavior during drying toward process optimization, 3) investigate the effect of drying conditions on the drying effectiveness, 4) develop a large-scale laser drying scanning system with automatic scanning, and 5) conduct large-scale simulation to study the effect of dopant level and develop strategies for system development toward industry deployment.

42 ENGINEERING↗

DOE OSTI2025

LNPK 156 Geothermal Coalition: Designing and Deploying Clean Energy in a Justice40 Cold Climate Community

The LNPK 156 Geothermal Coalition formed around the idea of building a new geothermal heating and cooling system in Duluth, MN for the Lincoln Park Justice40 neighborhood. The Coalition has been spearheaded by the City of Duluth and the local non-profit Ecolibrium3, and is named after the census tract of the neighborhood containing large portions of Lincoln Park, as well as a wastewater treatment plant, managed by the Western Lake Superior Sanitary District (WLSSD). The idea of building a geothermal system came to fruition over the period of about a decade, with repeated observations that the warm water discharged from the wastewater treatment plant was an energy-rich resource that has currently been untapped and released into the St. Louis River. The objective of this project was to build a geothermal system that harnesses wastewater heat recovery methods and satisfies the demand in a portion of Lincoln Park – a neighborhood experiencing high energy burdens, low life-expectancy, and where the natural gas and fuel oil used to heat homes and businesses results in decreased air quality and increased carbon emissions. The goal was to utilize this wastewater heat recovery method to benefit the neighborhood hosting the wastewater plant itself.

15 GEOTHERMAL ENERGY↗

DOE OSTI2025

An Integrated Paradigm for the Management of Delivery Risk in Electricity Markets: From Batteries to Insurance and Beyond

If power systems transition to integrate higher amounts of variable renewable energy sources, storage technologies, and distributed energy resources (DERs), new risk management frameworks are necessary to ensure cost-effective and reliable power system operations. Projects funded by the Advanced Research Projects Agency-Energy (ARPA-E) Performance-based Energy Resource Feedback, Optimization, and Risk Management (PERFORM) program aim to contribute new risk management frameworks by developing methods to quantify and manage risk at grid asset and system levels. The National Renewable Energy Laboratory (NREL) led a PERFORM project in collaboration with the Johns Hopkins University, the Electric Power Research Institute (EPRI), kWh Analytics, Packetized Energy, and Imperial Consultants (ICON). The project addressed two challenges related to risk management in electricity markets: managing net load imbalances and flexibility from DERs. This final technical report presents a list of project accomplishments, activities, and outputs.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2025

Recent Collaborations and Innovations to Demonstrate Next-Generation Techniques for Monitoring Subsurface Carbon Storage

World Carbon Capture, Utilization, and Storage (CCUS) Conference, Bergen, Norway, September 1–4, 2025. This talk provides a high-level overview of many novel and sustainable carbon storage-monitoring methods to accelerate the deployment of CCUS technologies at future CCUS sites across the United States. The Energy & Environmental Research Center’s work impacts the general CCUS industry by providing novel low-impact methods for tracking the injected plume’s migration and more autonomous data collection and processing techniques for performing assurance monitoring. Specifically, the results benefit 1) CCUS community members through knowledge sharing of lessons learned; 2) CCUS operators through commercialization of additional methods, including improvements to workflows and simplification of fieldwork; and 3) CCUS project stakeholders through implementation of low-impact and more autonomous monitoring solutions.

02 PETROLEUM↗

DOE OSTI2026

Techno-Economic Evaluation of Electrified Vehicle Options in Drayage Fleets

The electrification of drayage fleets offers potential economic and operational benefits, but the financial viability of electrified vehicles remains sensitive to battery cost, energy price, and fleet usage patterns. While total cost of ownership (TCO) is a useful benchmark, fleet operators and investors are equally concerned with investment performance metrics such as payback period (PB) and Internal Rate of Return (IRR), which better reflect financial risks and investment return timelines. This study develops a unified techno-economic framework that jointly evaluates TCO, PB, and IRR to determine when electrified trucks become cost-effective alternatives to diesel trucks. Building on a previously developed cost modeling tool and using real-world telematics data from a Class 8 drayage fleet at the Port of Savannah, the analysis incorporates projected battery cost trajectories, electricity and diesel price trends, vehicle efficiency improvements, and multiple battery capacities. Parameter ranges reflect widely cited projections and observed drayage-duty-cycle variability. A surrogate-modeling method approximates economic performance across thousands of battery cost–electricity price combinations, enabling high-resolution identification of conditions that achieve TCO parity, acceptable PB thresholds, and target IRR levels. Additionally, the study estimates the evolving share of the fleet that can feasibly electrify over time under multiple economic metrics. This integrated framework offers a novel, data-driven approach to inform risk-aware decision-making for fleet electrification and supports investment planning under evolving cost and operational conditions.

Sun, Ruixiao [ORNL] (ORCID:0000000341768676)↗

DOE OSTI2023

On the characteristics of the wake of a wind turbine undergoing large motions caused by a floating structure: an insight based on experiments and multi-fidelity simulations from the OC6 project Phase III

This study reports the results of the second round of analyses of the Offshore Code Comparison, Collaboration, Continued, with Correlation and unCertainty (OC6) project Phase III. While the first round investigated rotor aerodynamic loading, here, focus is given to the wake behavior of a floating wind turbine under large motion. Wind tunnel experimental data from the UNsteady Aerodynamics for FLOating Wind (UNAFLOW) project are compared with the results of simulations provided by participants with methods and codes of different levels of fidelity. The effect of platform motion on both the near and the far wake is investigated. More specifically, the behavior of tip vortices in the near wake is evaluated through multiple metrics, such as streamwise position, core radius, convection velocity, and circulation. Additionally, the onset of velocity oscillations in the far wake is analyzed because this can have a negative effect on stability and loading of downstream rotors. Results in the near wake for unsteady cases confirm that simulations and experiments tend to diverge from the expected linearized quasi-steady behavior when the rotor reduced frequency increases over 0.5. Additionally, differences across the simulations become significant, suggesting that further efforts are required to tune the currently available methodologies in order to correctly evaluate the aerodynamic response of a floating wind turbine in unsteady conditions. Regarding the far wake, it is seen that, in some conditions, numerical methods overpredict the impact of platform motion on the velocity fluctuations. Moreover, results suggest that the effect of platform motion on the far wake, differently from original expectations about a faster wake recovery in a floating wind turbine, seems to be limited or even oriented to the generation of a wake less prone to dissipation.

17 WIND ENERGY↗

DOE OSTI2020

Stochastic Analysis for Long Term Capital Structures, Systems, and Components Refurbishment and Replacement

As commercial Nuclear Power Plants (NPPs) pursue extended plant operation in the form of Second License Renewal (SLR), opportunities exist for these plants to provide capital investments to ensure long-term safe and economic performance. At the current time, several utilities have announced an intention to pursue extended operation for one or more of their NPPs via SLR . The goal of this research is to develop a risk-informed approach to evaluate and prioritize plant capital investments made in preparation for, and during the period of, extended plant operations to support decisions for NPP operations. Since the capital investments are influenced by various factors, such as markets, safety and regulatory, the decision-making process of NPP operations should take into account relevant factors for balancing risks, costs and profits. The traditional method of capital budgeting is based on the priority list of candidate projects using economic measures such as benefit-investment ratio, net present value (NPV) and internal rate of return. In the literatures, the problem of capital budgeting or the variant can be represented by an appropriate knapsack problem. The knapsack approach to capital budgeting takes as input as investment, along with the cost and profit of each project. The objective of capital budgeting is to find the combination of the binary decisions for every investment such that the overall profit is as large as possible. The output is a collection of projects to be carried out, and we refer this selected collection of projects as a project portfolio. One limitation of traditional optimization models for capital budgeting is that they do not account for risk/uncertainty in profit and cost streams associated with individual projects, they do not account for risk in resource availability in future years [1,2,3]. Projects can incur cost over-runs, especially when projects are large, performed infrequently, and when there is risk regarding technical viability, external contractors, and/or suppliers of requisite parts and materials. Occasionally, projects are performed ahead of schedule and with cost savings. Planned budgets for capital improvements can be cut and key personnel may be lost. Or, there may be surprise windfalls in budgets for maintenance activities due to decreased costs for “unplanned” maintenance. In these cases, how should we resolve capital budgeting when we have risk forecasts for costs, profits and budgets? One approach we proposed in this summary is to re-solve the optimization models based on assumed statistical distributions of given parameters. If these distributions were not available, a two-stage stochastic optimization approach can be used to provide priority lists to decision-makers to support better risk-informed decisions [4, 5]. In this summary, we will only focus on the first approach.

42 ENGINEERING↗

DOE OSTI2021

Modular High Temperature Gas-cooled Reactor: Accident Analysis

Types of Potential Accidents and Reactor Response Codes and Tools Experimental Validation Safety Analysis Approach Licensing Modernization Project Use of PRA in LMP, ASME/ANS, Non-LWR PRA Standard Methods for Incorporating Passive System Reliability into a PRA

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

DOE OSTI2023

Machine learning for detection of 3D features using sparse x-ray tomographic reconstruction

In many inertial confinement fusion (ICF) experiments, the neutron yield and other parameters cannot be completely accounted for with one and two dimensional models. This discrepancy suggests that there are three dimensional effects that may be significant. Sources of these effects include defects in the shells and defects in shell interfaces, the fill tube of the capsule, and the joint feature in double shell targets. Due to their ability to penetrate materials, x rays are used to capture the internal structure of objects. Methods such as computational tomography use x-ray radiographs from hundreds of projections, in order to reconstruct a three dimensional model of the object. In experimental environments, such as the National Ignition Facility and Omega-60, the availability of these views is scarce, and in many cases only consists of a single line of sight. Mathematical reconstruction of a 3D object from sparse views is an ill-posed inverse problem. These types of problems are typically solved by utilizing prior information. Neural networks have been used for the task of 3D reconstruction as they are capable of encoding and leveraging this prior information. We utilize half a dozen, different convolutional neural networks to produce different 3D representations of ICF implosions from the experimental data. Deep supervision is utilized to train a neural network to produce high-resolution reconstructions. These representations are used to track 3D features of the capsules, such as the ablator, inner shell, and the joint between shell hemispheres. Machine learning, supplemented by different priors, is a promising method for 3D reconstructions in ICF and x-ray radiography, in general.

Wolfe, Bradley T. (ORCID:0000000268301614)↗

DOE OSTI2022

Coal Fired Power Plant Configuration and Operation Impact on Plant Effluent Contaminants and Conditions

The primary objective of this project is to characterize coal contaminants in coal-fired power plant wastewater as a function of coal type, unit configurations, and unit operation profile with uncertainty analysis. This project was in response to the U.S. Department of Energy (DOE) Solicitation DE-FOA-0001842. The project duration was between September 01, 2018, and December 31, 2021 (no-cost extension filed, due to the Covid-19 pandemic restrictions, and approved). Field and lab test program was conducted with the main goal to characterize coal contaminants in coal-fired power plant wastewater as a function of coal type, unit configurations, and unit operation profile with uncertainty analysis. In this project, the team of Lehigh University (prime recipient) and Western Kentucky University identified two suitable Thermoelectric Power Plants (TTPs) firing bituminous and sub-bituminous coals respectively, designed test plans, and performed sample collection. Sampling included coal from each TTPs power generation units, Wet Flue Gas Desulfurization (WFGD) slurry material and waste-water samples taken from the outlet of the water treatment tank prior to discharge and other pertinent locations. Coal samples are dried, crushed, and pulverized according to the American Society for Testing and Materials (ASTM) methods. The prepared coal samples are analyzed for normal proximate and ultimate analysis tests in addition to the toxic metals and anions according to ASTM methods. The FGD slurry materials are analyzed for toxic metals and anions according to Electric Power Research Institute (EPRI) or Environmental Protection Agency (EPA) methods, as appropriate. The wastewater samples from the water treatment tank outlet are analyzed for toxic metals and anions according to EPA methods. The effluent species analyzed include mercury, arsenic, selenium, nitrate/nitrite, bromide, and chlorine. This project provided results of effluent conditions as a function of coal type, unit configuration, and unit operation profile, and identified the levels of uncertainty in the effluent results.

01 COAL, LIGNITE, AND PEAT↗

DOE OSTI2023

An Incremental Gradient Method for Optimization Problems With Variational Inequality Constraints

We consider minimizing a sum of agent-specific nondifferentiable merely convex functions over the solution set of a variational inequality (VI) problem in that each agent is associated with a local monotone mapping. This problem finds an application in computation of the best equilibrium in nonlinear complementarity problems arising in transportation networks. We develop an iteratively regularized incremental gradient method where at each iteration, agents communicate over a directed cycle graph to update their solution iterates using their local information about the objective and the mapping. The proposed method is single-timescale in the sense that it does not involve any excessive hard-to-project computation per iteration. We derive nonasymptotic agent-wise convergence rates for the suboptimality of the global objective function and infeasibility of the VI constraints measured by a suitably defined dual gap function. Finally, the proposed method appears to be the first fully iterative scheme equipped with iteration complexity that can address distributed optimization problems with VI constraints over cycle graphs.

convergence↗

DOE OSTI2024

Bottom-up design of actinide materials from molecular clusters: Demonstration of a general-purpose simulation capability leveraging machine-learned atomic potentials

Actinide thin-film coatings such as uranium dioxide (UO 2 ) play an important role in nuclear reactors and other mission-relevant applications, but realization of their potential requires a deep fundamental understanding of the chemical vapor deposition (CVD) processes used for their growth. The slow experimental progress can be attributed, in part, to the standard safety guidelines associated with handling uranium byproducts, which are often corrosive, toxic, and radioactive. Accurate simulation techniques, when used in concert with experiment, can improve laboratory safety, material durability, and deliverable timeframes. However, state-of-the-art computational methods are either insufficiently accurate or intractably expensive. To remedy this situation, in this project we suggested a machine-learning (ML) accelerated workflow for simulating molecular clustering toward deposition. As a benchmark test case, we considered molecular clustering in steam and assessed independent components of our workflow by comparing with measured thermodynamic properties of water. After analyzing each component individually and finding no fundamental barrier to realization of the workflow, we attempted to integrate the ML component, a Sandia-developed tool called FitSNAP. As this was the first application of FitSNAP to atoms and molecules in the gas phase at Sandia, the method required more fitting data than was originally anticipated. Systematic improvements were made by including in the fit data diatomic potentials, molecular single-bond-breaking curves, and symmetry-constrained intermolecular potentials. We concluded that our strategy provides a feasible pathway toward modeling CVD and related processes, but that extensive training data must be generated before it can be of practical use.

36 MATERIALS SCIENCE↗