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At least 307 records · Page 17

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DOE OSTI2026

Propagation method and planting density influence canopy developmental transition and biomass productivity in Miscanthus × giganteus

Understanding how establishment practices influence the mechanisms underlying Miscanthus × giganteus (miscanthus) productivity and canopy development is critical for optimizing management. Data was collected during the juvenile (2011–2013) and mature (2024) phases of a long-term field experiment established in Urbana, Illinois, to evaluate the effects of propagation method (plug propagation [PP] and rhizome propagation [RP]), planting density (1.0, 0.75, and 0.25 plants m⁻²), and nitrogen application (0 and 67 kg N ha⁻¹) on end-of-season biomass yield, tiller mass, tiller density, and tiller height. Linear regression models identified the dominant predictors of yield across stand ages and management regimes. Planting density, nitrogen (N) application, and propagation method significantly influenced early yield and canopy development. During the juvenile phase, biomass yield was driven by tiller density due to canopy expansion; in the mature phase, yield became driven by tiller mass. The PP plots produced higher tiller density than the RP plots, resulting in faster canopy closure and higher juvenile-phase yields. Rhizome-propagated (RP) plots produced lower tiller density, but individual tillers were 3.3–6.4 g tiller −1 heavier than PP tillers. After the canopy reached equilibrium, the PP and RP yields were similar because greater RP tiller mass compensated for its lower tiller density. Higher planting density resulted in greater yield and tiller density during the second year (2012), but this effect was absent from the third year (2013) onward. In the juvenile phase, N fertilization enhanced yield by 1.6–3.4 Mg ha −1 . Initiating fertilization in 2013 on unfertilized plots produced biomass similar to that in fertilized plots, suggesting yield recovery in the mature phase. These findings revealed that establishment strategies, including propagation method and planting density, influence juvenile miscanthus canopy development and productivity, transitioning from tiller-density- to mass-dominated yields, but not mature phase productivity.

09 BIOMASS FUELS↗

DOE OSTI2025

Development and characterization of an aerosol-generated multi-method analytical particle test material

Ceria (CeO 2 ) particles with low to ultra-low loading of nickel dopant were produced using an aerosol-based, droplet-to-particle synthesis via an in-line calcination technique. This aerosol-based synthesis method enables the production of particles with a monodisperse size distribution. These produced and well-characterized, multi-element, ceria-based particles demonstrate a material exemplar for multi-method analytical testing. They were prepared from a cerium nitrate feedstock where low loading nickel dopant was added at target Ni/(Ni + Ce) atomic percents of 1 %, 0.1 %, and 0.01 %, using a nickel nitrate spike. This methodology proved to produce ceria particles doped with a dynamic range of low to ultra-low loadings of nickel over a 24-h period, with consistent size distribution, morphology, and composition. The successful incorporation of nickel was demonstrated with bulk and single particle inductively coupled plasma mass spectroscopy and revealed notable particle-to-particle elemental homogeneity. X-ray photoelectron spectroscopy demonstrated the presence of a high concentration of nickel dopant incorporated preferentially toward the surface of the particles, and that this dopant aided oxidation of surface Ce(III) atoms to Ce(IV). These particle test materials were then validated through X-ray absorption near edge spectroscopy, comparing the ultra-low 0.01 % Ni and low 1 % Ni-doped ceria samples. This revealed a more-reduced oxidation state of the nickel with an increase in dopant concentration. Finally, this work demonstrates a synthesis and systematic characterization scheme to produce multi-method analytical test particulates.

36 MATERIALS SCIENCE↗

DOE OSTI2024

A regularized-interface method as a unified formulation for simulations of high-pressure multiphase flows

The injection of multi-species fluids into high-pressure and high-temperature environments beyond the species' critical points is commonly found in engineering applications. At these conditions, for immiscible species, both subcritical interfacial dynamics and supercritical mixing can coexist due to variations in temperature around the mixture critical point. The modeling of these complex transcritical phenomena for large-scale configurations is so far not possible. To address this issue, we propose the Regularized-Interface Method (RIM) as a unified formulation that can describe both sub- and supercritical processes as well as the transition between them. The proposed method is derived via filtering of the nanoscale interface-resolving formulation based on van der Waals' linear gradient theory. Thus, this approach allows for the consistent modeling of interfacial dynamics that vanishes at supercritical conditions, while significantly reducing the temporal and spatial resolution constraints of the original nanoscale formulation. The resulting RIM formulation is examined in interface-capturing simulations of sub-, trans-, and supercritical fuel injection processes, involving droplets and jets. Furthermore, these results highlight the importance of resolving spatio-temporal transitions from subcritical interfacial dynamics to supercritical mixing in high-pressure multiphase simulations, in contrast to commonly employed diffused-interface methods, where interfacial dynamics are often neglected.

Interface capturing↗

DOE OSTI2024

A performant energy-conserving particle reweighting method for Particle-in-Cell simulations

A new particle-based reweighting method is developed and demonstrated in the Aleph Particle-in-Cell with Direct Simulation Monte Carlo (PIC-DSMC) program. Novel splitting and merging algorithms ensure that modified particles maintain physically consistent positions and velocities. This method allows a single reweighting simulation to efficiently model plasma evolution over orders of magnitude variation in density, while accurately preserving energy distribution functions (EDFs). Demonstrations on electrostatic sheath and collisional rate dynamics show that reweighting simulations achieve accuracy comparable to fixed weight simulations with substantial computational time savings. This highly performant reweighting method is recommended for modeling plasma applications that require accurate resolution of EDFs or exhibit significant density variations in time or space.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

DOE OSTI2025

A time-parallel multiple-shooting method for large-scale quantum optimal control

Quantum optimal control plays a crucial role in quantum computing by providing the interface between compiler and hardware. Solving the optimal control problem is particularly challenging for multi-qubit gates, due to the exponential growth in computational complexity with the system's dimensionality and the deterioration of optimization convergence. To ameliorate the computational complexity of time-integration, this paper introduces a multiple-shooting approach in which the time domain is divided into multiple windows and the intermediate states at window boundaries are treated as additional optimization variables. Further, this enables parallel computation of state evolution across time-windows, significantly accelerating objective function and gradient evaluations. Since the initial state matrix in each window is only guaranteed to be unitary upon convergence of the optimization algorithm, the conventional gate trace infidelity is replaced by a generalized infidelity that is convex for non-unitary state matrices. Continuity of the state across window boundaries is enforced by equality constraints. A quadratic penalty optimization method is used to solve the constrained optimal control problem, and an efficient adjoint technique is employed to calculate the gradients in each iteration. We demonstrate the effectiveness of the proposed method through numerical experiments on quantum Fourier transform gates in systems with 2, 3, and 4 qubits, noting a speedup of 80x for evaluating the gradient in the 4-qubit case, highlighting the method's potential for optimizing control pulses in multi-qubit quantum systems.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

DOE OSTI2025

Extending wire-arc directed energy deposition using non-gravity aligned (NGA) torch methods

For standard Additive Manufacturing (AM) processes, traditional path planning typically relies on a 2.5-dimensional approach. In wire-arc directed energy deposition (DED), commonly referred to as wire-arc additive manufacturing (WAAM), this 2.5D approach inherently limits final near net shape due to the stair-step effect and restricts the maximum overhang angle achievable without part degradation. To achieve better near net shape and part quality, a 3D planning approach that modulates the tool tip position and angle without process changes is demonstrated in components containing up to 105° of unsupported overhang. The experimental methods are validated with half and fully enclosed cylinder sections containing 90° of overhang. The non-gravity aligned methods are then applied to a commercial WAAM system for a composite tool mold demonstrator part. As a result, the methods developed in this paper enable expansion of WAAM system capabilities to parts containing large overhangs without compromising the net shape or material structure of the resulting parts and without the need for a part positioner.

Additive manufacturing↗

DOE OSTI2025

Benchmarking optimization methods for materials research: Gradient descent and Bayesian optimization for lithium-ion battery aging diagnostics

Accurate and efficient parameter estimation is essential for battery diagnostics and aging analysis. Here, in this study, we compare two optimization-based approaches—gradient descent and Bayesian optimization—for extracting parameters from differential voltage analysis in lithium-ion batteries. While these techniques are widely used, their relative strengths and limitations for this application are not well understood. The study evaluates the trade-offs between these methods in terms of result quality, computational cost, and reliability within this specific application. The diagnostic results from our battery data suggest adopting gradient descent as an initial method for rapid and efficient analysis, while employing more stable optimization techniques, such as Bayesian optimization, as a verification step to mitigate potential instability. Comparing the two methods provides information on algorithmic choice, while inspiring further discussions on selecting appropriate techniques for specific research tasks.

Zhao, Ziqing [Boston Univ., MA (United States)] (O↗

DOE OSTI2025

A review of quantum imaging methods and enabling technologies

The field of quantum imaging has recently seen an explosion of new methods and techniques. At the heart of most quantum imaging approaches are three enabling technologies: the source of the quantum light, the detectors that turn sensing into imaging, and the method by which the quantum nature of light is leveraged to improve the desired measurement. This review addresses the dominant and emerging technologies in each of these components as well as details concerning the implementations of various quantum imaging methods.

36 MATERIALS SCIENCE↗

DOE OSTI2024

Iterative methods in GPU-resident linear solvers for nonlinear constrained optimization

Linear solvers are major computational bottlenecks in a wide range of decision support and optimization computations. The challenges become even more pronounced on heterogeneous hardware, where traditional sparse numerical linear algebra methods are often inefficient. For example, methods for solving ill-conditioned linear systems have relied on conditional branching, which degrades performance on hardware accelerators such as graphical processing units (GPUs). To improve the efficiency of solving ill-conditioned systems, our computational strategy separates computations that are efficient on GPUs from those that need to run on traditional central processing units (CPUs). Our strategy maximizes the reuse of expensive CPU computations. Iterative methods, which thus far have not been broadly used for ill-conditioned linear systems, play an important role in our approach. In particular, we extend ideas from Arioli et al., (2007) to implement iterative refinement using inexact LU factors and flexible generalized minimal residual (FGMRES), with the aim of efficient performance on GPUs. In conclusion, we focus on solutions that are effective within broader application contexts, and discuss how early performance tests could be improved to be more predictive of the performance in a realistic environment.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2025

Non-destructive evaluation and machine learning methods for inspection of spent nuclear fuel canisters: A state-of-the-art review

Nuclear energy is among the cleanest and most efficient energy sources currently available. The operation of nuclear power plants (NPPs) produces large amounts of high-level radioactive waste known as spent nuclear fuel (SNF). Currently, large amounts of SNF is stored in dry cask storage systems (DCSSs) for extended interim storage until a permanent disposal solution becomes available. During the extended interim storage, the DCSS, particularly the SNF canisters, may degrade and abnormal conditions may occur. Therefore, non-destructive evaluation (NDE) and machine learning (ML) approaches are necessary for inspection of SNF canisters. This paper presents a state-of-the-art review of literature by summarizing recent progress made on the applications of NDE and ML for inspection of SNF canisters. Sixteen NDE methods are examined and compared: visual inspection, ultrasonic guided waves (UGWs), laser-based approaches, acoustic emission (AE), eddy current testing (ECT), non-invasive acoustic sensing, dynamic modal testing, cosmic ray muons tomography, neutron imaging, gamma rays detection, fiber optical sensors, through-wall communications, X-ray computed tomography (CT), vibrothermography, monoenergetic photon sources, and surface acoustic wave (SAW) sensors. The technology readiness level (TRL) for each method is assessed and compared. Recent publications on ML-enhanced visual inspection, AE, non-invasive acoustic sensing, dynamic modal testing, and neutron imaging for SNF canisters are summarized and future research needs are identified. In conclusion, this review article provides a convenient reference on the state-of-the-art applications of NDE and ML methods for inspection of SNF canisters.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

DOE OSTI2025

Comparative Analysis of Rear Irradiance Modeling Methods for Bifacial PV Systems on Single-Axis Trackers Under Varying Albedo Conditions

This study compares three rear-side irradiance modeling methods for bifacial PV systems on single-axis trackers: (i) the 2D View Factor (VF) model in PVsyst(R), (ii) the open-source PVFactors VF model, and (iii) the Ray Tracing (RT) technique using bifacial_radiance. Simulations were benchmarked against field measurements from a pilot PV plant with rear-side sensors at the torque tube height. Results show that all models underestimated the non-uniformity of rear irradiance along the module length and overestimated the total rear irradiance incident on the module. However, since bifacial gain represents only a fraction of the system's total energy, the resulting energy yield differences among methods remained within +- 2 % of measured values, which is typical for such simulations. While the overall energy impact is limited, this study characterizes the specific limitations of each modeling approach, supporting further improvements in bifacial PV performance assessment methods.

14 SOLAR ENERGY↗

DOE OSTI2026

Prediction of dynamic behavior of a system undergoing an exchange of components using a transmission simulator method

In systems with modular design, it is of interest to perform component exchanges to upgrade components and explore new system configurations. If the dynamics of the components are known, then dynamic substructuring can be used to obtain a prediction of system performance for the new configuration. The transmission simulator method is a substructuring technique that utilizes an additional fixture to apply an elastic boundary condition to a test article such that an appropriate dynamic response can be obtained for a given substructuring application, and the technique has been used widely for performing coupling and decoupling operations. Here, in this work, a modified transmission simulator method is proposed that allows a direct exchange of components to be performed. In a numerical demonstration, the proposed method is shown to accurately predict the dynamics of an assembly that has undergone a component exchange in which the exchanged components have different material properties and different geometric properties. This approach can be applied to dynamic qualification frameworks in which structures undergo modifications or upgrades rather than complete redesigns.

Component exchange↗

DOE OSTI2024

The collisional particle-in-cell method for the Vlasov–Maxwell–Landau equations

We introduce an extension of the particle-in-cell method that captures the Landau collisional effects in the Vlasov–Maxwell–Landau equations. The method arises from a regularisation of the variational formulation of the Landau equation, leading to a discretisation of the collision operator that conserves mass, charge, momentum and energy, while increasing the (regularised) entropy. The collisional effects appear as a fully deterministic effective force, thus the method does not require any transport–collision splitting. The scheme can be used in arbitrary dimension, and for a general interaction, including the Coulomb case. We validate the scheme on scenarios such as the Landau damping, the two-stream instability and the Weibel instability, demonstrating its effectiveness in the numerical simulation of plasma.

Bailo, Rafael (ORCID:0000000180183799)↗

DOE OSTI2025

A Method for Rapid and Precise Triple Oxygen Isotope Measurements via High-Temperature Conversion to CO Followed by Nickel-Catalyzed CO to CO 2 Conversion and Laser Spectroscopy

Triple oxygen isotopic compositions ( 16 O, 17 O, 18 O) have conventionally been measured via isotope ratio mass spectrometry using O 2 as an analyte. Conversion of sample oxygen to O 2 typically utilizes fluorination chemistry or catalytic equilibration between CO 2 and O 2 . Recently, laser spectroscopy has become a viable alternative for triple oxygen isotope (Δ' 17 O) measurements due to its ease and rapid throughput. Laser spectrometers are currently available for Δ' 17 O analysis of either H 2 O or CO 2 as the analyte gas. So far, these instruments have been used to measure Δ' 17 O of water, carbonate (CO 2 liberated by acid digestion), and atmospheric CO 2 samples. Here, we present a new method for high-precision Δ' 17 O analysis of CO 2 via tunable infrared laser direct absorption spectroscopy that is compatible with a wider range of geochemically important materials. This approach involves converting sample oxygen to CO 2 in two steps. First, the sample oxygen is liberated and reduced to CO by high-temperature conversion at 1450 °C in the presence of excess elemental carbon. Then, CO is catalytically converted to CO 2 over hot nickel at 350 °C. The conversion process is rapid (10 to 30 min) and quantitative. Spectroscopic Δ' 17 O analysis of the resulting CO 2 takes approximately 45 min. By measuring several oxygen isotope standards, we demonstrate that the method is precise (1σ = 12 per meg for procedural replicates) and accurate (within 11 per meg of previously reported values). The method can be applied to most pyrolytic materials where quantitative oxygen conversion is attainable, such as sulfate, phosphate, nitrate, and oxide minerals, water, and organic molecules.

Ellis, Nicholas M. [University of California, Berk↗

DOE OSTI2024

Emerging Atomistic Modeling Methods for Heterogeneous Electrocatalysis

Heterogeneous electrocatalysis lies at the center of various technologies that could help enable a sustainable future. However, its complexity makes it challenging to accurately and efficiently model at an atomic level. Herein, we review emerging atomistic methods to simulate the electrocatalytic interface with special attention devoted to the components/effects that have been challenging to model, such as solvation, electrolyte ions, electrode potential, reaction kinetics, and pH. Additionally, we review relevant computational spectroscopy methods. Then, we showcase several examples of applying these methods to understand and design catalysts relevant to green hydrogen. We also offer experimental views on how to bridge the gap between theory and experiments. Finally, we provide some perspectives on opportunities to advance the field.

36 MATERIALS SCIENCE↗

DOE OSTI2026

Multireference Embedding and Fragmentation Methods for Classical and Quantum Computers: From Model Systems to Realistic Applications

One of the primary challenges in quantum chemistry is the accurate modeling of strong electron correlation. While multireference methods effectively capture such correlation, their steep scaling with system size prohibits their application to large molecules and extended materials. Quantum embedding offers a promising solution by partitioning complex systems into manageable subsystems. In this Review, we highlight recent advances in multireference density matrix embedding and localized active space self-consistent field approaches for complex molecules and extended materials. We discuss both classical implementations and the emerging potential of these methods on quantum computers. Here, by extending classical embedding concepts to the quantum landscape, these algorithms have the potential to expand the reach of multireference methods in quantum chemistry and materials.

Algorithms↗

DOE OSTI2026

A Proxy Method to Bridge LCA Data Gaps Using Automated Material Classification and Probabilistic Under-Specification

Life cycle assessments (LCAs) are essential for understanding the environmental impacts of material production. However, gaps in life cycle inventory (LCI) data for material and chemical inputs present a key challenge for LCA practitioners, especially in the early design stages. Strategies for filling in these gaps require additional time and expertise, which can hinder the LCA’s completion. This study combined automatic material classification and probabilistic under-specification to create a time-efficient method to fill material LCI data gaps. To illustrate the proposed method, proxy environmental impact distributions were generated using publicly available material LCI data classified into the ChemOnt chemical taxonomy using the open-source chemical classification software ClassyFire. Input materials with data gaps were then classified into the same taxonomy, where proxy environmental impact values could be selected from the available distributions to quickly fill in any data gaps. Although these methods were applied to classify material production processes available in the Federal LCA Commons and Ecoinvent databases, they can be applied to any LCA database. This study shows that classifying materials by their chemical structure produces taxonomies with increased granularity relative to industrial classification, improving the ability of under-specified proxy data to be used for differentiating the environmental impacts of competing designs.

biological databases↗

DOE OSTI2024

The Localized Active Space Method with Unitary Selective Coupled Cluster

Here, we introduce a hybrid quantum-classical algorithm, the localized active space unitary selective coupled cluster singles and doubles (LAS-USCCSD) method. Derived from the localized active space unitary coupled cluster (LAS-UCCSD) method, LAS-USCCSD first performs a classical LASSCF calculation, then selectively identifies the most important parameters (cluster amplitudes used to build the multireference UCC ansatz) for restoring interfragment interaction energy using this reduced set of parameters with the variational quantum eigensolver method. We benchmark LAS-USCCSD against LAS-UCCSD by calculating the total energies of (H 2 ) 2 , (H 2 ) 4 , and trans-butadiene, and the magnetic coupling constant for a bimetallic compound [Cr 2 (OH) 3 (NH 3 ) 6 ] 3+ . For these systems, we find that LAS-USCCSD reduces the number of required parameters and thus the circuit depth by at least 1 order of magnitude, an aspect which is important for the practical implementation of multireference hybrid quantum-classical algorithms like LAS-UCCSD on near-term quantum computers.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗