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

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At least 91 records · Page 5
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Tracking precipitation features and associated large-scale environments over southeastern Texas

Abstract. Deep convection initiated under different large-scale environmental conditions exhibits different precipitation features and interacts with local meteorology and surface properties in distinct ways. Here, we analyze the characteristics and spatiotemporal patterns of different types of convective systems over southeastern Texas using 13 years of high-resolution observations and reanalysis data. We find that mesoscale convective systems (MCSs) contribute significantly to both mean and extreme precipitation in all seasons, while isolated deep convection (IDC) plays a role in intense precipitation during summer and fall. Using self-organizing maps (SOMs), we found that convection can occur under unfavorable conditions without large-scale lifting or moisture convergence. In spring, fall, and winter, front-related large-scale meteorological patterns (LSMPs) characterized by low-level moisture convergence act as primary triggers for convection, while the remaining storms are associated with an anticyclonic pattern and orographic lifting. In summer, IDC events are mainly associated with front-related and anticyclonic LSMPs, while MCSs occur more in front-related LSMPs. We further tracked the life cycle of MCS and IDC events using the Flexible Object Tracker algorithm over southeastern Texas. MCSs frequently initiate west of Houston, traveling eastward for around 8 h to southeastern Texas, while IDC events initiate locally. The average duration of MCSs in southeastern Texas is 6.1 h, approximately 4.1 times the duration of IDC events. Diurnally, the initiation of convection associated with favorable LSMPs peaks at 11:00 UTC, 3 h earlier than that associated with anticyclones.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Phased array-based nonlinear wave mixing technique: Application to lack-of-fusion porosity characterization in additively manufactured metals

The objective of this research is to demonstrate the effectiveness of a phased array-based nonlinear wave mixing technique to characterize internal, localized microscale damage in an additively manufactured (AM) component. By using phased arrays for the generation of the incident waves, it is possible to produce a nonlinear wave mixing scanning technique without the need for immersion or changing coupling conditions. The phased arrays can be configured to generate incident waves in multiple directions that meet the resonance conditions required for nonlinear wave mixing at a variety of internal locations. This allows for the scanning of a specimen without the removal and re-coupling of the source transducers, leading to greater scanning speed and repeatability. To demonstrate the accuracy of this phased array wave mixing approach, measurements of acoustic nonlinearity in an AM component are first made with a bulk wave second harmonic generation through thickness measurement. Next, nonlinear wave mixing measurements are made with single element transducers to confirm the sensitivity of the proposed nonlinear wave mixing approach to lack-of-fusion porosity in AM metals. Finally, phased arrays are used to highlight the effectiveness of the proposed nonlinear wave mixing technique in these same AM components.

Acoustics↗
DOE OSTI2025

Infrared Thermometer Instrument Handbook

The infrared thermometer (IRT) is a ground-based radiation pyrometer that provides measurements of the brightness temperature of the scene in its field of view. The ground IRT (sometimes referred to as the upwelling instrument) lens has a wide field of view for measuring the radiating temperature of the ground surface. It measures in a narrow range of the infrared band, details of which can be found in Table 2. It provides data to the user averaged to 1-minute resolution. The infrared thermometer for sea-surface temperature (IRTSST) is a ship-based radiation pyrometer that provides measurements of the temperature of the sea surface. The upwelling infrared emission is determined with two different IRTs for over-ocean field campaigns and the data is at 1-second temporal resolution. The IRTs are mounted at heights varying between 2 and 42 m above the ground, oriented so the mounting platform and other human made objects are not in the field of view, and also to ensure that the ground and vegetation cover are representative of the local area. The Heitronics IRT is generally mounted inside a small enclosure, and the Apogee IRT is shielded by a cylindrical enclosure that is part of the design from the manufacturer.

47 OTHER INSTRUMENTATION↗
DOE OSTI2026

An Intermediate-Scale Version of the Volturnus + Floating Offshore Wind Turbine Platform Concept in a Real Ocean Environment with an Operating Turbine off the Coast of Maine (Final Scientific/Technical Report)

This project was undertaken to advance the technical readiness and commercial viability of a next-generation, industrialized concrete floating foundation for offshore wind turbines called VolturnUS +. The design objective is to deliver a platform that is lower cost, faster to manufacture, simpler to deploy, and optimized for domestic supply chains and local workforce participation. To enable project financing and commercial adoption, an at-sea demonstration under representative operating conditions was required and therefore this project aimed to deploy a ¼-scale VolturnUS+ prototype offshore the Coast of Maine.

17 WIND ENERGY↗
DOE OSTI2024

Hybridized Discontinuous Galerkin Methods for Computational Fluid Dynamics

Hybridizable Discontinuous Galerkin (HDG) methods hold promise for any applications with significant advection character, including thermal hydraulics in light water reactors and advanced reactor concepts and fluid models of plasmas in magnetic confinement fusion. Its features include natural upwinding, local element conservation, and extensibility to arbitrarily high order accuracy. In the last fiscal year we have implemented HDG in the Multiphysics Object-Oriented Simulation Environment (MOOSE). We developed a first-of-its-kind automatic static condensation system in MOOSE’s underlying finite element library libMesh which can condense out arbitrarily many internal variables. Finally, we developed the first preconditioner for HDG discretizations of the Navier-Stokes equations which shows robust performance across a wide range of problem sizes and Reynolds numbers. This preconditioner yields solution times that are equivalent to the fastest developed for industry standard finite volume methods. Moreover, the arbitrarily high-order nature of HDG makes it a prime candidate for acceleration via graphical processing units (GPUs). We believe these developments will hold significant importance for future DOE Nuclear Energy (NE) and Fusion Energy Science (FES) programs.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Self‐Potential Tomography Preconditioned by Particle Swarm Optimization—Application to Monitoring Hyporheic Exchange in a Bedrock River

Abstract A self‐potential (SP) data‐inversion algorithm was developed and tested on an analytical model of electrical‐potential profile data attributed to single and multiple polarized electrical sources. The developed algorithm was then validated by an application to SP‐monitoring field data measured on the floodplain of East Fork Poplar Creek, Oak Ridge, Tennessee, to image electrical sources in areas conducive to preferential flow into the flood plain from the bedrock‐lined riverbed. The algorithm combined stochastic source‐localization by particle‐swarm‐optimization (PSO) of electrical sources characterized by simplified geometries with source tomography by regularized weighted least‐squares minimization of a quadratic objective function. Prior information was incorporated by preconditioning the tomography algorithm by PSO results. Variable percentages of random noise were added to analytical‐model data to evaluate the algorithm performance. Results indicated that true parameters of single‐source models were inverted and approximated with small residual error, whereas inversion of analytical‐model data representing multiple electrical sources accurately approximated the locations of the sources but miscalculated some parameters because of the non‐uniqueness of the inverse‐model solution. Source tomography applied to analytical model data during testing produced a spatially continuous parameter field that identified the locations of point‐scale synthetic dipole sources of electrical current flow with varying degrees of accuracy depending on the prior information incorporated into the tomography. When applied to SP‐monitoring field data, the algorithm imaged electrical sources within a known fault that intersects the bedrock riverbed and flood plain of East Fork Poplar Creek and depicted dynamic electrical conditions attributed to hyporheic exchange.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Invasive Plant Species Management Plan for Los Alamos National Laboratory (Rev.3)

Native species are plants and animals that continually occupy a natural range without direct or indirect introduction and/or care by humans. They are adapted to the environmental conditions and processes of the ecosystem in which they reside. A species introduced into a novel ecosystem can either exploit that ecosystem and thrive or be unable to survive in that ecosystem (Hobbs et al. 2006). Alien or non-native species are species that are intentionally or accidentally introduced into a novel ecosystem and are capable of living and propagating within the physical parameters of that ecosystem. Invasive species is a species that is non-native (or alien) to the ecosystem under consideration and a noxious species are those whose introduction causes or is likely to cause economic or environmental harm. The term invasive species is applicable to plants and animals alike; however, this invasive species management plan currently focuses on invasive vegetation. Invasive plant species are usually capable of rapid colonization of disturbed ground, such as after a change in wildfire regime intensity and frequency (Reilly et al. 2020) or anthropogenic ground disturbance (Burke and Grime 1996; Hobbs and Huenneke 1992). Climate change facilitates the spread and establishment of many alien species and creates new opportunities for them to become invasive (Turbelin and Catford 2021). As climate change impacts increase in the coming decades, we may see in an increase in invasive species establishment. See a list of definitions of terms pertinent to this document in Appendix A: Definitions. Los Alamos National Laboratory (LANL) hosts populations of non-native and invasive species all typical of the northern New Mexico region (Martin 2004; NMDA 2020). By implementing an invasive species management plan, LANL will have readily accessible management strategies for invasive species that are found on-site. The benefits of managing invasive species include a decrease in wildland fire risks, an increase in soil productivity, an increase in (productive or beneficial) wildlife habitat, an increase in water quantity and quality, and the restoration of impacted areas (Burke and Grime 1996; Hobbs and Huenneke 1992; D’Antonio and Hobbie 2005; MacDougall et al. 2013; Reilly et al. 2020). The aforementioned benefits from invasive species management directly enable the LANL mission by ensuring compliance requirements are met and site-wide programs, such as the Vulnerability Assessment and Resilience Plan, are supported for a mutually beneficial outcome. An example for the LANL site specifically, controlling non-native annual plant species, for example, could reduce the costs associated with stabilizing soils during the stormwater pollution prevention compliance process. Roadway and utility right-of-way areas are another place where an integrated vegetation management strategy would promote low growing perennial plants in a way that is mutually beneficial to habitat and the institution through lowered maintenance costs. There is also an economic benefit to managing invasive species. In one nationwide study, invasive plants had an estimated impact cost of $190.45 billion (Fantel-Lepczyk et al. 2022). Investing in preventative measures and surveillance could help to offset future control and management costs of invasive species that have the potential to become established. The State of New Mexico has developed plant species lists and recommendations through the New Mexico Department of Agriculture’s Noxious Weeds Management Act, Article 7D (NM Statute § 76-7D-4 2021); however, effective invasive species management must rely on local knowledge of the site and region. Los Alamos County (LAC) has already compiled a target invasive plant species list and species-specific management objectives (Martin 2004). Therefore, the New Mexico Noxious Weed List and the LAC invasive plant species list, as well as management objectives from those documents, are integrated into LANL’s invasive plant species management plan.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Quantum Monte Carlo Calculations of Chemical Binding and Reactions

The auxiliary field quantum Monte Carlo method developed by the PIs has been shown to provide the most accurate description of strongly correlated electronic systems, from molecules to solids. Unlike other explicitly many‐body approaches, the quantum Monte Carlo method scales as a low order polynomial of systems size, similar to mean‐field methods such as density functional theory. However, the auxiliary field quantum Monte Carlo algorithm is significantly more expensive than traditional density functional calculations. This creates a bottleneck for applications to extended systems, such as large molecules and solids. One principal objective of this proposal was to develop new auxiliary field quantum Monte Carlo computational strategies to achieve improved scaling with system size, using downfolding and localization schemes, without sacrificing the predictive power of the calculations. A second goal is to extend the reach of auxiliary field quantum Monte Carlo to calculate excited states. This final report summarizes what has been achieved during the course the project toward these goals.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

State Requirements for Electric Distribution System Planning

Utilities have conducted distribution planning since they first began building and operating electricity systems. But filing these plans for regulatory and stakeholder review is a relatively recent phenomenon. This report summarizes legislative and regulatory requirements for regulated electric utilities to file some type of distribution system plan in 20 U.S. jurisdictions. Some plans focus on expedited cost recovery for certain types of distribution system improvements; other plans focus on investments for grid modernization or distributed energy resources. Increasingly, states are adopting requirements for Integrated Distribution Plans. Such plans provide holistic grid investment strategies that address state and local policies and increasing complexity at the grid edge. The report covers the following topics for distribution system plans, highlighting advanced practices: -State goals and objectives -Procedural requirements -Forecasting loads and distributed energy resources -Hosting capacity analysis -Baseline information requirements -Grid modernization strategy -Grid needs assessment -Non-wires solutions -Reliability and resilience analyses -Stakeholder engagement -Equity -Pilots -Coordination with other planning processes The report includes links to legislation; regulatory requirements, proceedings, and orders; and filed utility plans. The U.S. Department of Energy’s Office of Electricity provided funding support.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2025

2024 Second Half Semi Annual Report: Modeling plasticity-mediated flow in metals with pressurized cavities

The objective is to better predict the bulk-scale mechanical behavior of porous metals that have over pressurized cavities (e.g., irradiated metals with helium bubbles) by quantifying the complex coupling among cavity aspects (e.g., size distribution, inhomogeneous overpressure values, spatial arrangement) and metal properties (e.g., rate-dependency, crystallographic lattice). This requires up-scaling local mechanical fields from the single crystal scale and will be accomplished using a homogenization approach that combines full-field numerical simulations, analytical formalisms, and physics-informed machine learning to produce symbolically-defined constitutive equations (e.g., gauge functions). These equations will satisfy the objective because they enable computationally efficient predictions that approach the accuracy of computationally expensive full-field numerical simulations, abide by theoretical requirements (e.g., conservation of energy, work conjugacy), and retain the transparency of analytical models.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Photometric redshifts probability density estimation from recurrent neural networks in the DECam local volume exploration survey data release 2

Photometric wide-field surveys are imaging the sky in unprecedented detail. These surveys face a significant challenge in efficiently estimating galactic photometric redshifts while accurately quantifying associated uncertainties. In this work, we address this challenge by exploring the estimation of Probability Density Functions (PDFs) for the photometric redshifts of galaxies across a vast area of 17,000 square degrees, encompassing objects with a median 5 σ point-source depth of g = 24.3, r = 23 . 9 , i = 23.5, and z = 22.8 mag. Our approach uses deep learning, specifically integrating a Recurrent Neural Network architecture with a Mixture Density Network, to leverage magnitudes and colors as input features for constructing photometric redshift PDFs across the whole DECam Local Volume Exploration (DELVE) survey sky footprint. Subsequently, we rigorously evaluate the reliability and robustness of our estimation methodology, gauging its performance against other well-established machine learning methods to ensure the quality of our redshift estimations. Our best results constrain photometric redshifts with the bias of − 0 . 0013 , a scatter of 0.0293, and an outlier fraction of 5.1%. These point estimates are accompanied by well-calibrated PDFs evaluated using diagnostic tools such as Probability Integral Transform and Odds distribution. We also address the problem of the accessibility of PDFs in terms of disk space storage and the time demand required to generate their corresponding parameters.We present a novel Autoencoder model that reduces the size of PDF parameter arrays to one-sixth of their original length, significantly decreasing the time required for PDF generation to one-eighth of the time needed when generating PDFs directly from the magnitudes.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2025

HIPED: Machine learning framework for spherical tokamak pedestal prediction and optimization

We introduce a Machine Learning framework, HIPED (HeIght and width Predictor for Edge Dynamics), for predicting and optimizing pedestal and core performance in spherical tokamak plasmas. Trained on pedestal and core datasets from the third MAST-U campaign, HIPED provides accurate estimates of pedestal height and width. The results reveal notable differences compared with conventional aspect-ratio studies; for instance, a simple power-law relation between pedestal width and height has very low accuracy. Instead, additional parameters such as normalized plasma pressure, elongation, and Greenwald fraction significantly improve accuracy. HIPED can also be trained only on `control room parameters' to inform experimentalists of which controllable parameters to adjust for improving core-integrated performance. The framework further includes a multi-objective optimization scheme that helps guide experimental planning and optimization. We find Pareto-optimal discharges with respect to various features, including distance from edge-localized modes and normalized plasma pressure, track their parameter trajectories over time, and identify the control room parameters required for these Pareto-optimal discharges. This provides a framework for systematically optimizing core and edge performance according to different experimental priorities.

Parisi, Jason F. [Princeton Plasma Physics Laborat↗
DOE OSTI2025

Convergence analysis for a nonlocal gradient descent method via directional Gaussian smoothing

We analyze the convergence of a nonlocal gradient descent method for minimizing a class of high-dimensional non-convex functions, where a directional Gaussian smoothing (DGS) is proposed to define the nonlocal gradient (also referred to as the DGS gradient). The method was first proposed in [Zhang et al., Enabling long-range exploration in minimization of multimodal functions, UAI 2021], in which multiple numerical experiments showed that replacing the traditional local gradient with the DGS gradient can help the optimizers escape local minima more easily and significantly improve their performance. However, a rigorous theory for the efficiency of the method on nonconvex landscape is lacking. In this work, we investigate the scenario where the objective function is composed of a convex function, perturbed by deterministic oscillating noise. We provide a convergence theory under which the iterates exponentially converge to a tightened neighborhood of the solution, whose size is characterized by the noise wavelength. Here, we also establish a correlation between the optimal values of the Gaussian smoothing radius and the noise wavelength, thus justifying the advantage of using moderate or large smoothing radii with the method. Furthermore, if the noise level decays to zero when approaching the global minimum, we prove that DGS-based optimization converges to the exact global minimum with linear rates, similarly to standard gradient-based methods in optimizing convex functions. Several numerical experiments are provided to confirm our theory and illustrate the superiority of the approach over those based on the local gradient.

Tran, Hoang [Oak Ridge National Laboratory (ORNL),↗
DOE OSTI2025

DOE Repository Metadata Profile (DRMP): A Metadata Framework for Advancing Interoperability and AI Readiness Across Scientific Repositories

The Department of Energy (DOE) funds a diverse and distributed ecosystem of repositories that steward scientific data, publications, and software across its research programs, user facilities, and national laboratories. While significant progress has been made in standardizing dataset-level metadata, the metadata describing repositories themselves (their identity, governance, access interfaces, policies, and technical capabilities) remains inconsistent and fragmented across DOE-funded systems. This variability limits discoverability, interoperability, automated validation, and AI-driven analysis, all of which are increasingly essential for modern scientific workflows. To address this gap, the DOE Data Curation Working Group (DCWG) developed the DOE Repository Metadata Profile (DRMP). The DRMP is a practical, community-driven framework that defines how repositories can describe themselves in a consistent, machine-actionable, and scalable manner. The DRMP is not a new metadata schema. Instead, it is a mapping profile and structured element set capturing the essential characteristics of DOE repositories. It harmonizes repository-level metadata across six widely adopted community schemas: RE3Data; DCAT-US v3; Schema.org; Dublin Core; DataCite 4.6; and PREMIS 3.0. This harmonization eliminates reinvention and enables interoperability within DOE and across the broader scientific ecosystem. A core objective of the DRMP is to reduce burden on repositories by allowing them to reuse their existing metadata through a Rosetta-style crosswalk rather than redesigning local implementations. The profile introduces a three-level conformance model that supports incremental adoption: • Level 1 – Minimum Viable Record (MVR): foundational identification elements required for workflows, project registration, and basic repository presence. • Level 2 – Interoperable: structured metadata enabling alignment with national and international discovery systems. • Level 3 – AI-Ready: enhanced provenance, policy transparency, fixity, semantic context, and capabilities that support automated reasoning, model training governance, and machine-assisted curation. To support implementation, the DRMP includes JSON Schema definitions, OpenAPI patterns, and MCP templates that allow repositories to publish machine-readable metadata directly within existing platforms. These resources are modular and lightweight, enabling adoption without major architectural change. Adopting the DRMP enables repositories to: • Enhance discoverability and interoperability by aligning identifiers, classifications, and descriptive elements across widely used schema standards. • Support federated discovery and cross-registration across DOE systems, Data.gov, and international catalogs. • Enable AI agents and workflow orchestration systems to interpret repository-level metadata within the American Science Cloud (AmSC) through Model Context Protocol (MCP)-based context publication. • Demonstrate alignment with DOE’s open science, stewardship, and FAIR data priorities. This guidance represents a community-driven step forward. Through voluntary adoption and continued feedback, the DRMP advances a cohesive, machine-actionable description of DOE repositories that supports FAIR data practices, preparing the infrastructure for AI-enabled research, and strengthening the discoverability and reuse of DOE’s scientific outputs.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗
DOE OSTI2026

Close-Out Project Description for Koepke's Dept of Energy grant DE-SC0021405

Objectives: To establish, for lab & space conditions, EM-IEDDI’s (electromagnetic shear-driven instability's) dispersion relation, unstable range, instability threshold, and mode characteristics, we need LAPD’s Alfven-wave-favorable electromagnetic-style conditions, including higher "beta" (0.001 < beta ≤ 0.3) and low-collisionality. Also, we attempted to intentionally launch or spontaneously destabilize compressional and shear Alfven waves in the strong, localized, perpendicular-velocity-shear region at the interface between coaxial plasmas (one plasma cylinder inside an outer, otherwise hollow, tube, each having a different, controllable, value of plasma electrostatic potential, i.e., “space” potential (not to be confused with the temperature-dependent “floating” potential of an object immersed in the plasma). Nonlinear wave-wave interactions between same-family (EM-IEDD or Alfven) and cross-family (EM-IEDD-with-Alfven) fluctuations were targeted for documentation over a range of spectral overlap. Although laboratory experiments were conducted, the following theoretical work was left unfinished: Analytical non-modal prediction Computational non-modal prediction Check to see if Mikhailenko’s theory formulation leads to his published graphs

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2024

Surrogate Model Guided Optimization of Expensive Black-Box Multi-Objective Problems: A Posteriori Methods

Many engineering applications require the simultaneous optimization of multiple conflicting objective functions. Often, these objective functions are evaluated using highly accurate computer simulations that are computationally too expensive to be evaluated hundreds or thousands of times during optimization. Thus, the goal is to find good approximations of the Pareto front using as few of these expensive simulations as possible. Here, we describe an optimization approach based on surrogate models and diverse sampling strategies to accelerate the search for the Pareto solutions. We use a separate surrogate model for approximating each objective function and then we use the surrogate models to inform where additional expensive simulations should be run. The surrogate models are updated in an active learning framework whenever new information from the expensive simulations becomes available. The sampling strategies aim at balancing local improvements of the approximate Pareto front and global exploration to identify the extrema and fill in large gaps of the approximate Pareto front. We demonstrate on a large set of benchmark problems the effectiveness of the method for finding good approximations of the Pareto front.

MATHEMATICS AND COMPUTING↗
DOE OSTI2024

The environmental impact of hydropower: a systematic review of the ecological effects of sub-daily flow variability on riverine fish

Hydropower can help facilitate power grid decarbonization because it can respond to short-term changes in power demand and is comparatively more reliable than intermittent wind and solar. However, flexible hydropower operations can create rapid and abnormal fluctuations in downstream flow conditions, which can negatively impact aquatic ecosystems. Accordingly, we conducted a systematic review on the ecological effects of hydropower-driven sub-daily flow variability (SDFV) on riverine fishes. We reviewed and synthesized 109 articles relevant to fish-SDFV relationships from seven sources, most of which focused on Salmonids in North America and northern and western Europe and were published in the last 15 years. We found strong agreement in the literature that SDFV increases fish stranding risk, destabilizes habitat, and decreases production and diversity. We found moderate agreement that SDFV interrupts fish reproduction, increases or has no impact on condition, and prompts or discourages movement depending on local channel conditions. We found little to no agreement for relationships between SDFV and mortality, physiology, and behavior. The effects of SDFV on riverine fish ecology are intertwined in the complex suite of biotic and abiotic characteristics that structure aquatic ecosystems and are highly site-, species-, and life stage-specific. Assessments of the impact of SDFV on fish ecology should first characterize local habitat and channel quality and fish community composition to identify specific, measurable ecological outcomes to sustain or enhance, and then design mitigation strategies tailored to those ecological objectives.

13 HYDRO ENERGY↗
DOE OSTI2025

A direct-adjoint approach for material point model calibration with application to plasticity

Here, this paper proposes a new approach for the calibration of material parameters in local elastoplastic constitutive models. The calibration is posed as a constrained optimization problem, where the constitutive model evolution equations for a single material point serve as constraints. The objective function quantifies the mismatch between the stress predicted by the model and corresponding experimental measurements. To improve calibration efficiency, a novel direct-adjoint approach is presented to compute the Hessian of the objective function, which enables the use of second-order optimization algorithms. Automatic differentiation is used for gradient and Hessian computations. Two numerical examples are employed to validate the Hessian matrices and to demonstrate that the Newton–Raphson algorithm consistently outperforms gradient-based algorithms such as L-BFGS-B.

36 MATERIALS SCIENCE↗