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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 19 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Fast, Nondestructive and Precise Biomass Measurements Are Possible Using Lidar-Based Convex Hull and Voxelization Algorithms

Light detection and ranging (lidar) scanning tools are available that can make rapid digital estimations of biomass. Voxelization and convex hull are two algorithms used to calculate the volume of the scanned plant canopy, which is correlated with biomass, often the primary trait of interest. Voxelization splits the scans into regular-sized cubes, or voxels, whereas the convex hull algorithm creates a polygon mesh around the outermost points of the point cloud and calculates the volume within that mesh. In this study, digital estimates of biomass were correlated against hand-harvested biomass for field-grown corn, broom corn, and energy sorghum. Voxelization (r = 0.92) and convex hull (r = 0.95) both correlated well with plant dry biomass. Lidar data were also collected in a large breeding trial with nearly 900 genotypes of energy sorghum. In contrast to the manual harvest studies, digital biomass estimations correlated poorly with yield collected from a forage harvester for both voxel count (r = 0.32) and convex hull volume (r = 0.39). However, further analysis showed that the coefficient of variation (CV, a measure of variability) for harvester-based estimates of biomass was greater than the CV of the voxel and convex-hull-based biomass estimates, indicating that poor correlation was due to harvester imprecision, not digital estimations. Overall, results indicate that the lidar-based digital biomass estimates presented here are comparable or more precise than current approaches.

Environmental Sciences & Ecology↗
DOE OSTI2026

FOCAL Campaign IV: Integrated System Control: Turbine + Hull

Campaign IV of the Floating Offshore-wind Controls Advanced Laboratory Experimental Program (FOCAL) aimed to generate a dataset enabling the validation of a floating offshore wind turbine with turbine and hull controls. The turbine considered in this campaign is the scaled IEA 15MW Reference Wind Turbine similar to the setup considered in FOCAL Campaign I. This turbine is mounted on the VolturnUS platform with integrated hull controls similar to the hull considered in FOCAL Campaign II/III. This system is deployed for a fully-coupled wind and wave testing campaign at the University of Maine's Harold Alfond Wind and Wave (W2) facility. System dynamics and turbine characteristics are measured to determine global performance and assess the effect of the control strategies employed. NREL's reference open-source controller (ROSCO) is used for active blade pitch control and hull structural control is examined through the use of tuned mass dampers (TMD's) tuned to the systems pitch and tower-bending natural frequency. The naming convention of the load cases considered are described in the data details section. Detailed properties on the modeled system are found in the following reference: Lenfest E., Floating Offshore-wind Controls Advanced Laboratory (FOCAL) Experimental Program - Campaign IV: 1:70 Model-scale Testing of the IEA-Wind 15MW Reference Turbine and the VolturnUS-S Platform. UMaine ASCC Report Number 23-57-1183.

17 WIND ENERGY↗
DOE OSTI2026

FOCAL Campaign II/III: Applying Active Hull Controls Using Tuned-mass Dampers/Hull Flexibility and Internal Loads

Campaign II and III of the Floating Offshore-wind Controls Advanced Laboratory Experimental Program (FOCAL) aimed to generate a dataset enabling the validation of the performance and loads of a scaled hull, with and without structural hull control. The floating platform was subjected to a variety of wave environments and controlled using tuned-mass dampers (TMDs) tuned to two of the systems natural frequencies (Platform Pitch and Tower-bending). The floating platform is fully instrumented to record a variety of parameters in real time such as platform dynamics, accelerations, and loads at different points in the structure. The test data considered was generated at the University of Maine's Harold Alfond Wind and Wave (W2) testing facility. This testing was focused only on validation of wave loading, and wind conditions were not considered. As such the platform does not support a working turbine, and instead supports a structure designed to have the same mass properties as the 1:70 IEA 15MW Reference turbine. The Load Cases (LC) considered in this testing campaign are as follows: LC 1.X - Platform Static Offset (TMD off); LC 2.X - Platform Free-decays (TMD off); LC 3.X - Wave Cases (Regular Wave, Irregular Wave, Pink Noise Wave) with and without TMDs active. Detailed properties on the model system are found in the following reference: Lenfest E., Floating Offshore-wind Controls Advanced Laboratory (FOCAL) Experimental Program - Campaigns 2 and 3: 1:70 Model-scale Testing of the IEA-Wind 15MW Reference Turbine and the VolturnUS-S Hull. UMaine ASCC Report Number 23-56-1183.

17 WIND ENERGY↗
DOE OSTI2025

Synthesis and Characterization of Soy Hull Biochar-Based Flexible Polyurethane Foam Composites

Flexible polyurethane foams are a diverse class of materials encompassing furniture, packaging, automotive, and many other industrial and domestic applications. Polyurethane foams are synthesized by the addition of polyols and isocyanates; however, the petroleum origin and toxic nature of isocyanates have driven many to look for more sustainable routes to production. Renewable fillers have emerged as a biobased resource to decrease the carbon footprint of this widely used polymeric material. In this study, soy hulls, as mass-produced, industrial by-products of soybean production, were used to create a biochar beneficial in the synthesis of flexible polyurethane foam composites. The addition of soy hull biochar was found to maintain the compression properties of foams at a decreasing isocyanate index, reducing the amount of isocyanates needed for production. In addition, the addition of biochar decreased the flammability of foams, important for many applications where consumer safety is important. The results point to the ability to create safer, more sustainable, and even more cost-effective polyurethane foams through the reduction in isocyanate use while maintaining the properties of this important class of polymers.

Chemistry↗
DOE OSTI2024

Smart Adaptive Structures for an Ocean Wave Energy Converter

Ocean wave energy converters face significant challenges including cost-effectiveness, minimizing maintenance requirements, and withstanding extreme conditions. However, by utilizing smart materials, these converters could overcome these challenges. Such energy harvesters could use dielectric elastomer generators to convert ocean wave energy into electricity through their dynamic straining. Conversely, by applying electricity to these generators, they become actuators - dielectric elastomer actuators - thereby enabling them to alter their stiffness and adapt to the ever-changing ocean energy environments. Such active adaptation could enhance the converter's ability to: reach resonance with ocean waves and protect itself from dangerous waves. This study utilizes numerical analyses through the COMSOL software framework to evaluate the potential energy that could be harvested by a conceptual ocean wave energy converter based upon dielectric elastomer generators/actuators. The converter is composed of an external hull (that is a hollow cylinder), an inertial mass (that is a hollow cylinder and concentric with the hull), and 'spokes' - made of dielectric elastomer generators/actuators - that connect the hull to the inertial mass. Results of the numerical analyses include those outcomes arising from the conceptual converter being simulated via a sinusoidal motion analogous to ocean waves. That motion, therefore, causes relative motion between the converter's hull and inertial mass thereby dynamically stretching the corresponding connecting elastomers. The stretching of the elastomers enables them to 'gain elastic strain energy' and is, therefore, considered to be the theoretical limit of possible electrical energy conversion for the dielectric elastomer generator/actuator spokes. Additionally, the elastomer material properties of the spokes were altered to simulate the actuation of those same elastomers; with overall strain energy being subsequently investigated. Ultimately this is a preliminary study exploring the ability of such smart materials - electricity-generating and self-actuating elastomers - to actively adapt an ocean wave energy converter's structure to address and overcome the aforementioned challenges.

active materials↗
DOE OSTI2024

Optimized Floating Offshore Wind Turbine Substructure Design Trends for 10–30 MW Turbines in Low-, Medium-, and High-Severity Wave Environments

Floating offshore wind is a promising renewable energy source, as 60% of the wind resources globally are found at depths requiring floating technologies, it minimizes construction at sea, and provides opportunities for industrialization given a lower site dependency. While floating offshore wind has numerous advantages, a current obstacle is its cost in comparison to more established energy sources. One cost-reduction approach for floating wind is increasing turbine capacities, which minimizes the amount of foundations, moorings, cables, and O&M equipment. This work presents trends in mass-optimized VolturnUS hull designs as turbine capacity increases for various wave environments. To do this, a novel rapid hull optimization framework is presented that employs frequency domain modeling, estimations of statistical extreme responses, industry constructability requirements, and genetic algorithm optimization to generate preliminary mass-optimal VolturnUS hull designs for a given turbine design and set of site conditions. Using this framework, mass-optimized VolturnUS hull designs were generated for 10–30 MW turbines for wave environments of varying severities. These design studies show that scaling up turbine capacities increases the mass efficiency of substructure designs, with decreasing returns, throughout the examined turbine capacity range. Additionally, increased wave environment severity is shown to increase the required mass of a given substructure design.

VolturnUS↗
DOE OSTI2024

Utilizing biochars to stabilize mercury in contaminated floodplain sediment: Implications on mercury remediation

Major weather events contribute to the mobility and remobilization of legacy mercury (Hg) contamination and sequestration within sediments. Remediation using biochar as a soil amendment is a useful technique to immobilize and decrease Hg toxicity. This study explored whether biochar application is effective at stabilizing labile mercury (LaHg) from floodplain sediment. Controlled mesocosms simulating contamination events and flooding conditions were conducted. Floodplain sediment, which experiences annual periodic flooding, was collected. Sediment was spiked with inorganic Hg, applied with different types of biochar, and experienced simulated flooding events. Four types of biochar, pure rice husk (RH), pure peanut hull (PH), sulfur-modified rice husk (SMRH), and sulfur-modified peanut hull (SMPH), were applied at 10 and 40 g/kg rates (i.e., RH 10, RH 40; PH 10, PH 40, SMRH 10, SMRH 40, SMPH 10, SMPH 40). Total Hg, methylmercury, and LaHg concentrations were analyzed by coupling with redox potential measurements. Results indicate that SMRH 10, PH 10, PH 40, SMPH 10, and SMPH 40 successfully remediate Hg by stabilizing and reducing LaHg species from floodplain sediment. However, a high Hg methylation potential was observed with unsulfated and sulfated peanut hulls (PH 10, PH 40, SMPH 10, and SMPH 40), as they tend to create a reducing microenvironment that favors sulfate reduction reactions. Additionally, sulfur-modified biochar tends to promote Hg methylation potential at high application rates (i.e., 40 g/kg). We thus recommend using SMRH at a relatively low application rate (SMRH 10) for the remediation of Hg from floodplain sediment.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Machine learning guided search for energetically favorable metal borocarbide ternary compounds

In this work, we employ machine-learning (ML) combined with first principles calculations to efficiently search for the energetically favorable metal borocarbide (M-B-C) ternary compounds with M being the group 1–3 metal elements. Using a crystal graph convolutional neural network (CGCNN) ML approach followed by first-principles calculations, we predicted 47 energetically favorable stable and metastable ternary Na-B-C, Ca-B-C, and La-B-C ternary compounds with their decomposition energy (E d ) below or within 100 meV/atom from the currently known convex hulls. Phonon spectra and electronic structures of the 14 energetically favorable stable structures are also investigated by first-principles calculations. By substituting the metal atoms in the 29 energetically favorable non-equivalent template structures of Na (Ca, La)-B-C with other group 1–3 elements in the periodic table, we further obtain 22 stable structures and 52 metastable structures (E d ≤100 meV/atom with respect to the known convex hulls) for Li-B-C, K-B-C, Rb-B-C, Mg-B-C, Sr-B-C, Ba-B-C, Sc-B-C and Y-B-C ternary compounds. New convex hulls including our newly predicted stable ternary structures and the known stable structures are constructed for the M-B-C systems. The results obtained by our ML guided first-principles calculations enrich our knowledge in the structure and energy landscape of metal borocarbide ternary compounds and provide useful guidance for further experimental synthesis and discovery.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Data mining the missing ordered phases of Li/Na metal oxides

Data-driven discovery of Li-ion and Na-ion battery materials has been pioneered by generic materials data platforms such as the Materials Project. After decades of progress, it is timely to ask whether there remain underexplored compositional spaces. Here, in this work, we present a systematic data-mining effort to uncover missing ordered binary, ternary and quaternary Li/Na-containing metal oxides using high-throughput density functional theory (DFT). Building on 19,120 stable and metastable oxides entries from the Materials Project, we performed 13,245 additional calculations through isovalent substitutions of known ground states, experimentally reported compounds, and specific prototype structures. Our study identifies 36 new ground states within the GGA/GGA + U convex hull and 45 within the r 2 SCAN convex hull. Additionally, we identified 840 metastable compounds from GGA/GGA + U and 979 from r 2 SCAN that are absent in the present Materials Project databases. Moreover, we have tripled the metastable materials in compositional spaces with a molar ratio of cation/anion >1, highlighting the overlooked opportunities in this compositional space.

25 ENERGY STORAGE↗
DOE OSTI2025

Optimal Control of Differentially Private EV Charging: A Scalable Learning Approach Under Uncertainty

Internet of Things (IoT)-enabled electric vehicles (IoEVs) enable intelligent charging coordination that accounts for grid congestion. However, increased data exchange raises privacy concerns, as charging patterns can reveal sensitive driver behavior to grid operators. Here, we propose a differentially private (DP) EV charging framework that enables coordinated control while protecting driver data with theoretical privacy guarantees. Nevertheless, integrating DP inevitably introduces uncertainty into the control strategy for EVs, which can lead to infeasible solutions. To tackle this challenge, we develop a feasible and scalable control algorithm based on constrained reinforcement learning (CRL) and convex hulls. While our framework is designed to handle the uncertainty introduced by DP, it is general and also applicable to other sources of uncertainty in EV charging, such as the stochastic nature of driver behavior and renewable variability. This ensures feasible and privacy-preserving coordination of EV charging at scale. Our method constructs convex hulls within the action space to guarantee feasibility under stochastic constraints and incorporates constraint reduction techniques to improve scalability. Case studies based on IEEE benchmark systems demonstrate that the proposed approach effectively balances feasibility under uncertainty, scalability, and privacy in large-scale EV charging control.

Engineering - Power transmission and distribution↗
DOE OSTI2024

Remark on Algorithm 1012: Computing Projections with Large Datasets

In ACM TOMS Algorithm 1012, the DELAUNAYSPARSE software is given for performing Delaunay interpolation in medium to high dimensions. When extrapolating outside the convex hull of the training set, DELAUNAYSPARSE calls the nonnegative least squares solver DWNNLS to compute projections onto the convex hull. However, DWNNLS and many other available sum-of-squares optimization solvers were not intended for usage with many variable problems, which result from the large training sets that are typical in machine learning applications. Thus, a new PROJECT subroutine is given, based on the highly customizable quadratic program solver BQPD. This solution is shown to be as robust as DELAUNAYSPARSE for projection onto both synthetic and real-world datasets, where other available solvers frequently fail. Although it is intended as an update for DELAUNAYSPARSE, due to the difficulty and prevalence of the problem, this solution is likely to be of external interest as well.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

CalWave's xWave Design for PacWave (Final Technical Report)

CalWave Inc. (CalWave) is developing a wave energy converter (WEC) technology that can generate electricity from ocean waves. CalWave’s design offers a unique approach to wave energy conversion that operates fully submerged and can actively adjust the wave excitation. This capability gives the architecture enhanced survivability in ocean storms without adding significant costs. Prior to this project, CalWave had completed a demonstration of a fully functional WEC system in an open ocean demonstration at nominal 1:5 scale under FOA 1663. The goal of this project was the detailed design, following relevant standards and industry best-practices, of a variant of the xWave WEC technology that can safely and efficiently operate at the DOE’s PacWave South test site for a targeted deployment of up two years. The WEC design and associated review processes proceeded in two distinct project phases: a ‘Preliminary’ and a ‘Final’ design phase. The first phase of the project consisted of the systematic design of the WEC’s key features with regards to appropriate IEC standards. The work resulted in a preliminary design of the xWave hull including structural and Power Take-Off (PTO) load estimates, as well as performance estimates for all ocean conditions the WEC would operate in at PacWave South. Following the first open-water demonstration of CalWave’s small-scale “x1” device under FOA 1663, lessons learned were fed directly into a comprehensive review of the xWave design in the second design phase of this FOA project. CalWave’s work was supported by Sandia National Lab (SNL) and the National Renewable Energy Lab (NREL) on the holistic WEC design, and detailed feedback from specialized partners on hull design, mooring and anchoring specification, and electrical grid interconnection. Optimization of the WEC system was performed using a novel numerical optimization tool developed by Sandia and optimization trends were confirmed via an experimental model scale tank test campaign. Performance estimates for PacWave and a detailed xWave design including integration of all relevant system components were concluded. The mooring design was also concluded in the Final design phase using the most up to date sea floor characterization (CPT) data.

16 TIDAL AND WAVE POWER↗
DOE OSTI2025

Exploring the Potential of Solid-state H2 Storage for Fuel Cell Vessels

This is a ~ 7 minute oral poster talk which is meant to briefly tell the audience about the subject of a poster, and then direct them to the poster itself. It describes a new project to investigate if storing hydrogen in a metal hydride enables a "shape optimized" hull conforming hydrogen tank that can be placed in the hull of a ship, increasing the amount of hydrogen that can be stored on emerging H2 Fuel Cell vessels. it is for the 2025 Hydrogen Metal Systems Gordon Research Conference.

Klebanoff, Leonard E. [Sandia National Laboratorie↗
DOE OSTI2025

A bilevel multistage stochastic self-scheduling model with indivisibilities for trading in the continuous intraday electricity market

In this paper, we study the profit maximization problem of a virtual power plant trading in the continuous intraday electricity market. Our virtual power plant model is compatible with renewable, and thermal assets, covering a range of virtual power plants currently participating in energy markets. We model the trading problem as a bilevel multistage stochastic program. The upper level of the problem accounts for the profit maximization of the virtual power plant with explicit modeling of the technical constraints of the operational status of the thermal power plant including minimum start-up and shut-down times, ramp-up and ramp-down rates, and minimum generation level. The upper level also decides which continuous and indivisible (fill-or-kill) orders are submitted to the market. The lower-level problem accounts for the clearing of the continuous intraday market, i.e., matching of buy and sell orders. Because of the presence of fill-or-kill orders, the lower-level problem is mixed-integer, which prevents its direct conversion to a single-level problem using duality. In order to solve this challenging problem, we develop a convex-hull extended formulation for the lower-level problem, apply duality theory to obtain a single-level stochastic equivalent formulation, and employ McCormick envelopes to turn the problem into a multistage stochastic mixed-integer linear problem, which we solve using the stochastic dual dynamic integer programming algorithm. We conduct numerical experiments and analyze the optimal trading behavior of a virtual power plant trading in an ideal continuous market without arbitrage.

Bilevel multistage stochastic programming problem↗
DOE OSTI2025

Multi‐Omics Analyses Reveal Divergent Molecular Mechanisms Underlying Plant Biomass Conversion by Five Fungi

Fungal plant biomass conversion (FPBC) is of great importance to the global carbon cycle and has been increasingly applied for the production of biofuel and biochemicals from lignocellulose. However, the comprehensive understanding of relevant molecular mechanisms in different fungi remains challenging. Here, we comparatively analyzed the transcriptome, proteome and metabolome profile of four ascomycetes and one basidiomycete fungi during their growth on two common agricultural feedstocks (soybean hulls and corn stover). We revealed strong time‐, substrate‐ and species‐specific responses at multi‐omics levels for the tested fungi, highlighting species‐specific carbon utilization approaches and evolutionary adaptation to environmental niches. Notably, a remarkable expressional diversity of lignocellulose degrading enzymes, sugar transporter and metabolic genes, as well as industrially relevant metabolites were identified across different fungi and cultivation conditions. The findings improves our understanding of complex molecular networks underlying FPBC and fungal ecological roles, offering novel insights that can guide future genetic engineering of fungi for valorization of agriculture waste into value‐added bioproducts.

CAZy↗
DOE OSTI2026

Statistics of base polytopes in F-theory

We propose a new statistical ensemble of toric bases for elliptic Calabi-Yaus used in F-theory models, by focusing on only the convex hull of the base, i.e., the base polytope. This physically motivated coarse-graining greatly simplifies the combinatorial complexity of the part of the 4d F-theory landscape with toric bases. We develop a Monte Carlo approach that randomly samples the base polytopes within fixed boxes, with proper statistical weights. We first apply the algorithm to the set of 2d base polytopes, generating an enlarged set of toric 2d bases that include certain types of codimension-two (4,6) points, and we validate our approach against exact numbers. We then explore the set of 3d base polytopes which fit in a set of “maximal” 3d boxes, and estimate the total number of inequivalent 3d base polytopes to be 10 85 –10 90 . We provide statistical data such as the distribution of non-Higgsable gauge groups on these bases. Amusingly, a similar method can also be applied to generate reflexive polytopes in various dimensions. In both the reflexive and base polytope cases, the number of relevant polytopes obeys a Gaussian distribution as a function of the number of vertices, which can be understood in terms of other results on random polytopes in the math literature.

Differential and algebraic geometry↗
DOE OSTI2025

A method for bounding high-order finite element functions: Applications to mesh validity and bounds-preserving limiters

We introduce a novel method for bounding high-order multi-dimensional polynomials in finite element approximations. The method involves precomputing optimal piecewise-linear bounding boxes for polynomial basis functions, which can then be used to locally bound any combination of these basis functions. This approach can be applied to any element/basis type at any approximation order, can provide local (i.e., subcell) extremum bounds to a desired level of accuracy, and can be evaluated efficiently on-the-fly in simulations. Furthermore, we show that this approach generally yields more accurate bounds in comparison to traditional methods based on convex hull properties (e.g., Bernstein polynomials). Furthermore, the efficacy of this technique is shown in applications such as mesh validity checks and optimization for high-order curved meshes, where positivity of the element Jacobian determinant can be ensured throughout the entire element, and continuously bounds-preserving limiters for hyperbolic systems, which can enforce maximum principle bounds across the entire solution polynomial.

Bounding box↗
DOE OSTI2025

Co-design of a wave energy converter through bi-conjugate impedance matching

As with other oscillatory power conversion systems, the design of wave energy converters can be understood as an impedance matching problem. By representing the wave energy converter as a multi-port network, two separate but related impedance matching conditions can be established. Satisfying these conditions maximizes power transfer to the load. In practice, these impedance matching conditions may be used to influence the design of the system (including the hull, power take-off, controller, mooring, etc.). To this end, this paper considers some example applications of wave energy converter design with the help of the impedance matching framework.

WEC↗