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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 253 records · Page 14

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DOE OSTI2024

Geothermal Uncertainty Representation in reV: the Renewable Energy Potential Model

We present a preliminary methodology for including geothermal resource uncertainty into the Renewable Energy Potential model, which estimates potential capacity and costs on a gridded surface at the national scale. The uncertainty outputs characterize the 10th, 50th and 90th percentile for geothermal resources using two energy capacity estimation equations. We then present a method and results that demonstrate how other geologic data layers, which may be indicative of permeability, can be used to inform the mean and standard deviation of the geothermal capacity. We demonstrate how the mean and standard deviation can be defined or partially informed by using collocated regression estimates and estimate errors, respectively . These regression results are from 36 observed geothermal power plants in the Great Basin region and are also used to benchmark the P10-P90 calculations.

exclusions↗

DOE OSTI2025

Characterization of ablator dynamics initiated by picket-pulse conditions

Direct-drive laser-fusion targets may include low-cost plastic ablator material. Here, this work describes a tabletop experimental system designed to enable the investigation of the interaction of such ablator material as a function of the laser wavelength and the associated excitation mechanism. This experimental system offers time-resolved imaging of the material response with subpicosecond temporal resolution and ≈1-μm spatial resolution. The system involves synchronized femtosecond and picosecond lasers to initiate and characterize the dynamics of polystyrene planar targets irradiated by conditions similar to a “picket” prepulse from a direct-drive pulse shape. Ultrafast imaging of the initial excitation process followed by the resulting critical plasma formation, shock-wave propagation, and blowoff plasma expansion is used to compare the impact of excitation wavelength, 355 nm or 266 nm, on the ensuing dynamics. This work aims to investigate fundamental laser material interactions to help advance physics models that can inform the development of laser driver and target designs.

42 ENGINEERING↗

DOE OSTI2025

QuadTune version 1: a regional tuner for global atmospheric models

When a new, better-formulated physical parameterization is introduced into a global atmospheric model, aspects of the global model solutions are sometimes degraded. Then, in order to use the new global model to address science questions, there is an incentive to restore its accuracy. Oftentimes this restoration is achieved by tuning of model parameter values. Unfortunately, the retuning process is expensive because characterizing the parameter dependence requires numerous time-consuming global simulations. To reduce the cost of tuning, this manuscript introduces a “poor man's” model tuner, “QuadTune”. QuadTune carves the globe into regions and approximates the model parameter dependence through the use of an uncorrelated quadratic emulator (i.e., response surface). The simplicity of the emulator reduces the required number of global model simulations and aids explainability of tuner behavior. Tuning removes parametric error but leaves behind model structural error. Structural error manifests itself as regional residual biases, such as stubborn biases and tuning trade-offs. To visualize these residual biases, QuadTune's software includes a set of diagnostic plots. This paper illustrates the use of the plots for characterizing residual biases with an example tuning problem.

Environmental sciences↗

DOE OSTI2022

Strong Correspondence in Evapotranspiration and Carbon Dioxide Fluxes Between Different Eddy Covariance Systems Enables Quantification of Landscape Heterogeneity in Dryland Fluxes

Abstract The eddy covariance method is widely used to investigate fluxes of energy, water, and carbon dioxide at landscape scales, providing important information on how ecological systems function. Flux measurements quantify ecosystem responses to environmental perturbations and management strategies, including nature‐based climate‐change mitigation measures. However, due to the high cost of conventional instrumentation, most eddy covariance studies employ a single system, limiting spatial representation to the flux footprint. Insufficient replication may be limiting our understanding of ecosystem behavior. To address this limitation, we deployed eight lower‐cost eddy covariance systems in two clusters around two conventional eddy covariance systems in the Chihuahuan Desert of North America for a period of 2 years. These dryland settings characterized by large temperature variations and relatively low carbon dioxide fluxes represented a challenging setting for eddy covariance. We found very good closure of energy and water balance across all systems (within ±9% of unity). We found very good correspondence between the lower‐cost and conventional systems' fluxes of sensible heat (with concordance correlation coefficient (CCC) of ≥0.87), latent energy (evapotranspiration; CCC ≥ 0.89), and useful correspondence in the net ecosystem exchange ((NEE); with CCC ≥ 0.4) at the daily temporal resolution. Relative to the conventional systems, the low‐frequency systems were characterized by a higher level of random error, particularly in the NEE fluxes. Lower‐cost systems can enable wider deployment affording better replication and sampling of spatiotemporal variability at the expense of greater measurement noise that might be limiting for certain applications. Replicated eddy covariance observations may be useful when addressing gaps in the existing monitoring of critical and underrepresented ecosystems and for measuring areas larger than a single flux footprint.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2020

Infrastructure and Metocean Technology

Research Problem: Changes in the ocean environment (i.e., mudslides or burial from subsea currents, strong weather events or natural fluctuations) have been linked to billions of dollars of impacts. These events can have a significant effect on the success and longevity of offshore infrastructure, as well as affect safety and cost during exploration and production activities.Research Approach: (1) Determine current state of knowledge regarding hazardous metocean and bathymetric conditions, and data availability regarding these conditions and historic events. (2) EY19 -Evaluate if Ml/AI model can be developed to better identify current hazardous metocean and bathymetric conditions. (3) EY 20 + -develop, train, and test ML/AI model to identify current conditions and forecast changes and vulnerability that may impact offshore infrastructure and operations. Benefit: Improved characterization of seabed related hazards in the offshore environment will help to manage and minimize costs and risks during drilling and production operations, and help prevent catastrophic incidents.

Duran, Rodrigo↗

DOE OSTI2023

Internal defect detection and characterization of samarium-cobalt sintered magnets by ultrasonic testing technique

Excessive quantities of samarium-cobalt (Sm-Co) magnet material are being scrapped needlessly due to a lack of understanding of inhomogeneity distribution and unacceptable internal defects. If there is a way to identify, locate, characterize and when needed separate the defective portions of magnet material, utilization can be increased and product quality improved. Further, the magnets’ magnetic and mechanical performance can be improved by reducing the occurrence of internal defects. This paper reports on a cost-effective and efficient nondestructive evaluation method based on an ultrasonic testing (UT) technique applied for detecting and characterizing internal defects in Sm-Co sintered magnets. Applying the UT technique will allow users to comprehensively analyze internal defects, such as inclusions, porosity, microcracks, and other structural irregularities, check for homogeneity and anomalous regions and give the locations of these internal anomalies and defects within the Sm-Co sintered magnets. The UT technique can also be applied to other rare-earth permanent magnets, such as sintered or die-upset neodymium-iron-boron (Nd-Fe-B) magnets. The UT technique can effectively guide quality control and acceptable product selection, in addition to optimizing the magnet alloy design and production processes. Therefore, it can facilitate the improvement of magnet manufacturing efficiency and machinability, reduce scrap, prolong service life, increase the use of what would be post-production waste, and enhance product reuse and recycling at end-of-life disposition.

36 MATERIALS SCIENCE↗

DOE OSTI2021

Fischer-Tropsch Synthesis: The Characterization and Testing of Pt-Co/SiO 2 Catalysts Prepared with Alternative Cobalt Precursors

Different low-cost cobalt precursors (acetate, chloride) and thermal treatments (air calcination/H 2 reduction versus direct H 2 -activation) were investigated to alter the interaction between cobalt and silica. H 2 -activated catalysts prepared from cobalt chloride had large Co 0 particles (XRD, chemisorption) formed by weak interactions between cobalt chloride and silica (temperature programmed reduction (TPR), TPR with mass spectrometry (TPR-MS), TPR with extended X-ray absorption fine structure (EXAFS) and X-ray absorption near edge spectroscopy (XANES) techniques) and retained Cl-blocked active sites, resulting in poor activity. In contrast, unpromoted Co/SiO 2 catalysts derived from cobalt acetate had strong interactions between Co species and silica (TPR/TPR-MS, TPR-EXAFS/XANES); adding Pt increased the extent of the Co reduction. For these Pt-promoted catalysts, the reduction of uncalcined catalysts was faster, resulting in larger Co 0 clusters (19.5 nm) in comparison with the air-calcined/H 2 -activated catalyst (7.8 nm). Both catalysts had CO conversions 25% higher than that of the Pt-promoted catalyst prepared in the traditional manner (air calcination/H 2 reduction using cobalt nitrate) and three times higher than that of the traditional unpromoted Co/silica catalyst. The retention of residual cobalt carbide (observed in XANES) from cobalt acetate decomposition impacted performance, resulting in a higher C 1 –C 4 selectivity (32.2% for air-calcined and 38.7% for uncalcined) than that of traditional catalysts (17.5–18.6%). The residual carbide also lowered the α-value and olefin/paraffin ratio. Future work will focus on improving selectivity through oxidation–reduction cycles.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2024

Redesigning large-scale multimodal transit networks with shared autonomous mobility services

Here, this study addresses a large-scale multimodal transit network design problem, with Shared Autonomous Mobility Services (SAMS) as both transit feeders and an origin-to-destination mode. The framework captures spatial demand and modal characteristics, considers intermodal transfers and express services, determines transit infrastructure investment and path flows, and generates transit routes. A system-optimal multimodal transit network is designed with minimum total door-to-door generalized costs of users and operators, satisfying transit origin-destination demand within a pre-set infrastructure budget. Firstly, the geography, demand, and modes in each zone are characterized with continuous approximation. The decisions of network link investment and multimodal path flows in zonal connection optimization are formulated as a minimum-cost multi-commodity network flow (MCNF) problem and solved efficiently with a mixed-integer linear programming (MILP) solver. Subsequently, the route generation problem is solved by expanding the MCNF formulation to minimize intramodal transfers. The model is illustrated through a set of experiments with the Chicago network comprised of 50 zones and seven modes, under three scenarios. The computational results present savings in traveler journey time and operator cost demonstrating the potential benefits of collaboration between multimodal transit systems and SAMS.

Autonomous vehicles↗

DOE OSTI2020

Large-area low-noise flexible organic photodiodes for detecting faint visible light

Silicon photodiodes are the foundation of light-detection technology; yet their rigid structure and limited area scaling at low cost hamper their use in several emerging applications. A detailed methodology for the characterization of organic photodiodes based on polymeric bulk heterojunctions reveals the influence that charge-collecting electrodes have on the electronic noise at low frequency. The performance of optimized organic photodiodes is found to rival that of low-noise silicon photodiodes in all metrics within the visible spectral range, except response time, which is still video-rate compatible. Solution-processed organic photodiodes offer several design opportunities exemplified in a biometric monitoring application that uses ring-shaped, large-area, flexible, organic photodiodes with silicon-level performance.

47 OTHER INSTRUMENTATION↗

DOE OSTI2023

Artificial intelligence for materials research at extremes

Abstract Materials development is slow and expensive, taking decades from inception to fielding. For materials research at extremes, the situation is even more demanding, as the desired property combinations such as strength and oxidation resistance can have complex interactions. Here, we explore the role of AI and autonomous experimentation (AE) in the process of understanding and developing materials for extreme and coupled environments. AI is important in understanding materials under extremes due to the highly demanding and unique cases these environments represent. Materials are pushed to their limits in ways that, for example, equilibrium phase diagrams cannot describe. Often, multiple physical phenomena compete to determine the material response. Further, validation is often difficult or impossible. AI can help bridge these gaps, providing heuristic but valuable links between materials properties and performance under extreme conditions. We explore the potential advantages of AE along with decision strategies. In particular, we consider the problem of deciding between low-fidelity, inexpensive experiments and high-fidelity, expensive experiments. The cost of experiments is described in terms of the speed and throughput of automated experiments, contrasted with the human resources needed to execute manual experiments. We also consider the cost and benefits of modeling and simulation to further materials understanding, along with characterization of materials under extreme environments in the AE loop. Graphical abstract AI sequential decision-making methods for materials research: Active learning, which focuses on exploration by sampling uncertain regions, Bayesian and bandit optimization as well as reinforcement learning (RL), which trades off exploration of uncertain regions with exploitation of optimum function value. Bayesian and bandit optimization focus on finding the optimal value of the function at each step or cumulatively over the entire steps, respectively, whereas RL considers cumulative value of the labeling function, where the latter can change depending on the state of the system (blue, orange, or green).

36 MATERIALS SCIENCE↗

DOE OSTI2020

Development of Tools Training and Outreach to Address Solar Glare and Flux-Related Avian Impacts

Millions of dollars and significant resources are being spent by developers of utility-scale solar photovoltaic (PV) and concentrating solar power (CSP) plants to address federal and local requirements regarding glare and avian hazards. Solar glare can occur from the glass surfaces of PV modules and from mirrors in CSP systems, which can produce safety and health risks for pilots, motorists, and residents located near these systems. In addition, concentrated solar flux at CSP plants has the potential to singe birds as they fly through regions of high solar flux. This work will develop tools to characterize and mitigate these potential hazards, which will address regulatory policies and reduce costs and efforts associated with the proposed deployment of gigawatts of solar energy systems throughout the nation. The development of standardized and publicly available tools to address these regulatory policies and ensure public and environmental safety is an appropriate role for the government.

14 SOLAR ENERGY↗

DOE OSTI2021

Breast Cancer Imaging and Characterization

Researchers at Los Alamos National Laboratory are developing a suite of ultrasound-waveform tomography and super-resolution ultrasound imaging technology that, while investigational at this time, may be able to provide significantly improved image resolution and characterization of early-stage cancers in a non-invasive and cost-effective manner. Breast cancer is the most frequently diagnosed cancer in U.S. women. Although current medical imaging technologies have improved, false positive rates for mammograms still range 50-60% after 10 yearly mammograms. This innovative ultrasound-imaging technology is patented and copyright protected. It has great potential to differentiate between cancerous and healthy tissue without the negative side effects of mammography. We are seeking a commercialization partner to license these technologies or participate in a Cooperative Research and Development Agreement (CRADA) to further develop a practical suite of products for commercial purposes, and where applicable seek any necessary FDA authorization of the technology.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2026

Generalizing synthetic data-trained acoustic predictive models to real-world measurements

Acoustic Resonance Spectroscopy (ARS) is highly sensitive to structural properties such as material, geometry, and environmental conditions; as a consequence, it can noninvasively measure internal properties that are unobservable by most other methods. Because of its sensing capabilities and low implementation cost and complexity, ARS has potential as a paradigm shift in noninvasive sensing, characterization, and monitoring applications. However, extracting specific properties from ARS measurements, comprising the vibration spectrum of a test object, is challenging due to the sensitivity of the spectra to other structural changes not being measured, e.g. manufacturing tolerances, component coupling, environmental variation, etc. Neural Networks are promising tools for identifying trends in ARS measurements, but their training typically requires large datasets, which are often impractical to obtain for real-world systems. Synthetic data can be simulated efficiently, but discrepancies between synthetic and real-world data frequently lead to poor generalization when testing on the real-world data. We propose a novel ARS model training framework that enables networks trained exclusively on synthetic ARS data to generalize effectively to real-world measurements. Our approach leverages the Correlation Alignment (CORAL) technique to enforce the extraction of features common to both synthetic and real-world domains. As a case study, we demonstrate noninvasive ARS-based pressure measurements in sealed systems. Finite element method (FEM) simulations were used to generate synthetic training data across diverse vessel configurations and pressure conditions, and model performance was then tested on real-world measurements. We demonstrate that robust machine learning models for ARS can be developed without large real-world datasets, significantly broadening the applicability of ARS for noninvasive sensing. Moreover, the approach is extensible to other sensing modalities where synthetic data are abundant but real-world data are limited.

36 MATERIALS SCIENCE↗

DOE OSTI2025

reVRt (reV Routing) [SWR-25-112]

The reV Routing (reVRt) tool is a computational framework for modeling and optimizing transmission infrastructure requirements for electrical grid connections. By employing a spatially-aware least-cost-path methodology, it allows users to incorporate a wide range of factors including siting constraints, regional component costs, land composition costs, point-of-interconnection costs, and network upgrade costs. Additionally, the tool enables advanced follow-on analyses, such as land characterization for potential transmission line routes, to support informed decision-making. Although it's designed to integrate seamlessly with the reV model, the reV Routing tool is versatile and can also be utilized independently for standalone analyses in transmission planning and resource assessment scenarios.

Pinchuk, Pavlo (Paul) [National Renewable Energy L↗

DOE OSTI2022

hifem_transport

SAND2022-14657 O The hifem-transport software is used to simulate transport processes in large geologic environments characterized by complex fractures and man-made infrastructure at an affordable computational cost. Sandia National Laboratories is a multimission laboratory managed and operated by National Technology & Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International Inc., for the U.S. Department of Energy’s National Nuclear Security Administration under contract DE-NA0003525.

Beskardes, Gungor↗

DOE OSTI2025

Chelation ion chromatography as an automated, and cost-effective analytical technique for REE determination: method development and applications

Rare earth elements (REEs), as critical minerals, have important uses in modern energy and technologies, yet are vulnerable to potential supply chain disruptions. To establish domestic REE supply chain, efficient REE detection methods for resource characterization and mineral processing will be needed to accelerate innovations for domestic REE recovery. This study developed a rapid, novel, and cost-effective for REE detection method using ion chromatography (IC) for aqueous samples. Various REE-targeted eluent gradients and post-column agent compositions were tested on the chelation ion chromatography (CIC) with UV-vis detector for optimal separation and quantification of REEs within approximately 20 min. The single-channel pump to deliver the post-column solution to UV-vis detector was replaced with a 4-channel gradient pump, to increase operation and maintenance efficiencies. After method optimization, resulting calibration curves for more than ten REEs achieved high coefficients of determination (R2>0.999) and low relatively standard deviations (below 3.24%), demonstrating sub-ppm level detection limits (0.0897 to 0.1149 mg/L). The reliability of the CIC method was validated through comparison with inductively coupled plasma mass spectrometry (ICP-MS), showing strong agreement in REE recovery from certified standards. The impact of metal ions and salts on REE recovery using CIC was also systematically investigated. CIC consistently exhibited reliable performance in the presence of salt solutions such as NaCl and Na₂SO₄ (up to 10,000 mg/L). Our study also found the presence of high concentrations of Al ions (at 10,000 mg/L) significantly influenced REE determination, and elevated concentrations of Ca ions affected the recovery of specific REEs, including La, Ce, and Pr. The CIC method was further tested on REE-containing eluents from solvent extraction tests out of fly ash leachates. REE detection from these real processing fluids were reported to achieve 90% to 100% recovery rate from our IC method, compared to ICP-MS results. This study underscores the potential of CIC as a reliable and efficient alternative for REE determination in complex matrices. It also highlights the importance of minimizing select interfering metal ions in solutions to ensure accurate results. The REE CIC method presents a promising, low-maintenance, salt-tolerant, and cost-effective alternative to traditional analytical methods for REE analysis.

detection of rare earth elements (REE)↗

DOE OSTI2020

Full circle mechanical dynamic characterization including experimental modal analysis and finite element analysis

During operation, it was observed that a specific mechanical system experienced undesirable vibration and it became necessary to understand and mitigate this phenomenon. This document investigates the tools, methodology, and results of the dynamic characterization of the system. The characterization makes use of the experimental modal analysis (EMA) methods of single input multiple output (SIMO) and single input single output (SISO). The validity of the theory of reciprocity is confirmed to minimize measurement error, cost, and time of repeat testing. Finite element analysis (FEA) is used in choosing transducer and modal impact locations to adequately characterize the system. Single degree of freedom (SDOF) and multiple degree of freedom (MDOF) curve fitting is used to fully characterize the system’s mode shapes and natural frequencies. The EMA characterization results are used to modify and validate the FEA model so that FEA can be used to model potential structural modifications to the system to mitigate the undesirable vibration. Structural modifications are chosen, implemented, and their effectiveness is quantified using EMA. Finally, a qualitative evaluation of the methodology of FEA validation by EMA and tuning of the model to match the experimental results is discussed.

42 ENGINEERING↗

DOE OSTI2020

Fabrication and Characterization of Composite Membranes for the Concentration of Lithium Containing Solutions Using Forward Osmosis

In this study, an energy and highly time efficient, cost effective, and scalable forward osmosis (FO) composite polyvinylidene fluoride (PVDF) membrane is developed to concentrate lithium chloride recovered from naturally occurring geothermal brine resources. Dense thin robust defect-free Kynar-PVDF membranes are fabricated on porous PVDF hollow fiber supports. Various membrane fabrication parameters such as membrane thickness, coating solution concentration, cross-linker concentration, and curing temperature are studied to develop a defect-free dense membrane. Advanced characterization tools such as FIB-SEM, Raman, FTIR spectroscopy, and XRD are used to study the surface and cross-sectional morphology, chemistry of crosslinking, crystallinity and chemical structure of the dense PVDF membrane. FO is utilized to concentrate LiCl in the solution from 36 to 175 g L -1 with an average water transfer rate of 0.450 L m -2 h -1 (LMH) at 85 °C. Furthermore, the membrane showed stable performance for more than 550 h while maintaining the purity of the concentrated lithium chloride solution of >99.99 wt%. Overall, the results suggest that the FO-based technology is an innovative, time and energy efficient approach compared to the conventional technologies for concentration of battery grade LiCl for application in lithium ion batteries and other clean energy technologies.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗