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At least 1,171 records · Page 65
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

A new method for measuring the 3D turbulent velocity dispersion of molecular clouds

ABSTRACT The structure and star formation activity of a molecular cloud are fundamentally linked to its internal turbulence. However, accurately measuring the turbulent velocity dispersion is challenging due to projection effects and observational limitations, such as telescope resolution, particularly for clouds that include non-turbulent motions, such as large-scale rotation. Here, we develop a new method to recover the 3D turbulent velocity dispersion (σv,3D) from position–position–velocity (PPV) data. We simulate a rotating, turbulent, collapsing molecular cloud, and compare its intrinsic σv,3D with three different measures of the velocity dispersion accessible in PPV space: (1) the spatial mean of the 2nd-moment map, σi, (2) the standard deviation of the gradient/rotation-corrected 1st-moment map, σ(c − grad), and (3) a combination of (1) and (2), called the ‘gradient-corrected parent velocity dispersion’, $\sigma _{\mathrm{(p}-\mathrm{grad)}}=(\sigma _{\mathrm{i}}^2+\sigma _{(\mathrm{c}-\mathrm{grad)}}^2)^{1/2}$. We show that the gradient correction is crucial in order to recover purely turbulent motions of the cloud, independent of the orientation of the cloud with respect to the line of sight. We find that with a suitable correction factor and appropriate filters applied to the moment maps, all three statistics can be used to recover σv,3D, with method 3 being the most robust and reliable. We determine the correction factor as a function of the telescope beam size for different levels of cloud rotation, and find that for a beam full width at half-maximum f and cloud radius R, the 3D turbulent velocity dispersion can best be recovered from the gradient-corrected parent velocity dispersion via $\sigma _{v,\mathrm{3D}}= \left[(-0.29\pm 0.26)\, f/R + 1.93 \pm 0.15\right] \sigma _{\mathrm{(p}-\mathrm{grad)}}$ for f/R < 1, independent of the level of cloud rotation or LOS orientation.

Stewart, Madeleine↗
DOE OSTI2023

Freeform thermoelectrics in single-step manufacturing: additive manufacturing of bismuth-telluride thermoelectrics

The project succeeded in producing crack-free Bismuth Telluride thermoelectric parts with density exceeding 98% through laser powder bed fusion (LPBF) additive manufacturing (AM). This greatly exceeded the highest previously reported density of 88% and is the highest among all semiconducting materials processed by LPBF. The additively manufactured material shows comparable Seebeck coefficient as conventional form and can be made into complex geometries with reduced material loss. On the other hand, measured properties are dramatically sensitive to the AM process parameters used, such that with identical composition, the Seebeck coefficient can be controllably tuned from +120 µV/K to -207 µV/K, which means the material switches between an n-type to a p-type semiconductor depending on processing. These changes are accompanied by significant differences in the as-processed microstructure due to rapid solidification. A machine learning protocol was developed and greatly reduced the experimental burden of the project, reducing the typical process optimization period of 2 years to 6 months. The project was fully successful in the objective of producing defect-free, complex geometry of bismuth-telluride parts through LPBF, but only partially successful in achieving performance goals. First, cost reduction of manufacturing, as measured by material waste, was successfully reduced by up to 70% compared to conventional manufacturing methods. This exceeded the proposed 30% reduction in materials waste needed to reach the 20% cost reduction goal of the project. On the other hand, the device performance, as measured by Seebeck coefficient, failed to reach the 40% improvement in efficiency. Rather, we observe comparable Seebeck coefficient between AM samples and conventionally processed counterparts. The device-level efficiency improvement does exceed 40% for complex geometry samples due to shape-induced increase in temperature gradients, but this was not the originally proposed metric. The machine learning approach developed in this project greatly accelerated the process optimization and can be adopted for fast development of AM processing parameters for other brittle and otherwise difficult-to-print materials. For the public, we deliver an efficient and widely adoptable process for incorporating waste-heat harvesting thermoelectric devices in both industrial and commercial heat exchangers. The geometric flexibility allows the capturing device to conform to the shape of the heat source to improve the system-level conversion efficiency. The technique can be deployed on any commercial LBPF systems with zero modifications, thus poses minimal adoption barrier for any manufacturer that already employed AM technology. Beyond bismuth-telluride, the machine-learning guided optimization protocol can be used in the future to reduce both the time and cost of process development for AM of other energy conversion and harvesting materials.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Towards a Verifiable Domain-Specific Language for Hardware-Accelerated Stencils

Defining a domain-specific language (DSL) that supports vector-calculus abstractions eases the porting of partial differential equation (PDE) solvers to specialized architectures. Sufficiently high-level abstractions empower users to express universal laws with sufficient generality that the laws must always hold true within their domain of validity. A broad class of PDE solvers employs stencil-based algorithms, the target domain of Berkeley Lab's stencil accelerator chip co-design project. First released as open-source in January 2026, the Formal software framework lays a foundation for defining an embedded DSL based on composable operators that implement mimetic numerical methods -- stencil algorithms that guarantee satisfaction of discrete versions of important vector calculus theorems. The Formal DSL will be the frontend to a new class of stencil-PDE accelerators developed jointly by LBNL, UHCL, and UC Berkeley through the DOE Competitive Portfolios for Computer Science Project. This offers the potential of an order of magnitude acceleration for this important category of computational methods to serve the DOE mission. Future work on the Formal DSL will facilitate software verification via type-safe templates that enable problem-specific correctness proofs relying upon generic function theory and carefully crafted unit tests.

Rouson, Damian↗
DOE OSTI2026

Improved Microalgal Carbon Utilization Efficiency via Integrated CO 2 Electro-Conversion to Formate and Microalgal Sequestration

This project developed a process to convert industrial carbon dioxide (CO 2 ) emissions into high-value, sustainable products through genetically engineered algae cultivation. While traditional microalgae cultivation depends on sparging CO 2 gas through water, this method is often inefficient because much of the gas escapes into the atmosphere before the algae can consume it. To overcome this challenge, the project designed an integrated system that first uses a CO 2 to formic acid electrolyzer to convert CO 2 into water-soluble formic acid/formate, then introduces formic acid/formate into the algae pond for cultivation, which allows the algae to access and utilize nearly all of the provided carbon, greatly increasing the efficiency of carbon utilization. The project team has successfully scaled up the CO 2 to formic acid electrolyzer from lab-scale to 1000 cm² and demonstrated industrially relevant current densities with the scaled-up electrolyzers using a CO 2 source that simulates industrial CO 2 waste.

42 ENGINEERING↗
DOE OSTI2023

Agilent AgileBioFoundry CRADA (Final Report)

The mission of this CRADA with Agilent was to couple powerful MS platforms (QQQ, IM-QTOF-MS) with Agilent’s novel Ultra-High-Performance Liquid Chromatography (UHPLC) fast metabolomic workflows and perform ABF Machine Learning (ML) to generated datasets. Agilent transferred UHPLC methods to PNNL and LBNL and methods were implemented and demonstrated in both labs, achieving total acquisition times of < 10 min. Metabolites analyzed using Agilent’s shared methods included metabolites from central carbon metabolism, common across hosts, and metabolites unique to engineered strains. Standards were acquired in an UHPLC-Drift Tube Ion Mobility Mass Spectrometer (DTIMS) system for the first time within the context of ABF and methods were optimized based on Agilent’s protocols. Samples from ABF hosts Pseudomonas putida, Aspergillus pseudoterreus, Aspergillus niger and Rhodosporidium toruloides were analyzed using the UHPLC-DTIMS platform for a total of 276 runs. A data analysis workflow compatible with the Experimental Data Depot (EDD) and completely shareable was developed for the acquired UHPLC-DTIMS data. Samples were analyzed using a Data Independent Acquisition Approach (DIA), which for most of the standards provided more transitions therefore increasing detection confidence. Using the data acquired by PNNL, LBNL, and Agilent’s specifications from previous ML projects, SNL applied an ensemble ML strategy to pick the best performing model for automated LC-method selection. Finally, with the contribution of the participant labs and Agilent, SNL developed an Automated Method Selection (AMS) software tool to predict the best liquid chromatography method for analysis of any new molecules of interest. Samples with novel pathways and new metabolite targets of interest are generated at a high pace in the ABF. Therefore, our accomplishment in this CRADA improved the efficiency and accuracy of strain testing by developing and implementing fast analytical methods, robust processing tools, and software for predicting the best methods for UHPLC analysis.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Physics-Reinforced Machine Learning Algorithms for Multiscale Closure Model Discovery

The central objective of this project was to address the challenge of modeling and simulating complex multiscale turbulence phenomena by leveraging physics-guided machine learning (PGML) and hybrid modeling approaches. By integrating physics-based methods with data-driven models, the research focused on achieving robust and scalable solutions for geophysical turbulence, enhancing numerical weather prediction and climate research tools. The project resulted in significant advancements in computational modeling paradigms, predictive tools for reduced-order modeling, and innovative algorithms for fluid dynamics.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2025

Future Opportunities for LWR Irradiations in US Test Reactors

After several years of relatively low activity in the field of Light Water Reactor (LWR) fuel development, the Department of Energy again began to engage in developing new fuel technologies and irradiation performance data prompted by the Fukushima Daichi nuclear accidents. New competencies for irradiation testing in material test reactors in the United States began to be developed at this time using the Advanced Test Reactor (ATR), High Flux Isotope Reactor (HFIR), Massachusetts Institute of Technology Reactor (MITR), and the Transient Reactor Test Facility (TREAT). Capsules for testing fuel and cladding materials in ATR and HFIR were deployed, a Pressurized Water Reactor (PWR) condition loop for testing fuel rods was established in ATR, cladding corrosion studies were performed using a water loop in MITR, and TREAT pulse testing capabilities were commissioned for fuel rods in water capsules. The more recent and unexpected closure of the Halden Boiling Water Reactor (HBWR) also prompted further investments in Loss of Coolant Accident (LOCA) testing capabilities at TREAT. New configurations of these test devices show further potential in enhanced steam condition control and other investigations are building toward a flowing water loop for testing transient to dryout conditions. The closure of HBWR also prompted a major project currently underway to construct additional water loops in ATR where a novel approach is being pursued to enable Boiling Water Reactor (BWR) conditions. A meaningful collaborative project was awarded to MITR which, amidst an unexpected major overhaul of the reactor, has expanded cladding corrosion test capabilities at MITR. New explorations have led to methods for unique experiments at HFIR including channel box irradiations. New device developments are also bridging toward future potential for instrumented capsule irradiation tests in ATR and HFIR. Finally, a new project referred to as the System Physics Advanced Reactor Critical facility (SPARC) is gaining traction towards a large zero-power reactor able to produce physics validation data for LWR fuel bundle designs with increased enrichment and enhanced absorbers for 24-month operation cycles. This paper provides a brief summary of the status of these irradiation testbed capabilities with an emphasis on current efforts toward future capabilities to obtain new data and maximize the performance potential of LWR fuel technologies.

Woolstenhulme, Nicolas [Idaho National Laboratory ↗
DOE OSTI2023

Non-parametric projections of the net-income distribution for all U.S. states for the Shared Socioeconomic Pathways

Abstract Income distributions are a growing area of interest in the examination of equity impacts brought on by climate change and its responses. Such impacts are especially important at subnational levels, but projections of income distributions at these levels are scarce. Here, we project U.S. state-level income distributions for the Shared Socioeconomic Pathways (SSPs). We apply a non-parametric approach, specifically a recently developed principal components algorithm to generate net income distributions for deciles across 50 U.S. states and the District of Columbia. We produce these projections to 2100 for three SSP scenarios in combination with varying projections of GDP per capita to represent a wide range of possible futures and uncertainties. In the generation of these scenarios, we also generated tax adjusted historical deciles by U.S. states, which we used for validating model performance. Our method thus produces income distributions by decile for each state, reflecting the variability in state income, population, and tax regimes. Our net income projections by decile can be used in both emissions- and impact-related research to understand distributional effects at various income levels and identify economically vulnerable populations.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2020

Start-up of Silo 130 Waste Retrieval at La Hague: first Step Towards Reducing Legacy Inventory - 20020

On June 25, 2019, the first grapple of waste and debris was lifted from Silo 130, one of the most challenging legacy waste storage silos at La Hague. This was the achievement of more than ten years of studies and efforts focused on solving some very unique technical and safety challenges, and on designing an innovative, efficient solution. The waste is from de-cladding materials from past Gas-Cooled Reactor fuel reprocessing operations. It is an unsorted mixture of magnesium cladding pieces, graphite debris, and various other metallic waste types. These materials can be chemically reactive and therefore need specific dispositions for handling and storing. The building is more than 40 years old and does not meet modern standards. French safety authorities have required the silo to be emptied. After about three years of operation, the bulk of this waste will be removed and placed in safe storage, pending the selection of a final conditioning solution. Removal of bottom debris will then be possible. When finished, this operation will bring a conclusion to a significant legacy-related hazard on the site. The silo will then be available for final cleaning and de-activation. This paper will describe the technical, safety, and economical challenges, and the various decisions and optimizations that led to the definition of the process and technology to be implemented. Several innovative features were developed and implemented, in all sections of the project: project organization, progressive approach for the waste disposition strategy, light building design, customized and robust retrieval technology, processing technologies involving the most modern robotic and sorting techniques, storage method... Several of these first-of-a-kind innovative features have been developed and qualified specifically for this application, using the detailed qualification procedure specified for all La Hague projects. The facility was built in less than five years, overcoming various difficulties along the line. Commissioning is now finished with the combined efforts of the project, engineering, start-up, and operating teams assisted by dedicated suppliers. The facility is now ramping-up. The paper will also report on the commissioning and start-up of the facility and describe the first operational results. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2021

Science & Technology Review: The Road to Exascale Computing

At Lawrence Livermore National Laboratory, we focus on science and technology research to ensure our nation’s security. We also apply that expertise to solve other important national problems in energy, bioscience, and the environment. Science & Technology Review is published eight times a year to communicate, to a broad audience, the Laboratory’s scientific and technological accomplishments in fulfilling its primary missions. The publication’s goal is to help readers understand these accomplishments and appreciate their value to the individual citizen, the nation, and the world. The Department of Energy’s Exascale Computing Project (ECP) and Lawrence Livermore’s RADIUSS (Rapid Application Development via an Institutional Universal Software Stack) initiative benefit from strategically developed software tools. The front cover shows a simulation of advection under twisting rotation that uses high-order finite elements from Livermore’s Modular Finite Element Methods (MFEM) software library and GLVis visualization tool. On the back cover, the logo (also created with GLVis) for the MFEM project illustrates the curved mesh and sub-element resolution used in high-order simulations. MFEM and GLVis are key components of the ECP’s co-design Center for Efficient Exascale Discretizations (CEED) and RADIUSS. MFEM is also part of ECP’s Extreme-Scale Scientific Software Development Kit (xSDK).

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Potential of U.S. Marine and Offshore Wind Energy to Power Marine Carbon Dioxide Removal

The goal of this project was to provide a preliminary feasibility assessment of powering different marine carbon dioxide removal (mCDR), carbon capture (mCC), and carbon sequestration (mCS) strategies with marine energy. The investigation focused on methods with clear power requirements at sea beyond monitoring such as artificial upwelling, deep ocean storage, electrochemical (eChem) mCDR and mCC, offshore microalgae cultivation, and seaweed farming and sinking. The project found that eChem mCDR powered by marine energy and offshore wind energy available in the United States could meet global CDR scales needed by 2040 and 2050 to limit warming to 1.5 degrees C by 2100 and marine energy alone could greatly contribute to reaching scales needed by 2040. Note that this preliminary estimate assumes that it is possible to harvest all the marine and offshore wind resources available in the United States with existing technology options. Though the biological methods were limited in scale they still hold promise in developing carbon negative fuels and products which can reduce emissions in the short term.

afforestation↗
DOE OSTI2025

A Scalable and Cost-Effective Solution to the U.S. Housing Crisis: A Case Study on Locally Manufactured Modular Multifamily Housing

This case study assesses waste management efficiencies in modular buildings compared to traditional construction methods. We focus on the modular 1-bedroom Model/Z unit by Model Z Modular, LLC. As part of the collaboration between Model Z Modular and the National Renewable Energy Laboratory, we analyzed waste metrics from design through construction, contrasting these findings against conventional stick-built and site-built multifamily buildings. The Model/Z unit is part of a strategic effort to address affordable housing shortages in South Los Angeles, where household income challenges are pronounced. The unit is produced in a state-of-the-art 150,000 sq. ft. modular manufacturing facility located within the city it is serving, and has so far supported the production of over 1,500 affordable housing units. Model/Z units have been used in projects with as many as 195 units, achieving large economies of scale and time. Our analysis demonstrates that modular construction reduces waste compared to traditional methods. This reduction is achieved through precise prefabrication techniques, the implementation of new framing methods, the concentration of workforce expertise, and streamlined logistics, which optimize material use and greatly reduce on-site handling. Additionally, local manufacturing minimizes transportation needs, improving overall project efficiency. The Model/Z unit exemplifies a scalable solution for enhancing housing affordability by optimizing resource utilization and minimizing associated costs. These methods support the delivery of high-quality units at reduced expenses, addressing critical urban housing shortages effectively. This case study underscores Model Z Modular's commitment to producing affordable, quality housing while fostering economic opportunities through job creation and training programs.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2020

Wave function methods for canonical ensemble thermal averages in correlated many-fermion systems

We present a wave function representation for the canonical ensemble thermal density matrix by projecting the thermofield double state against the desired number of particles. Furthermore, the resulting canonical thermal state obeys an imaginary-time evolution equation. Starting with the mean-field approximation, where the canonical thermal state becomes an antisymmetrized geminal power (AGP) wave function, we explore two different schemes to add correlation: by number-projecting a correlated grand-canonical thermal state and by adding correlation to the number-projected mean-field state. As benchmark examples, we use number-projected configuration interaction and an AGP-based perturbation theory to study the hydrogen molecule in a minimal basis and the six-site Hubbard model.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

Finite and infinite matrix product states for Gutzwiller projected mean-field wave functions

Matrix product states (MPS) and “dressed” ground states of quadratic mean fields (e.g., Gutzwiller projected Slater determinants) are both important classes of variational wave functions. This latter class has played important roles in understanding superconductivity and quantum spin liquids. We present a method to obtain both the finite and infinite MPS (iMPS) representation of the ground state of an arbitrary fermionic quadratic mean-field Hamiltonian (which in the simplest case is a Slater determinant and in the most general case is a Pfaffian). We also show how to represent products of such states (e.g., determinants times Pfaffians). From this representation one can project to single occupancy and evaluate the entanglement spectra after Gutzwiller projection. We then obtain the MPS and iMPS representation of Gutzwiller projected mean-field states that arise from the variational slave-fermion approach to the S = 1 bilinear-biquadratic quantum spin chain. To accomplish this, we develop an approach to orthogonalize degenerate iMPS to find all the states in the degenerate ground-state manifold. We find the energies of the MPS and iMPS states match the variational energies closely, indicating the method is accurate and there is minimal loss due to truncation error. We then present an exploration of the entanglement spectra of projected slave-fermion states, exploring their qualitative features and finding good qualitative agreement with the respective exact ground-state spectra found from density matrix renormalization group.

1-dimensional spin chains↗
DOE OSTI2022

A reusable neural network pipeline for unidirectional fiber segmentation

Abstract Fiber-reinforced ceramic-matrix composites are advanced, temperature resistant materials with applications in aerospace engineering. Their analysis involves the detection and separation of fibers, embedded in a fiber bed, from an imaged sample. Currently, this is mostly done using semi-supervised techniques. Here, we present an open, automated computational pipeline to detect fibers from a tomographically reconstructed X-ray volume. We apply our pipeline to a non-trivial dataset by Larson et al . To separate the fibers in these samples, we tested four different architectures of convolutional neural networks. When comparing our neural network approach to a semi-supervised one, we obtained Dice and Matthews coefficients reaching up to 98%, showing that these automated approaches can match human-supervised methods, in some cases separating fibers that human-curated algorithms could not find. The software written for this project is open source, released under a permissive license, and can be freely adapted and re-used in other domains.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2021

Evidence against the Efimov effect in 12 C from spectroscopy and astrophysics

Background: The Efimov effect is a universal phenomenon in physics whereby three-body systems are stabilized via the interaction of an unbound two-body subsystems. A hypothetical state in 12 C at 7.458-MeV excitation energy, comprising a loose structure of three α particles in mutual two-body resonance, has been suggested in the literature to correspond to an Efimov state in nuclear physics. The existence of such a state has not been demonstrated experimentally. Purpose: Using a combination of γ spectroscopy, charged-particle spectroscopy, and astrophysical rate calculations allowing for strict limits on the existence of such a state to been established here. Method: Using the combined data sets from two recent experiments, one with the TexAT (Texas Active Target) TPC (Time Projection Chamber) to measure α decay and the other with Gammasphere to measure γ decay of states in 12 C populated by 12 N and 12 B β decay, respectively, we achieve high sensitivity to states in close proximity to the α threshold in 12 C. Results: No evidence of a state at 7.458 MeV is seen in either data set. Using a likelihood method, the 95% confidence limit γ -decay branching ratio is determined as a function of the β-decay feeding strength relative to the Hoyle state. In parallel, calculations of the 3α reaction rate show the inclusion of the Efimov corresponds to a large increase in the reaction rate around 5 × 10 7 K. Conclusion: From decay spectroscopy—at the 95% confidence limit, the Efimov state cannot exist at 7.458 MeV with any γ-decay branching ratio unless the β strength is less than 0.7% of the Hoyle state. This limit is evaluated for a range of different excitation energies and the results are not favorable for existence of the hypothetical Efimov state in 12 C . Furthermore, the 3α reaction rate with the inclusion of a state between 7.43 and 7.53 MeV exceeds the rate required for stars to undergo the red giant phase.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2021

ECAR-6601 MARVEL Project Seismic Accelerations

The MARVEL reactor design requirements are currently listed within TFR-2576. Several MARVEL subsystems are required to be designed to seismic criteria of IBC 2015. This Engineering Analysis (EA) report documents the accelerations that need to be used in an equivalent-static method seismic analysis to satisfy this requirement.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2023

A hybrid deep neural operator/finite element method for ice-sheet modeling

One of the most challenging and consequential problems in climate modeling is to provide probabilistic projections of sea level rise. A large part of the uncertainty of sea level projections is due to uncertainty in ice sheet dynamics. At the moment, accurate quantification of the uncertainty is hindered by the cost of ice sheet computational models. In this work we develop a hybrid approach to approximate existing ice sheet models at a fraction of their cost. Our approach consists of replacing the finite element model for the momentum equations for the ice velocity, the most expensive part of an ice sheet model, with a Deep Operator Network, while we retain a classic finite element discretization for the evolution of the ice thickness. We show that the resulting hybrid model is very accurate and it is an order of magnitude faster than the traditional finite element model. Further, a distinctive feature of the proposed model, compared to other neural network approaches, is that it can handle high-dimensional parameter spaces (parameter fields) such as the basal friction at the bed of the glacier and can therefore be used for generating samples for uncertainty quantification. Further, we study the impact of hyper-parameters, number of unknowns and correlation length of the parameter distribution on the training and accuracy of the Deep Operator Network on a synthetic ice sheet model. We then target the evolution of the Humboldt glacier in Greenland and show that our hybrid model can provide accurate statistics of the glacier mass loss and can be effectively used to accelerate the quantification of uncertainty.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗