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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 847 records · Page 47
All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

Overview of design developments for Condensable Vapor Devices for ITER

The Condensable Vapor Devices (CVDs), which are part of the ITER Roughing Pump System (RPS), are designed to separate tritiated water content to protect downstream pumps and transfer the water to Tokamak Exhaust Processing (TEP) systems for further separation. CVDs operate on the principle that a cooling fluid, such as liquid nitrogen, cools the exhaust gas stream (process gas) from the Tokamak, allowing the water present in the process gas to condense and freeze, thereby trapping it in the CVDs. The CVDs once saturated with the ice are then regenerated to release the trapped water molecules, which are sent to the TEP. The present article will describe the technical requirements, challenges, and design progress made so far in the development of the CVDs.

Choukekar, Ketan [ORNL] (ORCID:0009000288016383)↗
DOE OSTI2024

Numerical modeling of Joule heated ceramic melter

Computational fluid dynamics and heat transfer models of waste glass melters are being developed to support the vitrification campaign at the Waste Treatment and Immobilization Plant (WTP) as part of the United States nuclear waste stewardship and environmental protection programs. The WTP mission aims to achieve the stable and durable processing of vitrified waste in the safest, most efficient, and economical manner possible. Hanford’s tank waste is far more complex and varied than the waste treated by other vitrification facilities and both startup and long-term operational challenges are likely to be even greater than prior experience has shown. There are thousands of different waste compositions, which makes processing the waste a complex and challenging task. Models of test melters, pilot-scale, and full-scale melters have been developed to provide understanding and insight into the various physical and chemical phenomena occurring during vitrification. So the results of these modeling activities will be used to support the WTP operations to process legacy nuclear waste into a stable form for disposal.

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

Optimization models for integrated biorefinery operations

Variations of physical and chemical characteristics of biomass lead to an uneven flow of biomass in a biorefinery, which reduces equipment utilization and increases operational costs. Uncertainty of biomass supply and high processing costs increase the risk of investing in the US’s cellulosic biofuel industry. We propose a stochastic programming model to streamline processes within a biorefinery. A chance constraint models system’s reliability requirement that the reactor is operating at a high utilization rate given uncertain biomass moisture content, particle size distribution, and equipment failure. The model identifies operating conditions of equipment and inventory level to maintain a continuous flow of biomass to the reactor. Furthermore, the sample average approximation method approximates the chance constraint and a bisection search-based heuristic solves this approximation. A case study is developed using real-life data collected at Idaho National Laboratory’s biomass processing facility. An extensive computational analysis indicates that sequencing of biomass bales based on moisture level, increasing storage capacity, and managing particle size distribution, increases utilization of the reactor and reduces operational costs.

09 BIOMASS FUELS↗
DOE OSTI2024

Analysis of a Prototype Multi-Detector Fast-Neutron Radiography Panel

A multi-detector fast neutron radiography panel was built using the previous work on scalable neutron radiography using the IDEAS ROSSPAD readout module. A new aluminum housing was built to accommodate a large number of detectors tiled together. Additional changes to startup and processing code were made to operate the detector as one cohesive unit. Spatial resolution of the full panel using Cs-137 gammas was reported to be 0.42 line pairs per centimeter at 90% MTF and 2.09 line pairs per centimeter at 10% MTF. Here, three neutron radiographs generated using a Cf-252 fission neutron source were used to determine the spatial resolution of the panel for neutrons. The experiments had 90% MTF values of 0.24, 0.3, and 0.27 line pairs per centimeter and 10% MTF values of 1.30, 1.46, and 1.40 line pairs per centimeter. An example neutron radiograph was also used to prove that the radiography panel can perform true neutron radiography.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2023

Simulations of biomass compression-screw feeding using a compressible non-Newtonian constitutive model

There is global interest in the conversion of biomass into sustainable low-carbon-footprint fuels and chemicals as an alternative to non-renewable fossil feedstocks. Feeding biomass solids into pressurized reactors is one of the key steps in biomass conversion. Predicting mechanical failure and energy requirements for this step helps avoid upstream processing bottlenecks and enables efficient operation of a biorefinery. Here, in this work, we developed a predictive computational model for biomass screw feeders that capture the highly viscous, non-Newtonian and compressible behavior of biomass slurries. Biomass compressible behavior is formulated by an equation of state and the non-Newtonian rheology is represented by a density-dependent viscosity model. Experimental data from two compression screw-feeder systems are presented as a validation for our model. Our model successfully predicted the location of the compressed biomass “plug”, biomass flow rate, and the required torque at different operating conditions for the experimental conditions studied in this work.

09 BIOMASS FUELS↗
DOE OSTI2020

Building commissioning costs and savings across three decades and 1500 North American buildings

B.V. Building commissioning (Cx) is a process for assuring efficient building operations that can be applied to new construction and existing buildings, resulting in energy and non-energy benefits. Quantifying the benefits of commissioning is challenging, but a 2009 study of 643 commercial buildings provided a solid initial data set to which we added 839 additional buildings for a significantly expanded and updated meta-analysis representing 34.7 million square meters (373 million square feet) of floor area. Since 2009 the commissioning industry has continued to grow, driven by building codes, utility programs, and rising awareness of commissioning benefits. In parallel, building controls have become more sophisticated, and analytics software has emerged to assist with commissioning. We find that delivery mechanism and market segment are key determinants of outcomes, although significant and cost-effective savings are found across the spectrum. Median primary energy savings for Cx projects in existing buildings ranged from 5 percent for those conducted under utility programs, 9 percent for monitoring-based commissioning utility programs (i.e., augmented with submetering and diagnostics), and 14 percent for Cx projects outside of utility programs. Across all project types, median savings ranged from 3 percent for the lodging market segment to 16 percent for public order and safety facilities. Outcomes did not vary significantly by building size or by market segment. Energy savings are rarely estimated for new construction commissioning. We found that the median costs of Cx were lower for the 2018 sample than for the 2009 sample—$\$$2.85 per square meter ($\$$0.26 per square foot) for existing buildings (a 33 percent reduction) and $\$$8.78 per square meter ($\$$0.82 per square foot) for new construction (a reduction of almost 50 percent). The median simple payback time for existing buildings was 1.7 years, with a 25th–75th percentile range of 0.8–3.5 years. Overall, this article summarizes these and other key findings, and discusses how the 2018 data reflects shifts in commissioning practice and outcomes.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2023

In-depth analysis on parallel processing patterns for high-performance Dataframes

The Data Science domain has expanded monumentally in both research and industry communities during the past decade, predominantly owing to the Big Data revolution. Artificial Intelligence (AI) and Machine Learning (ML) are bringing more complexities to data engineering applications, which are now integrated into data processing pipelines to process terabytes of data. Typically, a significant amount of time is spent on data preprocessing in these pipelines, and hence improving its efficiency directly impacts the overall pipeline performance. The community has recently embraced the concept of Dataframes as the de-facto data structure for data representation and manipulation. However, the most widely used serial Dataframes today (R, pandas) experience performance limitations while working on even moderately large data sets. We believe that there is plenty of room for improvement by taking a look at this problem from a high-performance computing point of view. In a prior publication, we presented a set of parallel processing patterns for distributed dataframe operators and the reference runtime implementation, Cylon. In this paper, we are expanding on the initial concept by introducing a cost model for evaluating the said patterns. Furthermore, we evaluate the performance of Cylon on the ORNL Summit supercomputer.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Optimizing lossy state preparation for quantum sensing using Hamiltonian engineering

One of the most prominent platforms for demonstrating quantum sensing below the standard quantum limit is the spinor Bose–Einstein condensate. While a quantum advantage using several tens of thousands of atoms has been demonstrated in this platform, it faces an important challenge: atom loss. Atom loss is a Markovian error process modeled by Lindblad jump operators, and a no-go theorem, which we also show here, states that the loss of atoms in all spin components reduces the quantum advantage to a constant factor. Here, we show that this no-go theorem can be circumvented if we constrain atom losses to a single spin component. Moreover, we show that in this case, the maximum quantum Fisher information with N atoms scales as N 3/2 , establishing that a scalable quantum advantage can be achieved despite atom loss. Although Lindblad jump operators are generally non-Hermitian and non-invertible, we use their Moore–Penrose inverse to develop a framework for constructing several states with this scaling of Fisher information in the presence of losses. We use Hamiltonian engineering with realistic Hamiltonians to develop experimental protocols for preparing these states. Finally, we discuss possible experimental techniques to constrain the losses to a single spin mode.

74 ATOMIC AND MOLECULAR PHYSICS↗
DOE OSTI2021

Initial demonstration of automated fuel performance modeling with 1977 EBR-II metallic fuel pins using BISON code with FIPD and IMIS databases

Using the BISON fuel performance code, simulations were conducted using an automated process to read initial and operating conditions from the Fuels Irradiation and Physics Database (FIPD) and Integral Fast Reactor materials information system (IMIS) database, which contains metallic fuel data from the Experimental Breeder Reactor-II (EBR-II). This work demonstrates use of an integrated framework to access the vast majority of EBR-II experimental fuel pin data to support rapid development of fuel performance models for next-generation metallic fuel systems. With this capability, validation for fuel qualification can be performed rapidly. Between IMIS and FIPD, there is enough information to conduct 1977 unique EBR-II metallic fuel pin histories from 24 different experiments, at varying levels of detail between the two databases. Each of these histories includes a high-resolution power history, flux history, coolant channel flow rates, and coolant channel temperatures. Fission gas release (FGR), cumulative damage fraction (CDF), fuel axial swelling, cladding profilometry, and burnup were all simulated in BISON. The results were compared to post-irradiation examination (PIE) results for the initial demonstration of automated BISON modeling. BISON simulations conducted with IMIS and FIPD were in rough agreement with PIE measurements and calculations. Cladding profilometry, FGR, and fuel axial swelling were found to be in rough agreement with PIE measurements, depending on the physics used within the BISON input files. Here, the mechanical contact solver chosen was found to significantly impact axial fuel swelling and cladding strain predictions. CDF values were assessed to see whether pin failure may have been predicted (CDF ≥ 1). This work suggests that continued development of an automated tool for BISON should focus on inclusion of the Fast Flux Test Facility (FFTF) experimental data for a larger database for metallic fuel, improved physical models to better capture fuel performance, such as fuel-cladding interactions, and a more detailed comparison with available PIE data to further the BISON model development.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2021

Rahasak—Scalable blockchain architecture for enterprise applications

Blockchain-based decentralized infrastructure has been adapted in various industries to handle the sensitive data in a privacy-preserving manner without trusting third parties. However, integrating state-of-the-art blockchain platforms with the scalable, enterprise-level applications result in several challenges. Current blockchain platforms do not support high transaction throughput, lack high scalability, and cannot provide real-time transaction processing and back-pressure operation handling in high transaction throughput applications(e.g Big data, IoT). In this paper, we propose a novel permissioned blockchain platform “Rahasak” for highly scalable, enterprise applications. Rahasak blockchain adopts the Apache Kafka-based consensus on top of a “Validate-Execute-Group” blockchain architecture to handle realtime transaction execution on the blockchain. The architecture is equipped with a functional programming and actor-based smart contract platform that enables concurrent execution of transactions in the blockchain. Rahasak supports high transaction throughput, high scalability, concurrent transaction execution, data analytics features. Finally, with Rahasak, we make blockchain more scalable, secure, structured and meaningful for further data analytics.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2026

Intrinsically Conductive {pi}‑d Conjugated Layers with Co–N4 Active Sites for Efficient Nitrate Electrocatalysis and Zinc-Nitrate Batteries

Electrochemical synthesis of ammonia from nitrate has been extensively investigated as a potential alternative to the energy-intensive Haber-Bosch process. This approach not only operates under ambient conditions but also simultaneously removes nitrate contaminants while producing ammonia as a value-added product. However, the ongoing quest lies in designing an efficient electrocatalyst that achieves a high ammonia yield rate, high selectivity, and long-term stability. Herein, we report the outstanding performance of a Co–N4 coordinated π-d layered Co3(HITP)2 (HITP = 2,3,6,7,10,11-hexaiminotriphenylene) in nitrate electrocatalysis. The unique combination of abundant Co–N4 active sites and superior electrical conductivity enables significant electrocatalytic activity, delivering a maximum ammonia yield rate of 56.8 mg cm–2 h–1 at −0.8 V vs RHE and a Faradaic efficiency of ∼91% at −0.4 V vs RHE. Mechanistic analysis reveals that alkaline conditions accelerate water dissociation to generate adsorbed hydrogen intermediates (H*), which are utilized by Co–N4 sites to drive the stepwise hydrogenation of nitrate to ammonia while suppressing competing hydrogen evolution reaction (HER) pathways. Furthermore, integration of this catalyst into a zinc-nitrate battery resulted in a maximum power density of 5.3 mW cm–2 and an open-circuit potential of ∼1.45 V. These results highlight the potential of π-d conjugated Co–N4 materials as an efficient catalyst for both environmental remediation and energy conversion.

Namvar, shahrirar↗
DOE OSTI2025

Simultaneous Heat and Electricity Storage in a Flow Battery System

This study investigates the dual-storage capability of a redox flow battery (RFB) system, enabling simultaneous storage of heat and electricity within a single platform. Through electrochemical and thermal experiments, we evaluated how heat storage affects battery performance and vice versa using a counterflow heat exchanger integrated in a conventional RFB configuration. The results show that incorporating heat storage had a minimal impact on the electrochemical charging and discharging processes. Further, the heat discharge operated independently of electrochemical storage, confirming that both functions can coexist without interference. The combined system also enhanced overall energy conversion efficiency, demonstrating its potential as an efficient solution for supplying both thermal and electrical energythe two most widely used energy forms.

Electrical energy↗
DOE OSTI2022

Splitting the Ring: Impact of Ortho and Meta Pi Conjugation Pathways through Disjointed [8]Cycloparaphenylene Electronic Materials

In this report, we describe the synthesis and electronic properties of small-molecule and polymeric [8]cycloparaphenylenes ([8]CPPs) with disjointed pi conjugated substituents. Arylene-ethynylene linkers were installed on opposite sides of the [8]CPP nanohoop as separated by three phenyl units on either side such that the monomer systems have syn (C2 symmetry) and anti (C1 symmetry) conformers with a small energy gap (0.1-0.6 kcal/mol). This disjoined substitution pattern necessarily forces delocalization through and around the CPP radial structure. We demonstrate new electronic states from this radial/linear mixing in both the small molecules and the pi extended polymers. Quantum chemical calculations reveal these electronic processes to arise from multiple operative radial/linear conjugation pathways, as the disjoint pattern results in both ortho and meta connections to the CPP ring. Furthermore, these results affirm the unique nature of hybrid radial and linear pi electron delocalization operative in these new conjugation pathways.

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

A Binomial Stochastic Framework for Efficiently Modeling Discrete Statistics of Convective Populations

Abstract Understanding the coupling between convective clouds and the general circulation, as well as addressing the gray zone problem in convective parameterization, requires insight into the genesis and maintenance of spatial patterns in cumulus cloud populations. In this study, a simple toy model for recreating populations of interacting convective objects as distributed over a two‐dimensional Eulerian grid is formulated to this purpose. Key elements at the foundation of the model include i) a fully discrete formulation for capturing discrete behavior in convective properties at small population sample sizes, ii) object age‐dependence for representing life‐cycle effects, and iii) a prognostic number budget allowing for object interactions and co‐existence of multiple species. A primary goal is to optimize the computational efficiency of this system. To this purpose the object birth rate is represented stochastically through a spatially aware Bernoulli process. The same binomial stochastic operator is applied to horizontal advection of objects, conserving discreteness in object number. The applicability to atmospheric convection as well as behavior implied by the formulation is assessed. Various simple applications of the BiOMi model (Binomial Objects on Microgrids) are explored, suggesting that important convective behavior can be captured at low computational cost. This includes i) subsampling effects and associated powerlaw scaling in the convective gray zone, ii) stochastic predator‐prey behavior, iii) the downscale turbulent energy cascade, and iv) simple forms of spatial organization and convective memory. Consequences and opportunities for convective parameterization in next‐generation weather and climate models are discussed.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Surface restructuring of a perovskite-type air electrode for reversible protonic ceramic electrochemical cells

Reversible protonic ceramic electrochemical cells (R-PCECs) are ideally suited for efficient energy storage and conversion; however, one of the limiting factors to high performance is the poor stability and insufficient electrocatalytic activity for oxygen reduction and evolution of the air electrode exposed to the high concentration of steam. Here we report our findings in enhancing the electrochemical activity and durability of a perovskite-type air electrode, Ba 0.9 Co 0.7 Fe 0.2 Nb 0.1 O 3-δ (BCFN), via a water-promoted surface restructuring process. Under properly-controlled operating conditions, the BCFN electrode is naturally restructured to an Nb-rich BCFN electrode covered with Nb-deficient BCFN nanoparticles. When used as the air electrode for a fuel-electrode-supported R-PCEC, good performances are demonstrated at 650 °C, achieving a peak power density of 1.70 W cm –2 in the fuel cell mode and a current density of 2.8 A cm –2 at 1.3 V in the electrolysis mode while maintaining reasonable Faradaic efficiencies and promising durability.

30 DIRECT ENERGY CONVERSION↗
DOE OSTI2025

Quantum optical classifier with superexponential speedup

Abstract Classification is a central task in deep learning algorithms. Usually, images are first captured and then processed by a sequence of operations, of which the artificial neuron represents one of the fundamental units. This paradigm requires significant resources that scale (at least) linearly in the image resolution, both in terms of photons and computational operations. Here, we present a quantum optical pattern recognition method for binary classification tasks. It classifies objects without reconstructing their images, using the rate of two-photon coincidences at the output of a Hong-Ou-Mandel interferometer, where both the input and the classifier parameters are encoded into single-photon states. Our method exhibits the behaviour of a classical neuron of unit depth. Once trained, it shows a constant $${{\mathcal{O}}}(1)$$ O ( 1 ) complexity in the number of computational operations and photons required by a single classification. This is a superexponential advantage over a classical artificial neuron.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗