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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 613 records · Page 34
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Manufacturing Cost Analysis of Advanced Parabolic Trough Collector

The research team performed a detailed bottom-up manufacturing cost estimate for an advanced parabolic trough design - the Solar Dynamics Sunbeam-MT (Sunbeam Mid-Term). This includes all components for manufacturing and assembly in a manufacturing facility (e.g. space frame and arms), and the purchased parts (e.g. mirrors and receiver tubes). Estimates of the construction and assembly activities have been made to then determine an estimated installed cost. Prior analysis has already undertaken detailed bottom-up manufacturing, assembly, and construction analysis for the Ultimate Trough from schlaich bergermann partner (sbp), and this work updates the solar field cost estimates based on a similar aperture area as the SunBeam-MT. For this analysis, the Ultimate Trough is considered the commercial parabolic trough and the Sunbeam-MT as the advanced parabolic trough. For similarity, both the Sunbeam-MT and the Ultimate Trough have been modelled with a solar field with approximately 800,000 square meters (m2) in aperture area - the equivalent of a large CSP plant. The analysis has found a potential installed cost estimate of the Sunbeam-MT could be $120/m2 but must be built at scale to confirm this estimate. Compared to prior analysis, the commercial Ultimate Trough using U.S. conditions, has reduced in installed cost from $178/m2 to $152/m2. Both designs could be even cheaper with Chinese steel.

cost analysis↗
DOE OSTI2024

Hydrogen underground storage for grid electricity storage: An optimization study on techno-economic analysis

Here, this study performs a techno-economic analysis of hydrogen underground storage systems for grid electricity storage, evaluating their economic viability at the plant scale using dynamic optimization. It explores the feasibility of various system configurations and revenue models in the context of volatile electricity prices and the necessity for multiple revenue streams. The hypothesis tested is that large-scale hydrogen storage, despite its low round-trip efficiency, can be economically viable with the right mix of revenue streams. This study uses scenario-based analysis to assess the impacts of different system configurations, including engaging in time-shifting arbitrage, ancillary service markets and blending hydrogen with natural gas. Results indicate potential annual net cash flows of up to $\$$1.5 million from ancillary services integration and $\$$5.2 million from natural gas blending, contingent on specific system sizes. The study concludes that hydrogen underground storage for grid electricity storage can be profitable, and emphasizes that proper system design and precise electricity price forecasting are crucial for optimizing system performance and economic returns. This research sets the stage for further investigations into the scalability of hydrogen storage systems and their broader implications for grid electricity storage and energy market dynamics.

25 ENERGY STORAGE↗
DOE OSTI2024

Integrating HPC, AI, and Workflows for Scientific Data Analysis: Report from Dagstuhl Seminar 23352

The Dagstuhl Seminar 23352, titled “Integrating HPC, AI, and Workflows for Scientific Data Analysis,” held from August 27 to September 1, 2023, was a significant event focusing on the synergy between High-Performance Computing (HPC), Artificial Intelligence (AI), and scientific workflow technologies. The seminar recognized that modern Big Data analysis in science rests on three pillars: workflow technologies for reproducibility and steering, AI and Machine Learning (ML) for versatile analysis, and HPC for handling large data sets. These elements, while crucial, have traditionally been researched separately, leading to gaps in their integration. The seminar aimed to bridge these gaps, acknowledging the challenges and opportunities at the intersection of these technologies. The event highlighted the complex interplay between HPC, workflows, and ML, noting how ML has increasingly been integrated into scientific workflows, thereby enhancing resource demands and bringing new requirements to HPC architectures, like support for GPUs and iterative computations. The seminar also addressed the challenges in adapting HPC for large-scale ML tasks, including in areas like deep learning, and the need for workflow systems to evolve to leverage ML in data analysis fully. Moreover, the seminar explored how ML could optimize scientific workflow systems and HPC operations, such as through improved scheduling and fault tolerance. A key focus was on identifying prestigious use cases of ML in HPC and understanding their unique, unmet requirements. The stochastic nature of ML and its impact on the reproducibility of data analysis on HPC systems was also a topic of discussion.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Urban mining from biomass, brine, sewage sludge, phosphogypsum and e-waste for reducing the environmental pollution: Current status of availability, potential, and technologies with a focus on LCA and TEA

We discuss how rapid industrialization, improved standards of living, growing economies and ever-increasing population has led to the unprecedented exploitation of the finite and non-renewable resources of minerals in past years. It was observed that out of 100 BMT of raw materials processed annually only 10% is recycled back. This has resulted in a strenuous burden on natural or primary resources of minerals (such as ores) having limited availability. Moreover, severe environmental concerns have been raised by the huge piles of waste generated at landfill sites. To resolve these issues, ‘Urban Mining’ from waste or secondary resources in a Circular Economy’ concept is the only sustainable solution. The objective of this review is to critically examine the availability, elemental composition, and the market potential of the selected secondary resources such as lignocellulosic/algal biomass, desalination water, sewage sludge, phosphogypsum, and e-waste for minerals sequestration. This review showed that, secondary resources have potential to partially replace the minerals required in different sectors such as macro and microelements in agriculture, rare earth elements (REEs) in electrical and electronics industry, metals in manufacturing sector and precious elements such as gold and platinum in ornamental industry. Further, inputs from the selected life cycle analysis (LCA) & techno economic analysis (TEA) were discussed which showed that although, urban mining has a potential to reduce the greenhouse gaseous (GHG) emissions in a sustainable manner however, process improvements through innovative, novel and cost-effective pathways are essentially required for its large-scale deployment at industrial scale in future.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Benchmark study of the DTU OWC chamber with both two-way and one-way absorption

This paper reports on a benchmark study based on small-scale (1:50) measurements of a single, oscillating water column chamber mounted sideways in a long flume. The geometry of the OWC chamber is extracted from a barge-like, attenuator-type floating concept “KNSwing” with 40 chambers targeted for deployment in the Danish part of the North Sea. In addition to traditional two-way energy extraction we also consider one-way energy extraction with passive venting and compare chamber response, pressures and total absorbed energy between the two methods. A blind study was established for the numerical modeling, with participants applying several implementations of weakly nonlinear potential flow theory and commercial Navier–Stokes solvers (CFD). Both compressible and incompressible models were used for the air phase. Potential flow calculations predict more energy absorption near the chamber resonance for one-way absorption than for two-way absorption, but the opposite is found from the experimental measurements. This outcome is mainly attributed to energy losses in the experimental passive valve system, but this conclusion must be confirmed by better experimental measurements. Modeling the one-way valve in CFD proved to be very challenging and only one team was able to provide results which were generally closer to the experiments. The study illustrates the challenges associated with both numerical and experimental analysis of OWC chambers. Air compressibility effects were not found to be important at this scale, even with the large volume of additional air used for the one-way case.

16 TIDAL AND WAVE POWER↗
DOE OSTI2022

Integrated Pumped Thermal and Liquid Air Energy Storage

Liquid air energy storage (LAES) and pumped thermal energy storage (PTES) are two closely related technologies that employ thermodynamic cycles to store electricity in the form of thermal energy. This chapter describes the integration of PTES and LAES in a single combined cycle (PT-LAES), where the PTES side provides the cold source required to drive the air liquefaction process. This presents several advantages compared to the separate cycles, including the partial or complete removal of cold stores, a higher energy density and the achievement of full air liquefaction. Notably, PT-LAES does not depend on external sources of heat or cold and thus constitutes a stand-alone system. As a result, PT-LAES can also be viewed as an advanced LAES cycle with full liquefaction.

Carnot batteries↗
DOE OSTI2021

Fast multiscale contrast independent preconditioners for linear elastic topology optimization problems

The goal of this work is to present a fast and viable approach for the numerical solution of the high-contrast state problems arising in topology optimization. The optimization process is iterative, and the gradients are obtained by an adjoint analysis, which requires the numerical solution of large high-contrast linear elastic problems with features spanning several length scales. The size of the discretized problems forces the utilization of iterative linear solvers with solution time dependent on the quality of the preconditioner. The lack of clear separation between the scales, as well as the high-contrast, imposes severe challenges on the standard preconditioning techniques. Thus, here we propose new methods for the high-contrast elasticity equation with performance independent of the high-contrast and the multi-scale structure of the elasticity problem. The solvers are based on two-levels domain decomposition techniques with a carefully constructed coarse level to deal with the high-contrast and multi-scale nature of the problem. The construction utilizes spectral equivalence between scalar diffusion and each displacement block of the elasticity problems and, in contrast to previous solutions proposed in the literature, is able to select the appropriate dimension of the coarse space automatically. The new methods inherit the advantages of domain decomposition techniques, such as easy parallelization and scalability. Finally, the presented numerical experiments demonstrate the excellent performance of the proposed methods.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Power modeling of degraded PV systems: Case studies using a dynamically updated physical model (PV-Pro)

Power modeling, widely applied for health monitoring and power prediction, is crucial for the efficiency and reliability of Photovoltaic (PV) systems. The most common approach for power modeling uses a physical equivalent circuit model, with the core challenge being the estimation of model parameters. Traditional parameter estimation either relies on datasheet information, which does not reflect the system's current health status, especially for degraded PV systems, or requires additional I-V characterization, which is generally unavailable for large-scale PV systems. Thus, we build upon our previously developed tool, PV-Pro (originally proposed for degradation analysis), to enhance its application for power modeling of degraded PV systems. PV-Pro extracts model parameters from production data without requiring I-V characterization. This dynamic model, periodically updated, can closely capture the actual degradation status, enabling precise power modeling. PV-Pro is compared with popular power modeling techniques, including persistence, nominal physical, and various machine learning models. The results indicate that PV-Pro achieves outstanding power modeling performance, with an average nMAE of 1.4 % across four field-degraded PV systems, reducing error by 17.6 % compared to the best alternative technique. Furthermore, PV-Pro demonstrates robustness across different seasons and severities of degradation. The tool is available as a Python package at https://github.com/DuraMAT/pvpro.

14 SOLAR ENERGY↗
DOE OSTI2026

Deciphering the Solvation Structure of Aqueous ZnCl 2 Solutions from X-ray Absorption Spectra Using the Interpretable Graph Neural Network

Machine learning (ML) provides powerful pathways for predicting spectroscopic observables from atomic structures, but its broader impact depends on making model predictions interpretable in terms of physical and chemical principles. Here, we introduce a physics-guided graph neural network (GNN) model that predicts Zn K-edge X-ray spectroscopy (XAS) spectra of aqueous ZnCl 2 solutions. Training data are generated from ab initio XAS calculations on molecular dynamics snapshots obtained using a machine learning interatomic potential. The GNN reproduces experimental spectra across concentrations from dilute (<0.1 m) to highly concentrated (30 m, “water-in-salt”) regimes and scales efficiently to large, disordered liquid systems beyond the reach of conventional ab initio approaches. Gradient-based attribution analysis reveals that the model learns physically meaningful structure-spectrum relationships. Ligand-specific attributions reflect orbital hybridization patterns and the origin of the excitations derived from the density functional theory. Bond-length attributions recover spectral shifts consistent with multiple-scattering theory. Finally, this work bridges data-driven prediction with electronic-structure theory, establishing a general paradigm for interpretable ML that links atomic structure, electronic structure, and spectroscopic observables.

25 ENERGY STORAGE↗
DOE OSTI2020

Three-State Majority-vote Model on Scale-Free Networks and the Unitary Relation for Critical Exponents

Abstract We investigate the three-state majority-vote model for opinion dynamics on scale-free and regular networks. In this model, an individual selects an opinion equal to the opinion of the majority of its neighbors with probability 1 − q , and different to it with probability q . The parameter q is called the noise parameter of the model. We build a network of interactions where z neighbors are selected by each added site in the system, a preferential attachment network with degree distribution k − λ , where λ = 3 for a large number of nodes N . In this work, z is called the growth parameter. Using finite-size scaling analysis, we obtain that the critical exponents $$\beta /\bar{\nu }$$ β / ν ¯ and $$\gamma /\bar{\nu }$$ γ / ν ¯ associated with the magnetization and the susceptibility, respectively. Using Monte Carlo simulations, we calculate the critical noise parameter q c as a function of z for the scale-free networks and obtain the phase diagram of the model. We find that the critical exponents add up to unity when using a special volumetric scaling, regardless of the dimension of the network of interactions. We verify this result by obtaining the critical noise and the critical exponents for the two and three-state majority-vote model on cubic lattice networks.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2023

Socially-aware evaluation framework for transportation

Technological advancements are rapidly changing traffic management in cities. Navigation applications, in particular, have impacted cities in many ways by rerouting traffic. As different routing strategies distribute traffic differently, understanding these disparities across multiple city-relevant dimensions is extremely important for decision-makers. We develop a multi-themed framework called Socially- Aware Evaluation Framework for Transportation (SAEF), which assists in understanding how traffic routing and the resultant dynamics affect cities. The framework is presented for four Bay Area cities, for which we compare three routing strategies - user equilibrium travel time, system optimal travel time, and system optimal fuel. The results demonstrate that many neighborhood impacts, such as traffic load on residential streets and around minority schools, degraded with the system-optimal travel time and fuel routing in comparison to the user-equilibrium travel time routing. The findings also show that all routing strategies subject the city's disadvantaged neighborhoods to disproportionate traffic exposure. Our intent with this work is to provide an evaluation framework that enables reflection on the consequences of traffic routing and management strategies, allowing city planners to recognize the trade-offs and potential unintended consequences.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2020

Economic losses from extreme weather in the U.S. Gulf Coast region: spatially differential contributions of climate hazard and socioeconomic exposure and vulnerability

Worldwide economic losses from extreme weather events (EWE) have increased over recent decades, with significant geographic heterogeneity in damages. The IPCC defines the risk from EWE as a function of the climate hazard, socioeconomic exposure, and vulnerability. Although these three drivers vary at fine spatial scales, spatial variability largely has been overlooked in assessments of the drivers of economic loss from EWE. Using cluster analysis, we developed a novel socio-climate hazard typology (SCT) that integrates locally defined climate hazard and socioeconomic exposure and social vulnerability typologies. The results identified 838 unique SCT types impacted by EWE across the Gulf Coastal United States during 1981–2010. We regressed the SCT types and their constituent hazard and socioeconomic components against the cumulative economic loss (1981–2010) from EWE for each SCT type. Across the landscape, economic damages of SCT types were determined by unique, spatially explicit combinations of different risk factors, even in explaining the same level of economic loss. For example, multi-billion-dollar damages in the central Gulf Coast and peninsular Florida were explained by different drivers of risk, with damages in the former explained by additive interaction between climate hazard and multiplicative interaction between climate hazard and socioeconomic exposure and vulnerability, and in the latter explained by socioeconomic exposure and vulnerability. These results highlight the need to diagnose additive and multiplicative interactions among drivers of EWE risk in a spatially explicit context.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Development of a high-fidelity multi-physics coupling between MCNP6.2 and CTF4.0 for VVER applications

Ensuring system safety in the design, licensing, and operation phases is a priority in the nuclear industry. Performing extensive, full-scale reactor safety experiments is often prohibitive due to the large associated costs. Computational simulations offer an alternative safety analysis method, typically with significant cost reductions. Recent high-level developments in technology and increased availability of computational resources have allowed the development of high-fidelity, high-resolution multi-physics coupled codes. Such developments may be used to generate reference models for deterministic core calculations. Under this framework, the high-fidelity continuous energy Monte Carlo-based neutron transport code MCNP6.2 was coupled externally with the state-of-the-art thermal-hydraulics subchannel code, CTF4.0 for VVER (Water-Water Energetic Reactor) applications. A VVER-1000 fuel assembly model was used to demonstrate the capability of the coupled code. The converged coupled solution is compared to initial results, consisting of the first MCNP evaluation and first CTF evaluation after initialization. The VVER-1000 type assembly results are compared to other evaluations of the same assembly model. The findings indicate good agreement with expectations and reference cases, where available. The initial results of the coupled MCNP6.2/CTF4.0 calculations at the assembly level for steady-state calculations are presented in this study, which may support future work toward high-fidelity coupled full-core modeling capabilities. The multi-physics model may be further improved, enhanced, and expanded for both cycle depletion and transient applications. Such a multi-physics system will also be applicable to the VVER-1200 and other triangular lattice designs and support their deployment and operation safely and economically. (authors)

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2025

Expansion of Carbon Calculator for Land Use and Land Management Change from Biofuels Production (CCLUB) to Address Induced Land Use Changes and Other Indirect Effects of Clean Fuel Production for R&D GREET ® 2024

Since the late 2000s, biofuel life-cycle analysis (LCA) has included induced land use change (ILUC) and other indirect effects (I-effects) of large-scale feedstock production for biofuels. In ILUC and I-effect emissions modeling, economic models are used to simulate the area of land conversion among different land types and other I-effects such as non-feedstock crop production and livestock production that are driven by the scenarios of biofuel production volume shocks. On the other hand, emission factors (EF) models estimate carbon stock changes and GHG emissions associated with these changes. Finally, the area and type of ILUC and I-effects are combined with the EFs to estimate the biofuel ILUC/I-effect GHG emissions in the unit of grams of CO 2 equivalent per MJ biofuel produced (g CO 2 e/MJ).

09 BIOMASS FUELS↗
DOE OSTI2020

On Performance Prediction of Big Data Transfer in High-performance Networks

Big data generated by large-scale scientific and industrial applications need to be transferred between different geographical locations for remote storage, processing, and analysis. High-speed dedicated connections provisioned in High-performance Networks (HPNs) are increasingly utilized to carry out such big data transfer. HPN management highly relies on an important capability of performance (mainly throughput) prediction to reserve sufficient bandwidth and meanwhile avoid over-provisioning that may result in unnecessary resource waste. This capability is critical to improving the resource (mainly bandwidth) utilization of dedicated connections and meeting various user requests for data transfer. Conventional methods conduct performance prediction by fitting prior observed transfer history with predefined loss functions, without considering unobservable latent factors such as competing loads on end hosts. Such latent factors also have a significant impact on the application-level data transfer performance, which may result in an inaccurate prediction model. In this paper, we first investigate the impact of latent factors and propose a clustering-based method to eliminate their negative impact on performance prediction. We then develop a robust machine learning-based performance predictor by: i) incorporating the proposed latent factor elimination method into data preprocessing, and ii) adopting a customized domain guided loss function. Extensive experimental results show that our predictor achieves significantly higher prediction accuracy than several other state-of-the-art methods.

Liu, Wuji↗
DOE OSTI2025

TPCpp-10M: Simulated proton-proton collisions in a time projection chamber for AI foundation models

Scientific foundation models hold great promise for advancing nuclear and particle physics by improving analysis precision and accelerating discovery. Yet, progress in this field is often limited by the lack of openly available large scale datasets, as well as standardized evaluation tasks and metrics. Furthermore, the specialized knowledge and software typically required to process particle physics data pose significant barriers to interdisciplinary collaboration with the broader machine learning community. This work introduces a large, openly accessible dataset of 10 million simulated proton-proton collisions, designed to support self-supervised training of foundation models. To facilitate ease of use, the dataset is provided in a common NumPy format. In addition, it includes 70,000 labeled examples spanning three well defined downstream tasks: track finding, particle identification, and noise tagging, to enable systematic evaluation of the foundation model's adaptability. The simulated data are generated using the Pythia Monte Carlo event generator at a center of mass energy of $\sqrt{s}$ = 200 GeV and processed with Geant4 to include realistic detector conditions and signal emulation in the sPHENIX Time Projection Chamber at the Relativistic Heavy Ion Collider, located at Brookhaven National Laboratory. This dataset resource establishes a common ground for interdisciplinary research, enabling machine learning scientists and physicists alike to explore scaling behaviors, assess transferability, and accelerate progress toward foundation models in nuclear and high energy physics. The complete simulation and reconstruction chain is reproducible with the sPHENIX software stack. All data and code locations are provided under Data Accessibility.

Data Analysis, Statistics and Probability (physics↗
DOE OSTI2022

Heterogeneous Visualization: A Call for Empirically Driven Technology Advancement and Adoption; Preprint

Our ability to generate data from simulations and experimental platforms is generally outpacing our ability to analyze those data efficiently and effectively. There are a growing number and variety of possible visualization display modalities, from commodity head-mounted displays to large-scale immersive environments, from desktop and touch displays to high-resolution display walls. Many of these visualization technologies and visual environments are rapidly evolving and could revolutionize analysis for certain classes of complex scientific and engineering data. However, the choice of what technology or combination of technologies to use has historically been largely ad hoc and lacks a carefully validated understanding of how to best utilize different visualization technologies to advance the science. To maximize the impact of our data and analyses, it is imperative that we use the most effective visualization environments for the analysis task at hand.

heterogeneous visualization↗
DOE OSTI2021

Entropy and Boundary Based Adversarial Learning for Large Scale Unsupervised Domain Adaptation

Supervised semantic segmentation methods provide state-of-the-art performance, but their performance is limited by the amount of quality labeled data they need for training. Scarcity of labeled data and non-transferablity of models, due to cross-domain discrepancy makes it a bigger challenge for remote sensing imagery analysis. In this work, we approach this problem through adversarial learning, driven by entropy and boundary of region-of-interest for unsupervised domain adaptation. This concept helps with better boundary prediction and encourages target domain entropy maps (probability/uncertainty maps) to be similar to source domains. In particular, we showed that deriving informative entropy through the adversarial learning is essential to enable the adaptation. We used a large scale cross country building extraction dataset to validate the framework. The experimental results show the usefulness of considering boundary and entropy driven adversarial learning for adaptation.

Makkar, Nikhil↗