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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 1,729 records · Page 96
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Derivation of physical equations for high-speed laser welding using large language models

It is challenging to formulate complex physical phenomena that occur in a manufacturing process, particularly when the available data are limited, rendering conventional data-driven approaches ineffective. This study aims to predict humping onset in high-speed laser welding by introducing a novel framework, namely text-to-equations generative pre-trained transformer (T2EGPT). This method leverages the capabilities of large language models (LLMs), in combination with sparse experimental data and enriched literature data, to derive an interpretable and generalizable equation for predicting humping initiation. By capturing key correlations among physical parameters, T2EGPT generates a compact and dimensionless expression that accurately predicts hump formation. The equation reveals that humping arises from the interplay between inertia-driven backward melt flow and capillary-driven surface stabilization, where inertial forces drive molten metal backward and capillary forces resist surface deformation. Furthermore, compared to traditional data-driven models, T2EGPT demonstrates enhanced predictive accuracy and cross-material transferability. More broadly, this study highlights the potential of LLMs to integrate textual information with data-driven discovery, enabling the extraction of physical laws in data-scarce scientific domains.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Visualization of hydraulic fracture using physics-informed clustering to process ultrasonic shear waves

Ultrasonic transmission is sensitive to the spatial variation in mechanical properties of materials due to the presence of cracks/fractures. Wave propagation through fractured media introduces changes in the frequency content, travel time and transmission coefficient of the wave. A workflow based on physics-informed unsupervised learning is developed to process the transmitted ultrasonic-shear waveforms to non-invasively visualize the geomechanical alterations due to hydraulic fracturing. Novelty of the work involves the assignment of both statistically consistent and physically consistent clusters to the measurements of shear waveforms acquired across the one axial and two frontal planes. Physically consistent/relevant information is incorporated by considering the travel time of the peak of spectral energy and transmission coefficient of the transmitted waveform. The proposed workflow generates maps of geomechanical alterations across the frontal and axial planes of the sample. The outputs of the workflow are in good agreement with independent techniques viz. acoustic emission and X-ray computed tomography. Finally, the proposed workflow can be adapted for improved fracture characterization in the subsurface when processing sonic-logging, cross-wellbore seismic or surface seismic waveform data.

42 ENGINEERING↗
DOE OSTI2011

The American Society for Radiation Oncology's 2010 Core Physics Curriculum for Radiation Oncology Residents

Purpose: In 2004, the American Society for Radiation Oncology (ASTRO) published its first physics education curriculum for residents, which was updated in 2007. A committee composed of physicists and physicians from various residency program teaching institutions was reconvened again to update the curriculum in 2009. Methods and Materials: Members of this committee have associations with ASTRO, the American Association of Physicists in Medicine, the Association of Residents in Radiation Oncology, the American Board of Radiology (ABR), and the American College of Radiology. Members reviewed and updated assigned subjects from the last curriculum. The updated curriculum was carefully reviewed by a representative from the ABR and other physics and clinical experts. Results: The new curriculum resulted in a recommended 56-h course, excluding initial orientation. Learning objectives are provided for each subject area, and a detailed outline of material to be covered is given for each lecture hour. Some recent changes in the curriculum include the addition of Radiation Incidents and Bioterrorism Response Training as a subject and updates that reflect new treatment techniques and modalities in a number of core subjects. The new curriculum was approved by the ASTRO board in April 2010. We anticipate that physicists will use this curriculum for structuring their teaching programs, and subsequently the ABR will adopt this educational program for its written examination. Currently, the American College of Radiology uses the ASTRO curriculum for their training examination topics. In addition to the curriculum, the committee updated suggested references and the glossary. Conclusions: The ASTRO physics education curriculum for radiation oncology residents has been updated. To ensure continued commitment to a current and relevant curriculum, the subject matter will be updated again in 2 years.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2011

Comparison of Physical Examination and Fluorodeoxyglucose Positron Emission Tomography/Computed Tomography 4-6 Months After Radiotherapy to Assess Residual Head-and-Neck Cancer

Purpose: To retrospectively compare fluorodeoxyglucose positron emission tomography/computed tomography (PET/CT) and physical examination 4-6 months after radiotherapy for assessing residual head-and-neck cancer (HNC). Methods and Materials: From July 2002 through March 2006, 52 HNC patients underwent definitive radiotherapy or chemoradiotherapy. Categoric assessments of residual tumor by PET/CT and physical examination 4-6 months after therapy were correlated and compared with clinical outcomes. Pretreatment data, including tumor stage and primary site standardized uptake value, were also gathered retrospectively and correlated with clinical outcomes. Median follow-up time was 58 months. Results: Twenty-one patients had either locoregionally 'positive' (17 of 21) or 'equivocal' (4 of 21) PET/CT scans, whereas 31 patients had locoregionally negative scans. Four patients failed treatment and had biopsy-confirmed residual or recurrent local disease. All patients, including patients with locally suspicious scans or examinations who refused biopsies, were followed clinically for a minimum of 29 months after therapy, with no other cases of treatment failure detected during this time. No patient had residual nodal disease after therapy. Sensitivities of PET/CT vs. physical examination for early detection of treatment failure were 100% vs. 50%, whereas the specificities of the two modalities were 64.6% vs. 89.6%, respectively. Higher initial T stage and American Joint Commission on Cancer stage correlated with increased incidence of positive/equivocal PET/CT results and treatment failure. Maximal standardized uptake value was not predictive of any clinical outcome. Conclusions: A negative result on PET/CT obtained 4-6 months after radiotherapy is highly sensitive and correlates with successful locoregional control. Patients with negative scans may reasonably be spared invasive diagnostic procedures, such as biopsy and neck dissection, unless recurrent disease is suspected on clinical grounds. Close follow-up is prudent for HNC patients with abnormal findings on posttherapy PET/CT scan.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2022

Effects of Exercise During Radiation Therapy on Physical Function and Treatment-Related Side Effects in Men With Prostate Cancer: A Systematic Review and Meta-Analysis

Radiation therapy is a commonly used treatment for prostate cancer; however, the side effects may negatively affect quality of life and cause patients to be less physically active. Although exercise has been shown to mitigate radiation therapy–related fatigue in men with prostate cancer during radiation therapy, other adverse effects of treatment such as physical deconditioning, urinary symptoms, or sexual dysfunction have not been systematically reviewed in this patient population. Thus, the purpose of this review was to investigate the effect of exercise on physical function and treatment-related side effects in men with prostate cancer undergoing radiation therapy.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2013

A flexible uncertainty quantification method for linearly coupled multi-physics systems

Highlights: •We propose a “modularly hybrid” UQ methodology suitable for independent development of module-based multi-physics simulation. •Our algorithmic framework allows for each module to have its own UQ method (either intrusive or non-intrusive). •Information from each module is combined systematically to propagate “global uncertainty”. •Our proposed approach can allow for easy swapping of new methods for any modules without the need to address incompatibilities. •We demonstrate the proposed framework on a practical application involving a multi-species reactive transport model. -- Abstract: This paper presents a novel approach to building an integrated uncertainty quantification (UQ) methodology suitable for modern-day component-based approach for multi-physics simulation development. Our “hybrid” UQ methodology supports independent development of the most suitable UQ method, intrusive or non-intrusive, for each physics module by providing an algorithmic framework to couple these “stochastic” modules for propagating “global” uncertainties. We address algorithmic and computational issues associated with the construction of this hybrid framework. We demonstrate the utility of such a framework on a practical application involving a linearly coupled multi-species reactive transport model.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Physics constrained learning for data-driven inverse modeling from sparse observations

Deep neural networks (DNN) have been used to model nonlinear relations between physical quantities. Those DNNs are embedded in physical systems described by partial differential equations (PDE) and trained by minimizing a loss function that measures the discrepancy between predictions and observations in some chosen norm. This loss function often includes the PDE constraints as a penalty term when only sparse observations are available. As a result, the PDE is only satisfied approximately by the solution. However, the penalty term typically slows down the convergence of the optimizer for stiff problems. We present a new approach that trains the embedded DNNs while numerically satisfying the PDE constraints. We develop an algorithm that enables differentiating both explicit and implicit numerical solvers in reverse-mode automatic differentiation. This allows the gradients of the DNNs and the PDE solvers to be computed in a unified framework. We demonstrate that our approach enjoys faster convergence and better stability in relatively stiff problems compared to the penalty method. Furthermore, our approach allows for the potential to solve and accelerate a wide range of data-driven inverse modeling, where the physical constraints are described by PDEs and need to be satisfied accurately.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2026

Physics-aware adaptive checkpointing with shadow systems for nonlinear PDE simulations

Large-scale simulations of nonlinear partial differential equations (PDEs) that exhibit strongly transient behavior and pattern-forming dynamics produce enormous amounts of data, which, even with modern storage systems, cannot be stored for later curation. Current I/O strategies either write dense time series of snapshots, which is often prohibitive in I/O and storage, or store a few checkpoints that enable restart but incur expensive recomputation cost and provide no control over post-restart error growth, especially when lossy compression is used. Moreover, most, if not all, existing strategies take no account of the actual physical state of the system. Here, we present a simple physics-aware I/O framework in which a low-cost shadow system adaptively triggers lossy checkpoints when the shadow system deviates from the fine-scale simulation. The shadow system can be a coarsened replica of the fine-scale simulation that evolves concurrently. This means that checkpoints are taken based on the physical state of the system: fewer checkpoints are triggered when the system is quiescent while more are taken when the system undergoes a rapid change. This type of behavior is observed in many systems such as Brusselator and FitzHugh–Nagumo. We illustrate that our framework maintains stable restarts, keeps fine-scale restart errors bounded by shadow errors, and reconstructs the time history with significantly lower error and storage than interpolating fixed-interval snapshots, with low-cost shadow replay and modest online synchronization overhead.

Gong, Qian [ORNL] (ORCID:0000000235704142)↗
DOE OSTI2026

Beyond interpolation: Physics-inspired gating transformers for extrapolating irradiation conditions to novel nuclear fuels

The qualification of advanced nuclear fuels relies on irradiation experiments in test reactors that emulate commercial conditions. Designing these tests requires accurate prediction of key irradiation quantities, particularly heat generation rate and burnup, yet obtaining them typically involves computationally expensive multi-step simulation workflows. We propose a physics-inspired gating transformer (PIGT) that integrates an inverse-square, distance-based attenuation into the encoder representation to bias attention toward physically relevant spatial relationships while retaining data-driven flexibility. Using MiniFuel irradiation data from the High Flux Isotope Reactor at Oak Ridge National Laboratory, we benchmark against ensemble methods, feedforward and recurrent networks, convolutional models, and standard transformers. While baseline models perform well under interpolation, they exhibit a pronounced generalization gap when evaluated on fuels not included in the training set. The proposed model consistently improves extrapolative accuracy and stability, yielding the strongest performance on unseen fuel configurations. These results indicate that a lightweight physics structure embedded within attention mechanisms can substantially improve robustness, enabling more reliable surrogate predictions to accelerate the design of nuclear fuel irradiation experiments.

Fuel qualification↗
DOE OSTI2020

Lift & Learn: Physics-informed machine learning for large-scale nonlinear dynamical systems

In this work, we present Lift & Learn, a physics-informed method for learning low-dimensional models for large-scale dynamical systems. The method exploits knowledge of a system’s governing equations to identify a coordinate transformation in which the system dynamics have quadratic structure. This transformation is called a lifting map because it often adds auxiliary variables to the system state. The lifting map is applied to data obtained by evaluating a model for the original nonlinear system. This lifted data is projected onto its leading principal components, and low-dimensional linear and quadratic matrix operators are fit to the lifted reduced data using a least-squares operator inference procedure. Analysis of our method shows that the Lift & Learn models are able to capture the system physics in the lifted coordinates at least as accurately as traditional intrusive model reduction approaches. This preservation of system physics makes the Lift & Learn models robust to changes in inputs. Numerical experiments on the FitzHugh–Nagumo neuron activation model and the compressible Euler equations demonstrate the generalizability of our model.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2026

Uncertain quantum computing futures and potential energy and physical resource impacts at scale

Considerable attention has recently focused on the vast energy and water demands of supercomputing, namely large-scale data centers that underpin artificial intelligence (AI), one of the great disruptors of contemporary society. Looking ahead some years from now, quantum computing is poised to disrupt established computing paradigms once again. Scientists and engineers are now working intensely to bring this century-old dream of physicists to fruition. Yet, as quantum computers begin to be integrated with classical supercomputing architectures, the implications for energy and physical resource use also need to be understood, especially how they compare to today’s AI data centers. These impacts have not yet been quantified by the research community – a notable gap in the literature, even if commercial-scale deployment of Quantum-Accelerated Computing Infrastructure (QuACI) is not expected for a few more years. This study is the first to conduct such an assessment. Using publicly available information from academic sources and private industry, we characterize multiple configurations of superconducting qubit-based, fault-tolerant quantum computers (FTQC) that could plausibly be deployed at scale in the 2030s and into the 2040s. By parameterizing these FTQC systems at a process level, we conduct a prospective scenario analysis to quantify their energy and physical resource needs. While these estimates are uncertain, given the current state of quantum technologies and their unknown future trajectories, important insights can already be drawn. One key finding is that while the electricity needs for a fleet of FTQCs are within the bounds of previous modeling studies that have explored high electricity demand futures, the needs for certain physical resources, namely water and helium-3, could pose bottlenecks to QuACI scale-up.

Computing↗
DOE OSTI2024

Use of physics to improve solar forecast: Part III, impacts of different cloud types

Cloud-type impacts present a great challenge to solar forecasting due to diverse and complex cloud-radiation interactions. This third part of our paper sequence seeks to address this challenge by quantifying the forecast accuracies under eight cloud types: cumulus (Cu), stratified clouds (St), altocumulus (Ac), altostratus (As), cirrostratus/anvil (Cr), cirrus (Ci), congestus (Co), deep convective clouds (Dc) across four physics-informed persistence models reported in Part I. To generalize the cloud impacts, the eight cloud types are further grouped into three cloud categories based on their common features: weak convective clouds, stratiform clouds, and strong convective clouds. Here, the decade-long (2001 ~ 2014) collocated measurements of irradiances and cloud types at the U.S. Department of Energy (DOE) Atmospheric Radiation Measurement (ARM) Program South Great Plain (SGP) Central Facility site are used for model evaluation. Results reveal a clear performance hierarchy for global horizontal irradiance (GHI) and direct normal irradiance (DNI): best for weak convective clouds and cirrus, intermediate for stratiform clouds, and worst for strong convective clouds. Performance for diffuse horizontal irradiance (DHI) is less influenced by cloud types. Cloud albedo dominates all three irradiances for Dc, while both cloud albedo and cloud fraction are influential for other cloud types. A 12 %~33 % improvement in accuracy at 6-hour lead time compared to the benchmark smart model confirms the effectiveness of incorporating physics into the models for various cloud types; further improvements are expected by directly integrating cloud type information into forecasting models by modifying the physical formulation of cloud-radiation interaction, and/or using more advanced machine learning models.

14 SOLAR ENERGY↗
DOE OSTI2021

Flow over an espresso cup: inferring 3-D velocity and pressure fields from tomographic background oriented Schlieren via physics-informed neural networks

Tomographic background oriented Schlieren (Tomo-BOS) imaging measures density or temperature fields in three dimensions using multiple camera BOS projections, and is particularly useful for instantaneous flow visualizations of complex fluid dynamics problems. We propose a new method based on physics-informed neural networks (PINNs) to infer the full continuous three-dimensional (3-D) velocity and pressure fields from snapshots of 3-D temperature fields obtained by Tomo-BOS imaging. The PINNs seamlessly integrate the underlying physics of the observed fluid flow and the visualization data, hence enabling the inference of latent quantities using limited experimental data. In this hidden fluid mechanics paradigm, we train the neural network by minimizing a loss function composed of a data mismatch term and residual terms associated with the coupled Navier–Stokes and heat transfer equations. We first quantify the accuracy of the proposed method based on a two-dimensional synthetic data set for buoyancy-driven flow, and subsequently apply it to the Tomo-BOS data set, where we are able to infer the instantaneous velocity and pressure fields of the flow over an espresso cup based only on the temperature field provided by the Tomo-BOS imaging. Moreover, we conduct an independent PIV experiment to validate the PINN inference for the unsteady velocity field at a centre plane. To explain the observed flow physics, we also perform systematic PINN simulations at different Reynolds and Richardson numbers and quantify the variations in velocity and pressure fields. Furthermore, the results in this paper indicate that the proposed deep learning technique can become a promising direction in experimental fluid mechanics.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2019

Evaluation of U.S. Manufacturing Subsectors at Risk of Physical Water Shortages

The potential impact of water shortages on U.S. manufacturing is unknown. While water for manufacturing constitutes an estimated 6% of U.S. water intake, the data (i.e., location, quantity, and purpose of water intake) needed to determine this impact does not exist. This paper will identify manufacturing subsectors at risk of physical water shortages by applying a method for estimating U.S. manufacturing water intake at the necessary spatial and sectoral resolutions. First, the data requirements to quantify a manufacturing facility’s water footprint within the context of the watershed are developed. Second, using international data, water intake at the national, state, and county-levels by each U.S. manufacturing subsector is estimated. Third, manufacturing subsectors that are most vulnerable to risks of physical water shortages are identified. Based on the results, the Paper, Primary Metals, Chemical, Petroleum and Coal Products, and Food subsectors have the largest intake, respectively. However, the Primary Metals, Fabricated Metals, Transportation Equipment, Petroleum and Coal Products, and Plastics and Rubber subsectors are at the greatest risk of physical water shortages based on concentrations of water intake in water-stressed regions. Lastly, the results can be used to develop strategies to mitigate the risks of water shortages on the U.S. manufacturing sector.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2019

Quantifying the Role of the Relative Humidity-Dependent Physical State of Organic Particulate Matter in the Uptake of Semivolatile Organic Molecules

The uptake of gas-phase dicarboxylic acids to organic particulate matter (PM) was investigated to probe the role of the PM physical state in exchange processes between gas-phase semivolatile organic molecules and organic PM. A homologous series of probe molecules, specifically isotopically labeled 13C-dicarboxylic acids, was used in conjunction with aerosol mass spectrometry to obtain a quantitative characterization of the uptake to organic PM for different relative humidities (RHs). The PM was produced by the dark ozonolysis of unlabeled α-pinene. The uptake of 13C-labeled oxalic, malonic, and α-ketoglutaric acids increased stepwise by 5 to 15 times with increases in RH from 15 to 80%. The enhanced uptake with increasing RH was explained primarily by the higher molecular diffusivity in the particle phase, as associated with changes in the physical state of the organic PM from a nonliquid state to a progressively less-viscous liquid state. At high RH, the partitioning of the probe molecules to the particle phase was more associated with physicochemical interactions with the organic PM than that with the co-absorbed liquid water. Additionally, uptake of the probe molecules also increased with a decrease in volatility along the homologous series. This study quantitatively shows the key roles of the particle physical state in governing the interactions of organic PM with semivolatile organic molecules.

42 ENGINEERING↗
DOE OSTI2025

Comparing Machine Learning and Physics-Based Nanoparticle Geometry Determinations Using Far-Field Spectral Properties

Anisotropic metal nanostructures exhibit polarization-dependent light scattering, a property which has been widely studied and exploited to determine orientations of subwavelength structures using far-field microscopy. Here we explore the use of variational autoencoders (VAEs) to determine the geometries of gold nanorods (NRs) such as in-plane orientation and aspect ratio under linearly polarized dark-field illumination in an optical microscope. We enforce a shared latent space to connect two VAEs trained separately with polarized dark-field scattering spectra and electron microscopy images and achieve image prediction (shape, orientation, and size) of Au NRs using only polarized dark-field scattering spectra. We determine the geometrical parameters of orientational angle and aspect ratio quantitatively via both our dual-VAE and physics-based analysis on the input scattering spectra. We show that orientational angle prediction by dual-VAE performs well with only a small (~300 particle) training set, yielding a mean absolute error (MAE) of 14.4° and a concordance correlation coefficient (CCC) of 0.95. This performance is only marginally worse than the physics-based cos(2?) fitting approach between the scattering intensity and the polarizing angle, which achieves MAE of 8.78° and CCC of 0.99. Aspect ratio determination is also comparable for the dual-VAE and physics-based fitting comparison (MAE of 0.21 vs. 0.23 and CCC of 0.53 vs. 0.68). Here, this dual encoder-decoder architecture effectively exploits the structure-property relationships of plasmonic nanostructures to construct a cross-modal machine learning (ML) approach, providing a pathway to employ ML approaches to address other structure-property relationships in materials science.

Dark-field scattering↗
DOE OSTI2025

Reversible Physical Gelation of Thermotropic Liquid Crystals Driven by Nanoplate Self-Assembly

Physically gelled soft materials, driven by the self-assembly of low-molecular-mass gelators (LMGs), have emerged as a platform for designing advanced gels that exhibit reversible gelation and property tunability. Liquid crystal (LC) gels are of great interest due to their supramolecular orderings as gel hosts and their enhanced electro-optical properties. In this study, we demonstrate the physical gelation of a nematic LC driven by nanoplate self-assembly, expanding the concept of gelators from small molecules to nanoparticles. These nanoplates are functionalized with promesogenic ligands and form a fibrillar network in LCs with face-to-face interplate stacking, resembling LMGs. The critical gelation volume fraction in the tilt test is only 0.14 v %, comparable to values reported for LMGs. Rheological analyses confirm viscoelastic properties characteristic of gelation. In situ small-angle X-ray scattering (SAXS) characterizes the formation of nanoplate networks in the LC with decreasing temperature, wherein LC mesogens become trapped in pores. Molecular dynamics (MD) simulations reveal that the interaction between ligand-coated nanoplates and LC-forming mesogens induces a multidomain LC structure, increasing friction between LC domains and stabilizing the gel. This study establishes direct relationships among molecular interactions, nanostructures, and mechanical properties in physically gelled LCs. In conclusion, the findings inspire the future gelator design of both LMGs and nanoplates, with potential applicability in bioscaffold engineering and liquid crystalline nanocomposites.

36 MATERIALS SCIENCE↗
DOE OSTI2013

Development of a Conceptual Process for Selective CO 2 Capture from Fuel Gas Streams Using [hmim][Tf 2 N] Ionic Liquid as a Physical Solvent

The Ionic Liquid (IL) [hmim][Tf 2 N] was used as a physical solvent in an Aspen Plus simulation, employing the Peng-Robinson Equation of State (P-R EOS) with Boston-Mathias (BM) alpha function and standard mixing rules, to develop a conceptual process for CO 2 capture from a shifted warm fuel gas stream produced from Pittsburgh # 8 coal for a 400 MWe power plant. The physical properties of the IL, including density, viscosity, surface tension, vapor pressure and heat capacity were obtained from literature and modeled as a function of temperature. Also, available experimental solubility values for CO 2 , H 2 , H 2 S, CO, and CH 4 in this IL were compiled and their binary interaction parameters (Δ ij and l ij ) were optimized and correlated as functions of temperature. The Span-Wager Equation-of-State EOS was also employed to generate CO 2 solubilities in [hmim][Tf 2 N] at high pressures (up to 10 MPa) and temperatures (up to 510 K). The conceptual process developed consisted of 4 adiabatic absorbers (2.4 m ID, 30 m high) arranged in parallel and packed with Plastic Pall Rings of 0.025 m for CO 2 capture; 3 flash drums arranged in series for solvent (IL) regeneration with the pressure-swing option; and a pressure-intercooling system for separating and pumping CO 2 up to 153 bar to the sequestration sites. The compositions of all process streams, CO 2 capture efficiency, and net power were calculated using Aspen Plus simulator. The results showed that, based on the composition of the inlet gas stream to the absorbers, 95.67 mol% of CO 2 was captured and sent to sequestration sites; 99.5 mol% of H 2 was separated and sent to turbines; the solvent exhibited a minimum loss of 0.31 mol%; and the net power balance of the entire system was 30.81 MW. These results indicated that [hmim][Tf 2 N] IL could be used as a physical solvent for CO 2 capture from warm shifted fuel gas streams with high efficiency.

20 FOSSIL-FUELED POWER PLANTS↗