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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 163 records · Page 9
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Cortical cell size regulates root metabolic cost

SUMMARY It has been hypothesized that vacuolar occupancy in mature root cortical parenchyma cells regulates root metabolic cost and thereby plant fitness under conditions of drought, suboptimal nutrient availability, and increased soil mechanical impedance. However, the mechanistic role of vacuoles in reducing root metabolic cost was unproven. Here we provide evidence to support this hypothesis. We first show that root cortical cell size is determined by both cortical cell diameter and cell length. Significant genotypic variation for both cortical cell diameter (~1.1‐ to 1.5‐fold) and cortical cell length (~ 1.3‐ to 7‐fold) was observed in maize and wheat. GWAS and QTL analyses indicate cortical cell diameter and length are heritable and under independent genetic control. We identify candidate genes for both phenes. Empirical results from isophenic lines contrasting for cortical cell diameter and length show that increased cell size, due to either diameter or length, is associated with reduced root respiration, nitrogen content, and phosphorus content.RootSlice, a functional‐structural model of root anatomy, predicts that an increased vacuolar: cytoplasmic ratio per unit cortical volume causes reduced root respiration and nutrient content. Ultrastructural imaging of cortical parenchyma cells with varying cortical diameter and cortical cell length confirms thein silicopredictions and shows that an increase in cell size is correlated with increased vacuolar volume and reduced cytoplasmic volume. Vacuolar occupancy and its relationship with cell size merits further investigation as a phene for improving crop adaptation to edaphic stress.

Plant Sciences↗
DOE OSTI2022

Spectroscopic analysis reveals that soil phosphorus availability and plant allocation strategies impact feedstock quality of nutrient-limited switchgrass

Abstract The perennial native switchgrass adapts better than other plant species do to marginal soils with low plant-available nutrients, including those with low phosphorus (P) content. Switchgrass roots and their associated microorganisms can alter the pools of available P throughout the whole soil profile making predictions of P availability in situ challenging. Plant P homeostasis makes monitoring of P limitation via measurements of plant P content alone difficult to interpret. To address these challenges, we developed a machine-learning model trained with high accuracy using the leaf tissue chemical profile, rather than P content. By applying this learned model in field trials across two sites with contrasting extractable soil P, we observed that actual plant available P in soil was more similar than expected, suggesting that adaptations occurred to alleviate the apparent P constraint. These adaptations come at a metabolic cost to the plant that have consequences for feedstock chemical components and quality. We observed that other biochemical signatures of P limitation, such as decreased cellulose-to-lignin ratios, were apparent, indicating re-allocation of carbon resources may have contributed to increased P acquisition. Plant P allocation strategies also differed across sites, and these differences were correlated with the subsequent year’s biomass yields.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Structure-Property Relationships of Hydrogel-Salt Composites for Extreme Sorption Performance

Hydrogel-salt composites have emerged as a promising low-cost material for a wide range of sorption applications including thermal energy storage, atmospheric water harvesting, and dehumidification. Despite significant efforts devoted to the synthesis of novel hydrogels and their implementation in sorption devices, very little is known about the fundamental connection between the hydrogel structure and composition with their material-level properties. This knowledge, however, is critical to enable the rational design of high-performance hydrogels targeted at the various applications. In this work, we elucidate via experiments and modeling the link between the hydrogel structure, including its salt content and polymer nanostructure, and its sorption properties. Using lithium chloride-embedded polyacrylamide hydrogels as model systems, we demonstrate that the hydrogel uptake and its kinetics are dominated by the content and properties of the salt. In fact, the polymer nanostructure has no a minor effect in its sorption performance. Based on our experimental insights, we develop a model for the simultaneous diffusion of water and salts in hydrogels, which helps to explain the observed relationship between hydrogel structure and its sorption properties. Furthermore, we leverage these insights to synthesize hydrogels with record-high water vapor uptakes of 1.79 g/g, 2.58 g/g, 3.86 g/g, and 8.51 g/g at 30%, 50%, 70%, and 90% relative humidity, respectively, owing to the use of large amounts of highly hygroscopic salts. By demonstrating the structure-property relationships, this work enables the rational design of salt-embedded hydrogels for low-cost and high-performance thermal energy storage, atmospheric freshwater production, and space conditioning.

Díaz-Marín, Carlos D↗
DOE OSTI2026

Wake-Resolving Acoustic Tomography: Advances through Numerical Covariance Methods

Acoustic tomography offers path-integrated measurements of atmospheric velocity and temperature fluctuations with high spatial resolution. Classical implementations of time-dependent stochastic inversion rely on homogeneous, isotropic covariance models that are poorly suited to the anisotropic structure of wind turbine wakes. By directly estimating heterogeneous covariances from large-eddy simulations (LESs) into the time-dependent stochastic inversion operator, we relax implicit assumptions in the analytical models used historically. Retrievals using these LES-informed models improve agreement with true fields in variance, turbulent kinetic energy, and spectral content compared to analytical and precursor-based covariance models. The results indicate that LES-informed covariance models can enhance the accuracy of acoustic tomography retrievals in complex, anisotropic flows such as wind turbine wakes in some cases and highlight instances where analytical models still offer competitive performance, despite their simplifying assumptions.

17 WIND ENERGY↗
DOE OSTI2020

Investigating the Chemical Reactivity of Lithium Silicate Model SEI Layers

Silicon anodes suffer from an unstable solid electrolyte interphase (SEI) layer that contributes to undesirable capacity fade with cycling. A key part to addressing this unstable SEI formation is to examine how certain components of the SEI react with the electrolyte over time. One SEI component that has not been thoroughly studied in the context of the chemical reactivity against the electrolyte is lithiated silicate. Four model silicate thin films with increasing lithium content were deposited by radio frequency (RF) magnetron sputtering to study how the lithiation of the native oxide on a silicon anode affects the chemical stability of the anode surface. SiO2, Li2Si2O5, Li2SiO3, and Li3SiOx films were exposed to 1.2 M LiPF6 in the 3:7 wt% ethylene carbonate/ethyl methyl carbonate (EC/EMC) electrolyte for periods of time that are representative of the amount of time it takes to undergo cell formations. Soaked samples were rinsed, dried, and characterized by a combination of attenuated total reflectance-infrared spectroscopy (ATR-IR), focused ion beam-secondary electron microscopy (FIB-SEM), and X-ray photoelectron spectroscopy (XPS) depth profiling. It was found that the rate of the decrease in film thickness of the silicates exposed to the electrolyte over time increases as a function of the lithium content in the thin film. This reaction involves HF etching and LiPF6 salt degradation leading to silicate loss and fluorination throughout the bulk. Understanding this chemical instability is critical to determining the overall mechanism of SEI degradation over time.

ADVANCED PROPULSION SYSTEMS,ENERGY STORAGE↗
DOE OSTI2023

Challenges in computationally designing high temperature Fe-based austenitic alloys: Addressing the role of Ni additions

Alumina-forming austenitic (AFA) alloys are relatively inexpensive high performance materials which combine the creep resistance of low-cost austenitic alloys and the oxidation resistance of expensive alumina forming alloys. However, a fundamental understanding of the role of key alloying elements such as Ni, Cr, Al, Nb, Ti, V, B and C in the experimentally observed oxidation behavior of these alloys is still lacking. The present work is a first in a series of studies aiming to quantitatively describe the role of Ni in promoting or disrupting protective Al 2 O 3 scale formation on AFA alloys. Here, ternary Fe-Al-xNi model alloys with three different Ni contents were isothermally exposed in an atmosphere with a low partial pressure of oxygen between 800–1000 °C for 24 h to evaluate the role of Ni in the observed internal oxidation behavior. Increasing Ni contents had no impact on the internal oxidation behavior of the alloys. The experimental and theoretical analyses in the present work suggested a negligible effect of the internal oxide precipitates on the inward diffusion of oxygen, typically expected in these systems, while simultaneously highlighting the barriers in the development of reliable models for computation-assisted design of these alloys.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Evolution of oxygen and stratification and their relationship in the North Pacific Ocean in CMIP6 Earth system models

Abstract. This study examines the linkages between the upper-ocean (0–200 m) oxygen (O2) content and stratification in the North Pacific Ocean using four Earth system models (ESMs), an ocean hindcast simulation, and an ocean reanalysis. The trends and variability in oceanic O2 content are driven by the imbalance between physical supply and biological demand. Physical supply is primarily controlled by ocean ventilation, which is responsible for the transport of O2-rich surface waters to the subsurface. Isopycnic potential vorticity (IPV), a quasi-conservative tracer proportional to density stratification that can be evaluated from temperature and salinity measurements, is used herein as a dynamical proxy for ocean ventilation. The predictability potential of the IPV field is evaluated through its information entropy. The results highlight a strong O2–IPV connection and somewhat higher (as compared to the rest of the basin) predictability potential for IPV across the tropical Pacific, where the El Niño–Southern Oscillation occurs. This pattern of higher predictability and strong anticorrelation between O2 and stratification is robust across multiple models and datasets. In contrast, IPV at mid-latitudes has low predictability potential and its center of action differs from that of O2. In addition, the locations of extreme events or hotspots may or may not differ between the two fields, with a strong model dependency, which persists in future projections. On the one hand, these results suggest that it may be possible to monitor ocean O2 in the tropical Pacific based on a few observational sites co-located with the more abundant IPV measurements; on the other, they lead us to question the robustness of the IPV–O2 relationship in the extratropics. The proposed framework helps to characterize and interpret O2 variability in relation to physical variability and may be especially useful in the analysis of new observation-based data products derived from the BGC-Argo float array in combination with the traditional but far more abundant Argo data.

Novi, Lyuba↗
DOE OSTI2026

Influence of Rigidity–Hydration Coupling on Size-Dependent Diffusion in Hydrated Polymer Membranes

Selective ion transport in polymer membranes depends critically on how penetrant motion couples to polymer dynamics and hydration. Yet, the mechanistic interplay between polymer rigidity, water content, and penetrant size remains poorly understood, especially in the regime where the penetrant diameter, polymer Kuhn length, and correlation length are comparable. Here, we employ coarse-grained molecular dynamics simulations to systematically investigate penetrant diffusion in hydrated polymer networks across a broad range of water volume fractions, chain rigidities, and penetrant sizes. The results reveal a transition from a decoupled regime, where small penetrants diffuse nearly independently of polymer relaxation, to a coupled regime in which large penetrants require cooperative polymer motion for transport. Increasing polymer rigidity amplifies the sensitivity of diffusivity to hydration, particularly at low water content, leading to pronounced deviations from Stokes−Einstein scaling. Comparison with scaling theories and free-volume models shows that classical nanoparticle-based frameworks fail to capture this intermediate regime. To address this gap, we extend the Yasuda model to incorporate polymer rigidity through a single parameter that quantifies the dynamic contribution of chain stiffness to free-volume fluctuations. The resulting model collapses diffusivity data across all sizes, water contents, and rigidities, providing a unified description of penetrant transport in hydrated polymer matrices. Furthermore, these findings establish polymer rigidity as a key, tunable determinant of diffusion and offer a framework for interpreting size-dependent transport in ion-selective membranes.

diffusion↗
DOE OSTI2020

Using Robotic Manipulators for Radioactive Waste Inspection - 20090

The global nuclear industry has a growing volume of nuclear waste which needs to be scanned, sorted according to its activity and material type, then processed into the correct waste packages for long term storage and disposal. It is vital that there is a detailed understanding of the waste inventory stored in long term waste containers, as knowledge of their contents could predict or prevent any adverse effects in storage. The numerous 'scan and sort' tables which are currently used at many different facilities around the world to sort waste into their correct containers are human operated and require very slow gamma scanning procedures combined with educated guesswork to manually sort the waste. This often leads to excessive conservatisms, with placement of lower activity wastes in higher activity containers, which in turn costs significantly more to store. In the United Kingdom it costs UK Pounds 46 k per cubic meter to store intermediate level waste compared to just UK Pound 2.9 k per cubic meter to store low level waste according to a 2008 Department of Energy and Climate Change report in the UK. A proposed solution to this problem, is the use of a robotic manipulator to automatically inspect the 'scan and sort table' in order to produce an accurate 3D model of the table's waste contents and attach an overlaid radiation map. The radiation map contains spectrometry data and can in consequence be used to distinguish and locate specific radioisotopes. The 3D model should be as accurate as possible in order to allow for a second robot arm with an attached gripper to grasp the objects and place them into their designated long-term storage container. Various scanning procedures are explored in this study including basic raster scanning, adaptive raster scanning and point sampling. The optimal solution will in practice be defined by the required application and activity level of the wastes being inspected. The results presented in this study indicate that it is possible to produce a centimeter accurate 3D model of a mixed assortment of components on a nuclear waste 'scan and sort' table. In addition, it was shown that the waste objects emitting radiation could be accurately identified and located, with an overlaid radiation map. This study is applicable across the nuclear waste management sector. Many of the ideas and concepts developed in this study are applicable in other decommissioning settings for example, dismantling of legacy gloveboxes or routine inspection of nuclear waste packages in storage. (authors)

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

Leveraging Community and Author Context to Explain the Performance and Bias of Text-Based Deception Detection Models

Deceptive news posts shared in online communities can be detected with NLP models, and much recent research has focused on the development of such models. In this work, we use characteristics of online communities and authors --- the context of how and where content is posted --- to explain the performance of a neural network deception detection model and identify sub-populations who are disproportionately affected by model accuracy or failure. We examine who is posting the content, and where the content is posted to. We find that while author characteristics are better predictors of deceptive content than community characteristics, both characteristics are strongly correlated with model performance. Traditional performance metrics such as F1 score may fail to capture poor model performance on isolated sub-populations such as specific authors, and as such, more nuanced evaluation of deception detection models is critical.

machine learning (ML), machine learning explanatio↗
DOE OSTI2021

The oscillatory anisotropy in the spatially flat cosmological models

Highlights: • Relations among asymptotic anisotropy and matter content. • A new scheme for obtaining physically motivated exact solutions. • The process of isotropization/anisotropization in cosmology. • The conversion among the anisotropy and the energy density into one another. In this manuscript, we investigate the oscillatory behaviour of the anisotropy in the diagonal Bianchi-I spacetimes. Our starting point is a simplification of Einstein’s equations using only observable or physical variables. As a consequence, we are able to: (a) Prove general results concerning the existence of oscillations of the anisotropy in the primordial and the late-time universe. For instance, in the expanding scenario, we show that a past weakly mixmaster behaviour (oscillations as we approach the Kasner solutions) might appear even with no violation of the usual energy conditions, while in the future, the pulsation (oscillations around isotropic solutions) seems to be most favoured; (b) Determine a large scheme for deriving classes of physically motivated exact solutions, and we give some (including the general barotropic perfect fluid and the magnetic one); (c) Understand the physical conditions for the occurrence of the isotropization or anisotropization during the cosmological evolution; (d) Understand how anisotropy and energy density are converted one into another. In particular, we call attention to the presence of a residue in the energy density in a late-time isotropic universe coming from its past anisotropic behaviour.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2020

Algebraic expressions for estimating the impact depths of a surface barrier over a homogeneous soil

Engineered surface barriers are used to isolate subsurface contaminants for effective long-term containment of municipal solid waste, other nonhazardous solid and liquid waste, hazardous and toxic wastes, and radioactive waste. The impact depths of a surface barrier are affected by the pre-barrier recharge rate and the properties of the soil beneath the barrier. In this paper, the pore-size-specific (PSS) water velocity is defined and an algebraic expression of PSS velocity is derived based on the stream tube concept and the Brooks and Corey hydraulic retention model. Algebraic expressions are developed to estimate drainage velocities and barrier impact depths after the emplacement of a surface barrier. Four impact depth terms are used to convey the protective effect: drainage front, average drainage, the location with 50% impact, and drainage tail. The drainage front depth is the deepest point at which the barrier has a detectable impact at a specific time (also called the near zero-impact depth). The average-impact depth is the depth at which average drainage occurs. At the 50% impact depth, the water flux rate is reduced by half because of the surface barrier. Lastly, the drainage tail depth (also called the full-impact depth) is the deepest depth at which the water conditions above it are in equilibrium with the barrier. The algebraic expressions show that the average-impact depth is no more than 1/3 of near zero-impact depth, while the 50% impact depth is slightly larger than 1/2 of the near zero-impact depth. The full-impact depth, depending on the final recharge rate from the surface barrier, is usually much smaller than the other impact depths. These differences lead to a very large transition zone beneath a surface barrier. Numerical simulations were conducted to replicate the same conditions. The numerical results corroborated the analytical models by predicting very similar water content profiles and near zero-, average-, 50%, and full-impact depths. The algebraic expressions provided in this paper are useful for quickly identifying sites where the depth of the existing contaminants could be beyond the protection of a surface barrier.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Interpreting the spatial distribution of soil properties with a physically-based distributed hydrological model

Digital soil maps are commonly data-driven as the development of physically-based models for soil mapping is difficult due to the complexity of soils. However, physically-based hydrologic models have been successful in simulating water dynamics. Since water movement is a major driver of pedogenesis, the physical rules that govern water movement might help explain and predict the spatial variation of soil properties. Here, we demonstrate the novel use of a physically-based, distributed hydrologic model to inform the spatial distribution of soil properties. The Distributed Hydrology Soil Vegetation Model (DHSVM) was utilized to simulate soil moisture content (SM) and water table depth (WTD) in two hillslope catchments under pasture and forest management wherein hydrologic model outputs were then compared with soil properties measured in situ. SM sensors and wells were installed in both catchments to validate simulations of soil water movement via Nash-Sutcliffe Efficiency (E). In-situ observations were made at 87 sites within both catchments to study the connection between simulated water movement (SM and WTD) and observed soil properties, namely the depth and thickness of the argillic (Bt), fragic (Btx), and C horizons, and the depth of redoximorphic features. The simulated time series of SM and WTD were also clustered per season using Dynamic Time Warping (DTW), which identified similarity among time series at varying timescales. Model validation suggested that simulations of surficial SM (0–20 cm) were reasonable (E = 0.45), however, simulated subsurface SM (45–60 cm) and WTD were not sufficiently accurate. The thickness of Btx horizons were spatially grouped into different populations by SM clusters from every season except spring. For the other properties, only SM dynamics of specific seasons grouped into significantly different populations, suggesting that the explanatory power of simulated water movement varies seasonally and was greater during winter. Here, we show clusters of simulated SM separated soil properties into statistically different populations, showing that hydrologic models could inform areas that followed different water dynamics related to pedogenic trajectories and related biogeochemical processes not necessarily simulated by the model. As such, physically-based modeling of water dynamics can, therefore, inform and advance digital soil mapping by linking water movement patterns stemming from hydrologic model outputs to spatial patterns of soil properties and pedogenesis.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

Towards AI-assisted neutrino flavor theory design

Particle physics theories, such as those which explain neutrino flavor mixing, arise from a vast landscape of model-building possibilities. A model’s construction typically relies on the intuition of theorists. It also requires considerable effort to identify appropriate symmetry groups, assign field representations, and extract predictions for comparison with experimental data. We develop Autonomous Model Builder (AMBer), a framework in which a reinforcement learning agent interacts with a streamlined physics software pipeline to search these spaces efficiently. AMBer selects symmetry groups, particle content, and group representation assignments to construct models while minimizing the number of free parameters introduced. We validate our approach in well-studied regions of theory space and extend the exploration to a previously unexamined symmetry group. While demonstrated in the context of neutrino flavor theories, this approach of reinforcement learning with physics software feedback may be extended to other theoretical model-building problems in the future.

Baretz, Jason Benjamin↗
DOE OSTI2023

Vertical Structure and Ice Production Processes of Shallow Convective Postfrontal Clouds over the Southern Ocean in MARCUS. Part II: Modeling Study

Abstract Part I of this series presented a detailed overview of postfrontal mixed-phase clouds observed during the Measurements of Aerosols, Radiation, and Clouds over the Southern Ocean (MARCUS) field campaign. In Part II, we focus on a multiday (23–26 February 2018) case with the aim of understanding ice production as well as model sensitivity to ice process parameterizations using the Weather Research and Forecasting (WRF) Model. The control simulation with the Predicted Particle Properties (P3) microphysics scheme underestimates the ice content and overestimates the supercooled liquid water content, contrary to the bias common in global climate models. The simulations targeted at ice production processes show negligible sensitivity to cloud droplet number concentrations. Further, neither increasing ice nuclei particle (INP) concentrations to an unrealistic level nor adjusting it to MARCUS field estimations alone guarantees more ice production in the model. However, the simulated clouds are found to be highly sensitive to the implementation of immersion freezing, the thresholding of condensation/deposition freezing initiation, and the rime splintering process. By increasing immersion freezing of cloud droplets, relaxing thresholds for condensation/deposition freezing, or removing rime splintering thresholds, the model significantly improves its performance in producing ice. The relaxation of the immersion freezing temperature threshold to the observed cloud-top temperature suggests an in-cloud seeder–feeder mechanism. The results of this work call for an increase in observations of INP, especially over the remote Southern Ocean and at relatively high temperatures, and measurements of ice particle size distributions to better constrain ice nucleating processes in models.

Meteorology & Atmospheric Sciences↗
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

Characteristics of Vertical Ground Motions and Their Effect on the Seismic Response of Bridges in the Near-Field: A State-of-the-Art Review

Despite the evidence from past earthquakes and several numerical investigations demonstrating the detrimental impact of vertical ground motions (VGMs) on the integrity of bridge structures, incorporating their effects into seismic assessment and design procedures has traditionally been given limited consideration. Current codes utilize rather simplistic approaches to account for the concurrent effects of vertical and horizontal motions in structural performance evaluations, potentially leading to unconservative estimates of structural demands. This paper reviews the main features of VGMs and their effect on the seismic response of bridges. The methods and empirical models available to estimate vertical motions for design purposes are discussed, and research gaps and related research needs are identified. Finally, the emerging role of physics-based ground-motion simulations, as well as their limitations, in supporting future research and informing the development of simplified design procedures is examined. The main areas of interest for future research are identified in the need to carry out systematic sensitivity studies to gain insight into the main earthquake parameters that influence key VGMs features, understand the influence of soil nonlinearities on VGMs amplitude and frequency content, inform the development of empirical models that cover a range of site conditions and source-to-site distances where current models are poorly constrained, generate arrays of motions to update coherency models to properly inform the analysis of distributed infrastructure, investigate the impulsive character of VGMs, and assess the approximations made in estimating VGMs with 1D site response analyses. Furthermore, specific focus is laid on large-magnitude earthquakes in the near-field.

Asynchronous ground motion↗
DOE OSTI2024

Predicting biomass comminution: Physical experiment, population balance model, and deep learning

An extended population balance model (PBM) and a deep learning-based enhanced deep neural operator (DNO+) model are introduced for predicting particle size distribution (PSD) of comminuted biomass through a large knife mill. Experimental tests using corn stalks with varied moisture contents, mill blade speeds, and discharge screen sizes are conducted to support model development. A novel mechanism in the extended PBM allows for including additional input parameters such as moisture content, which is not possible in the original PBM. The DNO+ model can include influencing factors of different data types such as moisture content and discharge screen size, which significantly extends the engineering applicability of the standard DNO model that only admits feed PSD and outcome PSD. Test results show that both models are remarkably accurate in the calibration or training parameter space and can be used as surrogate models to provide effective guidance for biomass preprocessing design.

09 BIOMASS FUELS↗