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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 775 records · Page 43
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Parallel transport modeling of linear divertor simulators with fundamental ion cyclotron heating *

Abstract The Material Plasma Exposure eXperiment (MPEX) is a steady state linear device with the goal to perform plasma material interaction studies at future fusion reactor relevant conditions. A prototype of MPEX referred as ‘Proto-MPEX’ is designed to carry out research and development related to source, heating and transport concepts on the planned full MPEX device. The auxiliary heating schemes in MPEX are based on cyclotron resonance heating with radio frequency (RF) waves. Ion cyclotron heating (ICH) and electron cyclotron heating in MPEX are used to independently heat the ions and electrons and provide fusion divertor conditions ranging from sheath-limited to fully detached divertor regimes at a material target. A hybrid particle-in-cell code- PICOS++ is developed and applied to understand the plasma parallel transport during ICH in MPEX/Proto-MPEX to the target. With this tool, evolution of the distribution function of MPEX/Proto-MPEX ions is modeled in the presence of (a) Coulomb collisions, (b) volumetric particle sources and (c) quasi-linear RF-based ICH. The code is benchmarked against experimental data from Proto-MPEX and simulation data from B2.5 EIRENE. The experimental observation of ‘density-drop’ near the target in Proto-MPEX and MPEX during ICH is demonstrated and explained via physics-based arguments using PICOS++ modeling. In fact, the density drops at the target during ICH in Proto-MPEX/MPEX to conserve the flux and to compensate for the increased flow during ICH. Furthermore, sensitivity scans of various plasma parameters with respect to ICH power are performed for MPEX to investigate its role on plasma transport and particle and energy fluxes at the target.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2023

Scalable Graph Analytics and HPC Operational Enhancement: Parallel Computing and ML/DL Innovations

Parallel computing plays a pivotal role in the efficient processing of large-scale graphs. Complex network analysis stands as a capti- vating research frontier, holding promise across diverse scientific domains such as sociology, biology, online media, and recommenda- tion systems. In this era, Machine Learning (ML) and Deep Learning (DL) have emerged as indispensable tools, underpinning remarkable technological achievements. Within this dynamic landscape, my research revolves around advancing parallel algorithms tailored for large-scale graph operations. To achieve this, I harness the power of cutting-edge technologies including OpenMP, MPI, HIP, and CUDA, on the High-Performance Computing (HPC) platforms to unlock optimal performance. I also apply ML/DL techniques to HPC operational data, to streamline the monitoring and maintenance of supercomputers, alleviating the complexities associated with their upkeep and enhancing user support. My research echoes the syn- ergy between parallel computing, large-scale graph analysis, and ML/DL, improving computational efficiency and user experience.

Sattar, Naw Safrin↗
DOE OSTI2020

CyDER: A Cyber Physical Co-simulation Platform for Distributed Energy Resources in Smartgrids

The CyDER project aimed at developing an open-source, modular and scalable co-simulation platform for power grids with large shares of Distributed Energy Resources (DERs). The project partners are the Lawrence Berkeley National Lab (LBNL), Lawrence Livermore National Lab (LLNL), PG&E, SolarCity, and ChargePoint. The prime recipient is LBNL; SolarCity and ChargePoint were partners for the project’s first two years. Increased DER integration introduces a number of challenges in power grid operation including a more dynamic interaction between the transmission grid and distribution grids, and increased modeling complexity. Although specialized software exists to precisely model different components of the power system, it is far from trivial to integrate all various models and perform a holistic simulation. Instead of replicating all models in a common simulation program, a commonly accepted approach to tackle this model diversity is to couple third-party simulators and models through a co-simulation platform that coordinates information exchange among the various components. Following this line of research, this project’s objective was to develop a co-simulation platform based on a widely accepted industrial standard called Functional Mockup Interface (FMI). Within this process, the project developed models compliant with the FMI standard, called Functional Mockup Units (FMUs), and used them to perform various operational and planning power system analyses. Relying and building upon an industrial standard is the main differentiation of this project compared with previous or parallel efforts in the co-simulation area. Particular emphasis was put on delivering software utilities to facilitate setting up and running co-simulations by end-users. Furthermore, a strong aspect of this project is demonstrating that co-simulation techniques can be used to perform Hardware-in-the-Loop (HIL) simulations that couple software components (e.g., simulated models) with hardware components (e.g., real devices such PV systems and batteries). The long-term goal of CyDER project is to help establish FMI as a powerful standard for co-simulation and promote adoption by electric utilities and other interested stakeholders. The main accomplishments of the project include the development of several FMUs including distribution and transmission grid models, PV inverters with Volt/Var/Watt controllers, batteries, and predictive optimal controllers. Additionally, a unique software package was developed, called SimulatorToFMU, which is capable of exporting any Python-driven simulator or Python script as an FMU. This is an important contribution towards establishing FMI as one of the main co-simulation standards, because more and more third-party programs for sub-system modeling and simulation are delivered with Python APIs. The CyDER platform was used to perform PV hosting capacity analyses in real utility feeders with and without smart inverter controls, battery storage, and EV charging. Smart inverter controls include conventional Volt/Var/Watt controls for reactive power support and active power curtailment, but also predictive controls that optimize the charging and discharging profile of the battery connected on the DC side in order to minimize the customer’s economic benefit. Finally, an important result of this project is delivering an experimental setup that consists of residential-scale PV inverters with battery storage, a real-time grid simulator with an ideal voltage source as grid emulator, and micro Phasor Measurement Units (PMUs). All these components and additional software modules are coupled to one another using the FMI standard and can be co-simulated with the CyDER platform.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2020

Design optimization of semiconductor piezoresistors with Schottky diode contacts

A modeling theory is developed to predict the performance of piezoresistors which incorporate Schottky diode electrical contacts. This new theory allows the design of high performance gauges which can be fabricated using NonLithographically-Based Microfabrication (NLBM) techniques. These semiconductor piezoresistors can be designed in customizable sizes and fabricated in parallel in order to integrate position sensing into MEMS flexural positioners. Customizable nanopositioning platforms will enable advances in a range of nano-scale fabrication and metrology applications. A semiconductor piezoresistor with Schottky diode contacts was fabricated and attached to a titanium flexure. This device is shown to match predicted electrical performance within about 8% and to show a gauge factor of 116, within 2% of the predicted value. Performance limits for Schottky diode semiconductor piezoresistors are identified to be about 127dB full noise dynamic range for a quarter bridge over a 10kHz sensor bandwidth on a 600µm width titanium flexure, making them ideal for sensing on meso-/micro-scale flexural positioners. Methods of reaching these performance bound are suggested and their impact on the sensor dynamic range are studied.

42 ENGINEERING↗
DOE OSTI2024

Design and Validation of a High-Throughput Reductive Catalytic Fractionation Method

Reductive catalytic fractionation (RCF) is a promising method to extract and depolymerize lignin from biomass, and bench-scale studies have enabled considerable progress in the past decade. RCF experiments are typically conducted in pressurized batch reactors with volumes ranging between 50 and 1000 mL, limiting the throughput of these experiments to one to six reactions per day for an individual researcher. Here, we report a high-throughput RCF (HTP-RCF) method in which batch RCF reactions are conducted in 1 mL wells machined directly into Hastelloy reactor plates. The plate reactors can seal high pressures produced by organic solvents by vertically stacking multiple reactor plates, leading to a compact and modular system capable of performing 240 reactions per experiment. Using this setup, we screened solvent mixtures and catalyst loadings for hydrogen-free RCF using 50 mg poplar and 0.5 mL reaction solvent. The system of 1:1 isopropanol/methanol showed optimal monomer yields and selectivity to 4-propyl substituted monomers, and validation reactions using 75 mL batch reactors produced identical monomer yields. To accommodate the low material loadings, we then developed a workup procedure for parallel filtration, washing, and drying of samples and a 1H nuclear magnetic resonance spectroscopy method to measure the RCF oil yield without performing liquid-liquid extraction. As a demonstration of this experimental pipeline, 50 unique switchgrass samples were screened in RCF reactions in the HTP-RCF system, revealing a wide range of monomer yields (21-36%), S/G ratios (0.41-0.93), and oil yields (40-75%). These results were successfully validated by repeating RCF reactions in 75 mL batch reactors for a subset of samples. We anticipate that this approach can be used to rapidly screen substrates, catalysts, and reaction conditions in high-pressure batch reactions with higher throughput than standard batch reactors.

BIOMASS FUELS,INORGANIC, ORGANIC, PHYSICAL, AND AN↗
DOE OSTI2022

Attribution of North American Subseasonal Precipitation Prediction Skill

The skill of NOAA’s official monthly U.S. precipitation forecasts (issued in the middle of the prior month) has historically been low, having shown modest skill over the southern United States, but little or no skill over large portions of the central United States. The goal of this study is to explain the seasonal and regional variations of the North American subseasonal (weeks 3–6) precipitation skill, specifically the reasons for its successes and its limitations. The performances of multiple recent-generation model reforecasts over 1999–2015 in predicting precipitation are compared to uninitialized simulation skill using the atmospheric component of the forecast systems. This parallel analysis permits attribution of precipitation skill to two distinct sources: one due to slowly evolving ocean surface boundary states and the other to faster time-scale initial atmospheric weather states. A strong regionality and seasonality in precipitation forecast performance is shown to be analogous to skill patterns dictated by boundary forcing constraints alone. The correspondence is found to be especially high for the North American pattern of the maximum monthly skill that is achieved in the reforecast. The boundary forcing of most importance originates from tropical Pacific SST influences, especially those related to El Niño–Southern Oscillation. Furthermore, we discuss physical constraints that may limit monthly precipitation skill and interpret the performance of existing models in the context of plausible upper limits.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Toward Large-Scale Image Segmentation on Summit

Semantic segmentation of images is an important computer vision task that emerges in a variety of application domains such as medical imaging, robotic vision and autonomous vehicles to name a few. While these domain-specific image analysis tasks involve relatively small image sizes (~ 10 2 × 10 2 ), there are many applications that need to train machine learning models on image data with extents that are orders of magnitude larger (~10 4 × 10 4 ). Training deep neural network (DNN) models on large extent images is extremely memory-intensive and often exceeds the memory limitations of a single graphical processing unit, a hardware accelerator of choice for computer vision workloads. Here, an efficient, sample parallel approach to train U-Net models on large extent image data sets is presented. Its advantages and limitations are analyzed and near-linear strong-scaling speedup demonstrated on 256 nodes (1536 GPUs) of the Summit supercomputer. Using a single node of the Summit supercomputer, an early evaluation of a recently released model parallel framework called GPipe is demonstrated to deliver ~ 2X speedup in executing a U-Net model with an order of magnitude larger number of trainable parameters than reported before. Performance bottlenecks for pipelined training of U-Net models are identified and mitigation strategies to improve the speedups are discussed. Together, these results open up the possibility of combining both approaches into a unified scalable pipelined and data parallel algorithm to efficiently train U-Net models with very large receptive fields on data sets of ultra-large extent images.

Seal, Sudip↗
DOE OSTI2025

Summer Internship Report: ARA2 Benchmarking

Over the past decade, the RISC-V Instruction Set Architecture (ISA) has emerged as a significant player in both academic and industrial processor design due to its open-source nature, modular extension system, and versatility across domains ranging from microcontrollers to high-performance computing (HPC). One of its most important recent advancements is the RISC-V Vector Extension (RVV), which enables explicit data-level parallelism through vector registers and vectorized instructions. Unlike traditional SIMD (Single Instruction, Multiple Data) architectures that fix vector lengths at design time, RVV uses the concept of VLEN (vector register length) as a hardware-independent parameter and allows software to adapt dynamically to the available vector width. This flexible approach ensures portability across implementations while enabling scalable performance. The ARA2 core is a parameterizable RISC-V vector processor developed at the Integrated Systems Lab at ETH Zürich and the University of Bologna. Designed as a tightly-coupled accelerator to a scalar RISC-V core, ARA2 implements the RVV 1.0 specification and offers tunable architectural parameters such as the number of vector lanes, VLEN, and cache sizes.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Enabling machine learning-ready HPC ensembles with Merlin

With the growing complexity of computational and experimental facilities, many scientific researchers are turning to machine learning (ML) techniques to analyze large scale ensemble data. With complexities such as multi-component workflows, heterogeneous machine architectures, parallel file systems, and batch scheduling, care must be taken to facilitate this analysis in a high performance computing (HPC) environment. Here, we present Merlin, a workflow framework to enable large ML-friendly ensembles of scientific HPC simulations. By augmenting traditional HPC with distributed compute technologies, Merlin aims to lower the barrier for scientific subject matter experts to incorporate ML into their analysis. As a producer–consumer workflow model, Merlin enables multi-machine, cross-batch job, dynamically allocated yet persistent workflows capable of utilizing surge-compute resources. Key features of Merlin are a flexible HPC-centric interface, low per-task overhead, multi-tiered fault recovery, and a hierarchical sampling algorithm that allows for $\mathscr{O}$(N) task execution and $\mathscr{O}$(N ln N) task queuing to ensembles of millions of tasks. In addition to Merlin’s design, we test the algorithm’s performance in an HPC center and demonstrate the ability to enqueue 40 million simulations in 100 s, with a 30 millisecond per-task overhead that is independent of ensemble size. Finally, we describe some example applications that Merlin has enabled on leadership-class HPC resources, such as the ML-augmented optimization of nuclear fusion experiments and the calibration of infectious disease models to study the progression of and possible mitigation strategies for COVID-19.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Build orientation, surface roughness, and scan path influence on the microstructure, mechanical properties, and flexural fatigue behavior of Ti–6Al–4V fabricated by electron beam melting

In the present work, microstructure and fatigue behavior of Electron Beam Melted (EBM) Ti–6Al–4V have been systematically studied. Fatigue behavior was investigated using four-point bend tests of rectangular bars with internal features fabricated with different build orientations, scan paths, and surface conditions. Microstructural variations were evaluated by optical microscopy and Vickers micro-hardness indentations, both of which were found to be consistent throughout the build and within ranges reported in the literature. The fatigue performance of the EBM material was comparable to wrought Ti–6Al–4V when the loading direction was parallel to the build direction, but severely limited when loaded perpendicular to the build direction or if surface roughness defects were present. Scanning electron microscopy and interrupted fatigue tests were utilized to characterize fracture surfaces, failure mechanisms, crack initiation, and propagation behavior. Crack initiation sites in the vertically-oriented specimens were identified to be melt-related defects formed by insufficient melting.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Measurement of the Ar ( e , e ' p ) and Ti ( e , e ' p ) cross sections in Jefferson Lab Hall A

The E12-14-012 experiment, performed in Jefferson Lab Hall A, has collected exclusive electron-scattering data ( e , e ' p ) in parallel kinematics using natural argon and natural titanium targets. In this study we report the first results of the analysis of the data set corresponding to beam energy 2222 GeV, electron scattering angle 21 . 5 ° , and proton emission angle – 50 ° . The differential cross sections, measured with ≈ 4 % uncertainty, have been studied as a function of missing energy and missing momentum, and compared to the results of Monte Carlo simulations, obtained from a model based on the distorted-wave impulse approximation.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2024

Building and Sustaining a Community Resource for Best Practices in Scientific Software: The Story of BSSw.io

The development of scientific software—a cornerstone of long-term collaboration and scientific progress—parallels the development of other types of software but still poses distinct challenges, especially in high-performance computing. Although web searches yield numerous resources on software engineering, there is still a scarcity specifically for scientific software development. Here, this article introduces the Better Scientific Software site (https://bssw.io), a platform that hosts a community of researchers, developers, and practitioners who share their experiences and insights on scientific software development. Since 2017, this collaborative hub has gained traction within the scientific computing community, attracting a growing number of readers and contributors eager to share ideas and elevate their software development practices. In sharing the BSSw.io site’s story, we hope to encourage further growth of the BSSw.io community through both readership and contributors, with a long-term goal of fostering culture change by increasing emphasis on best practices in scientific software.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Exaflops Biomedical Knowledge Graph Analytics

We are motivated by newly proposed methods for mining large-scale corpora of scholarly publications (e.g., full biomedical literature), which consists of tens of millions of papers spanning decades of research. In this setting, analysts seek to discover relationships among concepts. They construct graph representations from annotated text databases and then formulate the relationship-mining problem as an all-pairs shortest paths (APSP) and validate connective paths against curated biomedical knowledge graphs (e.g., Spoke). In this context, we present Coast (Exascale Communication-Optimized All-Pairs Shortest Path) and demonstrate 1.004 EF/s on 9,200 Frontier nodes (73,600 GCDs). We develop hyperbolic performance models (HYPERMOD), which guide optimizations and parametric tuning. The proposed Coast algorithm achieved the memory constant parallel efficiency of 99% in the single-precision tropical semiring. Looking forward, Coast will enable the integration of scholarly corpora like PubMed into the Spoke biomedical knowledge graph.

Kannan, Ramakrishnan {ramki}↗
DOE OSTI2025

Analysis of Screening Current Effects in a Hybrid Nb$_{3}$Sn/REBCO Superconducting Accelerator Magnet Using a T - A Formulation

To explore the feasibility of using high-temperature superconducting (HTS) REBCO coated conductors in future accelerator magnets, two REBCO flat racetrack coils were fabricated using 4-mm wide EuBCO tapes at the High Energy Accelerator Research Organization (KEK). These coils were tested as an insert inside a Nb$_{3}$Sn common-coil dipole magnet, which provides a background field of up to $\sim$ 9.5T, at the Brookhaven National Laboratory (BNL). REBCO tapes offer exceptionally high critical current density under strong magnetic fields; however, they also exhibit significant magnetization due to screening currents, leading to magnetic field errors. Here, this study presents a 2D finite element model of screening current-induced fields (SCIF) in REBCO coils using the T-A formulation, along with the results obtained. Simulations were then performed for two KEK test cases: one where the REBCO conductors were oriented with the HTS tapes parallel to the background field, and another where the tapes were perpendicular to it. Since screening currents also influence the stress distribution and increase the peak stress in the coils, the mechanical effects of these currents were analyzed. The implications of these simulation and test results for the design of Nb$_{3}$Sn/REBCO superconducting accelerator magnets are discussed.

Accelerator magnets↗
DOE OSTI2022

Hydrogen-powered Electrochemically-driven CO 2 Removal from Air Containing 400 to 5000 ppm CO 2

The performance of a hydrogen-powered, electrochemically-driven CO 2 separator (EDCS) was demonstrated at cathode inlet CO 2 concentrations from 400 ppm to 5,000 ppm. The impact of current density and CO 2 concentration were evaluated to predict operating windows for various applications. The single-cell data was used to scale a 100 cm 2 , multi-cell stack using a shorted-membrane design for four applications: direct air capture (DAC), hydroxide exchange membrane fuel cell (HEMFC) air pretreatment, submarine life support, and space habitation. For DAC, a 339-cell EDCS stack (7.7 L, 17 kg) was projected to remove 1 tonne CO 2 per year. The addition of the EDCS in HEMFC systems would result in nearly a 30% increase in volume, and therefore further improvements in performance would be necessary. A module containing five 338-cell EDCS stacks (38 L, 85 kg) in parallel can support a 150 person crew at 2.1% of the volume of the liquid amine system employed in submarines. For space habitation, a 109-cell EDCS stack (3.2 L, 10 kg) is adequate for 6 crewmembers, and is less than 1% the size and 5% the weight of the current CO 2 removal system installed on the International Space Station.

25 ENERGY STORAGE↗
DOE OSTI2020

Initial Implementation Of Machine Learning System for SRF Cavity Fault Classification at CEBAF

The Continuous Electron Beam Accelerator Facility (CEBAF) at Jefferson Laboratory is high power CW electron accelerator. It uses a mixture of two types of SRF modules, older, lower energy C20/C50 modules and newer higher energy C100 modules, arrayed in two anti-parallel linear accelerators. Accurately classifying the type of cavity faults is essential to maintaining and improving accelerator performance. Each C100 cryomodule contains eight 7-cell cavities. When a cavity fault occurs within a cryomodule, all eight cavities generate 17 waveforms each containing 8192 points. This data is exported from EPICS and saved for review. Analysis of these waveforms is time intensive and requires subject matter expertise (SME). SMEs examine the data from each event and label it according to one of several known cavity fault types. Multiple machine learning models have been developed on this labeled dataset with sufficient performance to warrant the creation of a limited machine learning software system for use by accelerator operations staff. This paper discusses the transition from model development to implementation of a prototype system.

Carpenter, A.↗
DOE OSTI2020

Development and Characterization of 3D-Printed Cementitious Sytems for Innovative Nuclear Systems

The Transformational Challenge Reactor (TCR) is being developed to demonstrate a revolutionary approach to deploying new nuclear power systems by building and operating an additively manufactured microreactor. This initiative provides a unique opportunity to investigate the possibility of taking advantage of the recent development of the 3D-printed concrete system using the Sky Big Area Additive Manufacturing (SkyBAAM) printer at the Manufacturing Demonstration Facility (MDF) at Oak Ridge National Laboratory. Two pathways have been pursued in parallel since the end of the second quarter of FY19: 1. Development of methods to characterize the performance of additive manufacturing concrete using traditional Portland-cement based solution, and 2. Investigation of the opportunity to develop innovation of printable materials with higher irradiation-resistance performance using nontraditional concrete solutions in favor of carbonated cementitious materials (CCMs). This report describes the results achieved to date for both pathways. The main results are listed below: 1. A test protocol has been established to characterize the fracture properties of 3D-printed concrete; this protocol will be used to assess the performance of printed traditional and nontraditional cementitious materials, with a focus on performance of the interfaces inherent to the additive manufacturing process. 2. A state-of-the-art review of the mechanisms, fabrication method, and performance of CCMs was conducted. CCMs appear to be a viable, highly innovative, more performant alternative to Portland-based cementitious solution for the erection of biological shield pending some materials development.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2023

Algal Biomass Conversion to Fuels via Combined Algae Processing (CAP): 2022 State of Technology and Future Research

The annual State of Technology (SOT) assessment is an essential activity for platform research conducted under the Bioenergy Technologies Office (BETO). It allows for the impact of research progress (both directly achieved in-house at NREL and furnished by partner organizations) to be quantified in terms of economic improvements in the overall biofuel production process for a particular biomass processing pathway, whether based on terrestrial or algal biomass feedstocks. As such, initial benchmarks can be established for currently demonstrated performance, and progress can be tracked toward out-year goals to ultimately demonstrate economically viable biofuel technologies. NREL's algae SOT benchmarking efforts focus both on front-end algal biomass production and separately on back-end conversion to fuels through NREL's "combined algae processing" (CAP) pathway. The production model is based on outdoor long-term cultivation data, enabled by comprehensive algal biomass production trials conducted under Development of Integrated Screening, Cultivar Optimization, and Verification Research (DISCOVR) consortium efforts and driven by data furnished by Arizona State University's (ASU's) Arizona Center for Algae Technology and Innovation (AzCATI) test bed site. The CAP model is primarily based on experimental efforts conducted under NREL research and development projects, with some process parameters provided by partner organizations. Assumptions regarding the wet storage of algae use data provided by Idaho National Laboratory (INL), while parts of the polyurethane production process leverage BETO-funded research from collaborators at Algenesis and the University of California, San Diego (UCSD). This report focuses on back-end conversion of algal biomass through the CAP pathway, highlighting the 2022 updates to minimum fuel selling price (MFSP). This update incorporates improvements to fermentation performance for two biological pathways through carboxylic acid and 2,3-butanediol (BDO) intermediates, as demonstrated through parallel research on the biochemical conversion of corn stover. Improvements are applied to the glucose fraction of the biomass only, while parameters regarding the conversion of the mannose fraction (not a significant component in corn stover) are maintained consistently with prior CAP SOTs. Additional parameters are also updated to reflect the most current understanding of each pathway, including an increase in the catalyst loading requirement in the ketonization step of the acids pathway and a decrease in the fermentation productivity in the BDO pathway. Additionally, the biomass feedstock costs (minimum biomass selling price [MBSP]), yields, and seasonal variability from the upstream cultivation SOT model were also incorporated into downstream Aspen Plus CAP models.

09 BIOMASS FUELS↗