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DOE OSTI2022

Electron Cloud Simulations for the Electron-Ion Collider in Brookhaven National Laboratory

For high-intensity circular accelerators and storage rings, if the secondary electron emission yield (SEY) of the vacuum chamber surfaces is high, an EC (Electron Cloud) could build up with the passage of the circulating beam. The presence of EC can strongly affect the beam quality, such as transverse instabilities, transverse emittance growth, and beam loss. Furthermore, the heat load from EC can exceed the available cryogenic capability. For the RHIC superconducting (SC) arc magnets, to be used for the hadron storage ring of the Electron-Ion Collider (EIC), the dynamic heat load budget is 0.5 W/m to the 4.5 K stainless steel beam pipe. This can limit the maximum beam bunches or intensity of the EIC, hence diminishing the luminosity provided by the EIC. The EC has affected the beam instability and significantly contributed to cryogenic heat load in the Large Hadron Collider (LHC). Positron storage rings for which ECs have been a critical factor in the design and performance include KEKB in Japan and EC buildup remains one of the concerns for future high-intensity accelerators design. EC considerations have driven the SuperKEKB collider design and the positron damping ring for the proposed International Linear Collider (ILC). The LHC luminosity upgrade is contingent on reducing the bunch spacing to 25 ns; at this bunch spacing, severe EC buildup has been observed. The success of the upgrade is likely contingent on limiting EC buildup. To study the EC heat load, we did some EC simulations with PyECLOUD code for the dipole, quadrupole, sextupole magnets, and the warm (drift) section of the EIC hadron storage ring. PyECLOUD is an EC simulation code developed by CERN. The code has been validated and used to study EC in the LHC, SPS, and PS. To eliminate EC buildup, the sources of electrons must be minimized. First, we will reduce the production of electrons due to residual gas ionization by specifying the maximum gas density, which requires a vacuum chamber with low electron-stimulated desorption (ESD) yields and a sufficient (preferably distributed) pumping speed. Second, we should reduce the secondary electrons with a lower secondary electron yield (SEY) material.

43 PARTICLE ACCELERATORS↗

NASA NTRS1993

Genetic learning in rule-based and neural systems

The design of neural networks and fuzzy systems can involve complex, nonlinear, and ill-conditioned optimization problems. Often, traditional optimization schemes are inadequate or inapplicable for such tasks. Genetic Algorithms (GA's) are a class of optimization procedures whose mechanics are based on those of natural genetics. Mathematical arguments show how GAs bring substantial computational leverage to search problems, without requiring the mathematical characteristics often necessary for traditional optimization schemes (e.g., modality, continuity, availability of derivative information, etc.). GA's have proven effective in a variety of search tasks that arise in neural networks and fuzzy systems. This presentation begins by introducing the mechanism and theoretical underpinnings of GA's. GA's are then related to a class of rule-based machine learning systems called learning classifier systems (LCS's). An LCS implements a low-level production-system that uses a GA as its primary rule discovery mechanism. This presentation illustrates how, despite its rule-based framework, an LCS can be thought of as a competitive neural network. Neural network simulator code for an LCS is presented. In this context, the GA is doing more than optimizing and objective function. It is searching for an ecology of hidden nodes with limited connectivity. The GA attempts to evolve this ecology such that effective neural network performance results. The GA is particularly well adapted to this task, given its naturally-inspired basis. The LCS/neural network analogy extends itself to other, more traditional neural networks. Conclusions to the presentation discuss the implications of using GA's in ecological search problems that arise in neural and fuzzy systems.

Smith, Robert E.↗

NASA NTRS2014

Upgrading Custom Simulink Library Components for Use in Newer Versions of Matlab

The Spaceport Command and Control System (SCCS) at Kennedy Space Center (KSC) is a control system for monitoring and launching manned launch vehicles. Simulations of ground support equipment (GSE) and the launch vehicle systems are required throughout the life cycle of SCCS to test software, hardware, and procedures to train the launch team. The simulations of the GSE at the launch site in conjunction with off-line processing locations are developed using Simulink, a piece of Commercial Off-The-Shelf (COTS) software. The simulations that are built are then converted into code and ran in a simulation engine called Trick, a Government off-the-shelf (GOTS) piece of software developed by NASA. In the world of hardware and software, it is not uncommon to see the products that are utilized be upgraded and patched or eventually fade away into an obsolete status. In the case of SCCS simulation software, Matlab, a MathWorks product, has released a number of stable versions of Simulink since the deployment of the software on the Development Work Stations in the Linux environment (DWLs). The upgraded versions of Simulink has introduced a number of new tools and resources that, if utilized fully and correctly, will save time and resources during the overall development of the GSE simulation and its correlating documentation. Unfortunately, simply importing the already built simulations into the new Matlab environment will not suffice as it will produce results that may not be expected as they were in the version that is currently being utilized. Thus, an upgrade execution plan was developed and executed to fully upgrade the simulation environment to one of the latest versions of Matlab.

Matlab Simulation↗

NASA NTRS2019

Physical Simulation of Rocket Exhaust Aerodynamics Using Heated Ethane: Conceptual Foundations

Heated ethane (C2H6) has been proposed as an alternative to inert gases for use as a motive fluid in the experimental simulation of rocket exhaust plumes. By adjusting stagnation temperature, the isentropic exponent of ethane can be tuned to approximate those produced by common rocket propellants including hydrogen, hypergols, alcohols, and hydrocarbons. As a result, ethane can be made to follow a nozzle expansion process which is nearly identical to realistic rocket engine flow fields. Additionally, its high auto-ignition temperature and resistance to condensation enable the testing of expansion ratios much larger than conventional inertgas testing. NASA SSC has performed quasi-one-dimensional analyses using the Chemical Equilibrium with Applications (CEA) code as a preliminary means to compare flow fields produced by non-reacting ethane to those of reacting combustion products. A LO2/LH2 rocket engine operating at a chamber pressure of 5.0 MPa and a mixture ratio of 6.1 was used as an example case to demonstrate ethane’s efficacy as a simulant. Errors for key similarity parameters were compared to legacy cold-flow test methods. Additional errors induced by machining tolerances and chemical impurities were also examined. Results suggest that at a 3% geometric scale and ~500 K ethane stagnation temperature, an error of less than 2.5% throughout the flow field is realistically achievable along the dimensions of Mach number, Reynolds number, pressure ratio, and isentropic exponent. The development of an experimental test bed for validation of this configuration is currently underway.

Jones, Daniel R.↗

DOE OSTI2019

Visualization of Simulation and TAP Interaction

The Temporal Analysis of Products (TAP) Reactor applies transient kinetics to analyze sophisticated chemical reaction mechanisms, rapidly changing temperature, flow rate, pressure or concentration and then monitoring the system’s time dependent response. A simulation tool exists for the TAP reactor, but it requires a visualization platform to interface effectively with researchers. This graphical user interface remedies this problem, fixing many of the difficulties caused by users interacting directly with the simulation code by providing an intuitive web application that satisfies the simulation input parameters for fast generation of data. This tool will ensure robust and repeatable simulation results, expedite the data generating process and enhance the overall ability of TAP to produce significant findings.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

NASA NTRSDate not supplied

Fusing Surface BRDF from Geostationary and Polar-Orbiting Satellite Sensors

The bi-directional reflectance distribution function (BRDF) describes the fundamental optical property of a surface and therefore has been retrieved from both geostationary (GEO) and polar-orbiting (or Low-Earth Orbit, LEO) satellite observations. In theory, although GEO and LEO observations feature different illumination-view geometries, they reflect the same physical property and the retrieved BRDF should be mutually consistent. This fact also suggests that we may derive a better BRDF product by synergistically fusing the GEO and LEO datasets. Here we demonstrate the idea by fusing Terra/Aqua MODIS and GOES16/17 ABI surface BRDF with the Multi-Angle Implementation of Atmospheric Correction (MAIAC) algorithm. Our processing is implemented in two steps. First, we compile and project both MODIS and ABI data on the same GeoNEX common grid, and then use the MODIS MAIAC BRDF (MCD19A3) as the prior information to perform atmospheric correction of ABI data and retrieve surface BRDF with the ABI MAIAC code. In the second step, we run the MAIAC code in the forward mode to simulate MODIS and ABI TOA radiances from the jointly retrieved surface BRDF and the corresponding illumination-view geometries. By comparing the simulated TOA radiances with corresponding observations (e.g., MOD02 and ABI05), we can quantitatively evaluate whether the jointly retrieved ABI surface BRDF improves over the separately retrieved MODIS and ABI BRDF products. We expect that the jointly retrieved BRDF data are more robust than the standard products and can help us reduce uncertainties in higher-level earth observation satellite products.

Geostationary satellite↗

DOE OSTI2016

Hybrid-view programming of nuclear fusion simulation code in the PGAS parallel programming language XcalableMP

Recently, the Partitioned Global Address Space (PGAS) parallel programming model has emerged as a usable distributed memory programming model. XcalableMP (XMP) is a PGAS parallel programming language that extends base languages such as C and Fortran with directives in OpenMP-like style. XMP supports a global-view model that allows programmers to define global data and to map them to a set of processors, which execute the distributed global data as a single thread. In XMP, the concept of a coarray is also employed for local-view programming. In this study, we port Gyrokinetic Toroidal Code - Princeton (GTC-P), which is a three-dimensional gyrokinetic PIC code developed at Princeton University to study the microturbulence phenomenon in magnetically confined fusion plasmas, to XMP as an example of hybrid memory model coding with the global-view and local-view programming models. In local-view programming, the coarray notation is simple and intuitive compared with Message Passing Interface (MPI) programming while the performance is comparable to that of the MPI version. Thus, because the global-view programming model is suitable for expressing the data parallelism for a field of grid space data, we implement a hybrid-view version using a global-view programming model to compute the field and a local-view programming model to compute the movement of particles. Finally, the performance is degraded by 20% compared with the original MPI version, but the hybrid-view version facilitates more natural data expression for static grid space data (in the global-view model) and dynamic particle data (in the local-view model), and it also increases the readability of the code for higher productivity.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2023

Application of NEAMS Multiphysics Framework for Species Tracking in Molten Salt Reactors

This report from Idaho National Laboratory (INL) summarizes the key modeling and simulation activities conducted under the Department of Energy (DOE) Molten Salt Reactor (MSR) Campaign during the Fiscal Year 2023 (FY23). The focus of the work was to leverage state-of-the-art modeling capabilities from the DOE Nuclear Energy Advanced Modeling and Simulation (NEAMS) codes to enable novel multiphysics and multiscale modeling and simulation of MSRs. Through collaboration with NEAMS code developers, advanced multiphysics analysis capabilities for MSR systems were demonstrated by coupling depletion, thermal-hydraulics, and thermochemistry into an innovative framework for chemical species transport in MSRs. As a result, the framework can track nuclides throughout their lifetimes in the core, from production (depletion) to advection throughout the salt volume (thermal-hydraulics) and off-gassing or precipitation outside of the salt (thermochemistry). This work supports the near-term deployment of MSRs by integrating the synergistic efforts between the DOE’s MSR Campaign and NEAMS program. The resulting framework will help better connect system design modelers with experimentalists to better understand and predict complex physical behaviors in MSRs. Researchers and MSR developers alike can now leverage these new modeling and simulation capabilities to perform novel analyses with applications including: • MSR dynamics during normal operational transients and accident scenarios • Off-gas system design and performance for fuel cycle and depletion analysis • Corrosion and active chemistry control for reactor component health and lifetime determination • Source term, decay heat and activity determination in accident scenarios • Special nuclear material accountancy and chemical forensic analysis for safeguards • Digital twin development of experiments and experimental reactor demonstrations • Measurement requirements for instrumentation and control design • Uncertainty and sensitivity analysis of missing data to inform future experimental data collection.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

NASA NTRS2022

Development of an Effective Finite-rate Oxidation Model for NuSil-coated Charred Carbon Preform Ablators

Recently, a detailed effective finite-rate surface chemistry model was developed for the oxidation of FiberForm [1] using the molecular beam experimental data of Poovathingal et al., [2]. FiberForm is the major building block of the thermal protection system (TPS) material Phenolic Impregnated Carbon Ablator (PICA), commonly used by NASA. The surface chemistry model consists of detailed surface reaction mechanisms such as adsorption, desorption, and several types of Langmuir-Hinshelwood (LH) reactions to characterize the oxygen-carbon interactions at the surface. This model provides excellent agreement with the experimental data for oxidation product compositions and corresponding translational energy distributions. Further, an effective oxidation model was constructed that captures the equivalent interaction of oxygen inside the microstructure (including multiple surface collisions) via a modified reactivity for a smooth wall boundary condition. This enables the use of this model directly into Computational Fluid Dynamics (CFD) codes and Material Response (MR) codes to accurately simulate the gas–surface interactions within FiberForm without using the detailed micro-structure. However, this effective model is valid only for the virgin FiberForm. As the TPS material undergoes ablation, the preform carbon burns and turns into char. In addition, PICA is also coated with a protective silicon coating called NuSil for the purpose of mitigating the spread of phenolic dust, and limit contamination during clean room operations. Fig. 1 shows a X-ray microtomography image of a charred NuSil-coated PICA. The NuSil layer introduces species containing silicon into the product mixture. Further, the reactivity of the carbon within the char layer is different from the virgin FiberForm as shown in Fig. 2. Recently a new set of molecular beam experiments were performed on this NuSil-coated TPS material [3] with the same type of oxygen beam used in the previous experiments. Using the latest experimental data, the previously developed effective model will be extended to account for the charred carbon as well as the NuSil coating. Finally, this new effective model with three phases – preform carbon, char, and NuSil; will be compared and validated against the experimental product compositions.

K Swaminathan Gopalan↗

DOE OSTI2014

The Coal-Seq III Consortium. Advancing the Science of CO 2 Sequestration in Coal Seam and Gas Shale Reservoirs

The Coal-Seq consortium is a government-industry collaborative that was initially launched in 2000 as a U.S. Department of Energy sponsored investigation into CO2 sequestration in deep, unmineable coal seams. The consortium’s objective aimed to advancing industry’s understanding of complex coalbed methane and gas shale reservoir behavior in the presence of multi-component gases via laboratory experiments, theoretical model development and field validation studies. Research from this collaborative effort was utilized to produce modules to enhance reservoir simulation and modeling capabilities to assess the technical and economic potential for CO2 storage and enhanced coalbed methane recovery in coal basins. Coal-Seq Phase 3 expands upon the learnings garnered from Phase 1 & 2, which has led to further investigation into refined model development related to multicomponent equations-of-state, sorption and diffusion behavior, geomechanical and permeability studies, technical and economic feasibility studies for major international coal basins the extension of the work to gas shale reservoirs, and continued global technology exchange. The first research objective assesses changes in coal and shale properties with exposure to CO2 under field replicated conditions. Results indicate that no significant weakening occurs when coal and shale were exposed to CO2, therefore, there was no need to account for mechanical weakening of coal due to the injection of CO2 for modeling. The second major research objective evaluates cleat, Cp, and matrix, Cm, swelling/shrinkage compressibility under field replicated conditions. The experimental studies found that both Cp and Cm vary due to changes in reservoir pressure during injection and depletion under field replicated conditions. Using laboratory data from this study, a compressibility model was developed to predict the pore-volume compressibility, Cp, and the matrix compressibility, Cm, of coal and shale, which was applied to modeling software to enhance model robustness. Research was also conducted to improve algorithms and generalized adsorption models to facilitate realistic simulation of CO2 sequestration in coal seams and shale gas reservoirs. The interaction among water and the adsorbed gases, carbon dioxide (CO2), methane (CH4), and nitrogen (N2) in coalbeds is examined using experimental in situ laboratory techniques to comprehensively model CBM production and CO2 sequestration in coals. An equation of state (EOS) module was developed which is capable of predicting the density of pure components and mixtures involving the wet CBM gases CH4, CO2, and N2 at typical reservoir condition, and is used to inform CO2 injection models. The final research objective examined the effects adsorbed CO2 has on coal strength and permeability. This research studied the weakening or failure of coal by the adsorption of CO2 from empirically derived gas production data to develop models for advanced modeling of permeability changes during CO2 sequestration. The results of this research effort have been used to construct a new and improved model for assessing changes in permeability of coal reservoirs due CO2 injection. The modules developed from these studies and knowledge learned are applied to field validation and basin assessment studies. These data were used to assess the flow and storage of CO2 in a shale reservoir, test newly developed code against large-scale projects, and conduct a basin-oriented review of coal storage potential in the San Juan Basin. The storage potential and flow of CO2 was modeled for shale sequestration of a proprietary Marcellus Shale horizontal gas production well using COMET3 simulation software. Simulation results from five model runs indicate that stored CO2 quantities are linked to the duration of primary production preceding injection. Matrix CO2 saturation is observed to increase in each shale zone after injection with an increase in primary production, and the size of the CO2 plume is also observed to increase in size the longer initial production is sustained. The simulation modules developed around the Coal-Seq experimental work are also incorporated into a pre-existing large-scale numerical simulation model of the Pump Canyon CO2-ECBM pilot in the San Juan Basin. The new model was applied to re-history match the data set to explore the improvements made in permeability prediction against previously published data sets and to validate this module. The assessment of the new data, however, indicates that the impact of the variable Cp is negligible on the overall behavior of the coal for CO2 storage purposes. Applying these new modules, the San Juan Basin and the Marcellus Shale are assessed for their technical ECBM/AGR and CO2 storage potential and the economic potential of these operations. The San Juan Basin was divided into 4 unique geographic zones based on production history, and the Marcellus was divided into nine. Each was assessed based upon each zone’s properties, and simulations were run to assess the potential of full Basin development. Models of a fully developed San Juan Basin suggest the potential for up to 104 Tcf of CO2 storage, and 12.3 Tcf of methane recovery. The Marcellus models suggest 1,248 Tcf of CO2 storage and 924 Tcf of AGR. The economics are deemed favorable where credits cover the cost of CO2 in the San Juan Basin, and in many cases in the Marcellus, but to maximize storage potential, credits need to extend to pay the operator to store CO2.

01 COAL, LIGNITE, AND PEAT↗

DOE OSTI2015

An Implicit Correlation Method for Cross-Correlation Sampling, with MCNPX-PoliMi Validation

Monte Carlo particle transport codes used to model detector response are traditionally run in analog mode. However, analog simulations of cross-correlation measurements are extremely time-consuming because the probability of coincident detection is small, approximately equal to the product of the probabilities of a single detection in each detector. The new “implicit correlation” method described here increases the number of correlated event scores, thereby reducing variance and required computation times. The cost of the implicit correlation method is comparable to the cost of simulating single-event detection for the lowest absolute detector efficiency in the problem. The new method is especially useful in the nuclear nonproliferation and safeguards fields for simulating correlation measurements of shielded special nuclear material. The new method was implemented in MCNPX-PoliMi for neutron-neutron cross-correlations with a Cf-252 spontaneous fission source measured by 14 detectors at various angles. The method demonstrated good agreement with analog simulation and reference measurement results. Small differences between nonanalog and analog cross-correlation distributions are attributed to discretization errors that are often not present in practical applications. Improvement in the figure of merit was greater than a factor of 100 in all tested cases.

Marcath, Matthew J.↗

DOE OSTI2014

NEAMS FPL M2 Milestone Report: Development of a UO₂ Grain Size Model using Multicale Modeling and Simulation

This report summarizes development work funded by the Nuclear Energy Advanced Modeling Simulation program's Fuels Product Line (FPL) to develop a mechanistic model for the average grain size in UO₂ fuel. The model is developed using a multiscale modeling and simulation approach involving atomistic simulations, as well as mesoscale simulations using INL's MARMOT code.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

DOE OSTI2024

CLimate Impact: Determining Etiology thRough pAthways (CLDERA)

Climate impacts have broad economic, health, political, and national security ramifications. Societally relevant impacts are typically farther downstream, are the product of multiple interacting processes, and can arise over small regions and timeframes because their sources are short-term and localized. Short-term forcings (as can be seen in volcanic eruptions, climatic tipping points (e.g., the collapse of rainforests or the disappearance of sea ice), or in increasingly plausible climate interventions) fundamentally possess low signal-to-noise and could benefit from accounting for the multiple conditional processes through which a downstream impact arises. Under the Grand Challenge LDRD CLDERA (CLimate impacts: Discovering Etiology thRough pAthways), we have developed tools to enable downstream impact attribution from geographically and temporally localized source forcings in the climate. CLDERA developed methods that can distinguish how a localized source drives the climate system to respond with particular impacts. The how is embodied in pathways – the spatio-temporally evolving chain of physical processes that connects a source to a series of increasingly distant impacts. Novel analytic methods in pursuit of downstream impact attribution were developed and demonstrated on simulations and observations of the 1991 eruption of Mt. Pinatubo in the Philippines. As described within this report we have • developed stratospheric expertise and aerosol modeling capabilities in E3SM, • created original methods to detect and model pathways from source-to-impact, and • advanced climate attribution through novel methods, cases, and approaches. Further, CLDERA developed a tiered verification process consisting of controlled datasets to prototype, verify, and refine the original method development. CLDERA increased Sandia’s footprint in the climate analytics community and developed new climate collaborations whilst also creating a cadre of climate analysts at Sandia. The products from CLDERA have been extensive with a total of 9 journal articles published, 12 articles submitted and under review, and an additional 8 articles in preparation. We have produced 1750 simulated years and developed 9 code-bases. This report details these accomplishments and serves as a summary of the work completed during the CLDERA Grand Challenge.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2024

Extension of SCALE/Sampler’s sensitivity index for the assessment of cross section, fission yield, and decay data uncertainties

Accurate prediction of nuclide compositions and neutron and gamma sources and spectra for fresh and spent nuclear fuel through computational modeling and simulations is the basis for safeguards instruments design, optimization, and calibration, and for validation of the measured responses. The accuracy of these simulations can be significantly impacted by uncertainties in input parameters to the applied computer code. Input parameters are, for example, dimensions, material compositions, and temperatures of the model. Additionally, important but sometimes neglected input parameters for simulations of nuclear systems are the nuclear data that include nuclear reaction cross sections, fission product yields, and decay data. It has been shown that uncertainties of calculated results are dominated by uncertainties of these nuclear data.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

DOE OSTI2014

Simulation of Radiation Quantities for Accelerator-based Experiments

Results of the MARS15 code application to the design of target stations for two experiments – a muon-to-electron conversion experiment Mu2e and the prospective multi-purpose ProjectX Energy Station at Fermilab – are presented. A detailed MARS15 analysis has been carried out focusing on the most important radiation quantities such as displacements per atom (DPA), peak temperature and power density in the coils, absorbed dose in the insulation, dynamic heat load; DPA and neutron volumes, tritium production are also simulated for the ProjectX target, a benchmark of the neutron fission model is presented.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

DOE OSTI2014

Sensitivity studies for the main r process: nuclear masses

The site of the rapid neutron capture process (r process) is one of the open challenges in all of physics today. The r process is thought to be responsible for the creation of more than half of all elements beyond iron. The scientific challenges to understanding the origin of the heavy elements beyond iron lie in both the uncertainties associated with astrophysical conditions that are needed to allow an r process to occur and a vast lack of knowledge about the properties of nuclei far from stability. One way is to disentangle the nuclear and astrophysical components of the question. On the nuclear physics side, there is great global competition to access and measure the most exotic nuclei that existing facilities can reach, while simultaneously building new, more powerful accelerators to make even more exotic nuclei. On the astrophysics side, various astrophysical scenarios for the production of the heaviest elements have been proposed but open questions remain. This paper reports on a sensitivity study of the r process to determine the most crucial nuclear masses to measure using an r-process simulation code, several mass models (FRDM, Duflo-Zuker, and HFB-21), and three potential astrophysical scenarios.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2016

Agricultural Baseline (BL0) scenario of the 2016 Billion-Ton Report

Scientific reason for data generation: to serve as the reference case for the BT16 volume 1 agricultural scenarios. The agricultural baseline runs from 2015 through 2040; a starting year of 2014 is used. Date the data set was last modified: 02/12/2016 How each parameter was produced (methods), format, and relationship to other data in the data set: simulation was developed without offering a farmgate price to energy crops or residues (i.e., building on both the USDA 2015 baseline and the agricultural census data (USDA NASS 2014). Data generated are .txt output files by year, simulation identifier, county code (1-3109). Instruments used: POLYSYS (version POLYS2015_V10_alt_JAN22B) supplied by the University of Tennessee APAC The quality assurance and quality control that have been applied: • Check for negative planted area, harvested area, production, yield and cost values. • Check if harvested area exceeds planted area for annuals. • Check FIPS codes.

09 BIOMASS FUELS↗

DOE OSTI2016

Agricultural Baseline (BL0) scenario

09 BIOMASS FUELS↗