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DOE OSTI2026

Semi-Analytical Hierarchical Bayesian Inference of Nonlinear Model Structure in Stochastic Dynamics: Applied to Compartmental Models of Infectious Diseases

A Bayesian computational framework for parsimonious inference in stochastic nonlinear dynamical systems is presented. This framework enables the concurrent estimation of system states, time-varying parameters, time-invariant parameters, and the optimal sparsity structure of the model parameters. Because differential equation-based models are often simplified mechanistic or phenomenological representations, robust inference from noisy measurement data requires explicit treatment of model error and uncertainty. Model error and time-varying parameters can be represented as random processes, enabling inference while making minimal assumptions about the underlying sources of discrepancy and variability. Adopting stochastic differential equation representations affords the model significant flexibility, but can also render it susceptible to overfitting during statistical inversion, where the inferred model may track noise rather than the underlying signal. To alleviate the effects of overfitting and to enable the discovery of the optimal sparse representation of the time-invariant parameters, a Bayesian sparse learning algorithm is embedded within the framework. This sparse learning framework adopts an approximate hierarchical Bayesian setting defined by a series of semi-analytical expressions. The model structure inference framework is validated using a stochastic compartmental model for tracking and forecasting active cases of an infectious disease. Compartmental models describe population-level infectious disease dynamics through interactions among population fractions grouped by disease state. Mathematically, such models consist of a system of coupled ordinary differential equations. This example adopts an expressive compartmental model that includes multiple possible interactions between disease states, motivated by early uncertainty surrounding COVID-19 reinfection dynamics and their implications for long-term epidemic forecasting. The sparse learning exercise permits the inference of a priori unknown epidemiological dynamics from simulated public health data, discovering the nested compartmental model that optimizes the trade-off between average data-fit and model complexity. It is shown that inducing sparsity among the model parameters eliminates redundant interactions between compartments, equivalently revealing the optimal coupling structure between differential equations.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2025

Coal to Carbon Fiber (C2CF) Continuous Processing for High Value Composites (Final Report)

Coal tar is a condensed and recovered by-product of the coking of metallurgical coal for steel production. The heaviest fraction of distilled coal tar is an isotropic pitch largely used as a binder in the manufacturing of carbonaceous electrodes for primary aluminum smelting and in electric arc furnaces. Coal tar pitch offers high carbon yield upon carbonization. In this project, a process to convert the domestically sourced isotropic coal tar pitch, containing very low particulates (QI = 0.32 wt.%), to form flow-domain mesophase pitch amenable for melt spinning into precursor (green) fibers for carbon fiber, was developed and optimized. The final reproducible processing developed is reviewed in this report, along with several characterizations of the mesophase pitch. A final definition of the characteristics of a ‘spinnable’ mesophase pitch is presented. With this mesophase pitch, reproducible and stable multifilament melt spinning was developed, producing green fiber tows with filament diameters of approximately 20 m. The multifilament melt spinning was the most challenging aspect of the project and required the most effort. The best practices learned from this project for melt spinning are reviewed in this report. Once spun, the green fibers were oxidatively stabilized, carbonized and graphitized under inert gas atmosphere to form the final carbon fibers. Given the relatively high softening point of the mesophase pitch, no issues of interfilament fusion were observed during batch oxidation, and subsequent batch carbonization and graphitization went smoothly in all cases. An ~ 80 wt.% conversion of the green fiber mass to final carbon fiber was achieved. After graphitization, the carbon fibers showed high tensile moduli (most were ~ 600 GPa, or 87 Msi) consistent with commercially-available high-performance pitch-based carbon fiber. However, tensile strength and strain to failure were comparatively low. Further work to reduce defects in and on the fiber surfaces would increase these properties. SEM imaging of the graphitic fiber textures is presented herein. Rudimentary composites were fabricated from the carbon fibers and characterized showing similar modulus to baseline composites fabricated with commercial carbon fiber. Finally, a basic economic analysis was done showing the potential to increase the value of the isotropic coal tar pitch by up to 13.6 to 136 times based on a carbon fiber value of $\$$5/lb to $\$$50/lb, respectively. Moreover, the site case study suggests that the coal tar from the single integrated steel mill could supply production of up to 16 kt/yr of carbon fiber. Finally, a technological gap analysis was done which shed light on remaining technical challenges. These challenges included: recovery and utilization of condensates from the mesophase pitch processing, further advancing and increasing stability of the multifilament melt spinning processing, optimization of the oxidation processing, defect reduction for increased carbon fiber strength, and the development of a weaving process towards carbon fiber fabrics. To maximize the coal value chain, the primary objectives of this project were to (a) develop and scale efficient processing technology for producing melt-spinnable mesophase pitch from isotropic coal tar pitch, (b) clarify and simplify tedious continuous fiber processing technologies (particularly multifilament melt spinning of mesophase pitch) towards the efficient production of high performance carbon fiber, and (c) demonstrate and characterize representative composite parts derived from the final carbon fiber. Immense progress was made on all 3 objectives and is detailed in this report.

01 COAL, LIGNITE, AND PEAT↗

DOE OSTI2020

Measurement of neutron-proton capture in the $\mathrmSNO+ water phase

The SNO+ experiment collected data as a low-threshold water Cherenkov detector from September 2017 to July 2019. Measurements of the 2.2-MeV γ 's produced by neutron capture on hydrogen were made using an Am-Be calibration source, for which a large fraction of emitted neutrons are produced simultaneously with a 4.4-MeV γ. Analysis of the delayed coincidence between the 4.4-MeV γ and the 2.2-MeV capture γ revealed a neutron detection efficiency that is centered around 50% and varies at the level of 1% across the inner region of the detector, which to our knowledge is the highest efficiency achieved among pure water Cherenkov detectors. Finally, in addition, the neutron capture time constant was measured and converted to a thermal neutron-proton capture cross section of $336.3^{+1.2}_{-1.5}$ mb .

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

DOE OSTI2022

Air classification of forest residue for tissue and ash separation efficiency

The goal of this Case Study was to evaluate the performance of air classification of logging residues toward meeting conversion CMAs for carbon and ash contents, as compared to the static status quo Base Case system in which the residues are first dried and then ground in a hammer mill with a 6 mm screen and fines < 1.18 mm are removed. Also considered were moisture and ash impacts on throughput and Overall Operating Effectiveness (OOE), as well as delivered feedstock cost and minimum fuel selling price (MFSP). Laboratory data on the impacts of fan speed and moisture content on the separation efficiency of soil ash, needles and bark from white wood were received from FCIC Subtask 5.2. Average throughput and energy consumption data were obtained from the Bioenergy Feedstock National User Facility (BFNUF) for the same air classifier. These data were utilized to develop the necessary response surface equations to perform throughput analysis using discrete event simulation. Because the Base Case status quo system utilizes drying prior to grinding, we modeled the Case Study with drying prior to air classification and subsequent grinding of the separated white wood to isolate the individual quality and cost impacts of air classification relative to the Base Case system. Key takeaways from this Case Study are that air classification improves the quality of the final material, but increases the production cost, especially when lights are disposed; this becomes a tradeoff between increased conversion yield and the extra cost. Removing material should be done as early in the process as possible. Each operation that occurs prior to removing the material increases the cost of the disposed material and leads to wasted energy expenditures. As processes are included or modified, the impact on monetary and energy cost should be included in the decision process. Identifying alternative uses and the associated value for material that is separated from the feedstock stream will have a significant impact on the delivered cost of the material. Although there was an increase in production cost by adding air classification, there was a resulting benefit to the MFSP when meeting or exceeding all of the conversion CMAs; this was an important finding that should be explored further with FCIC Subtask 8.3 and potentially FCIC Task 6.

09 BIOMASS FUELS↗