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At least 271 records · Page 15
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

First Results From Nb3Sn Coatings of 2.6 GHz Nb SRF Cavities Using DC Cylindrical Magnetron Sputtering System

A DC cylindrical magnetron sputtering system has been commissioned and operated to deposit Nb3Sn onto 2.6 GHz Nb SRF cavities. After optimizing the deposition conditions in a mock-up cavity, Nb-Sn films are deposited first on flat samples by multilayer sequential sputtering of Nb and Sn, and later annealed at 950 °C for 3 hours. X-ray diffraction of the films showed multiple peaks for the Nb3Sn phase and Nb (substrate). No peaks from any Nb- Sn compound other than Nb3Sn were detected. Later three 2.6 GHz Nb SRF cavities are coated with ~1 μm thick Nb3Sn. The first Nb3Sn coated cavity reached close to Eacc = 8 MV/m, demonstrating a quality factor Q0 of 3.2 × 108 at Tbath = 4.4 K and Eacc = 5 MV/m, about a factor of three higher than that of Nb at this temperature. Q0 was close to 1.1 × 109, dominated by the residual resistance, at 2 K and Eacc = 5 MV/m. The Nb3Sn coated cavities demonstrated Tc in the range of 17.9 – 18 K. Here we present the commissioning experience, s ystem optimization, and the first results from the Nb3Sn fabrication on flat samples and SRF cavities.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2023

FIRST RESULTS FROM Nb3Sn COATINGS OF 2.6 GHz Nb SRF CAVITIES USING DC CYLINDRICAL MAGNETRON SPUTTERING SYSTEM

A DC cylindrical magnetron sputtering system has been commissioned and operated to deposit Nb3Sn onto 2.6 GHz Nb SRF cavities. After optimizing the deposition conditions in a mock-up cavity, Nb-Sn films are deposited first on flat samples by multilayer sequential sputtering of Nb and Sn, and later annealed at 950 °C for 3 hours. X-ray diffraction of the films showed multiple peaks for the Nb3Sn phase and Nb (substrate). No peaks from any Nb-Sn compound other than Nb3Sn were detected. Later three 2.6 GHz Nb SRF cavities are coated with ~1 µm thick Nb3Sn. The first Nb3Sn coated cavity reached close to Eacc = 8 MV/m, demonstrating a quality factor Q0 of 3.2 × 108 at Tbath = 4.4 K and Eacc = 5 MV/m, about a factor of three higher than that of Nb at this temperature. Q0 was close to 1.1 × 109, dominated by the residual resistance, at 2 K and Eacc = 5 MV/m. The Nb3Sn coated cavities demonstrated Tc in the range of 17.9 ? 18 K. Here, we present the commissioning experience, system optimization, and the first results from the Nb3Sn fabrication on flat samples and SRF cavities.

Shakel, M.S.↗
DOE OSTI2023

FIRST RESULTS FROM Nb3Sn COATINGS OF 2.6 GHz Nb SRF CAVITIES USING DC CYLINDRICAL MAGNETRON SPUTTERING SYSTEM

A DC cylindrical magnetron sputtering system has been commissioned and operated to deposit Nb3Sn onto 2.6 GHz Nb SRF cavities. After optimizing the deposition conditions in a mock-up cavity, Nb-Sn films are deposited first on flat samples by multilayer sequential sputtering of Nb and Sn, and later annealed at 950 °C for 3 hours. X-ray diffraction of the films showed multiple peaks for the Nb3Sn phase and Nb (substrate). No peaks from any Nb-Sn compound other than Nb3Sn were detected. Later three 2.6 GHz Nb SRF cavities are coated with ~1 µm thick Nb3Sn. The first Nb3Sn coated cavity reached close to Eacc = 8 MV/m, demonstrating a quality factor Q0 of 3.2 × 108 at Tbath = 4.4 K and Eacc = 5 MV/m, about a factor of three higher than that of Nb at this temperature. Q0 was close to 1.1 × 109, dominated by the residual resistance, at 2 K and Eacc = 5 MV/m. The Nb3Sn coated cavities demonstrated Tc in the range of 17.9 ? 18 K. Here, we present the commissioning experience, system optimization, and the first results from the Nb3Sn fabrication on flat samples and SRF cavities.

Shakel, M. S.↗
DOE OSTI2024

Uncertainty Propagation within Chained Models for Machine Learning Reconstruction of Neutrino-LAr Interactions

Sequential or chained models are increasingly prevalent in machine learning for scientific applications, due to their flexibility and ease of development. Chained models are particularly useful when a task is separable into distinct steps with a hierarchy of meaningful intermediate representations. In reliability-critical tasks, it is important to quantify the confidence of model inferences. However, chained models pose an additional challenge for uncertainty quantification, especially when input uncertainties need to be propagated. In such cases, a fully uncertainty-aware chain of models is required, where each step accepts a probability distribution over the input space, and produces a probability distribution over the output space. In this work, we present a case study for adapting a single model within an existing chain, designed for reconstruction within neutrino-Argon interactions, developed for neutrino oscillation experiments such as MicroBooNE, ICARUS, and the future DUNE experiment. We test the performance of an input uncertainty-enabled model against an uncertainty-blinded model using a method for generating synthetic noise. By comparing these two, we assess the increase in inference quality achieved by exposing models to upstream uncertainty estimates.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

First Results From Nb3Sn Coatings of 2.6 GHz Nb SRF Cavities Using DC Cylindrical Magnetron Sputtering System

A DC cylindrical magnetron sputtering system has been commissioned and operated to deposit Nb3Sn onto 2.6 GHz Nb SRF cavities. After optimizing the deposition conditions in a mock-up cavity, Nb-Sn films are deposited first on flat samples by multilayer sequential sputtering of Nb and Sn, and later annealed at 950°C for 3 hours. X-ray diffraction of the films showed multiple peaks for the Nb3Sn phase and Nb (substrate). No peaks from any Nb- Sn compound other than Nb3Sn were detected. Later three 2.6 GHz Nb SRF cavities are coated with ~1 μm thick Nb3Sn. The first Nb3Sn coated cavity reached close to Eacc = 8 MV/m, demonstrating a quality factor Q0 of $3.2 \times 108$ at Tbath = 4.4 K and Eacc = 5 MV/m, about a factor of three higher than that of Nb at this temperature. Q0 was close to $1.1 \times 109$, dominated by the residual resistance, at 2 K and Eacc = 5 MV/m. The Nb3Sn coated cavities demonstrated Tc in the range of $17.9 -- 18$ K. Here we present the commissioning experience, system optimization, and the first results from the Nb3Sn fabrication on flat samples and SRF cavities.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2023

First Results from Nb3Sn Coatings of 2.6 GHz Nb SRF Cavities Using DC Cylindrical Magnetron Sputtering System

A DC cylindrical magnetron sputtering system has been commissioned and operated to deposit Nb3Sn onto 2.6 GHz Nb SRF cavities. After optimizing the deposition conditions in a mock-up cavity, Nb-Sn films are deposited first on flat samples by multilayer sequential sputtering of Nb and Sn, and later annealed at 950 {\deg}C for 3 hours. X-ray diffraction of the films showed multiple peaks for the Nb3Sn phase and Nb (substrate). No peaks from any Nb3Sn compound other than Nb3Sn were detected. Later three 2.6 GHz Nb SRF cavities are coated with ~1 $\mu$m thick Nb3Sn. The first Nb3Sn coated cavity reached close to Eacc = 8 MV/m, demonstrating a quality factor Q0 of 3.2 x 108 at Tbath = 4.4 K and Eacc = 5 MV/m, about a factor of three higher than that of Nb at this temperature. Q0 was close to 1.1 x 109, dominated by the residual resistance, at 2 K and Eacc = 5 MV/m. The Nb3Sn coated cavities demonstrated Tc in the range of 17.9 - 18 K. Here we present the commissioning experience, system optimization, and the first results from the Nb3Sn fabrication on flat samples and SRF cavities.

43 PARTICLE ACCELERATORS↗
DOE OSTI2021

An adaptive Hessian approximated stochastic gradient MCMC method

Bayesian approaches have been successfully integrated into training deep neural networks. One popular family is stochastic gradient Markov chain Monte Carlo methods (SG-MCMC), which have gained increasing interest due to their ability to handle large datasets and the potential to avoid overfitting. Although standard SG-MCMC methods have shown great performance in a variety of problems, they may be inefficient when the random variables in the target posterior densities have scale differences or are highly correlated. Here, we present an adaptive Hessian approximated stochastic gradient MCMC method to incorporate local geometric information while sampling from the posterior. The idea is to apply stochastic approximation (SA) to sequentially update a preconditioning matrix at each iteration. The preconditioner possesses second-order information and can guide the random walk of a sampler efficiently. Instead of computing and saving the full Hessian of the log posterior, we use limited memory of the samples and their stochastic gradients to approximate the inverse Hessian-vector multiplication in the updating formula. Moreover, by smoothly optimizing the preconditioning matrix via SA, our proposed algorithm can asymptotically converge to the target distribution with a controllable bias under mild conditions. To reduce the training and testing computational burden, we adopt a magnitude-based weight pruning method to enforce the sparsity of the network. Our method is user-friendly and demonstrates better learning results compared to standard SG-MCMC updating rules. The approximation of inverse Hessian alleviates storage and computational complexities for large dimensional models. Numerical experiments are performed on several problems, including sampling from 2D correlated distribution, synthetic regression problems, and learning the numerical solutions of heterogeneous elliptic PDE. The numerical results demonstrate great improvement in both the convergence rate and accuracy.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Sequential Inference of Hospitalization Electronic Health Records Using Probabilistic Models

In the dynamic hospital setting, decision support can be a valuable tool for improving patient outcomes. Data-driven inference of future outcomes is challenging in this dynamic setting, where long sequences such as laboratory tests and medications are updated frequently. This is due in part to heterogeneity of data types and mixed-sequence types contained in variable length sequences. In this work we design a probabilistic unsupervised model for multiple arbitrary-length sequences contained in hospitalization Electronic Health Record (EHR) data. The model uses a latent variable structure and captures complex relationships between medications, diagnoses, laboratory tests, neurological assessments, and medications. It can be trained on original data, without requiring any lossy transformations or time binning. Inference algorithms are derived that use partial data to infer properties of the complete sequences, including their length and presence of specific values. We train this model on data from subjects receiving medical care in the Kaiser Permanente Northern California integrated healthcare delivery system. The results are evaluated against held-out data for predicting the length of sequences and presence of Intensive Care Unit (ICU) in hospitalization bed sequences. Our method outperforms a baseline approach, showing that in these experiments the trained model captures information in the sequences that is informative of their future values.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Multi-site Milling Strategy Reveals Significant Variation in Biomass Composition of Switchgrass ( Panicum virgatum ) Grown at Ten Locations

Cell wall composition influences biomass use as a forage and as a feedstock for biofuel and chemical conversion. To examine the influence of environment on composition of switchgrass (Panicum virgatum L.), we utilized a multi-environment experiment consisting of clones of switchgrass genotypes grown at up to ten locations in the continental US. We tested the influence of different milling treatments on biomass composition trait predictions via near-infrared reflectance spectroscopy (NIRS). We found that most compositional trait predictions (29/34) were significantly different (P < 0.05) when a single lot of biomass was subjected to disparate milling treatments, i.e., knife milling vs. knife milling with an additional cyclone milling. Further, depending on the plant material tested, three to eight compositional trait predictions vary (P < 0.05) when identical biomass was knife milled at different sites followed by cyclone milling at a single site, including for traits such as Klason lignin, nitrogen, and carbon. In some cases, variation due to milling site exceeded environmentally induced compositional variation of a single switchgrass genotype grown at different sites. From these observations, we recommend a protocol with two sequential millings that decouples growth environment from a particular mill. Utilizing this approach, we found that 46/46 biomass composition traits from the warm season herbaceous forage and switchgrass bioethanol NIRS equations vary significantly (P < 0.001) in clones of a switchgrass genotype (WBC) grown at ten sites, with the growth site representing the largest average source of variation (41%). This multi-site milling approach can be used to examine environmental and gene-by-environment influences on composition with the goal of optimizing cell wall composition in different environments for biomass utilization.

09 BIOMASS FUELS↗
DOE OSTI2021

Experimental and computational investigation of the bond energy of thorium dicarbonyl cation and theoretical elucidation of its isomerization mechanism to the thermodynamically most stable isomer, thorium oxide ketenylidene cation, OTh + CCO

Collision-induced dissociation (CID) of [Th,2C,2O] + with Xe is performed using a guided ion beam tandem mass spectrometer (GIBMS). The only products observed are ThCO + and Th + by sequential loss of CO ligands. The experimental findings and theoretical calculations support that the structure of [Th,2C,2O] + is the bent homoleptic thorium dicarbonyl cation, Th + (CO) 2 , having quartet spin, which is both thermodynamically and kinetically stable enough in the gas phase to be observed in our GIBMS instrument. Analysis of the kinetic energy-dependent cross sections for this CID reaction yields the first experimental determination of the bond dissociation energy (BDE) of (CO)Th + –CO at 0 K as 1.05 ± 0.09 eV. A theoretical BDE calculated at the CCSD(T) level with cc-pVXZ (X = T and Q) basis sets and a complete basis set (CBS) extrapolation is in very good agreement with the experimental result. Although the doublet spin bent thorium oxide ketenylidene cation, OTh + CCO, is calculated to be the most thermodynamically stable structure, it is not observed in our experiment where [Th,2C,2O] + is formed by association of Th + and CO in a direct current discharge flow tube (DC/FT) ion source. Potential energy profiles of both quartet and doublet spin are constructed to elucidate the isomerization mechanism of Th + (CO) 2 to OTh + CCO. The failure to observe OTh + CCO is attributed to a barrier associated with C–C bond formation, which makes OTh + CCO kinetically inaccessible under our experimental conditions. Furthermore, chemical bonding patterns in low-lying states of linear and bent Th + (CO) 2 and OTh + CCO isomers are also investigated.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2024

Interlaced Characterization and Calibration (ICC) for Improved Computational Simulation Credibility

Accurate material characterization and model calibration are pivotal for simulations used for high-consequence engineering decisions. Current characterization and calibration methods (1) use simplified test specimen geometries and global data, (2) cannot guarantee that sufficient characterization data is collected for a specific model of interest, (3) provide only mean parameter values with no uncertainty quantification, and (4) are sequential, inflexible, and time-consuming. This work developed a new paradigm—coined Interlaced Characterization and Calibration (ICC)—which drives forward the state-of-the-art in model calibration by bringing together recent advancements into one improved workflow. The ICC paradigm (1) employs tools to efficiently use full-field data to calibrate high-fidelity material models, (2) aligns the data needed with the data collected by adopting an optimal experimental design protocol, (3) provides uncertainty metrics on the calibrated model parameters, and (4) incorporates these advances into a quasi real-time feedback loop. The ICC framework was validated synthetically with both low-fidelity and high-fidelity simulations paired with several different elastoplastic material models, and was also demonstrated experimentally with an aluminum 6061 cruciform exemplar specimen. Results showed that the ICC framework—in which Bayesian optimal experimental design actively guided the experiment— resulted in calibrations with similar or better accuracy than predetermined experiments based on subject matter expertise. Moreover, the ICC framework produced a complete model calibration— with quantified uncertainties on model parameters—in 1 week, a 5 - 10× increase in efficiency over traditional approaches. Thus, the ICC paradigm improves both the calibration process and quality, by (1) improving efficiency, which increases agility of solid mechanics modeling and enables utilization of computational simulation (CompSim) at earlier stages of the design cycle and (2) providing quantified, and in some cases reduced, parameter uncertainties, which increases confidence in model predictions and supports credible decision making.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Persistent Sampling: Enhancing the Efficiency of Sequential Monte Carlo

Sequential Monte Carlo (SMC) samplers are powerful tools for Bayesian inference but suffer from high computational costs due to their reliance on large particle ensembles for accurate estimates. We introduce persistent sampling (PS), an extension of SMC that systematically retains and reuses particles from all prior iterations to construct a growing, weighted ensemble. By leveraging multiple importance sampling and resampling from a mixture of historical distributions, PS mitigates the need for excessively large particle counts, directly addressing key limitations of SMC such as particle impoverishment and mode collapse. Crucially, PS achieves this without additional likelihood evaluations-weights for persistent particles are computed using cached likelihood values. This framework not only yields more accurate posterior approximations but also produces marginal likelihood estimates with significantly lower variance, enhancing reliability in model comparison. Furthermore, the persistent ensemble enables efficient adaptation of transition kernels by leveraging a larger, decorrelated particle pool. Experiments on high-dimensional Gaussian mixtures, hierarchical models, and non-convex targets demonstrate that PS consistently outperforms standard SMC and related variants, including recycled and waste-free SMC, achieving substantial reductions in mean squared error for posterior expectations and evidence estimates, all at reduced computational cost. PS thus establishes itself as a robust, scalable, and efficient alternative for complex Bayesian inference tasks.

Karamanis, Minas↗
DOE OSTI2023

Validation of the Cossee–Arlman mechanism for propylene oligomerization on Ni/UiO-66

Steady state rate expressions can be derived to distinguish the Cossee–Arlman and metallacycle mechanisms postulated for propylene oligomerization on nickel-based catalysts based on product selectivities, where product selectivities for the former are a function of olefin pressure because sequential coordination and insertion steps lead to independent mechanistic pathways for different hexene isomers. In contrast, the metallacycle mechanism presents pressure-independent product selectivities due to successive coordination prior to the kinetically relevant steps in each mechanism. In this work, steady state propylene oligomerization rates and selectivities were measured in the absence of an activator on nickel functionalized UiO-66 metal organic framework (MOF), Ni/UiO-66, to validate the Cossee–Arlman mechanism for light olefin oligomerization. In situ NO titrations reveal that ~5% of nickel sites were active during the reaction, and thus, not all nickel sites are relevant for catalysis. Propylene dimerization was first order in propylene pressure from 5 to 500 kPa with an apparent activation energy of ~20 kJ mol -1 from 453 to 493 K. Calculated apparent activation energies with density functional theory (DFT) calculations on cluster models of Ni/UiO-66 are in agreement with experiment to corroborate the Cossee–Arlman mechanism. Selectivities of hexene products and the ratio of hexene product selectivities on Ni/UiO-66 are in accordance with selectivity expressions derived from the Cossee–Arlman mechanism. In conclusion, analysis of product selectivities can be used more extensively to demarcate the Cossee–Arlman and metallacycle mechanisms for olefin oligomerization on metal-based catalysts.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Deep learning-enabled probing of irradiation-induced defects in time-series micrographs

Modeling time-series data with convolutional neural networks (CNNs) requires building a model to learn in batches as opposed to training sequentially. Coupling CNNs with in situ or operando techniques opens the possibility of accurately segmenting dynamic reactions and mass transport phenomena to understand how materials behave under the conditions in which they are used. In this article, in situ ion irradiation transmission electron microscopy (TEM) images are used as inputs into the CNN to assess the defect generation rate, defect cluster density, and saturation of defects. We then use the output segmentation maps to correlate with conventional TEM micrographs to assess the model’s ability to detail nanoscale interactions. Next, we discuss the implications of preprocessing and hyperparameters on model variability, accuracy when expanded to other datasets, and the role of regularization when controlling model variance. Ultimately, we eliminate human bias when extrapolating physical metrics, speed up analysis time, decouple reactions that happen at 100 ms intervals, and deploy models that are both accurate and transferable to similar experiments.

Burns, Kory (ORCID:0000000158019909)↗
DOE OSTI2024

Microgrid Service Restoration Incorporating Unmonitored Automatic Voltage Controllers and Net Metered Loads

Islanded microgrids may experience voltage and frequency instability due to uncontrolled state changes of voltage regulation devices and inaccurate demand forecasts. Uncontrolled state changes can occur if optimal microgrid restoration and dispatch algorithms, used for generating control commands for distributed energy resources, do not incorporate the behavior of automatic controllers of voltage regulation devices. Inaccurate demand forecasts may be encountered since post-outage demand of behind-the-meter net metered (NM) loads can vary significantly from their historical NM profiles. Here, this paper proposes an optimization formulation which allows optimal control of voltage regulators and capacitor banks without remote control and communication capabilities. A generalized demand model for NM loads is proposed which incorporates the cold load pickup phenomenon and their time varying post-outage demand in accordance with the IEEE 1547 standard. The time dependent optimal control formulation and the NM demand model are integrated in a sequential microgrid restoration algorithm by linearization of the involved logic propositions. A detailed case study on the unbalanced IEEE 123-node test system in OpenDSS validates the effectiveness of the proposed approach.

30 DIRECT ENERGY CONVERSION↗
DOE OSTI2020

Robust Online Sequential RVFLNs for Data Modeling of Dynamic Time-Varying Systems with Application of an Ironmaking Blast Furnace

In a world where the increasing complexity of modern industrial processes brings difficulties for accurate mathematical modeling, taking advantage of data has become an efficient solution to complex dynamic process modeling issue. In this paper, we develop a novel robust online sequential version of random vector functional-link networks (RVFLNs) for data-driven modeling of dynamic time-varying system and applied it in a blast furnace (BF) ironmaking process. First, to overcome the time-varying dynamics of process and to enable the RVFLNs to learn online with avoiding data saturation, an improved online sequential version of RVFLNs (OS-RFVLNs) is first presented by online sequential learning with forgetting factor. This improved OS-RVFLNs algorithm is not only suitable for the real-time and large data transfer situation, but also can adjust the sensitivity of the algorithm to different samples with the help of the introduced forgetting factor. Second, since the output weights of the improved OS-RVFLNs as well as other RVFLNs algorithms are obtained by the least squares approach, a robustness problem may occur when the training dataset is contaminated with various outliers. To solve this problem, a Cauchy distribution weighted M-estimator is introduced to improve the robustness of the improved OS- RVFLNs. For this proposed robust OS-RVFLNs (R-OS- RVFLNs), since the weights of different outlier data are properly determined by the Cauchy distribution function, their corresponding contribution on modeling can be properly distinguished. Thus robust and better modeling results can be achieved. Experiments using actual industrial data of BF ironmaking process and comparative studies have demonstrated that the proposed method produces a better estimation accuracy and stronger robustness than other methods.

Blast furnace (BF), Modelling, Dynamic systems↗
DOE OSTI2024

Understanding Process–Structure Relationships during Lamination of Halide Perovskite Interfaces

Fabrication of halide perovskite (HP) solar cells typically involves the sequential deposition of multiple layers to create a device stack, which is limited by the thermal and chemical incompatibility of top contact layers with the underlying HP semiconductor. One emerging strategy to overcome these restrictions on material selection and processing conditions is lamination, where two half-stacks are independently processed and then diffusion bonded to complete the device. Lamination reduces the processing constraints on the top side of the solar cell to allow new device designs, expanded use of deposition methods, and self-encapsulation of devices. While laminated perovskite solar cells with high efficiencies and novel interlayer combinations have been demonstrated, there is a limited understanding of how the lamination process parameters affect the diffusion-bond quality and material properties of the resulting HP layer. In this study, we systematically vary temperature, pressure, and time during lamination and quantify the resulting impacts on bonded area, grain domain size, and photoluminescence. A design of experiments is performed, and statistical analysis of the experimental results is used to quantitatively evaluate the resulting process–structure–property relationships. The lamination temperature is found to be the key parameter controlling these properties. Furthermore, a temperature of 150 °C enables successful bonding over 95% of the substrate area and also results in increases in apparent grain domain size and photoluminescence intensity. Based on these insights, the lamination temperature of functional perovskite solar cell devices is varied, demonstrating the importance of the resulting bond quality on device performance metrics.

14 SOLAR ENERGY↗
DOE OSTI2021

Geomechanical and flow implications with continued bioconversion of coal to methane: Experiments and modeling

Microbial conversion of coal to methane is a promising technology during transition of coal-based energy from conventional coal mining to natural gas recovery from coal. Significant research advances have been made towards engineering ideal microbial communities and nutrients for bio-stimulation of coal. However, actual field applications require geomechanical and flow behavior characterization of coal during the bioconversion process as well as gas production over the life of the created “biogenic gas reservoirs”. This work presents the results of an experimental investigation to estimate gas/methane production using bioconversion of coal and analyses of variations in bulk modulus, strain and permeability. Bulk modulus of coal demonstrated time-dependent behavior with continued bioconversion of solid coal. The phenomenon was modeled using mass balancing in a closed environment and the logistic equation based biogenic gas production with time. The modeled gas production and changes in modulus of coal showed excellent agreement with the corresponding experimental results. Next, numerical simulation of biogenic conversion of coal in a constant stress in-situ condition, replicating the Huff ‘n Puff method under field conditions, was carried out. The results showed that bioconversion can lead to decrease in effective stress, increase in permeability and decrease in the modulus of coal with time. Repeated recharge of coal with nutrients and recovering the produced gas, that is, sequential cycles of Huff n’ Puff, showed further increase in permeability and decrease in coal strength, potentially leading to coal failure in-situ, further increasing the permeability, thus enhancing the prospect of field application of the technology.

01 COAL, LIGNITE, AND PEAT↗