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Presentation given at the 2024 annual AICHE meeting held October 27-31, 2024. The presentation focuses on modeling performance and economics of a solvent absorption system for CO2 capture with towers utilizing intensified packing. The packing is an alternative to traditional structured packing by incorporating cooling channel for simultaneous mass and heat transfer.
The use of benchmark datasets has become an important engine of progress in machine learning (ML) over the past 15 years. Recently there has been growing interest in utilizing machine learning to drive advances in research-level mathematics. However, off-the-shelf solutions often fail to deliver the types of insights required by mathematicians. This suggests the need for new ML methods specifically designed with mathematics in mind. The question then is: what benchmarks should the community use to evaluate these? On the one hand, toy problems such as learning the multiplicative structure of small finite groups have become popular in the mechanistic interpretability community whose perspective on explainability aligns well with the needs of mathematicians. While toy datasets are a useful benchmark for initial work, they lack the scale, complexity, and sophistication of many of the principal objects of study in modern mathematics. To address this, we introduce a new collection of benchmark datasets, Algebraic Combinatorics Benchmarks (ACBench), representing either classic or open problems in algebraic combinatorics, a subfield of mathematics that studies discrete structures arising from abstract algebra. After describing the datasets, we discuss the challenges involved in constructing “good” mathematics benchmarks, describe baseline model performance, and discuss some of the insights these datasets can provide that may be of interest even to those who are not interested in mathematics research itself.
As part of the SiC-based material development at General Atomics Electromagnetic Systems (GA-EMS), this project involved the first-of-a-kind experimental post-irradiation examination of the irradiation-induced bowing response in SiC composite structures under a neutron flux gradient. The SiC composite miniature channel specimen was provided by GA-EMS for the irradiation experiment. To quantify the irradiation-induced bowing of the channel specimen, a series of visual and dimensional inspections were conducted using the unique capabilities at Oak Ridge National Laboratory (ORNL), including a custom profilometry rig. The post-irradiation examination results of this project will assist GA-EMS in validating their fuel performance model for SiC-based core structures in neutron irradiation environments.
Resource adequacy concerns may be very different in electricity systems that have higher levels of solar and storage, requiring changes to existing planning models. This study explores a novel approach to evaluating resource adequacy under future scenarios with higher solar and storage in the Southeast U.S. It uses NREL’s Probabilistic Resource Adequacy Suite (PRAS), a collection of probabilistic resource adequacy modeling tools, and compares results when interacting PRAS and a portfolio planning tool with a more traditional modeling approach. The results suggest that traditional models perform reasonably well with lower levels of solar PV, but at higher levels of solar probabilistic tools better capture the changes in resource adequacy concerns—such as winter energy availability—associated with higher solar systems. This is the final study in the Preparing Southeast Markets for Reliable and Affordable Integration of Solar into Operations and Planning project. Two prior reports can be found at: Solar and Storage Integration in the Southeastern United States: Economics, Reliability, and Operations. https://emp.lbl.gov/publications/solar-and-storage-integration Solar and Wind Forecast Error Reserve Sharing in a Multi-Utility Region. https://eta-publications.lbl.gov/sites/default/files/2024-11/multiutility_fe_reserve_sharing_final.pdf
Improving our representation of convective cell processes requires better quantification of convective clouds throughout their entire life cycle. This includes gaining a clearer understanding of the controls on key convective cloud properties, such as updraft intensity, particle size distributions, rainfall rates, and hydrometeor species. Our inability to improve convective cloud process modeling stems, in part, from a limited understanding of convective cell properties, particularly given how rapidly these storms evolve. This lack of detailed observations in the most intense and organized convective storms is especially significant, as large errors remain in representing these clouds, which are critical for severe weather prediction and Earth system model performance. Cloud and precipitation radars are essential tools for studying cloud microphysics and dynamics, particularly in deeper convective clouds. The recent U.S. Department of Energy Atmospheric Radiation Measurement (ARM) User Facility’s third Mobile Facility (AMF3) Bankhead National Forest (BNF) deployment provides a unique opportunity to investigate important land–atmosphere interactions, as well as the environmental controls on deep convective cloud processes, in a location favorable for frequent convection. We operate the X/Ka-band Scanning ARM Cloud Radar (X/Ka SACR) using a scan strategy optimized to capture these rapidly evolving clouds and their properties, thereby improving studies of deep convective cloud processes. This effort is strengthened by a complementary and coordinated partnership with ongoing university and multi-agency radar activities collocated in north Alabama—a unique opportunity to examine clouds and precipitation from a lifetime-centric perspective.
In the talk, I will present our recent results on constraining cosmology from static strong lenses using Simulation Based inference algorithm, neural ratio estimation (NRE). I will present the results from our analysis and also present diagnostics for NRE to check model performance.
The mission of this CRADA with Agilent was to couple powerful MS platforms (QQQ, IM-QTOFMS) with Agilent’s novel Ultra-High-Performance Liquid Chromatography (UHPLC) fast metabolomic workflows and perform ABF Machine Learning (ML) to generated datasets. Agilent transferred UHPLC methods to PNNL and LBNL and methods were implemented and demonstrated in both labs, achieving total acquisition times of < 10 min. Metabolites analyzed using Agilent’s shared methods included metabolites from central carbon metabolism, common across hosts, and metabolites unique to engineered strains. Standards were acquired in an UHPLC-Drift Tube Ion Mobility Mass Spectrometer (DTIMS) system for the first time within the context of ABF and methods were optimized based on Agilent’s protocols. Samples from ABF hosts Pseudomonas putida, Aspergillus pseudoterreus, Aspergillus niger and Rhodosporidium toruloides were analyzed using the UHPLC-DTIMS platform for a total of 276 runs. A data analysis workflow compatible with the Experimental Data Depot (EDD) and completely shareable was developed for the acquired UHPLC-DTIMS data. Samples were analyzed using a Data Independent Acquisition Approach (DIA), which for most of the standards provided more transitions therefore increasing detection confidence. Using the data acquired by PNNL, LBNL, and Agilent’s specifications from previous ML projects, SNL applied an ensemble ML strategy to pick the best performing model for automated LC-method selection. Finally, with the contribution of the participant labs and Agilent, SNL developed an Automated Method Selection (AMS) software tool to predict the best liquid chromatography method for analysis of any new molecules of interest. Samples with novel pathways and new metabolite targets of interest are generated at a high pace in the ABF. Overall, the project advanced rapid metabolomics by combining liquid chromatography, ion mobility spectrometry, and data-independent mass spectrometry with machine learning. This multidimensional approach uses retention time, collision cross-section, precursor mass, and fragment-ion information to distinguish chemically similar metabolites that can be difficult to resolve using conventional liquid- or gas-chromatography methods. The resulting workflow also provided automated metabolite-identification error estimates, addressing a recognized need for statistical confidence measures in metabolomics.
This dataset is an extension and subset of the Existing Hydropower Assets (EHA) Plant Database, 2025, released by Oak Ridge National Laboratory through HydroSource covering hydropower plants in Puerto Rico. The extension enhanced EHA to support modeling performed under the Department of Energy Hydropower Office's SECURE Water Act Section 9505 Fourth Assessment (9505). Included is a spatial dataset containing the location of the powerhouse and plant characteristics and a spatial dataset containing the location of the point-of-diversion and plant characteristics. Enhancements made in this dataset from EHA include updating plant’s spatial locations to explicitly represent the location of the powerhouse; identifying a plant’s point-of-diversion (source of water) spatial location; adding additional plants not included in the 2025 EHA, and revising attributes with updated information.
This dataset is an extension and subset of the Existing Hydropower Assets (EHA) Plant Database, 2025, released by Oak Ridge National Laboratory through HydroSource covering hydropower plants in Hawaii. The extension enhanced EHA to support modeling performed under the Department of Energy Hydropower Office's SECURE Water Act Section 9505 Fourth Assessment (9505). Included is a spatial dataset containing the location of the powerhouse and plant characteristics and a spatial dataset containing the location of the point-of-diversion and plant characteristics. Enhancements made in this dataset from EHA include updating plant’s spatial locations to explicitly represent the location of the powerhouse; identifying a plant’s point-of-diversion (source of water) spatial location; adding Intertie Region as and attributes, and revising the County, Pt_Own, OwType, and Dam_Own attributes with updated information.
This dataset is an extension and subset of the Existing Hydropower Assets (EHA) Plant Database, 2025, released by Oak Ridge National Laboratory through HydroSource covering hydropower plants in Alaska. The extension enhanced EHA to support modeling performed under the Department of Energy Hydropower Office's SECURE Water Act Section 9505 Fourth Assessment (9505). Included is a spatial dataset containing the location of the powerhouse and plant characteristics and a spatial dataset containing the location of the point-of-diversion and plant characteristics. Enhancements made in this dataset from EHA include updating plant’s spatial locations to explicitly represent the location of the powerhouse; identifying a plant’s point-of-diversion (source of water) spatial location; adding State of Alaska Plant ID, Intertie Region, and Alaska Energy Region as attributes, and revising the County, Pt_Own, OwType, and Dam_Own attributes with updated information.
In powder bed fusion additive manufacturing, machines are often equipped with in-situ sensors to monitor the build environment as well as machine actuators and subsystems. The data from these sensors offer rich information about the consistency of the fabrication process within a build and across builds. This information may be used for process monitoring and defect detection; however, little has been done to leverage this data from the machines for more than just coarse-grained process monitoring. In this work we demonstrate how these inherently temporal data may be mapped spatially by leveraging scan path information. We then train a XGBoost machine learning model to predict localized defects—specifically soot–using only the mapped process data of builds from a laser powder bed fusion process as input features. The XGBoost model offers a feature importance metric that will help to elucidate possible relationships between the process data and observed defects. Finally, we analyze the model performance spatially and rationalize areas of greater and lesser performance.
U-Zr metallic fuel is a promising fuel candidate for Gen Ⅳ fast spectrum reactors. Previous experimental irradiation campaigns showed that the sodium thermal bonded U-10Zr fuel design can achieve a burnup of 10% fissions per initial heavy metal atom (FIMA). Advanced metallic fuel designs are pushing the burnup limit to 20% or even 30% FIMA. To achieve the higher burnup and eliminate the pyrophoric sodium, a prototypical annular fuel has been designed, fabricated, clad with HT-9 in the Materials and Fuels Complex, and irradiated in the Advanced Test Reactors of Idaho National Laboratory (INL) to a peak burnup of 3.3% FIMA. During irradiation, the mechanical contact between fuel and cladding acts as a thermal bond. The irradiation lasted for 132 days in the reactor. Recently, the archived fresh and irradiated fuel samples were characterized using advanced characterization capabilities in the Irradiated Materials Characterization Laboratory (IMCL) of INL. This article summarizes the results of advanced characterization and computer vision-based materials informatics to reveal the irradiation effects on U-Zr metallic fuel. Future work will focus on further implementation of advanced characterization and statistical data mining to improve the fidelity of fuel performance modeling and support U-Zr metallic fuel qualification for fast spectrum reactors.
Metallic U alloys have high U density and thermal conductivity and thus have been explored since the beginning of nuclear power research. Alloys of U with modest amounts of Mo, such as U-10 wt % Mo (U-10Mo), are of particular interest because the γ-U crystal structure in this alloying addition shows prolonged stability in reactor service. Historically, radiation data on U-10Mo fuels were collected in Na fast reactors or lower temperature research reactor conditions, but little is known about irradiation behavior, particularly swelling and creep, at irradiation temperatures between 250 and 500°C. This work discusses the methodology and pre-irradiation characterization results from a U-Mo irradiation campaign performed in the High Flux Isotope Reactor at Oak Ridge National Laboratory. U-10Mo and U-17Mo samples irradiations are being completed at temperatures ranging from 250 to 500°C to three targeted fission densities between 2 × 10 20 and 1.5 × 10 21 fissions per cubic centimeter. Swelling measurement of the specimen sizes studied here required development and assessment of new methods for volume determination before and after irradiation. Laser profilometry and X-ray computation tomography (XCT) were used to provide preirradiation characterization of samples to determine the error and applicability of each to determine swelling following irradiation. These outcomes are contextualized through use of BISON simulations performed to assess the predicted expansion of U-Mo fuels subjected to the irradiation conditions of this work. Use of existing BISON fuel performance models predicted a maximum of 7% swelling under the irradiation conditions of this study. Pre-irradiation characterization revealed the as-cast U-Mo fuel samples were uniformly large-grained fully cubic U crystals with small U-C/N bearing precipitates and pores distributed throughout. Samples were found to contain a bulk porosity between .4 and 3% because of the casting process. Local porosity in areas far from large, interconnected pores was found by Slice-and-View to be under .2%. Nanometer-sized precipitates rich in C and N were identified in all samples, likely because of impurities during the fabrication process. Dendritic bands were also observed throughout the samples. These bands were characterized by variable Mo content that deviated from the overall Mo content by 2–3 wt %. No other microstructural features were correlated to these bands. Mechanical properties were found to be slightly strengthened compared to literature reports of bulk U-Mo fuels due to the nano-scale precipitates throughout the sample.
Sulfuric acid (SA) is a governing gaseous precursor for atmospheric new particle formation (NPF), a major source of global ultrafine particles, in environments studied around the world. In polluted urban atmospheres with high condensation sinks (CSs), the formation of stable SA–amine clusters, such as SA–dimethylamine (DMA) clusters, usually initializes intense NPF events. Coagulation scavenging and cluster evaporation are dominant sink processes of SA–amine clusters in urban atmospheres, yet these loss processes are not quantitatively included in the present parameterizations of SA–amine nucleation. We herein report a parameterization of SA–DMA nucleation, based on cluster dynamic simulations and quantum chemistry calculations, with certain simplifications to greatly reduce the computational costs. Compared with previous SA–DMA nucleation parameterizations, this new parameterization was able to reproduce the dependences of particle formation rates on temperature and CSs. We then incorporated it in a three-dimensional (3-D) chemical transport model to simulate the evolution of the particle number size distributions. Simulation results showed good consistency with the observations in the occurrence of NPF events and particle number size distributions in wintertime Beijing and represented a significant improvement compared to that using a parameterization without coagulation scavenging. Quantitative analysis shows that SA–DMA nucleation contributes significantly to nucleation rates and aerosol population during the 3-D simulations in Beijing (>99 % and >60 %, respectively). These results broaden the understanding of NPF in urban atmospheres and stress the necessity of including the effects of coagulation scavenging and cluster stability in simulating SA–DMA nucleation in 3-D simulations. Representing these processes is thus likely to improve model performance in particle source apportionment and quantification of aerosol effects on air quality, human health, and climate.
Mitigation of carbonaceous aerosol emissions is expected to provide climate and health co-benefits. The accurate representation of carbonaceous aerosols in climate models is critical for reducing uncertainties in their climate feedback. In this regard, emission fluxes and aerosol life cycle processes are the two primary sources of uncertainties. Here, we demonstrate that the incorporation of a dynamic ageing scheme and emission estimates that are updated for the local sources improves the representation of carbonaceous aerosols over the Indian monsoon region in a regional climate model, RegCM, compared with its default configuration. The respective mean black carbon (BC) and organic carbon (OC) surface concentrations in 2010 are estimated to be 4.25 and 10.35 µg m -3 over the Indo-Gangetic Plain (IGP) in the augmented model. The BC column burden over the polluted IGP is found to be 2.47 mg m -2 , 69.95 % higher than in the default model configuration and much closer to available observations. The anthropogenic aerosol optical depth (AOD) increases by more than 19 % over the IGP due to the model enhancement, also leading to a better agreement with observed AOD. The respective top-of-the-atmosphere, surface, and atmospheric anthropogenic aerosol short-wave radiative forcing are estimated at -0.3, -9.3, and 9.0 W m -2 over the IGP and -0.89, -5.33, and 4.44 W m -2 over Peninsular India (PI). Our results suggest that the combined effect of two modifications leads to maximum improvements in the model performance in regions where emissions play a dominant role.
Abstract. Wildfires are becoming an increasing challenge to the sustainability of boreal peatland (BP) ecosystems and can alter the stability of boreal carbon storage. However, predicting the occurrence of rare and extreme BP fires proves to be challenging, and gaining a quantitative understanding of the factors, both natural and anthropogenic, inducing BP fires remains elusive. Here, we quantified the predictability of BP fires and their primary controlling factors from 1997 to 2015 using a two-step correcting machine learning (ML) framework that combines multiple ML classifiers, regression models, and an error-correcting technique. We found that (1) the adopted oversampling algorithm effectively addressed the unbalanced data and improved the recall rate by 26.88 %–48.62 % when using multiple datasets, and the error-correcting technique tackled the overestimation of fire sizes during fire seasons; (2) nonparametric models outperformed parametric models in predicting fire occurrences, and the random forest machine learning model performed the best, with the area under the receiver operating characteristic curve ranging from 0.83 to 0.93 across multiple fire datasets; and (3) four sets of factor-control simulations consistently indicated the dominant role of temperature, air dryness, and climate extreme (i.e., frost) for boreal peatland fires, overriding the effects of precipitation, wind speed, and human activities. Our findings demonstrate the efficiency and accuracy of ML techniques in predicting rare and extreme fire events and disentangle the primary factors determining BP fires, which are critical for predicting future fire risks under climate change.
Abstract. Temporally continuous snow depth estimates are vital for understanding changing snow patterns and impacts on permafrost in the Arctic. We trained a random forest machine learning model to predict snow depth from variability in snow–ground interface temperature. The model performed well on Alaska's Seward Peninsula where it was trained and at Arctic evaluation sites (RMSE ≤ 0.15 m). It performed poorly at temperate sites with deeper snowpacks, partially due to training data limitations. Small temperature sensors are cheap and easy to deploy, so this technique enables spatially distributed and temporally continuous snowpack monitoring at high latitudes to an extent previously infeasible.
Accurately reconstructing past changes to the shape and volume of the Antarctic ice sheet relies on the use of physically based and thus internally consistent ice sheet modeling, benchmarked against spatially limited geologic data. The challenge in model benchmarking against geologic data is diagnosing whether model-data misfits are the result of an inadequate model, inherently noisy or biased geologic data, and/or incorrect association between modeled quantities and geologic observations. In this work we address this challenge by (i) the development and use of a new model-data evaluation framework applied to an uncurated data set of geologic constraints, and (ii) nested high-spatial-resolution modeling designed to test the hypothesis that model resolution is an important limitation in matching geologic data. While previous approaches to model benchmarking employed highly curated datasets, our approach applies an automated screening and quality control algorithm to an uncurated public dataset of geochronological observations (specifically, cosmogenic-nuclide exposure-age measurements from glacial deposits in ice-free areas). This optimizes data utilization by including more geological constraints, reduces potential interpretive bias, and allows unsupervised assimilation of new data as they are collected. We also incorporate a nested model framework in which high-resolution domains are downscaled from a continent-wide ice sheet model. We highlight the application of this framework by applying these methods to a small ensemble of deglacial ice-sheet model simulations, and demonstrate that the nested approach improves the ability of model simulations to match exposure age data collected from areas of complex topography and ice flow. We develop a range of diagnostic model-data comparison metrics to provide more insight into model performance than possible from a single-valued misfit statistic, showing that different metrics capture different aspects of ice sheet deflation.