Heat in them there hills - developing a modern geophysical workflow for geothermal resource exploration in the U.S. Basin & Range
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Schwering, Paul Conrad↗
SEARCH · Engineering Papers
Results for “workflow development”
Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.
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Develop High-Throughput Workflows for Whole-Genome Sequencing and Insertion Site Screening (CRADA Final Report)
The engineering of microbes for biomanufacturing (e.g. of fuels, chemicals, materials) applications has advanced to a stage where researchers screen genetic libraries with millions of variations each for those with enhanced productivity. This screening, however, can be slow and expensive, as screening individual variants in a high-throughput yet cost-effective manner is challenging. In this project, we aimed to reduce by 3-fold costs associated with the sequencing aspects of the screening process (to determine which genetic variant is responsible for an observed change in productivity), while being able to process over 1,000 samples per batch.
60 APPLIED LIFE SCIENCES↗
Develop High-Throughput Workflows for Whole-Genome Sequencing and Insertion Site Screening
The engineering of microbes for biomanufacturing (e.g. of fuels, chemicals, materials) applications has advanced to a stage where researchers screen genetic libraries with millions of variations each for those with enhanced productivity. This screening, however, can be slow and expensive, as screening individual variants in a high-throughput yet cost-effective manner is challenging. In this project, we aimed to reduce by 3-fold costs associated with the sequencing aspects of the screening process (to determine which genetic variant is responsible for an observed change in productivity), while being able to process over 1,000 samples per batch.
60 APPLIED LIFE SCIENCES↗
FORCE Update 2024
The Framework for Optimization of Resources and Economics (FORCE) tool suite is the U.S. Department of Energy’s Nuclear Integrated Energy Systems (IES) Program flagship tool suite for technoeconomic IES analysis of IES. This tool suite is useful for analysis designed to evaluate and improve the technoeconomics of energy production systems, particularly for systems including nuclear technology. In this report, we document the development activity for the FORCE tool suite to extend its capabilities as performed during fiscal year 2024. In addition to reliability and accessibility, capability is one of the three standards guiding the development of the FORCE tool suite and the software codes that are its constituent parts. Extending the capabilities of the FORCE tool suite allows analysis both within the IES program as well as industry, university, and laboratory partners to perform analysis with more accuracy, insight, and impactful narrative. Four areas of capability development were the focus of activity this year: economic parameter uncertainty quantification, multiresolution analysis, components-to-optimization workflow automation, and statespace construction workflows for real-time optimal control. In economic parameter uncertainty quantification, the ability of HERON to capture risk due to scenarios (weather and energy demand uncertainty) was expanded to also include uncertainties in financial parameters such as capital cost or operation and maintenance costs. By including these sources of uncertainty, which are sometimes very large compared with scenario uncertainty, HERON is better able to capture the risk posed by investment in various IES technology. Because of this, analysts can also consider the reduction in risks that can be realized by choice of some technologies. In multiresolution analysis, development activity extended on work completed previously. In fiscal year 2023, methods for decomposing time series signals, such as demand, solar and wind availability, and price profiles, were analyzed and down-selected to those most effective at splitting signals into different resolutions. These resolutions allow considering the influence of different energy demand and supply behaviors across different time scales. For example, energy demand might be divided into seasonal, weekly, and hourly profiles. In fiscal year 2024, this preliminary work was extended and implemented within the Risk Analysis Virtual Environment (RAVEN) risk and uncertainty analysis platform, which is used throughout the FORCE framework. This development of the “multi-resolution time series analysis” (MR-TSA) module in RAVEN allows training synthetic history generators on complex time series. These synthetic history generators can then be used in HERON for generating scenarios that represent possible market and weather scenarios that can be analyzed on different time scales. We envision completing this work in the future, implementing multiresolution dispatch optimization strategies that can make the most beneficial use of these stratified time histories. In components-to-optimization workflow development, workflows for translating user inputs of components into algorithms for algebraic optimization were selected and implemented. Similar algorithms within the Holistic Energy Resource Optimization Network (HERON) were separated from the main code base of HERON and gathered with the components-to-optimization workflows in the new Dispatch Optimization Variable Engine (DOVE) software library. This modularization allows FORCE users to analyze dispatch optimization and energy system duty cycles independently of HERON, which previously was a burdensome task. Additionally, these dispatch optimization algorithms, set up in an independent library, can now be used across all software applications within FORCE, especially including the real-time optimal control software Optimization of Real-time Capacity Allocation (ORCA). Allowing FORCE software to share dispatch optimization algorithms within a single library allows for improved software maintenance and reliability. In statespace characterization workflow development, alternative workflows for optimizing dispatch with additional technical accuracy was the focus, particularly to improve the real-time optimization decision making in ORCA. Using algorithms and workflows initially developed for the Feasible Actuator Range Modifier (FARM), workflows for determining the statespace representation of IES were identified and demonstrated. The resulting dispatch optimization required a more robust optimization algorithm than that originally used in HERON (and moved to DOVE), which required adding an alternate workflow to DOVE that can more accurately match the behavior of physical systems using a partial differential equation representation. In conclusion, capability developments in the FORCE tool suite in fiscal year 2024 have improved the ability of the FORCE tool suite to perform
29 ENERGY PLANNING, POLICY, AND ECONOMY↗
CI/CD Efforts for Validation, Verification and Benchmarking OpenMP Implementations
Software developers must adapt to keep up with the changing capabilities of platforms so that they can utilize the power of High-Performance Computers (HPC), including exascale systems. OpenMP, a directive-based parallel programming model, allows developers to include directives to existing C, C++, or Fortran code to allow node level parallelism without compromising performance. This paper describes our CI/CD efforts to provide easy evaluation of the support of OpenMP across different compilers using existing testsuites and benchmark suites on HPC platforms. Our main contributions include (1) the set of a Continuous Integration (CI) and Continuous Development (CD) workflow that captures bugs and provides faster feedback to compiler developers, (2) an evaluation of OpenMP (offloading) implementations supported by AMD, HPE, GNU, LLVM, and Intel, and (3) evaluation of the quality of compilers across different heterogeneous HPC platforms. With the comprehensive testing through the CI/CD workflow, we aim to provide a comprehensive understanding of the current state of OpenMP (offloading) support in different compilers and heterogeneous platforms consisting of CPUs and GPUs from NVIDIA, AMD, and Intel.
Jarmusch, Aaron↗
A terminology for scientific workflow systems
The term “scientific workflow” has evolved over the last two decades to encompass a broad range of compositions of interdependent compute tasks and data movements. It has also become an umbrella term for processing in modern scientific applications. Today, many scientific applications can be considered as workflows made of multiple dependent steps, and hundreds of workflow systems have been developed to manage and run these scientific workflows. However, no turnkey solution has emerged from the field to address the diversity of scientific processes and the infrastructure on which they are supposed to be implemented. Instead, new research problems requiring the execution of scientific workflows with some novel feature often lead to the development of an entirely new workflow system. A direct consequence of this situation is that many existing workflow management systems (WMSs) share some salient features, offer similar functionalities, and can manage the same categories of workflows but at the same time also have some distinct capabilities that can be important for specific applications. This situation makes researchers who develop workflows face the complex question of selecting a WMS. This selection can be driven by technical considerations, to find the system that is the most appropriate for their application and for the computing and storage resources available to them, or other factors such as reputation, adoption, strong community support, or long-term sustainability. To address this problem, a group of WMS developers and practitioners joined their efforts to produce a community-based terminology of WMSs. This paper summarizes their findings and introduces this new terminology to characterize WMSs. Furthermore, this terminology is composed of fives axes: workflow structure and characteristics, composition, orchestration, data management, and metadata capture. Each axis comprises several concepts that capture the prominent features of WMSs. Based on this terminology, this paper also presents a classification of 23 existing WMSs according to the proposed axes and terms.
Community-based terminology↗
High-throughput dataset of impurity adsorption on common catalysts in biomass upgrading applications
Abstract An extensive dataset consisting of adsorption energies of pernicious impurities present in biomass upgrading processes on common catalysts and support materials has been generated. This work aims to inform catalyst and process development for the conversion of biomass-derived feedstocks to fuels and chemicals. A high-throughput workflow was developed to execute density functional theory calculations for a diverse set of atomic (Al, B, Ca, Cl, Fe, K, Mg, Mn, N, Na, P, S, Si, Zn) and molecular (COS, H 2 S, HCl, HCN, K 2 O, KCl, NH 3 ) species on 35 unique surfaces for transition-metal (Ag, Au, Co, Cu, Fe, Ir, Ni, Pd, Pt, Re, Rh, Ru) and metal-oxide (Al 2 O 3 , MgO, anatase-TiO 2 , rutile-TiO 2 , ZnO, ZrO 2 ) catalysts and supports. Approximately 3,000 unique adsorption geometries and corresponding adsorption energies were obtained.
09 BIOMASS FUELS↗
Extreme-scale workflows: A perspective from the JLESC international community
The Joint Laboratory for Extreme-Scale Computing (JLESC) focuses on software challenges in high-performance computing systems to meet the needs of today’s science campaigns, which often require large resources, consist of multiple tasks, and generate vast amounts of data. In this context, extreme-scale workflows have been the key factor in enabling scientific discoveries by helping scientists automate the dependencies and data exchanges between workflow tasks, instead of managing those manually. Here, in this paper, we present representative extreme-scale workflows and feature workflow systems developed by JLESC participating institutions. We present lessons learned while developing these tools, alongside with the open challenges and future research directions in the field of extreme-scale workflows.
97 MATHEMATICS AND COMPUTING↗
Final Technical Report - Center for Simulation of Fusion Relevant RF Actuators
We have developed a suite of 3D electromagnetic field solvers, both FEM and FDTD based, that account for the RF antenna and vacuum vessel geometries with unprecedented accuracy. Workflows were developed that make it possible to translate CAD models for the antenna and vacuum vessel to physics meshes for RF wave simulation. Nonlinear RF sheath formation has been incorporated self-consistently as a boundary condition in these solvers. We have also carried out extensive studies of the impact of RF sheaths on the ion energy angle distribution at plasma-material interfaces, using high fidelity particle-in-cell codes. Comprehensive simulation models were developed to assess the impact of blob-like edge turbulence on RF wave propagation and the impact of the RF ponderomotive force on the plasma scrape-off layer (SOL). A fluid transport solver for the far-SOL was also developed which accounts for the high parallel to perpendicular heat anisotropy on an unstructured mesh, thus making it possible to precisely represent an antenna structure in the presence of edge transport. Finally we have developed a hierarchy of core wave propagation and absorption models that self-consistently combine continuum Fokker Planck and Monte Carlo treatments of fast ion evolution with ICRF full-wave field solvers and continuum Fokker Planck treatments of fast electron evolution with both full-wave and ray tracing models.
70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
Soil porous microstructure control over soil organic matter mobility: A multimethod workflow for understanding chemistry-dependent organic matter binding in soil
Soil organic matter (SOM) has attracted a great deal of interest; particularly for its potential to mitigate human derived CO 2 emissions. Studies have demonstrated that SOM plays a critical role in carbon storage and CO 2 sequestration. However, the sorption properties of SOM, which influence its transport in pore water and stabilization within the soil, remain poorly understood. This study develops a workflow to: (1) examine compound-specific advective and diffusive transport and desorption behaviors, (2) quantify desorption rates through stop-flow and continuous-flow column experiments, and (3) evaluate the impact of soil microporosity on SOM mobility using high-resolution imaging and extractions. Intact core column experiments were conducted on Uncultivated (Natural) and Cultivated soil samples, both were arid soils, collected in Washington State. X-ray computed tomography was employed to measure porosity and pore connectivity, while Fourier-transform ion cyclotron resonance mass spectrometry was used to analyze SOM composition. The findings revealed that cultivation increased total carbon and nitrogen levels due to irrigation and fertilization, enhancing carbon capture potential in arid soils. In contrast, the Natural soil, characterized by higher porosity and connectivity, contained more oxidized carbon. Pore network analysis indicated that soil compaction in the Cultivated soil may lead to longer diffusion pathways, significantly influencing SOM transport and stability.
Hydraulic Properties↗
Preliminary analysis of TREAT free-field experiments using openmc
This work analyses activation calculations for dosimetry materials during a steady-state irradiation in the Transient Reactor Test (TREAT) reactor core. Hence, we developed a workflow based on the Monte Carlo code OpenMC alongside a custom depletion solver. The irradiation-induced activity as a function of time is computed, and several sensitivity studies are performed to evaluate uncertainty. This study has shown activity computations are sensitive to flux amplitude, irradiation time, atoms quantity and microscopic cross sections. Stochastic uncertainties have been propagated to evaluate the activity uncertainty for each dosimetry material. Most uncertainties are below our target of 3%, which demonstrates OpenMC as a powerful predictive and analysis tool. The precise results obtained through this newly developed computation scheme will be used in future experiments to characterize quantities of interest when operating the TREAT reactor in new configurations.
22 GENERAL STUDIES OF NUCLEAR REACTORS↗
Preliminary analysis of TREAT free-field experiments using OpenMC
This work analyses activation calculations for dosimetry materials during a steady-state irradiation in the Transient Reactor Test (TREAT) reactor core. Hence, we developed a workflow based on the Monte Carlo code OpenMC alongside a custom depletion solver. The irradiation-induced activity as a function of time is computed, and several sensitivity studies are performed to evaluate uncertainty. This study has shown activity computations are sensitive to flux amplitude, irradiation time, atoms quantity and microscopic cross sections. Stochastic uncertainties have been propagated to evaluate the activity uncertainty for each dosimetry material. Most uncertainties are below our target of 3%, which demonstrates OpenMC as a powerful predictive and analysis tool. The precise results obtained through this newly developed computation scheme will be used in future experiments to characterize quantities of interest when operating the TREAT reactor in new configurations.
61 - RADIATION PROTECTION AND DOSIMETRY↗
Modification and analysis of context-specific genome-scale metabolic models: methane-utilizing microbial chassis as a case study
ABSTRACT Context-specific genome-scale model (CS-GSM) reconstruction is becoming an efficient strategy for integrating and cross-comparing experimental multi-scale data to explore the relationship between cellular genotypes, facilitating fundamental or applied research discoveries. However, the application of CS modeling for non-conventional microbes is still challenging. Here, we present a graphical user interface that integrates COBRApy, EscherPy, and RIPTiDe, Python-based tools within the BioUML platform, and streamlines the reconstruction and interrogation of the CS genome-scale metabolic frameworks via Jupyter Notebook. The approach was tested using -omics data collected for Methylotuvimicrobium alcaliphilum 20Z R , a prominent microbial chassis for methane capturing and valorization. We optimized the previously reconstructed whole genome-scale metabolic network by adjusting the flux distribution using gene expression data. The outputs of the automatically reconstructed CS metabolic network were comparable to manually optimized i IA409 models for Ca-growth conditions. However, the CS model questions the reversibility of the phosphoketolase pathway and suggests higher flux via primary oxidation pathways. The model also highlighted unresolved carbon partitioning between assimilatory and catabolic pathways at the formaldehyde-formate node. Only a very few genes and only one enzyme with a predicted function in C1 metabolism, a homolog of the formaldehyde oxidation enzyme ( fae1-2 ), showed a significant change in expression in La-growth conditions. The CS-GSM predictions agreed with the experimental measurements under the assumption that the Fae1-2 is a part of the tetrahydrofolate-linked pathway. The cellular roles of the tungsten (W)-dependent formate dehydrogenase ( fdhAB ) and fae homologs ( fae1-2 and fae3 ) were investigated via mutagenesis. The phenotype of the f dhAB mutant followed the model prediction. Furthermore, a more significant reduction of the biomass yield was observed during growth in La-supplemented media, confirming a higher flux through formate. M. alcaliphilum 20Z R mutants lacking fae1-2 did not display any significant defects in methane or methanol-dependent growth. However, contrary to fae1, the fae1-2 homolog failed to restore the formaldehyde-activating enzyme function in complementation tests. Overall, the presented data suggest that the developed computational workflow supports the reconstruction and validation of CS-GSM networks of non-model microbes. IMPORTANCE The interrogation of various types of data is a routine strategy to explore the relationship between genotype and phenotype. An efficient approach for integrating and cross-comparing experimental multi-scale data in the context of whole-genome-based metabolic network reconstruction becomes a powerful tool that facilitates fundamental and applied research discoveries. The present study describes the reconstruction of a context-specific (CS) model for the methane-utilizing bacterium, Methylotuvimicrobium alcaliphilum 20Z R . M. alcaliphilum 20Z R is becoming an attractive microbial platform for the production of biofuels, chemicals, pharmaceuticals, and bio-sorbents for capturing atmospheric methane. We demonstrate that this pipeline can help reconstruct metabolic models that are similar to manually curated networks. Furthermore, the model is able to highlight previously overlooked pathways, thus advancing fundamental knowledge of non-model microbial systems or promoting their development toward biotechnological or environmental implementations.
Kulyashov, M. A.↗
Surrogate model evaluation and building energy benchmarking for commercial buildings
Building energy consumption benchmarking involves challenges associated with various energy patterns for different building types; heating, ventilating, and air-conditioning (HVAC) system types; and climates. Given significant variation in energy use patterns, accurate prediction of long-term energy use using surrogate models remains challenging. Multiple linear regression (MLR) is commonly used for building energy benchmarking because of its simple structure; however, it lacks accuracy compared to other black-box models. Although many studies have compared surrogate models and offer guidance on model selection based on metrics, they do not provide detailed analysis on improving the surrogate model accuracy. In this paper, we implement a surrogate model using polynomial ridge regression (i.e., MLR with interaction terms combined with ridge regularization) for small office and retail strip mall buildings across six HVAC system types and all climate zones, for electricity and natural gas in baseline and proposed scenarios. A simulation workflow is developed using OpenStudio TM /EnergyPlus TM to generate simulation data using measures over a wide range of efficiency inputs. Enhancements based on statistical insights are used for improving the model accuracy using filters, input transformations, and change points. Surrogate models achieved average coefficient of variation of the root mean squared error (CVRMSE) values of 2.17, 1.06, 2.05, and 3.26 for proposed electricity, proposed natural gas, baseline electricity, and baseline natural gas, respectively, with enhancements reducing CVRMSE by an average of 14.9% across all combinations. We provide model interpretation via Shapley additive explanations to determine which input variables most influence energy consumption and provide supportive arguments for enhancements.
32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
Accelerated Exploration of Empty Material Compositional Space: Mg–Fe–B Ternary Metal Borides
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Zhang, Zhen↗
Rapid Generation of Tandem Mass Spectrometry Reference Libraries Using Immediate Drop-on-Demand Liquid Handling Coupled to an Open Port Sampling Interface
In metabolomics, tandem MS (MS2) fragmentation libraries are important for the identification of unknown features, but generating these libraries takes many valuable hours of instrument and operator time. Here, an immediate droplet-on-demand/open port sampling interface was used to rapidly acquire tandem MS of standards arrayed in a 96-well plate format. A workflow was developed for automated, high-throughput control of MS2 library generation. Pure standard mass spectral libraries were collected on Orbitrap and Q-TOF mass spectrometers for 192 compounds using 6 different collision energies with a throughput of 4 and 7.8 s/spectrum, respectively. Libraries were acquired using different solvent additives, precursor adducts, and ion polarities.
Cahill, John [ORNL] (ORCID:0000000298664010)↗
Exploring electron-beam induced modifications of materials with machine-learning assisted high temporal resolution electron microscopy
Directed atomic fabrication using an aberration-corrected scanning transmission electron microscope (STEM) opens new pathways for atomic engineering of functional materials. In this approach, the electron beam is used to actively alter the atomic structure through electron beam induced irradiation processes. One of the impediments that has limited widespread use thus far has been the ability to understand the fundamental mechanisms of atomic transformation pathways at high spatiotemporal resolution. Here, we develop a workflow for obtaining and analyzing high-speed spiral scan STEM data, up to 100 fps, to track the atomic fabrication process during nanopore milling in monolayer MoS 2 . An automated feedback-controlled electron beam positioning system combined with deep convolution neural network (DCNN) was used to decipher fast but low signal-to-noise datasets and classify time-resolved atom positions and nature of their evolving atomic defect configurations. Through this automated decoding, the initial atomic disordering and reordering processes leading to nanopore formation was able to be studied across various timescales. Using these experimental workflows a greater degree of speed and information can be extracted from small datasets without compromising spatial resolution. This approach can be adapted to other 2D materials systems to gain further insights into the defect formation necessary to inform future automated fabrication techniques utilizing the STEM electron beam.
36 MATERIALS SCIENCE↗
Machine learned potential for high-throughput phonon calculations of metal—organic frameworks
Metal–organic frameworks (MOFs) are highly porous and versatile materials studied extensively for applications such as carbon capture and water harvesting. However, computing phonon-mediated properties in MOFs, like thermal expansion and mechanical stability, remains challenging due to the large number of atoms per unit cell, making traditional Density Functional Theory (DFT) methods impractical for high-throughput screening. Recent advances in machine learning potentials have led to foundation atomistic models, such as MACE-MP-0, that accurately predict equilibrium structures but struggle with phonon properties of MOFs. In this work, we developed a workflow for computing phonons in MOFs within the quasi-harmonic approximation with a fine-tuned MACE model, MACE-MP-MOF0. The model was trained on a curated dataset of 127 representative and diverse MOFs. The fine-tuned MACE-MP-MOF0 improves the accuracy of phonon density of states and corrects the imaginary phonon modes of MACE-MP-0, enabling high-throughput phonon calculations with state-of-the-art precision. The model successfully predicts thermal expansion and bulk moduli in agreement with DFT and experimental data for several well-known MOFs. These results highlight the potential of MACE-MP-MOF0 in guiding MOF design for applications in energy storage and thermoelectrics.
Elena, Alin Marin↗