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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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At least 181 records · Page 10
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Absorber Column CFD model validation against PNNL’s device-scale absorber column on the LCFS unit

Absorber column has been widely used for CO 2 capture in the coal-fired power plants. High-fidelity CFD models play an important role in absorber column design and solvents optimization, which help enhance the CO 2 capture efficiency and reduce the operation cost. This report provides a comprehensive description of the development of CFD absorber models at two different levels, the design and implementation of PNNL’s device-scale absorber column experiment, and the methodology to combine the CFD results, experiment data, and Aspen model for a better understanding of the interface area in packed column. A composite model is firstly proposed in the Discrete Element Method (DEM) packing process, which can model the complex geometry of various packing elements. This generates a realistic packing pattern and accurate packing porosity ε and specific area a p compared to the actual values for absorber column used in LCFS. Two level of CFD absorber models were developed, namely the full-size column model (FCM) to simulate the entire packed column with a focus on the wall/entrance effects, and the representative column model (RCM) to simulate a section of column with a focus on the sensitivity study of interface area. A Design of Experiment (DoE) plan was developed to guide the collection of 100 run CFD data and 12 experiment runs. The 100 CFD runs were carried out in the RCM with Pro-Pak packing and cover a wide operation range and solvent properties. Impact of influential factors on the interface area in packed column were investigated in details. The CFD interface area was then combined with the experimental data and Aspen model to infer some information of the effective contact angle in the column. The accuracy and uncertainties in the interface area and contact angle can be quantified.

01 COAL, LIGNITE, AND PEAT↗
DOE OSTI2024

Generalization error guaranteed auto-encoder-based nonlinear model reduction for operator learning

Many physical processes in science and engineering are naturally represented by operators between infinite-dimensional function spaces. The problem of operator learning, in this context, seeks to extract these physical processes from empirical data, which is challenging due to the infinite or high dimensionality of data. An integral component in addressing this challenge is model reduction, which reduces both the data dimensionality and problem size. In this paper, we utilize low-dimensional nonlinear structures in model reduction by investigating Auto-Encoder-based Neural Network (AENet). AENet first learns the latent variables of the input data and then learns the transformation from these latent variables to corresponding output data. Our numerical experiments validate the ability of AENet to accurately learn the solution operator of nonlinear partial differential equations. Furthermore, we establish a mathematical and statistical estimation theory that analyzes the generalization error of AENet. Finally, our theoretical framework shows that the sample complexity of training AENet is intricately tied to the intrinsic dimension of the modeled process, while also demonstrating the robustness of AENet to noise.

Auto-encoder↗
DOE OSTI2026

Rolling Root Mean Square Based Multimodal Anomaly Detection for Real Time Monitoring of Smart Grid

Reliable real-time monitoring is valuable for maintaining the operational integrity of modern electrical smart grids. Deployment of heterogeneous sensing technologies in substations has enabled high-resolution, multichannel waveform monitoring, but also introduces challenges for anomaly detection due to noise, baseline drift, and modality-dependent signal characteristics. In this work, we present a computationally efficient unsupervised method for multimodal event detection based on Rolling Root Mean Square based Event Detection (RRMSED). The method is developed using in-house, field deployed sensors collecting data at a utility substation. The sensing system comprises voltage and current sensors, triaxial accelerometers, and magnetometers, collectively capturing electrical, vibrational, and magnetic waveform measurements at high temporal resolution. RRMSED operates by extracting rolling RMS energy features and their first-order temporal differences from consecutive waveform segments for each channel and then applying channel-specific statistical thresholds learned from historical data. A persistence-based exceedance logic is employed to robustly identify transient events while suppressing impulsive noise, and to provide precise temporal localization with high resolution. The framework is designed for continuous server-side operation and can be deployed in real time without requiring complex models. Experiments on simulated waveform data with known ground truth demonstrate low false positive (FP) and false negative (FN) rates. Application to real substation data shows RRMSED to identify events that are not captured by conventional monitoring indicators including fast transient detection algorithm currently deployed in the system. These results indicate that rolling RMS based features provide an effective and practical basis for real-time multimodal event detection in smart-grid substations.

Mukherjee, Subrata [ORNL] (ORCID:0000000309930338)↗
DOE OSTI2024

AEcroscopy: A Software–Hardware Framework Empowering Microscopy Toward Automated and Autonomous Experimentation

Microscopy has been pivotal in improving the understanding of structure-function relationships at the nanoscale and is by now ubiquitous in most characterization labs. However, traditional microscopy operations are still limited largely by a human-centric click-and-go paradigm utilizing vendor-provided software, which limits the scope, utility, efficiency, effectiveness, and at times reproducibility of microscopy experiments. Here, in this work, a coupled software–hardware platform is developed that consists of a software package termed AEcroscopy (short for Automated Experiments in Microscopy), along with a field-programmable-gate-array device with LabView-built customized acquisition scripts, which overcome these limitations and provide the necessary abstractions toward full automation of microscopy platforms. The platform works across multiple vendor devices on scanning probe microscopes and electron microscopes. It enables customized scan trajectories, processing functions that can be triggered locally or remotely on processing servers, user-defined excitation waveforms, standardization of data models, and completely seamless operation through simple Python commands to enable a plethora of microscopy experiments to be performed in a reproducible, automated manner. This platform can be readily coupled with existing machine-learning libraries and simulations, to provide automated decision-making and active theory-experiment optimization to turn microscopes from characterization tools to instruments capable of autonomous model refinement and physics discovery.

47 OTHER INSTRUMENTATION↗
DOE OSTI2021

Big PanDa Workflow Management on Titan for High Energy and Nuclear Physics and for Future Extreme Scale Scientific Application

Over a three year period, from 2016-2019, this project demonstrated the scientific benefits of integrating the Titan supercomputer at Oak Ridge Leadership Computing Facility into traditional high throughput grid based distributed computing systems managed by PanDA, the workflow management system used for the execution of all distributed computing applications by the ATLAS experiment at the Large Hadron Collider. PanDA manages millions of batch jobs daily at hundreds of clusters worldwide on request by thousands of physicist users, and processes more than an exabyte of data annually using grid middleware. High levels of operational use of Titan was sustained by PanDA in order to meet the physics goals of ATLAS. The success of this project led to the use of other supercomputers worldwide by ATLAS, and to the adoption of PanDA by other experiments and other scientists. Multiple innovative operational and computer science research goals were achieved supporting the use of supercomputers for scientific domains with large scale distributed data and distributed processing needs.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Metabolic multireactor: Practical considerations for using simple oxygen sensing optodes for high-throughput batch reactor metabolism experiments

We present a system for carrying out small batch reactor oxygen consumption experiments on water and sediment samples for environmental questions. In general, it provides several advantages that can help researchers achieve impactful experiments at relatively low costs and high data quality. In particular, it allows for multiple reactors to be operated and their oxygen concentrations to be measured simultaneously, providing high throughput and high time-resolution data, which can be advantageous. Most existing literature on similar small batch-reactor metabolic studies is limited to either only a few samples, or only a few time points per sample, which can restrict the ability for researchers to learn from their experiments. The oxygen sensing system is based very directly on the work of Larsen, et al. [2011], and similar oxygen sensing technology is widely used in the literature. As such we do not delve deeply into the specifics of the fluorescent dye sensing mechanism. Instead, we focus on practical considerations. We describe the construction and operation of the calibration and experimental systems, and answer many of the questions likely to come up when other researchers choose to build and operate a similar system themselves (questions we ourselves had when we first built the system). In this way, we hope to provide an approachable and easy to use research article that can help other researchers construct and operate a similar system that can be tailored to ask their own research questions, with a minimum of confusion and missteps along the way.

3D printing↗
DOE OSTI2023

SNM Radiation Signature Classification Using Different Semi-Supervised Machine Learning Models

The timely detection of special nuclear material (SNM) transfers between nuclear facilities is an important monitoring objective in nuclear nonproliferation. Persistent monitoring enabled by successful detection and characterization of radiological material movements could greatly enhance the nuclear nonproliferation mission in a range of applications. Supervised machine learning can be used to signal detections when material is present if a model is trained on sufficient volumes of labeled measurements. However, the nuclear monitoring data needed to train robust machine learning models can be costly to label since radiation spectra may require strict scrutiny for characterization. Therefore, this work investigates the application of semi-supervised learning to utilize both labeled and unlabeled data. As a demonstration experiment, radiation measurements from sodium iodide (NaI) detectors are provided by the Multi-Informatics for Nuclear Operating Scenarios (MINOS) venture at Oak Ridge National Laboratory (ORNL) as sample data. Anomalous measurements are identified using a method of statistical hypothesis testing. After background estimation, an energy-dependent spectroscopic analysis is used to characterize an anomaly based on its radiation signatures. In the absence of ground-truth information, a labeling heuristic provides data necessary for training and testing machine learning models. Supervised logistic regression serves as a baseline to compare three semi-supervised machine learning models: co-training, label propagation, and a convolutional neural network (CNN). In each case, the semi-supervised models outperform logistic regression, suggesting that unlabeled data can be valuable when training and demonstrating value in semi-supervised nonproliferation implementations.

98 NUCLEAR DISARMAMENT, SAFEGUARDS, AND PHYSICAL P↗
DOE OSTI2024

Improving Self-Driving Labs: Quantifying System-Level Experiment Repeatability and Broadening Instrument-Level Compatibility

Modular Autonomous Research System (MARS) is a self-driving laboratory (SDL) which performs wet-lab science with peptide-lanthanide combinations in an automated and, ultimately, an autonomous manner to aid in soil analysis for domestic lithium mining. Autonomous experimentation involves automated experimentation, experiment planning, and active learning. MARS consists of a 6-axis robotic arm (UR5e) on a linear rail, pipette robots (Opentrons 2), and microplate readers. These components transport, operate on, and collect data with chemical solutions in standard labware. For effective autonomy, MARS must perform system-level labware operations repeatably, plan experiments autonomously, and be portable between research-domains. Repeatability is evaluated by labware placement precision, such that future operations can properly locate labware, as well as the elapsed time, so that low variance mean estimates of experiment duration can inform high-level researcher decision making. Autonomous experiment planning is the next step to decouple experimentation from human management; however, there is a conflict between the ideal system-level experiment goals and the constraints imposed by instruments’ limitations. Sub-domain portability is a long-term goal to extend MARS’ research beyond the chemistry of peptide-lanthanide binding to other sub-domains without having to invest significant overhead to system retrofitting. To address these goals, we manually trained the robotic arm labware placement and modelled statistical failurerate and uncertainty Additionally, we benchmarked the duration and variance of each experiment sub-operation as a heuristic for research decision making. Next, we use a parameterized geometric program (PGP) approach to design experiments that optimize system-level objectives and satisfy instrument-level constraints. Lastly, we proposed a Python framework to maximize MARS’ extensibility to other scientific sub-domains through a JSON-based experiment specification.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Validation of Numerical Tools for Calculating Reactivity Feedback in Sodium Fast Reactors Using SEFOR Experimental Data

The Southwest Experimental Fast Oxide Reactor (SEFOR) was an experimental sodium-cooled fast breeder reactor operated from 1969 to 1972 with experiments designed to measure Doppler reactivity feedback in a wide temperature range from around 350 °F to temperatures approaching the melting point of mixed oxide fuel of around 5000 °F, providing valuable data for code validations. Co-supported by the Department of Energy (DOE) Fast Reactor Program (FRP) and the DOE Nuclear Energy Advanced Modeling and Simulation (NEAMS) program, the SEFOR benchmark project focused on using the experimental data to validate numerical tools that are used in industry and academia to design and license sodium-cooled fast reactors (SFRs). By the end of FY-25, substantial progress was achieved in the SEFOR benchmark study. A variety of numerical tools commonly used for modeling SFRs were applied to develop models for SEFOR core configurations I-D, I-E, I-I, and I-J. These included Monte Carlo codes such as MCNP, Serpent, and Shift; deterministic codes such as the legacy Argonne Reactor Computation (ARC) suite and the high-fidelity NEAMS code Griffin; and the system analysis code SAS4A/SASSYS-1 (SAS). Using these models, both SEFOR zero-power experiments and power-ascending tests were successfully simulated. Comparisons were performed against experimental measurements of core criticalities, reflector worth, kinetics parameters (Λ/βeff), isothermal reactivity feedback (from 350 °F to 760 °F at zero power), and power-ascending reactivity feedback (as power increased from 0.4 MW to 17 MW). In general, these comparisons demonstrated very good agreement between numerical results and experimental data. In Fiscal Year 26 (FY-26), the SEFOR benchmark project will continue to address the modeling issues identified in FY-25. Effort will focus on the simulation of reactivity insertion transients in SEFOR core II using the ARC/SAS model. Future work will also focus on incorporating BISON into the SEFOR core modeling process to enable the first Multiphysics simulations of the isothermal tests based on the MOOSE framework.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2026

A large-scale benchmarking of deterministic and stochastic derivative-free optimization algorithms

This presentation summarizes our work in the PrOMMiS project on benchmarking of data-driven optimization algorithms and their applications in self-driving laboratories. This work supports the broader project goal of accelerating the identification of promising separation methods and operating conditions for critical minerals separation processes. We present a systematic benchmarking study of 42 data-driven optimization algorithms on a broad collection of 502 test problems. The results identify BAM, GLCCLUSTER, and MULTIMIN as the most effective optimization solvers, with BAM showing the highest overall performance and solving more than 80% of the benchmark problems. The study also shows that no single solver consistently outperforms the others across all problem types, indicating that our future laboratory applications may benefit from using a small set of strong solvers rather than relying on a single method. The presentation also illustrates an in-silico chemical reactor case study showing that data-driven optimization methods can guide autonomous experimentation in a self-driving laboratory and identify optimal operating conditions within a small number of experiments. Overall, the results provide a basis for selecting efficient optimization methods and demonstrate the practical use of data-driven optimization in self-driving laboratory workflows.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Algorithm-guided experimentation for autonomous AI systems in self-driving laboratories

This presentation summarizes our work in the PrOMMiS project on benchmarking of data-driven optimization algorithms and their applications in self-driving laboratories. This work supports the broader project goal of accelerating the identification of promising separation methods and operating conditions for critical minerals separation processes. We present a systematic benchmarking study of 42 data-driven optimization algorithms on a broad collection of 502 test problems. The results identify BAM, GLCCLUSTER, and MULTIMIN as the most effective optimization solvers, with BAM showing the highest overall performance and solving more than 80% of the benchmark problems. The study also shows that no single solver consistently outperforms the others across all problem types, indicating that our future laboratory applications may benefit from using a small set of strong solvers rather than relying on a single method. The presentation also illustrates an in-silico chemical reactor case study showing that data-driven optimization methods can guide autonomous experimentation in a self-driving laboratory and identify optimal operating conditions within a small number of experiments. Overall, the results provide a basis for selecting efficient optimization methods and demonstrate the practical use of data-driven optimization in self-driving laboratory workflows.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Flexible and Accessible 4D Subsurface Visualization Using a Web-Based Platform

4D subsurface visualization using a web platform can provide improved communication, education and outreach to non-experts and stakeholders. It allows for the improved understanding of complex relationships and interactions that occur in inaccessible locations of which many experimental testbeds exist in. The use of a web-based visualization tool lessens the cognitive impact further by reducing the need of specialized software. Users can define their own exploration of a 3D or 4D scene, adding and removing data as needed and are able to make informed decisions based on accurate model visualizations, while providing a collaborative tool that can be accessed on any platform using a browser window. The tool is also able to point to real time streams to display up-to-the-second data as an experiment is ongoing. This level of latency can also provide operators with essential information that can direct an experiment’s progress. Additionally, this tool can leverage augmented and virtual reality (AR, VR) capabilities of certain mobile devices and head-mounted displays, providing further engaging visualization possibilities.

Pratt, Martin J.↗
DOE OSTI2020

Development and Validation of a Simulation Testbed for the Intelligent Building Agents Laboratory (IBAL) Using TRNSYS

This paper documents the development and validation of a dynamic primary cooling and thermal storage system simulation testbed. The system simulation testbed, sIBAL, is based on the Intelligent Building Agents Laboratory (IBAL) at the National Institute of Standards and Technology (NIST), which is a research infrastructure and testbed for the development, evaluation, and demonstration of intelligent control algorithms. The sIBAL testbed developed in this project will serve as a virtual twin of the real facility for future control algorithm development. The details of the methodologies used to develop and validate the simulation testbed, which replicates the dynamic behaviors of the primary cooling and ice storage system in the IBAL facility, are presented. The simulation testbed was developed in TRNSYS using built-in component models and MATLAB functions to replicate the two water-cooled chillers, a thermal storage tank, pumps, valves and other components for four different operation modes. Experiments on IBAL components were designed and executed to generate experimental data for model development and verification of the simulation platform. The validation of the simulation results was carried out in two phases: 1) independent component simulations for the chillers and thermal storage tank, and 2) a combined testbed simulation of the entire hydronic system. Comparison of simulation results to the experimental data obtained from the IBAL facility showed errors within 1 °C for the temperatures outputs of both the chiller and the thermal storage model. The error is within an acceptable range for further intelligent control algorithms development. The findings from the study are summarized and presented along with areas where additional research is needed. In addition, data filtering procedures and model refinement measures utilized to improve the accuracy and accelerate the computation time of the simulation are presented.

cooling systems↗
DOE OSTI2024

Dark Matter Reconstruction in LBAI Experiments with Imperfect Data

Long-baseline atom interferometer (LBAI) experiments offer unprecedented sensitivity to ultralight scalar dark matter (DM) [1], however reconstruction of a putative DM signal with traditional frequency-domain analysis requires ``perfect data (i.e., regularly-sampled with no missing samples). In a real LBAI experiment, there will undoubtedly be imperfections in the data leading to downtime. This downtime can arise from operational considerations (e.g., maintenance), the operational environment (motion of people and animals [2] or elevators), and robustness of the experimental apparatus (e.g., bad atom launches). In this work, we investigate the impact of various downtime models on the overall DM sensitivity of such an experiment. We compare the sensitivity for each downtime model as determined by a ``compound FFT analysis to a baseline no-downtime case. We also show how much sensitivity can be regained by moving to a Lomb-Scargle frequency analysis, as in [2]. Furthermore, we demonstrate reconstruction of the DM wave s phase as well as its frequency. [1] D. Antypas, et al, ``New Horizons: Scalar and Vector Ultralight Dark Matter (2022). arXiv:2203.14915 [2] J. Carlton and C. McCabe, ``From RATs to riches: mitigating anthropogenic and synanthropic noise in atom interferometer searches for ultra-light dark matter (2023). arXiv:2308.101731

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2026

A machine learning based approach to online electron reconstruction at CLAS12

Online reconstruction is key for monitoring purposes and real time analysis in High Energy and Nuclear Physics experiments. A necessary component of reconstruction algorithms is particle identification that combines information left by a particle passing through several detector components to identify the particle’s type. Of particular interest to electro-production Nuclear Physics experiments such as CLAS12 is electron identification which is used to trigger data recording. A machine learning approach was developed for CLAS12 to reconstruct and identify electrons by combining raw signals at the data acquisition level from several detector components. Here, this approach achieves an electron identification purity above 75% whilst retaining an efficiency close to 100%. The machine learning tools are capable of running at high rates exceeding the data acquisition rates and will allow electron reconstruction in real-time. This work enhances online analyses and monitoring and can contribute to improved triggering at CLAS12. This machine learning driven approach will also be crucial for experiments aiming to transition to streaming readout operations where online reconstruction will be a key component of the data taking paradigm.

Artificial intelligence↗
DOE OSTI2022

What We Learned From Analyzing 18 Million Rows of Commercial Buildings’ HVAC Fault Data

To achieve ambitious decarbonization goals it is critical that buildings operate to their full potential. Commercial HVAC systems, however, experience a wide range of operational faults, adversely affecting energy consumption, occupant comfort, and maintenance costs. Analytical tools such as fault detection & diagnostics (FDD) software identify and help diagnose these types of sensing, mechanical, or control-related faults. While significant energy savings has been documented for FDD, along with limited-scale studies on technical capabilities, there is a lack of empirical data on faults being reported by FDD tools. With FDD deployment accelerating significantly over the past decade there is an opportunity to gather and analyze data on commercial HVAC operational problems at an unprecedented scale. Such data could address many questions such as: [a] What faults are most commonly reported?; and [b] How does fault reporting vary by time of year and other possible drivers? A recent study into FDD fault reporting amassed the largest U.S. dataset of commercial HVAC air-side fault records, drawn from multi-year monitoring across over 60,000 pieces of HVAC equipment. The results of this study provide granular data on fault reporting for over 90 unique fault types. In this paper we provide an overview of the research process and highlight key findings and lessons learned. This study presents an extraordinary level of detail on FDD fault reporting characteristics across many climate zones and building types. Armed with these new insights, commercial building industry stakeholders can make better informed decisions when designing, configuring, and operating commercial HVAC systems.

Crowe, Eliot↗
DOE OSTI2023

Machine learning for automated experimentation in scanning transmission electron microscopy

Abstract Machine learning (ML) has become critical for post-acquisition data analysis in (scanning) transmission electron microscopy, (S)TEM, imaging and spectroscopy. An emerging trend is the transition to real-time analysis and closed-loop microscope operation. The effective use of ML in electron microscopy now requires the development of strategies for microscopy-centric experiment workflow design and optimization. Here, we discuss the associated challenges with the transition to active ML, including sequential data analysis and out-of-distribution drift effects, the requirements for edge operation, local and cloud data storage, and theory in the loop operations. Specifically, we discuss the relative contributions of human scientists and ML agents in the ideation, orchestration, and execution of experimental workflows, as well as the need to develop universal hyper languages that can apply across multiple platforms. These considerations will collectively inform the operationalization of ML in next-generation experimentation.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Advances in Metallic Fuel Database Development and Data Qualification

The Fuels Irradiation and Physics Database (FIPD [1]) is a comprehensive repository of data and documents related to Uranium-Zirconium based metallic fuel test pins. This database stores operational conditions of these pins, calculated using a suite of Argonne National Laboratory analysis codes developed during the Integral Fast Reactor (IFR) program. Key calculated data include axial distributions of power, temperature, fluence, burnup, and isotopic densities. Additionally, the FIPD holds post-irradiation examination (PIE) data such as fission gas release, gas chemistry measurements, and axial distributions derived from profilometry, gamma scanning, and neutron radiography. Complementing these data is an extensive archive of documents related to various pins and experiments. These include raw PIE records, design details, safety analyses, and operational reports. More detail about FIPD can be found in ref. [2]. The database development is an ongoing effort covering metallic fuel experiments from the Experimental Breeder Reactor II (EBR-II) and the Fast Flux Test Facility (FFTF). The recent improvements to the database and the data QA status are summarized in this paper.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗