Building Re-tuning Simulator
The Building Re-tuning Simulator (BRS) is a Re-tuning focused modeling and building automation system simulation environment.
Fernandez, Nick↗
SEARCH · Engineering Papers
Results for “automated modelling”
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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COMPASS (Continuous Ordinance Mapping for Planning and Siting Systems) [SWR-25-62]
COMPASS (Formerly, INFRA-COMPASS) is an innovative software tool that harnesses the power of Large Language Models (LLMs) to automate the compilation and continued maintenance of an inventory of state and local codes and ordinances pertaining to energy infrastructure.
Pinchuk, Pavlo [National Renewable Energy Laborato↗
From Text to Maps: LLM-Driven Extraction and Geotagging of Epidemiological Data
Epidemiological datasets are essential for public health analysis and decision-making, yet they remain scarce and often difficult to compile due to inconsistent data formats, language barriers, and evolving political boundaries. Traditional methods of creating such datasets involve extensive manual effort and are prone to errors in accurate location extraction. To address these challenges, we propose utilizing large language models (LLMs) to automate the extraction and geotagging of epidemiological data from textual documents. Our approach significantly reduces the manual effort required, limiting human intervention to validating a subset of records against text snippets and verifying the geotagging reasoning, as opposed to reviewing multiple entire documents manually to extract, clean, and geotag. Additionally, the LLMs identify information often overlooked by human annotators, further enhancing the dataset’s completeness. Our findings demonstrate that LLMs can be effectively used to semi-automate the extraction and geotagging of epidemiological data, offering several key advantages: (1) comprehensive information extraction with minimal risk of missing critical details; (2) minimal human intervention; (3) higher-resolution data with more precise geotagging; and (4) significantly reduced resource demands compared to traditional methods.
Harrod, Karly↗
SCALE 6.2.4 Validation: Radiation Shielding
For safe and reliable use of computer codes by the community, accuracy must be clearly evaluated. In particular, the nuclear reactor engineering and licensing field needs accurate tools for radiation shielding modeling. Monaco with Automated Variance Reduction using Importance Calculations (MAVRIC) is one such tool, with built-in variance reduction methods distributed within the SCALE code, and its validity is demonstrated in this report for the released version 6.2.4. Representative benchmarks corresponding to shielding analysis are selected for the validation study. Typical experimental results analyzed from those benchmarks include neutron fluxes, detector count rates, detector energy response functions, neutron and gamma doses, foil neutron activation rates and activities, neutron leakage fluxes, and skyshine dose rates. Thousands of points of comparison between experiment and calculation are presented in this work. Other than rare outliers typically explained by either a lack of information or large uncertainties in the experiment conditions, material, or dimensions, MAVRIC agrees well with the experiment results. MAVRIC is also compared to Monte Carlo N-Particle (MCNP) calculations when available, and both codes generally produce good agreements within estimated uncertainties. The selected benchmarks are obtained from reliable sources such as the International Criticality Safety Benchmark Evaluation Project Handbook (ICSBEP Handbook), the Shielding Integral Benchmark Archive & Database (SINBAD), and other shielding validation work found in the literature. Additional datapoints and benchmarks will be added to future versions of this report to incrementally expand the shielding validation suite incrementally.
61 RADIATION PROTECTION AND DOSIMETRY↗
Numerical Modelling of the Maiden Wave Energy WEC Device: Cooperative Research and Development Final Report, CRADA Number CRD-22-22640
NREL will develop meshing and modelling scripts to automate exploration of the design space for Maiden Wave Energy's Wave Energy Converter ("WEC") device. This will aid Maiden Wave Energy to assess a range of different device configurations and explore how different variables in the system affect the concept's performance.
CRADA↗
HPC-FAIR: A Framework Managing Data and AI Models for Analyzing and Optimizing Scientific Applications
The increasing reliance on machine learning (ML) to analyze and optimize large-scale scientific applications on supercomputers faces a significant bottleneck: the lack of readily available, high-quality training datasets and the difficulty in reusing existing AI models. This project was motivated by the urgent need to address the “FAIR” principles (Findability, Accessibility, Interoperability, Reusability) for both training datasets and AI models in the high-performance computing (HPC) domain. The project developed HPC-FAIR, a high-performance computing data management framework designed to centralize HPC-related datasets and AI models within a unified hub. To ensure interoperability, the framework established a standardized representation and vocabulary (ontology) for both data and models. HPC-FAIR also implemented automated workflows to streamline data processing, model access, and benchmarking. Additionally, the project focused on optimizing data harnessing efficiency through advanced techniques like deep reuse and compression-based analytics.
97 MATHEMATICS AND COMPUTING↗
End-to-end deep learning pipeline for real-time Bragg peak segmentation: from training to large-scale deployment
X-ray crystallography reconstruction, which transforms discrete X-ray diffraction patterns into three-dimensional molecular structures, relies critically on accurate Bragg peak finding for structure determination. As X-ray free electron laser (XFEL) facilities advance toward MHz data rates (1 million images per second), traditional peak finding algorithms that require manual parameter tuning or exhaustive grid searches across multiple experiments become increasingly impractical. While deep learning approaches offer promising solutions, their deployment in high-throughput environments presents significant challenges in automated dataset labeling, model scalability, edge deployment efficiency, and distributed inference capabilities. We present an end-to-end deep learning pipeline with three key components: (1) a data engine that combines traditional algorithms with our peak matching algorithm to generate high-quality training data at scale, (2) a modular architecture that scales from a few million to hundreds of million parameters, enabling us to train large expert-level models offline while deploying smaller, distilled models at the edge, and (3) a decoupled producer-consumer architecture that separates specialized data source layer from model inference, enabling flexible deployment across diverse computing environments. Using this integrated approach, our pipeline achieves accuracy comparable to traditional methods tuned by human experts while eliminating the need for experiment-specific parameter tuning. Although current throughput requires optimization for MHz facilities, our system's scalable architecture and demonstrated model compression capabilities provide a foundation for future high-throughput XFEL deployments.
Wang, Cong↗
Deep learning models for interpretation of point of care ultrasound in military working dogs
Introduction: Military working dogs (MWDs) are essential for military operations in a wide range of missions. With this pivotal role, MWDs can become casualties requiring specialized veterinary care that may not always be available far forward on the battlefield. Some injuries such as pneumothorax, hemothorax, or abdominal hemorrhage can be diagnosed using point of care ultrasound (POCUS) such as the Global FAST® exam. This presents a unique opportunity for artificial intelligence (AI) to aid in the interpretation of ultrasound images. In this article, deep learning classification neural networks were developed for POCUS assessment in MWDs. Methods: Images were collected in five MWDs under general anesthesia or deep sedation for all scan points in the Global FAST® exam. For representative injuries, a cadaver model was used from which positive and negative injury images were captured. A total of 327 ultrasound clips were captured and split across scan points for training three different AI network architectures: MobileNetV2, DarkNet-19, and ShrapML. Gradient class activation mapping (GradCAM) overlays were generated for representative images to better explain AI predictions. Results: Performance of AI models reached over 82% accuracy for all scan points. The model with the highest performance was trained with the MobileNetV2 network for the cystocolic scan point achieving 99.8% accuracy. Across all trained networks the diaphragmatic hepatorenal scan point had the best overall performance. However, GradCAM overlays showed that the models with highest accuracy, like MobileNetV2, were not always identifying relevant features. Conversely, the GradCAM heatmaps for ShrapML show general agreement with regions most indicative of fluid accumulation. Discussion: Overall, the AI models developed can automate POCUS predictions in MWDs. Preliminarily, ShrapML had the strongest performance and prediction rate paired with accurately tracking fluid accumulation sites, making it the most suitable option for eventual real-time deployment with ultrasound systems. Further integration of this technology with imaging technologies will expand use of POCUS-based triage of MWDs.
59 BASIC BIOLOGICAL SCIENCES↗
Evaluation of Deep Learning Model Architectures for Point-of-Care Ultrasound Diagnostics
Point-of-care ultrasound imaging is a critical tool for patient triage during trauma for diagnosing injuries and prioritizing limited medical evacuation resources. Specifically, an eFAST exam evaluates if there are free fluids in the chest or abdomen but this is only possible if ultrasound scans can be accurately interpreted, a challenge in the pre-hospital setting. In this effort, we evaluated the use of artificial intelligent eFAST image interpretation models. Widely used deep learning model architectures were evaluated as well as Bayesian models optimized for six different diagnostic models: pneumothorax (i) B- or (ii) M-mode, hemothorax (iii) B- or (iv) M-mode, (v) pelvic or bladder abdominal hemorrhage and (vi) right upper quadrant abdominal hemorrhage. Models were trained using images captured in 27 swine. Using a leave-one-subject-out training approach, the MobileNetV2 and DarkNet53 models surpassed 85% accuracy for each M-mode scan site. The different B-mode models performed worse with accuracies between 68% and 74% except for the pelvic hemorrhage model, which only reached 62% accuracy for all model architectures. These results highlight which eFAST scan sites can be easily automated with image interpretation models, while other scan sites, such as the bladder hemorrhage model, will require more robust model development or data augmentation to improve performance. With these additional improvements, the skill threshold for ultrasound-based triage can be reduced, thus expanding its utility in the pre-hospital setting.
47 OTHER INSTRUMENTATION↗
G2PDeep-v2: A Web-Based Deep-Learning Framework for Phenotype Prediction and Biomarker Discovery for All Organisms Using Multi-Omics Data
Multi-omics data offers rich insights into complex traits across organisms, yet integrating and analyzing these datasets for phenotype prediction and marker discovery remains challenging. Researchers need accessible tools that combine deep learning, hyperparameter optimization, visualization, and downstream analysis in a unified web platform. To address this, we developed G2PDeep-v2, a web-based platform powered by deep learning for phenotype prediction and marker discovery from multi-omics data across a wide range of organisms, including humans and plants. The server provides multiple services for researchers to create deep-learning models through an interactive interface and train these models using an automated hyperparameter tuning algorithm on high-performance computing resources. Users can visualize the results of phenotype and markers predictions and perform Gene Set Enrichment Analysis for the significant markers to provide insights into the molecular mechanisms underlying complex diseases, conditions and other biological phenotypes being studied.
59 BASIC BIOLOGICAL SCIENCES↗
Modeling the Spectral Diversity of Quasars in the Sixteenth Data Release from the Sloan Digital Sky Survey
Abstract We present a new approach to capturing the broad diversity of emission-line and continuum properties in quasar spectra. We identify populations of spectrally similar quasars through pixel-level clustering on 12,968 high signal-to-noise ratio (S/N) spectra from the Sloan Digital Sky Survey (SDSS) in the redshift range of 1.57 < z < 2.4. Our clustering analysis finds 396 quasar spectra that are not assigned to any population, 15 misclassified spectra, and 6 quasars with incorrect redshifts. We compress the quasar populations into a library of 684 high-S/N composite spectra, anchored in redshift space by the Mg ii emission line. Principal component analysis on the library results in an eigenspectrum basis spanning 1067–4007 Å. We model independent samples of SDSS quasar spectra with the eigenbasis, allowing for a free redshift parameter. Our models achieve a median reduced χ 2 on non–broad absorption line quasar spectra that is reduced by 8.5% relative to models using the eigenspectra from the SDSS spectroscopic pipeline. A significant contribution to the relative improvement is from the ability to reconstruct the range of emission-line variation. The redshift estimates from our model are consistent with the Mg ii emission-line redshift with an average offset that displays 51.4% less redshift-dependent variation relative to the SDSS eigenspectra. Our method for developing quasar spectra models can improve automated classification and predict the intrinsic spectrum in regions affected by intervening absorbers such as Ly α , C iv , and Mg ii , thus benefiting studies of large-scale structure.
79 ASTRONOMY AND ASTROPHYSICS↗
Surface Water Chemistry and Water and Nitrogen Isotopes, Teller Road Site, Seward Peninsula, Alaska, 2016 and 2017
Data include results from water chemistry and water isotope analyses for surface water samples collected at the NGEE Arctic Teller Road site at mile marker 27 (TL_MM27) on the Seward Peninsula, Alaska, July through September of 2016 and May through September of 2017. The samples were collected using automated samplers (ISCO Model 6712; Teledyne, USA), with regular collection (every 24 hrs in 2016 and every 48 hrs in 2017). Surface water samples were collected from three locations along the main drainage: ISCO 1 from the tributary (64.729060°, -165.946210°), ISCO 3 which was located upstream (64.733640°, -165.955410°) and ISCO 2 which was located downstream (64.726410°, -165.946820°). Dataset includes a *.pdf user guide and one *.csv data file.The Next-Generation Ecosystem Experiments: Arctic (NGEE Arctic), was a to reduce uncertainty in Earth System Models by developing a predictive understanding of carbon-rich Arctic ecosystems and feedbacks to climate. NGEE Arctic was supported by the Department of Energy's Office of Biological and Environmental Research. The NGEE Arctic project had two field research sites: 1) located within the Arctic polygonal tundra coastal region on the Barrow Environmental Observatory (BEO) and the North Slope near Utqiagvik (Barrow), Alaska and 2) multiple areas on the discontinuous permafrost region of the Seward Peninsula north of Nome, Alaska. Through observations, experiments, and synthesis with existing datasets, NGEE Arctic provided an enhanced knowledge base for multi-scale modeling and contributed to improved process representation at global pan-Arctic scales within the Department of Energy's Earth system Model (the Energy Exascale Earth System Model, or E3SM), and specifically within the E3SM Land Model component (ELM).
54 ENVIRONMENTAL SCIENCES↗
Knowledge-based and integrated monitoring and diagnosis in autonomous power systems
A new technique of knowledge-based and integrated monitoring and diagnosis (KBIMD) to deal with abnormalities and incipient or potential failures in autonomous power systems is presented. The KBIMD conception is discussed as a new function of autonomous power system automation. Available diagnostic modelling, system structure, principles and strategies are suggested. In order to verify the feasibility of the KBIMD, a preliminary prototype expert system is designed to simulate the KBIMD function in a main electric network of the autonomous power system.
Momoh, J. A.↗
Overcoming limitations of model-based diagnostic reasoning systems
The development of a model-based diagnostic system to overcome the limitations of model-based reasoning systems is discussed. It is noted that model-based reasoning techniques can be used to analyze the failure behavior and diagnosability of system and circuit designs as part of the system process itself. One goal of current research is the development of a diagnostic algorithm which can reason efficiently about large numbers of diagnostic suspects and can handle both combinational and sequential circuits. A second goal is to address the model-creation problem by developing an approach for using design models to construct the GMODS model in an automated fashion.
Holtzblatt, Lester J.↗
Design and fabrication of an autonomous rendezvous and docking sensor using off-the-shelf hardware
NASA Marshall Space Flight Center (MSFC) has developed and tested an engineering model of an automated rendezvous and docking sensor system composed of a video camera ringed with laser diodes at two wavelengths and a standard remote manipulator system target that has been modified with retro-reflective tape and 830 and 780 mm optical filters. TRW has provided additional engineering analysis, design, and manufacturing support, resulting in a robust, low cost, automated rendezvous and docking sensor design. We have addressed the issue of space qualification using off-the-shelf hardware components. We have also addressed the performance problems of increased signal to noise ratio, increased range, increased frame rate, graceful degradation through component redundancy, and improved range calibration. Next year, we will build a breadboard of this sensor. The phenomenology of the background scene of a target vehicle as viewed against earth and space backgrounds under various lighting conditions will be simulated using the TRW Dynamic Scene Generator Facility (DSGF). Solar illumination angles of the target vehicle and candidate docking target ranging from eclipse to full sun will be explored. The sensor will be transportable for testing at the MSFC Flight Robotics Laboratory (EB24) using the Dynamic Overhead Telerobotic Simulator (DOTS).
Grimm, Gary E.↗
The Stanford how things work project
We provide an overview of the Stanford How Things Work (HTW) project, an ongoing integrated collection of research activities in the Knowledge Systems Laboratory at Stanford University. The project is developing technology for representing knowledge about engineered devices in a form that enables the knowledge to be used in multiple systems for multiple reasoning tasks and reasoning methods that enable the represented knowledge to be effectively applied to the performance of the core engineering task of simulating and analyzing device behavior. The central new capabilities currently being developed in the project are automated assistance with model formulation and with verification that a design for an electro-mechanical device satisfies its functional specification.
Fikes, Richard↗
Magnetospheric Image Unfolding
The Grant was a three year grant funded under the Space Physics Supporting Research and Technology and Suborbital Program. Our objective was to develop automated techniques needed to unfold or "invert" global images of the magnetospheric ion populations obtained by the new magnetospheric imaging techniques (ENA, EUV) in anticipation of future missions such as the Magnetospheric Imager and, now, IMAGE. Our focus on the present three year grant is to determine the degree to which such images can quantitatively constrain the global electromagnetic properties of the magnetosphere. In a previous three year grant period we successfully automated a forward modeling inversion algorithm, demonstrated that these inversions are robust in the face of realistic instrumental considerations such as counting statistics and backgrounds, applied error analysis techniques to the extracted parameters using variational procedures, implemented very realistic magnetospheric test images to test the inversion algorithms using the Rice University Magnetospheric Specification Model, and began the process of generating parametric models with the flexibility to handle the realistic magnetospheric images (e.g. Roelof et al, 1992; 1993). Our plan for the present 3 year grant period was to complete the development of the inversion tools needed to handle realistic magnetospheric images, assess the degree to which global electrodynamics is quantitatively constrained by ENA images of the magnetosphere, and bring the inversion of EUV images up to the maturity that we will have achieved for the ENA imaging. Below the accomplishments of our three year effort are present followed by a list of our presentations and publications. The accomplishments of all three years are presented here, and thus some of these items appeared on interim progress reports.
Source record↗
Systems Characterization of Temperature, Ph and Electrical Conductivity in Aerobic Biodegradation of Wheat Biomass at Differing Mixing Rates
The purpose of this preliminary study is to observe and relate the rate of mixing to pH and electrical conductivity in an aerobic, continuously stirred bioreactor. The objective is to use data collected from successive experiments as a means of a system characterization. Tests were conducted to obtain these data using a continuously stirred 20 L Cytostir glass reaction vessel as a bioreactor operated without built-in temperature or pH control. The tests were conducted on the lab bench at ambient temperatures. The substrate in the bioreactor was ground wheat biomass obtained from the Biomass Production Chamber at NASA Kennedy Space Center. In this study, the data reflect characteristics of the native (uninoculated) systems as well as inoculated systems. In the native systems, it was found that pi levels became stable after approximately 2 to 3 days. The electrical conductivity levels for the native systems tended to decrease over time. In contrast, ion activity was increased after the introduction of bacteria into the system. This could be correlated with the release of nutrients, due to the activity of the bacteria. Also, there were slight increases in pH in the inoculated system, a result which is expected for a system with no active pr controls. The data will be used to test a mathematical model in an automated system.
Calhoun, M.↗