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All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Myna: Connecting powder bed fusion build data to simulation tools for digital twin applications

Additive manufacturing (AM), as a digital process, can generate a detailed digital thread linking a part’s design and manufacturing to its operational performance. As AM systems advance, an increasing amount of process data is stored in manufacturing databases. In principle, this data can be utilized by simulation-based digital twin approaches, such as real-time process control and asynchronous post-processing guidance. However, few tools currently exist for systematically integrating digital thread data with computational tools. Here, in this study, we propose a software package, called Myna, for connecting data from powder bed fusion processes to simulation tools. The utility of such a platform is demonstrated using build data from the Oak Ridge National Laboratory Manufacturing Demonstration Facility “Peregrine v2023-10” public dataset to automatically configure and run 54 semi-analytical 3DThesis melt pool simulations, 78 numerical Additive FOAM melt pool simulations, and 3 ExaCA microstructure simulations. The simulated, spatially registered microstructures are then compared directly with electron backscatter diffraction characterization of the corresponding as-built part locations. The resulting simulated microstructure showed variation as a function of process parameters, particularly stripe width; however, the experimental data had little variation between the microstructure texture and grain size resulting from different processing conditions. Analysis of the discrepancies suggest that it is possible a two-phase ferritic-austenitic solidification model is needed to accurately predict grain size and texture for certain stainless steel 316L feedstock compositions under powder bed fusion conditions, providing direction for future research. As illustrated here, due to the number and complexity of the simulations involved in AM process-structure–property predictions, automated methods to connect process data and simulations will remain necessary tools for testing hypotheses and implementing digital twin applications.

Knapp, Gerald L. [Oak Ridge National Laboratory (O↗
DOE OSTI2025

Packaging HEP Heterogeneous Mini-apps for Portable Benchmarking and Facility Evaluation on Modern HPCs

High Energy Physics (HEP) experiments are making increasing use of GPUs and GPU dominated High Performance Computer facilities. Both the software and hardware of these systems are rapidly evolving, creating challenges for experiments to make informed decisions as to where they wish to devote resources. In its first phase, the High Energy Physics Center for Computational Excellence (HEP-CCE) produced portable versions of a number of heterogeneous HEP mini-apps, such as p2r, FastCaloSim, Patatrack and the WireCell Toolkit, that exercise a broad range of GPU characteristics, enabling cross platform and facility benchmarking and evaluation. However, these miniapps still require a significant amount of manual intervention to deploy on a new facility. We present our work in developing turn-key deployments of these mini-apps, where by means of containerization and automated configuration and build techniques such as Spack, we are able to quickly test new hardware, software, environments and entire facilities with minimal user intervention, and then track performance metrics over time.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2016

TU-AB-202-03: Prediction of PET Transfer Uncertainty by DIR Error Estimating Software, AUTODIRECT

Purpose: Deformable image registration (DIR) is a powerful tool, but DIR errors can adversely affect its clinical applications. To estimate voxel-specific DIR uncertainty, a software tool, called AUTODIRECT (automated DIR evaluation of confidence tool), has been developed and validated. This work tests the ability of this software to predict uncertainty for the transfer of standard uptake values (SUV) from positron-emission tomography (PET) with DIR. Methods: Virtual phantoms are used for this study. Each phantom has a planning computed tomography (CT) image and a diagnostic PET-CT image set. A deformation was digitally applied to the diagnostic CT to create the planning CT image and establish a known deformation between the images. One lung and three rectum patient datasets were employed to create the virtual phantoms. Both of these sites have difficult deformation scenarios associated with them, which can affect DIR accuracy (lung tissue sliding and changes in rectal filling). The virtual phantoms were created to simulate these scenarios by introducing discontinuities in the deformation field at the lung rectum border. The DIR algorithm from Plastimatch software was applied to these phantoms. The SUV mapping errors from the DIR were then compared to that predicted by AUTODIRECT. Results: The SUV error distributions closely followed the AUTODIRECT predicted error distribution for the 4 test cases. The minimum and maximum PET SUVs were produced from AUTODIRECT at 95% confidence interval before applying gradient-based SUV segmentation for each of these volumes. Notably, 93.5% of the target volume warped by the true deformation was included within the AUTODIRECT-predicted maximum SUV volume after the segmentation, while 78.9% of the target volume was within the target volume warped by Plastimatch. Conclusion: The AUTODIRECT framework is able to predict PET transfer uncertainty caused by DIR, which enables an understanding of the associated target volume uncertainty.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2014

Predictive Engineering Tools for Injection-Molded Long-Carbon-Fiber Thermoplastic Composites - FY 2014 First Quarterly Report

The CRADA between PNNL, Autodesk, Toyota and Magna has been effective since October 28th, 2013. The whole team including CRADA and subcontract partners kicked off the project technically on November 1st, 2013. This report describes work performed during the first quarter of FY 2014. The following technical progresses have been made toward project milestones: 1) The project kickoff meeting was organized at PlastiComp, Inc. in Winona on November 13th, 2013 involving all the project partners. During this meeting the research plan and Gantt chart were discussed and refined. The coordination of the research activities among the partners was also discussed to ensure that the deliverables and timeline will be met. 2) Autodesk delivered a research version of ASMI to PNNL for process modeling using this tool under the project. PNNL installed this research version on a PNNL computer and tested it. Currently, PNNL is using ASMI to prepare the models for PlastiComp plaques. 3) PlastiComp has compounded long carbon-fiber reinforced polypropylene and polyamide 6,6 compounds for rheological and thermal characterization tests by the Autodesk laboratories in Melbourne, Australia. 4) Initial mold flow analysis was carried out by PlastiComp to confirm that the 3D complex part selected by Toyota as a representative automotive part is moldable. 5) Toyota, Magna, PlastiComp and PNNL finalized the planning for molding the Toyota 3D complex part. 6) Purdue University worked with PNNL to update and specify the test matrix for characterization of fiber length/orientation. 7) Purdue University developed tools to automate the data collection and analysis of fiber length and orientation measurements. 8) Purdue University designed and specified equipment to replace the need for equipment using the technology established by the University of Leeds at General Motors.

42 ENGINEERING↗
DOE OSTI2020

Acid and heat stable enzymes for economical fuel production from biomass

CinderBio’s vision is to harness three billion years of biology to transform many industrial practices into sustainable processes with superior performance. Environments such as volcanic hot springs have evolved extremophilic microbes and enzymes that are naturally heat and acid stable. CinderBio was founded based on technologies developed at Lawrence Berkeley National Laboratory. Our core technologies are the molecular tools and know how to efficiently express extremophilic genes in an extremophilic host such that they retain their natural heat and acid stability. During this project, we tested our enzymes at the Advanced Biofuels and Bioproducts Process Development Unit (ABPDU), automated our fermentation and downstream processing, and built a comprehensive technoeconomic assessment (TEA) model for our manufacturing costs.

09 BIOMASS FUELS↗
DOE OSTI2022

Radiographic Imaging and Tomography

Radiographic imaging and tomography (RadIT) come in many flavors such as X-ray imaging and tomography (IT), proton IT, neutron IT, muon IT, neutrino IT, etc. We identify five RadIT themes: Physics, Sources, Detectors, Methods, and Data Science, which are integral parts of image interpretation and three-dimensional (3D) tomographic reconstruction. Traditionally, RadIT have been driven by medicine, non-destructive testing, material sciences, and security applications. The latest thrusts of growth come from automation, machine vision, additive manufacturing and virtual reality (‘metaverse’). The five RadIT themes parallel their counterparts in optical IT. Synergies among different forms of RadIT and with optical IT motivate further advances towards multi-modal IT and quantum IT.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2025

Packaging HEP Heterogeneous Mini-apps for Portable Benchmarking and Facility Evaluation on Modern HPCs

High Energy Physics (HEP) experiments are making increasing use of GPUs and GPU dominated High Performance Computer facilities. Both the software and hardware of these systems are rapidly evolving, creating challenges for experiments to make informed decisions as to where they wish to devote resources. In its first phase, the High Energy Physics Center for Computational Excellence (HEP-CCE) produced portable versions of a number of heterogeneous HEP mini-apps, such as \ptor, FastCaloSim, Patatrack and the WireCell Toolkit, that exercise a broad range of GPU characteristics, enabling cross platform and facility benchmarking and evaluation. However, these mini-apps still require a significant amount of manual intervention to deploy on a new facility. We present our work in developing turn-key deployments of these mini-apps, where by means of containerization and automated configuration and build techniques such as Spack, we are able to quickly test new hardware, software, environments and entire facilities with minimal user intervention, and then track performance metrics over time.

Atif, Mohammad [Brookhaven] (ORCID:000000026889770↗
DOE OSTI2019

An Automated, Disruption-Tolerant Device Authentication and Key Management Framework for Critical Systems

Key management is critical to secure operation. Distributed control systems, such as Supervisory Control and Data Acquisition (SCADA) systems, have unique operational requirements that make conventional key management solutions less effective and burdensome. In this paper a novel Kerberos based framework for automated, disruption-tolerant key management for control system environments is presented. Experimental tests and their results are presented to quantify expected performance overhead of this approach. In addition, Zeek sensor analytics are presented to monitor the health and security of the key management framework operation.

Edgar, Thomas W.↗
DOE OSTI2023

Sensitivity analysis of an automated fault detection algorithm for residential air-conditioning systems

The state of the art of fault detection and diagnosis (FDD) for residential air-conditioning systems is expensive and not yet amenable to widespread implementation. FDD for homes can significantly reduce utility costs, and increase the lifespan of the equipment. The cost barriers currently, however, make FDD for homes economically unviable for large scale implementation. In prior work, we offered a solution to reduce FDD costs by proposing an automated fault detection algorithm to serve as a screening step before more expensive FDD tests can be conducted. The algorithm uses only the home thermostat and local weather information to identify thermodynamic parameters and detect high-impact air-conditioning faults, including those that occur during equipment installation. We had tested the algorithm on a single EnergyPlus™ model of a home in Orlando, Florida. The thermodynamic parameter identification process is highly nonconvex involving several local optimal solutions. In this paper we propose a novel method to select the best model for fault detection from among the list of local optimal solutions to make the algorithm more robust to homes of different construction, without which the fault detection process would be infeasible. Another unique contribution of the paper is implementing the solution on real-world data. We also bring the algorithm closer to market by testing it on real-world data. We implement the algorithm on data obtained from experiments conducted by the Florida Solar Energy Center (FSEC) on a laboratory home equipped with a heat pump where faults were intentionally added for a period of seven months. The algorithm successfully detected an undercharge fault with 70.6% accuracy, concurrent duct leakage and undercharge faults with 85.2% accuracy, and duct leakage faults with 69.1% accuracy. A sensitivity analysis is also performed on EnergyPlus models of nine types of homes that vary in construction to demonstrate the robustness of the algorithm. Finally, the algorithm achieves an average accuracy of 71% for no-fault condition, 77% for 40% undercharge fault, and 76% for duct-leak fault.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2026

Generalizable Web User Interface for Scalable and Streamlined Deployment of Building Energy Management Systems in Small and Medium-Sized Commercial Buildings

Small and medium-sized commercial buildings (SMCBs) comprise 94% of US commercial buildings yet face significant barriers to implementing building energy management systems despite advances in smart device technology. Existing solutions present critical limitations: cloud-based API solutions simplify deployment but create vendor lock-in constraints; commercial integrated software solutions ensure compatibility via standardized protocols but require substantial cost and technical expertise; open-source IoT platforms offer cost-effective vendor independence but provide insufficient standardized protocol support for commercial building automation. This research presents a generalizable web user interface framework that bridges the gap between evolving smart device capabilities and lagging software infrastructure for SMCBs. The proposed system integrates VOLTTRON open-source middleware with an automated configuration converter that transforms unified specifications written in YAML, a human-readable data-serialization format, into system-specific files, streamlining manual setup processes. The vendor-agnostic architecture supports industry-standard protocols (BACnet and Modbus) and semantic building models while providing adaptive web interfaces that dynamically adjust to various building configurations. Demonstrations through simulation-based testing and a field deployment show automatic interface adaptation across heterogeneous HVAC systems and multizone monitoring. The automated configuration converter also substantially reduces labor-intensive setup.

Chung, Jihoon [ORNL] (ORCID:0000000184880815)↗
DOE OSTI2021

EXPLAINABLE AND TRUSTWORTHY DIAGNOSTICS ACHIEVABLE THROUGH PROCESS-BASED AUTOMATED REASONING

An approach has been developed that incorporates domain knowledge to obtain a more explainable and trustworthy equipment health monitoring diagnosis than might otherwise be obtained from a purely data-driven method. Physics-based models serve to constrain the realizable solution space and render a more trusted diagnosis. An automated reasoning algorithm performs backward chaining to infer a diagnosis that is consistent with logic statements that have been evaluated as true. This diagnosis is made explainable to an operator by providing the forward chaining path that elucidates for inspection and validity testing those truths implied by the diagnosis.

automated reasoning↗
DOE OSTI2025

Modular Autonomous Experimentation for Biological Applications

The Modular Autonomous Research System (MARS) was created to address a key challenge in scientific discovery: experiments are often slow, require significant manual labor, and generate data that is not easily integrated across different tools. This limits how quickly scientists can explore new materials, processes, and chemical reactions. Our motivation was to design a system that makes research faster, more reliable, and adaptable by combining automation with artificial intelligence. By doing so, we aimed to reduce human error, accelerate discovery, and allow researchers to quickly test many possibilities that would otherwise take months or years. Our approach was to build a flexible platform that connects laboratory robots, measurement instruments, and a central data system, all guided by artificial intelligence. MARS integrates liquid handling robots, robotic arms, and plate readers with an intelligent decision-making system that chooses the most informative experiments to run next. This creates a closed loop where experiments are performed automatically, the data is analyzed in real time, and new conditions are immediately tested. Through this work, we demonstrated that MARS can carry out multiple experiments with little or no human intervention, adapt to different scientific problems, and handle uncertain or noisy measurements in a robust way. The results show that modular and intelligent automation can significantly accelerate the pace of discovery, providing a model for future self-driving laboratories. This approach addresses the growing scientific need for adaptable, data-driven research platforms that can keep up with the complexity and scale of modern science.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2015

Daily QA of linear accelerators using only EPID and OBI

Purpose: As treatment delivery becomes more complex, there is a pressing need for robust quality assurance (QA) tools to improve efficiency and comprehensiveness while simultaneously maintaining high accuracy and sensitivity. This work aims to present the hardware and software tools developed for comprehensive QA of linear accelerator (LINAC) using only electronic portal imaging devices (EPIDs) and kV flat panel detectors. Methods: A daily QA phantom, which includes two orthogonally positioned phantoms for QA of MV-beams and kV onboard imaging (OBI) is suspended from the gantry accessory holder to test both geometric and dosimetric components of a LINAC and an OBI. The MV component consists of a 0.5 cm water-equivalent plastic sheet incorporating 11 circular steel plugs for transmission measurements through multiple thicknesses and one resolution plug for MV-image quality testing. The kV-phantom consists of a Leeds phantom (TOR-18 FG phantom supplied by Varian) for testing low and high contrast resolutions. In the developed process, the existing LINAC tools were used to automate daily acquisition of MV and kV images and software tools were developed for simultaneous analysis of these images. A method was developed to derive and evaluate traditional QA parameters from these images [output, flatness, symmetry, uniformity, TPR{sub 20/10}, and positional accuracy of the jaws and multileaf collimators (MLCs)]. The EPID-based daily QA tools were validated by performing measurements on a detuned 6 MV beam to test its effectiveness in detecting errors in output, symmetry, energy, and MLC positions. The developed QA process was clinically commissioned, implemented, and evaluated on a Varian TrueBeam LINAC (Varian Medical System, Palo Alto, CA) over a period of three months. Results: Machine output constancy measured with an EPID (as compared against a calibrated ion-chamber) is shown to be within ±0.5%. Beam symmetry and flatness deviations measured using an EPID and a 2D ion-chamber array agree within ±0.5% and ±1.2% for crossline and inline profiles, respectively. MLC position errors of 0.5 mm can be detected using a picket fence test. The field size and phantom positioning accuracy can be determined within 0.5 mm. The entire daily QA process takes ∼15 min to perform tests for 5 photon beams, MLC tests, and imaging checks. Conclusions: The exclusive use of EPID-based QA tools, including a QA phantom and simultaneous analysis software tools, has been demonstrated as a viable, efficient, and comprehensive process for daily evaluation of LINAC performance.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2024

Uncertainty-aware particle segmentation for electron microscopy at varied length scales

Electron microscopy is indispensable for examining the morphology and composition of solid materials at the sub-micron scale. To study the powder samples that are widely used in materials development, scanning electron microscopes (SEMs) are increasingly used at the laboratory scale to generate large datasets with hundreds of images. Parsing these images to identify distinct particles and determine their morphology requires careful analysis, and automating this process remains challenging. In this work, we enhance the Mask R-CNN architecture to develop a method for automated segmentation of particles in SEM images. We address several challenges inherent to measurements, such as image blur and particle agglomeration. Moreover, our method accounts for prediction uncertainty when such issues prevent accurate segmentation of a particle. Recognizing that disparate length scales are often present in large datasets, we use this framework to create two models that are separately trained to handle images obtained at low or high magnification. By testing these models on a variety of inorganic samples, our approach to particle segmentation surpasses an established automated segmentation method and yields comparable results to the predictions of three domain experts, revealing comparable accuracy while requiring a fraction of the time. These findings highlight the potential of deep learning in advancing autonomous workflows for materials characterization.

36 MATERIALS SCIENCE↗
DOE OSTI2015

TU-F-CAMPUS-I-05: Semi-Automated, Open Source MRI Quality Assurance and Quality Control Program for Multi-Unit Institution

Purpose: Phantom measurements allow for the performance of magnetic resonance (MR) systems to be evaluated. Association of Physicists in Medicine (AAPM) Report No. 100 Acceptance Testing and Quality Assurance Procedures for MR Imaging Facilities, American College of Radiology (ACR) MR Accreditation Program MR phantom testing, and ACR MRI quality control (QC) program documents help to outline specific tests for establishing system performance baselines as well as system stability over time. Analyzing and processing tests from multiple systems can be time-consuming for medical physicists. Besides determining whether tests are within predetermined limits or criteria, monitoring longitudinal trends can also help prevent costly downtime of systems during clinical operation. In this work, a semi-automated QC program was developed to analyze and record measurements in a database that allowed for easy access to historical data. Methods: Image analysis was performed on 27 different MR systems of 1.5T and 3.0T field strengths from GE and Siemens manufacturers. Recommended measurements involved the ACR MRI Accreditation Phantom, spherical homogenous phantoms, and a phantom with an uniform hole pattern. Measurements assessed geometric accuracy and linearity, position accuracy, image uniformity, signal, noise, ghosting, transmit gain, center frequency, and magnetic field drift. The program was designed with open source tools, employing Linux, Apache, MySQL database and Python programming language for the front and backend. Results: Processing time for each image is <2 seconds. Figures are produced to show regions of interests (ROIs) for analysis. Historical data can be reviewed to compare previous year data and to inspect for trends. Conclusion: A MRI quality assurance and QC program is necessary for maintaining high quality, ACR MRI Accredited MR programs. A reviewable database of phantom measurements assists medical physicists with processing and monitoring of large datasets. Longitudinal data can reveal trends that although are within passing criteria indicate underlying system issues.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2022

express: Extensible, high-level workflows for swifter ab initio materials modeling

In this work, we introduce an open-source Julia project, express, an extensible, lightweight, high-throughput, high-level workflow framework that aims to automate ab initio calculations for the materials science community. express is shipped with well-tested workflow templates, including structure optimization, equation of state (EOS) fitting, phonon spectrum (lattice dynamics) calculation, and thermodynamic property calculation in the framework of the quasi-harmonic approximation (QHA). It is designed to be highly modularized so that its components can be reused across various occasions, and customized workflows can be built on top of that. Users can also track the status of workflows in real-time, and rerun failed jobs thanks to the data lineage feature express provides. Finally, two working examples, i.e., all workflows applied to lime and akimotoite, are also presented in the code and this paper.

36 MATERIALS SCIENCE↗
DOE OSTI2017

Automating High-Precision X-Ray and Neutron Imaging Applications with Robotics

Los Alamos National Laboratory and the University of Texas at Austin recently implemented a robotically controlled nondestructive testing (NDT) system for X-ray and neutron imaging. This system is intended to address the need for accurate measurements for a variety of parts and, be able to track measurement geometry at every imaging location, and is designed for high-throughput applications. This system was deployed in a beam port at a nuclear research reactor and in an operational inspection X-ray bay. The nuclear research reactor system consisted of a precision industrial seven-axis robot, 1.1-MW TRIGA research reactor, and a scintillator-mirror-camera-based imaging system. The X-ray bay system incorporated the same robot, a 225-keV microfocus X-ray source, and a custom flat panel digital detector. The robotic positioning arm is programmable and allows imaging in multiple configurations, including planar, cylindrical, as well as other user defined geometries that provide enhanced engineering evaluation capability. The imaging acquisition device is coupled with the robot for automated image acquisition. The robot can achieve target positional repeatability within 17 μm in the 3-D space. Flexible automation with nondestructive imaging saves costs, reduces dosage, adds imaging techniques, and achieves better quality results in less time. Specifics regarding the robotic system and imaging acquisition and evaluation processes are presented. In conclusion, this paper reviews the comprehensive testing and system evaluation to affirm the feasibility of robotic NDT, presents the system configuration, and reviews results for both X-ray and neutron radiography imaging applications.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2026

Generative large language models for predictive maintenance planning

Maintenance planning and the generation of necessary components for tasks can prove time-consuming and complex. Automating the creation of recurring or similar tasks by leveraging previous planning packages and data, while uncovering insights to automate planning package generation, presents an opportunity to conserve valuable time and resources. This work aims to harness the textual and probabilistic capabilities of large language models (LLMs) to automate the generation of planning packages. Utilizing diverse data sources ranging from raw data to handwritten text, both singular and collaborative LLMs are trained and tested. Results demonstrate their capability to generate essential planning package components, effectively replicating the statistical patterns in the data. This demonstrates the use of these tools inside a digital asset for automated planning. This work outlines a methodology for constructing datasets, a training suite, and evaluation methods for LLM-based textual and conversational planning tools utilized in an asset digital twin. Results indicate that the fine-tuned models generate estimated planning information within the statistical ranges observed in real maintenance data. The models achieve high accuracy (>90%) in document question-answering and instruction generation tasks. Furthermore, the conversational retrieval-augmented generation (RAG) assistant system achieves 100% document retrieval accuracy, while conversational information capture exceeds 98% across the majority of work-package assistant modules.

97 MATHEMATICS AND COMPUTING↗