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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 109 records · Page 6
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Genetic circuit design automation for yeast

Cells can be programmed to monitor and react to their environment using genetic circuits. Design automation software maps a desired circuit function to a DNA sequence, a process that requires units of gene regulation (gates) that are simple to connect and behave predictably. This poses a challenge for eukaryotes due to their complex mechanisms of transcription and translation. To this end, we have developed gates for yeast (Saccharomyces cerevisiae) that are connected using RNA polymerase flux as the signal carrier and are insulated from each other and host regulation. They are based on minimal constitutive promoters (~120 base pairs), for which rules are developed to insert operators for DNA-binding proteins. Using this approach, in this work we constructed nine NOT/NOR gates with nearly identical response functions and 400-fold dynamic range. In circuits, they are transcriptionally insulated from each other by placing ribozymes downstream of terminators to block nuclear export of messenger RNAs resulting from RNA polymerase readthrough. Based on these gates, Cello 2.0 was used to build circuits with up to 11 regulatory proteins. A simple dynamic model predicts the circuit response over days. Genetic circuit design automation for eukaryotes simplifies the construction of regulatory networks as part of cellular engineering projects, whether it be to stage processes during bioproduction, serve as environmental sentinels or guide living therapeutics.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Automated Systems for Solvent Extraction

An automated solvent extraction system for the eventual handling of radioactive materials has been designed and procured for rapid, efficient and safe liquid-liquid extraction processes. This system will be configured to carry out liquid-liquid separations from beginning to end, automating tedious and time-consuming tasks such as organic/aqueous phase prep, pH checks, phase separation, and metal ion analysis. The system features advanced liquid handling capabilities, a robotic arm for precise sample transfer, and sophisticated analytical tools including UV-VIS spectrophotometry for real-time monitoring. Additionally, it incorporates automated capping of vials, vortex mixing, and centrifugation to ensure thorough mixing and phase separation. This report provides an overview of the system and general capabilities as well as initial efforts to develop an automated workflow for liquid-liquid separations.

38 RADIATION CHEMISTRY, RADIOCHEMISTRY, AND NUCLEA↗
DOE OSTI2025

Integration and Demonstration of Monitoring, Modeling, and Prediction of DV-1 Amendment Performance at the Bench Scale: DV-1 Amendment Demonstration

During fiscal years 2024 and 2025, the U.S. Department of Energy’s Hanford Field Office commissioned Pacific Northwest National Laboratory to conduct applied research aimed at reducing the cost, time, and uncertainty associated with in situ treatment of vadose zone contaminants at the Hanford Site. This report outlines the integration of three key research efforts into a meso-scale demonstration designed to advance field-scale solutions that aim to (1) optimize the delivery of chemical amendments to contaminated soils, (2) reduce uncertainty in amendment delivery performance assessment using advanced monitoring techniques, and (3) provide real-time insights into when and where amendment-induced precipitation reactions occur in the subsurface. To achieve these objectives, the tank-scale (~ 1 cubic meter) Geophysical Imaging of Flow and Transport (GIFT) system was developed. GIFT enables experimental testing of amendment delivery while incorporating automated multi-modal monitoring approaches, including pressure measurements, direct fluid sampling, and remote time-lapse geophysical imaging. The data generated from these monitoring techniques will serve as inputs for a generative artificial-intelligence-driven digital twin – a numerical simulation model designed to honor observed data while quantifying uncertainty in simulation accuracy. Using this simulator, researchers will refine an amendment injection strategy to maximize delivery efficiency within a low-permeability soil zone. Monitoring data will be interpreted through simulated outputs to enhance understanding of the injection process. The efficacy of this integrated approach will be evaluated through direct sampling at the conclusion of the experiment.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2025

Findings on subtask 1.6 – basin electric carbon storage research project: novel monitoring techniques

The Energy & Environmental Research Center (EERC) led a study to validate novel and emerging technologies as commercial monitoring techniques for application in carbon dioxide (CO 2 ) injection operations. This applied research was conducted at Basin Electric Power Cooperative’s (Basin Electric’s) active CO 2 -injection operations in Beulah, North Dakota, in two phases. The EERC previously completed a set of baseline (preinjection) activities in Phase 1, which included 1) design of an automated, integrated, modular (AIM) monitoring station; 2) time-lapse electromagnetic (EM) field surveys; 3) drone-based surveillance studies; 4) time-lapse monitoring with seismic methods; and 5) advanced wellbore-monitoring methods.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Continuous Water Quality Sensing for FGD Wastewater

The coal fired plant power industry requires new solutions for on-line monitoring of flue gas desulfurization (FGD) wastewaters for regulated trace metals to reduce recurring operating and off-site laboratory analysis costs and provide a high level of confidence of compliance with state and federal discharge guidelines. The University of Alabama at Birmingham (UAB) developed an integrated water sensor package for continuous water quality monitoring of flue gas desulfurization (FGD) wastewaters for concentration measurements of Selenium (Se). UAB utilized a diverse team of University Professors/Staff Members/Student(s) (Engineering and Innovative Technology Development Group), a University Affiliated Research Institution from the local area (Southern Research), and a cost sharing Industry Partner (Metrohm). The team developed and validated a proprietary wet chemistry batch process for water sample preparation; and implemented this process as a fully automated on-line monitor prototype system. This prototype system’s performance was evaluated in both the laboratory and during in-field deployment to a coal fired power plant. In both environments it was found that the prototype system was unable to recreate the batch process’s success at preparing FGD water samples for total Se measurements with a commercial-off-the-shelf (COTS) cyclic anodic stripping voltammetry (CSV) system. Through this experience, UAB has provided insight into a number of specific areas where improvements can be made with respect to the COTS systems used in this project to yield a more complete and competitive industry solution for on-line monitoring of total Se in industrial wastewater.

01 COAL, LIGNITE, AND PEAT↗
DOE OSTI2020

Simulation of DWPF Chemical Process Cell Using a Reaction Calorimeter

The Defense Waste Processing Facility (DWPF) converts liquid radioactive waste into glass using a Sludge Receipt Adjustment Tank (SRAT) and Slurry Mix Evaporator (SME) in addition to other processes. The SRAT and SME processes can be simulated on a small-scale using the Mettler-Toledo Reaction Calorimeter (RC1). This equipment is able to automate the execution and monitoring of experiments involving nonradioactive chemical simulants. During the chemical processing of sludge in the SRAT process, foam begins to form and can foam over the reaction vessel. The current antifoam used at DWPF creates a risk of increased flammability and has a limited pH range. Usually, mercury is circulated back into the reaction vessel during the SRAT process, making it difficult to remove. To combat foam over, novel antifoams will be tested during the boil-up process using the RC1. To improve mercury extraction, 3D-printed coalescer columns will be tested within the Mercury Water Wash Tank (MWWT) of the RC1. Testing of Simulant Sludge Batch 6 (SB6) was performed to ensure: the sludge would form a stable foam at boiling during full SRAT/SME runs; the antifoam would reduce the amount of foam within the vessel The sludge and a Precipitate Reactor Feed Tank (PRFT) simulant with monosodium titanate (MST) were mixed separately, combined, and then heated to boiling. As shown below, the antifoam used was able to eliminate the foam within the vessel, as expected. The FormLabs Form2 3D Printer can be used to create roughly 3-inch tall and 1-inch diameter columns from clear and ceramic resins. The columns will utilize chemical and physical forces to draw the mercury out of a condensed offgas stream leaving the RC1 reaction vessel. These columns will be tested during full SRAT/SME runs on the basis of how well they are able to collect elemental mercury. In preparation for full-scale runs, the RC1 equipment was assembled onto a custom table and tested with water runs. The software used to control automation, iControl, has a user-friendly interface that allows the experiment to be altered as needed. An example of the data recorded during an experimental run is represented in Figure 1 below. Water runs were utilized as a way to prove if the RC1 system was functioning in the following areas: Boil-up rates, Chemical additions using pumps, Temperature readings, Balance readings, Probe readings, Offgas system, Agitation. Future Plans: Five novel antifoams will be tested during full SRAT/SME runs. The most effective antifoam will be utilized in DWPF's Chemical Process Cell. The design of the Mercury Extraction Coalescer can be further manipulated to increase the extraction of not only elemental mercury, but other heavy metals in environmental remediation processes as well.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2026

Embedded EPICS server for PowerPMAC motion controllers

An embedded server layer of Experimental Physics and Industrial Control System (EPICS) for PowerPMAC motion controllers has been developed and deployed at two undulator beamlines of the National Institute of General Medical Sciences and the National Cancer Institute (GM/CA) Structural Biology Facility at the Advanced Photon Source (APS). This compact, open source solution makes the power and versatility of PowerPMAC motion controls directly accessible to distributed EPICS clients. At GM/CA the system controls about 200 servo and stepper motors — both encoded and unencoded — and multiple digital and analog I/O accessories. The server stack comprises two sublayers: a lower-level driver and database that communicates directly with PowerPMAC, and a facility-specific soft sublayer built on top. The paper describes installing EPICS on PowerPMAC, the implementation of both layers and client examples, including on-the-fly scanning.

EPICS↗
DOE OSTI2021

Directional Laplacian Centrality for Cyber Situational Awareness

Cyber operations is drowning in diverse, high-volume, multi-source data. To get a full picture of current operations and identify malicious events and actors, analysts must see through data generated by a mix of human activity and benign automated processes. Although many monitoring and alert systems exist, they typically use signature-based detection methods. We introduce a general method rooted in spectral graph theory to discover patterns and anomalies without a priori knowledge of signatures. We derive and propose a new graph-theoretic centrality measure based on the derivative of the graph Laplacian matrix in the direction of a vertex. To build intuition about our measure, we show how it identifies the most central vertices in standard network datasets and compare to other graph centrality measures. Finally, we focus our attention on studying its effectiveness in identifying important IP addresses in network flow data. Using both real and synthetic network flow data, we conduct several experiments to test our measure’s sensitivity to two types of injected attack profiles and show that vertices participating in injected attack profiles exhibit noticeable changes in our centrality measures, even when the injected anomalies are relatively small, and in the presence of simulated network dynamics.

45 MILITARY TECHNOLOGY, WEAPONRY, AND NATIONAL DEF↗
DOE OSTI2026

Carbon dioxide, water vapor and methane soil efflux (soil respiration) in a Pinus palustris root exclusion in Georgetown, SC

This dataset contains processed data from a combination of survey flux chambers and long-term automated flux chambers. Soil flux measurements were conducted from June 2023 through December 2025 in a mature longleaf pine forest in Georgetown, SC. Soil respiration measurements were conducted approximately biweekly for two and a half years, before and after a root exclusion that took place on May 5, 2024. Processed, QAQC’d data for the treatment (root exclusion) and control (roots intact) before and after the root exclusion can be found in the file: 1_DATA_ESS_DOE_HR_RS_HB2_QAQC_Survey_Data_20260223.csv. Two multiday deployments were also conducted prior to the root exclusion using long-term automated chambers to continuously monitor greenhouse gas soil efflux. Processed, QAQC’d data for both long-term deployments can be found in the file: 2_DATA_ESS_DOE_HR_RS_HB2_QAQC_Longterm_Data_20260209.csv. Raw and working data files (.json, .81x, & .82z format) from LI-COR equipment are included for reference and can be accessed using SoilFluxPro software. CSV metadata files describe the raw data and modifications made using SoilFluxPro v5 and Matlab R2024b, as well as formatting and units for processed CSVs. Matlab code is included for reading in the processed CSVs, with sample figures comparing treatment and control. This research was performed as part of the project: “Improving models of stand and watershed carbon and water fluxes with more accurate representations of soil-plant-water dynamics in southern pine ecosystems”, which examines in part the effects hydraulic redistribution on soil efflux of carbon dioxide, water vapor and methane, as well as soil moisture and temperature in a southern pine ecosystem with sandy soils and high water table.

CARBON DIOXIDE FLUX↗
DOE OSTI2020

Real-time Canister Welding Health Monitoring and Prediction System for Spent Fuel Dry Storage

In this report, the salt mist corrosion testing at controlled temperature was carried out successfully on 304L SS submerged arc welded coupons by Unifog Dispersion Tower at ORNL. Corrosion in-situ monitoring system from Intelligent Automation, Inc. (IAI) was setup in the corrosion test and running smoothly to record signals during the corrosion test. Parts and device selection, sensors attachment, and experimental procedures are discussed. There wasn’t any visible corrosion or crack propagation on the weld coupon after about a month of corrosion testing.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2022

Resilience Enhancements through Deep Learning Yields

This report documents the Resilience Enhancements through Deep Learning Yields (REDLY) project, a three-year effort to improve electrical grid resilience by developing scalable methods for system operators to protect the grid against threats leading to interrupted service or physical damage. The computational complexity and uncertain nature of current real-world contingency analysis presents significant barriers to automated, real-time monitoring. While there has been a significant push to explore the use of accurate, high-performance machine learning (ML) model surrogates to address this gap, their reliability is unclear when deployed in high-consequence applications such as power grid systems. Contemporary optimization techniques used to validate surrogate performance can exploit ML model prediction errors, which necessitates the verification of worst-case performance for the models.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2023

Integrated Process Optimization for Biochemical Conversion

This research is motivated by the challenges faced during biomass processing in bioenergy plants. It has been observed that variations in biomass characteristics, such as moisture, ash, and carbohydrate contents cause variations in feeding of the system which led to underutilization of equipment and the reactor. The objective of this research is to ensure a continuous flow of biomass to the reactor in plants that use the biochemical conversion process to generate liquid fuels. The overall goal is to lower the cost of producing biofuels, which could lead to improving US’s energy independency and growing US’s rural economy. The research team developed analytical models, such as discrete element method (DEM) models and mathematical models. The DEM models are unit-level models that explicitly capture biomass characteristics and quantify the impacts of biomass characteristics on bulk material properties and the performance of specific equipment. The mathematical models are system-level models that capture the impacts of system infeed rate, equipment processing rate, storage location and capacity, and biomass characteristics on system throughput. The functional relations predicting the bulk material properties from DEM models are incorporated to the mathematical models. The models developed were validated and evaluated using data collected at Idaho National Laboratory’s biomass processing facility. Via these models, we identified process control strategies that ensure a continuous flow of biomass to the reactor, while meeting the requirements of biochemical conversion process. Our analysis indicates that sequencing of biomass bales based on moisture level, and carbohydrate contents could have a positive impact on reducing processing time and inventory level and increasing throughput rate. Short bale sequences that repeat frequently, seem to have the greatest impact on improving system’s performance. Based on our experiments, the total annual system operating costs reduced by 20-30%, and the maximum inventory level reduced by 3 to 4 times. The operating costs include the annual equipment amortization cost and processing cost. The implementation of the models developed requires the use of standardized bale format, Radio Frequency Identification technology, sensing and real time monitoring of material attributes, automated material handling equipment, and automated process control. The scope of the model proposed can be extended to include the whole supply chain. The supply chain models help identify how many bales of different biomass feedstock to purchase given biomass availability in the region, biomass price and quality, and the biomass processing capabilities of the biorefinery. Thus, the outcomes of supply chain models can be used to inform the design of long-term contracts among farmers and the biorefinery.

09 BIOMASS FUELS↗
DOE OSTI2022

Integrated Risk-Informed Condition Based Maintenance Capability and Automated Platform: Technical Report 3

This project is a collaborative research effort between PKMJ Technical Services LLC, Idaho National Laboratory, and Public Service Enterprise Group (PSEG) Nuclear, LLC. The collaboration, led by PKMJ Technical Services LLC, is part of the industry Funding Opportunity Announcement (FOA) award under Advanced Nuclear Technology Development FOA #DE-FOA-0001817. The pilot demonstration focuses on the Circulating Water System (CWS), an important non-safety-related system that impacts the power generation capability of the plant site. Achieving riskinformed condition-based Predictive Maintenance (PdM) on the CWS will result in significant economic benefits, and the developed methodologies can also be applied to other plant systems. This approach supports an industry goal of ensuring that nuclear power generation remains a viable, economically competitive option in the energy market. Operation and Maintenance (O&M) costs include labor-intensive Preventive Maintenance (PM) programs that involve manually performed inspection, calibration, testing, and maintenance of plant assets at periodic frequencies as well as time-based replacement of assets, irrespective of condition. This project offers an alternative by focusing on riskinformed condition-based maintenance to reduce O&M costs while still maintaining plant health and safety. This report summarizes the progress made toward achieving a risk-informed condition-based maintenance approach. The research and development (R&D) activities presented in this report are associated with development of a nuclear digital platform application, integration of fault signature models, and automated work management processes. The fault signatures and Machine Learning (ML) models are key components in predictive analytics and are heavily leveraged to improve the insights received by existing plant process data sources. Availability of the analysis results within a centralized digital platform enhances efficiency by enabling automation of activities otherwise performed manually. Personnel are presented with enhanced information that can be used to evaluate plant status and risks. Utilizing the enhancements to data analytics supports automated responses, (i.e. issuance of work orders) to address developing equipment faults and thus preventing forced, unplanned shutdowns of components or systems. The R&D activities described within this report lay the foundation for developing and demonstrating a digital automated platform to centralize the implementation of condition monitoring and response to equipment faults. The digital automated platform is cloud-based and designed to enable improved efficiency of plant processes. The digital platform includes content related to maintenance optimization, fault signature analysis, and plant records, which can all be used to support efficiencies when located within a centralized digital platform. These efficiencies could be further enhanced when deployed through industry-wide deployment of the technology to improve insights and processes based upon economies of scale.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2020

A Data Processing Workflow for Fixed-wing Drone Based Radiation Mapping in the Chornobyl Exclusion Zone (CEZ) - 20119

April 2020 marks the 34. anniversary of the high-profile radiological release from the Chornobyl Nuclear Power Plant (ChNPP). The release of radioactive material from reactor number four began on the 26 April 1986 and continued over a period of about 10 days, releasing approximately 1700 PBq of radioactive material (including 85 PBq of 137-Cs) into the environment. To this day, the accident remains the most significant release of radioactive material since civil nuclear power generation began. In the years since the accident, automated and remote radiation monitoring technologies have advanced significantly in their capabilities. One such example of this is the use of unmanned aerial vehicles (UAVs) in radiation mapping investigations. In April 2019, a team of scientists from the University of Bristol showcased a novel radiation mapping system within the CEZ, specifically aiming to map radiation over a large portion of the area immediately surrounding the ChNPP. Over six days of data collection, the system flew a total distance of 583.8 km, covering an area of 14.6 sq.km with an exceptional spatial resolution (sub 20 m/pixel). The work presented herein outlines and explains the data processing procedure to convert the raw data into 137-Cs activity (kBq/sq.m) and cesium-equivalent dose-rate (CED) at 1 m above ground level (μSv/hr). A demonstration of the validity of the method is demonstrated through the successful reduction of the raw data into a single linear relationship between the measured {sup 137}Cs net peak intensity and the {sup 137}Cs activity. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2025

Human Factors Considerations for Anticipated Uses of Autonomy in Advanced Reactor Technologies

The objective of this report is examine anticipated advanced reactor designs that incorporate high levels of autonomy in operation of advanced reactor systems or facilities, and to begin identifying the human factors engineering (HFE) considerations relevant to those designs. The insights gained will help identify areas where U.S. Nuclear Regulatory Commission (NRC) may need to develop or update review guidance to adequately address HFE considerations essential for safe operation. The adequacy of existing guidance, along with recommendations for updates or new guidance, may be addressed in future reports. We use the term “landscape analysis” to describe the review, analysis, and characterization of publicly available information on the anticipated future state of the industry regarding particular technologies or operational concepts. This landscape analysis considers the potential range of advanced reactor automation concepts that may be used for monitoring and controlling nuclear systems and facilities, including the potential for highly automated, near autonomous, or fully autonomous operation of advanced nuclear reactors.

99 - GENERAL AND MISCELLANEOUS↗
DOE OSTI2022

A multi-scale temperature-based strategy to map hydrologic exchange flows in highly dynamic systems

Mapping and quantifying hydrologic exchange flows (HEFs) is critical to environmental monitoring and remediation at contaminated sites; however, these objectives are challenging in highly dynamic systems, e.g., along dam-regulated rivers, where HEFs vary rapidly. Direct seepage measurements are labor-intensive and difficult to automate, whereas indirect (e.g., thermal) and remote sensing methods have potential to allow continuous monitoring with limited field effort. We present a preliminary assessment of a multi-scale temperature-based strategy for monitoring HEFs along the Hanford Reach of the Columbia River, in eastern WA, United States. Five thermal methods were assessed. First, a vertical temperature profile (VTP) was installed into the streambed. The VTP data were analyzed using a data assimilation algorithm designed for automated real-time estimation in dynamic systems. Second, a thermal infrared (TIR) camera was used in roving surveys to identify seeps. Third, a TIR camera was stationed at the VTP site to collect images at 1-h intervals. Together, the two TIR datasets provided a basis to assess the potential for drone-based TIR. Fourth, temperature was measured at the sediment/water interface to assess fiber-optic distributed temperature sensing. Fifth, imagery from the ECOSTRESS satellite mission was acquired to assess the potential of spaceborne thermal monitoring. Based on our preliminary assessment, VTP, TIR, and bed temperature measurements provide complementary spatial coverage, temporal sampling, and resolution; these methods have potential for long-term, automated monitoring of HEFs. The publicly available spaceborne imagery, however, proved inadequate because of insufficient spatial resolution and data gaps resulting from cloud cover and revisit frequency.

54 ENVIRONMENTAL SCIENCES↗