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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 613 records · Page 34
All sourcesNASA NTRSDOE OSTI
DOE OSTI2016

TU-H-207A-09: An Automated Technique for Estimating Patient-Specific Regional Imparted Energy and Dose From TCM CT Exams Across 13 Protocols

Purpose: To develop an automated technique for estimating patient-specific regional imparted energy and dose from tube current modulated (TCM) computed tomography (CT) exams across a diverse set of head and body protocols. Methods: A library of 58 adult computational anthropomorphic extended cardiac-torso (XCAT) phantoms were used to model a patient population. A validated Monte Carlo program was used to simulate TCM CT exams on the entire library of phantoms for three head and 10 body protocols. The net imparted energy to the phantoms, normalized by dose length product (DLP), and the net tissue mass in each of the scan regions were computed. A knowledgebase containing relationships between normalized imparted energy and scanned mass was established. An automated computer algorithm was written to estimate the scanned mass from actual clinical CT exams. The scanned mass estimate, DLP of the exam, and knowledgebase were used to estimate the imparted energy to the patient. The algorithm was tested on 20 chest and 20 abdominopelvic TCM CT exams. Results: The normalized imparted energy increased with increasing kV for all protocols. However, the normalized imparted energy was relatively unaffected by the strength of the TCM. The average imparted energy was 681 ± 376 mJ for abdominopelvic exams and 274 ± 141 mJ for chest exams. Overall, the method was successful in providing patientspecific estimates of imparted energy for 98% of the cases tested. Conclusion: Imparted energy normalized by DLP increased with increasing tube potential. However, the strength of the TCM did not have a significant effect on the net amount of energy deposited to tissue. The automated program can be implemented into the clinical workflow to provide estimates of regional imparted energy and dose across a diverse set of clinical protocols.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2020

Machine learning prediction of incidence of Alzheimer’s disease using large-scale administrative health data

Nationwide population-based cohort provides a new opportunity to build an automated risk prediction model based on individuals’ history of health and healthcare beyond existing risk prediction models. We tested the possibility of machine learning models to predict future incidence of Alzheimer’s disease (AD) using large-scale administrative health data. From the Korean National Health Insurance Service database between 2002 and 2010, we obtained de-identified health data in elders above 65 years (N = 40,736) containing 4,894 unique clinical features including ICD-10 codes, medication codes, laboratory values, history of personal and family illness and socio-demographics. To define incident AD we considered two operational definitions: “definite AD” with diagnostic codes and dementia medication (n = 614) and “probable AD” with only diagnosis (n = 2026). We trained and validated random forest, support vector machine and logistic regression to predict incident AD in 1, 2, 3, and 4 subsequent years. For predicting future incidence of AD in balanced samples (bootstrapping), the machine learning models showed reasonable performance in 1-year prediction with AUC of 0.775 and 0.759, based on “definite AD” and “probable AD” outcomes, respectively; in 2-year, 0.730 and 0.693; in 3-year, 0.677 and 0.644; in 4-year, 0.725 and 0.683. The results were similar when the entire (unbalanced) samples were used. Important clinical features selected in logistic regression included hemoglobin level, age and urine protein level. This study may shed a light on the utility of the data-driven machine learning model based on large-scale administrative health data in AD risk prediction, which may enable better selection of individuals at risk for AD in clinical trials or early detection in clinical settings.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Machine learning-led semi-automated medium optimization reveals salt as key for flaviolin production in Pseudomonas putida

Although synthetic biology can produce valuable chemicals in a renewable manner, its progress is still hindered by a lack of predictive capabilities. Media optimization is a critical, and often overlooked, process which is essential to obtain the titers, rates and yields needed for commercial viability. Here, we present a molecule- and host-agnostic active learning process for media optimization that is enabled by a fast and highly repeatable semi-automated pipeline. Its application yielded 60% and 70% increases in titer, and 350% increase in process yield in three different campaigns for flaviolin production in Pseudomonas putida KT2440. Explainable Artificial Intelligence techniques pinpointed that, surprisingly, common salt (NaCl) is the most important component influencing production. The optimal salt concentration is very high, comparable to seawater and close to the limits that P. putida can tolerate. The availability of fast Design-Build-Test-Learn (DBTL) cycles allowed us to show that performance improvements for active learning are rarely monotonous. This work illustrates how machine learning and automation can change the paradigm of current synthetic biology research to make it more effective and informative, and suggests a cost-effective and underexploited strategy to facilitate the high titers, rates and yields essential for commercial viability.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Computational catalyst discovery: Active classification through myopic multiscale sampling

We report the recent boom in computational chemistry has enabled several projects aimed at discovering useful materials or catalysts. We acknowledge and address two recurring issues in the field of computational catalyst discovery. First, calculating macro-scale catalyst properties is not straightforward when using ensembles of atomic-scale calculations [e.g., density functional theory (DFT)]. We attempt to address this issue by creating a multi-scale model that estimates bulk catalyst activity using adsorption energy predictions from both DFT and machine learning models. The second issue is that many catalyst discovery efforts seek to optimize catalyst properties, but optimization is an inherently exploitative objective that is in tension with the explorative nature of early-stage discovery projects. In other words, why invest so much time finding a “best” catalyst when it is likely to fail for some other, unforeseen problem? We address this issue by relaxing the catalyst discovery goal into a classification problem: “What is the set of catalysts that is worth testing experimentally?” Here, we present a catalyst discovery method called myopic multiscale sampling, which combines multiscale modeling with automated selection of DFT calculations. It is an active classification strategy that seeks to classify catalysts as “worth investigating” or “not worth investigating” experimentally. Our results show an ~7–16 times speedup in catalyst classification relative to random sampling. These results were based on offline simulations of our algorithm on two different datasets: a larger, synthesized dataset and a smaller, real dataset.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Evaluation of antifouling surfaces using a method that employs mussel larvae settlement quantified by machine learning

Antifouling coating development requires extensive performance testing. Coatings that prevent aquatic larval settlement are of interest because many forms of macrofouling begin at the larval stage. However, field testing can be time consuming and poorly controlled. Herein is reported a screening tool, Settlement of Larvae Assay using Mussels (SLAM), for down-selecting materials prior to field testing. The method entails using a dense concentration of mussel larvae that are allowed to settle on submerged test surfaces. Settled larvae are then quantified to provide a measure of antifouling performance. The SLAM test differentiated coatings with only slight differences in formulation. To enable efficient quantification of dense larvae settlement, an automated counting method was developed that combines two analyses: a color thresholding identifies larvae clumps, and a machine learning algorithm identifies non-clumped larvae. Finally, this automated ‘hybrid’ approach rapidly quantifies settled larvae as effectively as manual counting but in a fraction of the time.

Mytilus↗
DOE OSTI2016

European sea bass show behavioural resilience to near-future ocean acidification

Ocean acidification (OA)-caused by rising concentrations of carbon dioxide (CO 2 )-is thought to be a major threat to marine ecosystems and has been shown to induce behavioural alterations in fish. Here we show behavioural resilience to near-future OA in a commercially important and migratory marine finfish, the Sea bass (Dicentrarchus labrax). Sea bass were raised from eggs at 19 °C in ambient or near-future OA (1000 μatm pCO 2 ) conditions and n = 270 fish were observed 59-68 days post-hatch using automated tracking from video. Fish reared under ambient conditions, OA conditions, and fish reared in ambient conditions but tested in OA water showed statistically similar movement patterns, and reacted to their environment and interacted with each other in comparable ways. Thus our findings indicate behavioural resilience to near-future OA in juvenile sea bass. Moreover, simulated agent-based models indicate that our analysis methods are sensitive to subtle changes in fish behaviour. Lastly, it is now important to determine whether the absences of any differences persist under more ecologically relevant circumstances and in contexts which have a more direct bearing on individual fitness.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2015

WE-G-207-05: Relationship Between CT Image Quality, Segmentation Performance, and Quantitative Image Feature Analysis

Purpose: Segmentation quality can affect quantitative image feature analysis. The objective of this study is to examine the relationship between computed tomography (CT) image quality, segmentation performance, and quantitative image feature analysis. Methods: A total of 90 pathology proven breast lesions in 87 dedicated breast CT images were considered. An iterative image reconstruction (IIR) algorithm was used to obtain CT images with different quality. With different combinations of 4 variables in the algorithm, this study obtained a total of 28 different qualities of CT images. Two imaging tasks/objectives were considered: 1) segmentation and 2) classification of the lesion as benign or malignant. Twenty-three image features were extracted after segmentation using a semi-automated algorithm and 5 of them were selected via a feature selection technique. Logistic regression was trained and tested using leave-one-out-cross-validation and its area under the ROC curve (AUC) was recorded. The standard deviation of a homogeneous portion and the gradient of a parenchymal portion of an example breast were used as an estimate of image noise and sharpness. The DICE coefficient was computed using a radiologist’s drawing on the lesion. Mean DICE and AUC were used as performance metrics for each of the 28 reconstructions. The relationship between segmentation and classification performance under different reconstructions were compared. Distributions (median, 95% confidence interval) of DICE and AUC for each reconstruction were also compared. Results: Moderate correlation (Pearson’s rho = 0.43, p-value = 0.02) between DICE and AUC values was found. However, the variation between DICE and AUC values for each reconstruction increased as the image sharpness increased. There was a combination of IIR parameters that resulted in the best segmentation with the worst classification performance. Conclusion: There are certain images that yield better segmentation or classification performance. The best segmentation Result does not necessarily lead to the best classification Result. This work has been supported in part by grants from the NIH R21-EB015053. R Nishikawa is receives royalties form Hologic, Inc.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2015

Extremely accurate sequential verification of RELAP5-3D

Large computer programs like RELAP5-3D solve complex systems of governing, closure and special process equations to model the underlying physics of nuclear power plants. Further, these programs incorporate many other features for physics, input, output, data management, user-interaction, and post-processing. For software quality assurance, the code must be verified and validated before being released to users. For RELAP5-3D, verification and validation are restricted to nuclear power plant applications. Verification means ensuring that the program is built right by checking that it meets its design specifications, comparing coding to algorithms and equations and comparing calculations against analytical solutions and method of manufactured solutions. Sequential verification performs these comparisons initially, but thereafter only compares code calculations between consecutive code versions to demonstrate that no unintended changes have been introduced. Recently, an automated, highly accurate sequential verification method has been developed for RELAP5-3D. The method also provides to test that no unintended consequences result from code development in the following code capabilities: repeating a timestep advancement, continuing a run from a restart file, multiple cases in a single code execution, and modes of coupled/uncoupled operation. In conclusion, mathematical analyses of the adequacy of the checks used in the comparisons are provided.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2024

Synchrophasor-Based Zonal Current Differential Protection for Secondary Low Voltage Networks

This report describes an approach to utilizing phasor measurement unit (PMU) data from multiple Intelligent Electronics Devices (IEDs) in a low-voltage network to produce a differential scheme for protecting the medium-voltage feeder and low-voltage network transformers. The proposed protection scheme is designed and prototyped on a real-time automation controller. Its performance is evaluated using real-time controller hardware-in-the-loop simulation. Lab testing results indicate that the proposed protection scheme allows significant distributed energy resources (DER) backfeed and enables selective and fast protection of medium voltage feeders.

42 ENGINEERING↗
DOE OSTI2023

The GammaSpec Package for Automated Gamma-ray Analysis (V.1.0)

This report describes the GammaSpec package, a tool developed to automate the analysis and comparison of gamma-ray spectra. The goal of this tool is to drastically reduce, or even eliminate, currently time consuming processes for comparing large sets of gamma-ray measurements. There are a number of potential applications for this work throughout the laboratory, but particularly within Global Security. Current work flows where large numbers of measured spectra must be compared for anomolies and outliers often consist of loading and visually inspecting individual spectra, or utilizing small-batch tools. One such example is the PeakEasy tool also developed at LANL. PeakEasy has a robust and tested feature set for calibrating, identifying, and fitting gamma-ray spectra. However it’s intention was not for batch processing and automated comparison of large numbers of spectra. This tool is not intended to replace PeakEasy in an analyzers arsenal, but to instead augment it. The intention here is to automate the isolation of spectra of particular interest, which can then be studied in further detail. This report is arranged as follows. Section 2 lays out the design goals and desired functionality of the package. Section 3 describes the algorithms developed and chosen to accomplish those goals. Section 4 describes the primary interaction with the tool, as well as it’s resulting output. Following these sections, results are presented from a set of example datasets from High Purity Germanium (HPGe) detectors in Sec. 5. Finally, the current status is summarized in Sec. 6.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2024

Mini Report: Jailbreaking Attacks and Defenses

Overall, jailbreaking defenses are unreliable, and no defenses proposed thus far can completely stop such attacks in any verifiable way. Even manual attacks can trivially bypass some claimed ‘defenses’, and over the course of 2023 automated attacks on prior models have been adapted to work on LLMs while other attacks draw on ideas such as fuzz testing. At best, some defenses can make jailbreaks more difficult, but with the developing landscape of attacks existing papers have not robustly evaluated how effective they are against all these methods. However, our opinion is that given the way these large generative models are trained and ‘aligned’ to stated goals of safety via fine tuning, it will be exceedingly difficult if not impossible to eliminate the possibility of jailbreaking attacks. A major barrier is that the feature space of LLMs is not sufficiently understood in a way where guarantees can be made about the outputs. Barring major changes, the expectation around jailbreaking defenses should be that they can mitigate misuse, but not verifiably prevent it. However, one defense we believe merits further investigation depends on the fact as automated attacks produce text, they need an automated way to identify a successful jailbreak - a judgment model. We have seen some attempts to repurpose models like these to defend against jailbreaks, but the evaluations are small scale and not robust.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2016

Activity and durability of Pt-Ni nanocage electocatalysts in proton exchange membrane fuel cells

We report a Ni-rich Pt-Ni alloy was synthesized via a solvothermal method and transformed into platinum-nickel nanocage catalysts (PNCs) by applying a two-phase corrosion process. The catalysts were physically and electrochemically characterized by X-ray diffraction (XRD), X-ray photoelectron spectroscopy (XPS), transmission electron microscopy (TEM) and both cyclic and linear sweep voltammetry using a rotating disk electrode (RDE). During the RDE testing, the half-wave potential of the PNC was 30 mV higher compared to that of a commercial Pt/C catalyst for the oxygen reduction reaction (ORR). The RDE experiments showed that the specific and mass activity of the PNC was 2 and 4 times greater than Pt/C, respectively, at 0.9 V. Catalyst-coated membranes (CCMs) were fabricated with the Pt-Ni nanocages using an automated air-assisted cylindrical liquid jet sprayer system. The PNC CCMs were loaded into proton exchange membrane fuel cells (PEMFC) for activity and stability testing. The CCMs showed no obvious Pt and Ni dissolution and redeposition in the membrane even after 30 K cycles. The performance and electrochemically active surface area (ECSA) retention of the PNC was far superior to commercial Pt/C, just short of the US Department of Energy (DOE) 2020 targets, suggesting that Pt-alloy nanocages are very promising candidates for high-performing commercial PEMFCs.

25 ENERGY STORAGE↗
DOE OSTI2016

WE-H-BRC-06: A Unified Machine-Learning Based Probabilistic Model for Automated Anomaly Detection in the Treatment Plan Data

Purpose: The purpose of this work was to investigate the ability of a machine-learning based probabilistic approach to detect radiotherapy treatment plan anomalies given initial disease classes information. Methods In total we obtained 1112 unique treatment plans with five plan parameters and disease information from a Mosaiq treatment management system database for use in the study. The plan parameters include prescription dose, fractions, fields, modality and techniques. The disease information includes disease site, and T, M and N disease stages. A Bayesian network method was employed to model the probabilistic relationships between tumor disease information, plan parameters and an anomaly flag. A Bayesian learning method with Dirichlet prior was useed to learn the joint probabilities between dependent variables in error-free plan data and data with artificially induced anomalies. In the study, we randomly sampled data with anomaly in a specified anomaly space.We tested the approach with three groups of plan anomalies – improper concurrence of values of all five plan parameters and values of any two out of five parameters, and all single plan parameter value anomalies. Totally, 16 types of plan anomalies were covered by the study. For each type, we trained an individual Bayesian network. Results: We found that the true positive rate (recall) and positive predictive value (precision) to detect concurrence anomalies of five plan parameters in new patient cases were 94.45±0.26% and 93.76±0.39% respectively. To detect other 15 types of plan anomalies, the average recall and precision were 93.61±2.57% and 93.78±3.54% respectively. The computation time to detect the plan anomaly of each type in a new plan is ∼0.08 seconds. Conclusion: The proposed method for treatment plan anomaly detection was found effective in the initial tests. The results suggest that this type of models could be applied to develop plan anomaly detection tools to assist manual and automated plan checks. The senior author received research grants from ViewRay Inc. and Varian Medical System.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2024

Development and Validation of Home Comfort System for Total Performance Deficiency/Fault Detection and Optimal Comfort Control

In this project, we developed and tested a learning-based home thermal model that facilitates the operation of a model predictive control (MPC)-based optimization agent and an automated fault detection and diagnosis (AFDD) agent. The home thermal model was constructed using a two-node resistor-capacitor model. Moreover, two accompanying parameter identification methods were introduced, least-squares and optimization. Based on the home thermal model, the MPC-based optimization agent was developed to optimize residential HVAC operation. Using two FDD methods, the AFDD agent was constructed to detect and diagnose two prevalent residential AC faults, airflow reduction and refrigerant undercharge. The home thermal model, along with the MPC-based optimization agent and AFDD agent, were tested at the Norman Test House, Miami Test House, Pacific Northwest National Laboratory (PNNL) Test House A, and PNNL Test House B. Finally, they were also field tested in nine demonstration homes with real occupants.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2016

WTP Waste Feed Qualification: Hydrogen Generation Rate Measurement Apparatus Testing Report

The generation rate of hydrogen gas in the Hanford tank waste will be measured during the qualification of the staged tank waste for processing in the Hanford Tank Waste Treatment and Immobilization Plant. Based on a review of past practices in measurement of the hydrogen generation, an apparatus to perform this measurement has been designed and tested for use during waste feed qualification. The hydrogen generation rate measurement apparatus (HGRMA) described in this document utilized a 100 milliliter sample in a continuously-purged, continuously-stirred vessel, with measurement of hydrogen concentration in the vent gas. The vessel and lid had a combined 220 milliliters of headspace. The vent gas system included a small condenser to prevent excessive evaporative losses from the sample during the test, as well as a demister and filter to prevent particle migration from the sample to the gas chromatography system. The gas chromatograph was an on line automated instrument with a large-volume sample-injection system to allow measurement of very low hydrogen concentrations. This instrument automatically sampled the vent gas from the hydrogen generation rate measurement apparatus every five minutes and performed data regression in real time. The fabrication of the hydrogen generation rate measurement apparatus was in accordance with twenty three (23) design requirements documented in the conceptual design package, as well as seven (7) required developmental activities documented in the task plan associated with this work scope. The HGRMA was initially tested for proof of concept with physical simulants, and a remote demonstration of the system was performed in the Savannah River National Laboratory Shielded Cells Mockup Facility. Final verification testing was performed using non-radioactive simulants of the Hanford tank waste. Three different simulants were tested to bound the expected rheological properties expected during waste feed qualification testing. These simulants were tested at different temperatures using purge gas spiked with varying amounts of hydrogen to provide verification that the system could accurately measure the hydrogen in the vent gas at steady state.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2016

An online replanning method using warm start optimization and aperture morphing for flattening-filter-free beams

Purpose: In a situation where a couch shift for patient positioning is not preferred or prohibited (e.g., MR-linac), segment aperture morphing (SAM) can address target dislocation and deformation. For IMRT/VMAT with flattening-filter-free (FFF) beams, however, SAM method would lead to an adverse translational dose effect due to the beam unflattening. Here the authors propose a new two-step process to address both the translational effect of FFF beams and the target deformation. Methods: The replanning method consists of an offline and an online step. The offline step is to create a series of preshifted-plans (PSPs) obtained by a so-called “warm start” optimization (starting optimization from the original plan, rather than from scratch) at a series of isocenter shifts. The PSPs all have the same number of segments with very similar shapes, since the warm start optimization only adjusts the MLC positions instead of regenerating them. In the online step, a new plan is obtained by picking the closest PSP or linearly interpolating the MLC positions and the monitor units of the closest PSPs for the shift determined from the image of the day. This two-step process is completely automated and almost instantaneous (no optimization or dose calculation needed). The previously developed SAM algorithm is then applied for daily deformation. The authors tested the method on sample prostate and pancreas cases. Results: The two-step interpolation method can account for the adverse dose effects from FFF beams, while SAM corrects for the target deformation. Plan interpolation method is effective in diminishing the unflat beam effect and may allow reducing the required number of PSPs. The whole process takes the same time as the previously reported SAM process (5–10 min). Conclusions: The new two-step method plus SAM can address both the translation effects of FFF beams and target deformation, and can be executed in full automation except the delineation of target contour required by the SAM process.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2025

Findings on subtask 3.3 – applicability of automated brine chemistry determinations for treatment and recovery processes through facility automation/modularization: engineering design study

Under the Energy & Environmental Research Center’s (EERC’s) ~$\$$22 million Phase II Brine Extraction and Storage Test (BEST) Program, a multimillion-dollar brine treatment technology test bed facility was established in western North Dakota to provide a platform for evaluating developing technologies and approaches for brine treatment and volume reduction. The initial facility, and associated research effort, was funded by the U.S. Department of Energy (DOE), with in-kind contributions provided by several industry participants and the state of North Dakota. Since its opening, the Brine Technology Test Facility (BTTF) has supported performance evaluations of desalination technologies capable of treating high-salinity produced water (PW) and enabled data collection for multiple approaches of PW management and critical material recovery. As part of the decommissioning process for the original project, facility ownership and liability were transferred to Select Water, which is providing the EERC with a continuing site access option for state or federal research and/or commercial technology development. This report documents the findings from a design study conducted by the EERC and the engineering firm that was originally contracted to design and construct the facility (Advanced Engineering and Environmental Services, LLC [AE2S]) that evaluated the current status of BTTF and its systems and developed a retrofit design to increase the facility’s capabilities through automation and modularization of its PW treatment infrastructure. The proposed retrofit will provide DOE and industry with an expanded range of conditioned PW that can be produced at the facility for evaluating fit-for-purpose water treatment technologies, online instrumentation for brine chemistry determination, and systems that recover critical materials like lithium and magnesium. The current facility consists of an 9600-square-foot facility that includes a 40-foot by 65-foot Class 1, Division 2-rated demonstration area and associated control rooms and lab-ready space capable of sourcing oil and gas PW and wastewater from industrial sources or tailoring brine compositions up to 300,000 mg/L total dissolved solids (TDS) and supplying them at rates up to 25 gpm for extended-duration technology demonstrations. The colocation of the facility with Select Water’s water management facilities allows for access and unloading of more than 10,000 bbl/day of trucked water delivered to site and associated access to on-site Class I and Class II brine disposal wells and nearby hazardous waste landfills operated and/or contracted by Select Water to dispose of concentrate and/or effluents associated with the testing.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗