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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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All sourcesNASA NTRSDOE OSTI
DOE OSTI2013

FINAL TECHNICAL REPORT: Underwater Active Acoustic Monitoring Network For Marine And Hydrokinetic Energy Projects

This project saw the completion of the design and development of a second generation, high frequency (90-120 kHz) Subsurface-Threat Detection Sonar Network (SDSN). The system was deployed, operated, and tested in Cobscook Bay, Maine near the site the Ocean Renewable Power Company TidGen™ power unit. This effort resulted in a very successful demonstration of the SDSN detection, tracking, localization, and classification capabilities in a high current, MHK environment as measured by results from the detection and tracking trials in Cobscook Bay. The new high frequency node, designed to operate outside the hearing range of a subset of marine mammals, was shown to detect and track objects of marine mammal-like target strength to ranges of approximately 500 meters. This performance range results in the SDSN system tracking objects for a significant duration - on the order of minutes - even in a tidal flow of 5-7 knots, potentially allowing time for MHK system or operator decision-making if marine mammals are present. Having demonstrated detection and tracking of synthetic targets with target strengths similar to some marine mammals, the primary hurdle to eventual automated monitoring is a dataset of actual marine mammal kinematic behavior and modifying the tracking algorithms and parameters which are currently tuned to human diver kinematics and classification.

16 TIDAL AND WAVE POWER↗
DOE OSTI2022

Multi‐objective optimization for cost‐efficient and resilient machining under tool wear

Abstract With the onset and rapid growth of smart manufacturing, there is a constant increase in the demand for automation technologies to enhance productivity while providing uninterrupted, cost‐efficient, and resilient machining. Traditional manufacturing systems, however, suffer from several losses due to machine faults and degradation. Specifically, tool wear directly impacts the precision and quality of the milled parts, which causes an increase in the scrap production. Hence, more attempts are required to meet the desired quota of successful parts, which in turn results in wasted material, longer delays, further tool degradation, and higher energy, machining, and labor costs. As such, this paper develops a multi‐objective optimization framework to generate the optimal control set points (e.g., feed rate and width of cut) that minimize the total cost of machining operations resulting from multiple contradictory cost functions (e.g., material, energy, tardiness, machining, labor, and tool) in the presence of tool wear. Notably, we estimate the total expected cost in dollars, which provides automatic and intuitive weighting in this multi‐objective formulation. The optimization framework is tested on a high‐fidelity face milling model that has been validated on real data from industry. Results show significant dollar savings of up to as compared to the default control scheme.

Wilson, James P.↗
DOE OSTI2020

Autonomous materials discovery driven by Gaussian process regression with inhomogeneous measurement noise and anisotropic kernels

Abstract A majority of experimental disciplines face the challenge of exploring large and high-dimensional parameter spaces in search of new scientific discoveries. Materials science is no exception; the wide variety of synthesis, processing, and environmental conditions that influence material properties gives rise to particularly vast parameter spaces. Recent advances have led to an increase in the efficiency of materials discovery by increasingly automating the exploration processes. Methods for autonomous experimentation have become more sophisticated recently, allowing for multi-dimensional parameter spaces to be explored efficiently and with minimal human intervention, thereby liberating the scientists to focus on interpretations and big-picture decisions. Gaussian process regression (GPR) techniques have emerged as the method of choice for steering many classes of experiments. We have recently demonstrated the positive impact of GPR-driven decision-making algorithms on autonomously-steered experiments at a synchrotron beamline. However, due to the complexity of the experiments, GPR often cannot be used in its most basic form, but rather has to be tuned to account for the special requirements of the experiments. Two requirements seem to be of particular importance, namely inhomogeneous measurement noise (input-dependent or non-i.i.d.) and anisotropic kernel functions, which are the two concepts that we tackle in this paper. Our synthetic and experimental tests demonstrate the importance of both concepts for experiments in materials science and the benefits that result from including them in the autonomous decision-making process.

36 MATERIALS SCIENCE↗
DOE OSTI2015

Pooling the resources of the CMS Tier-1 sites

The CMS experiment at the LHC relies on 7 Tier-1 centres of the WLCG to perform the majority of its bulk processing activity, and to archive its data. During the first run of the LHC, these two functions were tightly coupled as each Tier-1 was constrained to process only the data archived on its hierarchical storage. This lack of flexibility in the assignment of processing workflows occasionally resulted in uneven resource utilisation and in an increased latency in the delivery of the results to the physics community.The long shutdown of the LHC in 2013-2014 was an opportunity to revisit this mode of operations, disentangling the processing and archive functionalities of the Tier-1 centres. The storage services at the Tier-1s were redeployed breaking the traditional hierarchical model: each site now provides a large disk storage to host input and output data for processing, and an independent tape storage used exclusively for archiving. Movement of data between the tape and disk endpoints is not automated, but triggered externally through the WLCG transfer management systems.With this new setup, CMS operations actively controls at any time which data is available on disk for processing and which data should be sent to archive. Thanks to the high-bandwidth connectivity guaranteed by the LHCOPN, input data can be freely transferred between disk endpoints as needed to take advantage of free CPU, turning the Tier-1s into a large pool of shared resources. The output data can be validated before archiving them permanently, and temporary data formats can be produced without wasting valuable tape resources. Lastly, the data hosted on disk at Tier-1s can now be made available also for user analysis since there is no risk any longer of triggering chaotic staging from tape.In this contribution, we describe the technical solutions adopted for the new disk and tape endpoints at the sites, and we report on the commissioning and scale testing of the service. We detail the procedures implemented by CMS computing operations to actively manage data on disk at Tier-1 sites, and we give examples of the benefits brought to CMS workflows by the additional flexibility of the new system.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗
DOE OSTI2021

Sequential Perturbation-based FDIA Detection using DERs for Unbalanced Distribution System

The power distribution system with its reliance on automated monitoring and control infrastructure makes it a complicated cyber-physical system and vulnerable to various cyber-attacks. This paper proposes a moving target defense inspired false data injection attack (FDIA) detection mechanism for an unbalanced distribution system. In the proposed grid diagnosis service framework, the distribution system operator judiciously manipulates power outputs of a subset of inverter-based distribution energy resources (DERs) to create secret low magnitude voltage perturbations which are inconsequential to the normal operation of the grid. A mixed-integer-linear-programming algorithm is developed to select the optimal set of DERs that can create a voltage perturbation signal of the required magnitude at each sensor location at a minimum cost. Then, a sequential detector is applied that detects for the FDIA as measurements are received from individual sensors. The performance of the proposed perturbation-based FDIA detection framework is demonstrated via simulation of the IEEE 123 bus test system.

Attack Detection↗
DOE OSTI2025

Automation of Vulnerability and Patch Management: Information Extraction, Association, and Optimization

Vulnerability and patch management is an integral part of a robust cybersecurity program, yet it grows increasingly complex due to the sheer amount of data that must be analyzed. Particularly in Operational Technology (OT) environments, analysis must be done manually because of the lack of automated solutions. Additionally, there are many steps in this process, from the initial discovery of the vulnerability to the implementation of its remediation, and each step in the process requires different data in order to be performed effectively. In this work, we provide approaches and strategies to assist operators in industrial or OT environments throughout the vulnerability management cycle. Security advisories provide key information about mitigation strategies, or actions that can be taken when a patch is unavailable or cannot be installed. Details of these strategies are not shared in public vulnerability databases and must be found manually. We approach this problem by designing a solution to automatically identify that information within vendor security advisories and retrieve it for operator use. We start with an approach that requires domain-specific knowledge of certain frequently-seen reference websites. Next, an approach that can work on an arbitrary website but relies on certain keywords. Finally, an approach that uses Natural Language Processing (NLP) methods and does not require specific knowledge or keywords. Each of these approaches is more general than its predecessor; we demonstrate high accuracy for all approaches Advisories also often contain details of affected products in non-standard or natural language formats. While this information can be easily understood when read by an operator, the non-standard format acts as a barrier to effective automation. We provide an approach for the first step in this process: identifying vendors in security advisories and mapping them to a standard framework for representing digital assets and software products. We evaluate five established string similarity algorithms, plus one of our own design that combines string similarity and information theory, on the task of mapping vendors to their corresponding entries in the Common Platform Enumeration (CPE) repository. Our results show that our proposed metric outperforms all others. Due to the constraints on time, finances, and personnel for organizations, Large Language Models (LLMs) may seem like attractive opportunities for security operators to speed up information gathering; however, it is still not clear whether LLMs can handle vulnerability management tasks well. To answer this question, we perform an empirical study of LLMs’ ability to provide consistent, accurate information about vulnerabilities in order to guide organizations in their adoption of LLMs. We observe poor performance for all models tested, suggesting that these models are not well-suited to the consistent retrieval of accurate vulnerability information. Finally, once vulnerabilities have been identified and any additional information has been obtained, operators must decide which remediation actions to implement based on their available resources. This already-complex problem becomes even more so when we consider that a vulnerability may have multiple avenues for remediation. We formulate this scenario as two knapsack problems and provide solutions, which we then compare against several existing strategies for vulnerability prioritization seen in real operational environments.

McClanahan, Kylie↗
DOE OSTI2016

Bell Creek Test Site - Development of a Cost-Effective, Long-Term Monitoring Strategy

The Bell Creek oil field is operated by Denbury Onshore LLC (Denbury) as a commercial enhanced oil recovery (EOR) project which provides an opportunity to improve the understanding of associated carbon dioxide (CO 2 ) storage that occurs as a natural part of the EOR process as well as provides a large-scale CO 2 injection site that can be used to inform carbon storage in deep saline formations (DSFs). The Plains CO 2 Reduction (PCOR) Partnership Bell Creek project, which is conducted in collaboration with the commercial project, provides a substantive opportunity for the validation of viable monitoring, verification, and accounting (MVA) strategies with applicability to both large-scale carbon storage in DSFs and associated carbon storage through EOR. In collaboration with Denbury, a 5-year research MVA program was conducted to demonstrate 16 MVA techniques with applications to geologic CO 2 storage scenarios. The program represents 1.5 years of preinjection and over 3 years of operational MVA activities coinciding with the first 3.2 million tons of associated CO 2 storage. Insights provided through the Bell Creek project and investigation into a long-term monitoring plan have resulted in an ongoing field demonstration of two emerging geophysical techniques, the Krauklis seismic wave (K-wave) and scalable, automated, semipermanent seismic array (SASSA), through complementary projects. These techniques show promise for providing more cost-efficient, lower-impact, near-real-time solutions for long-term monitoring. If proven successful, these techniques will provide additional techniques compatible with long-term monitoring strategies. While the development of a long-term monitoring plan for the Bell Creek project will take into consideration specific operations and benefits to the project, the case study will provide recommendations that are expected to be broadly applicable for commercial carbon storage projects throughout the PCOR Partnership region and the wider deployment of carbon capture and storage.

02 PETROLEUM↗
DOE OSTI2015

TU-G-BRD-02: Automated Systematic Quality Assurance Program for Radiation Oncology Information System Upgrades

Purpose: To: (1) describe an independent, automated, systematic software-based protocol for verifying clinical data accuracy/integrity for mitigation of data corruption/loss risks following radiation oncology information system (ROIS) upgrades; and (2) report on application of this approach in an academic/community practice environment. Methods: We propose a robust approach to perform quality assurance on the ROIS after an upgrade, targeting four data sources: (1) ROIS relational database; (2) ROIS DICOM interface; (3) ROIS treatment machine data configuration; and (4) ROIS-generated clinical reports. We investigated the database schema for differences between pre-/post-upgrade states. Paired DICOM data streams for the same object (such as RT-Plan/Treatment Record) were compared between pre-/post-upgrade states for data corruption. We examined machine configuration and related commissioning data files for changes and corruption. ROIS-generated treatment appointment and treatment parameter reports were compared to ensure patient encounter and treatment plan accuracy. This protocol was supplemented by an end-to-end clinical workflow test to verify essential ROI functionality and integrity of components interfaced during patient care chain of activities. We describe the implementation of this protocol during a Varian ARIA system upgrade at our clinic. Results: We verified 1,638 data tables with 2.4 billion data records. For 222 under-treatment patients, 605 DICOM RT plans and 13,480 DICOM treatment records retrieved from the ROIS DICOM interface were compared, with no differences in fractions, doses delivered, or treatment parameters. We identified 82 new data tables and 78 amended/deleted tables consistent with the upgrade. Reports for 5,073 patient encounters over a 2-week horizon were compared and were identical to those before the upgrade. Content in 12,237 xml machine files was compared, with no differences identified. Conclusion: An independent QA/validation approach for ROIS upgrades was developed and implemented at our clinic. The success of this approach ensures a robust QA of ROIS upgrades without manual paper/electronic checks and associated intensive labor.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2015

Distribution System Secondary Circuit Parameter Estimation for Model Calibration

To analyze and coordinate the operation of distribution systems with rapidly increasing amounts of PV, more accurate distribution system models are required, especially for the distribution system secondary (low-voltage) circuits down to the point of common coupling for distributed PV. There is a growing need for automated procedures to calibrate the distribution system secondary circuit models that are typically either not modeled at all or are modeled with a lower level of detail than the better modeled medium-voltage systems. This report presents an accurate, flexible, and computationally efficient method to use measurement data to estimate secondary circuit series impedance parameters in existing utility feeder models. The parameter estimation method assumes well-modeled primary circuit models, known secondary circuit topologies, and AMI active power, and reactive power measurements at all the loads in the secondary circuit. The method also requires AMI voltage measurement at most of the loads in the secondary circuit but can handle loads that do not have voltage measurements. No existing secondary circuit model information is needed, except for topology. The method is based on the well-known linearized voltage drop approximation and linear regression. The performance of the method is demonstrated on a three-phase test circuit with ten different secondary circuit topologies and on the Georgia Tech campus distribution system with AMI data. The developed method can be utilized to improve existing utility feeder models for more accurate analysis and operation with ubiquitous distributed PV interconnected on the low-voltage circuits.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2016

A morphology independent methodology for quantifying planview river change and characteristics from remotely sensed imagery

Remotely sensed imagery of rivers has long served as a means for characterizing channel properties and detection of planview change. In the last decade the dramatic increase in the availability of satellite imagery and processing tools has created the potential to greatly expand the spatial and temporal scale of our understanding of river morphology and dynamics. To date, the majority of GIS and automated analyses of planview changes in rivers from remotely sensed data has been developed for single-threaded meandering river systems. These methods have limited applicability to many of the earth's rivers with complex multi-channel planforms. Here we present the methodologies of a set of analysis algorithms collectively called Spatially Continuous Riverbank Erosion and Accretion Measurements (SCREAM). SCREAM analyzes planview river metrics regardless of river morphology. These algorithms quantify both the erosion and accretion rates of riverbanks from binary masks of channels generated from imagery acquired at two time periods. Additionally, the program quantifies the area of change between river channels and the surrounding floodplain and area of islands lost or formed between these two time periods. To examine variations in erosion rates in relation to local channel attributes and make rate comparisons between river systems of varying sizes, the program determines channel widths and bank curvature at every bank pixel. SCREAM was developed and tested on rivers with diverse and complex planform morphologies in imagery acquired from a range of observational platforms with varying spatial resolutions. Here, validation and verification of SCREAM-generated metrics against manual measurements show no significant measurement errors in determination of channel width, erosion, and bank aspects. SCREAM has the potential to provide data for both the quantitative examination of the controls on erosion rates and for the comparison of these rates across river systems ranging broadly in size and planform morphology.

47 OTHER INSTRUMENTATION↗
DOE OSTI2024

An Empirical Validation of a Constrained Bin Packing Algorithm for a Home Energy Management System

The increasing number of intelligent electrical appliances and home energy management systems provide a big opportunity for demand response services from residential and small commercial buildings to the grid. Simultaneously, direct control of individual devices by utilities can cause communication bottlenecks, as well as coordination and privacy concerns. These challenges can be addressed by combining the constituent devices into a single house battery equivalent for the purposes of demand response, using Minkowski sum and a 2d bin packing problem. However, the well-studied traditional problems have not been tested in a real house, as implementation carries significant challenges of its own. We deploy the packing problem on residential devices in a controllable house. We report the barriers we found, such as charge forecast and scalability of the algorithm, and discuss our solutions. The study serves as an intermediate step between existing theoretical research and possible future steps, such as prototype deployments of systems that provide residential demand response.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2015

SU-C-BRD-05: Non-Invasive in Vivo Biodosimetry in Radiotherapy Patients Using Electron Paramagnetic Resonance (EPR) Spectroscopy

Purpose: Medical intervention following a major, unplanned radiation event can elevate the human whole body exposure LD50 from 3 to 7 Gy. On a large scale, intervention cannot be achieved effectively without accurate and efficient triage. Current methods of retrospective biodosimetry are restricted in capability and applicability; published human data is limited. We aim to further develop, validate, and optimize an automated field-deployable in vivo electron paramagnetic resonance (EPR) instrument that can fill this need. Methods: Ionizing radiation creates highly-stable, carbonate-based free radicals within tooth enamel. Using a process similar to nuclear magnetic resonance, EPR directly measures the presence of radiation-induced free radicals. We performed baseline EPR measurements on one of the upper central incisors of total body irradiation (TBI) and head and neck (H&N) radiotherapy patients before their first treatment. Additional measurements were performed between subsequent fractions to examine the EPR response with increasing radiation dose. Independent dosimetry measurements were performed with optically-stimulated luminescent dosimeters (OSLDs) and diodes to more accurately establish the relationship between EPR signal and delivered radiation dose. Results: 36 EPR measurements were performed over the course of four months on two TBI and four H & N radiotherapy patients. We observe a linear increase in EPR signal with increasing dose across the entirety of the tested range. A linear least squares-weighted fit of delivered dose versus measured signal amplitude yields an adjusted R-square of 0.966. The standard error of inverse prediction (SEIP) is 1.77 Gy. For doses up to 7 Gy, the range most relevant to triage, we calculate an SEIP of 1.29 Gy. Conclusion: EPR spectroscopy provides a promising method of retrospective, non-invasive, in vivo biodosimetry. Our preliminary data show an excellent correlation between predicted signal amplitude and delivered dose. With further development, a robust means of predicting delivered radiation dose from EPR measurements is expected. This project was funded by the Biomedical Advanced Research and Development Authority (BARDA) within the U.S. Department of Health and Human Services subcontracted through the Geisel School of Medicine at Dartmouth and by the Dartmouth Physically-Based Biodosimetry Center for Medical Countermeasures Against Radiation (Dart-Dose CMCR) Pilot Program.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2016

SU-G-201-09: Evaluation of a Novel Machine-Learning Algorithm for Permanent Prostate Brachytherapy Treatment Planning

Purpose: A novel, automated, algorithm for permanent prostate brachytherapy (PPB) treatment planning has been developed. The novel approach uses machine-learning (ML), a form of artificial intelligence, to substantially decrease planning time while simultaneously retaining the clinical intuition of plans created by radiation oncologists. This study seeks to compare the ML algorithm against expert-planned PPB plans to evaluate the equivalency of dosimetric and clinical plan quality. Methods: Plan features were computed from historical high-quality PPB treatments (N = 100) and stored in a relational database (RDB). The ML algorithm matched new PPB features to a highly similar case in the RDB; this initial plan configuration was then further optimized using a stochastic search algorithm. PPB pre-plans (N = 30) generated using the ML algorithm were compared to plan variants created by an expert dosimetrist (RT), and radiation oncologist (MD). Planning time and pre-plan dosimetry were evaluated using a one-way Student’s t-test and ANOVA, respectively (significance level = 0.05). Clinical implant quality was evaluated by expert PPB radiation oncologists as part of a qualitative study. Results: Average planning time was 0.44 ± 0.42 min compared to 17.88 ± 8.76 min for the ML algorithm and RT, respectively, a significant advantage [t(9), p = 0.01]. A post-hoc ANOVA [F(2,87) = 6.59, p = 0.002] using Tukey-Kramer criteria showed a significantly lower mean prostate V150% for the ML plans (52.9%) compared to the RT (57.3%), and MD (56.2%) plans. Preliminary qualitative study results indicate comparable clinical implant quality between RT and ML plans with a trend towards preference for ML plans. Conclusion: PPB pre-treatment plans highly comparable to those of an expert radiation oncologist can be created using a novel ML planning model. The use of an ML-based planning approach is expected to translate into improved PPB accessibility and plan uniformity.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2024

Aerosol Envelope Sealing of Existing Residences

This report explores the best methods for aerosol envelope sealing of unoccupied, existing residences and documents typical leakage reductions. The project consisted of three distinct efforts: (1) scaled field demonstrations of the sealing process, tracking from setup to sealing and cleanup, (2) laboratory testing of new sealants that dry clear, making them more appropriate for retrofit applications, and (3) BEopt™ (Building Energy Optimization Tool) modeling of the energy implications of the measured reductions in leakage. Overall, aerosol sealing performance in existing homes was effective, with an average leakage reduction of 47% across all 34 sites. Conventional approaches typically only produce leakage reductions of 25%–30%. The aerosol sealing technology could provide a process for existing homes and multifamily units to gain the benefits of a well-sealed home at a reasonable cost with minimal disruption.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2015

MO-G-304-02: Knowledge Based DVH Prediction Using a Geometric Dose Transform

Purpose: To demonstrate a novel method for predicting patient dose-volume histograms (DVHs) using a prior database of optimized radiotherapy treatment plans. Such predicted DVHs could be useful for automating treatment planning. Methods: Our initial demonstration utilized a database of 100 prostate intensity-modulated radiotherapy (IMRT) data-sets. Each data-set contained a CT image with contours of the planning target volume (PTV), rectum, and bladder, the parameters of a clinically approved IMRT plan, and a corresponding simulated dose distribution. We applied a novel geometric transformation to remove the influence of the PTV size, shape, and location on the dose distribution. We termed the transformed distribution the geometrically normalized dose distribution (GNDD). This normalization transform was applied to 80 data-sets randomly selected from the database, and a population GNDD was computed as the average. Next, the population GNDD was mapped onto each of the remaining 20 patient datasets using the reverse of the geometric normalization transform, and predicted DVHs were calculated from the reverse transformed dose distributions (GNDD-DVHs). In addition, a state of the art machine learning based method from the literature was tested for comparison. Results: DVH prediction accuracy was quantified by calculating the relative root mean squared error (rRMSE) on predicted DVHs for the 20 test patients using their known DVHs. For bladder, rectum, and PTV average rRMSEs for the GNDD method were 9.7 ± 4.2%, 13.9 ± 6.0%, and 2.3 ± 0.5% respectively. Prediction results using GNDD were roughly equivalent to that from the machine learning method. Conclusion: We developed a new method for predicting DVH curves from a database of prior patient plans. We demonstrated that our simple approach achieves accuracy comparable to a method using a complicated machine learning based approach.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2020

Evaluation of Material Balance Approaches for Hanford Direct-Feed Low Activity Waste Processing - 20022

The Hanford Site has accumulated millions of gallons of tank waste from reprocessing spent fuel to recover plutonium, uranium, cesium, and strontium. The supernatant from the accumulated tank waste will be treated using a Direct Feed Low-Activity Waste approach. The supernatant will be treated to remove solids and cesium in the Tank-Side Cesium Removal process in the Hanford tank farm, then vitrified in a semi-batch process in the Hanford Waste Treatment and Immobilization Plant (WTP). Currently, each batch of feed is sampled at three locations prior to being fed to the melter: the feed qualification tank in the Hanford tank farm as well as the concentrate receipt vessel (CRV) and melter feed preparation vessel (MFPV) in the WTP. The feed qualification sample is taken from a large batch of accumulated feed, only two to three samples are expected each year. Approximately 275 samples from the CRVs and 1100 samples from the MFPV are expected each year. An evaluation was performed to determine if a material balance based on the feed qualification sample could replace most of the sampling in the CRVs and MFPVs. The evaluation consisted of three elements: (1) determination of the practicality of using a material balance to estimate the stream composition of the CRV and MFPV contents, (2) evaluation of whether the material balance could be automated using the existing process control system, and (3) determination of the uncertainty in glass composition using the material balance approach. It is assumed that periodic sampling at the CRV and MFPV would be performed periodically to re-baseline the material balance, evaluations are in progress to determine the frequency of this periodic sampling. Process sample locations downstream of the melter were reviewed as well, but the partitioning of semi-volatile species in the melter was determined to preclude extending the material balance approach past the melter. It was determined that replacement of the CRV sample location was feasible and did not increase process uncertainty or significantly impact waste loading. Replacement of the MFPV sample was also determined to be feasible, but that measurement of the glass former chemical addition may be needed prior to addition of these chemicals to the MFPV. This measurement could be performed by an in situ laser-induced breakdown spectroscopy (LIBS) system. Limited tests were performed to evaluate LIBS for direct measurements of the low-activity waste melter feed. The use of a material balance would eliminate over 1200 samples each year if only 10% of the CRV and MFPV batches are sampled and could likely allow the WTP laboratory to operate on days only versus 24/7 operation. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2022

Energy and mobility impacts of connected autonomous vehicles with co-optimization of speed and powertrain on mixed vehicle platoons

Intersections are known to be traffic bottlenecks where a significant amount of energy consumption could be caused due to deceleration/acceleration in the presence of red signals. With an increased level of connectivity and automation of intelligent transportation systems, connected autonomous vehicles (CAVs) are expected to be able to proactively adjust their driving strategies subject to constraints imposed by the predicted future traffic. As a result, many potential benefits can be achieved, such as improved energy efficiency, enhanced traffic safety, among many others. Notably, the way CAVs are controlled affects the following legacy vehicles (LVs) due to complex traffic dynamics. Here, we are particularly interested in studying the energy and mobility impact of CAVs with an improved traffic prediction method on mixed vehicle platoons at various market penetration rates. Leveraging traffic prediction, CAVs are controlled with co-optimization of their speed and gear position. Specifically, a traffic prediction framework in a rolling horizon fashion is employed based upon a modified Payne–Whitham (PW) model capable of handling mixed traffic consisting of CAVs and LVs. The prediction error of the modified PW model is reduced by 53.62% compared to that of the standard PW model under test scenarios. According to the predicted traffic conditions, speed and gear position of CAVs are co-optimized with the primary goal of minimizing energy consumption when driving on a signalized arterial. The energy benefits achieved by CAVs and the impact of CAVs on LVs behind are studied comprehensively for mixed vehicle platoons. The lead LV follows a real-world speed profile collected on TH-55 in Minnesota. Numerical results show that energy benefits achieved by the vehicle platoon range from 2% to 16%, and a 1% to 5% reduction in travel time for LVs behind CAVs is also observed, at different penetration rates of CAVs in various traffic scenarios. Furthermore, it is observed that CAVs using the proposed eco-driving approach appear to have a positive impact on the LVs behind in terms of energy consumption, regardless of the driving styles of the LVs ahead.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2016

The dedicated monitor of exotransits (DEMONEX): Seven transits of XO-4b

The DEdicated MONitor of EXotransits (DEMONEX) was a 20-inch robotic and automated telescope to monitor bright stars hosting transiting exoplanets to discover new planets and improve constraints on the properties of known transiting planetary systems. We present results for the misaligned hot Jupiter XO-4b containing seven new transits from the DEMONEX telescope, including three full and four partial transits. We combine these data with archival light curves and archival radial velocity measurements to derive the host star mass M{sub ∗}=1.293{sub −0.029}{sup +0.030} M{sub ⊙} and radius R{sub ∗}=1.554{sub −0.030}{sup +0.042} R{sub ⊙}, the planet mass M{sub P}=1.615{sub −0.099}{sup +0.10} M{sub J} and radius R{sub P}=1.317{sub −0.029}{sup +0.040} R{sub J}, and a refined ephemeris of P=4.1250687±0.0000024 days and T{sub 0}=2,4547,58.18978±0.00024 BJD{sub TDB}. We include archival Rossiter–McLaughlin measurements of XO-4 to infer the stellar spin–planetary orbit alignment of λ=−40.0{sub −7.5}{sup +8.8} degrees. We test the effects of including various detrend parameters, theoretical and empirical mass–radius relations, and Rossiter–McLaughlin models. We infer that detrending against CCD position and time or airmass can improve data quality but can have significant effects on the inferred values of many parameters—most significantly R{sub P}/R{sub ∗} and the observed central transit times T{sub C}. In the case of R{sub P}/R{sub ∗} we find that the systematic uncertainty due to detrending can be three times that of the quoted statistical uncertainties. The choice of mass–radius relation has little effect on our inferred values of the system parameters. The choice of Rossiter–McLaughlin models can have significant effects on the inferred values of vsinI{sub ∗} and the stellar spin–planet orbit angle λ.

79 ASTRONOMY AND ASTROPHYSICS↗