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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 OSTI2024

Adsorption Thermodynamics for Process Simulation

Adsorption has rapidly evolved in recent decades and is an established separation technology extensively practiced in gas separation industries and others. However, rigorous thermodynamic modeling of multicomponent adsorption equilibrium remains elusive, and industrial practitioners rely heavily on expensive and time-consuming trial-and-error pilot studies to develop adsorption units. Here, this article highlights the need for rigorous adsorption thermodynamic models and the limitations and deficiencies of existing models such as the extended Langmuir isotherm, dual-process Langmuir isotherm, and adsorbed solution theory. It further presents a series of recent advances in the generalization of the classical Langmuir isotherm of single-component adsorption by deriving an activity coefficient model to account for the adsorbed phase adsorbate–adsorbent interactions, substituting adsorbed phase adsorbate and vacant site concentrations with activities, and extending to multicomponent competitive adsorption equilibrium, both monolayer and multilayer. Requiring a minimum set of physically meaningful model parameters, the generalized Langmuir isotherm for monolayer adsorption and the generalized Brunauer–Emmett–Teller isotherm for multilayer adsorption address various thermodynamic modeling challenges including adsorbent surface heterogeneity, isosteric enthalpies of adsorption, BET surface areas, adsorbed phase nonideality, adsorption azeotrope formation, and multilayer adsorption. Also discussed is the importance of quality adsorption data that cover sufficient temperature, pressure, and composition ranges for reliable determination of the model parameters to support adsorption process simulation, design, and optimization.

09 BIOMASS FUELS↗
DOE OSTI2016

Preventing Data Ambiguity in Infectious Diseases with Four-Dimensional and Personalized Evaluations

Background Diagnostic errors can occur, in infectious diseases, when anti-microbial immune responses involve several temporal scales. When responses span from nanosecond to week and larger temporal scales, any pre-selected temporal scale is likely to miss some (faster or slower) responses. Hoping to prevent diagnostic errors, a pilot study was conducted to evaluate a four-dimensional (4D) method that captures the complexity and dynamics of infectious diseases. Methods Leukocyte-microbial-temporal data were explored in canine and human (bacterial and/or viral) infections, with: (i) a non-structured approach, which measures leukocytes or microbes in isolation; and (ii) a structured method that assesses numerous combinations of interacting variables. Four alternatives of the structured method were tested: (i) a noise reduction oriented version, which generates a single (one data point-wide) line of observations; (ii) a version that measures complex, three-dimensional (3D) data interactions; (iii) a non-numerical version that displays temporal data directionality (arrows that connect pairs of consecutive observations); and (iv) a full 4D (single line-, complexity-, directionality based) version. Results In all studies, the non-structured approach revealed non-interpretable (ambiguous) data: observations numerically similar expressed different biological conditions, such as recovery and lack of recovery from infections. Ambiguity was also found when the data were structured as single lines. In contrast, two or more data subsets were distinguished and ambiguity was avoided when the data were structured as complex, 3D, single lines and, in addition, temporal data directionality was determined. The 4D method detected, even within one day, changes in immune profiles that occurred after antibiotics were prescribed. Conclusions Infectious disease data may be ambiguous. Four-dimensional methods may prevent ambiguity, providing earlier, in vivo, dynamic, complex, and personalized information that facilitates both diagnostics and selection or evaluation of anti-microbial therapies.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

An Assessment of the Error Due to Computing Waste Isolation Pilot Plant Porosity Using the Porosity Response Surface Approach

The Waste Isolation Pilot Plant Performance Assessment (WIPP PA) must predict the likelihood that radionuclides will escape into the biosphere via mechanisms that depend on geohydraulic flow. Ideally, one would predict the geohydraulic flow using coupled geohydraulic and geomechanical simulations, but such coupled simulations are not computationally tractable. Instead, Sandia has historically used a look-up table of porosities for a given fluid pressure and time, called the porosity response surface, but this approach can introduce porosity errors because it largely ignores the porosity’s dependence on the past fluid pressure history. This report discusses efforts to quantify these porosity errors for both the legacy and new porosity response surfaces. Six hundred different fluid pressure histories were fed through the legacy/new geomechanical model and the legacy/new porosity response surface to generate six hundred porosity error histories. The error associated with the legacy porosity surface was substantial, while the error associated with the new porosity surface was typically small, except when fluid pressures exceeded the lithostatic pressure at the repository. In response to the errors at high pressures, a preliminary study of the WIPP PA’s sensitivity to these porosity errors was conducted. The study found that reducing the porosity errors at high pressures negligibly affected predictions of radionuclide releases. Finally, an initial machine-learned model for porosity was developed. This ML model significantly reduced the porosity error at high pressures, but sizable errors remained, so more development is necessary before coupling an ML model to the geohydraulic model.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2020

Joint Channel Equalization and Symbol Detection for IoT Devices in Severe Multipath Channels

Internet of Things (IoT) devices often transmit very short message blocks without strong error correction coding or pilots for channel equalization. When the transmitted signals encounter a severe multipath channel, the receiver is often unable to equalize the resulting inter-symbol interference (ISI) via traditional methods, leading to a high retransmission rate. This paper proposes a joint channel equalization and symbol detection scheme for this scenario by utilizing the preamble sequence as a training pilot for equalization and the relatively weak cyclic redundancy check (CRC) 8-Dallas code for error correction. By assuming a sparse channel impulse response, the proposed joint channel equalization and symbol detection scheme formulates an l1 norm constrained optimization problem and solves it by a saddle-point algorithm. The proposed algorithm is applied to a set of real-world underwater animal tracking IoT data, and the results show improvement of correct message detection rate from 21:5% to 41:6%.

Jing, Shusen↗
DOE OSTI2016

MO-FG-202-06: Improving the Performance of Gamma Analysis QA with Radiomics- Based Image Analysis

Purpose: The use of gamma analysis for IMRT quality assurance has well-known limitations. Traditionally, a simple thresholding technique is used to evaluated passing criteria. However, like any image the gamma distribution is rich in information which thresholding mostly discards. We therefore propose a novel method of analyzing gamma images that uses quantitative image features borrowed from radiomics, with the goal of improving error detection. Methods: 368 gamma images were generated from 184 clinical IMRT beams. For each beam the dose to a phantom was measured with EPID dosimetry and compared to the TPS dose calculated with and without normally distributed (2mm sigma) errors in MLC positions. The magnitude of 17 intensity histogram and size-zone radiomic features were derived from each image. The features that differed most significantly between image sets were determined with ROC analysis. A linear machine-learning model was trained on these features to classify images as with or without errors on 180 gamma images.The model was then applied to an independent validation set of 188 additional gamma distributions, half with and half without errors. Results: The most significant features for detecting errors were histogram kurtosis (p=0.007) and three size-zone metrics (p<1e-6 for each). The sizezone metrics detected clusters of high gamma-value pixels under mispositioned MLCs. The model applied to the validation set had an AUC of 0.8, compared to 0.56 for traditional gamma analysis with the decision threshold restricted to 98% or less. Conclusion: A radiomics-based image analysis method was developed that is more effective in detecting error than traditional gamma analysis. Though the pilot study here considers only MLC position errors, radiomics-based methods for other error types are being developed, which may provide better error detection and useful information on the source of detected errors. This work was partially supported by a grant from the Agency for Healthcare Research and Quality, grant number R18 HS022244-01.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2021

Energy use of piperazine with the advanced stripper from pilot plant testing

Here, piperazine with the Advanced Stripper (PZAS™) was tested at the National Carbon Capture Center (NCCC) at coal and NGCC conditions. Energy performance was measured with accounting for heat loss. The net heat duty was found to be identical at 2.45 GJ/tonne for both 4 and 12% CO 2 at 90% CO 2 removal. The average measured heat loss was 0.38 GJ/tonne and represented 30% of the gross heat rate. For PZAS, the warm rich piping before the control valve is the largest in surface area. Heat loss at this location increased heat duty by 80% of the heat loss value. Therefore, the total heat duty was decreased by 80% of the heat loss. The Independence™ model was validated using 112 steady-state pilot plant runs and predicted average net heat duty with an error of 9% and lean loading with an error of 10%. The model was used to simulate process impacts of heat loss for the PZAS and simple stripper configurations. For both strippers, heat loss was found to impact heat duty at locations other than just the steam heater. While heat loss in the steam heater was the most important for PZAS, heat loss in the column was the most important for the simple stripper due to a large top-side pinch and CO 2 reabsorption at the same location. CO 2 reabsorption in PZAS occurred only at high heat loss values (~0.14 GJ/hr), compared to low heat loss values (~ 0.03 GJ/hr) in the PZ-simple stripper. Excessive packing in the PZ-simple stripper caused a large top-side pinch at 0.23 mol/mol lean loading, making the column very sensitive to heat loss. Heat loss in the PZ-simple stripper increased heat duty by 120–150% at typical heat loss values and lean loading of 0.23 mol/mol. Heat loss in the MEA-simple stripper only increased heat duty by 1–13% in a typical lean loading range of 0.1–0.2 mol/mol. A large bottom side pinch at low lean loading for 7 m MEA made the effect of heat loss less pronounced.

42 ENGINEERING↗
DOE OSTI2018

Elaborating a First Version of a KIF For The Manche Surface Repository in France - 18060

The concept of a Key Information File developed in the framework of the RK and M international initiative has been tested against the case of the Manche surface repository. Work is still in progress. The aim is to issue a final version before end of year 2018, with the file for the decennial safety assessment update. The KIF will be part of the regulatory required Summary Memory File. Testing the KIF brought several lessons, on the elaboration process of such document, on the adaptation of the KIF concept to a surface repository, on the necessary evolution of the document over time. The Manche repository is the first repository operated by Andra, and the one at its most advanced stage. Therefore, it is de facto a pilot for similar issues encountered on other Andra's repositories. The difficulties encountered, the errors made and analyzed are the basis for improved provisions in other cases. It has been the case for the repository design, when the second surface repository was implemented in the Aube district. It is now the case for the cover design, for monitoring and for memory provisions. (authors)

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

Flexible Pilot Jobs Framework for Distributed High Throughput Computing

Experimental particle physics has been at the forefront of analyzing the world’s largest datasets for decades. The high-energy physics (HEP) community was among the first to develop suitable software and computing tools for this purpose. GlideinWMS is a Glidein-based workload management system whose purpose is to provide experiments like CMS at CERN, DUNE at Fermilab, and others, a way to access and efficiently use vast amounts of computing resources. This system wants to provide a simple way to submit jobs to a set of computing resources, that will be provided to users behind the scenes. Glideins are the pilot jobs executed on the worker nodes at the grid sites, performing operations such as hardware detection, environment setup, and error handling. After all these operations, they will launch the actual user job. Many grid sites are supported, such as shared clusters, Google CE, and AWS. My internship aimed to design and code a flexible pilot jobs framework that will replace the one used by GlideinWMS, developing a modular and flexible skeleton of the Glidein and adding further functionalities. My project also focused on the application of machine learning techniques as support to this management system.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2017

Savannah River National Laboratory Collaborative Innovation Pilot Team - 17560

The Collaborative Innovation (COIN) program was established by the Savannah River National Laboratory (SRNL) in March 2016. The goal of the SRNL COIN program is to identify innovative options to significantly reduce program and project risks (improve safety, reduce schedule and cost) of high priority Department of Energy Environmental Management, National Nuclear Security Administration, and Nuclear Energy missions. The COIN Advisory Board selected as the target outcome of the first COIN effort a 50% improvement to the Savannah River Site (SRS) H-Canyon spent nuclear fuel (SNF) processing 'baseline,' or current plan, as defined in the SRS Nuclear Materials Planning Roadmap. A small team of SRNL and Savannah River Nuclear Solutions personnel with pertinent experience and expertise was co-located and largely sequestered for eight weeks to identify improvements to H-Canyon processing of SNF. Following the process defined in the SRNL COIN Charter, the COIN Pilot Team analyzed the baseline for processing SNF through H Canyon; generated alternative ideas; tested seemingly viable ideas by reaching out to other subject-matter experts; and identified options for consideration for implementation by the COIN Advisory Board. The COIN Pilot produced a number of important outcomes, including important lessons learned for subsequent COIN efforts. The two principal outcomes are these: 1. The COIN Pilot Team conducted a comprehensive examination of the individual unit operations, the integrated system, and external interfaces to produce a timeline of H-Canyon processing and sampling times that had not previously existed. The general timeline served as the baseline from which the Pilot Team identified processing improvements. In addition, the timeline has already been employed in other planning efforts. 2. The COIN Pilot Team determined that innovations to the H-Canyon Dissolving unit operation alone could increase the number of SNF bundles processed through H Canyon by the target of 50%, but with little margin for error. To ensure that the gains in throughput in the Dissolving unit operation would be maintained throughout the facility, the Pilot Team identified four additional improvements to create greater efficiency and flexibility in downstream H-Canyon unit operations. The COIN Pilot Team's innovations reduce the processing time of a single batch of SNF through H Canyon from about 70 days to about 50 days. This talk will review the COIN process and the results of the pilot of the process. (authors)

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2016

TH-CD-207A-11: Sensitivity Analysis of Action Limits for Real-Time EPID-Based Delivery Verification System Using Artificial Clinical Relevant Error Simulations

Purpose: A real-time patient treatment delivery verification system using EPID (Watchdog) has been developed as an advanced patient safety tool. In a pilot study data was acquired for 119 prostate and head and neck (HN) IMRT patient deliveries to generate body-site specific action limits using statistical process control. The purpose of this study is to determine the sensitivity of Watchdog to detect clinically significant errors during treatment delivery. Methods: Watchdog utilizes a physics-based model to generate a series of predicted transit cine EPID images as a reference data set, and compares these in real-time to measured transit cine-EPID images acquired during treatment using chi comparison (4%, 4mm criteria) after the initial 2s of treatment to allow for dose ramp-up. Four study cases were used; dosimetric (monitor unit) errors in prostate (7 fields) and HN (9 fields) IMRT treatments of (5%, 7%, 10%) and positioning (systematic displacement) errors in the same treatments of (5mm, 7mm, 10mm). These errors were introduced by modifying the patient CT scan and re-calculating the predicted EPID data set. The error embedded predicted EPID data sets were compared to the measured EPID data acquired during patient treatment. The treatment delivery percentage (measured from 2s) where Watchdog detected the error was determined. Results: Watchdog detected all simulated errors for all fields during delivery. The dosimetric errors were detected at average treatment delivery percentage of (4%, 0%, 0%) and (7%, 0%, 0%) for prostate and HN respectively. For patient positional errors, the average treatment delivery percentage was (52%, 43%, 25%) and (39%, 16%, 6%). Conclusion: These results suggest that Watchdog can detect significant dosimetric and positioning errors in prostate and HN IMRT treatments in real-time allowing for treatment interruption. Displacements of the patient require longer to detect however incorrect body site or very large geographic misses will be detected rapidly.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2026

Hydrogen Production System Scaling Using a High-Fidelity Simulation-Optimization Framework

Proton exchange membrane (PEM) electrolyzers are widely used for hydrogen production, yet few validated, high-fidelity tools can reliably guide scale-up. Using measured performance from a 50-hour hardware-in-the-loop pilot test, a physics-based, plant-level model of a 1.25 MW PEM electrolyzer and its balance-of-plant (BoP) subsystems is developed and validated. The model couples electrochemistry and thermal/flow submodels and is calibrated against pilot test data via a genetic algorithm (GA) workflow. Validation yields a mean absolute percentage error (APE) of 0.43% for cell voltage and stack power. Two scale-out strategies are then benchmarked under a common 7-day wind-and-photovoltaic (PV) profile: (i) linear duplication of 1.25 MW blocks and (ii) shared-BoP architectures. Sharing BoP between stacks reduces BoP energy by 27% at 10 MW and 34% at 100 MW (vs. linear duplication) and improves system specific energy consumption (SEC) to 52.9 and 52.6 kWh/kg, respectively (from 54.0 kWh/kg with linear duplication). Partial-load studies (25-100% set-point) show that cumulative hydrogen production remains nearly constant down to 50% load because all cases use the same weekly renewable-energy input. Below 50%, the power cap limits how much energy can be used within 168 h, which reduces hydrogen output. The model further indicates that the practical operating optimum lies between 50% and 85% load, where efficiency gains begin to appear without significant loss in hydrogen output. Moreover, the efficiency gains at lower loads are offset by reduced production. The validated framework supports scenario-based engineering trade-off studies for large configurations (10-100 MW) and for operating policies under variable renewables.

08 HYDROGEN↗
DOE OSTI2025

Quantifying UAS Observation Error Variance Used in Data Assimilation Systems and Its Impact on Predictive Skill

Observation error determines the weights of the observations and background state used in data assimilation to generate analyses. Quantifying observation error is critical for the optimal assimilation of observational data sets. Uncrewed Aircraft System (UAS) observations have shown potential benefits in filling observational gaps in the lower atmosphere; however, characterization of their error characteristics has been limited. To optimize the use of UAS observations in numerical weather prediction, UAS observation error is estimated based on the 3‐cornered hat diagnostic approach which uses three independent estimates of the atmospheric state. This approach is applied to data from the 2018 Lower Atmospheric Profiling Studies at Elevation‐a Remotely‐piloted Aircraft Team Experiment field campaign using collocated UAS and rawinsonde observations along with output from a set of convection‐permitting model simulations. The estimated observation error values for UAS temperature, wind, and relative humidity measurements were found to be only weakly dependent on height AGL with mean values equal to 0.5°C, 0.8 m s −1 , and 3%, respectively. Only the newly estimated observation error for temperature differed from that previously used to assimilate commercial aircraft observations into global models (1.0°C). However, using this reduced temperature observation error produced more accurate mesoscale analyses and forecasts of both terrain‐driven flows and convection initiation generated by colliding outflow boundaries within the San Luis Valley of Colorado.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Multi-scale dynamic modeling and validation of radial flow fixed bed contactors for post-combustion CO 2 capture using bench scale and pilot plant data

Here, in this work, a multi-scale model of a radial flow fixed bed contactor packed with a carbon sorbent is developed and validated with laboratory-scale and pilot plant scale dynamic data. For the lab scale system, the model results were compared with low, medium and high gas and sweep flowrates, yielding root mean square error (RMSE) of 0.80, 0.63, 0.96 CO 2 mol%, respectively, for the outlet CO 2 concentration profile considering the entire A-D cycle. For the bed outer temperature profile, maximum RMSE was found to be 5.5 °C considering all flowrates and entire A-D cycles. An experimental campaign was developed and applied to a pilot plant at Technology Center Mongstad (TCM), Norway. Approaches were developed for pre-processing of data including consideration of the effect of gas mixing, measurement delay, and determination of cyclic steady-state conditions. Considering profiles during A-D cycles for all test runs, it was found that the maximum RMSE for pressure drop, temperature for the outer section of the bed, temperature for the middle section of the bed, and outlet CO 2 concentration profile remained less than 1.5 mbar, 3.5 °C, 2.8 °C, and 1.3 CO 2 mol%, respectively. The validated model was used to perform sensitivity studies on several key design operating variables for the adsorption-desorption cycle. It was found that the flow rate and concentration of flue gas have dominant nonlinear effects on the breakthrough time while the desorption time was strongly affected by the sweep gas flowrate for the specific sorbent being evaluated in this study.

20 FOSSIL-FUELED POWER PLANTS↗
DOE OSTI2021

WSF PISA example for SAFER pilot

On October 15, 2021, the Radioactive and Hazardous Waste Management Facility Manager declared a Potential Inadequacy in the Safety Analysis (PISA) due to a new information relating to an error discovered in calculation AB-WSF-20-002. Calculation AB-WSF-20-002 is used to determine source terms and heat release rates for vehicle and aircraft impacts with fuel fires involving TRU waste containers. The source term for an aircraft’s impact has different source terms factors (i.e., DR, ARF, RF, LPF) that are applied to multiple categories of drums impacted in the accident scenario based on the physical stresses of the aircraft impact and involvement in the subsequent fuel pool fire. The source terms of the multiple categories of affected drums are summed to determine the final source term for the accident scenario. Certain categories of affected drums are modeled to lose their lid, causing material to eject and burn unconfined on the ground. For these specific categories, a lower ARF value was incorrectly applied in AB-WSF-20-002 (i.e., an ARF value of 1E-3 was used instead of the DOE-STD-5506-2007 directed ARF value of 1E-2). For these categories, the ARF is applied to a relatively small number of low-activity drums (i.e., 17 drums at 2.9 PE-Ci each) compared to the total number of drums affected (i.e., 166 drums totaling 621.8 PE-Ci). Therefore, this error is not expected to significantly increase final dose for the accident scenario. No immediate actions or compensatory measures are required to maintain the facility in a safe condition.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2017

Automated work package: capabilities of the future - 275

As part of the ongoing efforts at the U.S. Department of Energy's Light Water Reactor Sustainability Program, Idaho National Laboratory (INL) is conducting several pilot projects in collaboration with the nuclear industry to improve the reliability, safety, and economics of the nuclear power industry, especially as the nuclear power plants extend their operating licenses to 80 years. One of these pilot projects is the automated work package (AWP) pilot project. An AWP is an electronic intelligent and interactive work package. It uses plant condition, resources status, and user progress to adaptively drive the work process in a manner that increases efficiency while reducing human error. To achieve this mission, the AWP acquires information from various systems of a nuclear power plant and incorporates several advanced instrumentation and control technologies along with modern human factors techniques. With the current rapid technological advancement, it is possible to envision several available or soon-to-be-available capabilities that can play a significant role in improving the work package process. In this pilot project, the researchers develop an AWP prototype of an expanding set of capabilities and evaluates them in an industrial environment. While some of the proposed capabilities are based on using technological advances in other applications, others are conceptual; thus, they require significant research and development to be applicable in an AWP. The scope of this paper is to introduce a set of envisioned capabilities, their need for the industry, and the industry difficulties they resolve. (authors)

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2017

10th International Topical Meeting on Nuclear Plant Instrumentation, Control and Human Machine Interface Technologies, San Francisco, CA, USA, June 11–15, 2017

As part of the ongoing efforts at the U.S. Department of Energy’s Light Water Reactor Sustainability Program, Idaho National Laboratory is conducting several pilot projects in collaboration with the nuclear industry to improve the reliability, safety, and economics of the nuclear power industry, especially as the nuclear power plants extend their operating licenses to 80 years. One of these pilot projects is the automated work package (AWP) pilot project. An AWP is an electronic intelligent and interactive work package. It uses plant condition, resources status, and user progress to adaptively drive the work process in a manner that increases efficiency while reducing human error. To achieve this mission, the AWP acquires information from various systems of a nuclear power plant’s and incorporates several advanced instrumentation and control technologies along with modern human factors techniques. With the current rapid technological advancement, it is possible to envision several available or soon-to-be-available capabilities that can play a significant role in improving the work package process. As a pilot project, the AWP project develops a prototype of an expanding set of capabilities and evaluates them in an industrial environment. While some of the proposed capabilities are based on using technological advances in other applications, others are conceptual; thus, require significant research and development to be applicable in an AWP. The scope of this paper is to introduce a set of envisioned capabilities, their need for the industry, and the industry difficulties they resolve.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2016

Impact of Corrections to the Spallings Volume Calculation on Waste Isolation Pilot Plant Performance Assessment - 16135

The numerical code DRSPALL (from direct release spallings) is written to calculate the volume of Waste Isolation Pilot Plant solid waste subject to material failure and transport to the surface (i.e., spallings) as a result of a hypothetical future inadvertent drilling intrusion into the repository. An error in the implementation of the DRSPALL finite difference equations was discovered and documented in a software problem report in accordance with the quality assurance procedure for software requirements. This paper describes the corrections to DRSPALL and documents the impact of the new spallings data from the modified DRSPALL on previous performance assessment calculations. Updated performance assessments result in more simulations with spallings, which generally translates to an increase in spallings releases to the accessible environment. Total normalized radionuclide releases using the modified DRSPALL data were determined by forming the summation of releases across each potential release pathway, namely borehole cuttings and cavings releases, spallings releases, direct brine releases, and transport releases. Because spallings releases are not a major contributor to the total releases, the updated performance assessment calculations of overall mean complementary cumulative distribution functions for total releases are virtually unchanged. Therefore, the corrections to the spallings volume calculation did not impact Waste Isolation Pilot Plant performance assessment calculation results. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2017

Estimating traffic volumes for signalized intersections using connected vehicle data

Recently connected vehicle (CV) technology has received significant attention thanks to active pilot deployments supported by the US Department of Transportation (USDOT). At signalized intersections, CVs may serve as mobile sensors, providing opportunities of reducing dependencies on conventional vehicle detectors for signal operation. However, most of the existing studies mainly focus on scenarios that penetration rates of CVs reach certain level, e.g., 25%, which may not be feasible in the near future. How to utilize data from a small number of CVs to improve traffic signal operation remains an open question. In this work, we develop an approach to estimate traffic volume, a key input to many signal optimization algorithms, using GPS trajectory data from CV or navigation devices under low market penetration rates. To estimate traffic volumes, we model in this paper vehicle arrivals at signalized intersections as a time-dependent Poisson process, which can account for signal coordination. The estimation problem is formulated as a maximum likelihood problem given multiple observed trajectories from CVs approaching to the intersection. An expectation maximization (EM) procedure is derived to solve the estimation problem. Two case studies were conducted to validate our estimation algorithm. One uses the CV data from the Safety Pilot Model Deployment (SPMD) project, in which around 2800 CVs were deployed in the City of Ann Arbor, MI. The other uses vehicle trajectory data from users of a commercial navigation service in China. Mean absolute percentage error (MAPE) of the estimation is found to be 9–12%, based on benchmark data manually collected and data from loop detectors. Finally, considering the existing scale of CV deployments, the proposed approach could be of significant help to traffic management agencies for evaluating and operating traffic signals, paving the way of using CVs for detector-free signal operation in the future.

97 MATHEMATICS AND COMPUTING↗