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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 145 records · Page 8
All sourcesNASA NTRSDOE OSTI
DOE OSTI2018

Conditioning and Requalification of Legacy Waste Packages - 18648

In most German interim storage facilities, hundreds of waste packages are waiting for re-conditioning and qualification to comply with the waste acceptance criteria (WAC) for the Konrad repository. Especially the waste classification as well as some characteristics and the description of the material composition in its current state do not comply with the Konrad requirements since the conditioning of these waste packages took place up to 40 years ago. The German name for these waste packages is 'Altgebinde', hereafter referred to as 'legacy waste packages'. The present article will provide an overview of the measures for the qualification of these legacy waste packages to fulfill the Konrad requirements. The following measures, when necessary, are carried out for the qualification of legacy waste packages: - Collection and evaluation of all documents related to the primary conditioning process, e. g. the used equipment, documented waste and packaging properties, conditioning and radiology, if available (hereafter 'old documents'). - Preparation of a conceptual report and a process control quality plan based on existing data, with appropriate measures to qualify the waste packages. Assessment and approval of the process control quality plan by the competent authority. - Characterization of the waste and packaging properties by visual inspection or tomography, recording or updating of dose rate, contamination and mass, verification of product stability, for example by gas analysis of the interior atmosphere of the container. - Characterization of the nuclide inventory by gammas scans, sample taking to analyze nuclides which can not be measured by means of in-situ gamma spectrometry, and for material declaration. - Qualification or post-conditioning of the legacy waste packages if necessary, e. g. by drying the waste packages and packaging them in containers approved for disposal. - Compilation of all results of the above mentioned tests and steps to create a waste package documentation, including the calculations for the nuclide inventory and the description of the material composition of the waste packages. - Receiving approval by the competent authority after evaluation of the final documentation. The aim behind this structured approach is to prevent additional, unnecessary measures and treatment of the waste. Consequently, the additional characterization is preferably carried out on the basis of the available documents. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2018

Directly Deposited Binder-Free Sulfur Electrode Enabled by Air-Controlled Electrospray Process

Lithium–sulfur batteries are one of the most promising energy storage technologies to replace commercial Li-ion batteries due to 5-fold higher theoretical energy density, and lower cost. However, due to certain limitations, the technology is not ready to be deployed. To overcome some of these, scientists have been extensively employing graphene oxide (GO) or graphene material to improve the electrochemical performance. In this work, we present a unique, novel, and facile method to deposit the active materials onto an aluminum current collector by utilizing the van der Waals interaction between graphene oxide and aluminum via an air-controlled electrospray (ACES) process. The role of conventional polymer binder was replaced by graphene, resulting in a binder-free substrate. We demonstrated that the elimination of conventional polymer binder and graphene layers assembled via the ACES method resulted in higher capacity and retention, and offer almost 4 times higher capacity at 2C than the conventional slurry-cast system with polymer binder. Here, this ACES technique offers potential for improving the overall energy density of sulfur and being adapted for commercialization in the energy storage industry.

25 ENERGY STORAGE↗
DOE OSTI2017

Self-organizing sensing and actuation for automatic control

A Self-Organizing Process Control Architecture is introduced with a Sensing Layer, Control Layer, Actuation Layer, Process Layer, as well as Self-Organizing Sensors (SOS) and Self-Organizing Actuators (SOA). A Self-Organizing Sensor for a process variable with one or multiple input variables is disclosed. An artificial neural network (ANN) based dynamic modeling mechanism as part of the Self-Organizing Sensor is described. As a case example, a Self-Organizing Soft-Sensor for CFB Boiler Bed Height is presented. Also provided is a method to develop a Self-Organizing Sensor.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2018

Observations and Lessons Learned from Testing of the Hanford Waste Treatment Plant Standard High Solids Vessel Design - 18635

The Hanford Waste Treatment and Immobilization Plant (WTP) recently completed qualification testing of the mixing system for the standard high solids vessel design the U.S. Department of Energy (DOE) proposed to replace several vessels in the design of the plant Pretreatment Facility. Testing was split into two phases. The first phase exercised the pulse jet mixer control system to ensure that the control software could accommodate fluids across the complete range of expected densities and viscosities within the vessel for all operational modes. The second phase of testing was focused on the mixing performance of the vessel, specifically, whether mixing was adequate to support the transfer, de-inventory, throughput, and sampling functions of the vessel. Key observations and lessons learned from testing include: - Successful transfer of waste within and beyond the design basis slurry solids concentration; - Successful resuspension and transfer of large, heavy particles; - Effective performance of multi-level bubblers for process control and monitoring; - Effective controls performance; - Reduction of sparger flowrate from nominal design flow; - Liquid blending within about 15 minutes; - Insights into sample representativeness and uniformity of transfers to inform process control strategies. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2021

The Marine and Hydrokinetic ToolKit (MHKiT) for Data Quality Control and Analysis [Slides]

The ability to collect, ingest, condition, reduce, quality control, process, visualize, and store data in a standardized way is critical at all stages of Marine Energy (ME) research and technology/project development. MHKiT is an open-source, standardized suite of ME data processing functions that provides the ability to ingest, condition, reduce, quality control, process, visualize and store ME data. MHKiT is developed in both Python and Matlab.

16 TIDAL AND WAVE POWER↗
DOE OSTI2017

Nonlinear model predictive control for chemical looping process

A control system for optimizing a chemical looping ("CL") plant includes a reduced order mathematical model ("ROM") that is designed by eliminating mathematical terms that have minimal effect on the outcome. A non-linear optimizer provides various inputs to the ROM and monitors the outputs to determine the optimum inputs that are then provided to the CL plant. An estimator estimates the values of various internal state variables of the CL plant. The system has one structure adapted to control a CL plant that only provides pressure measurements in the CL loops A and B, a second structure adapted to a CL plant that provides pressure measurements and solid levels in both loops A, and B, and a third structure adapted to control a CL plant that provides full information on internal state variables. A final structure provides a neural network NMPC controller to control operation of loops A and B.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2016

TU-FG-201-09: Predicting Accelerator Dysfunction

Purpose: To develop an integrated statistical process control (SPC) framework using digital performance and component data accumulated within the accelerator system that can detect dysfunction prior to unscheduled downtime. Methods: Seven digital accelerators were monitored for twelve to 18 months. The accelerators were operated in a ‘run to failure mode’ with the individual institutions determining when service would be initiated. Institutions were required to submit detailed service reports. Trajectory and text log files resulting from a robust daily VMAT QA delivery were decoded and evaluated using Individual and Moving Range (I/MR) control charts. The SPC evaluation was presented in a customized dashboard interface that allows the user to review 525 monitored parameters (480 MLC parameters). Chart limits were calculated using a hybrid technique that includes the standard SPC 3σ limits and an empirical factor based on the parameter/system specification. The individual (I) grand mean values and control limit ranges of the I/MR charts of all accelerators were compared using statistical (ranked analysis of variance (ANOVA)) and graphical analyses to determine consistency of operating parameters. Results: When an alarm or warning was directly connected to field service, process control charts predicted dysfunction consistently on beam generation related parameters (BGP)– RF Driver Voltage, Gun Grid Voltage, and Forward Power (W); beam uniformity parameters – angle and position steering coil currents; and Gantry position accuracy parameter: cross correlation max-value. Control charts for individual MLC – cross correlation max-value/position detected 50% to 60% of MLCs serviced prior to dysfunction or failure. In general, non-random changes were detected 5 to 80 days prior to a service intervention. The ANOVA comparison of BGP determined that each accelerator parameter operated at a distinct value. Conclusion: The SPC framework shows promise. Long term monitoring coordinated with service will be required to definitively determine the effectiveness of the model. Varian Medical System, Inc. provided funding in support of the research presented.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2019

Maintenance Plan for the Performance Assessments and Composite Analyses for the Area 3 and Area 5 Radioactive Waste Management Sites at the Nevada National Security Site (Rev.3)

U.S. Department of Energy (DOE) Order 435.1, Radioactive Waste Management, requires that performance assessments (PAs) and composite analyses (CAs) for low-level radioactive waste (LLW) disposal facilities be maintained by the responsible field office. This plan describes the activities performed to maintain the PA and the CA for the Area 3 and Area 5 Radioactive Waste Management Sites (RWMSs) at the Nevada National Security Site (NNSS). The plan is based on DOE Order 435.1 Radioactive Waste Management (DOE 2007), DOE Manual 435.1-1, Radioactive Waste Management Manual (DOE 2011), and DOE Standard DOE-STD-5002-2017, Disposal Authorization Statement and Tank Closure Documentation (DOE 2017). The facility’s Disposal Authorization Statement (DAS) specifies conditions for the continuing operation of a LLW facility. The DAS is issued based on a review of the facility's PA, CA, the maintenance plan, preliminary closure plan, and preliminary monitoring plan. The DAS specifies the limits and conditions on construction, design, operations, and closure of the low-level waste facility based on these reviews. The Maintenance Plan uses an Unreviewed Disposal Question (UDQ)/Unreviewed Composite Analysis Question (UCAQ) change control process to identify, evaluate, and control planned or discovered changes affecting the PA, CA, DAS, or other documents composing the Radioactive Waste Management Basis (RWMB). The UDQ/UCAQ process uses the PA and CA GoldSim models to calculate updated results, assess the significance of changes, and to assess the need to revise the PA, CA, or DAS. In addition to the change control process, the Maintenance Plan requires an annual review of all changes and an annual assessment of the need to revise RWMB documents, plans, and programs. The cumulative effect of all changes is assessed annually by preparing a revised PA and CA baseline model that is used to update PA and CA results. The updated results are used to assess compliance with the PA performance objectives and the CA dose constraint, and to assess the need to revise the PAs, CAs, and other documents, plans, and programs supporting the RWMB. An Annual Summary Report documenting the results and conclusions of the annual review is prepared and submitted to DOE.

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

Processing temperature control of a diketopyrrolopyrrole- alt -thieno[2,3- b ]thiophene polymer for high-mobility thin-film transistors and polymer solar cells with high open-circuit voltages

Here, we synthesized a planar pDPPTTi-OD polymer based on diketopyrrolopyrrole (DPP) and thieno [2,3- b ] thiophene (TTi) and investigated the electrical properties of the pDPPTTi-OD polymer. pDPPTTi-OD films displayed a low optical bandgap of 1.57 eV, and HOMO and LUMO levels of –5.40 and –3.74 eV, respectively. The 150°C-annealed pDPPTTi-OD films showed a high hole mobility of 0.16 cm 2 V –1 s –1 in organic thin-film transistor (OTFT) devices. The photovoltaic properties of polymer solar cells (PSCs) incorporating the pDPPTTi-OD were also measured. A pDPPTTi-OD:PC 71 BM blend film was spin-coated at 25, 70 and 90 °C. High-temperature processing significantly improved the power conversion efficiency of PSCs by effectively reducing the PC 71 BM domain sizes, which improved the miscibility between pDPPTTi-OD and PC 71 BM. Furthermore, this work demonstrated that the TTi moiety is a useful donor building block for high-performance D–A type polymers in OTFTs and PSCs, and that processing temperatures should be controlled to fully realize the materials' beneficial intrinsic properties.

36 MATERIALS SCIENCE↗
DOE OSTI2016

Testing of the Defense Waste Processing Facility Cold Chemical Dissolution Method in Sludge Batch 9 Qualification

For each sludge batch that is processed in the Defense Waste Processing Facility (DWPF), the Savannah River National Laboratory (SRNL) tests the applicability of the digestion methods used by the DWPF Laboratory for elemental analysis of Sludge Receipt and Adjustment Tank (SRAT) Receipt samples and SRAT Product process control samples. DWPF SRAT samples are typically dissolved using a method referred to as the DWPF Cold Chemical or Cold Chem Method (CC), (see DWPF Procedure SW4- 15.201). Testing indicates that the CC method produced mixed results. The CC method did not result in complete dissolution of either the SRAT Receipt or SRAT Product with some fine, dark solids remaining. However, elemental analyses did not reveal extreme biases for the major elements in the sludge when compared with analyses obtained following dissolution by hot aqua regia (AR) or sodium peroxide fusion (PF) methods. The CC elemental analyses agreed with the AR and PF methods well enough that it should be adequate for routine process control analyses in the DWPF after much more extensive side-by-side tests of the CC method and the PF method are performed on the first 10 SRAT cycles of the Sludge Batch 9 (SB9) campaign. The DWPF Laboratory should continue with their plans for further tests of the CC method during these 10 SRAT cycles.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2024

Towards in-situ certification of additively manufactured parts: the vital roles of physics-based and data-driven models

Certifying additively manufactured (AM) parts in-situ at the completion of a build is an enticing prospect, as it can help reduce the high costs associated with post-build testing and evaluation. However, achieving this goal presents significant challenges that may keep it aspirational for the foreseeable future. Nonetheless, incremental progress can pave the way forward. A critical aspect of in-situ certification involves continuous quality checking due to the random nature of the AM process and the difficulties in detecting defects or anomalies once layers are built over. While real-time in-situ monitoring strategies assisted by machine learning (ML) play a pivotal role in auditing part quality, they must ideally be supported by real-time (or near real-time) adaptive process control enabled by ML-assisted decision-making. By analyzing in-situ monitoring data in real-time (or near real- time) to dynamically adjust manufacturing parameters, such intervention can ensure AM parts are built to meet stringent certification standards. This can be achieved virtually by using high-fidelity performance models for the physical testing and evaluation tasks. In this short editorial, we discuss the key contributions made by data-driven and physics-based models in providing intelligence to the monitoring and process control tasks underpinning in-situ certification and in the simulation of the build’s performance under test and service conditions. While our focus lies in metal AM, the concepts discussed here are also relevant to other AM processes.

: In-situ monitoring↗
DOE OSTI2016

SU-G-TeP4-07: Automatic EPID-Based 2D Measurement of MLC Leaf Offset as a Quality Control Tool

Purpose: The MLC dosimetric leaf gap (DLG) and transmission are measured parameters which impact the dosimetric accuracy of IMRT and VMAT plans. This investigation aims to develop an efficient and accurate routine constancy check of the physical DLG in two dimensions. Methods: The manufacturer’s recommended DLG measurement method was modified by using 5 fields instead of 11 and by utilizing the Electronic Portal Imaging Device (EPID). Validations were accomplished using an ion chamber (IC) in solid water and a 2D IC array. EPID data was collected for 6 months on multiple TrueBeam linacs using both Millennium and HD MLCs at 5 different clinics in an international consortium. Matlab code was written to automatically analyze the images and calculate the 2D results. Sensitivity was investigated by introducing deliberate leaf position errors. MLC calibration and initialization history was recorded to allow quantification of their impact. Results were analyzed using statistical process control (SPC). Results: The EPID method took approximately 5 minutes. Due to detector response, the EPID measured DLG and transmission differed from the IC values but were reproducible and consistent with changes measured using the ICs. For the Millennium MLC, the EPID measured DLG and transmission were both consistently lower than IC results. The EPID method was implemented as leaf offset and transmission constancy tests (LOC and TC). Based on 6 months of measurements, the initial leaf-specific action thresholds for changes from baseline were set to 0.1 mm. Upper and lower control limits for variation were developed for each machine. Conclusion: Leaf offset and transmission constancy tests were implemented on Varian HD and Millennium MLCs using an EPID and found to be efficient and accurate. The test is effective for monitoring MLC performance using dynamic delivery and performing process control on the DLG in 2D, thus enhancing dosimetric accuracy. This work was supported by a grant from Varian Medical Systems.

61 RADIATION PROTECTION AND DOSIMETRY↗
DOE OSTI2021

Overview of the Process Automation and Control Group (E-3) [Slides]

Process Automation and Control (E-3) is a part of the Weapons Associate Laboratory Directorate (DDW) which includes Weapons Engineering Directorate (WE) and the Engineering Technology and Design (E) Division. There are three teams: Fabrication and Assembly, Process Controls, and Automation and Robotics. After an overview of the group, information is provided about an internship and how to apply.

45 MILITARY TECHNOLOGY, WEAPONRY, AND NATIONAL DEF↗
DOE OSTI2021

Cyber-Physical System Implementation for Manufacturing With Analytics in the Cloud Layer

Effective and efficient modern manufacturing operations require the acceptance and incorporation of the fourth industrial revolution, also known as Industry 4.0. Traditional shop floors are evolving their production into smart factories. To continue this trend, a specific architecture for the cyber-physical system is required, as well as a systematic approach to automate the application of algorithms and transform the acquired data into useful information. This work makes use of an approach that distinguishes three layers that are part of the existing Industry 4.0 paradigm: edge, fog, and cloud. Each of the layers performs computational operations, transforming the data produced in the smart factory into useful information. Trained or untrained methods for data analytics can be incorporated into the architecture. A case study is presented in which a real-time statistical control process algorithm based on control charts was implemented. The algorithm automatically detects changes in the material being processed in a computerized numerical control (CNC) machine. The algorithm implemented in the proposed architecture yielded short response times. The performance was effective since it automatically adapted to the machining of aluminum and then detected when the material was switched to steel. The data were backed up in a database that would allow traceability to the line of g-code that performed the machining.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2019

A geometric approach for computing tolerance bounds for elastic functional data

We design an approach for constructing tolerance bounds for functional data with random warping variability. In particular, we define a generative, probabilistic model for the amplitude and phase components of such observations, which parsimoniously characterizes variability in the baseline data. Based on the proposed model, we define two different types of tolerance bounds that are able to measure both types of variability, and as a result, identify when the data has gone beyond the bounds of amplitude and/or phase. The first functional tolerance bounds are computed via a bootstrap procedure on the geometric space of amplitude and phase functions. The second functional tolerance bounds utilize functional Principal Component Analysis to construct a tolerance factor. Our report is motivated by two main applications: process control and disease monitoring. The problem of statistical analysis and modeling of functional data in process control is important in determining when a production has moved beyond a baseline. Moreover, in biomedical applications, doctors use long, approximately periodic signals (such as the electrocardiogram) to diagnose and monitor diseases. In this context, it is desirable to identify abnormalities in these signals. We additionally consider a simulated example to assess our approach and compare it to two existing methods.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2017

Electrolyte chemistry control in electrodialysis processing

Methods for controlling electrolyte chemistry in electrodialysis units having an anode and a cathode each in an electrolyte of a selected concentration and a membrane stack disposed therebetween. The membrane stack includes pairs of cationic selective and anionic membranes to segregate increasingly dilute salts streams from concentrated salts stream. Electrolyte chemistry control is via use of at least one of following techniques: a single calcium exclusionary cationic selective membrane at a cathode cell boundary, an exclusionary membrane configured as a hydraulically isolated scavenger cell, a multivalent scavenger co-electrolyte and combinations thereof.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

Integrated Process Optimization for Biochemical Conversion

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

09 BIOMASS FUELS↗
DOE OSTI2017

A novel tool for improving the data collection process during control room modernization human-system interface testing and evaluation activities - 271

The U.S. Department of Energy (DOE) Light Water Reactor Sustainability (LWRS) Program is developing a scientific basis to extend the existing U.S. nuclear power plant (NPP) operating life beyond the current 60-year licensing period and to ensure NPP long-term reliability, productivity, safety, and security. Under the Advanced Instrumentation, Information, and Control (II and C) Systems Technologies pathway, the LWRS Program conducts targeted research and development to address long-term aging and obsolescence of existing instrumentation and control technologies, as well as to implement and test new digital II and C technology that enables broad innovation and business improvement in the NPP operating model. To this end, the role of human factors engineering (HFE) is important as part of ensuring that the control room modernization (CRM) activities account for the operator's cognitive and physiological characteristics. Part of the HFE process for CRM entails conducting tests and evaluations (T and Es) that will inform the design of new human-system interface (HSI) technologies. Qualitative data that captures operator feedback and important human-system interactions are an important part of T and E. However, the analyzing this qualitative data in order to provide actionable design recommendations can be time-consuming and error prone. This paper presents an early version of a tool, which uses a combination of natural language processing and supervised machine learning to support the analysis of qualitative data. The objectives of this tool is to improve the process of (1) sifting through freeform text for improved design issue identification, (2) maintaining consistency in theme coding conventions, and (3) readily providing actionable design guidance that can be traceable to state-of-the-art HFE guidelines such as NUREG-0700. (authors)

22 GENERAL STUDIES OF NUCLEAR REACTORS↗