Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “usability testing”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 109 records · Page 6
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Development of Large Bore Rabbit Capsules in Support of BWR Cladding Irradiations in HFIR

The High Flux Isotope Reactor (HFIR) is an ideal tool for materials irradiation testing because of its intense steady-state neutron flux. Many programs take advantage of HFIR’s central flux trap for irradiation experiments using capsules, also known as rabbits, to support advanced materials development and reactor design. The facility that makes up the HFIR flux trap has recently undergone a design change that increases the HFIR primary coolant volumetric flow rate by removing restrictions in the system. As a result, the usable cross-sectional area within the facility increased, opening the door to increase the cross-sectional area of the rabbit capsules that fill the facility. This report documents a new large-diameter rabbit housing that increases the usable volume within the rabbit capsule by 22.6%. However, challenges arise with increasing the capsule size, such as establishing a new maximum capsule operating pressure and determining the thermal-hydraulic characteristics. This report addresses those challenges with previously adopted HFIR safety methods. The rupture pressure of the rabbit housings is demonstrated while verifying that capsule swelling during and after rupture will not block coolant flow. Then, a safety factor is applied to ascertain an administrative operating pressure. Additionally, the thermal-hydraulic performance of the HFIR facility filled with large-diameter rabbit capsules is shown to not violate previously determined safety criteria. Next, heat transfer coefficients are determined for use in design calculations. Furthermore, this report gives an example of internal configurations for the new, larger rabbit capsules that use relevant boiling water reactor (BWR) cladding geometry. Finally, this report documents an example thermal design performance for a rabbit capsule containing six gauge-curved tensile tube specimens. The thermal performance gives predicted temperature distributions within the capsule and shows the expected temperature of the passive thermometers for post-irradiation temperature comparisons.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2021

A Comparative Analysis of Lithium-Ion Battery Chemistries for Cold-Climate Maritime Applications

Energy storage, primarily in the form of electrochemical batteries, is critical for enabling integration of marine energy (e.g., power from waves and currents) with end-use applications at sea due to the periodicity of the resources and requirement of consistent, smooth power delivery. Powering high-latitude coastal or ocean-based observing systems has been identified as a high value-proposition use case, exemplifying the concept of Powering the Blue Economy. An existing power system implementation for such a platform uses solar panels coupled to rechargeable lithium-ion batteries, augmented by a non-rechargeable backup bank used to power heaters in winter months. As wave energy is a potential resource for powering this and similar use cases and is strongest when solar power is most limited, it was hypothesized that integration of wave power would alleviate some of the challenges in cold climates related to energy storage. In this work, we investigate the use case scenario in simulation and perform a comparative test of commercially-available lithium-ion battery chemistry formulations to determine the most appropriate choice for the application. The experiment compared the most commonly-used lithium-based battery types (NCM, NCA, and LFP) in laboratory conditions emulating a battery enclosure thermally coupled to freezing seawater using an industrial battery performance tester in PNNL's arctic simulation lab. Battery strings were repeatedly cycled (i.e., charged, discharged, rested) and their usable capacity was measured. Both NCM and NCA strings exhibited degradation upon cycling, while the LFP string maintained stable operation through over 700 complete cycles. Though lower in baseline capacity, LFP are recommended for this and similar low-temperature applications with access to high levels of bulk charging current (e.g., from high-energy wave events) and their use may reduce the cost and complexity of battery conditioning and protection apparatus.

16 TIDAL AND WAVE POWER↗
DOE OSTI2021

Replicated Computational Results (RCR) Report for “Adaptive Precision Block-Jacobi for High Performance Preconditioning in the Ginkgo Linear Algebra Software”

The article by Flegar et al. titled “Adaptive Precision Block-Jacobi for High Performance Preconditioning in the Ginkgo Linear Algebra Software” presents a novel, practical implementation of an adaptive precision block-Jacobi preconditioner. Performance results using state-of-the-art GPU architectures for the block-Jacobi preconditioner generation and application demonstrate the practical usability of the method, compared to a traditional full-precision block-Jacobi preconditioner. A production-ready implementation is provided in the Ginkgo numerical linear algebra library. In this report, the Ginkgo library is reinstalled and performance results are generated to perform a comparison to the original results when using Ginkgo’s Conjugate Gradient solver with either the full or the adaptive precision block-Jacobi preconditioner for a suite of test problems on an NVIDIA GPU accelerator. After completing this process, the published results are deemed reproducible.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Nevada National Security Site Environmental Report 2024 - Summary Report

This Nevada National Security Site Environmental Report (NNSSER) summarizes actions taken in 2024 to protect the environment and the public while achieving the NNSA/NFO mission goals. It is prepared for the public and our stakeholders in hopes that it is readily understandable and usable. It is a key component in our efforts to keep the public informed of environmental conditions at the NNSS and its support facilities in Las Vegas, Nevada. This supplemental Summary report provides an abbreviated version of the full report.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Software Verification Toolkit (SVT): Survey on Available Software Verification Tools and Future Direction

Writing software is difficult. However, writing complex, well tested and designed, and functionally correct software is incredibly difficult. An entire field of study is devoted to the validation and verification of software to address this problem, and in this paper we analyze the landscape of currently available third party software. We have divided our analyses into three separate subsections with regards to software validation: formal methods, static analysis, and test generation. Formal verification is the most complex method in which to validate software correctness, but also the most thorough as it truly validates the mathematical validity of the source code. Static analysis generally is relegated to abstract syntax tree traversal techniques to find errors related to faulty software such as memory leaks or stack overflow issues. Automatic test generation is similar in implementation to static analysis, but pushes a bit further in verifying the boundedness of function inputs and outputs with regards to annotated or parsed criteria. The crux of this report is to analyze and describe the software tools that implement these techniques to validate and verify software. Pros and cons related to installation, utilization, and capabilities of the frameworks are described, and reproducible examples are provided with a focus on usability. The initial survey concluded that the most interesting tools of note are Z3, Isabelle/HOL, and TLA+ with regards to formal verification; and Infer, Frama-C, and SonarQube with regards to static analysis. With these tools in mind, a final conjecture is provided that describes future avenues of utilizing these tools for developing a verification framework to assist in validating existing software at Sandia National Laboratories.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Recycling Disassembled Automotive Plastic Components for New Vehicle Components: Enabling the Automotive Circular Economy

As the automotive industry increasingly relies on plastic components to meet fuel efficiency and emissions targets, the challenge of managing end-of-life vehicle (ELV) plastics continues to grow. Currently, more than 80% of ELV plastics in the U.S. are landfilled due to limited economic incentives and technical barriers to recycling. This study examines a mechanical recycling pathway for thermoplastic components disassembled from ELVs and assesses their usability for reintegration into new vehicle parts. Four representative materials were chosen based on material labels embedded in recovered parts and aligned with their virgin industrial equivalents: polypropylene (PP), 10% talc-filled PP (PP-T10), 20% talc-filled PP (PP-T20), and a 20% glass-/mineral-filled polyamide (PA6 + GF7 + MF13). The materials underwent shredding, drying, and injection molding before being characterized by particle size analysis, density measurement, thermal analysis (TGA, DSC), mechanical testing, and heat deflection temperature (HDT) evaluation. The results in this work indicated that minor differences in crystallinity were observed and small differences between model materials and ELV materials could have contributed to these changes. Mechanical testing revealed that neat polypropylene suffered a 15–20% reduction in stiffness and tensile strength, but talc-filled polypropylene and glass/mineral-filled nylon retained >90% of their modulus, strength, and heat deflection temperature values relative to virgin controls. Differences between virgin and ELV materials could have been attributed to use life degradation, contamination during use life, or even chemical/processing differences in model materials and ELV materials. However, these findings suggest that mechanically recycled, disassembled ELV plastics can retain sufficient structural performance to support circularity efforts in the automotive sector.

automotive recycling↗
DOE OSTI2025

Using a Large Language Model as a Building Block to Generate Usable Validation and Verification Suite for OpenMP

In the HPC area, both hardware and software move quickly. Often new hardware is developed and deployed, the corresponding software stack, including compilers and other tools, are under active development while leading edge software developers are working to port and tune their applications, all at the same time. While the software ecosystem is in flux, one of the key challenges for users is obtaining insight into the state of implementation of key features in the programming languages and models their applications are using – whether they have been implemented, and whether the implementation conforms to the specification, especially for newly implemented features (less tested by widespread use). OpenMP is one of the most prominent shared memory programming models used for on-node programming in HPC. With the shift towards accelerators (such as GPUs and FPGAs) and heterogeneous programming OpenMP features are getting more complex. It is natural to ask whether generative AI approaches, and large language models (LLMs) in particular, can help in producing validation and verification test suites to allow users better and faster insights into the availability and correctness of OpenMP features of interest. In this work, we explore the use of ChatGPT-4 to generate a suite of tests for OpenMP features. We have chosen a set of directives and clauses, a total of 78 combinations, which first appeared in OpenMP 3.0 (released in May 2008) but are also relevant for accelerators. We prompted ChatGPT to generate tests in the C and Fortran languages, for both host (CPU) and device (accelerator). On the Summit super-computer using the GNU implementation, we found that, of the 78 generated tests 67 C tests and 43 Fortran tests compiled successfully and fewer than those executed to completion. On further analysis we show that not all generated tests are valid. We document the process, results, and provide detailed analysis regarding the quality of tests generated. With the aim of providing input to a production quality validation and verification suite, we manually implement the corrections required to make the tests valid according to the current OpenMP specification. We quantify this effort as small, medium, or large, and record the lines of code changed to correct the invalid tests. With the corrected tests we validate recent implementations from HPE, AMD, and GNU on the Frontier supercomputer. Our experiment and subsequent analysis show that although LLMs are capable of producing HPC specific codes, they are limited by their understanding of the deeper semantics and restrictions of programming models such as OpenMP. Unsurprisingly more commonly used features have better support, while some OpenMP 3.0 directives such as sections and tasking are not universally supported on accelerators. We demonstrate that successful compilation and execution to completion are inadequate metrics for evaluating generated code and that, at this time, commodity LLMs require expert intervention for code verification. This points to gaps in the training data that is currently available for HPC. We demonstrate that with "small" effort 37% of generated invalid C tests and 63% of generated invalid Fortran tests could be corrected. This improves productivity of test generation as we circumvent writing from scratch and the common programming errors associated with it.

Pophale, Swaroop [ORNL] (ORCID:0000000185446367)↗
DOE OSTI2023

Use of Sensors and Machine Learning for Signal Discovery in a Solvent Extraction Process – 23280

Reprocessing is an important step in the nuclear fuel cycle where usable nuclear materials are extracted from spent fuel for recycling. The extraction of materials for reuse simultaneously reduces not only the volume of nuclear waste, but its decay time to radioactivity levels similar to that of the originating uranium ore. As part of an initiative to steward research, development, and innovation into the nuclear fuel cycle, Idaho National Laboratory is designing and constructing a solvent extraction testbed named Beartooth. Beartooth will allow researchers to refine extraction processes, test innovative extraction processes, and give early career scientists opportunities to gain skills in performing separations chemistry that uses centrifugal contactors. In addition, the Beartooth testbed is being uniquely designed to enable novel technologies including machine learning capabilities for the characterization of chemical process operations in near real-time. To aid in the design of Beartooth, a team of researchers are installing a variety of atypical sensors into a system of contactors for signal discovery. The team will implement machine learning methods on sensor data to determine signal features with the goal of providing a process operator with a deeper understanding of the chemical process and equipment usage. This work will summarize sensors utilized and preliminary results from an infrared camera.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2023

Self-Sustainable IoT-Based Remote Sensing Powered by Energy Harvesting Using Stacked Piezoelectric Transducer and Thermoelectric Generator

We propose a self-powered remote multi-sensing system for traffic sensing which is powered by the collective energy harvested from the mechanical vibration of the road caused by the passing vehicles and from the temperature gradient between the asphalt of the road and the soil underneath. A stacked piezoelectric transducer converts mechanical vibrations into electrical energy and a thermoelectric generator harvests the thermal energy from the thermal gradient. Electrical energy signals from the stacked piezoelectric transducer and the thermoelectric generators are converted into usable DC power to recharge the battery using AC-DC and DC-DC converters working simultaneously. The multi-sensing system comprises an embedded system with a microcontroller that acquires data from the sensors and sends the sensory data to an IoT transceiver which transmits the data as RF packets to an ethernet gateway. The gateway converts the RF packets into Internet Protocol (IP) packets and sends them to a remote server. Laboratory and road-testing results showed over 98% sensory data accuracy with the system functioning solely powered by the energy harvested from the alternative energy sources. The successful maximum transmission distance obtained between the IoT, and the gateway was approximately 1 mile, which is a considerable transmission distance achieved in an urban environment. Successful operation of the self-powered multi-sensing system under both laboratory and road conditions contributes considerably to the fields of energy harvesting and self-powered remote sensing systems. The energy flow chart and efficiency for the steps in the system were found to be mechanical power from vehicles to the energy harvester of 0.25%, stacked PZT transducer efficiency was found to be 37%, and for the TEGs the efficiency is 11%. AC-to-DC and DC-to-DC converters’ efficiencies were found to be 90% and 11%. The wireless communication RF transceiver efficiency was found to be 62.5%.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2023

Use of Sensors and Machine Learning for Signal Discovery in a Solvent Extraction Process

Reprocessing is an important step in the nuclear fuel cycle where usable nuclear materials are extracted from used fuel for recycling. The separation of materials for reuse simultaneously reduces not only the volume of nuclear waste, but its decay time to radioactivity levels similar to that of the originating uranium ore. As part of an initiative to steward research, development, and innovation into the nuclear fuel cycle, Idaho National Laboratory is designing and constructing a solvent extraction testbed named Beartooth. This testbed will allow researchers to refine separation processes, test innovative extraction processes, and give early career scientists opportunities to gain skills in performing separations chemistry utilizing centrifugal contactors. In addition, the Beartooth testbed is being uniquely designed to enable novel technologies including machine learning capabilities for the characterization of chemical process operations in near real-time. To aid in the design of Beartooth, a team of researchers are installing a variety of atypical sensors into a system of contactors for signal discovery. The team will implement machine learning methods on acquired sensor data to extract signal features. The goal is to provide a process operator with a deeper understanding of the chemical process and equipment usage. This work will summarize sensors utilized and preliminary results from an infrared camera.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2020

Development and Implementation of Fault-Correction Algorithms in Fault Detection and Diagnostics Tools

A fault detection and diagnostics (FDD) tool is a type of energy management and information system that continuously identifies the presence of faults and efficiency improvement opportunities through a one-way interface to the building automation system and the application of automated analytics. Building operators on the leading edge of technology adoption use FDD tools to enable median whole-building portfolio savings of 8%. Although FDD tools can inform operators of operational faults, currently an action is always required to correct the faults to generate energy savings. A subset of faults, however, such as biased sensors, can be addressed automatically, eliminating the need for staff intervention. Automating this fault “correction” can significantly increase the savings generated by FDD tools and reduce the reliance on human intervention. Doing so is expected to advance the usability and technical and economic performance of FDD technologies. This paper presents the development of nine innovative fault auto-correction algorithms for Heating, Ventilation, and Air Conditioning pi(HVAC) systems. When the auto-correction routine is triggered, it overwrites control setpoints or other variables to implement the intended changes. It also discusses the implementation of the auto-correction algorithms in commercial FDD software products, the integration of these strategies with building automation systems and their preliminary testing.

field testing↗
DOE OSTI2024

Improved Earthquake Source Parameters with 3D Wavespeed Models in California and Nevada

Seismic tomography harnesses earthquake data to explore the inaccessible structure of the Earth. Adjoint waveform tomography (AWT), a method of seismic tomography, updates the tomographic model by optimizing the fit between observed earthquake data and synthetic waveforms. The synthetic data are calculated by solving the wave equation through a given 3D model. An important requirement to calculating synthetics is the source information (location, centroid time, depth, and moment tensor). Errors in source information affect the quality of the synthetics produced, which in turn can limit how structure can be inferred in the AWT workflow. Here, to test the effect of updating source information, we used MTTime (Chiang, 2020), a time-domain full-waveform moment tensor inversion code, to calculate the moment tensors and depths of 118 earthquakes that occurred in California and Nevada over a 20-yr period. We calculated 3D Green’s functions using a 3D seismic wavespeed model of California and Nevada (Doody et al., 2023b). We show that the inverted solutions provide better waveform fits than the Global Centroid Moment Tensor catalog and increase usable, well-correlated data by up to 7%. Therefore, we argue that recalculating source parameters should be considered in AWT workflows, particularly for smaller magnitude events (⁠M w > 5.0).

58 GEOSCIENCES↗
DOE OSTI2023

Data-driven key performance indicators and datasets for building energy flexibility: A review and perspectives

Energy flexibility, through short-term demand-side management (DSM) and energy storage technologies, is now seen as a major key to balancing the fluctuating supply in different energy grids with the energy demand of buildings. This is especially important when considering the intermittent nature of ever-growing renewable energy production, as well as the increasing dynamics of electricity demand in buildings. This paper provides a holistic review of (1) data-driven energy flexibility key performance indicators (KPIs) for buildings in the operational phase and (2) open datasets that can be used for testing energy flexibility KPIs. The review identifies a total of 48 data-driven energy flexibility KPIs from 87 recent and relevant publications. These KPIs were categorized and analyzed according to their type, complexity, scope, key stakeholders, data requirement, baseline requirement, resolution, and popularity. Moreover, 330 building datasets were collected and evaluated. Of those, 16 were deemed adequate to feature building performing demand response or building-to-grid (B2G) services. The DSM strategy, building scope, grid type, control strategy, needed data features, and usability of these selected 16 datasets were analyzed. This review reveals future opportunities to address limitations in the existing literature: (1) developing new data-driven methodologies to specifically evaluate different energy flexibility strategies and B2G services of existing buildings; (2) developing baseline-free KPIs that could be calculated from easily accessible building sensors and meter data; (3) devoting non-engineering efforts to promote building energy flexibility, standardizing data-driven energy flexibility quantification and verification processes; and (4) curating and analyzing datasets with proper description for energy flexibility assessm.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2021

ARC Software Validation Work for the FFTF Reactor

Extensive efforts have been carried out at ANL for the verification and validation of the Argonne Reactor Codes (ARC) software package currently used for the design of Versatile Test Reactor (VTR). The ARC software package consists of steady state neutronics and thermal hydraulics modeling capabilities which are being used by the VTR program to develop most of the VTR reactor design details which will be part of the licensing application. It is anticipated that this software will continue to be used for the design work and for initial operations although additional software may be introduced at a later time. The validation work was focused primarily on obtaining validation data consistent with VTR and usable for the ARC software. Because no critical facilities or operating fast spectrum reactors are available to do experiments for the VTR, the next best option is to identify historical experimental data that can be used as validation data. Early on in VTR, the ZPPR-15 set of experiments was identified as good validation data because of 1) the availability and quality of the data, 2) existing staff that are already familiar with the experimental machine and measurements, 3) most of the ZPPR-15 loadings of interest have already been processed into ARC models, and 4) a full uncertainty quantification has already been done for several loadings of ZPPR-15. The FFTF startup and operations data was identified as the most consistent reactor type that has validation data usable for VTR. Finally, the EBR-II fuel depletion measurements were identified as the best available validation data for VTR. It is important to note that both the FFTF and EBR-II reactors typically come with higher uncertainties than the ZPPR. In the frame of the discussed verification and validation efforts, the present document discusses the analysis of selected FFTF measurements included in the benchmark specifications of the International Reactor Physics Experiment (IRPhE) handbook. The FFTF reactor core configurations from the benchmark specification are presented in Section 2. The analysis is performed with the use of the ARC code suite available at ANL for fast reactor studies and is discussed in Section 3. The reactor parameters from the benchmark include criticality, neutron spectra, effective delayed neutron spectra, control rod worth, isothermal temperature coefficient and low energy gamma-ray spectra. The calculated values and the comparison with the experimental data is discussed in Sections 4 to 9 for each considered reactor parameter. Finally, conclusions are presented in Section 10.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2023

Visual Style Elements in Human–System Interface Design for Nuclear Power Operations: Does Style Affect Performance and Preference?

As the world increasingly adopts renewable and sustainable energy systems, transitionary solutions include nuclear power, which currently provides 20% of the United States’ electricity and is the largest single source of carbon-free electricity generation. Advanced reactors are a critical component of a carbon-free mixed energy portfolio that require careful design of first-of-a-kind control rooms. The application of Human Factors Engineering (HFE) is essential for scientific and iterative testing of novel human–system interface (HSI) concepts to ensure effective, efficient, and safe plant operations. Microworlds are simulators that use simplified physics models and control systems to distill nuclear power operations into essential functions. HFE scientists used the Rancor Microworld Simulator to obtain preference and performance metrics for novel and traditional static HSI design styles. Participants comprised advanced reactor company employees and nuclear industry consultants. A mixture of quantitative and qualitative data was captured. There was a preference for the basic graphical style that included high contrast and traditional color scheme elements. No single HSI design outperformed the others, and the participants did not perform better using their preferred HSI style. We report this experiment is the first in a series of HFE testing for HSIs in advanced reactor control room development. Clear user preferences emerged for elements within static displays. The cutting-edge neumorphic style was the least preferred. Future directions include tests of dynamic displays. HFE is used in evaluating and designing HSI devices that will improve the efficiency and safety of advanced nuclear power operations.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2023

High-Nickel Cathode Materials for High-Energy, Long-Life, Low-Cost Lithium-Ion Batteries (Final Report)

The overall goal of this project is to produce Li-ion cells, which deliver at least 600 Wh kg -1 at the cathode level with usable lifetimes of at least 1,000 cycles or 15 years of storage (calendar life). A secondary goal of this project is to minimize the use of cobalt in the cathode, with the ultimate goal of eliminating cobalt entirely. To develop such a cathode, the University of Texas at Austin (UT Austin) team extensively investigated the structural, physical, and chemical properties of layered oxide materials. A panel of dopant elements were surveyed with an emphasis on identifying those, which impart electrochemical and thermal stability. Of those tested, Al and Mg showed the greatest performance improvements while still being easily incorporated into the cathode through simple alterations of the precursor coprecipitation method. Other dopants, such as Ta, Nb, B, and F significantly improved cathode performance, yet their mode of incorporation required additional processing which negated their use under scaled-up conditions. From this work, a first-generation cathode LiNi 0.85 Mn 0.075 Co 0.05 Al 0.02 Mg 0.005 O 2 (NMCAM-85) was produced and an initial set of 2 Ah cells was fabricated by Tesla Inc. These cells delivered 650 Wh kg -1 and a cycle life of just over 700 cycles. Further improvements were garnered through careful study of electrolyte additives. A tailored, ethylene carbonate (EC)-free electrolyte was developed, which improved capacity retention from 60% to 82% after 500 cycles in model LiNi 0.9 Mn 0.05 Al 0.05 O 2 (NMA-90) full cells. This study identified the additives fluoroethylene carbonate (FEC) and lithium difluorophosphate (LiPO 2 F 2 ) as beneficial additives, which were included in the electrolyte for the end-of-project cells. The UT Austin team developed a high-Ni layered oxide cathode LiNi 0.85 Mn 0.1 Al 0.05 Mg 0.005 O 2 (NMAM-85) capable of meeting the project goals. This cathode was synthesized in kilogram quantities and delivered to Tesla for fabrication into 2 Ah pouch cells for DOE testing. These large-format cells achieved 630 Wh kg -1 at the cathode level under the designated testing parameters, meeting the project goal with a completely cobalt-free cathode. The large-format cells are currently undergoing cycle life and calendar life evaluation at Idaho National Lab.

25 ENERGY STORAGE↗
DOE OSTI2024

A nuclear power plant digital twin for developing robot navigation and interaction

As robot technologies are rapidly improving, an increasing number of new ideas on utilizing robots for automated operation and maintenance tasks in nuclear power plants (NPPs) are being studied. However, due to safety concerns, researchers hardly found opportunities to test their new robot solutions on physical NPPs. In that sense, an efficient and realistic simulation environment plays a vital role in the development of automation systems for an NPP. In this paper, we propose the design of a 3D digital twin system capable of simulating NPP in real-time. This system obtains the data from a full-scope NPP simulator to reproduce the operating conditions of the plant. In addition, a scenario of a team of robots performing inspection tasks like temperature and pressure measurements will highlight its usability. This system enables development of intelligent robot swarms to deploy for inspection and maintenance purposes. It will positively impact autonomous control and operations for many types of reactors by reducing uncertainty in autonomous control and providing the tools necessary for remote intervention.

Energy & Fuels↗
DOE OSTI2021

Modeling Nanosecond-Pulsed Spark Discharge and Flame Kernel Evolution

Dilute combustion, either using exhaust gas recirculation or with excess air, is considered a promising strategy to improve the thermal efficiency of internal combustion engines. However, the dilute air-fuel mixture, especially under intensified turbulence and high-pressure conditions, poses significant challenges for ignitability and combustion stability, which may limit the attainable efficiency benefits. In-depth knowledge of the flame kernel evolution to stabilize ignition and combustion in a challenging environment is crucial for effective engine development and optimization. To date, a comprehensive understanding of ignition processes that result in the development of fully predictive ignition models usable by the automotive industry does not yet exist. Spark-ignition consists of a wide range of physics that includes electrical discharge, plasma evolution, joule-heating of gas, and flame kernel initiation and growth into a self-sustainable flame. In this study, an advanced approach is proposed to model spark-ignition energy deposition and flame kernel growth. To decouple the flame kernel growth from the electrical discharge, a nanosecond-pulsed high-voltage discharge is used to trigger spark-ignition in an optically accessible small ignition test vessel with a quiescent mixture of air and methane. Initial conditions for the flame kernel, including its thermodynamic state and species composition, are derived from a plasma-chemical equilibrium calculation. The geometric shape and dimension of the kernel are characterized using a multi-dimensional thermal plasma solver. Here, the proposed modeling approach is evaluated using a high-fidelity computational fluid dynamics procedure to compare the simulated flame kernel evolution against flame boundaries from companion Schlieren images.

42 ENGINEERING↗