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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 OSTI2020

Benchmark Modeling and Simulation of the FFTF LOFWOS Test #13 Using SAM

The Fast Flux Test Facility (FFTF) was a 400 MW thermal powered, oxide-fueled, liquid sodium cooled test reactor, built to assist development and testing of advanced fuels and materials for fast breeder reactors. In July 1986, a series of unprotected Loss of Flow Without Scram (LOFWOS) transients were performed in FFTF as part of the Passive Safety Testing (PST) program. The LOFWOS Test #13, which was initiated at 50% power and 100% flow with the pump pony motors left off, has been chosen as a benchmark case by IAEA to support collaborative efforts within international partnerships on the validation of simulation tools and models in the area of sodium fast reactor passive safety in an IAEA Coordinated Research Project (CRP), launched in October 2018. The System Analysis Module (SAM) is an advanced and modern system analysis tool under development at Argonne National Laboratory for advanced non-LWR safety analysis. It utilizes the object-oriented application framework MOOSE to leverage the modern software environment and advanced numerical methods. The capabilities of SAM are being extended to enable the transient modeling, analysis, and design of various advanced nuclear reactor systems. To participate the IAEA CRP and enhance the SAM validation base for advanced reactor transient safety analysis, benchmark simulations of the FFTF LOFWOS Test #13 are performed using the SAM code. In this first phase of the validation effort, the thermal-hydraulic behavior of the reactor system is the focus and the reactor kinetics is not considered in the SAM FFTF model. Instead, the results of Argonne’s neutronics calculations are directly used, including the power shape of the active core region and the power history during the transient. The simulation results of FFTF at steady state agreed well with the measured data from the test. During the transient, reasonably good agreement were also obtained. Future work to improve the model will focus on introducing the reactivity predictions into the model, as well as better understanding or resolving the current discrepancies with the measured data.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2017

Methodology for assessing the safety of Hydrogen Systems: HyRAM 1.1 technical reference manual

The HyRAM software toolkit provides a basis for conducting quantitative risk assessment and consequence modeling for hydrogen infrastructure and transportation systems. HyRAM is designed to facilitate the use of state-of-the-art science and engineering models to conduct robust, repeatable assessments of hydrogen safety, hazards, and risk. HyRAM is envisioned as a unifying platform combining validated, analytical models of hydrogen behavior, a stan- dardized, transparent QRA approach, and engineering models and generic data for hydrogen installations. HyRAM is being developed at Sandia National Laboratories for the U. S. De- partment of Energy to increase access to technical data about hydrogen safety and to enable the use of that data to support development and revision of national and international codes and standards. This document provides a description of the methodology and models contained in the HyRAM version 1.1. HyRAM 1.1 includes generic probabilities for hydrogen equipment fail- ures, probabilistic models for the impact of heat flux on humans and structures, and computa- tionally and experimentally validated analytical and first order models of hydrogen release and flame physics. HyRAM 1.1 integrates deterministic and probabilistic models for quantifying accident scenarios, predicting physical effects, and characterizing hydrogen hazards (thermal effects from jet fires, overpressure effects from deflagrations), and assessing impact on people and structures. HyRAM is a prototype software in active development and thus the models and data may change. This report will be updated at appropriate developmental intervals.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

CRNT4SBML: a Python package for the detection of bistability in biochemical reaction networks

Motivation: Signaling pathways capable of switching between two states are ubiquitous within living organisms. They provide the cells with the means to produce reversible or irreversible decisions. Switchlike behavior of biological systems is realized through biochemical reaction networks capable of having two or more distinct steady states which are dependent on initial conditions. Investigation of whether a certain signaling pathway can confer bistability involves a substantial amount of hypothesis testing. The cost of direct experimental testing can be prohibitive. Therefore, constraining the hypothesis space is highly bene?cial. One such methodology is based on Chemical Reaction Network Theory which uses computational techniques to rule out pathways that are not capable of bistability regardless of kinetic constant values and molecule concentrations. Although useful, these methods are complicated from both pureandcomputationalmathematicsperspectives.Thus,theiradoptionisverylimitedamongstbiologists. Results: We brought Chemical Reaction Network Theory approaches closer to experimental biologists by automating all the necessary steps in CRNT4SMBL. The input is based on SBML format which is the community standard for biological pathway communication. The tool parses SBML and derives C-graph representations of the biological pathway with mass action kinetics. Next steps involve an ef?cient search for potential saddle-node bifurcation points using an optimization technique. This type of bifurcation is important as it has the potential of acting as a switching point between two steady states. Finally, if any bifurcation points are present, numerical continuation analysis extends the equilibria branches for generating the diagram. Presence of an S-shaped bifurcation diagram indicates that the pathway acts as a bistable switch for the given optimization parameters. Availability: CRNT4SBML is available via the Python Package Index. The documentation can be found at https://crnt4sbml.readthedocs.io. CRNT4SBML is licensed under the Apache Software License 2.0. Contact: vladislav.petyuk@pnnl.gov

Reyes, Brandon C.↗
DOE OSTI2019

Assessment of Physicochemical Properties of Electrical Arc Furnace Slag and Their Effects on Foamability

The composition of slags of an electric arc furnace (EAF) in a commercial melt shop was systematically analyzed with a focus on slag foaming. Basic behavior of FeO in an EAF slag was confirmed using fundamental thermodynamics. Monoxide ([Mg,Fe,Mn]O = M’O) and spinel ([Mg,Fe]Al{sub 2}O{sub 4}) phases in EAF slag were confirmed by X-ray diffraction analysis, and these results were interpreted in the context of equilibrium cooling calculation using FactSage™ software. Furthermore, the distribution of MgO with respect to the M’O-saturation limit at different basicity ratios (=CaO/SiO{sub 2}=C/S) and temperatures was evaluated. In particular, the relationship between MgO and FeO for C/S ratios ranging from 1.3 to 1.6 was considered with reference to phase equilibria. Foam height was affected by slag viscosity (η) and gas generation according to changes in C/S ratio and FeO content. Foaming index (Σ) decreased with the increasing C/S ratio and FeO content. The measured foam heights were relatively higher than the calculated values, and the result indicates that the effect of M’O on slag foaming in commercial process is remarkable. Therefore, slag chemistry should be optimized based on thermodynamic considerations and thermophysical properties to achieve good foaming characteristics.

36 MATERIALS SCIENCE↗
DOE OSTI2017

Development of a bayesian belief network model for the software reliability assessment of nuclear digital I and C safety systems - 206

Since the digital instrumentation and control systems are expected to play an important role for the safety systems in nuclear power plants (NPPs), the need has emerged to not only establish a basis for incorporating software behavior into digital I and C system reliability models, but also to quantify the failure probability of the software used in NPP digital protection systems. In this study, a Bayesian belief network (BBN) model is developed to quantitatively assess software reliability by estimating the number of faults in a software program considering its software development life cycle (SDLC). The model structure and parameters are established based on the information applicable to NPP safety-related systems and the evidence used to construct and quantify the BBN model was collected from three stages of expert elicitation. The software failure probability is estimated from the number of residual defects in a software program at the end of SDLC phase. As a case study, the BBN model was applied to quantify the software reliability of a typical digital protection software having the size of 50 function points and having the Medium development and validation and verification (V and V) qualities. The developed model can be applied to estimate the failure probability for both developing and deployed safety-related NPP software, and such results can be used to evaluate the quality of the digital I and C systems in addition to estimating potential reactor risk due to software failure. (authors)

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2016

Time Dependent Data Mining in RAVEN

RAVEN is a generic software framework to perform parametric and probabilistic analysis based on the response of complex system codes. The goal of this type of analyses is to understand the response of such systems in particular with respect their probabilistic behavior, to understand their predictability and drivers or lack of thereof. Data mining capabilities are the cornerstones to perform such deep learning of system responses. For this reason static data mining capabilities were added last fiscal year (FY 15). In real applications, when dealing with complex multi-scale, multi-physics systems it seems natural that, during transients, the relevance of the different scales, and physics, would evolve over time. For these reasons the data mining capabilities have been extended allowing their application over time. In this writing it is reported a description of the new RAVEN capabilities implemented with several simple analytical tests to explain their application and highlight the proper implementation. The report concludes with the application of those newly implemented capabilities to the analysis of a simulation performed with the Bison code.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

StructuredFuzzer: Fuzzing Structured Text-Based Control Logic Applications

Rigorous testing methods are essential for ensuring the security and reliability of industrial controller software. Fuzzing, a technique that automatically discovers software bugs, has also proven effective in finding software vulnerabilities. Unsurprisingly, fuzzing has been applied to a wide range of platforms, including programmable logic controllers (PLCs). However, current approaches, such as coverage-guided evolutionary fuzzing implemented in the popular fuzzer American Fuzzy Lop Plus Plus (AFL++), are often inadequate for finding logical errors and bugs in PLC control logic applications. They primarily target generic programming languages like C/C++, Java, and Python, and do not consider the unique characteristics and behaviors of PLCs, which are often programmed using specialized programming languages like Structured Text (ST). Furthermore, these fuzzers are ill suited to deal with complex input structures encapsulated in ST, as they are not specifically designed to generate appropriate input sequences. This renders the application of traditional fuzzing techniques less efficient on these platforms. To address this issue, this paper presents a fuzzing framework designed explicitly for PLC software to discover logic bugs in applications written in ST specified by the IEC 61131-3 standard. The proposed framework incorporates a custom-tailored PLC runtime and a fuzzer designed for the purpose. We demonstrate its effectiveness by fuzzing a collection of ST programs that were crafted for evaluation purposes. We compare the performance against a popular fuzzer, namely, AFL++. The proposed fuzzing framework demonstrated its capabilities in our experiments, successfully detecting logic bugs in the tested PLC control logic applications written in ST. On average, it was at least 83 times faster than AFL++, and in certain cases, for example, it was more than 23,000 times faster.

47 OTHER INSTRUMENTATION↗
DOE OSTI2023

Implementation of a feature selection algorithm in FARM to identify important state variables and time-invariant matrices

The FARM (Feasible Actuator Range Modifier) software module is a component of the RAVEN-based FORCE framework for analysis of Integrated Energy Systems (IES). FARM aids the HERON software module in the evaluation of the optimal dispatch for the different IES components. Set-point trajectories are required to meet limits on both production variables (i.e., the variables to be optimized such as the electrical power, the hydrogen production rate, etc.) and process variables tied to the service life of equipment (e.g., steam flowrate, vessel pressure, turbine firing temperature, etc.). To evaluate the feasibility of HERON generated set-points and to do so in an acceptable time, FARM employs reduced order models to represent the dynamic behavior of the systems to be dispatched. These surrogate models take the form of a linear dynamic system with sets of Linear Parameter Varying (LPV) matrices that are mapped to the system operating space. These matrices are derived from the trajectories of system state variables and system output variables during transients. The accuracy of LPV matrices depends on the selection of state variables. In previous reports, state variables were selected by adopting a complicated workflow requiring multiple software licenses and an advanced level of user expertise. In this report, a new workflow that automates the state variable selection process is presented. It significantly reduces the frequency of user interventions and does not require multiple software licenses. Each module in the new workflow is described in detail, and the input / output examples in each step of the workflow are provided. It was demonstrated that this workflow can greatly reduce the complexity of the state variable selection process, and that the updated FARM-Gamma and FARM-Delta validators can benefit from this workflow when solving the power dispatch problem of a representative IES test case. Finally, some code improvements that can further enhance the efficiency are suggested.

42 ENGINEERING↗
DOE OSTI2024

Simplified inelastic constitutive models for ASME Section III, Division 5 design by inelastic analysis

This report describes the development of simplified, universal constitutive model that captures the high temperature monotonic and cyclic behavior of a range of commonly-used high temperature materials. The goal of the work is to provide a simple, universal constitutive model to replace the current bespoke models for Grade 91, 316H, and Alloy 617 included in Nonmandatory Appendix HBB-Z of the ASME Boiler & Pressure Vessel Code, and to extend this model to cover Alloy 800H. We initiated this work in response to feedback from reactor vendors and other Code users requesting simplified models, compared to the current models, that are easier to implement and use in commercial finite element analysis software. This report describes the completion of this effort by developing a model to correct the defects in standard model forms presently used for high temperature material modeling, described in past work, developing and implementing new numerical methods to train this model against test data, and then actually training the model for the four materials. The report provides a complete mathematical description of the model along with the tabulated material coefficients for the four materials. The final step will be to formulate an ASME Code change to introduce the new models into the Code.

36 MATERIALS SCIENCE↗
DOE OSTI2015

Vacuum arc behavior and its voltage characteristics in drawing process controlled by composite magnetic fields along axial and transverse directions

In this research, drawing vacuum arc (VA) experiments were conducted using composite contacts under currents ranging from 5 kA to 20 kA root mean square (rms). The new type of contact comprised an axial magnetic field (AMF) configuration and a transverse magnetic field (TMF) configuration. The TMF plate was in the center, surrounded by the AMF plate. The contact generated both AMFs and TMFs simultaneously. VA appearances and arc voltages were recorded, and the VA was modeled as a conductor for electromagnetic force analysis in ANSYS software. The results showed that the coaxiality of operating mechanisms significantly influenced arc behavior just as the arc was ignited. When arc brightness did not increase after ignition, there was a voltage drop accompanied with diffusion of the VA. As to VA development, when an arc was ignited on an AMF plate, it spread on the plate and rotated. Over time the arc current increased, the constricting arc forms, and the arc column rotated on the TMF plate under the action of Ampere's force. With regard to the influence of a magnetic field on a VA at different stages, in the initial drawing arc stage the TMF was dominant, and the arc started to rotate under the action of Ampere's force. Afterwards, the AMF was dominant, with a steadily burning arc. As for contact melting, in the initial arcing period, a contracted short arc caused severe melting and erosion of the contact plate. When the ignition spot or root was close to the slot of plate, the electromagnetic force pushed the arc toward slot and contact edge, resulting in local erosion of the slot region.

42 ENGINEERING↗
DOE OSTI2020

Accelerating Hanford Site Cleanup through Operations Research Modeling - 20238

The Hanford Site cleanup effort will require the integration of dozens of unique facilities and processes, many of which will be first-of-a-kind in implementation and design. Each facility will be governed by its own set of operating logic, configured with a unique array of unit operations, and subject to a set of constraints that will affect its behavior. The collection of facilities have multiple points of interface, making the operations of any one facility potentially significant to the operations of other up- or downstream processes. It is therefore highly desirable to accurately predict these operations, as it allows for Site officials to identify and preempt bottlenecks and vulnerabilities before they unexpectedly inhibit the cleanup mission. With the quantity and complexity of the processes that will be on Site, building a pen-and-paper or even a spreadsheet-assisted model of the cleanup mission quickly becomes overwhelming in scope and inaccurate in execution. The Engineering organization for the Site's Tank Operations Contract (TOC) has therefore implemented the use of operations research (OR) modeling to simulate and predict future operations of Site facilities. These models are created using a discrete event simulation tool that allows for the development of detailed, versatile, and robust models. Not only can these models account for complex logical behaviors, but they can also simulate process details down to the level of vessel sizing, labor utilization, equipment reliability, and resource availability. To date, the TOC has developed OR models for several facilities on Site, including for single-shell tank (SST) farms, double-shell tank (DST) farms, the Effluent Treatment Facility (ETF), and the waste transfer system. These models have focused on identifying bottlenecks and operational constraints, and have been used to quantify the effects of implementing process changes. This latter point is particularly valuable, as it allows for several alternatives to be studied in a virtual setting before committing resources to making a change in the field. The decision to develop OR models has gained tremendous support from the Site's stakeholders and the U.S. Department of Energy (DOE) management, and has prompted the use of the tool to support additional internal and external initiatives. Recently, an initiative was proposed to use the models to help identify and provide quantitative backing for risks and opportunities for the TOC. This application of OR could not only help inform how the TOC manages its risks (e.g. quantities and types of spare parts), but could also help drive process improvements whose benefits might otherwise be hard to quantify. The models have also been used to drive the TOC's cloud computing, artificial intelligence (AI), and machine learning (ML) initiatives. These initiatives will not only improve the ability of the TOC to more rapidly respond to the needs of its customers, but it will also aid in the ability of the TOC to analyze and improve the processes it studies. Partnership with two external software development and consulting companies (Lanner and Ynformed) has furthered not only the application of AI and ML within the TOC, but has also spurred the development of new/improved software tools and platforms used by the companies. These partnerships have proven to be mutually beneficial and productive, and have set a precedent for the types of gains that can be made by exploring such options. (authors)

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

Argonne Reactor Code Software Verification and Validation Plan for the Versatile Test Reactor

This Argonne Reactor Code (ARC) software has taken a pivotal role in the design work of the Versatile Test Reactor. The ARC software itself consists of several independent or semiindependent pieces of software that are used in concert to carry out specific analysis and design objectives. In this report, we discuss the validation that is needed for the ARC software overall and discuss available, usable experimental data and how it relates to the VTR project. We also discuss the verification that is required for the ARC software with respect to its usage in the VTR project and give some concrete examples as to what it must accomplish. A list of goals for the verification and validation work are presented as the general procedure that will be implemented for every individual piece of software in ARC. The ARC software has been continuously developed for over 50 years and many of the features used for the VTR project have thus been rigorously tested for accuracy during that timeframe. The ARC software is presently under source code control and undergoes nightly regression testing of not only the individual pieces, but the interconnected aspects that are common to most analysis projects. Its greatest weakness, from a software verification and validation perspective, is both a lack of documentation and outdated documentation. As an example, much of the previous validation work will require contemporary effort to re-evaluate given the updated base data libraries and modifications to the software that have occurred since. This requires new documentation to be produced to support the usage of the software in the VTR project. The bulk of the ARC software is focused on steady state neutronics and TH behavior of a reactor system. In this regard, we have identified the ZPPR, FFTF, and EBR-II programs as the best candidates for sources of validation data for the VTR project. The ZPPR-15 series in the ZPPR program was chosen as it is the best representation of the VTR fuel form and reactor core layout of all of the ZPPR measurements. Additional support for it comes from the fact that most of the data has already been processed into usable ARC models for VTR validation. The FFTF and EBR-II reactors were both functional reactors that provide reactor scale measurements. FFTF is clearly a closer match to VTR and its startup measurements would be a very valuable validation basis. EBR-II has invaluable burnup related measurements that will play a key role in the ARC software validation work. Each piece of ARC software will require a report detailing the software verification. The validation work will be logically split into the results of modeling each identified experimental resource (ZPPR, EBR-II, FFTF). The intended timeline for the VTR project is to have the ARC software verified and validated by the end of fiscal year 2020. This is incumbent on sufficient funding allocated to V&V activities and no change in programmatic priorities.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2019

Experimental Investigation on Metallic Droplet Behavior in Molten BOF Slag

In order to better understand the metallic droplet behavior during a slag treatment process, a physical modeling based on the similarity principle was performed in a transparent scaled-down vessel at room temperature. Paraffin oil, 20 wt pct copper sulfate solution, and compressed air were used to simulate the molten slag, metallic droplet, and carrier gas, respectively. The droplets injected into paraffin oil during the experiment were captured by a high speed camera and were analyzed by Image Pro Plus software to obtain the droplet size distribution. The critical droplet size in the physical modeling and slag treatment process is quantitatively correlated. The results show that droplet breakage phenomenon is dominant over its coalescence in the current industrial practice, and droplet breakage is enhanced with increasing gas flow rate and/or lance depth. No significant effect of the nozzle configuration was found on the droplet breakage and coalescence. The droplet size distribution varies with the lance position. Gas flow rate and lance depth are the most important factors for droplet breakage, the extent of which can be reduced through a proper selection of the operational conditions. A linear relationship between the droplet size and the input energy flux is obtained.

36 MATERIALS SCIENCE↗
DOE OSTI2015

SU-C-210-07: Assessment of Intra-/Inter-Fractional Internal Tumor and Organ Movement in Radiotherapy of Head and Neck Cancer Using On-Board Cine MRI

Purpose: Head and neck (H&N) internal organ motion has previously been determined with low frequency and temporary nature based on population-based pre- and post-treatment studies. Using immobilization masks and adding a 4–6 mm planning-tumor-volume margin, geometric uncertainties of patients are routinely considered clinically inconsequential in H&N radiotherapy. Using the first commercially-available MR-IGRT system, we conducted the first quantitative study on inter-patient, intra- and inter-fractional H&N internal motion patterns to evaluate the necessity of individualized asymmetric internal margins. Methods: Ninety cine sagittal MR image sequences were acquired during the entire treatment course (6–7 weeks) of three H&N cancer patients using the ViewRay™ MR-IGRT system. The images were 5 mm thick and acquired at 4 frames/per second. One of the patients had a tracheostomy tube. The cross-sectional H&N airway (nasopharynx, oropharynx, and laryngopharynx portions) movement was analyzed comprehensively using in-house developed motion detection software. Results: Large inter-patient variations of swallowing frequency (0–1 times/per fraction), swallowing duration (1–3 seconds), and pharyngeal cross-sectional area (238–2516 mm2) were observed. Extensive pharyngeal motion occurred during swallowing, while nonzero and periodic change of airway geometry was observed in resting. For patient 1 with tracheostomy tube replacement, 30.3%, 30.0%, 48.7% and 0.3% of total frames showed ≥ 4 mm displacements in the anterior, posterior, inferior, and superior airway boundaries, respectively; similarly, (5.7%, 0.0%, 0.0%, 0.3%) and (23.3%, 0.0%, 35.7%, 1.7%) occurred for patients 2 and 3. Area overlapping coefficients with respect to the first frame were 76.3+/−6.4%, 90.3+/−0.6%, and 92.3+/−1.2% for the three patients, respectively. Conclusion: Both the resting and swallowing motions varied in frequency and amplitude among the patients and across fractions of a patient’s treatment. Patients receiving surgery that alters their respiratory and swallowing behavior can have significant intra-fractional internal motion. Patient-specific organ/tumor motion analysis may yield individualized asymmetric internal margins and improve the therapeutic ratio.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2022

Insert Modeling in UNF ST&DARDS

The Used Nuclear Fuel-Storage, Transportation & Disposal Analysis Resource and Data System (UNF-ST&DARDS) is a software tool that integrates a used nuclear fuel (UNF) or spent nuclear fuel (SNF) relational database and key analysis capabilities to simplify and automate numerous UNF management and fuel cycle–related activities. UNF-ST&DARDS is being developed for the US Department of Energy’s Office of Nuclear Energy Spent Fuel and Waste Disposition program. UNF-ST&DARDS provides an integrated framework that uses advanced modeling and simulation to predict the behavior of SNF over the timescales associated with permanent disposal in a geologic repository. After leaving the spent fuel pool, SNF is transferred to dry storage in a dual-purpose canister (DPC). DPCs are considered “dual purpose” because they are designed for both storage and transportation, removing the need to transfer the fuel to a separate transportation cask. However, much research has been conducted investigating the feasibility of directly disposing of DPCs in geologic repositories. Direct disposal of DPCs could reduce worker exposure during repackaging, reducing the amount of low-level waste from the discarded DPCs and potentially saving billions of dollars. Therefore, direct disposal of as-loaded DPCs is desirable if it can be done safely.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2019

Research on the Corrosion Behavior of X70 Pipeline Steel Under Coupling Effect of AC + DC and Stress

The coupling effect of stray AC (alternating current) + DC (direct current) and tensile stress-induced X70 pipeline corrosion is complicated with its mechanism uncovered. In this work, the corrosion behavior of X70 pipeline steel under three different corrosion (including an individual AC corrosion, mixed AC + 150 MPa tensile stress corrosion, coupling effect of AC + a 30 A/m{sup 2} DC interference and 150 MPa tensile stress corrosion) conditions in Dagang soil simulated solution was investigated by COMSOL Multiphysics software simulation, electrochemical tests, scanning electron microscope and other techniques. The results indicated that with the increase in AC density, the open-circuit potential of the X70 steel samples was negatively shifted and corrosion rate increased in three different corrosion conditions. The corrosion rates of X70 steel samples under individual AC interference were almost unchanged when 150.9 MPa tensile stress was applied. When applying 30 A/m{sup 2} DC interference, the corrosion rate of X70 steel increases significantly. Therefore, the factors affecting corrosion performance of X70 pipeline steel were DC > AC > stress. Under individual AC interference, uniform corrosion occurred on the X70 steel at an AC current density of less than 30 A/m{sup 2}, while the pitting corrosion occurred at an AC current density of greater than 100 A/m{sup 2}. The application of tensile stress and DC had a certain inhibitory effect on pitting corrosion.

36 MATERIALS SCIENCE↗
DOE OSTI2019

Economic Assessment of Nuclear Hybrid Energy Systems: Nuclear-Renewable-Water Integration in Arizona

One of the goals of the Nuclear-Renewable Hybrid Energy Systems (N-R HES) modeling and simulation (M&S) project is to assess the economic viability of N-R HES in a market that contains Variable Renewable Energy (VRE) sources like solar. The N-R HES M&S project is a multi-laboratory effort, led by Idaho National Laboratory (INL) and supported by Argonne National Laboratory (ANL) and Oak Ridge National Laboratory (ORNL). Development of the N-R HES software framework was initiated at INL in 2016 [1] and has reached some level of maturity such that it can be applied to more than simple demonstration cases, e.g. real industry problems. Nevertheless, more capabilities are constantly added to accommodate the special needs of challenging, real-life problems. The N-R HES framework is built on the Risk Analysis Virtual ENvironment (RAVEN) code [2], which it uses as a driver and workflow manager for all calculations. There are four main cornerstones of the N-R HES simulation framework: 1) The generation of stochastic synthetic time series. 2) A set of probabilistic analysis and optimization algorithms available in RAVEN. 3) A set of models for representing the physical behavior of N-R HES. These can be reduced-order models or high-fidelity Modelica [3] models. 4) The RAVEN plug-in called CashFlow [4] that maps physical performance into economic performance. Within this framework, a broad spectrum of questions related to N-R HES can be addressed. One of the challenges currently of high interest is the increasing penetration of VRE altering the profile of the net demand (demand after removing all non-curtailable renewable energy sources, e.g. rooftop solar). The N-R HES software framework is capable of analyzing the potential feasibility of mitigating the resultant volatility in the net electricity demand. One possible solution to manage net demand volatility is adding stabilizing loads to the grid. These loads can be external industrial processes that are able to ramp production up and down quickly, or for regulated markets, adding variable internal loads that will effectively increase the baseload of the power plant and therefore reduce the risk of internal demand that is lower than the capacity of the power plant. The latter is a possible solution currently being considered by Arizona Public Service (APS), the operating owner of the Palo Verde (PV) Nuclear Power Plant (NPP).

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

A Hardware and Software Co-design Framework for Energy Efficient Neuromorphic Systems

Neuromorphic systems can be realized by a variety of algorithms and architectures. A common understanding is that spiking neuromorphic designs, which encode information into spatio-temporal spiking events, are both a biologically-accurate and efficient way of processing information. However, representing the information through timing relationships induces sophisticated circuit designs in traditional CMOS-based implementations. In recent years, high-capacity resistive memory (RRAM, aka, memristor) has demonstrated great potential in mimicking synaptic behaviors. Several RRAM-based spiking neuromorphic designs exist, most of which focus on rate coding schemes. These designs simplify circuit implementations of neuron models and explore challenges such as unsatisfactory speed, resolution, and performance. As an alternative, we will explore temporal coding spiking neuromorphic systems that encode information as the relative timing of neuron activations (spikes), which have been proven to be more adaptive and energy-efficient. Developing a neuromorphic system for spiking neural network (SNN) inference and online training, however, faces some major technical challenges: (1) It lacks circuit implementation support for temporal-coding SNN to achieve satisfying power efficiency and accuracy; (2) Although existing research works have investigated memristive synapse and neuron designs for spike-timing-dependent plasticity, the non-ideal conditions in implementation, such as device variations and signal degradation, degrade online learning accuracy of large scale systems; and (3) Non-optimized, inter-layer data traffic in SNNs, leads to unnecessary data communication costs. In this project, we plan to address these challenges by a hardware and software co-design framework that incorporates solutions at the circuit, architecture, and algorithm levels. At the circuit-level, we will elaborate on the in-situ SNN processing element designs for supporting both inference and online training modes. Variation-aware schemes will be studied to improve reliability. At the architecture level, we propose a pipelined, asynchronous architecture to retain the timing resolution of spikes. At the algorithm level, we will investigate an innovative SNN training algorithm for enabling activation sparsification and reducing unnecessary data communication costs. This neuromorphic system will provide an effective solution to real-life energy-constrained applications and significantly contribute to the exploration of next-generation high-performance computing systems under the DOE context.

97 MATHEMATICS AND COMPUTING↗