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At least 253 records · Page 14

Instruction Roofline: An insightful visual performance model for GPUs

The Roofline performance model provides an intuitive approach to identify performance bottlenecks and guide performance optimization. However, the classic FLOP-centric approach is inappropriate for the emerging applications that perform more integer operations than floating point operations. In this article, we reintroduce our Instruction Roofline Model on NVIDIA GPUs and expand our evaluation of it. The Instruction Roofline incorporates instructions and memory transactions across all memory hierarchies together, and provides more performance insights than the FLOP-oriented Roofline Model, that is, instruction throughput, stride memory access patterns, bank conflicts, and thread predication. We use our Instruction Roofline methodology to analyze eight proxy applications: HPGMG from AMReX, Matrix Transpose benchmarks, ADEPT from MetaHipMer's sequence alignment phase, EXTENSION from MetaHipMer's local assembly phase, CUSP, cuSPARSE, cudaTensorCoreGemm, and cuBLAS. We demonstrate the ability of our methodology to understand various aspects of performance and performance bottlenecks on NVIDIA GPUs and motivate code optimizations.

Ding, N↗

A High Performance Sparse Tensor Algebra Compiler in MLIR

Sparse tensor algebra is widely used in many applications, including scientific computing, machine learning, and data analytics. The performance of sparse tensor algebra kernels strongly depends on the intrinsic characteristics of the input tensors, hence many storage formats are designed for tensors to achieve optimal performance for particular applications/architectures, which makes it challenging to implement and optimize every tensor operation of interest on a given architecture. We propose a tensor algebra domain-specific language (DSL) and compiler framework to automatically generate kernels for mixed sparse-dense tensor algebra operations. The proposed DSL provides high-level programming abstractions that resemble the familiar Einstein notation to represent tensor algebra operations. The compiler introduces a new Sparse Tensor Algebra dialect built on top of LLVM's extensible MLIR compiler infrastructure for efficient code generation while covering a wide range of tensor storage formats. Our compiler also leverages input-dependent code optimization to enhance data locality for better performance. Our results show that the performance of automatically generated kernels outperforms the state-of-the-art sparse tensor algebra compiler, with up to 20.92x, 6.39x, and 13.9x performance improvement over state-of-the-art tensor algebra compilers, for parallel SpMV, SpMM, and TTM, respectively.

Tian, Ruiqin↗

COMPARISON OF NONLINEAR DYNAMICS OPTIMIZATION METHODS FOR APS-U

Many different objectives and genetic algorithms have been proposed for storage ring nonlinear dynamics performance optimization. These optimization objectives include nonlinear chromaticities and driving/detuning terms, on-momentum and off-momentum dynamic acceptance, chromatic detuning, local momentum acceptance, variation of transverse invariant, Touschek lifetime, etc. In this paper, the effectiveness of several different optimization methods and objectives are compared for the nonlinear beam dynamics optimization of the Advanced Photon Source upgrade (APS-U) lattice. The optimized solutions from these different methods are preliminarily compared in terms of the dynamic acceptance, local momentum acceptance, chromatic detuning, and other performance measures.

APS-U∗↗

TH-A-18C-01: Design Optimization of Segmented Scintillators for Megavoltage Cone- Beam CT

Purpose: Active matrix flat-panel imagers incorporating thick, segmented scintillators for megavoltage cone-beam CT (MV CBCT) imaging have demonstrated strong potential for facilitating soft-tissue visualization at low, clinically practical doses. In order to identify scintillator design parameters that optimize performance for this purpose, a modeling technique which includes both radiation and optical effects and which lends itself to computationally practical implementation has been developed and explored. Methods: A hybrid modeling technique, based on Monte Carlo event-by-event simulation of radiation transport and separate determination of optical effects, was devised as an alternative to computationally prohibitive event-by- event simulations of both radiation and optical transport. The technique was validated against empirical results from a previously reported 1.13 cm thick, 1.016 mm element-to-element pitch BGO scintillator prototype. Using this technique, the contrast-to-noise ratio (CNR) and spatial resolution performance of numerous scintillator designs, with thicknesses ranging from 0.5 to 6 cm and pitches ranging from 0.508 to 1.524 mm, were examined. Results: CNR and spatial resolution performance for the various scintillator designs demonstrate complex behavior as scintillator thickness and pitch are varied - exhibiting a clear trade-off between these two imaging metrics up to a thickness of ~3 cm. Based on these results, an optimization map highlighting those regions of design that provide a balance between these metrics was created. The map indicates that, for a given set of optical parameters, scintillator thickness and pitch can be judiciously chosen to maximize performance without resorting to thicker, more costly scintillators. Conclusion: Modeling radiation and optical effects in thick, segmented scintillators through use of a hybrid modeling technique provides a practical way to gain insight as to how to optimize the performance of such devices for radiotherapy imaging. Assisted by such modeling, the development of practical designs should greatly facilitate low-dose, soft tissue visualization through MV CBCT imaging. This project was supported in part by NIH grant R01 CA051397.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

Humins-Derived Hard Carbon as a Low-Cost Material for Sodium-Ion Battery Anodes

The growing demand for sodium-ion batteries (SIBs) in grid storage underscores the need for electrode materials that balance performance and cost, including sustainable and robust carbon sources. The equitable and abundant distribution of materials for SIBs, along with their superior low-temperature performance, safety, and fast-charging capability, further distinguishes them from lithium-ion batteries (LIBs). Hard carbon (HC) is the state-of-the-art anode for SIBs, but current commercial HC production is localized mostly to one region of the world, raising concerns about supply chain vulnerability, critical material dependency, and environmental aspects. Here, the first demonstration of humins, an abundant biorefinery byproduct, as a precursor for HC anodes for sodium-ion storage is reported. Humins were carbonized at 1100 degrees C-1300 degrees C, and the resulting materials were subjected to comprehensive materials and electrochemical characterization. Among the temperatures studied, humins-derived hard carbon synthesized at 1200 degrees C delivers an initial reversible capacity 270mA h g-1 , with stable cycling performance up to 500 cycles and excellent rate capability, representing the optimal performance. This study establishes humins as a promising and low-cost carbon source that provides a route to mitigate supply chain risks and valorizes an underutilized biorefinery waste stream for high-performance SIB anodes.

25 ENERGY STORAGE↗

Exploring code portability solutions for HEP with a particle tracking test code

Traditionally, high energy physics (HEP) experiments have relied on x86 CPUs for the majority of their significant computing needs. As the field looks ahead to the next generation of experiments such as DUNE and the High-Luminosity LHC, the computing demands are expected to increase dramatically. To cope with this increase, it will be necessary to take advantage of all available computing resources, including GPUs from different vendors. A broad landscape of code portability tools—including compiler pragma-based approaches, abstraction libraries, and other tools—allow the same source code to run efficiently on multiple architectures. In this paper, we use a test code taken from a HEP tracking algorithm to compare the performance and experience of implementing different portability solutions. While in several cases portable implementations perform close to the reference code version, we find that the performance varies significantly depending on the details of the implementation. Achieving optimal performance is not easy, even for relatively simple applications such as the test codes considered in this work. Several factors can affect the performance, such as the choice of the memory layout, the memory pinning strategy, and the compiler used. The compilers and tools are being actively developed, so future developments may be critical for their deployment in HEP experiments.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

ATTNChecker: Highly-Optimized Fault Tolerant Attention for Large Language Model Training

Large Language Models (LLMs) have demonstrated remarkable performance in various natural language processing tasks. However, the training of these models is computationally intensive and susceptible to faults, particularly in the attention mechanism, which is a critical component of transformer-based LLMs. In this paper, we investigate the impact of faults on LLM training, focusing on INF, NaN, and near-INF values in the computation results with systematic fault injection experiments. We observe the propagation patterns of these errors, which can trigger non-trainable states in the model and disrupt training, forcing the procedure to load from checkpoints. To mitigate the impact of these faults, we propose ATTNChecker, the first Algorithm-Based Fault Tolerance (ABFT) technique tailored for the attention mechanism in LLMs. ATTNChecker is designed based on fault propagation patterns of LLM and incorporates performance optimization to adapt to both system reliability and model vulnerability while providing lightweight protection for fast LLM training. Evaluations on four LLMs show that ATTNChecker on average incurs on average 7% overhead on training while detecting and correcting all extreme errors. Compared with the state-of-the-art checkpoint/restore approach, ATTNChecker reduces recovery overhead by up to 49×.

Liang, Yuhang [University of Alabama - Birmingham]↗

Development and Commercialization of an IDAES-Based Power Plant Performance Monitoring and Optimization System

DOE and NETL have created an advanced, open-source computational platform through the Institute for the Design of Advanced Energy Systems (IDAES). The IDAES platform is a very extensive modeling environment that can be used for a broad range of power plant and process applications. MapEx Software is developing and commercializing a software application that makes it easier to set-up and run IDAES-based analyses. The MapEx-developed software application will replace the need for custom Python language coding with a user-friendly graphical user interface where the user can construct a flowsheet diagram of the IDAES model by inserting icons representing the plant equipment onto the screen. This application will make the implementation of modeling and optimization of existing fossil-fired power plants more straight-forward and less time-consuming. The effort focuses on performance monitoring and optimization of plant operations for the existing coal-fired power plant fleet but is built on a structure that allows expansion into the broad range of applications where IDAES methods may be applied.

20 FOSSIL-FUELED POWER PLANTS↗

Machine Learning-Assisted High-Temperature Reservoir Thermal Energy Storage Optimization: Numerical Modeling and Machine Learning Input and Output Files

This data set includes the numerical modeling input files and output files used to synthesize data, and the reduced-order machine learning models trained from the synthesized data for reservoir thermal energy storage site identification. In this study, a machine-learning-assisted computational framework is presented to identify High-Temperature Reservoir Thermal Energy Storage (HT-RTES) site with optimal performance metrics by combining physics-based simulation with stochastic hydrogeologic formation and thermal energy storage operation parameters, artificial neural network regression of the simulation data, and genetic algorithm-enabled multi-objective optimization. A doublet well configuration with a layered (aquitard-aquifer-aquitard) generic reservoir is simulated for cases of continuous operation and seasonal-cycle operation scenarios. Neural network-based surrogate models are developed for the two scenarios and applied to generate the Pareto fronts of the HT-RTES performance for four potential HT-RTES sites. The developed Pareto optimal solutions indicate the performance of HT-RTES is operation-scenario (i.e., fluid cycle) and reservoir-site dependent, and the performance metrics have competing effects for a given site and a given fluid cycle. The developed neural network models can be applied to identify suitable sites for HT-RTES, and the proposed framework sheds light on the design of resilient HT-RTES systems. All the simulations and the neural network model were done by Idaho National Laboratory. A detailed description of the work was reported in publication linked below.

15 GEOTHERMAL ENERGY↗

Big Data Analysis of Massive PMU Datasets: A Data Platform Perspective

The discovery of `event signatures' and useful insights from very large historical Phasor Measurement Unit (PMU) datasets is predicated on offline Big Data analysis approaches that rely on the generation of predictive features on a massive scale. This paper presents lessons learned from a data platform perspective towards reducing barriers to adoption of Big Data analytics against a real dataset of almost half a trillion data points drawn from over 400 PMUs distributed across the North American power grid. We demonstrate software abstractions and targeted performance optimizations that can lead to significant productivity gains for power systems researchers seeking to perform offline exploratory temporal analysis and modeling tasks, with a focus on feature generation. We describe how our optimized approach goes beyond a naive application of mainstream Big Data technologies, enabling feature generation tasks, that previously took days or even weeks, to now be completed in just a few hours.

Kumar, Vijay S.↗

Evaluating Optimization Strategies for Engine Simulations Using Machine Learning Emulators

This work evaluates different optimization algorithms for computational fluid dynamics (CFD) simulations of engine combustion. Due to the computational expense of CFD simulations, emulators built with machine learning algorithms were used as surrogates for the optimizers. Two types of emulators were used: a Gaussian process (GP) and a weighted variety of machine learning methods called SuperLearner (SL). The emulators were trained using a dataset of 2048 CFD simulations that were run concurrently on a supercomputer. The design of experiments (DOE) for the CFD runs was obtained by perturbing nine input parameters using a Monte-Carlo method. The CFD simulations were of a heavy duty engine running with a low octane gasoline-like fuel at a partially premixed compression ignition mode. Ten optimization algorithms were tested, including types typically used in research applications. Each optimizer was allowed 800 function evaluations and was randomly tested 100 times. The optimizers were evaluated for the median, minimum, and maximum merits obtained in the 100 attempts. Some optimizers required more sequential evaluations, thereby resulting in longer wall clock times to reach an optimum. The best performing optimization methods were particle swarm optimization (PSO), differential evolution (DE), GENOUD (an evolutionary algorithm), and micro-genetic algorithm (GA). These methods found a high median optimum as well as a reasonable minimum optimum of the 100 trials. Moreover, all of these methods were able to operate with less than 100 successive iterations, which reduced the wall clock time required in practice. Two methods were found to be effective but required a much larger number of successive iterations: the DIRECT and MALSCHAINS algorithms. A random search method that completed in a single iteration performed poorly in finding optimum designs but was included to illustrate the limitation of highly concurrent search methods. The last three methods, Nelder–Mead, bound optimization by quadratic approximation (BOBYQA), and constrained optimization by linear approximation (COBYLA), did not perform as well.

Probst, Daniel M.↗

Thermodynamically Driven Synthetic Optimization for Cation-Disordered Rock Salt Cathodes

Relating the synthesis conditions of materials to their functional performance has long been an experience-based trial-and-error process. However, this methodology is not always efficient in identifying an appropriate protocol and can lead to overlooked opportunities for the performance optimization of materials through simple modifications of the synthesis process. In this work, we systematically track the structural evolution in the synthesis of a representative disordered rocksalt (a promising next-generation Li-ion cathode material) at the scale of both the long-range crystal structure and the short-range atomic structure using various in situ and ex situ techniques, including transmission electron microscopy, X-ray diffraction, and pair distribution function analysis. Further, an optimization strategy is proposed for the synthesis protocol, leading to a remarkably enhanced capacity (specific energy) of 313 mAh g –1 (987 Wh kg –1 ) at a low rate (20 mA g –1 ), with a capacity of more than 140 mAh g –1 retained even at a very high cycling rate of 2,000 mA g –1 . This strategy is further rationalized using ab initio calculations, and important opportunities for synthetic optimization demonstrated in this study are highlighted.

36 MATERIALS SCIENCE↗

Improving wafer-scale Josephson junction resistance variation in superconducting quantum coherent circuits

Quantum bits, or qubits, are an example of coherent circuits envisioned for next-generation computers and detectors. A robust superconducting qubit with a coherent lifetime of O (100 µs) is the transmon: a Josephson junction functioning as a non-linear inductor shunted with a capacitor to form an anharmonic oscillator. In a complex device with many such transmons, precise control over each qubit frequency is often required, and thus variations of the junction area and tunnel barrier thickness must be sufficiently minimized to achieve optimal performance while avoiding spectral overlap between neighboring circuits. Simply transplanting our recipe optimized for single, stand-alone devices to wafer-scale (producing 64, 1x1 cm dies from a 150 mm wafer) initially resulted in global drifts in room-temperature tunneling resistance of ± 30%. Inferring a critical current variation from this resistance distribution, we present an optimized process developed from a systematic 38 wafer study that results in < 3.5% relative standard deviation (RSD) in critical current ( ) for 3000 Josephson junctions (both single-junctions and asymmetric SQUIDs) across an area of 49 cm 2 . Looking within a 1x1 cm moving window across the substrate gives an estimate of the variation characteristic of a given qubit chip. Our best process, utilizing ultrasonically assisted development, uniform ashing, and dynamic oxidation has shown = 1.8% within 1x1 cm, on average, with a few 1x1 cm areas having < 1.0% (equivalent to < 0.5%). Such stability would drastically improve the yield of multi-junction chips with strict critical current requirements.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

Ex situ metrology and data analysis for optimization of beamline performance of aspherical pre-shaped x-ray mirrors at the advanced light source

Super high quality aspherical x-ray mirrors with a residual slope error of ~100 nrad (root-mean-square) and a height error of ~1-2 nm (peak-to-valley), and even lower, are now available from a number of the most advanced vendors utilizing deterministic polishing techniques. The mirror specification for the fabrication is based on the simulations of the desired performance of the mirror in the beamline optical system and is normally given with the acceptable level of deviation of the mirror figure and finish from the desired ideal shape. For example, in the case of aspherical x-ray mirrors designed for the Advanced Light Source (ALS) QERLIN beamline, the ideal shape is defined with the beamline application (conjugate) parameters and their tolerances. Here, we first introduce an original procedure and dedicated software developed at the ALS X-Ray Optics Laboratory (XROL) for optimization of beamline performance of pre-shaped hyperbolic and elliptical mirrors. The optimization is based on results of ex situ surface slope metrology and consists in minimization of the mirror shape error by determining the conjugate parameters of the best-fit ideal shape within the specified tolerances. We describe novel optical metrology instrumentation, measuring techniques, and analytical methods used at the XROL for acquisition of surface slope data and optimization of the optic’s beamline performance. The high efficacy of the developed experimental methods and data analysis procedures is reflected in results of measurements with and performance optimization of hyperbolic and elliptical cylinder mirrors designed and fabricated for the ALS QERLIN beamline.

47 OTHER INSTRUMENTATION↗

Novel Carbon (C)-Boron (B)-Nitrogen (N)-Containing H2 Storage Materials

The following summarizes the research conducted for DOE project DE-EE0005658 “Novel Carbon(C)-Boron(B)-Nitrogen(N)-Containing H2 Storage Materials”. This work focused in part on the continued study of two materials identified from the preceding project DE-FG360GO18143 (“Hydrogen Storage by Novel CBN Heterocycle Materials”) as lead candidates to meet the DOE technical targets for either vehicular or non-automotive hydrogen storage applications. Specifically, a room-temperature liquid, 3-methyl-1,2-cyclopentane (B), and a high H2 capacity solid, 1,2-BN-cyclohexane (J), were selected for further characterization and performance optimization. In addition to these compounds, the current project also aimed to prepare several new materials predicted to be disposed towards direct reversibility of H2 release and uptake, a feature deemed critical to achieving efficient recycling of spent fuel end products. To assist in the rational design of these and other next-generation materials, this project undertook to investigate the mechanism of hydrogen release from established compounds (mainly B and J) using a combined experimental/computational approach. Among this project’s signature accomplishments, the preliminary synthetic route to B was optimized for production on decagram scale. With such quantities of material available, its performance in powering an actual 30 W proton exchange membrane (PEM) fuel cell stack was tested and found to be identical to that of facility H2. Despite this positive proof-of-concept achievement, however, further consideration of neat B as a potential hydrogen storage material was abandoned due to evidence of thermal instability. Specifically, mass spectrometry-coupled thermogravimetric analysis (TGA-MS) revealed significant H2 release from B to initiate at 50 °C, well below the 60 °C minimum threshold set by the DOE. This result prompted a more extensive investigation in the decomposition mechanism of B vis-à-vis that of J, which exhibited in neat form a substantially higher onset temperature for spontaneous H2 release (70 °C). Solution-phase kinetic experiments using ReactIR established a second-order dependence for the initial loss of H2 from both B and J; Arrhenius analysis, however, revealed the activation barrier for this reaction was lower for B than for J, which presumably contributes to the diminished thermal stability of the former. On the basis of these and other experimental results, extensive computational efforts yielded a reasonable mechanistic model for the dehydrogenation of 1,2-BN-cycloalkane materials. While the prospect of neat B as a suitable hydrogen storage material was discarded, it was proposed that the combination of B with more thermally stable amine-borane-based materials might afford mixtures with improved properties. Indeed, when B was combined with ammonia borane (AB) in a 2:1 molar ratio, the two materials formed a liquid. More significantly, this mixture remained liquid even after complete dehydrogenation, thus establishing the potential for a single-phase fuel cycle. (In contrast, the dehydrogenation product of neat B is a low melting solid (mp = 28-30 °C).) Another advantage conferred by the blend formulation was a dramatic reduction in the amount of borazine produced by AB. Borazine is a well-known contaminant of H2 produced by the thermal decomposition of neat AB, and exerts deleterious effects on fuel cell performance. Residual gas analysis (RGA) of the gas stream generated from the B-AB blend, however, detected just 0.01% borazine content when a Pt-Ni nanoparticle dehydrogenation catalyst was used. In all ii then, the 2:1 B-AB blend marks a major achievement in the effort to develop a suitable liquid amine-borane hydrogen storage material, and merits further investigation into the optimization for practical adoption. Similar realization of the potential of J as a high % wt. H2 material required a method to dehydrogenate the carbonaceous components of the molecule without the use of a sacrificial hydrogen acceptor, as had been reported in the previous project. Ultimately, this reaction was achieved for a B,N-disubstituted BN-cyclohexene model substrate using a gas flow system with a fixed Pd/C catalyst bed. Considerable work remains, however, to translate these initial results into a general protocol for complete dehydrogenation of fully saturated BN-cycloalkane materials such as J. With concrete confirmation of the possibility to perform both BN and CC dehydrogenation on a single theoretical substrate, COMSOL modeling was used to evaluate the effects of thermodynamically coupling the two reactions. It was hypothesized that the heat generated from exothermic BN dehydrogenation would partially drive the endothermic CC dehydrogenation reaction; this additional heat consumption was expected to in turn confer the benefit of lowering the maximum reactor temperature. A two-dimensional model of an axisymmetric reactor including experimental kinetic and calculated thermodynamic parameters for both reactions did indeed predict these outcomes. The extent to which the effects of thermodynamic coupling actually manifested, however, were also revealed to depend strongly on the relative rates of the two reactions, as well as the magnitude of the equilibrium constant governing the progress of the endothermic process. Given the evident complexity of attaining high effective % wt. H2 capacity with J, alternative systems were investigated for greater facility of extensive H2 release. Among those studied, 1,2,4,5-bis-BN-cyclohexane (H) demonstrated the most favorable properties, particularly with respect to thermal stability: rather than decompose, a neat sample instead sublimed when heated above 150 °C. Nevertheless, two commercially available catalytic systems were identified to effect release of two H2 equivalents from H. Release of further equivalents were apparently impeded by the formation of either polymeric material or one of two dimeric cage compounds depending on the catalyst used. Notably, a method to regenerate H from these product mixtures remains to be developed. Thus, while H may prove useful for certain long-term energy storage needs, it is currently less suited applications involving frequent fuel consumption. Similar difficulties were also encountered in attempts to realize the complete fuel cycle of 1,3-BN-cyclohexane (E) and B,N-substituted derivatives thereof. It had been initially proposed that E would provide for readily reversible BN dehydrogenation through a measure of frustrated Lewis pair-type character. Indeed, computations predicted this reaction would be essentially thermoneutral in solution. In the course of attempts to fully hydrogenate the spent fuel, however, dimeric species formed and proved resistant to further BN reduction. While a number of monomeric cyclic compounds were also successfully synthesized as formal boron-nitrogen frustrated Lewis pairs, none demonstrated any capacity to split H2 across the BN unit. The challenge of developing a practical amine-borane-based material for readily reversible hydrogen storage thus remains unresolved at this time. As such, it deserves consideration as a major objective of any future work.

08 HYDROGEN↗

High‐Efficiency Nonfullerene Organic Solar Cells: Critical Factors that Affect Complex Multi‐Length Scale Morphology and Device Performance

Organic solar cells (OSCs) made of donor/acceptor bulk‐heterojunction active layers have been of widespread interest in converting sunlight to electricity. Characterizing of the complex morphology at multiple length scales of polymer:nonfullerene small molecular acceptor (SMA) systems remains largely unexplored. Through detailed characterizations (hard/soft X‐ray scattering) of the record‐efficiency polymer:SMA system with a close analog, quantitative morphological parameters are related to the device performance parameters and fundamental morphology–performance relationships that explain why additive use and thermal annealing are needed for optimized performance are established. A linear correlation between the average purity variations at small length scale (≈10 nm) and photovoltaic device characteristics across all processing protocols is observed in ≈12%‐efficiency polymer:SMA systems. In addition, molecular interactions as reflected by the estimated Flory–Huggins interaction parameters are used to provide context of the room temperature morphology results. Comparison with results from annealed devices suggests that the two SMA systems compared show upper and lower critical solution temperature behavior, respectively. The in‐depth understanding of the complex multilength scale nonfullerene OSC morphology may guide the device optimization and new materials development and indicates that thermodynamic properties of materials systems should be studied in more detail to aid in designing optimized protocols efficiently.

Ye, Long↗

Advanced control techniques for modern inertia based inverters

In this research three artificial intelligent (AI)-based techniques are proposed to regulate the voltage and frequency of a grid-connected inverter. The increase in the penetration of renewable energy sources (RESs) into the power grid has led to the increase in the penetration of fast-responding inertia-less power converters. The increase in the penetration of these power electronics converters changes the nature of the conventional grid, in which the existing kinetic inertia in the rotating parts of the enormous generators plays a vital role. The concept of virtual inertia control scheme is proposed to make the behavior of grid connected inverters more similar to the synchronous generators, by mimicking the mechanical behavior of a synchronous generator. Conventional control techniques lack to perform optimally in nonlinear, uncertain, inaccurate power grids. Besides, the decoupled control assumption in conventional VSGs makes them nonoptimal in resistive grids. The neural network predictive controller, the heuristic dynamic programming, and the dual heuristic dynamic programming techniques are presented in this research to overcome the draw backs of conventional VSGs. The nonlinear characteristics of neural networks, and the online training enable the proposed methods to perform as robust and optimal controllers. The simulation and the experimental laboratory prototype results are provided to demonstrate the effectiveness of the proposed techniques.

24 POWER TRANSMISSION AND DISTRIBUTION↗

A HUMAN AUTOMATION INTERACTION CONCEPT FOR A SMALL MODULAR REACTOR CONTROL ROOM

Many advanced nuclear power plant (NPP) designs incorporate higher degrees of automation than the existing fleet of NPPs. Automation is being introduced or proposed in NPPs through a wide variety of systems and technologies, such as advanced displays, computer-based procedures, advanced alarm systems, and computerized operator support systems. Additionally, many new reactor concepts, both full scale and small modular reactors, are proposing increased automation and reduced staffing as part of their concept of operations. However, research consistently finds that there is a fundamental tradeoff between system performance with increased automation and reduced human performance. There is a need to address the question of how to achieve high performance and efficiency of high levels of automation without degrading human performance. One example of a new NPP concept that will utilize greater degrees of automation is the SMR concept from NuScale Power. The NuScale Power design requires 12 modular units to be operated in one single control room, which leads to a need for higher degrees of automation in the control room. Idaho National Laboratory (INL) researchers and NuScale Power human factors and operations staff are working on a collaborative project to address the human performance challenges of increased automation and to determine the principles that lead to optimal performance in highly automated systems. This paper will describe this concept in detail and will describe an experimental test of the concept. The benefits and challenges of the approach will be discussed.

99 GENERAL AND MISCELLANEOUS↗