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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 1,801 records · Page 100

Reinforcement Learning Control for Enhancing Marine Hydrokinetic Turbine Energy Generation

This paper proposes a reinforcement learning-based method to maximize power generation for a direct-drive marine hydrokinetic turbine. A high levelized cost of energy (LCOE) is preventative in the widespread adoption of many marine energy conversion technologies. A straightforward way to reduce LCOE is to increase conversion efficiency and ensure maximum energy generation. The proposed method utilizes a damping control methodology, varying applied generator torque via a linear relationship between the applied damping coefficient and rotor speed. A state-action-reward-state-action (SARSA) algorithm has been used to learn the optimal control action for a given flow velocity. The proposed SARSA methodology uses Gaussian radial basis functions to create a three-dimensional surface to estimate the relationship between damping coefficient, incoming flow velocity, and coefficient of power (C p ). Here, the SARSA algorithm was compared against a baseline optimal tip speed ratio controller over a year-long flow velocity case profile while considering the effects of biofouling on the turbine system, where the proposed RL method generated 0.92% more energy than the baseline.

Damp↗

Kinetic Properties of Microbial Exoenzymes Vary With Soil Depth but Have Similar Temperature Sensitivities Through the Soil Profile

Current knowledge of the mechanisms driving soil organic matter (SOM) turnover and responses to warming is mainly limited to surface soils, although over 50% of global soil carbon is contained in subsoils. Deep soils have different physicochemical properties, nutrient inputs, and microbiomes, which may harbor distinct functional traits and lead to different SOM dynamics and temperature responses. We hypothesized that kinetic and thermal properties of soil exoenzymes, which mediate SOM depolymerization, vary with soil depth, reflecting microbial adaptation to distinct substrate and temperature regimes. We determined the Michaelis-Menten (MM) kinetics of three ubiquitous enzymes involved in carbon (C), nitrogen (N) and phosphorus (P) acquisition at six soil depths down to 90 cm at a temperate forest, and their temperature sensitivity based on Arrhenius/ Q 10 and Macromolecular Rate Theory (MMRT) models over six temperatures between 4–50°C. Maximal enzyme velocity ( V max ) decreased strongly with depth for all enzymes, both on a dry soil mass and a microbial biomass C basis, whereas their affinities increased, indicating adaptation to lower substrate availability. Surprisingly, microbial biomass-specific catalytic efficiencies also decreased with depth, except for the P-acquiring enzyme, indicating distinct nutrient demands at depth relative to microbial abundance. These results suggested that deep soil microbiomes encode enzymes with intrinsically lower turnover and/or produce less enzymes per cell, reflecting distinct life strategies. The relative kinetics between different enzymes also varied with depth, suggesting an increase in relative P demand with depth, or that phosphatases may be involved in C acquisition. V max and catalytic efficiency increased consistently with temperature for all enzymes, leading to overall higher SOM-decomposition potential, but enzyme temperature sensitivity was similar at all depths and between enzymes, based on both Arrhenius/ Q 10 and MMRT models. In a few cases, however, temperature affected differently the kinetic properties of distinct enzymes at discrete depths, suggesting that it may alter the relative depolymerization of different compounds. We show that soil exoenzyme kinetics may reflect intrinsic traits of microbiomes adapted to distinct soil depths, although their temperature sensitivity is remarkably uniform. These results improve our understanding of critical mechanisms underlying SOM dynamics and responses to changing temperatures through the soil profile.

59 BASIC BIOLOGICAL SCIENCES↗

Experimental and Computational Investigation of a Translating-Throat Single-Expansion-Ramp Nozzle

An experimental and computational study was conducted on a high-speed, single-expansion-ramp nozzle (SERN) concept designed for efficient off-design performance. The translating-throat SERN concept adjusts the axial location of the throat to provide a variable expansion ratio and allow a more optimum jet exhaust expansion at various flight conditions in an effort to maximize nozzle performance. Three design points (throat locations) were investigated to simulate the operation of this concept at subsonic-transonic, low supersonic, and high supersonic flight conditions. The experimental study was conducted in the jet exit test facility at the Langley Research Center. Internal nozzle performance was obtained at nozzle pressure ratios (NPR's) up to 13 for six nozzles with design nozzle pressure ratios near 9, 42, and 102. Two expansion-ramp surfaces, one concave and one convex, were tested for each design point. Paint-oil flow and focusing schlieren flow visualization techniques were utilized to acquire additional flow data at selected NPR'S. The Navier-Stokes code, PAB3D, was used with a two-equation k-e turbulence model for the computational study. Nozzle performance characteristics were predicted at nozzle pressure ratios of 5, 9, and 13 for the concave ramp, low Mach number nozzle and at 10, 13, and 102 for the concave ramp, high Mach number nozzle.

Deere, Karen A.↗

Trash-to-Gas: Converting Space Trash into Useful Products

NASA's Logistical Reduction and Repurposing (LRR) project is a collaborative effort in which NASA is determined to reduce total logistical mass through reduction, reuse and recycling of various wastes and components of long duration space missions and habitats. LRR is focusing on four distinct advanced areas of study: Advanced Clothing System, Logistics-to-Living, Heat Melt Compactor and Trash to Supply Gas (TtSG). The objective of TtSG is to develop technologies that convert material waste, human waste and food waste into high-value products. High-value products include life support oxygen and water, rocket fuels, raw material production feedstocks, and other energy sources. There are multiple pathways for converting waste to products involving single or multi-step processes. This paper discusses thermal oxidation methods of converting waste to methane. Different wastes, including food, food packaging, Maximum Absorbent Garments (MAGs), human waste simulants, and cotton washcloths have been evaluated in a thermal degradation reactor under conditions promoting pyrolysis, gasification or incineration. The goal was to evaluate the degradation processes at varying temperatures and ramp cycles and to maximize production of desirable products and minimize high molecular weight hydrocarbon (tar) production. Catalytic cracking was also evaluated to minimize tar production. The quantities of CO2, CO, CH4, and H2O were measured under the different thermal degradation conditions. The conversion efficiencies of these products were used to determine the best methods for producing desired products.

Caraccio, Anne J.↗

High Thrust-to-Power Annular Engine Technology

Gridded ion engines have the highest efficiency and total impulse of any mature electric propulsion technology, and have been successfully implemented for primary propulsion in both geocentric and heliocentric environments with excellent ground-in-space correlation of performance. However, they have not been optimized to maximize thrust-to-power, an important parameter for Earth orbit transfer applications. This publication discusses technology development work intended to maximize this parameter. These activities include investigating the capabilities of a non-conventional design approach, the annular engine, which has the potential of exceeding the thrust-to-power of other EP technologies. This publication discusses the status of this work, including the fabrication and initial tests of a large-area annular engine. This work is being conducted in collaboration among NASA Glenn Research Center, The Aerospace Corporation, and the University of Michigan.

electric propulsion↗

Trash to Gas: Converting Space Trash into Useful Products

NASA's Logistical Reduction and Repurposing (LRR) project is a collaborative effort in which NASA is determined to reduce total logistical mass through reduction, reuse and recycling of various wastes and components of long duration space missions and habitats. LRR is focusing on four distinct advanced areas of study: Advanced Clothing System, Logistics-to-Living, Heat Melt Compactor and Trash to Supply Gas (TtSG). The objective of TtSG is to develop technologies that convert material waste, human waste and food waste into high-value products. High-value products include life support oxygen and water, rocket fuels, raw material production feedstocks, and other energy sources. There are multiple pathways for converting waste to products involving single or multi-step processes. This paper discusses thermal oxidation methods of converting waste to methane. Different wastes, including food, food packaging, Maximum Absorbent Garments (MAGs), human waste simulants, and cotton washcloths have been evaluated in a thermal degradation reactor under conditions promoting pyrolysis, gasification or incineration. The goal was to evaluate the degradation processes at varying temperatures and ramp cycles and to maximize production of desirable products and minimize high molecular weight hydrocarbon (tar) production. Catalytic cracking was also evaluated to minimize tar production. The quantities of C02, CO, CH4, and H20 were measured under the different thermal degradation conditions. The conversion efficiencies of these products were used to determine the best methods for producing desired products.

Trash-to-Gas↗

Efficient Measurement-Driven Eigenenergy Estimation with Classical Shadows

Quantum algorithms exploiting real-time evolution under a target Hamiltonian have demonstrated remarkable efficiency in extracting key spectral information. However, the broader potential of these methods, particularly beyond ground-state calculations, is underexplored. In this work, we introduce the framework of multiobservable dynamic mode decomposition (MODMD), which combines the observable dynamic mode decomposition (DMD), a measurement-driven eigensolver tailored for near-term implementation, with classical shadow tomography. MODMD leverages random scrambling in the classical shadow technique to construct, with exponentially reduced resource requirements, a signal subspace that encodes rich spectral information. Notably, we replace typical Hadamard-test circuits with a protocol designed to predict low-rank observables, thereby broadening the use of classical shadow tomography for predicting many low-rank observables. We establish theoretical guarantees on the spectral approximation from MODMD, taking into account distinct sources of error. In the ideal case, we prove that the spectral error scales as exp (−Δ⁢𝐸⁢𝑡 max ), where Δ⁢𝐸 is the Hamiltonian spectral gap and 𝑡 max is the maximal simulation time. This analysis provides a rigorous justification of the rapid convergence observed across simulations. To demonstrate the utility of our framework, we consider its application to fundamental tasks, such as determining the low-lying, i.e., ground or excited, energies of representative many-body systems. Our work paves the path for efficient designs of measurement-driven algorithms on near-term and early fault-tolerant quantum devices.

quantum algorithms & computation↗

Second-harmonic generation in nonlinear plasmonic lattices enhanced by quantum emitter gain medium

In this work, we report on a theoretical study of second-harmonic generation (SHG) in plasmonic nanostructures interacting with two-level quantum emitters (QEs) under incoherent energy pump. We generalize the driven-dissipative Tavis–Cummings model by introducing the anharmonic surface plasmon-polariton (SPP) mode coupled to QEs and examine physical properties of corresponding SPP-QE polariton states. Our calculations of the SHG efficiency for strong QE-SPP coupling demonstrate orders of magnitude enhancement facilitated by the polariton gain. We further discuss time-domain numerical simulations of SHG in a square lattice comprising Ag nanopillars coupled to QEs utilizing a fully vectorial nonperturbative nonlinear hydrodynamic model for conduction electrons coupled to Maxwell–Bloch equations for QEs. The simulations support the idea of gain enhanced SHG and show orders of magnitude increase in the SHG efficiency as the QEs are tuned in resonance with the lattice plasmon mode and brought above the population inversion threshold by incoherent pumping. By varying pump frequency and tuning QEs to a localized plasmon mode, we demonstrate further enhancement of the SHG efficiency facilitated by strong local electric fields. The incident light polarization dependence of the SHG is examined and related to the symmetries of participating plasmon modes.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

An efficient optimization of well placement and control for a geothermal prospect under geological uncertainty

This work applies an efficient optimization technique based on a multivariate adaptive regression spline (MARS) technique to determine the optimal design and engineering of a potential geothermal production operation at a prospect near Superstition Mountain in Southern California, USA. The faster MARS-based statistical model is used as a surrogate for higher-fidelity physical models within the intensive optimization process. Its use allows for the exploration of the impacts of specific engineering design parameters in the context of geologic uncertainty as a means to both understand and maximize profitability of the production operation. The MARS model is initially developed from a training dataset generated by a finite set of computationally complex hydrothermal models applied to the prospect. Its application reveals that the optimal engineering design variables can differ considerably assuming different choices of hydrothermal flow properties, which, in turn, indicates the importance of reducing the uncertainty of key geologic properties. The major uncertainty sources in the natural-system are identified and ranked first by an efficient MARS-enabled total order sensitivity quantification, which is then used to assist evaluating the effect of geological uncertainties on optimized results. At the Southern California prospect, this parameter sensitivity analysis suggests that groundwater circulation through high permeable structures, rather than heat conduction through impermeable granite, is the primary heat transfer method during geothermal extraction. Reservoir histories simulated using optimal parameters with different constraints are analyzed and compared to investigate the longevity and maximum profit of the geothermal resources. The comparison shows that the longevity and profit are very likely to be overestimated by optimizations without appropriate constraints on natural conditions. In addition to geothermal energy production, this optimization approach can also be used to manage other geologic resource operations, such as hydrocarbon production or CO 2 sequestration, under uncertain reservoir conditions.

15 GEOTHERMAL ENERGY↗

Yahoo! Compute Coop (YCC). A Next-Generation Passive Cooling Design for Data Centers

The purpose of the Yahoo! Compute Coop (YCC) project is to research, design, build and implement a greenfield "efficient data factory" and to specifically demonstrate that the YCC concept is feasible for large facilities housing tens of thousands of heat-producing computing servers. The project scope for the Yahoo! Compute Coop technology includes: - Analyzing and implementing ways in which to drastically decrease energy consumption and waste output. - Analyzing the laws of thermodynamics and implementing naturally occurring environmental effects in order to maximize the "free-cooling" for large data center facilities. "Free cooling" is the direct usage of outside air to cool the servers vs. traditional "mechanical cooling" which is supplied by chillers or other Dx units. - Redesigning and simplifying building materials and methods. - Shortening and simplifying build-to-operate schedules while at the same time reducing initial build and operating costs. Selected for its favorable climate, the greenfield project site is located in Lockport, NY. Construction on the 9.0 MW critical load data center facility began in May 2009, with the fully operational facility deployed in September 2010. The relatively low initial build cost, compatibility with current server and network models, and the efficient use of power and water are all key features that make it a highly compatible and globally implementable design innovation for the data center industry. Yahoo! Compute Coop technology is designed to achieve 99.98% uptime availability. This integrated building design allows for free cooling 99% of the year via the building's unique shape and orientation, as well as server physical configuration.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

Off-policy deep reinforcement learning with automatic entropy adjustment for adaptive online grid emergency control

Electric overloading conditions and contingencies put modern power systems at risk of voltage collapse and blackouts. Load shedding is crucial to maintain voltage stability for grid emergency control. However, the rule- or model-based schemes rely on accurate dynamic system models and face considerable challenges in adapting to various operating conditions and uncertain event occurrences. Here, to address these issues, this paper proposes a novel deep reinforcement learning (DRL)-based voltage stability control algorithm with automatic entropy adjustment (AEA) for grid emergency control. Various dynamic network components for complex system operations are modeled to construct the DRL environment. An off-policy soft actor-critic architecture is developed to maximize the expected reward and policy entropy simultaneously. The AEA mechanism is proposed to facilitate the policy maximum entropy procedure, and the proposed method can automatically provide effective discrete and continuous actions against various fault scenarios. Our approach accomplishes high sampling efficiency, scalability, and auto-adaptivity of the control policies under high uncertainties. Comparative studies with the existing DRL-based control methods in IEEE benchmarks indicate salient performance improvement of the proposed method for dynamic system emergency control.

24 POWER TRANSMISSION AND DISTRIBUTION↗

Geometric dependencies of the mean E × B shearing rate in negative triangularity tokamaks

Abstract This paper presents a comparative study of the poloidal distribution of the mean E × B shearing rate for positive triangularity (PT) and negative triangularity (NT) tokamaks. The effects of flux surface up–down asymmetry due to asymmetric upper and lower triangularities are also considered. Both direct eddy straining and the effects on Shafranov shift feedback loops are examined. Shafranov shift increases the shearing rate at all poloidal angles for all triangularities, due to flux surface compression. The maximum shearing rate bifurcates at a critical triangularity δ crit ( ≲ 0 ) . Thus, the shearing rate is maximal off the outboard midplane for NT, while it is maximal on the outboard midplane for PT. For up–down asymmetric triangularity, the usual up–down symmetry of the shearing rate is broken. The shearing rate at the outboard midplane is lower for NT than for PT, suggesting that the shearing efficiency in NT is reduced. Implications for turbulence stabilisation and confinement improvement in high- β p NT and internal transport barrier discharges are discussed.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

Beam delivery and beamstrahlung considerations for ultra-high energy linear colliders

As part of the Snowmass'21 community planning excercise, the Advanced Accelerator Concepts (AAC) community proposed future linear colliders with center-of-mass energies up to 15 TeV and luminosities up to 50 × 1034 cm -2 s -1 in a compact footprint. In addition to being compact, these machines must also be energy efficient. We identify two challenges that must be addressed in the design of these machines. First, the Beam Delivery System (BDS) must not add significant length to the accelerator complex. Second, beam parameters must be chosen to mitigate beamstrahlung effects and maximize the luminosity-per-power of the machine. In this paper, we review advances in plasma lens technology that will help to reduce the length of the BDS system, and we detail new Particle-in-Cell simulation studies that will provide insight into beamstrahlung mitigation techniques. We apply our analysis to both $e$ + $e$ - and $γγ$ colliders. The challenges and solutions described in this paper are considered independently. In conclusion, a unified, self-consistent concept for a BDS system for a 15 TeV linear collider will be the subject of future work.

47 OTHER INSTRUMENTATION↗

cuAlign: Scalable Network Alignment on GPU Accelerators

Given two graphs, the objective of network alignment is to find the best one-to-one mapping of vertices in one graph (??) to vertices in the other (??), such that the number of overlaps is maximized. We say that edges(??, ??) ???and(??', ??') ??? are overlapped if ?? is mapped to ??' and ?? is mapped to??'. Network alignment is an important optimization problem with several applications in bioinformatics, computer vision and ontology matching. Since it is an NP-hard problem, efficient heuristics and scalable implementations are necessary. In this work, we introduce a new framework that combines the concepts of intra-network proximity using vertex embedding,Belief Propagation (BP) and approximate weighted matching, and provides qualitative improvements up to22%over state-of-the-art approaches. We also provide scalable implementations on GPU accelerators, demonstrating up to19×speedup for Belief Propagation and 3× speedup for approximate weighted matching relative to previous multithreaded implementation. A combination of combinatorial and algebraic kernels within the network alignment algorithm poses significant hurdles for parallelization. Load imbalance and irregular DRAM traffic limit achievable performance on GPUs. Our novel approach identifies and exploits unique structural proper-ties of the BP-based algorithm and employs code fusion to reduce data movement between different steps of the algorithm. Using a diverse set of inputs, we demonstrate qualitative improvements of our algorithms, and performance gains of our GPU-accelerated implementation. We believe that our work will enable algorithmic improvements and practical applications of network alignment.

Xiang, Lizhi↗

Waste-to-Energy: Hawaii and Guam Energy Improvement Technology Demonstration Project

The National Renewable Energy Laboratory (NREL) and the U.S. Navy have worked together to demonstrate new or leading-edge commercial energy technologies whose deployment will support the U.S. Department of Defense (DOD) in meeting its energy efficiency and renewable energy goals while enhancing installation energy security. This is consistent with the 2010 Quadrennial Defense Review report1 that encourages the use of 'military installations as a test bed to demonstrate and create a market for innovative energy efficiency and renewable energy technologies coming out of the private sector and DOD and Department of Energy laboratories,' as well as the July 2010 memorandum of understanding between DOD and the U.S. Department of Energy (DOE) that documents the intent to 'maximize DOD access to DOE technical expertise and assistance through cooperation in the deployment and pilot testing of emerging energy technologies.' As part of this joint initiative, a promising waste-to-energy (WTE) technology was selected for demonstration at the Hickam Commissary aboard the Joint Base Pearl Harbor-Hickam (JBPHH), Hawaii. The WTE technology chosen is called high-energy densification waste-to-energy conversion (HEDWEC). HEDWEC technology is the result of significant U.S. Army investment in the development of WTE technology for forward operating bases.

09 BIOMASS FUELS↗

Efficient Machine Learning Approach for Optimizing Scientific Computing Applications on Emerging HPC Architectures

Efficient parallel implementations of scientific applications on multi-core CPUs with accelerators such as GPUs and Xeon Phis is challenging. This requires - exploiting the data parallel architecture of the accelerator along with the vector pipelines of modern x86 CPU architectures, load balancing, and efficient memory transfer between different devices. It is relatively easy to meet these requirements for highly structured scientific applications. In contrast, a number of scientific and engineering applications are unstructured. Getting performance on accelerators for these applications is extremely challenging because many of these applications employ irregular algorithms which exhibit data-dependent control-ow and irregular memory accesses. Furthermore, these applications are often iterative with dependency between steps, and thus making it hard to parallelize across steps. As a result, parallelism in these applications is often limited to a single step. Numerical simulation of charged particles beam dynamics is one such application where the distribution of work and memory access pattern at each time step is irregular. Applications with these properties tend to present significant branch and memory divergence, load imbalance between different processor cores, and poor compute and memory utilization. Prior research on parallelizing such irregular applications have been focused around optimizing the irregular, data-dependent memory accesses and control-ow during a single step of the application independent of the other steps, with the assumption that these patterns are completely unpredictable. We observed that the structure of computation leading to control-ow divergence and irregular memory accesses in one step is similar to that in the next step. It is possible to predict this structure in the current step by observing the computation structure of previous steps. In this dissertation, we present novel machine learning based optimization techniques to address the parallel implementation challenges of such irregular applications on different HPC architectures. In particular, we use supervised learning to predict the computation structure and use it to address the control-ow and memory access irregularities in the parallel implementation of such applications on GPUs, Xeon Phis, and heterogeneous architectures composed of multi-core CPUs with GPUs or Xeon Phis. We use numerical simulation of charged particles beam dynamics simulation as a motivating example throughout the dissertation to present our new approach, though they should be equally applicable to a wide range of irregular applications. The machine learning approach presented here use predictive analytics and forecasting techniques to adaptively model and track the irregular memory access pattern at each time step of the simulation to anticipate the future memory access pattern. Access pattern forecasts can then be used to formulate optimization decisions during application execution which improves the performance of the application at a future time step based on the observations from earlier time steps. In heterogeneous architectures, forecasts can also be used to improve the memory performance and resource utilization of all the processing units to deliver a good aggregate performance. We used these optimization techniques and anticipation strategy to design a cache-aware, memory efficient parallel algorithm to address the irregularities in the parallel implementation of charged particles beam dynamics simulation on different HPC architectures. Experimental result using a diverse mix of HPC architectures shows that our approach in using anticipation strategy is effective in maximizing data reuse, ensuring workload balance, minimizing branch and memory divergence, and in improving resource utilization.

Arumugam, Kamesh↗

Mercury speciation using microcolumns with a direct mercury analyzer (DMA). Development of analytical method for the Savannah River Site liquid system

Mercury in the Savannah River Site (SRS) Liquid Waste System (LWS) exists in various forms, including: a) ionic inorganic mercury, organomercury (e.g., methyl Hg), and other less abundant species dissolved in LWS fluids, b) mercury solids such as oxides, hydroxides, amalgams, sulfides, and sorbed mercury, c) accumulations of dense liquid elemental mercury, and d) vapor phase elemental and organomercury mercury in tank headspace gas and in evaporators. Strategic and proactive management of the estimated 60,000 Kg mercury in the LWS requires deployment of efficient and effective paradigms for sampling and analysis of the total quantity of mercury in the various physical-chemical forms. Scoping tests of selective gas-phase sorbents for mercury speciation were performed to support LWS objectives. This research specifically focused on developing and testing streamlined methods to obtain high-quality analytical results for gaseous mercury species. The proposed methods are straightforward, using small columns (microcolumns) filled with materials that selectively sorb one or more target forms of mercury. Simple differentiation of mercury species and quantification is then achieved using an efficient thermal-desorption-based total mercury analyzer as the final step. Benefits associated with transitioning from multistep procedures such as EPA Methods 245.1, 1630 and 1631to streamlined microcolumn approaches include reduced analysis time, labor, and waste generation. Such transitioning would maximize the value of the current deployment of direct mercury analysis (EPA Method 7473) at SRS. This work has two target end uses: 1) simplifying the analysis steps and reducing costs for liquid samples (e.g., developing a total organic mercury (TOM) method), and 2) providing options for high-quality onsite analysis and rapid turnaround for gas samples in support of SRS LWS industrial hygiene (IH) objectives.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

Demonstration and Evaluation of the Human-Technology Integration Function Allocation Methodology

There is an imminent need for the existing nuclear power plants to reduce their operating and maintenance (O&M) costs to remain economically viable. Digital technology, including automation, provides a significant opportunity for the existing nuclear power plant fleet to transform the way in which work is accomplished, reducing O&M costs, and allowing the fleet to remain economically competitive. One notable opportunity to significantly reduce O&M costs pertains to modifications to the plant equipment and main control room (MCR). Existing instrumentation and control (I&C) technologies in the MCR are highly analog, costly to operate and maintain, and demand a high cognitive and physical workload from plant staff (i.e., operators). Digitalizing the MCR has a range of broad economic benefits, including improved plant performance and reduced manual work. Further, digital I&C systems can fundamentally change the way in which plant staff operate the plant; this is the concept of operation. Human-technology integration is important to ensure that impacts to the concept of operation are done in a way that account for capabilities of people and technology. Human-technology integration employs human factors engineering (HFE) methods and principles to maximize the benefits of digital technology, reducing human error, improving overall decision-making and usability. The U.S. Department of Energy Light Water Reactor Sustainability Program is applying human-technology integration research to ensure digital technologies are safe, reliable, and efficient. This paper documents the demonstration of the human-technology guidance developed by the Light Water Reactor Sustainability Program from a first-of-a-kind digital I&C upgrade, specifically addressing function analysis and allocation for a new digital I&C system that included changes in automation levels. The program’s specific approach is included in this work, following lessons learned. This document serves as a resource for industry to follow in applying human-technology integration and HFE to digital modifications, specific to function analysis and allocation. The lessons learned should be considered in the planning and execution of HFE activities that support such digital modifications.

99 GENERAL AND MISCELLANEOUS↗

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