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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 1,117 records · Page 62
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DOE OSTI2021

Extreme wind shear events in US offshore wind energy areas and the role of induced stratification

As the offshore wind industry emerges on the US East Coast, a comprehensive understanding of the wind resource – particularly extreme events – is vital to the industry's success. Such understanding has been hindered by a lack of publicly available wind profile observations in offshore wind energy areas. However, the New York State Energy Research and Development Authority recently funded the deployment of two floating lidars within two current lease areas off the coast of New Jersey. These floating lidars provide publicly available wind speed data from 20 to 200 m height with a 20 m vertical resolution. In this study, we leverage a year of these lidar data to quantify and characterize the frequent occurrence of high-wind-shear and low-level-jet events, both of which will have a considerable impact on turbine operation. In designing a detection algorithm for these events, we find that the typical, non-dimensional power-law-based wind shear exponent is insufficient to identify many of these extreme, high-wind-speed events. Rather, we find that the simple vertical gradient of wind speed better captures the events. Based on this detection method, we find that almost 100 independent events occur throughout the year with mean wind speed at 100 m height and wind speed gradient of 16 m s -1 and 0.05 s -1 , respectively. The events have strong seasonal variability, with the highest number of events in summer and the lowest in winter. A detailed analysis reveals that these events are enabled by an induced stable stratification when warmer air from the south flows over the colder mid-Atlantic waters, leading to a positive air–sea temperature difference.

17 WIND ENERGY↗
DOE OSTI2021

Results from a wake-steering experiment at a commercial wind plant: investigating the wind speed dependence of wake-steering performance

Wake steering is a wind farm control strategy in which upstream wind turbines are misaligned with the wind to redirect their wakes away from downstream turbines, thereby increasing the net wind plant power production and reducing fatigue loads generated by wake turbulence. In this paper, we present results from a wake-steering experiment at a commercial wind plant involving two wind turbines spaced 3.7 rotor diameters apart. During the 3-month experiment period, we estimate that wake steering reduced wake losses by 5.6% for the wind direction sector investigated. After applying a long-term correction based on the site wind rose, the reduction in wake losses increases to 9.3%. As a function of wind speed, we find large energy improvements near cut-in wind speed, where wake steering can prevent the downstream wind turbine from shutting down. Yet for wind speeds between 6–8 m/s, we observe little change in performance with wake steering. However, wake steering was found to improve energy production significantly for below-rated wind speeds from 8–12 m/s. By measuring the relationship between yaw misalignment and power production using a nacelle lidar, we attribute much of the improvement in wake-steering performance at higher wind speeds to a significant reduction in the power loss of the upstream turbine as wind speed increases. Additionally, we find higher wind direction variability at lower wind speeds, which contributes to poor performance in the 6–8 m/s wind speed bin because of slow yaw controller dynamics. Further, we compare the measured performance of wake steering to predictions using the FLORIS (FLOw Redirection and Induction in Steady State) wind farm control tool coupled with a wind direction variability model. Although the achieved yaw offsets at the upstream wind turbine fall short of the intended yaw offsets, we find that they are predicted well by the wind direction variability model. When incorporating the expected yaw offsets, estimates of the energy improvement from wake steering using FLORIS closely match the experimental results.

17 WIND ENERGY↗
DOE OSTI2022

Can reanalysis products outperform mesoscale numerical weather prediction models in modeling the wind resource in simple terrain?

Mesoscale numerical weather prediction (NWP) models are generally considered more accurate than reanalysis products in characterizing the wind resource at heights of interest for wind energy, given their finer spatial resolution and more comprehensive physics. However, advancements in the latest ERA-5 reanalysis product motivate an assessment on whether ERA-5 can model wind speeds as well as a state-of-the-art NWP model – the Weather Research and Forecasting (WRF) Model. We consider this research question for both simple terrain and offshore applications. Specifically, we compare wind profiles from ERA-5 and the preliminary WRF runs of the Wind Integration National Dataset (WIND) Toolkit Long-term Ensemble Dataset (WTK-LED) to those observed by lidars at a site in Oklahoma, United States, and in a United States Atlantic offshore wind energy area. We find that ERA-5 shows a significant negative bias (~-1ms-1) at both locations, with a larger bias at the land-based site. WTK-LED-predicted wind speed profiles show a limited negative bias (~-0.5ms-1) offshore and a slight positive bias (~+0.5ms-1) at the land-based site. On the other hand, we find that ERA-5 outperforms WTK-LED in terms of the centered root-mean-square error (cRMSE) and correlation coefficient, for both the land-based and offshore cases, in all atmospheric stability conditions. We find that WTK-LED's higher cRMSE is caused by its tendency to overpredict the amplitude of the wind speed diurnal cycle. At the land-based site, this is partially caused by wind plant wake effects not being accurately captured by WTK-LED.

17 WIND ENERGY↗
DOE OSTI2022

Effectively using multifidelity optimization for wind turbine design

Abstract. Wind turbines are complex multidisciplinary systems that are challenging to design because of the tightly coupled interactions between different subsystems. Computational modeling attempts to resolve these couplings so we can efficiently explore new wind turbine systems early in the design process. Low-fidelity models are computationally efficient but make assumptions and simplifications that limit the accuracy of design studies, whereas high-fidelity models capture more of the actual physics but with increased computational cost. This paper details the use of multifidelity methods for optimizing wind turbine designs by using information from both low- and high-fidelity models to find an optimal solution at reduced cost. Specifically, a trust-region approach is used with a novel corrective function built from a nonlinear surrogate model. We find that for a diverse set of design problems – with examples given in rotor blade geometry design, wind turbine controller design, and wind power plant layout optimization – the multifidelity method finds the optimal design using 38 %–58 % of the computational cost of the high-fidelity-only optimization. The success of the multifidelity method in disparate applications suggests that it could be more broadly applied to other wind energy or otherwise generic applications.

17 WIND ENERGY↗
DOE OSTI2020

Herding Cats: Firm Non-Compliance in China’s Industrial Energy Efficiency Program

Here we study firm responses to a large-scale energy efficiency program in China, focusing on the quality of reporting and compliance outcomes. Using statistical methods to detect data manipulation in compliance reports, we find evidence that firms deliberately exaggerated performance during the first phase of the program (2006-2010), suggesting the high compliance rate was overstated. In its second phase (2011-2015), the number of firms in the program expanded by an order of magnitude, and the compliance rate decreased. We develop a simple model to show how the observed increase in non-compliance is consistent with reduced misreporting. Statistical tests find no evidence of manipulation in the second phase. Larger firms, especially those not controlled by the state, and firms in cities with relatively low growth were more likely to report non-compliance, which suggests a role for state control and local protectionism in shaping compliance decisions. Based on our findings, we offer several lessons for future program design.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2021

Hybrid deep learning architecture for general disruption prediction across tokamaks

In this paper, we present a new deep learning disruption prediction algorithm based on important findings from explorative data analysis which effectively allows knowledge transfer from existing devices to new ones, thereby predicting disruptions using very limited disruptive data from the new devices. The explorative data analysis conducted via unsupervised clustering techniques confirms that time-sequence data are much better separators of disruptive and non-disruptive behavior than the instantaneous plasma state data with further advantageous implications for a sequence-based predictor. Based on such important findings, we have designed a new algorithm for multi-machine disruption prediction that achieves high predictive accuracy on the C-Mod (AUC=0.801), DIII-D (AUC=0.947) and EAST (AUC=0.973). tokamaks with limited hyperparameter tuning. Through numerical experiments, we show that boosted accuracy (AUC=0.959) is achieved on EAST predictions by including in the training only 20 disruptive discharges, thousands of non-disruptive discharges from EAST, and combining this with more than a thousand discharges from DIII-D and C-Mod. The improvement of predictive ability obtained by combining disruptive data from other devices is found to be true for all permutations of the three devices. Furthermore, by comparing the predictive performance of each individual numerical experiment, we find that non-disruptive data are machine-specific while disruptive data from multiple devices contain device-independent knowledge that can be used to inform predictions for disruptions occurring on a new device.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2020

Is GW170817 a Multimessenger Neutron Star-Primordial Black Hole Merger?

We investigate the possibility of the gravitational-wave event GW170817 being a light, solar-mass black hole (BH) - neutron star (NS) merger. We explore two exotic scenarios involving primordial black holes (PBH) that could produce such an event, taking into account available observational information on NGC 4993. First, we entertain the possibility of dynamical NS-PBH binary formation where a solar-mass PBH and a NS form a binary through gravitational interaction. We find that while dynamical NS-PBH formation could account for the GW170817 event, the rate is highly dependent on unknown density contrast factors and could potentially be affected by galaxy mergers. We also find that PBH-PBH binaries would likely have a larger merger rate, assuming the density contrast boost factor of an order similar to the NS-PBH case. These exotic merger formations could provide new channels to account for the volumetric rate of compact-object mergers reported by LIGO/Virgo. Secondly, we consider the case where one of the NS's in a binary NS system is imploded by a microscopic PBH. We find that the predicted rate for NS implosion into a BH is very small, at least for the specific environment of NGC 4993. We point out that similar existing (e.g. GW190425 and GW190814) and future observations will shed additional light on these scenarios.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2020

How Should Machine Learning Be Successfully Used for Wind Speed Vertical Extrapolation?

An accurate characterization of the wind resource available at hub-height is required for an efficient and bankable wind farm project. However, direct measurement of wind speed at the constantly increasing height of the hub of commercial wind turbines is oftentimes challenging and expensive, so that it is common practice to vertically extrapolate the wind resource from lower and more easily accessible levels. Conventional techniques for wind speed vertical extrapolation include the use of a power law and a logarithmic profile. While simple, the limits in accuracy of these methods have been shown in various studies. Recently, machine learning has been proposed as a new method to vertically extrapolate winds. All the published studies on the topic assess the performance of machine learning techniques in vertically extrapolating the wind resource at the same location where the algorithm has been trained. However, in real-world applications, the wind resource is measured at the instrument location, but it then needs to be extrapolated at hub height at the location of the wind turbines within the find farm. To be able to fully recommend the use of machine learning techniques over the simple power law and logarithmic law, the spatial variability of the performance improvements of the machine learning approaches needs to be assessed. Here, we propose a round-robin validation of a machine learning-based method for wind speed extrapolation. We use 20 months of observations at four locations spanning a 100 km wide region at the Southern Great Plains (SGP) atmospheric observatory, in north-central Oklahoma. At each location, we train a random forest to predict 30-min average wind speed at 143 m AGL. We use as input features lidar wind speed at 65 m AGL, time of day, sonic anemometer wind speed at 4 m AGL, turbulent kinetic energy, and Obukhov length. First, we perform a same-site comparison of the performance of the proposed random forest against the conventional techniques for wind speed extrapolation (namely power law and logarithmic profile, with widely accepted stability corrections). We find that the random forest outperforms the power law in vertically extrapolating wind speed in all the considered stability regimes, with a 33% reduction in MAE for stable conditions, and a 31% reduction in unstable conditions. Similar results are found when comparing predictions of extrapolated winds from the logarithmic profile and the random forest with the observed values. Next, we propose a round-robin validation, to use the random forest trained at each site to extrapolate wind speed at the remaining three sites. We find that the performance of the random forest approach degrades when the algorithm is tested at a site different than the training one. However, even under those circumstances, the machine learning-based approach still outperforms the conventional techniques for wind speed extrapolation, with, on average, a reduction in mean absolute error between 15 and 20% over the conventional methods, with the largest benefits obtained under stable conditions.

Monte Carlo↗
DOE OSTI2020

High-Performance Windows -- More than just a Pretty Hole in the Wall

As more stringent building energy codes and better insulation products combine to yield better performing walls, window efficiency is coming into sharper focus. The U.S. Department of Energy has supported several projects through its national laboratories to improve the thermal performance of windows. At Pacific Northwest National Laboratory (PNNL) in Richland, Washington, the PNNL Lab Homes, two fully monitored identical side-by-side manufactured homes, have been used to test the performance of several window improvements including triple-pane windows, storm windows with low-emissivity (low-e) coatings, smart automated interior insulated shades, and exterior shading products. Findings will be presented on these studies, along with impacts. For example, PNNL’s Lab Home research on low-e storm windows has helped to support a new ENERGY STAR certification, industry standards, and utility incentives. Preliminary findings will also be presented on current Lab Homes experiments focusing on exterior shades. The session will also present findings from related field studies and a market assessment of emerging high-efficiency windows technologies and discuss some of PNNL’s planned field studies that will focus on validating the benefits and costs of the emerging thin triple-pane high-R window.

windows, high-r windows, insulating windows, windo↗
DOE OSTI2020

Integrated and Advanced Lighting Controls: The Next Frontier in Energy Savings?

Lighting energy use has significantly declined as a portion of overall building energy usage due largely to the uptake of more efficient light-emitting diode technology. Although the per unit cost of lighting controls has declined, the installation and use of lighting controls are low and the installation and use of advanced controls are abysmally low. Increased use of advanced and connected lighting controls, and the ability to integrate lighting with other building systems (e.g., heat and air, plug loads, miscellaneous loads) that make the building “smarter,” offer the potential for additional energy savings. Studies into the performance of new, advanced lighting control technologies in commercial buildings have generated new findings. Although each field evaluation has interesting results individually, an analysis of their collective findings across a spectrum of commercial building types will help establish the current state of the technology and implications related to market readiness. This paper will analyze results from available field evaluation studies (recently completed as well as ongoing) of retrofit sites and share findings and insights including: • The amount of energy savings that may be expected from these systems. • Cost effectiveness, monetization, and cost points of these systems. • Ideal characteristics of these systems the most viable. • Lessons learned from the installation, commissioning, and occupant response of these systems. The results will support Department of Energy research investments and help inform efficiency programs of the potential of these emerging technologies and how they may fit within the clean energy future.

Myer, Michael↗
DOE OSTI2021

Closed-Form Solutions for the Equations of Motion of the Heavy Symmetrical Top with One Point Fixed

The equations of motion (EOM) for the heavy symmetrical top with one point fixed are highly nonlinear. The literature describes the numerical methods that are used to resolve this classical system, including modern tools, such as the Runge–Kutta fourth–order method. Finding the derivate of closed-form solutions for the EOM is more difficult and, as mentioned in the literature, discovering the solution is not always possible for all the EOM. Fortunately, a few examples are available that serve as a guide to move further in this topic. The purpose of this paper is to find a methodology that will produce the solutions for a given subset of EOMs that fulfill certain requisites. This paper summarizes the literature available on this topic and then follows with the derivation of the EOM using the Euler–Lagrange method. The Routhian method will be used to reduce the size of the expression, and it continues with the formulation of the classical cubic function, ƒ(u), through a novel process. The roots of ƒ(u) are of the utmost importance in finding the EOM closed-form solution, and once the final roots are selected, the general method that will produce the closed-form solutions is presented. Two sets of examples are included to show the validity of the process, and comparisons of the results from the closed-form solutions vs. the numerical results for these examples are shown.

Laos, Hector↗
DOE OSTI2021

Dynamic Human-in-the-Loop Simulated Nuclear Power Plant Thermal Dispatch System Demonstration and Evaluation Study

An Idaho National Laboratory research team performed a human-in-the-loop study with two formerly licensed retired operators to evaluate thermal power dispatch operations supported by the modified GSE Systems GPWR plant simulator. Virtual representations of the analog control panels were presented on touch-screen bays configured to mimic the control room layout in the newly renovated Human Systems Simulation Laboratory. The operators performed 15 scenarios covering normal evolutions to transition the plant from full turbine operation to joint turbine and thermal power dispatch operations in addition to transient response scenarios induced with simulated faults to evaluate the impact of thermal power dispatch system on operator and plant responses. A prototype human-system interface (HSI) was developed and displayed in tandem with the virtual analog panels to support the operators executing the procedurally drive evolutions and transient responses. An interdisciplinary team of operations experts, nuclear engineers, and human factors experts observed the operators performing the scenarios to evaluate the operations. Only a preliminary analysis of the results has been performed. The full analysis will be shared in a milestone report scheduled for release at the end of September; however, some high-level conclusions were readily available. Two high level findings for the system design were captured in the study. The manual control supported by the HSI to transition from standard operations to thermal power dispatch operation imposed a considerable amount of workload on the operators due to tedious manual valve manipulations and system monitoring required to verify their intended effect. An additional operator would be required in the control room to support the daily evolution. Automatic control for the transition was deemed a requirement for plant adoption without imposing additional staffing costs. The second finding was the necessity for an automatic thermal power dispatch system trip isolation function linked to a turbine and reactor trip signal. The operators completed scenarios with automatic isolation functionality and manually required actuation of the thermal power dispatch system. The operator response was sufficiently slower in the manual trip condition, such that operators were unable to manually actuate key post trip safety functions an indicate a degraded control capability that should be avoided. With an automatic trip signal little to no impact of the thermal power dispatch system was identified on the primary plant response and therefore the system could be readily and safely adopted. Together these two findings represent the need to support the adoption of thermal power dispatch capability into existing operations by leveraging automation to augment any additional operator tasking required to control and monitor an additional system beyond existing operations.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2022

Operating Strategies for Dispatchable PEM Electrolyzers that Enable Low-Cost Hydrogen Production

Hydrogen is a pathway to enabling decarbonization across multiple economic sectors that cannot be directly decarbonized with electricity including heat for industrial operations, medium- and heavy-duty transportation applications, long-duration energy storage, and as a feedstock for chemical synthesis. Producing hydrogen at low costs and carbon footprint is likely essential to economically decarbonize these otherwise "hard to decarbonize" sectors. Low-temperature polymer electrolyte membrane (PEM) electrolysis produces hydrogen from water and electricity and is a rapidly developing pathway towards making hydrogen at the scales required for decarbonization applications. The levelized cost of electrolytic hydrogen is dependent on the capital cost, efficiency, and durability of the electrolyzer system as well as the price of electricity supplied to the electrolyzer and the annual utilization of the electrolyzer (capacity factor). Electricity price and capacity factor depend on the source of energy that the system uses and impact other economic factors. Electricity price and capacity factor and the connections between other aspects of hydrogen production via PEM electrolyzers are the focus of this work. PEM electrolyzers have conventionally been operated at high capacity factors using electricity purchased from utilities with a constant price throughout the year. To achieve lower effective electricity prices, recent work has investigated opportunities for electrolyzers to purchase electricity in wholesale markets, where the cost of electricity varies hourly. This option can lead to lower electricity costs when the electrolyzer is a controllable load that ramps up and down rapidly and turns on and off frequently. This configuration and operating strategy capitalizes on times of low wholesale electricity prices and results in lower hydrogen levelized costs than constant operation due to reduced electricity costs even though the reduced capacity factor increases the cost of recovering the capital investment. Cycling on and off frequently also has implications for electrolyzer durability. An electrolyzer configuration where it is directly connected to renewable generation such as wind or solar, only running when the generator is producing energy, has similar implications on operating strategy, hydrogen levelized cost, capacity factor, and durability. This work provides insight into the relationships between dispatchable electrolyzer operating strategies and the cost of producing hydrogen from these systems, outlining strategies and opportunities to minimize production costs while minimizing operations that are likely to negatively impact system durability and efficiency. We find strategies that minimize electrolyzer cycling and the resulting durability impacts while increasing the hydrogen levelized cost only slightly above the minimum. We also find opportunities for batteries to minimize the number of cycles in systems directly connected to renewable generation. These findings outline key opportunities for future electrolyzer deployments and the synergistic benefits between electrolyzers and increased deployment of renewable energy generation like wind and solar. They also inform research and development that is reducing electrolyzer capital cost while managing durability impacts.

electricity markets↗
DOE OSTI2022

Achieving 50% Energy Savings in Chicago Homes: A Case Study for Advancing Equity and Climate Goals

Since 2019, the National Renewable Energy Laboratory (NREL) and Elevate have collaborated to identify pathways to deep energy retrofits in Chicago's housing stock, document equity implications and co-benefits of this transition, and validate the findings by implementing retrofits in real Chicago homes. This document summarizes our analysis process to model advanced retrofit packages that lead to greater than 50% energy savings in Chicago homes. Based on these findings, we have also developed a roadmap with the City to guide implementation, and are deploying the recommended retrofit packages in real Chicago homes to realize these energy savings. This work was developed in collaboration with two key stakeholders - the City of Chicago and Commonwealth Edison (ComEd) - and funded by the U.S. Department of Energy (DOE). NREL's Residential Buildings team maintains the best-in-class ResStock™ energy model of the U.S. residential building stock. For this work, we calibrated ResStock to Chicago's unique local housing stock to accurately simulate energy use in Chicago homes both for current conditions and with various retrofit scenarios. We simulated a wide range of potential building retrofits covering all aspects of residential energy use and then grouped these into packages based on energy and utility bill savings. ResStock can model diverse building types and housing characteristics, so we're able to observe the range of outcomes that might occur when these upgrades are deployed across the entire housing stock. We can then estimate potential energy savings from an advanced retrofit program on Chicago's housing stock by comparing the modeled energy use before versus after a retrofit. This novel version of ResStock, calibrated to Chicago with data from Elevate, can help City officials, ComEd, and other partners plan for community-scale decarbonization via residential retrofits. Specifically, this work contributes the following project goals: Develop a building retrofit prioritization strategy for Chicago single-family and 2- to 4-unit buildings; Identify neighborhoods and home types that have the highest potential for savings from electrification; and Assess the impact of advanced building retrofits on energy use, utility bills, and CO2 emissions at the city and building level. Although this study is specific to Chicago, its methods and learnings are applicable across the United States. These findings are especially notable for heat pumps and electrification retrofits in cold climates.

building energy modeling↗
DOE OSTI2022

Renewable-Storage Hybrids in a Decarbonized Electricity Supply

We explore the potential impacts of growing industry interest in hybrid systems comprising PV and battery technologies on the results and findings of the Solar Futures Study (2021). Through comparison of Solar Futures Study scenarios with and without PV-battery hybrid configurations enabled (in the same grid planning model), we find that the highest net-value hybrid configuration depends strongly on power sector carbon policy, which increases the value of more forward-looking designs. While the availability of PV-battery hybrid configurations primarily displaces standalone PV and battery capacity, we find that it (a) increases solar's share of total capacity and generation and (b) reduces required transmission expansion and associated costs under scenarios that combine aggressive cost reductions and power sector decarbonization policy. Results will also be presented for the complementarity and economic value of wind-PV hybrids with varying amounts of energy storage.

complementarity↗
DOE OSTI2022

Basin-Wide Approaches to Hydropower Licensing

This presentation summarizes findings from a 2020 NREL technical report including the FERC relicensing process for non-federal hydropower projects, analyzes FERC-licensed projects with license expiration dates from 2018-2037, discusses FERC's authority to coordinate license terms of hydropower projects within a shared river basin and provides basin-wide case studies and considerations to hydropower relicensing. In the United States there are 1,043 active FERC-licensed hydroelectric projects with a total capacity of 56,097 MW. Of those, 647 with a total capacity of 21,870 MW have license expiration dates from 2018-2037. The expected workload in conjunction with the time, cost, and complexity associated with issuing a single new license has led to initiatives that aim to increase the efficiency of the relicensing process. Federal and state regulators and licensees in California, Maine, New York, and Wisconsin have begun to develop approaches to look at hydropower relicensing as part of a larger system - a river basin. These basin-wide approaches seek to coordinate different stages of the relicensing process for multiple projects at the same time. The goal of these basin-wide approaches is to increase the efficiency of the relicensing process and allow for a more comprehensive analysis of the cumulative impacts of the projects within the basin. These innovative approaches to relicensing could also reduce the workload for regulators and costs associated with relicensing. This presentation discusses some of the findings from the Basin-wide Approaches to Hydropower Relicensing: Case Studies and Considerations including a summary of the key considerations for the FERC relicensing process for non-federal hydropower projects, key findings from the analysis of FERC-licensed projects with license expiration dates from 2018-2037, discusses FERC's authority to coordinate license terms of hydropower projects within a shared river basin and provides basin-wide case studies and considerations to hydropower relicensing.

basin-wide↗
DOE OSTI2023

The Tiny Triplet Finder as a Versatile Track Segment Seeding Engine for Trigger Systems

In high energy physics experiment trigger systems, track segment seeding is a resource consuming function and the primary reason is the computing complexity of the segment finding process. As the Moore's Law is reaching its physical limit, reducing computing complexity should be carefully considered, rather than keep piling up silicon resources. The Tiny Triplet Finder is a scheme that reduces the computing complexity of the segment seeding. As a proof of concept, a 3D track segment seeding engine core based on the Tiny Triplet Finder has been implemented and tested in a low-cost FPGA device. The seeding engine is designed to preselect and group hits (stubs) from detector layers to feed subsequent track fitting stage. The seeding engine consists of a Hough transform space for r-z view and a Tiny Triplet Finder for r-phi view to implement 3D constraints. The seeding engine is organized as a pipeline so that each hit is processed in a single clock cycle. Taking advantage of the register-like storage block scheme which enables effectively resetting of a block RAM within a single clock cycle, clearing or refreshing the seeding engine takes only one clock cycles between two events. The Tiny Triplet Finder is also a generic coincidence finding scheme that can be used for many tasks. As a versatility demonstration, track segment finding performances for two distinctive detector geometries are tested in our seeding engine. In a collider barrel-layer geometry, the fake segment rates are studied for 3D (i.e., both r-phi and r-z views) and 2D (i.e., r-phi or r-z view only) configurations for high hit multiplicity events (>4000 hits/layer in the barrel region). Another detector geometry contains strip plane layers with timing information. The numbers of coincidences, both real or fake, with or without timing ("3D" or "2D") information at various hit multiplicities are studied.

43 PARTICLE ACCELERATORS↗
DOE OSTI2023

Operation Optimization Using Reinforcement Learning with Integrated Artificial Reasoning Framework

In large and complex systems, operational decision-making requires a systematic analysis with a vast amount of data from both process parameters and component status monitoring. In this paper, we present an integrated artificial reasoning approach for system state transition models that can help operational decision-making with explainable and traceable reasoning. The integrated artificial reasoning framework is a physics-based approach of defining the system structure in a Bayesian network, so we leveraged it in a Markov decision process (MDP) for finding optimal operational solutions. In our proposed framework, the MDP is implemented on a dynamic Bayesian network (DBN), which represents causalities in a system. The multilevel flow modeling was utilized in order to extract these causalities in a more efficient and objective manner. Since multilevel flow modeling is based on the fundamental energy and mass conservation laws, the target system is decomposed into several mass, energy, and information structures, which serve as the basis for a DBN. The MDP consists of the processes of finding a solution for the Bellman equation, which can be derived from the conditional probability equations of the constructed DBN. System operators can capture stochastic system dynamics as multiple subsystem state transitions based on their physical relations and uncertainties coming from the component degradation process or random failures. We analyzed a simplified example system to illustrate finding an optimal operational policy with this approach.

Kim, Junyung↗