Search NASA⌕ Search

SEARCH · Search NASA

Results for “Long Time Integration”

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

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

At least 127 records · Page 7
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Integrate FARM with PID controllers: IES Simulation Ecosystem Control System Development

The FARM (Feasible Actuator Range Modifier) software module is a component of the RAVEN-based FORCE framework for analysis of Integrated Energy Systems (IES). FARM was designed to support the HERON software module in the solution of the optimal dispatch problem for IES units. As the result of HERON-FARM dispatch simulation, the set-point trajectories are optimized to meet constraints on both the production variables (i.e., the variables to be optimized such as the electrical power, the hydrogen production rate, etc.) and the process variables tied to the service life of equipment (e.g., steam flowrate, vessel pressure, turbine firing temperature, etc.) at a coarse time resolution (every 10 or 100 seconds) over long time horizons (several days or weeks). In case the operational constraints need to be met at finer time resolution, the computational burden of HERON-FARM would linearly increase with the sampling rate, and sub-optimal solutions might be obtained. System responses characterized by overshoots and damped oscillations temporarily violating the imposed constraints might occur during abrupt power transients. In this report, a hierarchical control system architecture for the operation of the Dynamic Energy Technology and Integration Laboratory (DETAIL) facility constructed at INL was proposed. First, the preliminary studies on the proposed control strategy for operating the facility and the designed PI controllers were reviewed. In particular, the current approach for generating the set-point trajectories was studied, and its limits were identified. To this aim, the inclusion of a Supervisory Control layer embedding a modified version of the FARM algorithm for preserving the system safe operation over both long and real-time horizons was proposed. In this way, FARM would be applied twice, i.e., the original version (“FARM Validator”) aiding the solution of the power dispatch problem, and the modified version (“FARM Supervisory” coordinating the PI controllers to address the real-time control tasks. Despite the kernel of the two modules is the same algorithm, their roles, tasks, and capabilities are quite different. A detailed description of the role of FARM at addressing low-level control tasks is provided, along with tentative operational procedures for training the models embedded into the algorithm by using the collected experimental data.

42 ENGINEERING↗
DOE OSTI2022

Müllerian mimicry and the coloration patterns of sympatric coral snakes

Abstract Coral snakes in the genus Micrurus are venomous, aposematic organisms that signal danger to predators through vivid coloration. Previous studies found that they serve as models to several harmless species of Batesian mimics. However, the extent to which Micrurus species engage in Müllerian mimicry remains poorly understood. We integrate detailed morphological and geographical distribution data to investigate if coral snakes are Müllerian mimics. We found that coloration is spatially structured and that Micrurus species tend to be more similar where they co-occur. Though long supposed, we demonstrate for the first time that coral snakes might indeed be Müllerian mimics as they show some convergence in coloration patterns. Additionally, we found that the length of red-coloured rings in Micrurus is conserved, even at large geographic scales. This finding suggests that bright red rings may be under more substantial stabilizing selection than other aspects of coloration and probably function as a generalized signal for deterring predators.

Evolutionary Biology↗
DOE OSTI2022

In-situ imaging of nucleation and growth of superlattices from nanoscale colloidal nanoparticles

Nanoparticle-based superlattices have attracted extensive research efforts due to their versatile composition and packing-dependent optical, magnetic, electronic, catalytic, mechanical properties. Yet the experimental understanding of the thermodynamics and pathways of the crystallization processes for these systems, which govern their ultimate morphology, size, and packing structure, has for long remained underexplored. Here, this minireview highlights recent integration of liquid-phase transmission electron microscopy, which allows for real-space, real-time direct imaging of the crystallization processes at the single-particle level, with simulation and the conceptual framework established by pioneers such as Dr. A. A. Chernov. Different aspects of superlattice formation were revealed by direct imaging for the first time, such as the existence of prenucleation precursor in nonclassical crystallization, surface energy-dependent growth, and coalescence, allowing for the charting of phase coordinates and thermodynamic quantities at the nanoscale. We discuss the similarities and differences of crystal growth behaviors in atomic and nanoparticle systems, as well as engineering opportunities to fashion, shape, and achieve quality control in superlattice formation from nanoparticles, for next-generation optical and mechanical metamaterials.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Development of High Temperature (>700°C) molten Salt Pump Technology for Gen3 Solar Power Tower Systems

Generation 3 Concentrating Solar Power (CSP) Systems require thermal energy storage that can efficiently receive solar heat and deliver it to a working fluid at temperatures greater than 700°C. For liquid systems, the thermal transfer fluid must be phase stable over large temperature ranges. High temperature molten salts meet this criteria, but are difficult liquids to handle. The high heat and salt content can be very corrosive to several different materials that are wetted by the liquid salt. For power tower collectors, the molten salt must be pumped under high pressure to the top of the collector tower. These pumps have impellers and shafts as part of their rotating elements, and under load, require bearings to control shaft movement while rotating. These high temperature pumps require submerged bearings, and thus bearing materials that can withstand the temperature and molten salt contact over long periods of time. This is essential for keeping pump maintenance costs low, and downtime to a minimum. Long running bearing materials is an enabling technology for integrated liquid systems in Generation 3 CSP Systems. Keeping costs low and systems reliable are critical for the broad adoption of Gen 3 CSP Systems. Hayward Tyler, Inc (HTI) proposed development of journal bearing materials for use in vertical pumps designed for pumping high temperature molten salt on both the hot and cold sides of the solar power tower. To complete this objective, HTI utilized the testing capabilities of Oak Ridge National Laboratory (ORNL) and High Temperature System Design (HTSD). Materials would first be selected for static corrosion testing, then downselected for tribological testing. Successful tribologically tested materials would then be downselected for use in additional test regimes. Ten samples were selected for static corrosion testing. Three passed the static testing regime. Two of these materials were downselected for ball material in tribological testing against three metallic alloys: Sintered Y2O3 Partially Stabilized Zirconia YTZP and Silicon Nitride, Grade 147-31N. HTI also proposed collecting data from a thorough bearing test regime using a test rig designed and built as part of this project, and also a conceptual design for a high temperature molten salt vertical pump. HTI withdrew from the project prior to completion of this work and this report reflects only the work completed prior to withdrawal. The completed work produced two papers, one manuscript submitted for publication, and one paper published in the journal Solar Energy Materials and Solar Cells. The papers are: “Tribological behavior of ceramic-alloy bearing contacts in molten salt lubrication for concentrating solar power” and “Material Selection and Corrosion Studies Of Candidate Bearing Materials For Use In Molten Chloride Salt."

14 SOLAR ENERGY↗
DOE OSTI2020

Thermal Energy Storage Using Solid Particles for Long-Duration Energy Storage

The rapid growth of renewable energy increases the importance of economically firming the electricity supply from variable solar photovoltaic- and wind-power generators. Energy storage will be the key to manage variability and to bridge the generation gap over time scales of hours or days for high renewable grid integration. The integration of renewable power and storage of excess electricity has several significant and positive impacts including: 1) expanding the renewable energy portion of total electricity generation, 2) improving the peak-load response, and 3) coordinating the electricity supply and demand over the grid. Long-duration energy storage can potentially complement the reduction of fossil-fuel baseload generation that otherwise would risk grid security when a large portion of grid power comes from variable renewable sources. Several energy storage methods are deployed or under development, including mechanical, chemical or electrochemical, and thermal energy storage (TES). Comparing their economic potential for different scales and applications helps identify suitable technology to support high renewable grid integration. Despite the progress of TES technologies developed and deployed with concentrating solar power (CSP) systems, TES has been undervalued for its potential role in electric energy storage. This paper introduces TES methods applicable to grid energy storage and particularly focuses on solid-particle-based TES to serve the purpose of long-duration energy storage (LDES). The objective of this paper is to present a standalone particle-based TES system for electric storage and to show the potential of TES systems for LDES applications over other energy storage methods such as batteries, compressed-air energy storage, or pumped-storage hydropower.

27 ARPA - Advanced Research Projects Agency-Energy↗
DOE OSTI2023

Temporal Subsampling Diminishes Small Spatial Scales in Recurrent Neural Network Emulators of Geophysical Turbulence

The immense computational cost of traditional numerical weather and climate models has sparked the development of machine learning (ML) based emulators. Because ML methods benefit from long records of training data, it is common to use data sets that are temporally subsampled relative to the time steps required for the numerical integration of differential equations. Here, we investigate how this often overlooked processing step affects the quality of an emulator's predictions. We implement two ML architectures from a class of methods called reservoir computing: (a) a form of Nonlinear Vector Autoregression (NVAR), and (b) an Echo State Network (ESN). Despite their simplicity, it is well documented that these architectures excel at predicting low dimensional chaotic dynamics. We are therefore motivated to test these architectures in an idealized setting of predicting high dimensional geophysical turbulence as represented by Surface Quasi-Geostrophic dynamics. In all cases, subsampling the training data consistently leads to an increased bias at small spatial scales that resembles numerical diffusion. Interestingly, the NVAR architecture becomes unstable when the temporal resolution is increased, indicating that the polynomial based interactions are insufficient at capturing the detailed nonlinearities of the turbulent flow. The ESN architecture is found to be more robust, suggesting a benefit to the more expensive but more general structure. Spectral errors are reduced by including a penalty on the kinetic energy density spectrum during training, although the subsampling related errors persist. Future work is warranted to understand how the temporal resolution of training data affects other ML architectures.

58 GEOSCIENCES↗
DOE OSTI2020

Update on Full-Scale Retrieval and Transfer System Testing for Pneumatic Conveyance of High-Level Calcine Waste - 20241

The Idaho National Laboratory processed spent nuclear fuel from 1953 to 1994. The process used solvent extraction to separate uranium, resulting in a liquid waste. A calcination process was used to convert the liquid waste to a high-level radioactive solid powder, referred to as calcine. The calcine is stored in stainless steel bins contained within concrete vaults called the Calcined Solids Storage Facilities. There are a total of seven CSSFs. The Calcine Retrieval Project is tasked with retrieving the calcine from the bins of CSSF 1 and pneumatically transferring it to the bins of CSSF 6. CSSF 1 was constructed to be a final calcine repository with no forethought of retrieval. It represents the greatest technical hurdle to calcine extraction of all the CSSFs. The challenges inherent with accessing the calcine, safely extracting it, and securely transporting it are copious and complex. Once the calcine is removed from CSSF1, that facility may be closed, reducing the footprint of waste storage at the INL. The same process for removing Calcine from CSSF1 may then be utilized to remove the calcine from the remaining CSSFs for final treatment and transport to a long-term storage facility. To overcome these inherent challenges with accessing CSSF 1 and extracting the calcine in a timely manner, a full-scale integrated mockup has been constructed. The mockup includes a full-size model of a concentric group of bins from CSSF 1 and a storage vessel representing CSSF 6. All essential elements of the transport design are represented in the Mockup, including motive and makeup air supply (air compressor), a replica of the CSSF6 cyclone, pre-filter, HEPA filter, vacuum pump, and retrieval control devices. Construction on the Mockup was completed in 2018 and testing began in February 2019. It has already proven very beneficial to the CRP. Experience in running the Mockup have aided in the development of retrieval and transport methodologies and the selection of process parameter values. It has also uncovered some unforeseen technological challenges and will continue to be valuable to the testing team as these hurdles are overcome and the process is proven effective. Operations personnel have also benefitted from its use as a training tool and will continue to use the Mockup as a simulator to reproduce problems encountered during extraction and transfer of Calcine from CSSF 1. The knowledge gained during this process will also be of continued benefit as future work is done to remove various types of calcine from the remaining CSSFs. The methodology of safe and secure transfer of radioactive material may be of benefit to others throughout the U.S. Department of Energy complex. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2021

Time Refraction of Spin Waves

Here, we present an experimental study of time refraction of spin waves (SWs) propagating in microscopic waveguides under the influence of time-varying magnetic fields. Using space- and time-resolved Brillouin light scattering microscopy, we demonstrate that the broken translational symmetry along the time coordinate results in a loss of energy conservation for SWs and thus allows for a broadband and controllable shift of the SW frequency. With an integrated design of SW waveguide and microscopic current line for the generation of strong, nanosecond-long, magnetic field pulses, a conversion efficiency up to 39% of the carrier SW frequency is achieved, significantly larger compared to photonic systems. Given the strength of the magnetic field pulses and its strong impact on the SW dispersion relation, the effect of time refraction can be quantified on a length scale comparable to the SW wavelength. Furthermore, we utilize time refraction to excite SW bursts with pulse durations in the nanosecond range and a frequency shift depending on the pulse polarity.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2022

Liquid Salt Combined-Cycle Pilot Plant Design

The work described in this report is responsive to the Office of Fossil Energy program ‘Energy Storage for Fossil Power Generation.’ This Phase I report has been prepared by Pintail Power LLC, with support from Nexant ECA, Electric Power Research Institute (EPRI) and Southern Company Services as a deliverable for the U.S. Department of Energy for NETL Award DE-FE-00320016. The Liquid Salt Combined Cycle™ (LSCC™) technology provides large-scale energy storage integrated with Fossil Electric Generating Units (FEGUs) to meet critical needs in the energy transition by providing: • the lowest cost large-scale storage for time-shifting of renewable energy, • superior fuel efficiency to reduce GHGs from dispatchable resources, • flexible capacity and ramping to balance variability of wind and solar resources, • essential grid stability services to assure reliability of a low-carbon grid. The LSCC approach: • employs equipment that has already been proven in utility service, • uses safe, non-toxic, non-degrading, perpetual-life storage medium, • leverages and repurposes existing FEGU assets, • expands the value stack of energy storage to reduce market, financing, and commodity risks. Pintail Power has developed the LSCC technology to meet the need for reliable, efficient, and cost-effective integration of Variable Renewable Energy (VRE) into a low-carbon electric grid by coupling proven thermal energy storage with proven gas turbines, steam turbines, and heat transfer equipment. This novel approach is intended to address the key issues facing the grid and operators of renewable and fossil generating units including: • Overgeneration and curtailment of renewables, • Need for fast ramping dispatchable resources, • Improved efficiency and flexibility of fossil units, • Additional peaking capacity to support electrification of transportation and heating, • Provision of reliability services to support high penetration of VRE, especially synchronous inertia and fast frequency response. A Technology Readiness assessment by EPRI confirmed that LSCC technology consists of commercially proven hardware used in industrial and utility applications. Although the novel LSCC approach has not yet been demonstrated as a complete system, interfaces between major components have been conservatively specified. A Phase III pilot is planned to demonstrate equipment integration and operation. The patented innovation is removal of the evaporator section from the exhaust heat recovery system, with the evaporation performed by stored energy in a separate steam generator. This arrangement couples renewable and fossil power generation via long-duration energy storage to deliver cost, performance, and operational synergies, including superior charging and discharging flexibility, reduced fuel consumption and lower CO 2 emissions compared to conventional Combined Cycle Power Plants, and low-cost, large-scale energy storage. The LSCC technology is composed of proven equipment integrated with gas turbine exhaust heat in a novel system. During charging, electric heaters raise the salt temperature as it flows from the Cold Salt Tank to the Hot Salt Tank. During discharging, hot salt produces steam from feedwater that is heated with gas turbine exhaust, which also superheats steam to drive a steam turbine. LSCC technology can be added to any combustion-turbine to integrate renewable energy, provide needed grid services, and increase the value of fossil electric generating units based on the technology’s following attributes: • Long-duration storage enables time-shifting of VRE to avoid curtailment and impairment of renewable assets. • Long storage duration combined with fast-charging capability increases arbitrage opportunities by storing more energy when the price is low and discharging more hours when the price is high. • Long storage duration allows resource adequacy to be supplied across multiple days to increase reliability and reduce risk. • The stored energy reduces fuel heat rate and GHG emissions, and increases merit, so the LSCC dispatches earlier and longer to increase the plant’s capacity factor and asset value. • The stored energy enables pre-heating and startup of the steam cycle, without operating the gas turbine, to enable fast startup and ramping when dispatched for discharge. • The steam turbine can operate without the gas turbine so it can provide valuable synchronous inertia during charging without consuming fuel. • Fast frequency response and regulation services can be provided during charging using solid-state heater and pump controls to vary the charge power input in response to grid signals. • The LSCC system can be configured for resilience including black start, islanded/micro-grid operation, and even self-recharging of storage using either gas turbine power or gas turbine exhaust heat. The commercialization plan is to add LSCC technology to existing simple cycle gas turbine power plants with the 50MW GE LM6000 aero-derivative gas turbine as the reference design basis. A Techno-economic assessment of the reference design evaluated the benefits (Levelized Avoided Cost of Energy) and costs (Levelized Cost of Energy). The plant definition included all major systems and budgetary vendor quotes. Pintail Power and NexantECA developed the overall cost estimate for the LSCC plant up to the total plant cost level, following the DOE-NETL cost estimate guidelines at AACE Class 3 (-20%/+30%). This includes the equipment cost, bulk material, direct and indirect labor costs to arrive at the bare erected cost. Engineering costs are factored from the BEC and added to it to arrive at the EPC cost. Process and project contingencies were then factored from the EPC cost and rolled-up to yield the total plant cost of $\$$184 million for 1746 MWh of discharge electricity. • At $\$$105/kWh, the reference plant costs less than any of the Energy Storage Systems evaluated by PNNL in 2020 for the Energy Storage Grand Challenge. Operations and Maintenance cost estimates were scaled from combined cycle practice, assuming that the LSCC unit was co-located with and sharing some labor expense with other units, to arrive at $\$$2.2 million per year. Plant economics were evaluated using prices from the ERCOT Day-Ahead Market for calendar year 2019 (excluding the market disruptions from the COVID pandemic and the February 2020 deep freeze event). Assuming economic dispatch in the ERCOT Day-Ahead market, the reference plant capacity factor would have discharged for 2777 hours at 91.9 MW, a 31.66% capacity factor, with a marginal cost of $\$$25.59/MWh, and a LACE of $\$$82.41/MWh. Fixed charges were calculated according to EIA guidelines to arrive at an LCOE of $\$$83.48. The benefit-to-cost ratio of 0.99 suggests that the reference plant would have been cost-effective and competitive in the market. EPRI interviewed selected utilities to gauge the need for, applicability of and interest in the LSCC system. Several utilities are currently managing increased load growth along with the inclusion of increasing levels of renewable generation, putting pressure on conventional generation by requiring increased turndown requirements and ultimately lower capacity factors. All of the utilities interviewed have CO 2 reduction targets in the 2030-2050 timeframe that will severely limit the participation of fossil generation and require better utilization of carbon free generation. While there is limited opportunity for storage in the current markets, the utilities interviewed stated that there will be a substantial need for long duration energy storage in the future given the expected trends. Utilizing an energy storage system will generally be preferred over new gas capacity in some cases, with the capabilities of the LSCC system being a potential option for retrofit to existing simple cycle gas turbine units, allowing them to deliver greater participation in the market with lower carbon intensity. A technology gap assessment and technology maturation plan identified a pilot-scale demonstration as the final step before commercialization. Key gaps to be addressed during the Phase II FEED (Front-End Engineering Design) are component selection and design, commissioning procedures, and operational procedures and the control system for LSCC charging and discharging. The project team has been expanded to include Wood Group PLC as EPC. The proposed Phase II work leads to a pilot-scale engineering demonstration (TRL 6) to be conducted at Southern Company’s Plant Rowan, where the prototype system will perform “all the functions that will be required of the operational system.” The proposed pilot will facilitate commercialization (TRL-9) by scale-up to utility-scale systems integrated with peaking GTs or directly to facility scale systems using industrial GTs. The conceptual design for the pilot plant focuses on the novel integration aspects of LSCC technology. A slipstream of gas turbine exhaust will feed a waste heat recovery unit coupled to a molten salt steam generator heated by stored energy. The pilot is intended to demonstrate all key operating modes of the LSCC technology during charging, discharging and standby. The pilot equipment will be approximately one-seventh scale of the LM6000 commercial target and is expected to have commercial off-ramp potential for facility-scale applications.

01 COAL, LIGNITE, AND PEAT↗
DOE OSTI2023

Shadow energy functionals and potentials in Born–Oppenheimer molecular dynamics

In Born–Oppenheimer molecular dynamics (BOMD) simulations based on the density functional theory (DFT), the potential energy and the interatomic forces are calculated from an electronic ground state density that is determined by an iterative self-consistent field optimization procedure, which, in practice, never is fully converged. The calculated energies and forces are, therefore, only approximate, which may lead to an unphysical energy drift and instabilities. Here, we discuss an alternative shadow BOMD approach that is based on backward error analysis. Instead of calculating approximate solutions for an underlying exact regular Born–Oppenheimer potential, we do the opposite. Instead, we calculate the exact electron density, energies, and forces, but for an underlying approximate shadow Born–Oppenheimer potential energy surface. In this way, the calculated forces are conservative with respect to the approximate shadow potential and generate accurate molecular trajectories with long-term energy stabilities. We show how such shadow Born–Oppenheimer potentials can be constructed at different levels of accuracy as a function of the integration time step, δt, from the constrained minimization of a sequence of systematically improvable, but approximate, shadow energy density functionals. For each energy functional, there is a corresponding ground state Born–Oppenheimer potential. These pairs of shadow energy functionals and potentials are higher-level generalizations of the original “zeroth-level” shadow energy functionals and potentials used in extended Lagrangian BOMD. The proposed shadow energy functionals and potentials are useful only within this extended dynamical framework, where also the electronic degrees of freedom are propagated as dynamical field variables together with the atomic positions and velocities. The theory is quite general and can be applied to MD simulations using approximate DFT, Hartree–Fock, or semi-empirical methods, as well as to coarse-grained flexible charge models.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Enhanced minority carrier lifetime in bulk hydrogen-passivated InAsSbBi

We investigate the bulk passivation of the dilute bismide alloy InAsSbBi by plasma-assisted hydrogenation. InAsSbBi is of significant interest for mid- to long-wave infrared photodetection due to its bandgap flexibility and potential integration with heterostructured photodetector architectures. Epitaxially grown InAsSbBi samples are characterized by photoluminescence and time-resolved photoluminescence measurements for a range of hydrogenation conditions. Increases in the minority carrier lifetime of over 3× are reported, with no degradation over a period of months following the treatment. Photoluminescence measurements confirm that the hydrogenation process improves the InAsSbBi optical properties. These results offer a path toward the improved performance of InAsSbBi-based photodetectors and potentially other narrow bandgap semiconductor materials and material systems.

Physics↗
DOE OSTI2024

Complete quasilinear model for the acceleration-driven lower hybrid drift instability and a computational assessment of its validity

A complete quasilinear model is derived for the electrostatic acceleration-driven lower hybrid drift instability in a uniform two-species low-beta plasma in which current is perpendicular to the background magnetic field. The model consists of coupled nonlinear velocity space diffusion equations for the volume-averaged ion and electron distribution functions. Each species' diffusion coefficient depends on a time-evolving spectral density of the electric-field energy per unit volume and a time-evolving dispersion relation. The dispersion relation is expressed analytically in integral form without the use of asymptotic limits and applies to arbitrary distribution functions, so long as they can be expressed as a function of one velocity coordinate, e.g., f⁡(vy) or f⁡(v⊥). The quasilinear model conserves energy and is complete in that it fully describes the evolution of the distribution functions, including resonant and nonresonant particle-wave interactions, while accounting for distribution-function-dependent mixed-complex frequencies. Further, the quasilinear diffusion model is solved numerically and self-consistently using a Crank-Nicolson temporal discretization and a second-order finite-volume velocity-space discretization. Numerical solutions are compared to nonlinear fourth-order accurate continuum kinetic Vlasov-Poisson simulations. Evolution of electric-field energy, growth rates, distribution functions, and diffusion coefficients are shown to be in agreement with Vlasov simulations. The quasilinear model is shown to predict anomalous transport terms, like resistivity and heating, to within a factor of order unity. Discrepancies between the quasilinear model and Vlasov simulations are assessed and attributed primarily to lack of damping in the quasilinear description and to the use of unperturbed-orbit susceptibilities in the linear theory dispersion relation. The results illuminate the predictive accuracy of the quasilinear model, place approximate bounds on its validity, and provide much needed vetting of quasilinear theory's ability to predict the nonlinear state of a microturbulent plasma.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2022

Search for massive long-lived particles decaying semileptonically at ${\sqrt{s}}=13\,\hbox {TeV}$

A search is performed for massive long-lived particles (LLPs) decaying semileptonically into a muon and two quarks. Two kinds of LLP production processes were considered. In the first, a Higgs-like boson with mass from 30 to 200\,GeV\!/c 2 is produced by gluon fusion and decays into two LLPs. The analysis covers LLP mass values from 10\,GeV\!/c 2 up to about one half the Higgs-like boson mass. The second LLP production mode is directly from quark interactions, with LLP masses from 10 to 90\,GeV\!/c 2 . The LLP lifetimes considered range from 5 to 200 ps. This study uses LHCb data collected from proton-proton collisions at $\sqrt{s}$=13\,TeV, corresponding to an integrated luminosity of 5.4\,fb -1 . No evidence of these long-lived states has been observed, and upper limits on the production cross-section times branching ratio have been set for each model considered.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2025

Modular Autonomous Experimentation for Biological Applications (Full Report)

The Modular Autonomous Research System (MARS) was developed to address the pressing need for faster, more reliable, and more adaptable scientific discovery. Traditional experimentation is limited by manual labor, long cycle times, and fragmented data streams, which constrain the ability to explore complex chemical and materials design spaces. To overcome these limitations, we created an integrated, modular platform that combines laboratory robotics, diverse measurement instruments, and a central data infrastructure with artificial intelligence–driven decision-making. The system links liquid handling robots, robotic arms, and optical plate readers into a closed loop where experiments are executed automatically, data is analyzed in real time, and subsequent experimental conditions are adaptively chosen to maximize information gain. Over the course of the project, MARS was validated on two primary test cases—spectroscopic metal–ligand binding assays and peptide-directed mineralization—which highlighted the system’s ability to handle uncertainty and variability in experimental measurements. To further demonstrate modularity and extensibility, we also established additional testbeds in electrochemistry for catalyst discovery and electrolyte formulation for advanced batteries. The results show that MARS can reliably conduct autonomous campaigns with minimal human intervention, adapt to distinct scientific domains, and provide a scalable model for future self-driving laboratories. This work establishes new capabilities for modular, uncertainty-aware automation and directly supports the need for advanced, data-driven research platforms capable of accelerating discovery across a wide range of scientific and national security missions.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Assessing carbon storage capacity and saturation across six central US grasslands using data–model integration

Abstract. Future global changes will impact carbon (C) fluxes and pools in most terrestrial ecosystems and the feedback of terrestrial carbon cycling to atmospheric CO2. Determining the vulnerability of C in ecosystems to future environmental change is thus vital for targeted land management and policy. The C capacity of an ecosystem is a function of its C inputs (e.g., net primary productivity – NPP) and how long C remains in the system before being respired back to the atmosphere. The proportion of C capacity currently stored by an ecosystem (i.e., its C saturation) provides information about the potential for long-term C pools to be altered by environmental and land management regimes. We estimated C capacity, C saturation, NPP, and ecosystem C residence time in six US grasslands spanning temperature and precipitation gradients by integrating high temporal resolution C pool and flux data with a process-based C model. As expected, NPP across grasslands was strongly correlated with mean annual precipitation (MAP), yet C residence time was not related to MAP or mean annual temperature (MAT). We link soil temperature, soil moisture, and inherent C turnover rates (potentially due to microbial function and tissue quality) as determinants of carbon residence time. Overall, we found that intermediates between extremes in moisture and temperature had low C saturation, indicating that C in these grasslands may trend upwards and be buffered against global change impacts. Hot and dry grasslands had greatest C saturation due to both small C inputs through NPP and high C turnover rates during soil moisture conditions favorable for microbial activity. Additionally, leaching of soil C during monsoon events may lead to C loss. C saturation was also high in tallgrass prairie due to frequent fire that reduced inputs of aboveground plant material. Accordingly, we suggest that both hot, dry ecosystems and those frequently disturbed should be subject to careful land management and policy decisions to prevent losses of C stored in these systems.

58 GEOSCIENCES↗
DOE OSTI2021

A Multirange Vehicle Speed Prediction With Application to Model Predictive Control-Based Integrated Power and Thermal Management of Connected Hybrid Electric Vehicles

Abstract Connectivity and automated driving technologies have opened up new research directions in the energy management of vehicles which exploit look-ahead preview and enhance the situational awareness. Despite this advancement, the vehicle speed preview that can be obtained from vehicle-to-vehicle/infrastructure (V2V/I) communications is often limited to a relatively short time-horizon. The vehicular energy systems, specifically those of the electrified vehicles, consist of multiple interacting power and thermal subsystems that respond over different time-scales. Consequently, their optimal energy management can greatly benefit from long-term speed prediction beyond that available through V2V/I communications. Accurately extending the look-ahead preview, on the other hand, is fundamentally challenging due to the dynamic nature of the traffic environment. To address this challenge, we propose a data-driven multirange vehicle speed prediction strategy for arterial corridors with signalized intersections, providing the vehicle speed preview for three different ranges, i.e., short-, medium-, and long-range. The short-range preview is obtained by V2V/I communications. The medium-range preview is realized using a neural network (NN), while the long-range preview is predicted based on a Bayesian network (BN). The predictions are updated in real-time based on the current state of traffic and incorporated into a multihorizon model predictive control (MH-MPC) for integrated power and thermal management (iPTM) of connected vehicles. The results of design and evaluation of the performance of the proposed data-informed MH-MPC for iPTM of connected hybrid electric vehicles (HEVs) using traffic data for real-world city driving are reported.

Automation & Control Systems↗
DOE OSTI2020

Convergence acceleration of Monte Carlo many-body perturbation methods by direct sampling

In the Monte Carlo many-body perturbation (MC-MP) method, the conventional correlation-correction formula, which is a long sum of products of low-dimensional integrals, is first recast into a short sum of high-dimensional integrals over electron-pair and imaginary-time coordinates. These high-dimensional integrals are then evaluated by the Monte Carlo method with random coordinates generated by the Metropolis–Hasting algorithm according to a suitable distribution. The latter algorithm, while advantageous in its ability to sample nearly any distribution, introduces autocorrelation in sampled coordinates, which in turn increases the statistical uncertainty of the integrals and thus the computational cost. It also involves wasteful rejected moves and an initial “burn-in” step as well as displays hysteresis. Here, an algorithm is proposed that directly produces a random sequence of electron-pair coordinates for the same distribution used in the MC-MP method, which is free from autocorrelation, rejected moves, a burn-in step, or hysteresis. Furthermore, this direct-sampling algorithm is shown to accelerate second- (MC-MP2) and third-order Monte Carlo many-body perturbation (MC-MP3) calculations by up to 222% and 38%, respectively.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

A Modified Maximum Entropy Inverse Reinforcement Learning Approach for Microgrid Energy Scheduling

Increasing popularity of integrating distributed energy resources (DERs) into the power system brings a challenge to optimize the microgrid dispatch policy. The reinforcement learning methods suffer from a long-time problem with the theoretical assumption of the objective/reward function for the microgrid system. Although the traditional inverse reinforcement learning (IRL) approaches can solve this problem to some extent, they encounter a limitation of complex computations for state visitation frequency in the large and continuous state space. To alleviate this limitation, we propose a modified maximum entropy IRL (MMIRL) method to extract the reward function from the expert demonstrations for solving the microgrid energy scheduling problem. The proposed MMIRL algorithm is promising in recovering the reward function and learning the dispatch policy compared to conventional approaches. Case studies are performed in an energy arbitrage problem and a microgrid system with DERs. Results substantiate that the proposed MMIRL approach can learn the dispatch policy with more than 99% efficiency and outperforms other comparative methods.

artificial intelligence, reinforcement learning, m↗