Search NASA⌕ Search

SEARCH · Search NASA

Results for “predictive control”

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 217 records · Page 12
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Synthesizing intermetallic thermoelectric materials using metal flux growth method

With zero energy poverty by 2050 goals, exploring innovative energy technologies is essential. Thermoelectric materials (TEMs) have the potential to harness waste heat and convert it to electricity (e.g., for spacecraft power, sensors, and cryogenic cooling). Two limitations of thermoelectric compounds are their low efficiency and limited pool of materials. Optimizing the efficiency of TEMs is challenging due to the interdependent relationship between electrical and thermal conductivity. A deeper understanding of electronic properties and lattice thermal conductivity is necessary to “understand, predict and ultimately control” the thermoelectric properties of TEMs. Understanding the influence of light-atom-doping and intrinsic defects on the electronic properties of the f-element containing TEMs are important questions that need to be answered to design and synthesize TEMs with optimal parameters. The purpose of this work is to understand how doping f-element based TEMs with light elements, such as carbon and boron, would impact the crystal structure, bond lengths, band gaps, and thermoelectric characteristics. High-quality f¬-element containing intermetallic single crystals are grown in metal flux media. In molten metal flux growth method, one or more low-melting metals such as aluminum, gallium, tin, and bismuth are used in excess as the reaction medium. As these are solution-state reactions, we can use lower temperatures than solid-state reactions and isolate the kinetic products instead of the thermodynamically stable compound. Then these are characterized using X-ray diffraction, scanning electron microscopy, energy dispersive spectroscopy, magnetization, electrical and thermal conductivities, and thermoelectric power.

37 - INORGANIC, ORGANIC, PHYSICAL AND ANALYTICAL C↗
DOE OSTI2026

Combining Generative Modeling and Advanced Control for Building Scenario Generation

Buildings make up a large portion of energy consumption in the U.S. today. Understanding their energy consumption patterns can improve their efficiency, but requires detailed models that rely on incomplete or unknown information. Previous work has shown that artificial intelligence (AI) can be used to predict missing information and even suggest upgrades to improve building efficiency. However, building upgrades may require undesirable upfront costs. Oppositely, advanced control could improve building efficiency with negligible upfront cost. To explore the tradeoffs between these two approaches, in this work we propose a workflow to compute optimal temperature setpoint schedules to minimize energy consumption and operational cost. Results show that modifying the temperature setpoints in a building using model predictive control (MPC) can effectively reduce its energy consumption and operational cost. This optimal operation cannot fully meet a desired goal. However, we show that by considering MPC in addition to component upgrades, a desired goal can be met with significantly less upfront costs.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2024

Forward and inverse predictive transient models of TREAT using surrogate reactivity models

In this work, we present two novel approaches for predicting the power evolution and control rod height of the Transient Reactor Test Facility (TREAT) to support experiment modeling; specifically, transient analysis of the NASA-sponsored Sirius series of experiments. These approaches utilize steady-state Monte Carlo model, point kinetics model, and surrogate models to predict power evolution and control rod axial position during transient experiments. Both approaches were tested and validated against several Sirius experiments that were performed in the TREAT facility at different power levels. The validation test results show very good agreement with the experimental data, and the models were able to accurately predict the power evolution and the axial control rod position with an average error within 3.0%. Finally, this indicates that these approaches will help the reactor engineering team of the TREAT facility in preparing and predicting the power and temperature of the experiment.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2024

Digital Twin + AI: Control Room of the Future

A digital twin enhances power grid control room operations by providing real-time monitoring, predictive insights, simulation capabilities, remote control, training opportunities, data integration, and decision support. This technology empowers control room operators to effectively manage the grid, optimize performance, and ensure reliable and efficient energy distribution.

control room of the future↗
DOE OSTI2025

AI-Enabled Operations at Fermi Complex: Multivariate Time Series Prediction for Outage Prediction and Diagnosis

The Main Control Room of the Fermilab accelerator complex continuously gathers extensive time-series data from thousands of sensors monitoring the beam. However, unplanned events such as trips or voltage fluctuations often result in beam outages, causing operational downtime. This downtime not only consumes operator effort in diagnosing and addressing the issue but also leads to unnecessary energy consumption by idle machines awaiting beam restoration. The current threshold-based alarm system is reactive and faces challenges including frequent false alarms and inconsistent outage-cause labeling. To address these limitations, we propose an AI-enabled framework that leverages predictive analytics and automated labeling. Using data from $2,703$ Linac devices and $80$ operator-labeled outages, we evaluate state-of-the-art deep learning architectures, including recurrent, attention-based, and linear models, for beam outage prediction. Additionally, we assess a Random Forest-based labeling system for providing consistent, confidence-scored outage annotations. Our findings highlight the strengths and weaknesses of these architectures for beam outage prediction and identify critical gaps that must be addressed to fully harness AI for transitioning downtime handling from reactive to predictive, ultimately reducing downtime and improving decision-making in accelerator management.

Jain, Milan [PNL, Richland] (ORCID:000000021676111↗
DOE OSTI2024

PIP-II Beam Current Monitor Fault Case & Beam Position Monitor Linearity Studies in CST Studio

The use of non-invasive sensors & systems to measure particle beam characteristics is a crucial part of modern accelerator control systems. As such, simulations predicting the behavior of these sensors are essential for guiding beamline control system design. This poster details the results returned by two beam sensor models created using CST Studio software: the signal linearity of an elliptical beam position monitor (BPM), and the fault cases of an AC current transformer (ACCT) beam current monitor (BCM).

Rouzky, Adam↗
DOE OSTI2024

Effects of input gradient regularization on neural networks time-series forecasting of thermal power systems

This study proposes using neural networks, specifically gated recurrent unit (GRU), long-short-term memory (LSTM), and transformer networks, to improve control strategies in a 450 MW coal-fired power plant. However, neural networks face issues of becoming overly dependent on just a few variables to make predictions, which negatively impacts control decisions that rely on the model to determine the value of all manipulated variables. The paper introduces regularization techniques, including noise injection and input gradient regularization, during the training phase. Here, the work presents novel contributions in adapting neural networks to control industrial systems and applying regularization techniques from computer vision to industrial process control. Results demonstrate the effectiveness of input gradient regularization in reducing model dependence on subsets of variables, emphasizing the balance between fidelity and controllability. Further exploration is recommended, including the development of recurrent transformers, closed-loop control testing, and a sensitivity analysis on computer models to provide further insight.

20 FOSSIL-FUELED POWER PLANTS↗
DOE OSTI2026

Spherical tokamak physics research in preparation for the operation of NSTX-U

The National Spherical Torus Experiment Upgrade (NSTX-U) is preparing to resume operation, representing a crucial step toward realizing compact, cost-effective fusion pilot plants. In advance of this, extensive modeling and data analysis have been conducted to advance the physics basis for low-aspect-ratio, high-performance plasma regimes, focusing on three core objectives: confinement and stability, power and particle handling, and steady-state operation. Significant progress has been made in understanding the electron temperature flattening in high-β plasmas, which is shown to be driven by a complex interplay of magnetohydrodynamic instabilities (e.g. non-resonant infernal modes), fast-ion-driven Alfvén eigenmodes, and electron and ion-scale micro-instabilities, particularly Kinetic Ballooning Modes (KBMs), whose destabilization is strongly dependent on parallel magnetic field fluctuations (δB ∥ ). Furthermore, a new gyrokinetic critical pedestal model was developed, accurately predicting pedestal structure by identifying KBMs as the primary stability limit, offering a critical constraint for future high-confinement scenarios. To address the challenge of high heat flux, novel liquid lithium plasma-facing components were modeled. The analysis confirmed that lithium vapor shielding is a self-regulating mechanism for heat mitigation, while also emphasizing that strong main ion parallel flow is essential to minimize core lithium contamination. Finally, progress toward steady-state operation was anchored by developing the required physics basis and control tools. This includes predictive modeling for reversed magnetic shear sustainment, demonstrating that magnetic island-induced bootstrap current reduction is negligible in STs, and advancing real-time control and disruption avoidance capabilities. The development of high-speed surrogate models (e.g. MMMNet) provides computationally efficient tools vital for non-inductive scenario optimization and integrated, low-disruptivity operations planned for NSTX-U.

NSTX-U↗
DOE OSTI2024

Enabling "Fuel" Switching through On-Demand Wind Control

This work demonstrates that classical shear-flow stability theory can be successfully applied to modify wind turbine wakes and also explains the success of several emerging, empirically-arrived control methods (i.e., dynamic induction and helix control). Linear stability theory predictions are extended to include the effects of non-axisymmetric inflow profiles, such as wind shear, which is shown to not strongly affect the primary forcing frequency. The predictions, as well as idealized large-eddy simulations using actuator-line representation of the turbine blades, agree that the n = 0 and ±1 modes have faster initial growth rates than higher-order modes, suggesting the lower-order modes are more appropriate for wake control. Exciting the lower-order modes with periodic pitching of the blades produces higher entrainment into the wake and consequently faster wake recovery.

17 WIND ENERGY↗
DOE OSTI2026

Building Operation Model (Morpheus) for Dallas Fort Worth Airport (CRADA CRD-19-16301 Final Report)

The primary objective of this project was to leverage digital twin technology to enhance the design and operation of DFW Airport terminals and their associated energy systems. To achieve this, Morpheus, a building digital twin, was developed to guide improvements in airport operations, specifically targeting reductions in peak power demand and overall energy consumption.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2024

Methane fluxes in tidal marshes of the conterminous United States

Abstract Methane (CH 4 ) is a potent greenhouse gas (GHG) with atmospheric concentrations that have nearly tripled since pre‐industrial times. Wetlands account for a large share of global CH 4 emissions, yet the magnitude and factors controlling CH 4 fluxes in tidal wetlands remain uncertain. We synthesized CH 4 flux data from 100 chamber and 9 eddy covariance (EC) sites across tidal marshes in the conterminous United States to assess controlling factors and improve predictions of CH 4 emissions. This effort included creating an open‐source database of chamber‐based GHG fluxes ( https://doi.org/10.25573/serc.14227085 ). Annual fluxes across chamber and EC sites averaged 26 ± 53 g CH 4 m −2 year −1 , with a median of 3.9 g CH 4 m −2 year −1 , and only 25% of sites exceeding 18 g CH 4 m −2 year −1 . The highest fluxes were observed at fresh‐oligohaline sites with daily maximum temperature normals (MATmax) above 25.6°C. These were followed by frequently inundated low and mid‐fresh‐oligohaline marshes with MATmax ≤25.6°C, and mesohaline sites with MATmax >19°C. Quantile regressions of paired chamber CH 4 flux and porewater biogeochemistry revealed that the 90th percentile of fluxes fell below 5 ± 3 nmol m −2 s −1 at sulfate concentrations >4.7 ± 0.6 mM, porewater salinity >21 ± 2 psu, or surface water salinity >15 ± 3 psu. Across sites, salinity was the dominant predictor of annual CH 4 fluxes, while within sites, temperature, gross primary productivity (GPP), and tidal height controlled variability at diel and seasonal scales. At the diel scale, GPP preceded temperature in importance for predicting CH 4 flux changes, while the opposite was observed at the seasonal scale. Water levels influenced the timing and pathway of diel CH 4 fluxes, with pulsed releases of stored CH 4 at low to rising tide. This study provides data and methods to improve tidal marsh CH 4 emission estimates, support blue carbon assessments, and refine national and global GHG inventories.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Microstructure Scale Lithium-Ion Battery Modeling: Part III. When and Where Lithium Plating Occurs and its Correlation with the Electrode Microstructure

Li-ion battery performance and degradation are closely related to the cell’s underlying electrode microstructure. Electrode microstructures are typically characterized with volume-averaged properties that neglect the impact of local heterogeneities. However, local heterogeneities create hot spots that can trigger degradation onset. Herein, a microstructure scale electrochemical model is used to investigate the impact of microstructure heterogeneity on lithium plating. The model predicts lithium plating is not uniform, even when considering a relatively small portion of the electrode (a cross-sectional area of 154×144 µm 2 ), preferring to plate on larger particles as compared to smaller particles. While local heterogeneities control where plating occurs, the model predicts that volume-averaged properties control when plating occurs. Additionally, the model predicts that the active material specific surface area has a linear relationship with the plating onset. However, the linear relationship between increased active material surface area and delayed plating response appears to be sensitive to the microstructure feature used to increase the active interface area. Here, a comparative case-study is explored where the specific surface area is increased by either reducing the active material particle diameter, adding open-porosity cracks, or increasing the active material surface roughness. The model predicts that increasing the specific surface area by reducing the active material particle diameter is the most effective strategy for delaying lithium plating. At 6C, reducing particle size is shown to be 3 and 20 times more effective than, respectively, adding open-porosity cracks and increasing surface roughness. A dual-layer electrode architecture combining gradations both for average properties and uniformities is eventually proposed to improve homogeneous material utilization and reduce degradation at high charge rates.

25 ENERGY STORAGE↗
DOE OSTI2024

Observational benchmarks inform representation of soil organic carbon dynamics in land surface models

Abstract. Representing soil organic carbon (SOC) dynamics in Earth system models (ESMs) is a key source of uncertainty in predicting carbon–climate feedbacks. Machine learning models can help identify dominant environmental controllers and establish their functional relationships with SOC stocks. The resulting knowledge can be integrated into ESMs to reduce uncertainty and improve predictions of SOC dynamics over space and time. In this study, we used a large number of SOC field observations (n=54 000), geospatial datasets of environmental factors (n=46), and two machine learning approaches (namely random forest, RF, and generalized additive modeling, GAM) to (1) identify dominant environmental controllers of global and biome-specific SOC stocks, (2) derive functional relationships between environmental controllers and SOC stocks, and (3) compare the identified environmental controllers and predictive relationships with those in models used in Phase 6 of the Coupled Model Intercomparison Project (CMIP6). Our results showed that the diurnal temperature, drought index, cation exchange capacity, and precipitation were important observed environmental predictors of global SOC stocks. While the RF model identified 14 environmental factors that describe climatic, vegetation, and edaphic conditions as important predictors of global SOC stocks (R2=0.61, RMSE = 0.46 kg m−2), current ESMs oversimplify the relationships between environmental factors and SOC, with precipitation, temperature, and net primary productivity explaining > 96 % of the variability in ESM-modeled SOC stocks. Further, our study revealed notable disparities among the functional relationships between environmental factors and SOC stocks simulated by ESMs compared with observed relationships. To improve SOC representations in ESMs, it is imperative to incorporate additional environmental controls, such as the cation exchange capacity, and refine the functional relationships to align more closely with observations.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Benchmark microgravity experiments and computations for 3D dendritic-array stability in directional solidification

In this study, we present a comprehensive quantitative analysis of stability bands for dendritic arrays during directional solidification of a transparent succinonitrile-0.46 wt % camphor alloy, spanning a broad range of pulling velocities. Taking advantage of the microgravity environment aboard the International Space Station where most convection effects are suppressed, we obtain unique measurements that quantify the stable primary spacing range of spatially extended three-dimensional dendritic array structures under purely diffusive growth conditions. Through carefully designed velocity jump experiments and detailed examination of sub-grain boundary dynamics, we characterize key instabilities, including elimination and tertiary branching, shedding new light on the mechanisms governing dynamic dendritic spacing selection in extended 3D arrays. Phase field simulations are performed to characterize the stability limits of dendritic array structures for quantitative comparison with the flight experiments. Although the simulations capture general trends, significant deviations are noted at the upper stability boundary, indicating the influence of additional, unexplored factors. These findings contribute to a deeper understanding of dendritic growth dynamics and offer valuable benchmark data that could aid in refining predictive models and improving control of dendritic microstructures in metallurgical applications.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Good practices for documenting AI-based studies on energy and buildings

Artificial intelligence has transformed building science research over the past decade, with applications spanning energy modeling, energy prediction, HVAC optimization and controls, fault detection, and occupancy modeling. However, many studies lack adequate documentation of datasets, algorithms, training procedures, and validation methods. Building science research faces additional challenges including inconsistent evaluation metrics, limited generalizability across building types, climates, and significant gaps between experimental studies and deployed systems. This communication provides practical guidance for good practices in documenting and publishing AI-based research following established standards from the computer science and machine learning communities. By adopting frameworks such as Datasheets for Datasets, Model Cards, and standardized reproducibility checklists, researchers can ensure their work meets the rigorous documentation standards necessary for reproducible, comparable, and impactful building science research.

Hong, Tianzhen [Lawrence Berkeley National Laborat↗
DOE OSTI2025

Application of pH-dependent environmental protection agency test method 1313 to low-activity nuclear waste glass

Here, in this study, we present results of a room temperature, pH-dependent leach test established by United States Environmental Protection Agency (EPA) Leaching Environmental Assessment Framework (LEAF) (EPA Method 1313 and further abbreviated EPA 1313) to study the alteration behavior of low-activity nuclear waste (LAW) glasses. Implementation of EPA 1313, which is closer to LAW disposal temperatures, could allow for more flexibility in the formulation of Hanford LAW glasses, specifically in terms of increased waste loading. A set of 48 LAW glasses were selected to test with EPA 1313. Briefly, the test method consists of adding glass to a number of solutions at different initial pH values and at a fixed mass-to-solution-volume ratio. The glass is then allowed to corrode for two days and the final solution pH and concentration of different glass elements in solution is measured. The test was modified from the published procedure to constrain particle size to provide more reproducible results, reduce the studied pH range to a more applicable range, and to reduce solution volume to reduce costs associated with material preparation. Compositional modeling was used to predict the measured elemental releases at various pH. The model predictions improved in tests where there was a relatively small difference between initial and final solution pH indicating that pH controls would result in better predictive models.

Accelerated aging↗