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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 469 records · Page 26
All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

Optimizing Heat Recovery with Storage: Control Validation and Sensitivity Analysis of the Time-Independent Energy Recovery Plant Using Modelica

Heat recovery in large building central plants saves energy but traditionally requires simultaneous heating and cooling. The Time-Independent Energy Recovery (TIER) plant shifts this paradigm by integrating thermal energy storage (TES) to enable heat recovery regardless of concurrent demand, offering a highly efficient, space-saving solution to achieve California’s energy goals. However, its integration of heat recovery chillers, cooling-only chillers, cooling towers, and trim air-source heat pumps (ASHPs) creates growing control and sizing complexity. To overcome this, this study employs high-fidelity Modelica dynamic simulation to validate TIER control sequences and optimize equipment sizing. We translated the written Sequences of Operation into executable Control Description Language (CDL) to test logic against sub-hourly loads. This verification workflow successfully identified and resolved critical vulnerabilities, such as thermal storage freezing and equipment short-cycling, in a virtual environment prior to physical deployment. Then, the study analyzes TIER plant performance across three simulated building types in three locations, and a real building load profile, ensuring variety in heating and cooling loads, and simultaneity factors and explores sizing rules for the TES and ASHP capacity. The analysis shows that the TIER plant operates equipment efficiently leading to a plant SCOP of around 7.5 across all scenarios, higher than a traditional ASHP plant, and a viable pathway to de-risk complex system design and control through simulation to identify optimal designs that maximize energy efficiency, minimize operational costs, and ensure robust operation in varied environmental conditions, thereby facilitating the broader adoption of such a solution for large buildings.

Zanetti, Ettore↗
DOE OSTI2025

A simple centrifuge cell method for ex situ quantification of electrical conductivity of slurry electrode materials

We present the design, experimental procedure, and experimental evaluation of a system for fast, simple, and ex situ characterization of electrical conductivity of slurry electrode materials. The system uses a custom-designed electrochemical cell compatible with centrifugation in a swing-bucket centrifuge. The cell features cylindrical graphite electrodes that are partially sheathed so as to expose only 2 mm of the electrode surface to the bottom region of the packed particulate pellet. Also presented is a conduction model that provides a shape factor for estimating effective conductivity. We tested aqueous solutions of carbon black (CB), activated carbon (AC), and mixtures thereof. These particles were dispersed in 0.0 and 0.5 M NaCl solutions. Measurements show that the effective conductivity initially increases linearly with pellet mass and then saturates at higher masses. Notably, CB exhibited a fivefold increase in conductivity than AC at equal pellet masses. CB/AC mixtures at a fixed pellet mass were tested with CB mass fractions of 0 to 100%. Interestingly, the mixture conductivity was found to be a non-monotonic function of CB mass fraction, with a maximum conductivity at about 60 % CB mass fraction. At this maximum, the mixture conductivity is approximately 30 % higher than pure CB. NaCl concentration in the slurry solution had no effects on conductivity. These results highlight the interactions between slurry electrode composition and compaction, offering insights for optimizing slurry electrodes. Furthermore, the system and results may also be applicable to evaluation of particulate materials (including slurries) used for Li-ion batteries, capacitive deionization, fuel cells, and flow electrodes.

Capacitive deionization↗
DOE OSTI2024

Assessment of wafer scale MoS 2 atomic layers grown by metal–organic chemical vapor deposition using organo-metal, organo-sulfide, and H 2 S precursors

Transition Metal Dichalcogenides (TMDs) are a unique class of materials that exhibit attractive electrical and optical properties which have generated significant interest for applications in microelectronics, optoelectronics, energy storage, and sensing. Considering the potential of these materials to impact such applications, it is crucial to develop a reliable and scalable synthesis process that is compatible with modern industrial manufacturing methods. Metal–organic chemical vapor deposition (MOCVD) offers an ideal solution to produce TMDs, due to its compatibility with large-scale production, precise layer control, and high material purity. Optimization of MOCVD protocols is necessary for effective TMD synthesis and integration into mainstream technologies. Additionally, improvements in metrology are necessary to measure the quality of the fabricated samples more accurately. In this work, we study MOCVD of wafer-scale molybdenum disulfide (MoS 2 ) utilizing two common chalcogen precursors, H 2 S and DTBS. We then develop a metrology platform for wafer scale samples quality assessment. For this, the coalesced films were characterized using Raman spectroscopy, atomic force microscopy, transmission electron microscopy, X-ray photoelectron spectroscopy, and Kelvin probe force microscopy. We then correlate the structural analysis of these grown films with electrical performance by using aerosol jet printing to fabricate van der Pauw test structures and assess sheet resistance.

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

Optimal Droop Setting for Congestion Reduction in a 100% Grid-Forming Inverter-based Power System

he high penetration of inverter-based resources (IBRs) introduces new challenges to power systems due to the complex inverter control. However, IBRs can be configured to maximize their benefits to improve system resilience and reliability. This paper proposes a steady-state optimization model that aims to mitigate transmission congestion in a 100% grid- forming (GFM) IBR-based power system. This goal is achieved by determining the optimal droop settings for the GFM IBRs under different congestion conditions due to renewable energy and load variations. The numerical solution is rigorously verified by a high-fidelity model of the IEEE 39-bus test system with detailed GFM IBR control in the time-domain electromagnetic transient (EMT) simulation tool PSCAD. The numerical solution and simulation results show a significant congestion reduction while meeting all other operating requirements. It is also observed that the numerical solving time is substantially less compared to the EMT simulation time.

Nguyen, Quan H.↗
DOE OSTI2024

Control System for RF OPM

SAND2025-01910O This software built in LabVIEW FPGA provides experimental control of the RF OPM prototype via a reconfigurable NI I/O card. It samples the analog input, processes it on the FPGA in real time, and generates control and feedback outputs to control and optimize the operation of the device. Sandia National Laboratories is a multimission laboratory managed and operated by National Technology & Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International Inc., for the U.S. Department of Energy’s National Nuclear Security Administration under contract DE-NA0003525.

Bainbridge, Jonathan↗
DOE OSTI2025

Optimization of passive superconductors for shaping stellarator magnetic fields

Here, we consider the problem of optimizing a set of passive superconducting coils (PSCs) with currents induced by a background magnetic field rather than power supplies. In the nuclear fusion literature, such coils have been proposed to partially produce the 3D magnetic fields for stellarators and provide passive stabilization. We perform the first optimizations of PSC arrays with respect to the orientation, shape, and location of each coil, jointly minimized with the background fields. We conclude by generating passive coil array solutions for four stellarators.

coil optimization↗
DOE OSTI2024

Streamlining Ocean Dynamics Modeling with Fourier Neural Operators: A Multiobjective Hyperparameter and Architecture Optimization Approach

Training an effective deep learning model to learn ocean processes involves careful choices of various hyperparameters. We leverage DeepHyper’s advanced search algorithms for multiobjective optimization, streamlining the development of neural networks tailored for ocean modeling. The focus is on optimizing Fourier neural operators (FNOs), a data-driven model capable of simulating complex ocean behaviors. Selecting the correct model and tuning the hyperparameters are challenging tasks, requiring much effort to ensure model accuracy. DeepHyper allows efficient exploration of hyperparameters associated with data preprocessing, FNO architecture-related hyperparameters, and various model training strategies. We aim to obtain an optimal set of hyperparameters leading to the most performant model. Moreover, on top of the commonly used mean squared error for model training, we propose adopting the negative anomaly correlation coefficient as the additional loss term to improve model performance and investigate the potential trade-off between the two terms. The numerical experiments show that the optimal set of hyperparameters enhanced model performance in single timestepping forecasting and greatly exceeded the baseline configuration in the autoregressive rollout for long-horizon forecasting up to 30 days. Utilizing DeepHyper, we demonstrate an approach to enhance the use of FNO in ocean dynamics forecasting, offering a scalable solution with improved precision.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Electrochemical reduction of ammonia-captured CO 2 to CO over a nickel single-atom catalyst

Carbon reactive capture and conversion offers a sustainable route to valuable chemicals and fuels while aiding Green House Gas (GHG) reduction. Direct electrochemical conversion of capture solutions like bicarbonate avoids the energy demands of conventional CO 2 regeneration. Ammonium bicarbonate (NH 4 HCO 3 ) is particularly attractive due to its low decomposition temperature and ability to supply in situ CO 2 from dilute sources without requiring purified CO 2 . Meanwhile, single-atom catalysts (SACs) with nitrogen-coordinated metal sites further enhance CO 2 reduction efficiency using Earth-abundant materials. In this study, we demonstrate a nickel single-atom catalyst (Ni-SAC)-based electrolyzer that utilizes NH 4 HCO 3 as the CO 2 source, achieving significantly improved CO production performance compared to the conventional silver cathodes used in the CO 2 reduction reaction (CO 2 RR) to produce CO. The Ni-SAC cathode exhibited a Faradaic efficiency of 60.1% for CO production at −200 mA cm −2 , while the silver cathode achieved a Faradaic efficiency of only 2%, likely due to ammonium-induced poisoning. Furthermore, the integration of a customized microporous layer onto the electrode significantly increased the Faradaic efficiency from 64% to 83% at −100 mA cm −2 , emphasizing the crucial role of electrode structure optimization in enhancing CO selectivity. These findings demonstrate a sustainable and economically viable strategy for green CO production directly from CO 2 capture solutions.

30 DIRECT ENERGY CONVERSION↗
DOE OSTI2024

A comprehensive study on two types of supercapacitor composite electrodes comprising MnO 2 and activated carbon nanofibers: Self-supporting membrane and ground powder

Manganese dioxide (MnO 2 )@carbon composites have been attractively considered as electrode materials for supercapacitors (SCs) due to synergistic effects. This work systematically investigated the structure of MnO 2 @carbon nanofiber (CNF) composite electrodes with the different forms of CNFs and the corresponding electrochemical performance of SCs. In brief, novel activated carbon nanofibers were first fabricated by electrospinning the hydroxyl-containing poly(amic acid) solution, and then the preferred CNF material was decorated by MnO 2 crystals in the form of self-supporting membrane and ground powders, respectively. Additionally, the synthesis parameters were investigated and optimized based on the electrochemical performance of SCs. The results reveal that the powdered composite electrode exhibits a higher specific surface area of 501 m 2 g –1 compared to the self-supporting membrane composite electrode under the same conditions, resulting in a promising specific capacity of 214.1 mAh g –1 (770.8 Fg –1 ) in 6M KOH solution at 0.5 A g –1 . In addition, the capacitance of the symmetrical SC device assembled by G-HMC-1:3–80 reaches 179.8 Fg –1 , coupled with an energy density of 24.86 Whkg –1 at 230 Wkg –1 power density. This work provides valuable hints for designing SC composite electrode materials with outstanding performance.

25 ENERGY STORAGE↗
DOE OSTI2024

Chapter 7: Learning Stable Local Volt/Var Controllers in Distribution Grids

This chapter describes a framework to synthesize provably stable local Volt/Var controllers for distributed energy resources (DERs) in power distribution grids (DGs). The goal is to control the reactive power injections of DERs to improve the system performance as quantified by a generic optimal reactive power flow (ORPF) problem. To achieve this, we jointly design for each DER the control function, which prescribes the reactive power update rule, and the equilibrium function, which approximates the ORPF solutions from local measurements of voltages and powers. We provide conditions on the equilibrium functions and the control parameters ensuring the stability of the closed-loop system. In particular, we discuss the trade-offs between each set of conditions accounting for practical considerations, like fully exploiting the DERs' generation capabilities and reducing the optimality gap. These conditions are then translated into learning constraints on the neural networks' parameters that are enforced in the training phase. We validate our framework with numerical simulations on the IEEE 37-bus network and through a comparison with an optimized version of standard piece wise linear control rules.

closed-loop asymptotic stability↗
DOE OSTI2026

Optimizing district energy systems by integrating Borehole Thermal Energy Storage Using a Mixed-Integer Linear Programming g-function framework with a Multi-Timescale Rolling Horizon method

Shallow geothermal has gained increasing attention in recent years; however, a reliable framework for its accurate incorporation into large-scale energy system optimization remains lacking. This study proposes a Mixed-Integer Linear Programming (MILP) framework combined with the g-function approach to integrate Borehole Thermal Energy Storage (BTES) technology into energy system optimization. Validation against a Modelica-based reservoir network simulation demonstrates that the proposed framework effectively captures the ground thermal response under varying energy loads and accurately estimates the borefield energy supply. To enhance scalability, a Rolling Horizon with Multi-Timescale (RH-MTS) method is further introduced, reducing computational time by 73 % for the 1-year optimization model with only minor loss of optimality. The framework is demonstrated through the case study of the UC Berkeley campus. Results indicate that BTES is a cost-effective and low-carbon solution: two borefields comprising 382 boreholes can meet 8.0 % and 6.6 % of the total campus heating and cooling demand, respectively, at an average energy rate of 0.70–0.77 USD/kWh and carbon intensity of 0.54 kg-CO2/kWh. Short-term analysis reveals a 35%–65% decline in BTES energy flow after 3–6 months of continuous heating/cooling operation, while long-term simulation shows that annual energy production of BTES can vary by up to 12.0 % after four years before stabilizing. Overall, this study develops a novel optimization framework that couples physics-based g-function method with MILP optimization framework, thereby advancing methodological development for shallow-geothermal integration and providing actionable guidance for BTES deployment in district-energy systems.

Yang, Jiahui↗
DOE OSTI2026

Optimized manufacturing process for multilayer two-dimensional focusing mirrors in laboratory X-ray applications

Recent advances in laboratory X-ray applications require high-performance optical components that achieve exceptional imaging resolution and beam uniformity within compact experimental setups. Montel mirrors have become a preferred solution due to their unique dual-reflection focusing mechanism and a space-efficient design. Here, in this study, we present an effective manufacturing process for producing Montel mirrors tailored to focus laboratory X-ray beams. The mirrors were fabricated from single-crystal silicon substrates, chosen for their high mechanical stability and compatibility with precision polishing techniques. Our approach begins with the integration of a deterministic chemo-mechanical polishing (CMP)-based pre-shaping step followed by ion beam figuring (IBF), significantly improving manufacturing efficiency. Subsequently, our custom-developed advanced metrology and IBF techniques were employed for fabricating an off-axis, elliptical cylinder Montel mirror system with a 6-mrad total slope, with stringent optical specifications. While post-IBF processes, including multilayer coating, dicing, and gluing, introduced minor surface errors, yet their impact on performance remained negligible. The Montel mirrors manufactured with the optimized process exhibited significantly improved beam uniformity and a reduced focal spot size. These findings validate our approach as a viable solution for high-precision Montel mirror fabrication and facilitate further advancements in laboratory X-ray applications.

36 MATERIALS SCIENCE↗
DOE OSTI2025

IBPSA Project 2 BOPTEST: An update on the test cases available in the framework for testing advanced control strategies in buildings

Project 2 develops software infrastructure, test cases, and extensions for the Building Optimization Testing Framework (BOPTEST) to address the expanding needs of building and urban energy system controls through open international collaboration. This paper provides an overview of the new test cases available as of BOPTEST version 0.7.1. Each test case is developed using open-source Modelica libraries and Spawn of EnergyPlus, enabling the creation of high-fidelity building models that incorporate envelope dynamics, Heating Ventilation and Air Conditioning (HVAC) systems, and explicit control representations. Currently, eight test cases are available, with five additional cases under development. These test cases cover a wide range of climates, building types, and HVAC systems. This paper compiles and summarizes test case descriptions, cites original manuscripts that developed them for a more detailed description, and reports baseline control performance metrics. Furthermore, two example applications are presented: one illustrating different levels of control, from supervisory to low-level, and another demonstrating how Model Predictive Control (MPC) solutions must be adapted from continuous to integer to control some building actuators.

Zanetti, Ettore↗
DOE OSTI2025

RLGBS: Reinforcement Learning-Guided Beam Search for process optimization in a paper machine dryer section

Paper drying is responsible for over two-thirds of energy consumption in the U.S. pulp and paper industry, presenting significant potential for energy savings through optimization of process parameters. Current approaches often assume fixed operating conditions, neglecting dynamic ambient and process variations that limit achievable savings and real-world applicability. To this end, we develop a physics-based simulation environment for a paper machine dryer section and propose a reinforcement learning (RL) framework to minimize overall energy consumption by optimizing drying process parameters under diverse operating conditions. To mitigate overdrying and numerical instabilities caused by suboptimal local RL actions, we introduce Reinforcement Learning-Guided Beam Search (RLGBS), which explores multiple action sequences in parallel using beam search. Instead of making step-by-step decisions, RLGBS prioritizes solutions based on cumulative probability, reducing the impact of individual suboptimal actions. Experiments demonstrate that RLGBS achieves consistent energy savings under unseen operating conditions not encountered during training, outperforming conventional RL methods. While validated in drying optimization, this framework is broadly applicable to other RL-based industrial process control problems.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2024

High‐throughput combinatorial approach expedites the synthesis of a lead‐free relaxor ferroelectric system

Abstract Developing novel lead‐free ferroelectric materials is crucial for next‐generation microelectronic technologies that are energy efficient and environment friendly. However, materials discovery and property optimization are typically time‐consuming due to the limited throughput of traditional synthesis methods. In this work, we use a high‐throughput combinatorial synthesis approach to fabricate lead‐free ferroelectric superlattices and solid solutions of (Ba 0.7 Ca 0.3 )TiO 3 (BCT) and Ba(Zr 0.2 Ti 0.8 )O 3 (BZT) phases with continuous variation of composition and layer thickness. High‐resolution x‐ray diffraction (XRD) and analytical scanning transmission electron microscopy (STEM) demonstrate high film quality and well‐controlled compositional gradients. Ferroelectric and dielectric property measurements identify the “optimal property point” achieved at the composition of 48BZT–52BCT. Displacement vector maps reveal that ferroelectric domain sizes are tunable by varying {BCT–BZT} N superlattice geometry. This high‐throughput synthesis approach can be applied to many other material systems to expedite new materials discovery and properties optimization, allowing for the exploration of a large area of phase space within a single growth. image

36 MATERIALS SCIENCE↗
DOE OSTI2025

Parallel derivative-free optimization for simulation-based design of behind-the-meter energy systems

In this work, the integrated design and dispatch of behind-the-meter or distributed resources (e.g. stationary battery storage and solar PV generation) is considered. A simulation-based framework is employed, generating high-fidelity results with closed-loop predictive control at a fine resolution, at the expense of high computational cost (several minutes to a few hours per design point). To address this challenge, parallel derivative-free design methods are considered. Four methods are compared, including state-of-the-art surrogate-based methods (Radial-Basis Functions and Gaussian processes) and sampling strategies, an evolutionary-based method, and a simple sequential grid refinement method. As a case study, two types of design problem with increasing complexity are considered, namely, the design of behind-the-meter resources (three design variables) and the inclusion of grid capacity (four design variables). The second yields a constrained design problem for which violations can only be determined after solving the computationally expensive simulation. For the three-dimensional case, all methods present a good performance, achieving a solution within 1% of the optimum after the first iteration, with the sequential grid refinement exhibiting the fastest convergence and achieving the best final objective value. This indicates that the parallel evaluation of multiple sampling points may be more important than the choice of method for small decision spaces. For the four-dimensional constrained case, the Genetic Algorithm presents the best tradeoff between performance and computational effort, while the rough objective function terrain generated by constraint violation penalties reduces the performance of surrogate-based methods. Contour plots with flat regions indicate flexibility in the optimal design and highlight the importance of characterizing the solution space.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2024

Accelerating the Discovery of New, Single Phase High Entropy Ceramics via Active Learning

High-entropy ceramics have garnered interest due to their remarkable hardness, compressive strength, thermal stability, and fracture toughness; yet the discovery of new high-entropy ceramics (out of a tremendous number of possible elemental permutations) still largely requires costly, inefficient, trial-and-error experimental and computational approaches. The entropy forming ability (EFA) factor was recently proposed as a computational descriptor that positively correlates with the likelihood that a 5-metal high-entropy carbide (HECs) will form the desired single phase, homogeneous solid solution; however, discovery of new compositions is computationally expensive. If you consider 8 candidate metals, the HEC EFA approach uses 49 optimizations for each of the 56 unique 5-metal carbides, requiring a total of 2744 costly density functional theory calculations. Here, we describe an orders-of-magnitude more efficient active learning (AL) approach for identifying novel HECs. To begin, we compared numerous methods for generating composition-based feature vectors (e.g., magpie and mat2vec), deployed an ensemble of machine learning (ML) models to generate an average and distribution of predictions, and then utilized the distribution as an uncertainty. Here we then deployed an AL approach to extract new training data points where the ensemble of ML models predicted a high EFA value or was uncertain of the prediction. Our approach has the combined benefit of decreasing the amount of training data required to reach acceptable prediction qualities and biases the predictions toward identifying HECs with the desired high EFA values, which are tentatively correlated with the formation of single phase HECs. Using this approach, we increased the number of 5-metal carbides screened from 56 to 15,504, revealing 4 compositions with record-high EFA values that were previously unreported in the literature. Our AL framework is also generalizable and could be modified to rationally predict optimized candidate materials/combinations with a wide range of desired properties (e.g., mechanical stability, thermal conductivity).

36 MATERIALS SCIENCE↗
DOE OSTI2026

Ternary Phosphides Ba M 2 P 2 : Tailoring Crystal and Electronic Structures Enables Highly Efficient HER Electrocatalysis

Binary transition metal phosphides and their solid solutions have emerged as promising hydrogen evolution reaction (HER) catalysts. Although many research endeavors have adopted strategies to vary compositions to optimize catalytic performance, they mainly focus on binary structures, which represent only a small fraction of the abundant phase space of structure types among transition metal phosphides. Here, the largely unexplored class of ternary and multinary ordered phosphides in catalysis comprises two or more metals with quite different chemical nature, concealing the structure–property relationships essential for advancing catalyst design. Here, we explored phosphides crystallizing in one of the most abundant ordered intermetallic structure types, —the ThCr 2 Si 2 type, —where square nets of 3d transition metal M and P atoms are separated by layers of electropositive Ba cations. Four ternary BaM 2 P 2 (M = Fe, Fe/Cu, Fe/Ni, Ni) catalysts were synthesized and characterized. BaNi 2 P 2 showed high HER activity in acidic electrolyte, which required an overpotential, η 10 , of only 62 mV to drive current density j = –10 mA/cm 2 and high stability with a potential drop rate of 0.25 mV/h. BaNi 2 P 2 outperformed other Ni-based catalysts, such as Ni 2 P and Ni 5 P 4 . Notably, at current densities above –170 mA/cm 2 , BaNi 2 P 2 outperformed the standard Pt electrode measured under identical conditions. Electronic structure analysis revealed a volcano-type activity trend among the four BaM 2 P 2 catalysts based on their d-band center positions, highlighting the role of electropositive Ba cations in shifting the Ni-3d orbitals into an optimal position.

BaNi2P2↗