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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 181 records · Page 10
All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

Bipolar Membrane Electrodialyzers as Flexible Demand Response Resources: Co-Optimization of Cost Savings and Product Formation

Bipolar membrane electro dialyzers (BPMED) are widely used for chemical production and processing, including in the emerging ocean alkalinity enhancement (OAE) industry. In this paper, we explore the potential of BPMED devices as flexible electrochemical loads within power system operations. Using a multi-objective optimization framework, we evaluate BPMED operation across 24-hour and monthly horizons to examine how dispatch strategies respond to electricity price and grid conditions. Simulation results show that altering the relative weights of the choices in the objective function strongly shape the operating patterns, with cost-focused strategies that suppress the operation during peak prices. Furthermore, we propose alternative formulations that optimize operations to achieve both cost savings and alignment with periods of lower grid-side carbon intensity (CI), as low grid-side CI is key to maximize OAE efficiency. Additionally, a detailed sensitivity analysis highlights the importance of device properties, where low area-specific resistance (ASR) of membrane and high current efficiency (CE) are observed to jointly unlock cost-effective operation. However, even modest shunt efficiency losses are observed to erode performance and decrease system value. Importantly, the analysis demonstrates that BPMED can serve as a controllable and flexible demand response resource, shifting load to support multiple grid-side objectives, including (but not limited to) renewable integration, alleviate peak demand, and provide co-benefits for system reliability. These findings underscore BPMED’s dual role as a process technology and a grid-supporting asset, pointing to promising pathways for operational optimization of multiple objectives.

Bhattacharya, Saptarshi (ORCID:0000000308902060)↗
DOE OSTI2025

Preliminary study of auto-differentiation algorithm in beam dynamics with stochastic process

Modern particle accelerator optimization requires sophisticated computational methods to address the inherently stochastic nature of beam dynamics. This research develops a framework applying AD to SDEs that specifically addresses beam dynamics challenges in particle accelerators, focusing on accurately modeling and optimizing beam behavior in regimes dominated by stochastic processes. By incorporating key physical phenomena such as synchrotron radiation, wakefield effects, and quantum excitation, the framework aims to provide auto differentiation on the figure of merit of the phase space evolution and beam dynamics. The methodology will enable effective optimization method in a dynamic system with stochastic process.

Accelerator Physics↗
DOE OSTI2024

Quantum communications work at SQMS

The Superconducting Quantum Materials and Systems (SQMS) Center is focused on advancing low-loss interconnectivity between quantum processing units (QPUs) to enable scalable quantum computing. In the short term, our goals include the development and optimization of 2D and 3D platforms with remotely entangled modules, refinement in microwave design and control schemes, and the achievement of high-fidelity quantum state transfer between superconducting quantum modules. Looking ahead, we aim to realize modular quantum computing with low-loss interconnects, maximize remote entanglement fidelity and implement robust quantum operations with error correction. We will leverage advanced microwave engineering and material science to optimize the performance of quantum interconnects and the coupling interfaces between the interconnects and the QPUs.

Vallières, André↗
DOE OSTI2025

Fluid Mechanics Challenges in Direct-Ink-Writing Additive Manufacturing

Direct-ink writing (DIW) has rapidly become a versatile 3D fabrication method due to its ability to deposit a wide range of complex fluids into customizable 3D geometries. This review highlights key fundamental fluid mechanics and soft matter challenges across the different stages of the DIW printing process. The rheology of fluids and suspensions governs the flow behavior through narrow nozzles, posing questions about extrudability, confined flow dynamics, and clogging mechanisms. Downstream, the formation and deposition of extruded filaments involve extensional flows and potential instabilities, while postdeposition dynamics introduces complexities related to yield stress and structural stability. These stages are inherently interdependent, as optimizing material composition without considering filament stability risks compromising the final structure. As DIW applications expand through advanced ink formulations, developing fundamental fluid mechanics frameworks is essential to replace trial-and-error approaches with predictive design methodologies to enable more precise control and improved reliability of the printing process.

3D printing↗
DOE OSTI2024

Fuel Behavior Implications of Reactor Design Choices in Pressurized Water SMRs

Small pressurized water reactors (PWRs) can feature boron free operation, natural circulation mode, reduced height assemblies and/or long refueling cycles. This paper attempts to explore core design optimization for each of these design evolutions. In consequence, five core design layouts are developed incorporating boron free operation with continuous control rods insertion, natural circulation with low burnup/low power density design, natural circulation with high burnup/low power density design, forced circulation with standard core power density design, and forced circulation with high power density design. These cores’ performance is compared to a standard 4-loop PWR. The design process aims to improve the fuel cycle cost under safety constraints through core design optimization using CASMO4E/SIMULATE3 reactor physics codes and FRAPCON4.1 fuel performance assessment tool. Core modeling assumes standard 17x17 PWR fuel assemblies loaded with low enriched uranium (LEU) up to 5wt% or LEU+ (i.e., below 10wt% enrichment) pellets with gadolinium oxide (Gd2O3) as the burnable poison. Satisfactory core and fuel performances are obtained for all the designed cores under steady state and considered overpower transients. For low power density operation, long cycle lengths are achieved reaching a 2.5- and a 5-year cycles and peak rod-average burnup is pushed to 83 MWd/kgU. Other cycle lengths are maintained at 18 months. Boron free operation exhibits the ability to achieve longer cycle lengths at the cost of higher peaking factors leading to high local power and fuel temperatures which prevents sizable power uprates and is deemed uneconomical. Fuel assembly height reduction allows coolant velocity retrofit which enables higher core power density without violating structural integrity of the fuel assembly. As a result, a core power density of 123 kW/l is reached where total cladding hoop strain becomes the limiting parameter.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2024

Solid-State Mixed-Potential Electrochemical Sensors for Natural Gas Leak Detection and Quality Control (Final Technical Report)

Mitigation of methane emissions are a critical factor to limiting the impact of the natural gas industry on global climate change. Throughout the period of 2020-2024, the University of New Mexico and its commercialization partner and subcontractor, SensorComm Technologies, Inc. (SCT), have worked together to develop a low-cost Artificial Intelligence (AI)-driven Internet of Things (IoT)-based multi-gas sensor platform for methane emissions detection. In the final year of the project, we extended this work to include hydrogen detection in support of a transition to a hydrogen economy where hydrogen could be transported through existing natural gas infrastructure. Mixed potential electrochemical sensors were first prototyped by ceramic additive manufacturing and then transitioned to conventional ceramic manufacturing tape casting and screen-printing technologies in preparation for mass production. Demonstrated limits of detection of 5 ppm of methane in natural gas and 1 ppm of hydrogen were measured. These limits of detection are among the lowest of solid-state electrochemical sensors that have been reported in the literature or available in the industry. Machine learning algorithms were developed to identify natural gas mixtures with > 98% accuracy level and quantify methane concentrations at 97% accuracy. The presence of hydrogen could also be identified, and its concentration quantified at these accuracy levels. These algorithms were optimized for running on portable computing hardware which enabled > 1 Hz processing rates. A portable packaged IoT system was integrated with the electrochemical sensor in collaboration with SCT. The package consists of readout electronics with < 1 mV resolution, sensor temperature control, and data transmission over cellular wireless and/or Wi-Fi networks. Field testing was performed in two rounds at Colorado State University’s Methane Emissions Technology Evaluation Center (CSU METEC). The first round of testing demonstrated successful measurements of methane from an underground natural gas leak of 20 standard liters per minute (SLPM), which agreed with previously published literature using more sophisticated and expensive analytical equipment. The second round of testing showed that an above ground leak of 2 SLPM of hydrogen could be detected at 32 ft. This project has resulted in six published peer reviewed journal articles, over ten presentations at professional conferences, and one full patent application filed in 2023. Future work on this project includes increased sensitivity, higher production yields, and applications in the hydrogen safety and flare emissions monitoring spaces.

03 NATURAL GAS↗
DOE OSTI2026

The NREL Sensor Laboratory Detection of Hydrogen Emissions

The development of a functional hydrogen detection system is a multifaceted process that integrates hardware, deployments strategies, and analytics which can be supported by the NREL Sensor Laboratory: 1. Support of the design, validation and optimization of sensing prototypes; 2. Guide optimized sensing element development, including control electronics; 3. Laboratory testing to validate/optimize metrological performance (measurement range, detection limit, etc.); 4. Provide test sites for field deployments representative of real-world scenarios with controlled hydrogen releases; 5. Develop sensor placement and operation guidance; 6. Provide guidance on electronics to accommodate facility integration; 7. Electrical safety designs to allow for operation within restricted zones; 8. Integration into facility monitoring and control systems; 9. Guide incorporation of cyber security elements to protect facilities from malicious attacks; 10. Modeling and application of advanced analytics to detect and quantify emissions; 11. Higher Order dispersion models to guide sensor placement for reliable detection; 12. Advanced analytics for improved metrological performances, and to inform inverse modeling; 13. Market support and commercialization (national and international markets); 14. Commercial deployments in H2@SCALE markets (e.g., HUBs and other large-scale hydrogen markets); and 15. Leverage off international collaborations/partnerships (e.g., NREL is on the advisory board for the European initiative "pre-Normative Research on Hydrogen Releases Assessment"-NHyRA).

08 HYDROGEN↗
DOE OSTI2024

Federated Deep Reinforcement Learning for Decentralized VVO of BTM DERs

The future of grid control requires a hybrid approach combining centralized and decentralized methods to fully utilize the potential of smart edge devices with artificial intelligence (AI) capabilities. This paper aims to develop and evaluate a federated deep reinforcement learning (FDRL) framework for decentralized adaptive volt-var optimization (VVO) of behind-the-meter (BTM) distributed energy resources (DERs). First, this paper models a single deep reinforcement learning (DRL) agent using the Markov Decision Process (MDP) framework for decentralized adaptive VVO of BTM DERs. Two DRL algorithms, soft actor-critic (SAC) and twin-delayed deep deterministic policy gradient (TD3), are compared for their effectiveness in optimizing VVO. Results show that TD3 outperforms SAC, achieving a 71.3% improvement in mean reward. Finally, the DRL agent is deployed within the FDRL framework, using the Flower platform, to enhance learning, provide adaptive control, and ensure data privacy for BTM DERs.

Ravi, Abhijith↗
DOE OSTI2025

HP-MDR: High-performance and Portable Data Refactoring and Progressive Retrieval with Advanced GPUs

Scientific applications produce vast amounts of data, posing grand challenges in the underlying data management and analytic tasks. Progressive compression is a promising way to address this problem, as it allows for on-demand data retrieval with significantly reduced data movement cost. However, most existing progressive methods are designed for CPUs, leaving a gap for them to unleash the power of today’s heterogeneous computing systems with GPUs.In this work, we propose HP-MDR, a high-performance and portable data refactoring and progressive retrieval framework for GPUs. Our contributions are four-fold: (1) We carefully optimize the bitplane encoding and lossless encoding, two key stages in progressive methods, to achieve high performance on GPUs; (2) We propose pipeline optimization and incorporate it with data refactoring and progressive retrieval workflows to further enhance the performance for large data process; (3) We leverage our framework to enable high-performance data retrieval with guaranteed error control for common Quantities of Interest; (4) We evaluate HP-MDR and compare it with state of the arts using five real-world datasets. Experimental results demonstrate that HP-MDR delivers an average 13.68 × and 6.31 × throughput in data refactoring and progressive retrieval tasks, respectively. It also leads to 11.22 × throughput for recomposing required data representations under Quantity-of-Interest error control and 6.04 × performance for the corresponding end-to-end data retrieval, when compared with state-of-the-art solutions.

Li, Yanliang [University of Oregon]↗
DOE OSTI2024

Connected Traffic Signal Coordination Optimization Framework through Network-Wide Adaptive Linear Quadratic Regulator–Based Control Strategy

Traffic congestion in metropolitan areas causes several significant challenges, such as longer travel times, decreased productivity, increased fuel consumption and vehicle emissions, and even severe injuries during crashes. Traffic signal control is a management approach to reduce traffic congestion and allocate the appropriate right of way for safety and mobility efficiency, both in temporal and spatial domains. Here, this study proposes a network-wide adaptive signal control coordination optimization framework based on the linear quadratic regulator algorithm. The traffic flow conditions driven by signal control inputs are formulated based on their network-wide state-space representation. After modeling traffic control regulation constraints, an adaptive linear quadratic regulator algorithm is designed to maximize the network-wide total throughput under the current conditions. Optimal signal control split time durations for multiple intersections in the network are derived by solving the algebraic Riccati equation. Furthermore, the recursive least square parameter estimation method is employed to quantify dynamic traffic condition changes. To verify the effectiveness of this proposed signal control framework, both simulation and real-world experimental tests are conducted for multiple intersections in downtown Chattanooga, Tennessee, United States. In preparation for real-world experimental tests, pipelines for real-time data processing implementation and historical traffic flow data analysis are conducted. The test results demonstrate that the proposed control framework achieves a decrease in travel time by up to 19.4%, total time spent (TTS) by up to 11.9%, and relative queue balance (RQB) by up to 15.6%. The research findings indicate that the proposed signal control framework can be generalized to handle large scale signal control optimization network-wide.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Experimental study of airpath electrification in an opposed-piston two stroke (OP2S) engine architecture

The opposed-piston two stroke (OP2S) engine shows potential as an alternative engine architecture to the conventional four stroke engine due to its high-power density, thermal efficiency, and versatile airpath management system. Since the pistons of a two-stroke engine do not pump the air into and out of the cylinder like in a four-stroke engine, the selection of the air induction devices and airpath actuators becomes critical to optimize engine performance. Both the pumping losses and the in-cylinder combustion process can be affected by the scavenging process in a two-stroke engine. Therefore, this study compares two different airpath configurations for the same family of OP2S engines and investigates performance metrics like scavenging control, pumping work, net indicated and brake efficiencies, and engine-out emissions associated with each airpath. Data was collected on a 3.2 L, two-cylinder OP2S engine with an electrically assisted turbocharger (EAT) and a 4.9 L displacement, three-cylinder engine with a variable geometry turbocharger (VGT) and a supercharger. The experiments consisted of speed and load sweeps for both engines at the same operating conditions to compare scavenge control in both architectures. For the three-cylinder layout, the SE sweep range was much higher, and the intake pressure could be independently varied with air flowrate, thus providing more flexibility for scavenging control. The supercharger and the VGT usage was optimized based on its efficiency map and thus, this layout had lower pumping losses compared to the EAT. The two-cylinder engine had a higher overall SE as compared to the three-cylinder engine, but the intake pressure and air flowrate could not be decoupled, leading to over scavenging and increased short circuiting of fresh charge into the exhaust.

Bhatt, Ankur [Clemson University, Clemson, SC, USA↗
DOE OSTI2025

Predicting Initial Trans-Membrane Pressure for Optimized Operations in UF Unit Using Random Forest

With the growing scarcity of freshwater, innovative process design mechanisms like Ultra-filtration(UF) units are increasingly gaining attention among water treatment utilities to address the rising demand. Ensuring reliable water production necessitates efficient resource utilization, minimizing downtime in UF systems. Recent advancements in machine learning (ML) have enabled the development of accurate data-driven models for Model Predictive Control (MPC), often requiring minimal prior knowledge of underlying physical processes. In this study, we present predictive regression models based on Random Forest (RF) and Auto-Regressive (AR) approaches to forecast the initial Trans-Membrane Pressure (TMP) for each filtration cycle in data generated by Direct Potable Reuse (DPR) systems. The proposed RF-based model demonstrates superior performance compared to baseline methods, including historical mean, Last Observation Carried Forward (LOCF), and naïve AR models, across various forecasting horizons in terms of root mean square (RMSE) metric. Accurate prediction of initial TMP is critical for optimizing CCRO operations, as it enables the development of robust modelling frameworks that enhance process efficiency and reliability. The demonstrated efficacy of the RF-based approach highlights its potential as a tool for real-time decision-making in water treatment systems, paving the way for advanced process optimization and sustainable water resource management.

Mukherjee, Subrata [ORNL] (ORCID:0000000309930338)↗
DOE OSTI2025

Effect of 3D-printed surface textures on wear mechanism in 3-body abrasion of soil

This study systematically investigates the enhancement of wear resistance in 3D printed surface textures through both experimental and theoretical approaches. Three distinct surface morphologies (Smooth Surface, Surface with uniformly distributed Pits, and Surface with uniformly distributed Bumps) were fabricated using High-Impact Polystyrene, where the meso-scale textures were precisely controlled through the 3D printing process. Wear behavior was evaluated using a 3-body wear tester in an abrasive particle environment, analyzing the influence of surface textures under various operating conditions. Systematic wear tests revealed that optimally designed surface textures achieved a remarkable 77 % reduction in wear compared to the worst-performing sample. The wear mechanisms were comprehensively characterized through weight loss measurements, Scanning Electron Microscopy (SEM), and Energy Dispersive Spectroscopy (EDS) analyses, elucidating the surface morphology changes and their interaction with wear particles. Notably, the study identified how the geometric characteristics of surface textures influence the movement of wear particles and the distribution of contact stresses. Discrete element method simulations corroborated the experimental findings, providing theoretical validation for the enhanced wear resistance of the optimal structure. The high correlation between simulated wear patterns and experimental results validates the reliability of the proposed design methodology. In conclusion, these results demonstrate that 3D printed surface texturing offers a cost-effective and scalable approach to significantly improve wear resistance in engineering applications, presenting a practical alternative to conventional, high-cost surface engineering methods.

3-body abrasion↗
DOE OSTI2025

Phase-field predictions of the influence of cooling rates during AM on the Evolution of Microstructures in Nickel-Based Single Crystal Superalloys

Additive manufacturing of single crystals made of Ni-based superalloys offers major cost savings for gas turbine engines with the inclusion of internal cooling channels. However, the lack of understanding of the effect of transient thermal conditions on solidification grain structure during additive manufacturing hinders the potential for process control to maintain the single crystal quality. The use of high-fidelity simulations through high performance computing to predict the evolution of the solidification microstructure will enhance the abilities to tailor the microstructures through process optimization. Phase field simulations are used to determine the effect local thermal conditions and defects on the stability of the solidification morphology, specifically with respect to the onset of columnar-to-equiaxed transition that results in the loss of the single crystal. The results are expected to be instrumental for developing future surrogate models to speed up the integration of design and manufacturing of turbine blades under the harsh in-service conditions.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Crosstalk-robust quantum control in multimode bosonic systems

High-coherence superconducting cavities offer a hardware-efficient platform for quantum information processing. To achieve universal operations of these bosonic modes, the requisite nonlinearity is realized by coupling them to a transmon ancilla. However, this configuration is susceptible to crosstalk errors in the dispersive regime, where the ancilla frequency is Stark shifted by the state of each coupled bosonic mode. This leads to a frequency mismatch of the ancilla drive, lowering the gate fidelities. To mitigate such coherent errors, we employ quantum optimal control to engineer ancilla pulses that are robust to the frequency shifts. These optimized pulses are subsequently integrated into a recently developed echoed conditional displacement protocol for executing single- and two-mode operations. Through numerical simulations, we examine two representative scenarios: the preparation of single-mode Fock states in the presence of spectator modes and the generation of two-mode entangled Bell-cat states. Our approach markedly suppresses crosstalk errors, outperforming conventional ancilla control methods by orders of magnitude. These results provide guidance for experimentally achieving high-fidelity multimode operations and pave the way for developing high-performance bosonic quantum information processors.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2024

Machine learning for reducing noise in RF control signals at industrial accelerators

Industrial particle accelerators typically operate in dirtier environments than research accelerators, leading to increased noise in RF and electronic systems. Furthermore, given that industrial accelerators are mass produced, less attention is given to optimizing the performance of individual systems. As a result, industrial accelerators tend to underperform their own hardware capabilities. Improving signal processing for these machines will improve cost and time margins for deployment, helping to meet the growing demand for accelerators for medical sterilization, food irradiation, cancer treatment, and imaging. Our work focuses on using machine learning techniques to reduce noise in RF signals used for pulse-to-pulse feedback in industrial accelerators. Here we review our algorithms and observed results for simulated RF systems, and discuss next steps with the ultimate goal of deployment on industrial systems.

43 PARTICLE ACCELERATORS↗
DOE OSTI2024

Optimal Zeno Dragging for Quantum Control: A Shortcut to Zeno with Action-Based Scheduling Optimization

The quantum Zeno effect asserts that quantum measurements inhibit simultaneous unitary dynamics when the “collapse” events are sufficiently strong and frequent. This applies in the limit of strong continuous measurement or dissipation. It is possible to implement a dissipative control that is known as “Zeno dragging” by dynamically varying the monitored observable, and hence also the eigenstates, which are attractors under Zeno dynamics. This is similar to adiabatic processes, in that the Zeno-dragging fidelity is highest when the rate of eigenstate change is slow compared to the measurement rate. We demonstrate here two theoretical methods for using such dynamics to achieve control of quantum systems. The first, which we shall refer to as “shortcut to Zeno,” is analogous to the shortcuts to adiabaticity (counterdiabatic driving) that are frequently used to accelerate unitary adiabatic evolution. In the second approach, we apply the Chantasri-Dressel-Jordan stochastic action [PRA 88, 042110 (2013)], and demonstrate that the extremal-probability readout paths derived from this are well suited to setting up a Pontryagin-style optimization of the Zeno-dragging schedule. A fundamental contribution of the latter approach is to show that an action suitable for measurement-driven control optimization can be derived quite generally from statistical arguments. Implementing these methods on the Zeno dragging of a qubit, we find that both approaches yield the same solution, namely, that the optimal control is a unitary that matches the motion of the Zeno-monitored eigenstate. We then show that such a solution can be more robust than a unitary-only operation and we comment on solvable generalizations of our qubit example embedded in larger systems. These methods open up new pathways toward systematically developing dynamic control of Zeno subspaces to realize dissipatively stabilized quantum operations. Published by the American Physical Society 2024

Physics↗
DOE OSTI2024

Biofilm mitigation in hybrid chemical-biological upcycling of waste polymers

Accumulation of plastic waste in the environment is a serious global issue. To deal with this, there is a need for improved and more efficient methods for plastic waste recycling. One approach is to depolymerize plastic using pyrolysis or chemical deconstruction followed by microbial-upcycling of the monomers into more valuable products. Microbial consortia may be able to increase stability in response to process perturbations and adapt to diverse carbon sources, but may be more likely to form biofilms that foul process equipment, increasing the challenge of harvesting the cell biomass. To better understand the relationship between bioprocess conditions, biofilm formation, and ecology within the bioreactor, in this study a previously-enriched microbial consortium (LS1_Calumet) was grown on (1) ammonium hydroxide-depolymerized polyethylene terephthalate (PET) monomers and (2) the pyrolysis products of polyethylene (PE) and polypropylene (PP). Bioreactor temperature, pH, agitation speed, and aeration were varied to determine the conditions that led to the highest production of planktonic biomass and minimal formation of biofilm. The community makeup and diversity in the planktonic and biofilm states were evaluated using 16S rRNA gene amplicon sequencing. Results showed that there was very little microbial growth on the liquid product from pyrolysis under all fermentation conditions. When grown on the chemically-deconstructed PET the highest cell density (0.69 g/L) with minimal biofilm formation was produced at 30°C, pH 7, 100 rpm agitation, and 10 sL/hr airflow. Results from 16S rRNAsequencing showed that the planktonic phase had higher observed diversity than the biofilm, and that Rhodococcus, Paracoccus, and Chelatococcus were the most abundant genera for all process conditions. Biofilm formation by Rhodococcus sp. And Paracoccus sp. Isolates was typically lower than the full microbial community and varied based on the carbon source. Ultimately, the results indicate that biofilm formation within the bioreactor can be significantly reduced by optimizing process conditions and using pure cultures or a less diverse community, while maintaining high biomass productivity. The results of this study provide insight into methods for upcycling plastic waste and how process conditions can be used to control the formation of biofilm in bioreactors.

36 MATERIALS SCIENCE↗