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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 559 records · Page 31

Hierarchical Distributed Optimal Power Flow of HV and MV Distribution Networks With Continuous and Discrete Devices

With large-scale distributed photovoltaics (PVs) being integrated into distribution networks (DNs), coordinated optimal power flow (OPF) of high voltage (HV) and medium voltage (MV) DNs should be investigated to optimally dispatch the distributed PVs and other network devices. Here, this paper presents a hierarchical distributed OPF method for HV and MV DNs with on-load tap changers, reactive power compensators, feeder switches and distributed PVs. A hierarchical master-slave control architecture is applied to implement coordinated OPF of two-layer DNs. The HV master problem and MV subproblems are transformed into mixed-integer convex problems respectively with second order cone programming and LinDistFlow approximation. Since there is no efficient distributed algorithm to solve such OPF models with integer subproblems, a novel distributed algorithm is proposed in this paper to efficiently solve the hierarchical coordinated OPF model with integer subproblems in a distributed manner. In the proposed algorithm, the coordinated OPF model is solved in a branch-and-bound framework, where in each branch node generalized Benders decomposition (GBD) algorithm is applied to decompose the coordinated OPF model into a master problem and relaxed subproblems and solves them iteratively to get optimal solution. The GBD optimal and feasible cutting planes generated in a branch node are proved to be valid for its descendants. Moreover, three acceleration techniques are introduced into the proposed algorithm to improve computational efficiency. Finally, the effectiveness and accuracy of the proposed method are verified via simulation tests in Jinzhai DNs of China.

42 ENGINEERING↗

Ion-beam-assisted growth of cesium-antimonide photocathodes

We report on the novel use of a Cs+ ion gun for an ion-beam-assisted molecular-beam-epitaxy (IBA-MBE) method to sequentially deposit Cs-Sb cathodes on room temperature substrates as opposed to the standard technique of thermal evaporation on elevated-temperature substrates. The details of the ultrahigh-vacuum chamber, the Cs+ ion source, and the growth technique are elaborated. The final quantum efficiency (QE) is reasonably good for Cs-Sb cathodes grown on two different substrates—Si (100) and strontium titanate—and is comparable to the QE of cathodes grown using thermal sources. This suggests that IBA-MBE could be a viable alternative to grow alkali-antimonides without substrate heating, paving the way for the growth of epitaxial alkali-antimonides in a more reproducible fashion, which may help improve the efficiency of photon detectors and accelerator applications that use alkali-antimonides as electron sources.

Engineering↗

TEAMER: Pitching Foil Crossflow Turbine Efficiency Data

This dataset documents the efficiency testing of a pitching foil crossflow turbine, conducted at the University of New Hampshire's (UNH) Chase Ocean Engineering Laboratory tow tank facility. The tests explored various pitch phases and amplitudes, ranging from 0 to 18 degrees, across different flow speeds and turbine RPMs. Specifically, testing was performed at pitch amplitudes of 0,3,6,9,12,15 and 18 degrees. The results indicate that turbine efficiency improves with increased pitch amplitude, reaching optimal performance at 12 degrees. Modifications were made to the test frame by UNH to enhance the stiffness of the support structure. The testing specifically mapped the efficiency of a single quadrant of the Ocean Renewable Power Company (ORPC) Autonomous Turbine Generator Unit (ATGU), covering the full range of allowable pitching amplitudes and phases. The dataset provides both raw and processed data, including drag and torque measurements, and contains Python scripts used for data processing and visualization. This research was supported by funding from TEAMER RFTS 1 (Request for Technical Support).

16 TIDAL AND WAVE POWER↗

AOI.1 Application of Artificial Intelligence techniques enabling coal fired power plants the ability to achieve higher efficiency, improved availability, and increased reliability of their operations (Final Report)

During this effort, SparkCognition with support from the Electric Power Research Institute (EPRI) was tasked with applying artificial intelligence (AI) to improve the reliability, efficiency, and safety of operations at a coal-fired plant. By implementing AI techniques, like machine learning (ML), it is believed that operators can leverage existing data sources to gain more insights such as advanced warning of machine degradation. With enough lead time, a reliability engineer can take action to minimize, or even avoid, impact to production. To complete this work effort, SparkCognition developed and refined an ML-based model using sensor data for a Steam Turbine unit at a host site. The models were deployed in an online, web-based solution that allows users to visualize model outputs and supporting data. The final solution, based on SparkCognition’s proprietary software platform called SparkPredict®, was shared with EPRI who completed an online evaluation of results to determine the solution’s ability to detect actionable events.

20 FOSSIL-FUELED POWER PLANTS↗

Comparative Energetics of Various Membrane Distillation Configurations and Guidelines for Design and Operation

This paper presents a comparative performance study of single-stage desalination processes with major configurations of membrane distillation (MD) modules. MD modules covered in this study are (a) direct contact MD (DCMD), (b) vacuum MD (VMD), (c) sweeping gas MD (SGMD), and (d) air gap MD (AGMD). MD-based desalination processes are simulated with rigorous theoretical MD models supported by molecular thermodynamic property models for the accurate calculation of performance metrics. The performance metrics considered in MD systems are permeate flux and energy efficiency, i.e., gained output ratio (GOR). A general criterion is established to determine the critical length of these four MDs (at fixed width) for the feasible operation of desalination in a wide range of feed salinities. The length of DCMD and VMD is restricted by the feed salinity and permeate flux, respectively, while relatively large AGMD and SGMD are allowed. The sensitivity of GOR flux with respect to permeate conditions is investigated for different MD configurations. AGMD outperforms other configurations in terms of energy efficiency, while VMD reveals the highest permeate production. With larger MD modules, utilization of thermal energy supplied by the hot feed for evaporation is in the order of VMD > AGMD > SGMD > DCMD. Simulation results highlight that energy efficiency of the overall desalination process relies on the efficient recovery of spent for evaporation, suggesting potential improvement in energy efficiency for VMD-based desalination.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Integrated Approach to Post-Irradiation Examination of Nuclear Materials at Idaho National Laboratory

Idaho national Laboratory (INL) is the U.S. lead national laboratory for the Department of Energy’s Office of Nuclear Energy (DOE-NE), providing much of the nuclear research, development and demonstration capability needed to move nuclear innovation forward to deployment. INL’s Materials and Fuels Complex hosts a unique combination of personnel, facilities and infrastructure and offers the ability to perform post-irradiation examinations (PIE) of nuclear materials spanning multiple length scales. The ability to combine engineering-scale analysis and sub-microscopic characterization provides valuable insights into fuel and structural material behavior and degradation mechanisms. The holistic approach is used to accelerate these materials demonstration and deployment. Selected studies will be presented, highlighting the impact of these techniques on improving fuel reliability, safety, and efficiency, thereby advancing the development of sustainable and advanced nuclear energy technologies.

11 - NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

Improving PGM-free ORR Electrocatalyst Activity: Suggestions from DFT Studies

In this opinion, the limitations of platinum group metal–free oxygen reduction reaction electrocatalyst activity based on scaling laws and resulting Balandin-Sabatier volcano plots are presented. Previous theoretical work aimed at overcoming this limitation is discussed. In this work, a theoretical study of a novel approach toward catalyst activity improvement, dynamic strain engineering, is proposed and tested for a possible platinum group metal–free oxygen reduction reaction active site. It is shown that not only can this approach produce thermodynamic limiting potentials well beyond scaling law relationship limits but also be capable of doing so in an energy-efficient manner.

36 MATERIALS SCIENCE↗

Combining mechanistic and machine learning models for predictive engineering and optimization of tryptophan metabolism

Abstract Through advanced mechanistic modeling and the generation of large high-quality datasets, machine learning is becoming an integral part of understanding and engineering living systems. Here we show that mechanistic and machine learning models can be combined to enable accurate genotype-to-phenotype predictions. We use a genome-scale model to pinpoint engineering targets, efficient library construction of metabolic pathway designs, and high-throughput biosensor-enabled screening for training diverse machine learning algorithms. From a single data-generation cycle, this enables successful forward engineering of complex aromatic amino acid metabolism in yeast, with the best machine learning-guided design recommendations improving tryptophan titer and productivity by up to 74 and 43%, respectively, compared to the best designs used for algorithm training. Thus, this study highlights the power of combining mechanistic and machine learning models to effectively direct metabolic engineering efforts.

59 BASIC BIOLOGICAL SCIENCES↗

Randomized probe imaging through deep k-learning

Randomized probe imaging (RPI) is a single-frame diffractive imaging method that uses highly randomized light to reconstruct the spatial features of a scattering object. The reconstruction process, known as phase retrieval, aims to recover a unique solution for the object without measuring the far-field phase information. Typically, reconstruction is done via time-consuming iterative algorithms. In this work, we propose a fast and efficient deep learning based method to reconstruct phase objects from RPI data. The method, which we call deep k-learning, applies the physical propagation operator to generate an approximation of the object as an input to the neural network. This way, the network no longer needs to parametrize the far-field diffraction physics, dramatically improving the results. Deep k-learning is shown to be computationally efficient and robust to Poisson noise. The advantages provided by our method may enable the analysis of far larger datasets in photon starved conditions, with important applications to the study of dynamic phenomena in physical science and biological engineering.

Guo, Zhen (ORCID:0000000213473451)↗

TRUST Contact Thermal Conductance (TRUST-CTC) Report: FY24

The objective of the Delivery Environments (DE) Testbeds to Reduce Uncertainties in Simulations and Tests (TRUST) project is to quantify and help increase confidence in specific areas of computational and experimental capabilities that are applicable to current and future delivery environments. More complete quantification of confidence in experimental and computational capabilities and the sufficient increase of confidence in those capabilities is critical to improving weapons engineering design, qualification, and assessment efforts that are critical to the current and future stockpile. Staff development will include cross-discipline collaboration to provide engineers with experience in both numerical simulations and experimental methods. This work uses and provides feedback on analysis tools and experimental results databases for efficient and responsive engineering which are currently under development. TRUST currently includes five testbeds and their associated engineering analysis baseline models (EABMs): 1. contact thermal conductivity, (CTC) 2. nonlinear dynamics, (ND) 3. sensors in environments for accelerometers, (SEA) 4. sensors in environments for fiber optic displacement gages, and (SEFOD) 5. sensors in environments for thermocouples (SETC). The TRUST project uses single-feature testbeds to quantify uncertainties in specific models and experiments and to identify capability development needs that can help to reduce these uncertainties. Each testbed is designed, configured, and tested in collaboration with groups with design and experimental capability: E-14 and MPA-CINT. The complementary simulations are conducted using W-13 analysis tools and stored in model repositories with plans for incremental progress toward EABM requirements. W-13 extends and exercises the testbed simulations in collaboration with experimentalists for uncertainty quantification of current and future materials, geometries, and environments. Additionally, TRUST is intended to provide engineers in W-13 and E-14 with experience in both numerical simulations and experimental methods through cross-discipline collaborations. Following the introduction to the TRUST project, the remainder of this report focuses on experimental and analytical efforts conducted in Fiscal Year (FY) 2024 relevant to the TRUST Contact Thermal Conductance (CTC) testbed.

42 ENGINEERING↗

Developing multi-gene CRISPRa/i programs to accelerate DBTL cycles in ABF hosts engineered for chemical production

This project developed and implemented a modular CRISPR activation and interference (CRISPRa/i) platform to accelerate strain optimization and pathway development for industrially relevant microbial hosts. By integrating multiplexed transcriptional perturbation tools with data-driven Design–Build–Test–Learn (DBTL) workflows, the team achieved reductions in cycle time and enhanced production of industrial aromatics, particularly 4-aminocinnamic acid (4-ACA), in Pseudomonas putida. Key accomplishments included: ● Development of a robust, tunable CRISPRa/i system in P. putida that enabled efficient multi-target gene regulation via guide RNA (gRNA) programs ● Completion of two full DBTL cycles, guided by machine learning (ML) models trained on transcriptomic and performance data, reducing engineering time by over 30% ● Optimization of multi-gene regulatory programs to balance expression of host and pathway modules, improve 4-ACA titers, and resolve metabolic bottlenecks ● Demonstration of system portability through a limited proof-of-concept extension in Acinetobacter baylyi, underscoring the generalizability of the approach ● Evaluation of strain performance on lignocellulosic biomass-derived substrates, demonstrating the feasibility of converting renewable carbon into aromatic building blocks These results illustrate the feasibility of applying ML-guided CRISPRa/i perturbation strategies to accelerate strain development in complex microbial systems. The resulting tools and datasets contribute to DOE objectives by improving platform predictability, reducing development costs, and enabling broader access to sustainable, economically viable bioproduction technologies.

09 BIOMASS FUELS↗

TRUST Contact Thermal Conductance (TRUST-CTC) Report: FY25

The objective of the Delivery Environments (DE) Testbeds to Reduce Uncertainties in Simulations and Tests (TRUST) project is to quantify and help increase confidence in specific areas of computational and experimental capabilities that are applicable to current and future delivery environments [1]. More complete quantification of confidence in experimental and computational capabilities and the sufficient increase of confidence in those capabilities is critical to improving weapons engineering design, qualification, and assessment efforts that are critical to the current and future stockpile. Staff development will include cross-discipline collaboration to provide engineers with experience in both numerical simulations and experimental methods. This work uses and provides feedback on analysis tools and experimental results databases for efficient and responsive engineering which are currently under development.

42 ENGINEERING↗

Development and Validation of SAM Multi-dimensional Flow Model for Thermal Mixing and Stratification Modeling

Thermal mixing and stratification in large pools or enclosures are very important phenomena that are critical to nuclear reactor safety. Because of the wide ranges of time and length scales associated with such phenomena, accurate modeling and simulation of thermal mixing and stratification remain as the key unresolved, challenging problems for reactor transient analyses. In traditional system analysis codes, simplified zero-dimensional (0-D) models are widely used for their high numerical efficiency, but they generally suffer from very limited prediction accuracies or range of applicability. Like traditional system analysis codes, the current version of the SAM code has implemented such simplified 0-D and one-dimensional mixing models. On the other hand, high-resolution Computational Fluid Dynamics (CFD) tools are often used to model complex thermal mixing and stratification phenomena. They are, however, generally numerically expensive, and they require large amounts of computational resources. It is therefore desirable to implement advanced and efficient thermal mixing and stratification modeling capabilities embedded in a system analysis code. This approach will improve the accuracy of reactor safety analyses when thermal mixing and stratification are involved, and also avoid using the large computational resources needed for high-resolution CFD analysis. Currently, with the support of the U.S. DOE Office of Nuclear Energy’s Nuclear Energy Advanced Modeling and Simulation program, an effort has been launched to develop and implement a multi-dimensional flow model in the system analysis code SAM, and demonstrate its applications to model thermal mixing and stratification phenomena in large enclosures. The main outcomes of this research and development activity are summarized in this report, which presents an attempt to include a built-in advanced multi-dimensional flow model in a system analysis code with the focus on overcoming the simulation challenges of thermal mixing and stratification phenomena. In this report, we start with the introduction of existing SAM code capabilities to simulate thermal mixing and stratification phenomena, which is followed by a short summary of the multi-dimensional model implemented in the SAM code, including both the physical model and the Finite Element Method code implementation. In this study, two options were implemented in the SAM code to model turbulent flows: a relatively simple built-in turbulence model and an interface to accept externally computed turbulent viscosities (e.g., from a high- resolution CFD simulation). Code validation studies on this newly added capability were then carried out to compare SAM simulation results with experimental data from the SUPERCAVNA facility, which was designed to study the complex flow recirculation and thermal stratification phenomena relevant to sodium fast reactor designs. In this study, one transient and two steady-state test cases were used for code validation. Different approaches have been used to model the complex turbulence flow fields in the SUPERCAVNA facility. A highly simplified zero-equation turbulence model was first used, but it was determined that it is too simple to capture the complex turbulence flow fields in these test cases. Subsequently, the code validation continued with the use of turbulent viscosity data from high-resolution STAR-CCM+ CFD simulations to improve the accuracy of the results. Using this approach, the SAM simulation results showed very good agreement with both the SUPERCAVNA experimental data and STAR-CCM+ simulation results. In this report, we demonstrate the development, implementation, and successful validation of a multi-dimensional flow model in the SAM code, which aims to improve the simulation accuracy for complex thermal mixing and stratification phenomena. Lessons have also been learned, including that in cases where the flow fields are not well predicted by the zero-equation model, the iinput of turbulent viscosities from an external source can enhance the overall predictive capabilities needed to accurately capture complex thermal-hydraulic phenomena. Therefore, future research will be needed to further improve the code’s capabilities, such as by developing a more efficient and robust approach to capture the turbulence effects in the SAM code.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

Exploring the Limits of Cooling for Extreme Heat Flux Applications: Data Centers and Power Electronic

We developed an Extreme Heat Flux Micro-cooler (EHFµ-C) capable of removing heat flux of ~1 kW/cm 2 with a small temperature rise ~10°C. This represents greater than 10× reduction in thermal resistance compared to the state-of-the-art single-phase cooling technologies (~0.5 cm 2 -°C/W) that greatly increases the efficiency, resulting in improved performance and reliability for microprocessors and power electronics. The EHFµ-Cooler utilizes liquid wicking (no pump) and thin-film evaporation in a 30 µm thick wick porous microstructure such as copper inverse opal (CIO), silicon and AlN pin fins along with a 3D manifold (copper wiremesh and silicon) with liquid routing and vapor extraction channels achieving an unprecedented vapor quality ~ >0.9. The proposed EHFµ-Cooler significantly reduces device temperature, resulting in 25-50% improvement in reliability and device performance. Based on very conservative estimates, the total energy saving for data centers due to improved performance can be 6 billion kWh/year (>$300M). Better cooling, leading to more efficient, higher performance and more reliable power electronics, would save the nation 3 Quad of primary energy in 2030. Just for automotive application, a 10% increase in fuel efficiency for SiC-based power electronics equates to >20 million gallons of fuel savings annually. The proposed EHFµ-Cooler technology ensures the United States maintains its technological lead in high-performance computing and high-power electronics, while at the same time improving the energy efficiency, and as a result reducing energy-related emissions such as greenhouse gases.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

Knock-limited combustion of ethanol-, isobutanol-, and 2-methyl-3-buten-2-ol-gasoline blends in a direct-injected spark-ignition engine

Knock-limited combustion of alcohol-gasoline fuel blends was studied in a direct-injection spark-ignition engine. Ethanol, isobutanol (2-methyl-1-propanol), and methylbutenol (2-methyl-3-buten-2-ol) were splash-blended with a blendstock for oxygenate blending (BOB) gasoline. Ethanol was blended in fractions of 10%, 20%, and 30% by volume (E10, E20, and E30). Isobutanol and methylbutenol were blended to match the oxygen weight percentage of the ethanol blends, resulting in blends of 16%, 32%, and 49% (I16, I32, and I49) isobutanol by volume and 18%, 37%, and 56% (M18, M37, and M56) methylbutenol by volume. Neat BOB and gasoline primary reference fuels (PRF) with octane numbers (ON) of 87 (PRF87) and 100 (PRF100) were included for reference. The engine was operated at fixed speed, equivalence ratio, and injection timing while knock-limited spark advance was located across a range of intake pressures. Low-level alcohol blends E10 and M18 had similar knocking behavior and appeared to be slightly more knock-resistant than I16. For mid-level blends, E20 showed further improvement over I32 while M37 showed noticeably better knock resistance, able to match the knock-resistance of PRF100 for loads above 1000 kPa gross indicated mean effective pressure (GIMEP). For high-level blends, the difference between E30 and I49 was similar to their mid-level counterparts. However, M56 showed significant improvement over E30 and I49 throughout the entire range of loads tested. M56 exceeded the knock-resistance of PRF100 for loads above 700 kPa GIMEP. The improved knock-resistance combined with the increased volumetric energy density of methylbutenol resulted in improved fuel consumption and indicated efficiency. The overall results of this study indicate that the order of knock-resistance of these three alcohols, for direct-injection operation with injection during the intake stroke, is isobutanol < ethanol < methylbutenol when compared on an equal oxygen weight basis.

33 ADVANCED PROPULSION SYSTEMS↗

Demonstration of Improved CHP Systems utilizing Improved Gas Turbine and sCO2 Cycles Using Additive Manufacturing (AM) Components

This project seeks to integrate a sCO2 bottoming cycle with a 5.3 MW gas turbine to develop a CHP system that is able to transition rapidly between 50% and 100% load by engaging or bypassing the bottoming cycle while maintaining electrical system efficiency above 30% at all times. The project team will engineer an optimized design of the power systems. The needed new key components, such as advanced heat exchangers and a steam injection system for the gas turbine, will be developed and their performance demonstrated in rig tests. The feasibility of the whole integrated CHP system will be validated through a cyber-physical approach.

36 MATERIALS SCIENCE↗

Adaptable Technology Integration for Enhanced Worker Safety and Efficiency within the SPD Gloveboxes [Slides]

Surplus Plutonium Disposition (SPD) Overview: (1) Application Background – Location: Savannah River Site, Aiken, South Carolina – High backlog of plutonium material to process – Parallel lines of gloveboxes – Highly manual operations requiring full PPE and reader/worker steps; (2) Development Scope – Development of automated processes and opportunities to provide robust engineering design alternatives for baseline design that would meet process improvement goals.

61 RADIATION PROTECTION AND DOSIMETRY↗

A small number of point mutations confer formate tolerance in Shewanella oneidensis

ABSTRACT Microbial electrosynthesis (MES) is a sustainable approach to chemical production from CO 2 and clean electricity. However, limitations in electron transfer efficiency and gaps in understanding of electron transfer pathways in MES systems prevent full realization of this technology. Shewanella oneidensis could serve as an MES biocatalyst because it has a well-studied, efficient transmembrane electron transfer pathway. A key first step in MES in this organism could be CO 2 reduction to formate. However, we report that wild-type S. oneidensis does not tolerate high levels of formate. In this work, we created and characterized formate-tolerant strains of S. oneidensis for further engineering and future use in MES systems through adaptive laboratory evolution. Two different point mutations in a gene encoding a predicted sodium-dependent bicarbonate transporter and a DUF2721-containing protein separately confer formate tolerance to S. oneidensis . The mutations were further evaluated to understand their role in improving formate tolerance. We also show that the wild-type and mutant versions of the putative sodium-dependent bicarbonate transporter improve formate tolerance of Zymomonas mobilis , indicating the potential of transferring this formate tolerance phenotype to other organisms. IMPORTANCE Shewanella oneidensis is a bacterium with a well-studied, efficient extracellular electron transfer pathway. This capability could make this organism a suitable host for microbial electrosynthesis using CO 2 or formate as feedstocks. However, we report here that formate is toxic to S. oneidensis , limiting the potential for its use in these systems. In this work, we evolve several strains of S. oneidensis that have improved formate tolerance, and we investigate some mutations that confer this phenotype. The phenotype is confirmed to be attributed to several single point mutations by transferring the wild-type and mutant versions of each gene to the wild-type strain. Finally, the formate tolerance mechanism of one variant is studied using structural modeling and expression in another host. This study, therefore, presents a simple method for conferring formate tolerance to bacterial hosts.

Cross, Megan C. Gruenberg (ORCID:0000000291589900)↗