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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 505 records · Page 28
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Toward Accurate Modeling of Galaxy Clustering on Small Scales: Constraining the Galaxy-halo Connection with Optimal Statistics

Applying halo models to analyze the small-scale clustering of galaxies is a proven method for characterizing the connection between galaxies and their host halos. Such works are often plagued by systematic errors or limited to clustering statistics that can be predicted analytically. In this work, we employ a numerical mock-based modeling procedure to examine the clustering of Sloan Digital Sky Survey DR7 galaxies. We apply a standard halo occupation distribution (HOD) model to dark matter only simulations with a ΛCDM cosmology. To constrain the theoreStical models, we utilize a combination of galaxy number density and selected scales of the projected correlation function, redshift-space correlation function, group multiplicity function, average group velocity dispersion, mark correlation function, and counts-in-cells statistics. We design an algorithm to choose an optimal combination of measurements that yields tight and accurate constraints on our model parameters. Compared to previous work using fewer clustering statistics, we find a significant improvement in the constraints on all parameters of our halo model for two different luminosity-threshold galaxy samples. Most interestingly, we obtain unprecedented high-precision constraints on the scatter in the relationship between galaxy luminosity and halo mass. However, our best-fit model results in significant tension (>4σ) for both samples, indicating the need to add second-order features to the standard HOD model. To guarantee the robustness of these results, we perform an extensive analysis of the systematic and statistical errors in our modeling procedure, including a first of its kind study of the sensitivity of our constraints to changes in the halo mass function due to baryonic physics.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2021

Physics-informed, empirically constrained machine learning for designing Fe-9Cr alloys

<span style="font-family: Calibri, sans-serif; font-size: 12pt;">Materials data analytics can be used to significantly shorten development time of specialized alloys needed for next generation energy applications. Incorporation of the domain knowledge into deep-learning graph structure via fuzzy pre-training and causal process imitation presents a viable approach to developing accurate data-driven models and reliable alloy design tools, with limited datasets. It was demonstrated that the domain knowledge-based empirical constraints not only inhibit overfitting but also allow training more accurate and reliable ML models with improved transparency of the output interpretation. In this study, alloy tensile properties were interpreted with three competing virtual-microstructure models.</span>

Romanov, Vyacheslav↗
DOE OSTI2021

Towards a New μ→eγ Search with the MEG II Experiment: From Design to Commissioning

The MEG experiment represents the state of the art in the search for the Charged Lepton Flavour Violating μ + →e + γ decay. With its first phase of operations at the Paul Scherrer Institut (PSI), MEG set the most stringent upper limit on the BR (μ + →e + γ) ≤ 4.2×10 –13 at 90% confidence level, imposing one of the tightest constraints on models predicting LFV-enhancements through new physics beyond the Standard Model. An upgrade of the MEG experiment, MEG II, was designed and it is presently in the commissioning phase, aiming at a sensitivity level of 6×10 –14 . The MEG II experiment relies on a series of upgrades, which include an improvement of the photon detector resolutions, brand new detectors on the positron side with better acceptance, efficiency and performances and new and optimized trigger and DAQ electronics to exploit a muon beam intensity twice as high as that of MEG (7×10 7 μ + /s). This paper presents a complete overview of the MEG II experimental apparatus and the current status of the detector commissioning in view of the physics data taking in the upcoming three years.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2023

Preliminary IHLW Formulation Algorithm Description

This report documents the initial algorithm that could be used by the Waste Treatment and Immobilization Plant (WTP) in batching high-level waste (HLW) and glass-forming chemicals (GFCs) in the HLW melter feed preparation vessel (MFPV) (HFP-VSL-00001 and -00005). Not all Hanford tank waste can be accommodated by the models developed for this report and significant expansion of the model boundaries could be achievable to reduce the WTP mission life and total canister production count. The immobilized HLW (IHLW) must meet a series of constraints to be acceptable for disposal in the Monitored Geologic Repository, which are contained in the Specification 1 of the Contract (DOE 2000), the Waste Acceptance Product Specifications (WAPS, DOE 1996), and the Waste Acceptance System Requirements Document (WASRD, DOE 2007). The IHLW Waste Form Compliance Plan (WCP, 24590-HLW-PL-RT-07-0001, Rev 3) specifies that the formulation algorithm will be developed and used to comply with the constraints associated with glass composition and properties. This report is not an engineering calculation, does not provide design input, and is not an engineering study. Algorithm inputs include the chemical analyses of the blended HLW in the HLW blend vessel (HBV) (HLP-VSL-00028, the volume and composition of the MFPV heel, the volume and composition of the MFPV after waste addition, the volume and composition of MFPV batch after GFC addition, the compositions of individual GFCs, and the mass of glass in each canister. In addition to these inputs, uncertainties in the HLW composition and processing parameters are included in the algorithm. Using the above inputs, the algorithm calculates the following outputs: 1) the volume of HLW to be transferred from the HBV to the MFPV, 2) the mass of each GFC for addition to the MFPV, 3) the composition of the glass that will be produced along with uncertainties, and 4) the predicted properties, with associated uncertainties, of the resulting IHLW. The algorithm uses the property-composition models to calculate properties with associated uncertainties and compares them with various constraints to ensure that a processable feed is formulated and a compliant IHLW is produced. The GFC additions are determined using an optimization approach to provide high confidence that the HLW glass will meet all product quality requirements and key processing constraints. For most HLW batches there are many possible glass compositions that meet all constraints. In these cases, the glass composition is optimized for a series of target component concentrations and target property values. The algorithm also incorporates process measurement and product quality uncertainties, based on the work of Piepel et al. (2005). Estimates of the various process and measurement uncertainties that affect glass compositions and predicted glass properties have been previously reported (Piepel et al. 2005, 2006) and the impacts of these estimated uncertainties on the IHLW composition envelope that meets product quality and processing-related properties with sufficient confidence were evaluated. The details of work performed to date to develop this initial GFC addition and batching algorithm are summarized in Sections 4 and 5. An example data set is used to illustrate the calculations of the algorithm summarized in Section 6. Finally, in Section 7, there is a statement of the required work to achieve a final operational IHLW formulation control algorithm. This report is not an engineering calculation, does not provide design input, and is not an engineering study.

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

Solar collector installation design system including exceptional condition management and display

Embodiments may include systems and methods to create and edit a representation of a worksite, to create various data objects, to classify such objects as various types of pre-defined “features” with attendant properties and layout constraints. As part of or in addition to classification, an embodiment may include systems and methods to create, associate, and edit intrinsic and extrinsic properties to these objects. A design engine may apply of design rules to the features described above to generate one or more solar collectors installation design alternatives, including generation of on-screen and/or paper representations of the physical layout or arrangement of the one or more design alternatives. In some embodiments, metadata about the design process, including the process of classifying features and providing user input, generating layouts, and then modifying those layouts, may be generated. The metadata may include information about exceptional conditions in the project state information or design. A list of exceptions corresponding to exceptional conditions may be generated, and the designer may interact with these exceptions in a variety of ways, such as by complying with rules to remove an item from the exceptions list or overriding the application of the rules. The exceptions may be non-blocking relative to other user actions.

Wayne, Gary↗
DOE OSTI2020

Designing an Epitaxially-Integrated DBR for Dislocation Mitigation in Monolithic GaAsP/Si Tandem Solar Cells

This work explores epitaxially-integrated distributed Bragg reflectors (DBR) as a strategy to mitigate the impact of threading dislocations on the performance of monolithic GaAs 0.75 P 0.25 /Si tandem solar cells. The constraints present due to materials availability and the optical transmission profile of the GaAs 0.75 P 0.25 top cell require the use of an enhanced bandwidth DBR design achieved by combining two narrow-band DBR’s with different central wavelengths. Here, the impact of this DBR structure on JSC, along with the competing effects of threading dislocation density (TDD), are investigated using a robust, experimentally informed analytical model. Implementing this DBR within the GaAs 0.75 P 0.25 /Si tandem cell structure is predicted to yield a short circuit current enhancement equivalent to an ~1.8× reduction in TDD, providing a clear demonstration of its promise as a design methodology for mitigating the impact of non-negligible defect populations.

14 SOLAR ENERGY↗
DOE OSTI2025

Serpentine Magnet Designs for the Interaction Region of the Electron-Ion Collider (EIC)

The Electron-Ion Collider (EIC), hosted by Brookhaven National Laboratory, is designed to deliver a peak luminosity of 1 × 10 34 cm −2 sec −1 . The interaction region (IR) of the EIC imposes several constraints in terms of field quality, aperture, and spatial layout, which necessitates the development of several unique superconducting serpentine direct wind magnets. These magnets are constructed using either a single strand or a small-diameter 6-around-1 NbTi cable, presenting unique challenges for design and optimization. This paper introduces a new computational code specifically developed to streamline and integrate the design process for these magnets, enabling faster design iterations while addressing their complex requirements. Here, in this paper, we first introduce the code, which builds on established electromagnetic fundamentals. The code incorporates tools for optimizing winding patterns and for correcting magnetic multipoles; additionally, it interfaces with established magnet design software. We also present the design of several serpentine magnets for the EIC IR, demonstrating the code’s capability to deliver precise and efficient solutions. These designs highlight the code’s ability to accelerate the development cycle, ensuring the serpentine magnets meet the demanding specifications of the EIC project.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2026

Kinetics Modeling and Reactor Design Study of Glucose-to-Terpenes Cell-Free Conversion

Cell-free systems offer many advantages over traditional biological conversion by eliminating biological growth constraints. It also offers easy manipulation and finetuning of the reaction conditions for each individual enzyme. The conversion of cellulosic glucose to Limonene, a terpene, is a promising pathway for producing fuels and chemicals. Recent advances in developing cell-free systems focuses on bench scale optimization of terpene yield and to demonstrate its feasibility towards commercialization [1,2]. There is significant knowledge gap regarding reaction kinetics of these cell-free systems to further study how it will perform at larger scale. We present here, our studies on reaction kinetics and reactor design implications of cell-free glucose to Limonene conversion to facilitate the further development and commercialization of this process. We developed a novel kinetic model based on the metabolic-network structure of the cell-free system with multi-substrate reversible Michaelis-Menten rate law. To estimate kinetic parameters for this system of rate equations, we employed Bayesian optimization to perform global search with the assistance of gaussian processes to balance exploration and exploitation. The model parameters estimated showed good results compared with experimental data. The estimated parameters were used to perform sensitivity analysis. We found that Hexokinase is one of the most critical enzymes that affect the conversion of the glucose. We also observed that abundance of co-factors is also critical to the conversion of glucose to limonene. We investigated packed bed reactors with enzymes immobilized on the surface of particles to convert glucose stream into Limonene for larger scale production. The reactor design such as particle size, enzyme loading, and flow rate are found to be critical for improving yields. [1] Dudley, Q.M., Nash, C.J. and Jewett, M.C., 2019. Synthetic Biology, 4(1), p.ysz003. [2] Korman, T.P., Opgenorth, P.H. and Bowie, J.U., 2017. Nature communications, 8(1), p.15526.

09 BIOMASS FUELS↗
DOE OSTI2023

Efficient Integration of Algebraic Constraint for Exponential Time Integration

This work is a continuation of a previous project where the high efficiency of exponential integration methods for DRMHD systems was demonstrated. Often algebraic constraints must also be enforced for DRMHD systems of interest. Straightforward application of exponential methods to DRMHD equations with constraints leads to prohibitively computationally expensive methods. In this work, we propose new exponential schemes that allow the constraints to be removed from evaluation of exponential matrix functions which drastically reduces computational complexity by eliminating the need to perform computations enforcing constraints in exponential calculations while still preserving a high order of accuracy and allowing for a large time step even when the problem is stiff. This idea is similar to the W-methods and is achieved by carefully designing a method with the desired order of accuracy, even with an incomplete Jacobian matrix used as an argument of exponential-like functions. The constraints are accounted for by including them in the evaluation of the right-hand-side forcing function of the spatially discretized system. We study performance of the new methods on test problems and outline future research directions that this work opens.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2024

Representative Project Design Envelope for Floating Offshore Wind Energy: A Focus on the California 2023 Federal Leases

NREL developed recommendations for a representative project design envelope (RPDE) for floating offshore wind energy projects in the California lease areas, considering industry feedback from offshore wind farm developers. The RPDE provides estimates of minimum and maximum values for project design parameters that are relevant for assessing environmental impacts. The design envelope considers the practical range of technology options that may be deployed and accounts for major physical constraints, technology feasibility, and supply chain readiness. In addition to the RPDE, this report presents four scenarios that illustrate some of the differences between technologies that could be used offshore California, as well as descriptions of the typical installation processes that are expected to be used for floating offshore wind farms.

17 WIND ENERGY↗
DOE OSTI2023

Numerical Optimization of Spray-Guided Spark Assistance for Cold Idle Operation in a Heavy-Duty Gasoline Compression Ignition Engine

This article describes the results of a response surface model (RSM)-based numerical optimization campaign for spray-guided spark assistance at cold operations in a heavy-duty gasoline compression ignition (GCI) engine. On the basis of an earlier work on spark-assisted GCI cold combustion, a space-filling design of experiments (DoE) method was first undertaken to investigate a multitude of hardware design variables and engine operating parameters. The main design variables included the number of injector nozzles, fuel split quantities and injection timings, and spark timing. The objective variables were engine combustion efficiency (ŋ c ), maximum pressure rise rate (MPRR), and engine-out nitrogen oxide (NO x ) emissions. A total of 150 design candidates were automatically generated using the Sobol sequence method provided by the commercial software package, CAESES. Then, closed-cycle computational fluid dynamic (CFD) spark-assisted GCI simulations under cold idling operations were performed. The outcomes from the CFD-DoE design campaign were utilized to construct high-fidelity RSMs that allowed for further design optimization of the spark plug- and fuel injector-related design variables, along with fuel injection strategy parameters. A merit function with respect to objective variables was formulated with an appropriate weight assignment on each objective variable. Finally, the best design candidate was identified from the RSM-based optimization process and further validated in the CFD analysis. The best design candidate showed the potential to significantly improve combustion efficiency (ŋ c > 90%) over the baseline at cold idle while satisfying MPRR and NOx emissions constraints (MPRR < 5 bar/CAD and NO x < 4.5 g/kWh).

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2021

A Novel State-Constrained Primary Control for Grid-Forming Inverters

The problem of operating an AC microgrid with purely inverter-based generating units is considered, and a novel vector control for the constrained grid-forming (GFM) inverters is proposed. The design aims to achieve the primary control objectives of the GFM inverters: voltage establishment, synchronization, and voltage/frequency regulation. It is novel that the proposed control also ensures all operational variables constrained both real-time and for all the time except for low voltage during the period of grid forming. The constraints include frequency, output AC voltage, input DC power, and active/reactive power injections to the grid. Here, in this paper, outcomes of a detailed simulation are presented to demonstrated that the above design objectives are met and that the overall performance is superior due to its unique ability of simultaneously controlling the whole vectors of the inverter state and output.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2026

Explainable physics-based constraints on reinforcement learning for accelerator optimization

We present a reinforcement learning (RL) framework for optimizing particle accelerator experiments that builds explainable physics-based constraints on agent behavior. The goal is to increase transparency and trust by letting users verify that the agent’s decision-making process incorporates suitable physics. Our algorithm uses a learnable surrogate function for physical observables, such as energy, and uses them to fine-tune how actions are chosen. This surrogate can be represented by a neural network or by an interpretable sparse dictionary model. We test our algorithm on a range of particle accelerator optimization environments designed to emulate the Continuous Electron Beam Accelerator Facility at Jefferson Lab. By examining the mathematical form of the learned constraint function, we are able to confirm the agent has learned to use the established physics of each environment. In addition, we find that the introduction of a physics-based surrogate enables our RL algorithms to reliably converge for difficult high-dimensional accelerator optimization environments.

explainability↗
DOE OSTI2020

Variable-fidelity multipoint aerodynamic shape optimization with output-based adapted meshes

This work presents a method to control the discretization error in multipoint aerodynamic shape optimization using output-based adapted meshes. The meshes are adapted via adjoint-based error estimates, taking into account both the objective and constraint output errors. A multi-fidelity optimization framework is then developed by taking advantage of the variable fidelity offered by adaptive meshes. The objective functional and its sensitivity at each design point (operating condition) are first evaluated on the same initial coarse mesh, which is then subsequently adapted for each design point individually as the shape optimization proceeds. The effort to set up the optimization is minimal since the initial mesh can be fairly coarse and easy to generate. As the shape approaches the optimal design, the mesh at each design point becomes finer, in regions necessary for that particular operating condition. The multi-fidelity framework is tightly coupled with the objective error estimation to ensure the optimization accuracy at each fidelity. Computational savings arise from a reduction of the mesh size when the design is far from optimal and avoiding an exhaustive search on low-fidelity meshes. The proposed method is demonstrated on multipoint drag minimization problems of a transonic airfoil with lift and area constraints. Improved accuracy and efficiency are shown compared to traditional fixed-fidelity optimization with a fixed computational mesh.

42 ENGINEERING↗
DOE OSTI2025

Benchmarking and Fidelity Response Theory of High-Fidelity Rydberg Entangling Gates

The fidelity of entangling operations is a key figure of merit in quantum information processing, especially in the context of quantum error correction. High-fidelity entangling gates in neutral atoms have seen remarkable advancement recently. A full understanding of error sources and their respective contributions to gate infidelity will enable the prediction of fundamental limits on quantum gates in neutral atom platforms with realistic experimental constraints. In this work, we implement the time-optimal Rydberg controlled-Z (CZ) gate, design a circuit to benchmark its fidelity, and achieve a fidelity, averaged over symmetric input states, of 0.9971 ( 5 ) , downward corrected for leakage error, which together with our recent work [Nature 634, 321–327 (2024)] forms a new state of the art for neutral atoms. The remaining infidelity is explained by an error model, consistent with our experimental results over a range of gate speeds, with varying contributions from different error sources. Further, we develop a fidelity response theory to efficiently predict infidelity from laser noise with nontrivial power spectral densities and derive scaling laws of infidelity with gate speed. Besides its capability of predicting gate fidelity, we also utilize the fidelity response theory to compare and optimize gate protocols, to learn laser frequency noise, and to study the noise response for quantum simulation tasks. Finally, we predict that a CZ gate fidelity of ≳ 0.999 is feasible with realistic experimental upgrades. Published by the American Physical Society 2025

Tsai, Richard Bing-Shiun (ORCID:0000000286758677)↗
DOE OSTI2021

Trust-but-Verify in Cyber-Physical Systems

Cyber-physical systems span a wide spectrum, from long-lived legacy systems to more modern installations. Trust is an issue that arises across the spectrum, albeit with different variants of goals and constraints. On the one end of the spectrum, legacy systems are characterized by function-based designs in which trust is an implicitly in-built concept -- the operation is historically designed, implemented, and optimized in a benign stance with respect to intended use. On the other end of the spectrum, modern systems are characterized by offerings from manufacturers, vendors, and system installers -- the devices and deployments use a variety of security features that offer promises of increased trust. All along this spectrum of cyber-physical systems, extending trust beyond the traditional cyber portions to the arteries that connect the physical portions to the cyber portions is a major challenge. Here, we identify a Trust-but-Verify approach that spans this spectrum in addressing trust.

Perumalla, Kalyan↗
DOE OSTI2022

A deep learning-enhanced framework for multiphysics joint inversion

Joint inversion has drawn considerable attention due to the availability of multiple geophysical data sets, ever-increasing computational resources, the development of advanced algorithms, and its ability to reduce inversion uncertainty. A key issue of joint inversion is to develop effective strategies to link different geophysical data in a unified mathematical framework, in which the information obtained from different models can complement each other. We have developed a deep learning-enhanced joint inversion framework to simultaneously reconstruct different physical models by fusing different types of geophysical data. Traditionally, structure similarity constraints are pursued by joint inversion algorithms using manually crafted formulations (e.g., cross gradient). The constraint is constructed by a deep neural network (DNN) during the learning process. The framework is designed to combine the DNN and a traditional independent inversion workflow and improve the joint inversion result iteratively. The network can be easily extended to incorporate multiphysics without structural changes. Numerical experiments on the joint inversion of 2D DC resistivity data and seismic traveltime are used to validate our method. In addition, this learning-based framework demonstrates excellent generalization abilities when tested on data sets using different geologic structures. It also can handle different sensing configurations and nonconforming discretization.

Geochemistry & Geophysics↗
DOE OSTI2020

Challenges for and Pathways Toward Solid-State Batteries

Solid-state batteries utilizing lithium metal anodes have the potential to enable batteries with a specific energy of >500 Wh/kg and an energy density of >1,500 Wh/L for thousands of cycles. When optimized they will improve the energy efficiency, operating temperature range, sustainability, and safety at a lower cost compared to projections for advanced Li-ion batteries. This improved performance is critical for the widespread adoption of electric vehicles and may enable future applications such as electric aviation. Expectations for solid-state batteries are high, there are significant materials and processing challenges that need to be overcome. Some of these challenges are well known; others are more subtle and are just becoming known. The challenges and their solutions must be clearly identified to realize high-energy solid-state Li metal batteries. In the United States, the U.S. Department of Energy (DOE) funds the majority of energy storage research and development, including solid state battery research. Web of Science publication analysis shows that worldwide DOE is second largest funder. Work on solid state batteries is distributed across different arms of DOE and is coordinated at headquarters to ensure a broad national portfolio for advanced batteries, including lithium batteries and solid-state battery devices. We note that university and national laboratory researchers in the US with a long-standing interest in solid state batteries is a small, integrated community with recognized impact for publications, patents and startup companies. The opportunity for this group to engage for research with international colleagues is growing, fostered by periodic bilateral meetings supported by DOE and by the recent joint US German research program for lithium battery interface studies. These are valuable opportunities to speed solid-state battery development. The ORNL led virtual workshop, overviewed here, confirms the community’s shared vision of the exciting advances, opportunities, and challenges. Further, we are seeing that this workshop has spawned several new informal collaborations and draft proposals. This encourages us to propose future discussion. On May 11, 2020, Oak Ridge National Laboratory (ORNL) hosted a 6-hour, on-line national workshop to discuss recent advances and most the prominent obstacles to realizing solid-state Li metal batteries. The workshop included more than 30+ experts from national laboratories, universities, and companies, all of whom have worked on solid-state batteries for multiple years. The participants shared recent advances, many not yet in print, illustrating that the community has gained significant new insights for solid-state Li battery materials over the last 5 years. In this report, the major outcomes of the workshop are organized to identify the gaps in our scientific knowledge for four core materials science areas: (1) Li metal anodes, (2) the solid electrolyte in contact with Li metal, (3) active cathode materials and solid-state composite cathodes, and (4) solid electrolytes. Illustrative examples and discussions are reported with the more comprehensive issues in the report. Discussion of additional challenges related to processing of solid-state battery materials and to the designs and architectures for mechanically robust, long-lived batteries received less attention due to time constraints. These are good themes for deeper discussion at a follow-on workshop, where a complete list of critical research topics can be identified.

25 ENERGY STORAGE↗