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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 163 records · Page 9
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Differentiable modeling and optimization of non-aqueous Li-based battery electrolyte solutions using geometric deep learning

Electrolytes play a critical role in designing next-generation battery systems, by allowing efficient ion transfer, preventing charge transfer, and stabilizing electrode-electrolyte interfaces. In this work, we develop a differentiable geometric deep learning (GDL) model for chemical mixtures, DiffMix, which is applied in guiding robotic experimentation and optimization towards fast charging battery electrolytes. In particular, we extend mixture thermodynamic and transport laws by creating GDL-learnable physical coefficients. We evaluate our model with mixture thermodynamics and ion transport properties, where we show improved prediction accuracy and model robustness of Diff-Mix than its purely data-driven variants. Furthermore, with a robotic experimentation setup, Clio, we improve ionic conductivity of electrolytes by over 18.8% within 10 experimental steps, via differentiable optimization built on DiffMix gradients. By combining GDL, mixture physics laws, and robotic experimentation, DiffMix expands the predictive modeling methods for chemical mixtures and enables efficient optimization in large chemical spaces.

25 - ENERGY STORAGE↗
DOE OSTI2026

Sparsity Applications for Gradient‐Based Optimization of Wind Farms

Optimizing wind farms is essential for designing efficient energy systems, especially as farms grow larger and span multiple sites. However, this optimization becomes increasingly challenging due to the rising computational cost associated with more turbines. Gradient‐based optimization methods scale better than gradient‐free approaches for large problems, but the most computationally expensive component remains the calculation of gradients for the objective function and constraint Jacobians. To address this, we propose leveraging sparsity to accelerate gradient evaluations and reduce the size of the constraint Jacobian. Wind farms naturally exhibit sparsity—many turbines do not influence each other under certain wind directions. However, unlike traditional sparse problems with fixed patterns, wind farm sparsity is dynamic, requiring new strategies to handle changing interactions efficiently. This paper presents a study of sparsity in wind farm optimization and introduces several methods to exploit it. These strategies are tested on multiple farms using the analytic Cumulative Curl model, with gradients computed via automatic differentiation (AD). The same sparsity‐aware techniques are also applicable to finite difference (FD) methods, where they can yield even greater speedups due to the high cost of directional evaluations. Results show that sparse methods achieve up to a 10x speedup with less than ± 5% variance in optimized wake losses compared to traditional methods. These findings suggest that sparsity‐aware optimization not only maintains solution quality but also scales efficiently with farm size, enabling more comprehensive design exploration at reduced computational cost.

17 WIND ENERGY↗
DOE OSTI2025

Quantum Adiabatic Optimization with Rydberg Arrays: Localization Phenomena and Encoding Strategies

Quantum adiabatic optimization seeks to solve combinatorial problems using quantum dynamics, requiring the Hamiltonian of the system to align with the problem of interest. However, these Hamiltonians are often incompatible with the native constraints of quantum hardware, necessitating encoding strategies to map the original problem into a hardware-conformant form. While the classical overhead associated with such mappings is easily quantifiable and typically polynomial in problem size, it is much harder to quantify their overhead on the quantum algorithm, e.g., the transformation of the adiabatic timescale. In this work, we address this challenge on the concrete example of the encoding scheme proposed in [Nguyen , PRX Quantum , 010316 (2023)], which is designed to map optimization problems on arbitrarily connected graphs into Rydberg atom arrays. We consider the fundamental building blocks underlying this encoding scheme and determine the scaling of the minimum gap with system size along adiabatic protocols. Even when the original problem is trivially solvable, we find that the encoded problem can exhibit an exponentially closing minimum gap. We show that this originates from a quantum coherent effect, which gives rise to an unfavorable localization of the ground-state wave function. On the QuEra Aquila neutral atom machine, we observe such localization and its effect on the success probability of finding the correct solution to the encoded optimization problem. Finally, we propose quantum-aware modifications of the encoding scheme that avoid this quantum bottleneck and lead to an exponential improvement in the adiabatic performance. This highlights the crucial importance of accounting for quantum effects when designing strategies to encode classical problems onto quantum platforms. Published by the American Physical Society 2025

Bombieri, Lisa (ORCID:0009000950422897)↗
DOE OSTI2025

Scale-up study of enhancing algal growth through bioaugmentation with the indole-3-acetic acid producing bacteria Azospirillum brasilense

Algae-based wastewater treatment technologies can recover nutrients while generating algal biomass with diverse application potentials. While numerous bacteria have demonstrated the ability to enhance algal growth through symbiotic relationships in laboratory settings, scaling this benefit to larger applications remains challenging due to complex wastewater conditions and microbe-algae dynamics. This study explores the effectiveness of bioaugmentation with Azospirillum brasilense, a plant growth-promoting bacterium known for producing the phytohormone indole-3-acetic acid (IAA), in boosting algal productivity within an Algaewheel wastewater treatment system. Through a series of experiments conducted at lab-, pilot-, and full-scale levels, we evaluated the impact of bioaugmentation on algal growth and developed a bioaugmentation strategy for an Algaewheel system. The results indicate that a biweekly dosing, achieving a final bioaugmenting cell density of 0.2–4 × 10 9 cells/L, effectively doubles the biomass productivity in a residential subdivision wastewater treatment plant in Northern Illinois. The bacterial production of IAA was the primary mechanism driving this enhancement, as demonstrated by the similar growth and yield improvements observed with chemically supplemented IAA and bioaugmentation across all experimental scales. High-throughput sequencing of 18S rRNA genes revealed that the algal community in the bioaugmented tank exhibited more stable biodiversity than the control. Additionally, bioaugmentation improved nutrient removal efficiency during winter, highlighting its potential to enhance the overall performance and sustainability of algae-based wastewater systems. The developed bioaugmentation and chemical treatment methods offer further operational solutions for managing yield, optimizing biochemical profiles, and enhancing biofuel production potential.

Algae-bacterial symbiotic relationship↗
DOE OSTI2025

Computing material volume fractions on a superimposed mesh as applied to Monte Carlo particle transport simulations

Here, we present a newly implemented ray tracing algorithm in OpenMC for efficiently computing material volume fractions on superimposed meshes in complex geometries. By firing rays along each coordinate direction through the geometry, the approach accumulates track-length data in each mesh element, thereby determining the fractional composition of each material. Scaling studies on three different models—a random tetrahedra configuration, the Frascati Neutron Generator ITER dose rate benchmark, and a stellarator design—show excellent parallel performance, with nearly linear speedup on modern multi-threaded and distributed-memory systems. An analysis of the residual error relative to high-resolution reference solutions demonstrated that under optimal conditions it decreases as 1/R, where R is the number of rays fired, making it straightforward to achieve user-prescribed accuracy. This new functionality enables practical, mesh-based approaches for detailed nuclear analyses in production Monte Carlo workflows without resorting to expensive, fully conformal or unstructured meshing.

Monte Carlo↗
DOE OSTI2024

Power over fiber development for HEP detectors

Power-over-Fiber (PoF) technology has been used extensively in settings where high voltages require isolation from ground and electromagnetic isolation is critical. In cryogenic environments, PoF offers a reliable power transmission technology, leveraging optical fibers to transfer power with minimal system degradation. PoF technology excels in maintaining low noise levels and isolation when delivering power to sensitive electronic systems operating in extreme temperature ranges and high voltage environments. Here, in a novel application of PoF for a HEP detector, power is provided to photon detector modules located on a surface at ~300 kV with respect to ground in the planned DUNE experiment. This summary paper of the PoF talk at the 16th PISA Meeting on Advanced Detectors highlights the R&D effort of PoF in extreme conditions and underscores its capacity to revolutionize power delivery and management in critical applications offering a dependable solution with low noise, optimal efficiency, and superior isolation. The DUNE (Abi et al., 2020) experiment will soon deploy large liquid argon (LAr) time projection chambers (TPC) to detect neutrino interactions and other particle physics phenomena. In addition to the particle tracking provided by the TPC, photon detectors, powered by a first ever PoF system, in the cryostat will leverage the high scintillation light yield of LAr to provide crucial timing and additional calorimetric information.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2025

Rapid Screening of Single-Atom Catalyst Synthesis Conditions Using ToF-SIMS and Facet-Dependent Single-Crystal Substrates

Single-atom catalysts (SACs) offer superior catalytic performance compared to traditional nanoparticle catalysts but are challenging to develop because of the need for extensive optimization and specialized characterization techniques. Here, this study presents a rapid and versatile method for detecting synthesis conditions and elucidating deposition mechanisms of SACs on various substrates. By depositing active elements (Au, Cu, Ni and Rh) on facet-specific single-crystalline substrates (CeO 2 , TiO 2 , MgO and Al 2 O 3 ) and employing time-of-flight secondary ion mass spectroscopy (ToF-SIMS), we assessed facet-dependent deposition behaviors and identified optimal conditions for solution-based SAC synthesis. On CeO 2 and TiO 2 , we confirmed facet-dependent deposition, primarily influenced by oxygen vacancy density and photocatalytic activity, respectively. MgO exhibited the formation of metal oxide/hydroxide clusters for all active elements, and the degree of clustering for Cu and Ni was correlated with the facet hydrolysis susceptibility. Notably, Au and Rh deposition on MgO was facet-independent, attributed to the formation of hydroxide species in solution. Al 2 O 3 , due to its chemical stability and lack of surface defects, did not show active element deposition. This study not only provides a time and cost-efficient method for prescreening SAC synthesis conditions, but it also provides valuable insights into the various deposition mechanisms governing SAC formation on different substrates, paving the way for the rational design of tailored SACs for various catalytic applications.

ToF-SIMS↗
DOE OSTI2024

Characterization and novel application of power over fiber for electronics in a harsh environment

Power-over-Fiber (PoF) technology has been used extensively in settings where high voltages require isolation from ground. In a novel application of PoF, power is provided to photon detector modules located on a surface at ∼ 300 kV with respect to ground in the planned DUNE experiment. In cryogenic environments, PoF offers a reliable means of power transmission, leveraging optical fibers to transfer optical power. PoF technology excels in maintaining low noise levels when delivering power to sensitive electronic systems operating in extreme temperatures and high voltage environments. This paper presents the R&D effort of PoF in extreme conditions and underscores its capacity to revolutionize power delivery and management in critical applications, offering a dependable solution with low noise, optimal efficiency (∼ 51%), and superior isolation.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2024

Endogenizing Probabilistic Resource Adequacy Risks in Deterministic Capacity Expansion Models

In this work, we demonstrate how power system capacity expansion models can understate the stochastic effects of thermal outages when considering resource availabilities on an hourly expected value basis, yielding system designs with multiple orders of magnitude more shortfall risk than stated adequacy targets. We develop a novel approximation approach to efficiently endogenize awareness of this risk in a deterministic, linear capacity expansion framework. We compare this approach to exogenous tuning of an energy reserve margin, the leading alternative method to compensate for unmodeled probabilistic shortfall risk. Empirical results from a test system show that the new endogenous method cost-effectively meets all regional reliability targets with a single optimization solve, and produces a near-identical system design as the incumbent method without the need for repeated re-optimizations to find an appropriate reserve level. The endogenous method may also use iterative re-optimizations to further improve solution quality, although these incremental benefits were modest in the system studied.

capacity expansion modeling↗
DOE OSTI2025

Techno-Economic Analysis for the Addition of a Thermal Energy Storage System to a Central Plant

Increasing energy demand and rising peak loads present significant challenges for energy management in commercial and institutional settings. As climate change drives greater cooling needs, central plants must navigate the complex tradeoffs between operational efficiency, cost control, and grid stability. Thermal energy storage (TES) systems offer a viable solution by shifting energy consumption from peak to off-peak periods, thereby reducing peak demand, lowering utility expenses, and improving grid resilience. However, the success of TES implementation hinges on appropriate system sizing, effective control strategies, and alignment with local utility rate structures. This article presents a techno-economic analysis of integrating a chilled water TES system into the central plant at California State University, Dominguez Hills. Drawing on historical load profiles and utility tariffs, we assess three TES sizing approaches and their corresponding control strategies from both energy and economic perspectives. This article utilizes a model-based approach to assess the impact of TES sizing and control strategies on the techno-economic feasibility of integrating TES into an existing central plant. The models employed for this analysis were calibrated using 4 years of historical data. Here, the results demonstrated that utility tariffs and the campus's operational profiles dictate the most feasible sizing and control methods. The findings offer valuable insights for institutions and commercial building managers exploring sustainable energy solutions. By demonstrating how optimized TES strategies can improve operational efficiency while achieving financial savings, this study highlights the potential for TES to align performance with cost effectiveness in real-world applications.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2026

Integrating Resilience Planning in Distribution System Planning

Electric utilities, regulators, and stakeholders face increasing risks of severe storms, freezes, floods, and heat waves damaging grid infrastructure and causing power outages—and increasing risks of utility equipment igniting wildfires. At the same time, customer electricity rates have risen substantially in recent years, due in part to replacing aging infrastructure and improving resilience to natural hazards and physical threats. To address these challenges, utilities are beginning to move beyond traditional, siloed planning processes to balance resilience with other fundamental grid objectives such as affordability, reliability, safety, and serving new loads. This study presents a framework for states and utilities that want to advance integration of resilience and distribution planning processes to improve planning efficiency, better prioritize cost-effective grid expenditures, and balance planning objectives. The framework includes 7 key integration points between these planning processes: -Strategy process -Data -Threat assessments -Solution identification and prioritization -Optimization opportunities -Consideration of other grid needs -Metrics Lawrence Berkeley National Laboratory reviewed utility distribution system plans and interviewed subject matter experts to identify emerging practices for each of the 7 integration points. This report presents these practices, which can be used as a guide toward more holistic planning and cohesive investment strategies. It also includes 3 case studies to provide practical examples of how utilities apply such integrated planning processes: two pole hardening programs and one microgrid planning effort. The report concludes by identifying opportunities for future research.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2024

Unsupervised Learning for Equitable DER Control: Preprint

In the context of managing distributed energy resources (DERs) within distribution networks (DNs), this work focuses on the task of developing local controllers. We propose an unsupervised learning framework to train functions that can closely approximate optimal power flow (OPF) solutions. The primary aim is to establish specific conditions under which these learned functions can collectively guide the network towards desired configurations asymptotically, leveraging an incremental control approach. The flexibility of the proposed methodology allows to integrate fairness-driven components into the cost function associated with the OPF problem. This addition seeks to mitigate power curtailment disparities among DERs, thereby promoting equitable power injections across the network. To demonstrate the effectiveness of the proposed approach, power flow simulations are conducted using the IEEE 37-bus feeder. The findings not only showcase the guaranteed system stability but also underscore its improved overall performance.

asymptotic stability↗
DOE OSTI2025

Efficient Neural Network Approaches for Conditional Optimal Transport with Applications in Bayesian Inference

In this work, we present two neural network approaches that approximate the solutions of static and dynamic conditional optimal transport (COT) problems. Both approaches enable conditional sampling and conditional density estimation, which are core tasks in Bayesian inference—particularly in the simulation-based (“likelihood-free”) setting. Our methods represent the target conditional distribution as a transformation of a tractable reference distribution. Obtaining such a transformation, chosen here to be an approximation of the COT map, is computationally challenging even in moderate dimensions. To improve scalability, our numerical algorithms use neural networks to parameterize candidate maps and further exploit the structure of the COT problem. Our static approach approximates the map as the gradient of a partially input convex neural network. It uses a novel numerical implementation to increase computational efficiency compared to state-of-the-art alternatives. Our dynamic approach approximates the conditional optimal transport via the flow map of a regularized neural ODE; compared to the static approach, it is slower to train but offers more modeling choices and can lead to faster sampling. We demonstrate both algorithms numerically, comparing them with competing state-of-the-art approaches, using benchmark datasets and simulation-based Bayesian inverse problems.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Data-Driven Recommendation of Optimal Tuning Scheme for Range-Separated Hybrid Functionals in Solution-Phase UV/Vis Absorption Energy Prediction

Time-dependent density functional theory (TDDFT) combined with range-separated hybrid (RSH) functionals and a tuned range-separation parameter γ offers a computationally economical approach for high-throughput excited- state property predictions. The γ-tuning procedure in the gas phase is well established. However, no agreement on the best γ- tuning procedure has been made when considering the solvent effect with implicit solvent models like the polarizable continuum model (PCM). To answer that question, this study created a diverse dataset with 937 molecules with experimental solutionphase UV/vis absorption spectra. Three γ-tuning methods, the gasphase γ-tuning (GPγT), the partial vertical γ-tuning (PVγT), and the strict vertical γ-tuning (SVγT), were evaluated for the ωPBEh functional over the entire dataset. Additional benchmarks are done for the optimally tuned screened range-separated hybrid combined with the PCM approach (SRSH-PCM) and the solvation-mediated tuning procedure (sol-med-OT). Our findings revealed that the optimal γ-values obtained by the PVγT and the SVγT are significantly smaller than the GPγT. This trend holds consistently across all molecules in our dataset, and we explained the origin of this phenomenon. TDDFT calculations with PVγTand SVγT-tuned γ-values and default global Fock exchange fraction achieve superior performance compared to those using GPγTtuned or default γ and slightly outperform SRSH-PCM and sol-med-OT with similar or lesser computational cost. Furthermore, we found that the smaller γ-values from SVγT captured the expected 1/(εR) asymptotic behavior in the solution phase, resulting in accurate prediction of solution-phase CT excitations, consistent with the screened asymptote behavior encoded in SRSH-PCM. These results show that SVγT is the best scheme for high-throughput UV/vis absorption spectrum calculations using the ωPBEh functional from a data-driven perspective.

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

Bayesian optimization algorithms for accelerator physics

Accelerator physics relies on numerical algorithms to solve optimization problems in online accelerator control and tasks such as experimental design and model calibration in simulations. The effectiveness of optimization algorithms in discovering ideal solutions for complex challenges with limited resources often determines the problem complexity these methods can address. The accelerator physics community has recognized the advantages of Bayesian optimization algorithms, which leverage statistical surrogate models of objective functions to effectively address complex optimization challenges, especially in the presence of noise during accelerator operation and in resource-intensive physics simulations. In this review article, we offer a conceptual overview of applying Bayesian optimization techniques toward solving optimization problems in accelerator physics. We begin by providing a straightforward explanation of the essential components that make up Bayesian optimization techniques. We then give an overview of current and previous work applying and modifying these techniques to solve accelerator physics challenges. Finally, we explore practical implementation strategies for Bayesian optimization algorithms to maximize their performance, enabling users to effectively address complex optimization challenges in real-time beam control and accelerator design. Published by the American Physical Society 2024

43 PARTICLE ACCELERATORS↗
DOE OSTI2024

Strategically designed metal-free deep-ultraviolet birefringent crystals with superior optical properties

Finding new birefringent materials with deep-ultraviolet (DUV, λ < 200 nm) transparency is urgent, as current commercial materials cannot meet the rapidly growing demands in related application fields. Herein, three guanidinium-based compounds, C(NH 2 ) 3 CH 3 SO 3 , β-C(NH 2 ) 3 Cl, and γ-C(NH 2 ) 3 Cl, all featuring [C(NH 2 ) 3 ·X] ∞ (X = CH 3 SO 3 and Cl) pseudo layers, were designed through structural motif tailoring. Theoretical calculations indicate that these metal-free compounds all possess broad bandgaps (6.49–6.71 eV, HSE06) and remarkable birefringence (cal. 0.166–0.211 @ 1064 nm). Centimeter-sized C(NH 2 ) 3 CH 3 SO 3 crystals have been grown using a feasible aqua-solution method. Subsequently, to further optimize the properties, β/γ-C(NH 2 ) 3 Cl was remolded by further tailoring the [C(NH 2 ) 3 ] + cationic unit and the acceptor Cl - anion, and then the fourth compound NH 2 COF was theoretically constructed. Interestingly, NH 2 COF exhibits the desired coexistence of a wider bandgap (7.87 eV, HSE06) and giant birefringence (cal. 0.241 @ 1064 nm) attributed to its higher density of well-aligned birefringence-active groups (BAGs). Furthermore, among these four designed compounds, C(NH 2 ) 3 CH 3 SO 3 has been experimentally synthesized and exhibits a short UV cutoff edge. Centimeter-sized crystals have been grown using a feasible aqueous solution method. This study provides an effective strategy to optimize the density of BAGs for large birefringence and offers valuable insights into the strategic design of metal-free DUV birefringent crystals.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2026

Technoeconomic Design Optimization for Fast Reactors. Part I: Workflow Development and Case Study for Small LFR District Energy Application

The nuclear industry is developing small reactor designs that can target a variety of deployment locations and energy products. Smaller nuclear designs have traditionally struggled to handle the steep trade-offs between size and cost that have historically incentivized large reactors. This motivates computational optimization of small reactors to minimize costs and quantify the trade-off between size and cost. In this paper, the cost/size trade-off for a small fast reactor is derived using a multi-objective genetic algorithm optimization, with steady-state, transient, and cost analysis of the fast reactor being performed. Specifically, the method is demonstrated on a small 10- to 120-MW(thermal) U-Pu-Zr–fueled lead-cooled fast reactor with a 10-year core life for district energy applications, which can have a thermal load compatible with this range. The results reinforced that fast reactor cores at the lower end of this power range suffer cost penalties due to critical mass considerations. It was found that high power density cores with strong reactivity swings and many control rods were favored over designing to minimize reactivity swing. Furthermore, this contrasts with some traditional configurations designed using engineering judgment and demonstrates that optimizers can find nontraditional but realistic solutions, along with demonstrating the value of incorporating cost functions into whole-reactor design optimization.

Fast reactor↗