Search NASA⌕ Search

SEARCH · Search NASA

Results for “gradient design strategies”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 91 records · Page 5
All sourcesNASA NTRSDOE OSTI
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 OSTI2022

Diagnosing Barren Plateaus with Tools from Quantum Optimal Control

Variational Quantum Algorithms (VQAs) have received considerable attention due to their potential for achieving near-term quantum advantage. However, more work is needed to understand their scalability. One known scaling result for VQAs is barren plateaus, where certain circumstances lead to exponentially vanishing gradients. It is common folklore that problem-inspired ansatzes avoid barren plateaus, but in fact, very little is known about their gradient scaling. In this work we employ tools from quantum optimal control to develop a framework that can diagnose the presence or absence of barren plateaus for problem-inspired ansatzes. Such ansatzes include the Quantum Alternating Operator Ansatz (QAOA), the Hamiltonian Variational Ansatz (HVA), and others. With our framework, we prove that avoiding barren plateaus for these ansatzes is not always guaranteed. Specifically, we show that the gradient scaling of the VQA depends on the degree of controllability of the system, and hence can be diagnosed through the dynamical Lie algebra $\mathfrak{g}$ obtained from the generators of the ansatz. We analyze the existence of barren plateaus in QAOA and HVA ansatzes, and we highlight the role of the input state, as different initial states can lead to the presence or absence of barren plateaus. Taken together, our results provide a framework for trainability-aware ansatz design strategies that do not come at the cost of extra quantum resources. Moreover, we prove no-go results for obtaining ground states with variational ansatzes for controllable system such as spin glasses. Our work establishes a link between the existence of barren plateaus and the scaling of the dimension of $\mathfrak{g}$.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2026

Geothermal Heat Pump System Showcase: Short-Term Validation of Borehole Heat Exchanger Performance from Field Data to Numerical Modeling: Preprint

Since 2011, a geothermal heat pump (GHP) system has been operating to provide space heating and cooling for the Solar Radiation and Research Laboratory building at the National Laboratory of the Rockies (NLR) in Golden, Colorado. The system consists of 23 vertical boreholes, each extending to a depth of 300 ft (91 m), connected to 11 water-to-air heat pump units and four circulation pumps. Between fiscal years 2023 and 2025, additional power meters and temperature sensors were retrofitted to support detailed system performance assessment and model development. This study presents preliminary monitoring results and the development of an initial numerical model of the borehole heat exchanger field. The model incorporated site-specific geometry, ground thermal properties derived from thermal response tests, and ambient temperatures, and simulated system behavior over a representative operating day in September. Model predictions of outlet temperatures were compared against corresponding field measurements. Results showed that modeling initialized with a simplified linear subsurface temperature gradient presents systematic discrepancies in outlet temperature, whereas incorporating depth-resolved borehole temperature measurements for initialization yields substantially improved agreement with observations. The findings highlight the sensitivity of short-term predictive modeling to the representation of initial subsurface thermal conditions and underscore the value of high-resolution field measurements for model calibration and validation. These preliminary results inform ongoing efforts to extend the modeling framework to longer time horizons and to refine monitoring and modeling strategies that support the design guidance and operational optimization of GHP systems in research and commercial buildings.

15 GEOTHERMAL ENERGY↗
DOE OSTI2022

A Framework for Error-Bounded Approximate Computing, with an Application to Dot Products

Approximate computing techniques, which trade off the computation accuracy of an algorithm for better performance and energy efficiency, have been successful in reducing computation and power costs in several domains. However, error sensitive applications in high-performance computing are unable to benefit from existing approximate computing strategies that are not developed with guaranteed error bounds. While approximate computing techniques can be developed for individual high-performance computing applications by domain specialists, this often requires additional theoretical analysis and potentially extensive software modification. Hence, the development of low-level error-bounded approximate computing strategies that can be introduced into any high-performance computing application without requiring additional analysis or significant software alterations is desirable. In this paper, we provide a contribution in this direction by proposing a general framework for designing error-bounded approximate computing strategies and apply it to the dot product kernel to develop \bf qdot---an error-bounded approximate dot product kernel. Following the introduction of qdot, here we perform a theoretical analysis that yields a deterministic bound on the relative approximation error introduced by qdot. Empirical tests are performed to illustrate the tightness of the derived error bound and to demonstrate the effectiveness of qdot on a synthetic dataset, as well as two scientific benchmarks---the conjugate gradient (CG) and power methods. In some instances, using qdot for the dot products in CG can result in many components being quantized to half precision without increasing the iteration count required for convergence to the same solution as CG using a double precision dot product.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Building a genome-based understanding of bacterial pH preferences

The environmental preferences of many microbes remain undetermined. This is the case for bacterial pH preferences, which can be difficult to predict a priori despite the importance of pH as a factor structuring bacterial communities in many systems. We compiled data on bacterial distributions from five datasets spanning pH gradients in soil and freshwater systems (1470 samples), quantified the pH preferences of bacterial taxa across these datasets, and compiled genomic data from representative bacterial taxa. While taxonomic and phylogenetic information were generally poor predictors of bacterial pH preferences, we identified genes consistently associated with pH preference across environments. We then developed and validated a machine learning model to estimate bacterial pH preferences from genomic information alone, a model that could aid in the selection of microbial inoculants, improve species distribution models, or help design effective cultivation strategies. More generally, we demonstrate the value of combining biogeographic and genomic data to infer and predict the environmental preferences of diverse bacterial taxa.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Learning in continuous action space for developing high dimensional potential energy models

Reinforcement learning (RL) approaches that combine a tree search with deep learning have found remarkable success in searching exorbitantly large, albeit discrete action spaces, as in chess, Shogi and Go. Many real-world materials discovery and design applications, however, involve multi-dimensional search problems and learning domains that have continuous action spaces. Exploring high-dimensional potential energy models of materials is an example. Traditionally, these searches are time consuming (often several years for a single bulk system) and driven by human intuition and/or expertise and more recently by global/local optimization searches that have issues with convergence and/or do not scale well with the search dimensionality. Here, in a departure from discrete action and other gradient-based approaches, we introduce a RL strategy based on decision trees that incorporates modified rewards for improved exploration, efficient sampling during playouts and a “window scaling scheme" for enhanced exploitation, to enable efficient and scalable search for continuous action space problems. Using high-dimensional artificial landscapes and control RL problems, we successfully benchmark our approach against popular global optimization schemes and state of the art policy gradient methods, respectively. We demonstrate its efficacy to parameterize potential models (physics based and high-dimensional neural networks) for 54 different elemental systems across the periodic table as well as alloys. We analyze error trends across different elements in the latent space and trace their origin to elemental structural diversity and the smoothness of the element energy surface. Broadly, our RL strategy will be applicable to many other physical science problems involving search over continuous action spaces.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Learning with Adaptive Conservativeness for Distributionally Robust Optimization: Incentive Design for Voltage Regulation

Information asymmetry between the Distribution System Operator (DSO) and Distributed Energy Resource Aggregators (DERAs) obstructs designing effective incentives for voltage regulation. To capture this effect, we employ a Stackelberg game-theoretic framework, where the DSO seeks to overcome the information asymmetry and refine its incentive strategies by learning from DERA behavior over multiple iterations. We introduce a model-based online learning algorithm for the DSO, aimed at inferring the relationship between incentives and DERA responses. Given the uncertain nature of these responses, we also propose a distributionally robust incentive design model to control the probability of voltage regulation failure and then reformulate it into a convex problem. This model allows the DSO to periodically revise distribution assumptions on uncertain parameters in the decision model of the DERA. Finally, we present a gradient-based method that permits the DSO to adaptively modify its conservativeness level, measured by the size of a Wasserstein metric-based ambiguity set, according to historical voltage regulation performance. The effectiveness of our proposed method is demonstrated through numerical experiments.

adaptation models↗
DOE OSTI2024

Learning with Adaptive Conservativeness for Distributionally Robust Optimization: Incentive Design for Voltage Regulation: Preprint

Information asymmetry between the Distribution System Operator (DSO) and Distributed Energy Resource Aggregators (DERAs) obstructs designing effective incentives for voltage regulation. To capture this effect, we employ a Stackelberg game-theoretic framework, where the DSO seeks to overcome the information asymmetry and refine its incentive strategies by learning from DERA behavior over multiple iterations. We introduce a model-based online learning algorithm for the DSO, aimed at inferring the relationship between incentives and DERA responses. Given the uncertain nature of these responses, we also propose a distributionally robust incentive design model to control the probability of voltage regulation failure and then reformulate it into a convex problem. This model allows the DSO to periodically revise distribution assumptions on uncertain parameters in the decision model of the DERA. Finally, we present a gradient-based method that permits the DSO to adaptively modify its conservativeness level, measured by the size of a Wasserstein metric-based ambiguity set, according to historical voltage regulation performance. The effectiveness of our proposed method is demonstrated through numerical experiments.

distribution system operator↗
DOE OSTI2025

Non-dimensional performance and safety parameters for heat pipes

The use of heat pipes in safety-critical systems such as nuclear microreactors dictates the development of generalized, practical, scalable performance and safety parameters. Traditional dimensional metrics, while informative, lack the universality required for comparative analysis across varying designs and operating regimes. Here, this work introduces a comprehensive set of non-dimensional parameters to characterize heat pipe performance and safety, including capillary performance, effective thermal conductivity, response time, exergetic efficiency, allowable temperature gradients, allowable rate of temperature change, priming coefficients, and factor of safety. A reference heat pipe design representative of microreactor applications was analyzed via the developed parameters using both traditional analytical models and Sockeye simulations under transient and steady-state conditions. Sodium, potassium, and water were evaluated as working fluids to demonstrate the applicability of the framework across a broad temperature range. The proposed non-dimensional parameters effectively captured key thermal-hydraulic behaviors and safety concerns, as was demonstrated via Sockeye simulations. This framework supports the development of design optimization strategies, operational protocols, and safety assurance practices for advanced reactor systems and other high-reliability applications.

42 - ENGINEERING↗
DOE OSTI2024

Electrochemically driven carbon dioxide separation

This project explored the viability of a Ni(OH) 2 based hydroxide exchange membrane carbon capture (HEMCC) device for direct air capture. It is built off an H 2 fuel cell based HEMCC previously developed in the Yushan Yan group at University of Delaware. Taking the same membrane-based, electrochemically driven pH gradient concept, similar flux performance was able to be seen in the Ni(OH) 2 system as the H 2 system when using comparable current densities. The project produced two types of membrane electrode assemblies (MEA). The first was a traditional MEA with two electrodes and a membrane, the second was a flow-through membrane MEA. Consistent performance was achieved with the traditional MEA with an energy cost of 1.1 MWh∙ton -1 and flux of 82 kg∙m -2 ∙yr -1 . This was the most stable of the two designs. This project investigated strategies to improve performance with a flow-through membrane design. Two designs were made, one with a cast phase inversion membrane, and one with powdered membrane layer. The template phase inversion membrane achieved low pressure drop but had performance limitations due to a skin layer of membrane limiting CO 2 gas transport. The powdered membrane layer had better performance but higher pressure drop. Finally, using the powdered membrane, commercial battery materials were able to be used in order to achieve higher flux. This showed that the flow through membrane design does have the capability to overcome flux inefficiencies in the traditional MEA. Moreover, the process design of the system is proposed and given in this project. The process mass and energy balances were calculated for a reference plant of 1000 t/yr CO 2 capture, which contains several subsystems, e.g. air processing subsystem, electrical subsystem, and CO 2 purification and compression subsystem is designed and evaluated. Based on our calculation, When the power consumption for stack is 1 MWh t -1 CO 2 , Additional 382 kWh t -1 CO 2 will be consumed by other subsystem of the plant, i.e. The total power consumption of 1.38 MWh t -1 CO 2 , lower than the final milestone of 1.5 MWh t -1 CO 2 in this project.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2022

Accelerating Collective Communication in Data Parallel Training across Deep Learning Frameworks

This work develops new techniques within Horovod, a generic communication library supporting data parallel training across deep learning frameworks. In particular, we improve the Horovod control plane by implementing a new coordination scheme that takes advantage of the characteristics of the typical data parallel training paradigm, namely the repeated execution of collectives on the gradients of a fixed set of tensors. Using a caching strategy, we execute Horovod’s existing coordinator-worker logic only once during a typical training run, replacing it with a more efficient decentralized orchestration strategy using the cached data and a global intersection of a bitvector for the remaining training duration. Next, we introduce a feature for end users to explicitly group collective operations, enabling finer grained control over the communication buffer sizes. To evaluate our proposed strategies, we conduct experiments on a world-class supercomputer — Summit. We compare our proposals to Horovod’s original design and observe 2x performance improvement at a scale of 6000 GPUs; we also compare them against tf.distribute and torch.DDP and achieve 12% better and comparable performance, respectively, using up to 1536 GPUs; we compare our solution against BytePS in typical HPC settings and achieve about 20% better performance on a scale of 768 GPUs. Finally, we test our strategies on a scientific application (STEMDL) using up to 27,600 GPUs (the entire Summit) and show that we achieve a near-linear scaling of 0.93 with a sustained performance of 1.54 exaflops (with standard error +- 0.02) in FP16 precision.

Romero, Joshua↗
DOE OSTI2024

Different Strategies of Stratospheric Aerosol Injection Would Significantly Affect Climate Extreme Mitigation

Abstract Stratospheric aerosol injection (SAI) has been proposed as a potential supplement to mitigate some climate impacts of anthropogenic warming. Using Community Earth System Model ensemble simulation results, we analyze the response of temperature and precipitation extremes to two different SAI strategies: one injects SO 2 at the equator to stabilize global mean temperature and the other injects SO 2 at multiple locations to stabilize global mean temperature as well as the interhemispheric and equator‐to‐pole temperature gradients. Our analysis shows that in the late 21st century, compared with the present‐day climate, both equatorial and multi‐location injection lead to reduced hot extremes in the tropics, corresponding to overcooling of the mean climate state. In mid‐to‐high latitude regions, in comparison to the present‐day climate, substantial decreases in cold extremes are observed under both equatorial and multi‐location injection, corresponding to residual winter warming of the mean climate state. Both equatorial and multi‐location injection reduce precipitation extremes in the tropics below the present‐day level, associated with the decrease in mean precipitation. Overall, for most regions, temperature and precipitation extremes show reduced change in response to multi‐location injection than to equatorial injection, corresponding to reduced mean climate change for multi‐location injection. In comparison with equatorial injection, in response to multi‐location injection, most land regions experience fewer years with significant change in cold extremes from the present‐day level, and most tropical regions experience fewer years with significant change in hot extremes. The design of SAI strategies to mitigate anthropogenic climate extremes merits further study.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

A simplified, efficient approach to hybrid wind and solar plant site optimization

Abstract. Wind plant layout optimization is a difficult, complex problem with a large number of variables and many local minima. Layout optimization only becomes more difficult with the addition of solar generation. In this paper, we propose a parameterized approach to wind and solar hybrid power plant layout optimization that greatly reduces problem dimensionality while guaranteeing that the generated layouts have a desirable regular structure. Thus far, hybrid power plant optimization research has focused on system sizing. We go beyond sizing and present a practical approach to optimizing the physical layout of a wind–solar hybrid power plant. We argue that the evolution strategy class of derivative-free optimization methods is well-suited to the parameterized hybrid layout problem, and we demonstrate how hard layout constraints (e.g., placement restrictions) can be transformed into soft constraints that are amenable to optimization using evolution strategies. Next, we present experimental results on four test sites, demonstrating the viability, reliability, and effectiveness of the parameterized evolution strategy approach for generating optimized hybrid plant layouts. Completing the tool kit for parameterized layout generation, we include a brief tutorial describing how the parameterized evolutionary approach can be inspected, understood, and debugged when applied to hybrid plant layouts.

14 SOLAR ENERGY↗
DOE OSTI2024

Mechanochemically Robust LiCoO 2 with Ultrahigh Capacity and Prolonged Cyclability

Pushing intercalation-type cathode materials to their theoretical capacity often suffers from fragile Li-deficient frameworks and severe lattice strain, leading to mechanical failure issues within the crystal structure and fast capacity fading. This is particularly pronounced in layered oxide cathodes because the intrinsic nature of their structures is susceptible to structural degradation with excessive Li extraction, which remains unsolved yet despite attempts involving elemental doping and surface coating strategies. Herein, a mechanochemical strengthening strategy is developed through a gradient disordering structure to address these challenges and push the LiCoO 2 (LCO) layered cathode approaching the capacity limit (256 mAh g -1 , up to 93% of Li utilization). This innovative approach also demonstrates exceptional cyclability and rate capability, as validated in practical Ah-level pouch full cells, surpassing the current performance benchmarks. Comprehensive characterizations with multiscale X-ray, electron diffraction, and imaging techniques unveil that the gradient disordering structure notably diminishes the anisotropic lattice strain and exhibits high fatigue resistance, even under extreme delithiation states and harsh operating voltages. Consequently, this designed LCO cathode impedes the growth and propagation of particle cracks, and mitigates irreversible phase transitions. In conclusion, this work sheds light on promising directions toward next-generation high-energy-density battery materials through structural chemistry design.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Composition‐Conditioning Agent for Doped Spiro‐OMeTAD to Realize Highly Efficient and Stable Perovskite Solar Cells

Abstract The doped Spiro‐OMeTAD hole transport layer (HTL) formed using the lithium bis(trifluoromethane) sulfonimide salt and 4‐tert‐butylpyridine with phenethylammonium iodide surface treatment on a perovskite film has continuously dominated the record power conversion efficiencies (PCEs) of perovskite solar cells (pero‐SCs). However, unstable HTL compositions and iodide salts can cause severe device degradation. In this study, an HTL composition‐conditioning agent (CCA), Spiro‐BD‐2OEG, is designed, which contains a Spiro‐OMeTAD‐like backbone, functional pyridine units, and oligo (ethylene glycol) chains. This finely designed CCA presents good miscibility with Spiro‐OMeTAD and its dopants and acts as a conditioning agent through weak bond interactions. As a result, the CCA‐regulated HTL shows a pinhole‐free and smooth morphology with enhanced Spiro‐OMeTAD ordering and improves dopant stability. In addition, the gradient‐distributed CCA in the HTL can narrow the energy level offset with the valence band of the perovskite. The resultant pero‐SCs exhibit an excellent PCE of 24.19% without any interface treatment and weak size dependence. A remarkable PCE of 22.63% is obtained even for a 1.004‐cm 2 device. Importantly, the strategy shows good universality and significantly promotes the long‐term stability of the pero‐SCs based on the classical doped Spiro‐OMeTAD.

Chemistry↗
DOE OSTI2026

A variational framework for residual-based adaptivity in neural PDE solvers and operator learning

Residual-based adaptive strategies are widely used in scientific machine learning yet remain largely heuristic. We introduce a variational framework that formalizes these methods through convex transformations of the residual, where different transformations correspond to distinct objective functionals. For instance, exponential weights target uniform error minimization, while linear weights recover quadratic error minimization. This perspective reveals adaptive weighting as a means of selecting sampling distributions that optimize a primal objective, directly linking discretization choices to error metrics. This principled approach yields three key benefits: it enables systematic design of adaptive schemes, reduces discretization error by lowering estimator variance, and enhances learning dynamics by improving gradient signal-to-noise ratio. Extending the framework to operator learning, we demonstrate substantial performance gains across diverse optimizers and architectures. Our results provide a theoretical perspective for residual-based adaptivity and establish a foundation for principled discretization and training.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

The role of ionic blockades in controlling the efficiency of energy recovery in forward bias bipolar membranes

Limited understanding exists about the operation of bipolar membranes (BPMs) in forward bias to convert protonic gradients into electrical work, despite their emerging role in many electrochemical devices. In these device contexts, the BPM is typically exposed to complex electrolyte mixtures, but their impact on polarization remains poorly understood. Here, for this work, we develop a mechanistic model explaining the forward bias polarization behaviour of BPMs in mixed electrolytes with different acidities/basicities. This model invokes that weak acids/bases accumulate in the BPM and impose an ionic blockade that inhibits the recombination of stronger acids/bases, resulting in a substantial neutralization overpotential. We demonstrate the utility of our model for fuel cells and redox flow batteries and introduce two materials design strategies for mitigating this inhibition. Lastly, we apply our findings to enhance the energy efficiency of carbonate management in CO 2 electrolysers. This work highlights how non-equilibrium local environments at membrane–membrane interfaces can define the efficiency of protonic-to-electrical energy conversion.

25 ENERGY STORAGE↗
DOE OSTI2025

Enhancing Multi-Step Reservoir Inflow Forecasting: A Time-Variant Encoder–Decoder Approach

Accurate reservoir inflow forecasting is vital for effective water resource management. Reliable forecasts enable operators to optimize storage and release strategies to meet competing sectoral demands—such as water supply, irrigation, and hydropower scheduling—while also mitigating flood and drought risks. To address this need, in this study, we propose a novel time-variant encoder–decoder (ED) model designed specifically to improve multi-step reservoir inflow forecasting, enabling accurate predictions of reservoir inflows up to seven days ahead. Unlike conventional ED-LSTM and recursive ED-LSTM models, which use fixed encoder parameters or recursively propagate predictions, our model incorporates an adaptive encoder structure that dynamically adjusts to evolving conditions at each forecast horizon. Additionally, we introduce the Expected Baseline Integrated Gradients (EB-IGs) method for variable importance analysis, enhancing interpretability of inflow by incorporating multiple baselines to capture a broader range of hydrometeorological conditions. The proposed methods are demonstrated at several diverse reservoirs across the United States. Our results show that they outperform traditional methods, particularly at longer lead times, while also offering insights into the key drivers of inflow forecasting. These advancements contribute to enhanced reservoir management through improved forecasting accuracy and practical decision-making insights under complex hydroclimatic conditions.

58 GEOSCIENCES↗