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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 757 records · Page 42
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

aphBO-2GP-3B: a budgeted asynchronous parallel multi-acquisition functions for constrained Bayesian optimization on high-performing computing architecture

High-fidelity complex engineering simulations are often predictive, but also computationally expensive and often require substantial computational efforts. The mitigation of computational burden is usually enabled through parallelism in high-performance cluster (HPC) architecture. Optimization problems associated with these applications is a challenging problem due to the high computational cost of the high-fidelity simulations. In this paper, an asynchronous parallel constrained Bayesian optimization method is proposed to efficiently solve the computationally expensive simulation-based optimization problems on the HPC platform, with a budgeted computational resource, where the maximum number of simulations is a constant. The advantage of this method are three-fold. Firstly, the efficiency of the Bayesian optimization is improved, where multiple input locations are evaluated parallel in an asynchronous manner to accelerate the optimization convergence with respect to physical runtime. This efficiency feature is further improved so that when each of the inputs is finished, another input is queried without waiting for the whole batch to complete. Second, the proposed method can handle both known and unknown constraints. Third, the proposed method samples several acquisition functions based on their rewards using a modified GP-Hedge scheme. The proposed framework is termed aphBO-2GP-3B, which means asynchronous parallel hedge Bayesian optimization with two Gaussian processes and three batches. The numerical performance of the proposed framework aphBO-2GP-3B is comprehensively benchmarked using 16 numerical examples, compared against other 6 parallel Bayesian optimization variants and 1 parallel Monte Carlo as a baseline, and demonstrated using two real-world high-fidelity expensive industrial applications. The first engineering application is based on finite element analysis (FEA) and the second one is based on computational fluid dynamics (CFD) simulations.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Visual Analytics of Performance of Quantum Computing Systems and Circuit Optimization

Driven by potential exponential speedups in business, security, and scientific scenarios, interest in quantum computing is surging. This interest feeds the development of quantum computing hardware, but several challenges arise in optimizing application performance for hardware metrics (e.g., qubit coherence and gate fidelity). In this work, we describe a visual analytics approach for analyzing the performance properties of quantum devices and quantum circuit optimization. Our approach allows users to explore spatial and temporal patterns in quantum device performance data and it computes similarities and variances in key performance metrics. Detailed analysis of the error properties characterizing individual qubits is also supported. We also describe a method for visualizing the optimization of quantum circuits. The resulting visualization tool allows researchers to design more efficient quantum algorithms and applications by increasing the interpretability of quantum computations.

Chae, Junghoon↗
DOE OSTI2017

Optimizing fusion PIC code performance at scale on Cori Phase 2

In this paper we present the results of optimizing the performance of the gyrokinetic full-f fusion PIC code XGC1 on the Cori Phase Two Knights Landing system. The code has undergone substantial development to enable the use of vector instructions in its most expensive kernels within the NERSC Exascale Science Applications Program. We study the single-node performance of the code on an absolute scale using the roofline methodology to guide optimization efforts. We have obtained 2x speedups in single node performance due to enabling vectorization and performing memory layout optimizations. On multiple nodes, the code is shown to scale well up to 4000 nodes, near half the size of the machine. We discuss some communication bottlenecks that were identified and resolved during the work.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2017

Optimizing the query performance of block index through data analysis and I/O modeling

Indexing technique has become an efficient tool to enable scientists to directly access the most relevant data records. But, the time and space requirements of building and storing indexes are expensive in the traditional approaches, such as R-tree and bitmaps. Recently, we started to address this issue by using the idea of "block index", and our previous work has shown promising results from comparing it against other well-known solutions, including ADIOS, SciDB, and FastBit. In this work, we further improve the technique from both theoretical and implementation perspectives. Driven by an extensive effort in characterizing scientific datasets and modeling I/O systems, we presented a theoretical model to analyze its query performance with respect to a given block size configuration. We also introduced three optimization techniques to achieve a 2.3x query time reduction comparing to the original implementation.

Wu, Tzuhsien↗
DOE OSTI2025

Agentic AI vs ML-Based Autotuning: A Comparative Study for Loop Reordering Optimization

High Performance Computing (HPC) applications rely heavily on code optimizations to achieve good performance on modern CPU and GPU architectures. Traditional Machine Learning auto-tuning approaches have demonstrated success in exploring high-dimensional spaces, but they often require expensive compile-run evaluations and lack adaptability for large HPC applications. The recent advances in Large Language Models (LLMs) and Agentic AI systems raise intriguing questions about the potential of these approaches to address specific optimization methodologies. This work aims to answer an essential question for the HPC community: “How Agentic AI Systems Compare to Traditional ML Autotuning Techniques?” To address this question, we present a comparative analysis between a traditional ML-based optimization approach and an Agentic AI system, evaluating their respective capabilities and limitations for loop-level optimization. In addition, we introduced a new Agentic AI system named LoopGen-AI using three different Large Language Models: GPT-4.1, Claude 4.0, and Gemini 2.5. A key finding is that LoopGen-AI achieves competitive per-formance with only a few program runs, the reasoning logs from the agents revealed that their decisions rely heavily on the combination of semantic understanding of the target kernel with dynamic feedback from the environment, highlighting a promising new dimension in performance tuning. In contrast, ML-based autotuners focus on statistical exploration, and require orders of magnitude more runs to reach peak performance. Additionally, our analysis shows that prompt engineering, particularly using Persona + Context Manager patterns, significantly impacts the effectiveness of Agentic AI. Our results indicate that while Agentic AI systems are not yet a complete replacement for ML-based autotuners, it can effectively complement traditional methods.

Rosas, Miguel Romero↗
DOE OSTI2026

Robust Design Under Uncertainty in Quantum Error Mitigation

Error mitigation techniques are crucial to achieving near-term quantum advantage. Classical postprocessing of quantum computation outcomes is a popular approach for error mitigation, which includes methods, such as zero noise extrapolation, virtual distillation, and learning-based error mitigation. However, these techniques have limitations due to the propagation of uncertainty resulting from the finite shot number of a quantum measurement. In this work, we introduce general and unbiased methods for quantifying the uncertainty and error of error-mitigated observables based on the strategic sampling of error mitigation outcomes. We then extend our approach to demonstrate the optimization of performance and robustness of error mitigation under uncertainty. To illustrate our methods, we apply them to zero noise extrapolation and Clifford date regression in the ground state of the XY model simulated using depolarizing and International Business Machines Corporation (IBM) Toronto noise models, respectively. In particular, we optimize the choice of noise levels and the allocation of shots for zero noise extrapolation and the distribution of the training circuits for Clifford data regression. While our methods are readily applicable to any postprocessing-based error mitigation approach, in practice they must not be prohibitively expensive—even though they perform optimizations of the error mitigation hyperparameters requiring sampling of a statistical distribution of error mitigation outcomes. By leveraging surrogate-based optimization, we show that our methods can efficiently perform optimal design for a zero noise extrapolation implementation. We then further demonstrate the transferability of learned zero noise extrapolation hyperparameters to other similar circuits.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2017

Adjoint-based optimization of mechanical performance in polycrystalline materials and structures through texture control

The texture of a polycrystalline material refers to the preferred orientation of the grains within the material. In metallic materials, texture can significantly affect the mechanical properties such as elastic moduli, yield stress, strain hardening, and fracture toughness. Recent advances in additive manufacturing of metallic materials offer the possibility in the not too distant future of controlling the spatial variation of texture. In this work, we investigate the advantages, in terms of mechanical performance, of allowing the texture to vary spatially. We use an adjoint-based gradient optimization algorithm within a finite element solver (COMSOL) to optimize several engineering quantities of interest in a simple structure (hole in a plate) and loading (uniaxial tension) condition. As a first step to general texture optimization, we consider the idealized case of a pure fiber texture in which the homogenized properties are transversely isotropic. In this special case, the only spatially varying design variables are the three Euler angles that prescribe the orientation of the homogenized material at each point within the structure. This work paves a new way to design metallic materials for tunable mechanical properties at the microstructure level.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Structure optimization with stochastic density functional theory

Linear-scaling techniques for Kohn–Sham density functional theory are essential to describe the ground state properties of extended systems. Still, these techniques often rely on the localization of the density matrix or accurate embedding approaches, limiting their applicability. In contrast, stochastic density functional theory (sDFT) achieves linear- and sub-linear scaling by statistically sampling the ground state density without relying on embedding or imposing localization. In return, ground state observables, such as the forces on the nuclei, fluctuate in sDFT, making optimizing the nuclear structure a highly non-trivial problem. In this work, we combine the most recent noise-reduction schemes for sDFT with stochastic optimization algorithms to perform structure optimization within sDFT. We compare the performance of the stochastic gradient descent approach and its variations (stochastic gradient descent with momentum) with stochastic optimization techniques that rely on the Hessian, such as the stochastic Broyden–Fletcher–Goldfarb–Shanno algorithm. In conclusion, we further provide a detailed assessment of the computational efficiency and its dependence on the optimization parameters of each method for determining the ground state structure of bulk silicon with varying supercell dimensions.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2018

Field evaluation of performance of HVAC optimization system in commercial buildings

Smart new building technologies that offer continuous dynamic optimization of Heating, Ventilation, and Air Conditioning (HVAC) control hold promise to advance building operations for efficiency and grid response. These technologies use data from the control system to determine the analytically optimal setpoints, and then write back the optimal setpoints into the control system to minimize system energy consumption or costs. There are limited studies documenting field validations of these technologies. This paper presents the results from a long-term field evaluation of a model-predictive HVAC optimization system that installed in four commercial buildings. Energy savings analysis was conducted based on pre/post submetered energy use. Across the cohort of evaluation sites, HVAC savings following the implementation of the optimization system were mixed, ranging from 0–9%. Analysis of site operational data showed that occupant comfort was neither positively nor negatively impacted. Key technology adoption considerations and recommendations are summarized in the paper. The technology performs best when HVAC systems are in good working condition, and can be exercised to achieve the full range of its optimized setpoints–however it may not provide extensive additional savings over cases where best practice sequences of operation and reset strategies are already comprehensively implemented.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2016

A novel software and conceptual design of the hardware platform for intensity modulated radiation therapy

Purpose: To deliver high quality intensity modulated radiotherapy (IMRT) using a novel generalized sparse orthogonal collimators (SOCs), the authors introduce a novel direct aperture optimization (DAO) approach based on discrete rectangular representation. Methods: A total of seven patients—two glioblastoma multiforme, three head & neck (including one with three prescription doses), and two lung—were included. 20 noncoplanar beams were selected using a column generation and pricing optimization method. The SOC is a generalized conventional orthogonal collimators with N leaves in each collimator bank, where N = 1, 2, or 4. SOC degenerates to conventional jaws when N = 1. For SOC-based IMRT, rectangular aperture optimization (RAO) was performed to optimize the fluence maps using rectangular representation, producing fluence maps that can be directly converted into a set of deliverable rectangular apertures. In order to optimize the dose distribution and minimize the number of apertures used, the overall objective was formulated to incorporate an L2 penalty reflecting the difference between the prescription and the projected doses, and an L1 sparsity regularization term to encourage a low number of nonzero rectangular basis coefficients. The optimization problem was solved using the Chambolle–Pock algorithm, a first-order primal–dual algorithm. Performance of RAO was compared to conventional two-step IMRT optimization including fluence map optimization and direct stratification for multileaf collimator (MLC) segmentation (DMS) using the same number of segments. For the RAO plans, segment travel time for SOC delivery was evaluated for the N = 1, N = 2, and N = 4 SOC designs to characterize the improvement in delivery efficiency as a function of N. Results: Comparable PTV dose homogeneity and coverage were observed between the RAO and the DMS plans. The RAO plans were slightly superior to the DMS plans in sparing critical structures. On average, the maximum and mean critical organ doses were reduced by 1.94% and 1.44% of the prescription dose. The average number of delivery segments was 12.68 segments per beam for both the RAO and DMS plans. The N = 2 and N = 4 SOC designs were, on average, 1.56 and 1.80 times more efficient than the N = 1 SOC design to deliver. The mean aperture size produced by the RAO plans was 3.9 times larger than that of the DMS plans. Conclusions: The DAO and dose domain optimization approach enabled high quality IMRT plans using a low-complexity collimator setup. The dosimetric quality is comparable or slightly superior to conventional MLC-based IMRT plans using the same number of delivery segments. The SOC IMRT delivery efficiency can be significantly improved by increasing the leaf numbers, but the number is still significantly lower than the number of leaves in a typical MLC.

61 RADIATION PROTECTION AND DOSIMETRY↗
DOE OSTI2022

Performance Analysis and Optimization for Scientific Data Workloads

Scientific data generated at experimental and observational facilities are increasingly being processed on large-scale compute systems. Most of the experimental data analysis workflows are not designed or implemented to run on large scale environments and take full advantage of HPC compute and storage resources. These applications are unlike the traditional tightly-coupled scientific applications and hence face significant performance and scalability challenges as the volume of data increases exponentially. In this paper, we conduct a performance and scalability analysis for experimental analysis applications and workflows operating on data from light sources. Our analysis detects and quantifies I/O performance, scalability and runtime bottlenecks for three data analysis applications that run on NERSC resources. Based on our analysis we propose and implement a set of optimizations that lead to reducing the amount of time spent on I/O operations by almost 90%.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Multi-Material Topology Optimization for High Performance Heat Exchangers

This award allowed RTX Technology Research Center to demonstrate several key objectives related to the design and manufacture of additive heat exchangers. The focus of the project was on creating a multi-physics, multi-material topology optimization tool, then using it to create an ultra-compact, optimized, fully additive heat exchanger.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2024

Sampling Size Optimization for Bioburden Density Estimation in Planetary Protection

Planetary protection (PP) is a discipline that focuses on minimizing the biological contamination of spacecraft to ensure compliance with international policy. Precise estimation of bioburden - the total number of microbes in or on spacecraft hardware – and the bioburden density are of utmost importance for PP. Such estimation is the way concordance with requirements is demonstrated, and it is critical for quantifying the potential risk of inadvertently contaminating other planetary bodies. Although a suite of molecular techniques have been used to thoroughly characterize and profile the microbiome of various cleanroom environments and spacecraft, the gold standard remains the physical enumeration of microbes via culturing of samples directly taken from spacecraft and associated surfaces. However, due to technical, budgetary, and programmatic constraints, only a manageable portion (around 10%) of the entire spacecraft surface is directly sampled with cotton swabs or wipes. To generate the bioburden current best estimate (CBE) for components not directly verifiable, the accepted approach is to apply a NASA-defined bioburden estimate based on the components’ manufacturing or assembly environment. This approach utilizes a prespecified bioburden density estimation that applies a maximum value across the total surface area of the specified component. For hardware components that underwent similar assembly processes, an implied bioburden is adopted for all components, based on a direct verification of a representative component within the same lot. Once all components have a CBE, the bioburden estimates are generated. In previous publication [ 1], we have shown that statistical risks quantifying the accuracy of the estimates for sampled, prespecified, and implied components can be derived and ranked. For mean squared error (MSE) function, the risks are available analytically and hence a cost function can be obtained to optimize the risks with respect to the sampling area and sampling cost. Since the sampling area and sampling cost are two complimentary variables, their sum will have a well-defined minimum. This paper presents the multivariate optimization of the integrated risk of an empirical Bayes estimator to determine the optimal sampling schedule for a given number of components. It is assumed that given a number of components, N, the bioburden density for each component can either be sampled, implied, or prespecified. The multivariate optimization searches through different options to sample, imply or prespecify the bioburden density for a component, and account for the component’s surface area and cost of sampling. The idea of the optimization is based on the observation that the statistical risk of using an estimator is a monotonically decreasing function of the sampled area. The larger the sampled area, the lower the risk of using the estimator as the estimator becomes more and more accurate as the sampling area increases. On the other hand, the cost of sampling is monotonically increasing as the sampled surface grows. This makes the risk and total cost of sampling complimentary variables which can be counterbalanced to achieve an optimal overall value with respect to the sampled surface. In this paper, the integrated risk has been used to quantify the accuracy of the estimator. This risk has been selected because it depends on neither the true value of the parameter nor on the collected data. The cost of each sample was also available to obtain the total cost of sampling of N components. The paper will present the results based on computer-simulated data as well as the data collected during the InSight mission. The computer-simulated data have N components with randomly generated total areas and each component assigned to one of the three categories according to the method of estimating of bioburden density: sampled, implied, or prespecified. The cost of sampling is also available. The cost of sampling is estimated based on a cost model provided by the planetary protection group at JPL. For this paper, the overall cost was assumed to be a linear function of exposure. The optimization process finds the allocation of the components to the three categories that minimizes the tradeoff between integrated risk and total cost. For the InSight data, a set of components is selected representing all three categories, and optimization is performed to determine if the performed allocation was optimal or if a better allocation could have been obtained. To the best of our knowledge, this work is the first attempt not only perform an accurate estimation of bioburden density but also do it in an optimal way.

97 - MATHEMATICS AND COMPUTING↗
DOE OSTI2026

Tokamak level performance in the optimized stellarator Wendelstein 7-X with stable peaked density profiles

Progress towards achieving net gain from nuclear fusion is typically characterized by the triple product 𝑛⁢𝑇⁢𝜏. Although stellarators have several benefits for a reactor, such as the inherent steady-state capability and the lack of disruptions, the maximum achieved triple product has historically lagged behind that of tokamaks. In the large optimized stellarator Wendelstein 7-X, a triple product of (1.10 ± 0.15) × 10 20 m −3 ⁢ keVs has recently been achieved and held stable for 1.9 s. This marks an important step in stellarator core plasma performance and puts it in line with 𝐻-mode performance in similar sized tokamaks. The progress in Wendelstein 7-X performance is due to the suppression of ion-temperature-gradient turbulence by strong density gradients. In the record case, these are maintained by a fine balance of the core particle transport and central fueling using neutral beam injection. The duration of the scenario was limited in time only by technical restrictions of the neutral beam injectors and control of the divertor heat loads. This paper presents the result in the context of the leading magnetic confinement devices along with details of the scenario.

Bannmann, Sebastian [Max Planck Institute for Plas↗
DOE OSTI2023

Performance Evaluation and Optimal Design Analysis of Continuous-Operation Solar-Driven Cooling Absorption Systems With Thermal Energy Storage

A solar absorption cooling system consisting of a flat plate collector, thermal energy storage tank, and absorption chiller is analyzed in this work. A dimensionless model is developed from the energy balance on each component and the chiller's characteristic performance curves. The model is used to determine the interaction and influence of different parameters such as tank size, solar collector area, chiller size, cooling load, cooling temperature, heat loss, and mass flow rates on the performance. From the analysis, smaller solar collector areas are required for lower cooling loads and smaller tank volumes. A specific cooling load of 1 x 10^-5 will require a specific solar collector area between two and six times larger, depending on the initial tank temperature, than the area required for a baseline system that considers typical commercial design and operation parameters. A similar behavior was observed for the specific tank volume. For the baseline system, the minimum specific area of the collector of 9.57 is achieved for an initial tank temperature of 1.19. For a cooling load of 1 x 10^-5, the optimum initial tank temperature will be 1.11 that results in a minimum specific solar collector area of 25.26. A specific tank volume of 4 x 10^-4 will also have an optimum initial tank temperature of 1.11 that minimizes the specific solar collector area to a value of 28.18. The approach and analysis in this work can be used to determine design parameters for solar absorption cooling systems based on a proper relation among system's dimensions to achieve optimum operation.

cooling load↗
DOE OSTI2019

Performance analysis and optimization for scalable deployment of deep learning models for country-scale settlement mapping on Titan supercomputer

Here, we present a scalable object detection workflow for detecting objects, such as settlements, from remotely sensed (RS) imagery. We have successfully deployed this workflow on Titan supercomputer and utilized it for the task of mapping human settlement at a country scale. The performance of various stages in the workflow was analyzed before making it operational. The workflow implemented various strategies to address issues such as suboptimal resource utilization and long-tail effects due to unbalanced image workload, data loss due to runtime failures, and maximum wall-time constraints imposed by Titan's job scheduling policy. A mean shift clustering–based static load balancing strategy was implemented, which partitions the image load such that each partition contained similar-sized images. Furthermore, a checkpoint-restart strategy was added in the workflow as a fault-tolerance mechanism to prevent the data losses due to unforeseen runtime failures. The performance of the above-mentioned strategies was observed in various scenarios, such as node failure, exceeding wall time, and successful completion. Using this workflow, we have examined an RS data set that has a spatial resolution of 0.31 m and is comprised of 685 675 km 2 of area of the Republic of Zambia in under six hours using 5426 nodes of the Titan supercomputer.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Coupled beam-target-moderator optimization for the Second Target Station

To support the design of the Second Target Station, that aims to provide the world’s highest peak brightness of cold neutrons, studies that optimize the dimensions of the target and moderators are invaluable. In this work, we investigate the influence of the target dimensions and beam profile on the performance and optimal size of the moderators. We perform optimization runs with detailed MCNP6.2 simulations using high-fidelity unstructured mesh geometries generated from parametrized CAD models. We demonstrate that small changes in target height do not influence the moderator performance if the beam dimensions are chosen adequately. We quantify the effect of beam footprint and target width on the moderator performance and show that the optimal moderator dimensions are insensitive to limited changes in target and beam profile.

Dakota↗
DOE OSTI2018

CSULA Hydrogen Refueling Facility Performance Evaluation and Optimization

Throughout its history, Cal State LA has been in the forefront of advanced energy and transportation technologies. The station is located at the intersection of two major highways in Los Angeles region and has a convenient access. It is capable of producing up to 60 kg/day and can fuel 15-20 vehicles per day. Cal State LA had partnered with DOE to establish a robust station operation and performance data collection program. This consisted of equipping the station with additional instrumentation and collecting fueling performance data for several years. The data was further furnished to NREL for processing and agglomeration with data from other stations. In addition to fueling, maintenance and failure events were shared as well as hydrogen purity reports. The station has also undergone several upgrades in fueling protocols and hardware for improved performance and code compliance.

08 HYDROGEN↗