SEARCH · Engineering Papers

Results for “scalable performance”

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.

SourceResearch connectionFrom yearThrough year

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

At least 1,729 records · Page 96

All sources NASA NTRS DOE OSTI

DOE OSTI2024

Multi-objective sizing and dispatch for building thermal and battery storage towards economic and environmental synergy

The role of building thermal and battery storage is pivotal in advancing smart cities and achieving sustainability goals through effective energy management. Despite their significance, there are several limitations in the sizing approach and value stream analysis with various objectives for their widespread adoption in buildings. This work proposes a flexible and scalable multi-objective optimization framework for optimal sizing and dispatch of building thermal and battery storage, addressing conflicting objectives simultaneously using mixed-integer linear programming. The weighted-sum method is adapted, combining multiple objectives into a single function. The two-stage procedure iterates over different weights, generating optimal solutions and forming the Pareto front. Case studies are performed to assess the energy, economic, and environmental benefits of building energy storage systems for a large office building in three climate locations. The results demonstrate that the proposed framework efficiently determines optimal sizing and dispatch strategies, addressing the balance between economic viability and emission reduction. The dynamic relationship between time-of-use energy charges and emission factors leads to significantly different strategies based on whether economic or environmental concerns are prioritized. This research enhances our understanding of the benefits of TES and BES systems in buildings, providing valuable guidance to stakeholders.

25 ENERGY STORAGE↗

DOE OSTI2020

Parallel algorithms for finding connected components using linear algebra

Finding connected components is one of the most widely used operations on a graph. Optimal serial algorithms for the problem have been known for half a century, and many competing parallel algorithms have been proposed over the last several decades under various different models of parallel computation. This paper presents a class of parallel connected-component algorithms designed using linear-algebraic primitives. These algorithms are based on a PRAM algorithm by Shiloach and Vishkin and can be designed using standard GraphBLAS operations. Here, we demonstrate two algorithms of this class, one named LACC for Linear Algebraic Connected Components, and the other named FastSV which can be regarded as LACC’s simplification. With the support of the highly-scalable Combinatorial BLAS library, LACC and FastSV outperform the previous state-of-the-art algorithm by a factor of up to 12x for small to medium scale graphs. For large graphs with more than 50B edges, LACC and FastSV scale to 4K nodes (262K cores) of a Cray XC40 supercomputer and outperform previous algorithms by a significant margin. This remarkable performance is accomplished by (1) exploiting sparsity that was not present in the original PRAM algorithm formulation, (2) using high-performance primitives of Combinatorial BLAS, and (3) identifying hot spots and optimizing them away by exploiting algorithmic insights.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2024

Tailoring Interface Energies via Phosphonic Acids to Grow and Stabilize Cubic FAPbI 3 Deposited by Thermal Evaporation

Coevaporation of formamidinium lead iodide (FAPbI 3 ) is a promising route for the fabrication of highly efficient and scalable optoelectronic devices, such as perovskite solar cells. However, it poses experimental challenges in achieving stoichiometric FAPbI 3 films with a cubic structure (α-FAPbI 3 ). In this work, we show that undesired hexagonal phases of both PbI 2 and FAPbI 3 form during thermal evaporation, including the well-known 2H-FAPbI 3 , which are detrimental for optoelectronic performance. We demonstrate the growth of α-FAPbI 3 at room temperature via thermal evaporation by depositing phosphonic acids (PAc) on substrates and subsequently coevaporating PbI 2 and formamidinium iodide. We use density-functional theory to develop a theoretical model to understand the relative growth energetics of the α and 2H phases of FAPbI 3 for different molecular interactions. Experiments and theory show that the presence of PAc molecules stabilizes the formation of α-FAPbI 3 in thin films when excess molecules are available to migrate during growth. This migration of molecules facilitates the continued presence of adsorbed organic precursors at the free surface throughout the evaporation, which lowers the growth energy of the α-FAPbI 3 phase. Our theoretical analyses of PAc molecule–molecule interactions show that ligands can form hydrogen bonding to reduce the migration rate of the molecules through the deposited film, limiting the effects on the crystal structure stabilization. Our results also show that the phase stabilization with molecules that migrate is long-lasting and resistant to moist air. These findings enable reliable formation and processing of α-FAPbI 3 films via vapor deposition.

36 MATERIALS SCIENCE↗

DOE OSTI2026

Accelerating Bilevel Optimization With Hierarchical Many-Threaded Parallel Differential Evolution

Bilevel optimization is encountered in many relevant real-world applications. The main feature of this type of problem is that an upper-level optimization problem is constrained by a nested lower-level optimization problem. Because of this nested structure, bilevel problems (BLPs) are usually computationally expensive to solve. Differential evolution (DE) has demonstrated promising results in solving BLPs of relatively small scales. As the problem scale increases, the decision space becomes intrinsically larger, requiring a growing number of function evaluations for the method to work properly. In this context, heavy parallelization and high-performance computing techniques are indispensable to enable the resolution of more complex and challenging optimization problems. Hence, we propose a hierarchical many-threaded parallel DE approach for BLPs, where both levels are parallelized. The computational experiments demonstrate that the parallel implementation achieved runtime speeds ranging from 44 to 2559 times faster than the sequential version on a well-known scalable SMD benchmark test problem when executed on an NVIDIA A100 GPU. The findings indicate that the algorithm’s convergence is strongly influenced by the number of both upper- and lower-level generations. Moreover, the success of experiments with large-scale problems is closely linked to the choice of small population sizes.

Dufek, Amanda S↗

DOE OSTI2020

On-demand indistinguishable single photons from an efficient and pure source based on a Rydberg ensemble

Single photons coupled to atomic systems have shown to be a promising platform for developing quantum technologies. Yet a bright on-demand, highly pure, and highly indistinguishable single-photon source compatible with atomic platforms is lacking. In this work, we demonstrate such a source based on a strongly interacting Rydberg system. The large optical nonlinearities in a blockaded Rydberg ensemble convert coherent light into a single collective excitation that can be coherently retrieved as a quantum field. We simultaneously observe a fully single-mode (spectral, temporal, spatial, and polarization) efficiency up to 0.098(2), a detector-background-subtracted g ( 2 ) = 5.0 ( 1.6 ) × <#comment/> 10 − <#comment/> 4 , and indistinguishability of 0.980(7), at an average photon production rate of 1.18 ( 2 ) × <#comment/> 10 4 s − <#comment/> 1 . All of these make this system promising for scalable quantum information applications. Furthermore, we investigate the effects of contaminant Rydberg excitations on the source efficiency and observed single-mode efficiencies up to 0.18(2) for lower photon rates. Finally, recognizing that many quantum information protocols require a single photon in a fully single mode, we introduce metrics that take into account all degrees of freedom to benchmark the performance of on-demand sources.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

DOE OSTI2026

Satellite Embedding-Based Population Imputation for Areas with Missing Building Footprint Data: A Computer Vision-Based Approach

High-resolution population modeling is important for supporting effective decision-making across diverse sectors. LandScan Mosaic generates population estimates at the level of individual buildings and aggregates them to 3 arc-second grids, and this approach performs well in regions where building footprint data are comprehensive and reliable. However, large portions of the globe still suffer from incomplete, sparse, or entirely missing building stock datasets, creating a structural limitation for strictly building-based population models. To address this research gap, this study proposes a computer vision-based framework that employs Google Earth Engine satellite embeddings and UNet, which allows us to directly impute grid-level population estimates in building-data-deficient areas. Applied to Taiwan as a case study, the framework achieved strong predictive performance with R$^{2}$ of 0.89, RMSE of 18.70, and MAE of 8.41, outperforming traditional machine learning approaches. Notably, the proposed framework effectively addressed building false-positive errors inherent in Global Human Settlement Layer (GHSL) data, correctly identifying uninhabited areas that were erroneously classified as populated. The framework also offers significant advantages for global population mapping, particularly in terms of scalability and temporal consistency, thereby extending the coverage and accuracy of high-resolution population products in data-scarce regions worldwide. Urban planners, decision makers, and related stakeholders can obtain granular population distributions to support more accurate and targeted infrastructure investment, service delivery, resource allocation, and risk assessment decisions.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2020

Potential for Applying Continuous Directed Evolution to Plant Enzymes: An Exploratory Study

Plant evolution has produced enzymes that may not be optimal for maximizing yield and quality in today’s agricultural environments and plant biotechnology applications. By improving enzyme performance, it should be possible to alleviate constraints on yield and quality currently imposed by kinetic properties or enzyme instability. Enzymes can be optimized more quickly than naturally possible by applying directed evolution, which entails mutating a target gene in vitro and screening or selecting the mutated gene products for the desired characteristics. Continuous directed evolution is a more efficient and scalable version that accomplishes the mutagenesis and selection steps simultaneously in vivo via error-prone replication of the target gene and coupling of the host cell’s growth rate to the target gene’s function. However, published continuous systems require custom plasmid assembly, and convenient multipurpose platforms are not available. We discuss two systems suitable for continuous directed evolution of enzymes, OrthoRep in Saccharomyces cerevisiae and EvolvR in Escherichia coli, and our pilot efforts to adapt each system for high-throughput plant enzyme engineering. To test our modified systems, we used the thiamin synthesis enzyme THI4, previously identified as a prime candidate for improvement. Our adapted OrthoRep system shows promise for efficient plant enzyme engineering.

59 BASIC BIOLOGICAL SCIENCES↗

NASA NTRS2017

Robotically Assembled Aerospace Structures: Digital Material Assembly using a Gantry-Type Assembler

This paper evaluates the development of automated assembly techniques for discrete lattice structures using a multi-axis gantry type CNC machine. These lattices are made of discrete components called digital materials. We present the development of a specialized end effector that works in conjunction with the CNC machine to assemble these lattices. With this configuration we are able to place voxels at a rate of 1.5 per minute. The scalability of digital material structures due to the incremental modular assembly is one of its key traits and an important metric of interest. We investigate the build times of a 5x5 beam structure on the scale of 1 meter (325 parts), 10 meters (3,250 parts), and 30 meters (9,750 parts). Utilizing the current configuration with a single end effector, performing serial assembly with a globally fixed feed station at the edge of the build volume, the build time increases according to a scaling law of n4, where n is the build scale. Build times can be reduced significantly by integrating feed systems into the gantry itself, resulting in a scaling law of n3. A completely serial assembly process will encounter time limitations as build scale increases. Automated assembly for digital materials can assemble high performance structures from discrete parts, and techniques such as built in feed systems, parallelization, and optimization of the fastening process will yield much higher throughput.

Mechanical Structure↗

NASA NTRS2017

Robotically Assembled Aerospace Structures: Digital Material Assembly using a Gantry-Type Assembler

This paper evaluates the development of automated assembly techniques for discrete lattice structures using a multi-axis gantry type CNC machine. These lattices are made of discrete components called "digital materials." We present the development of a specialized end effector that works in conjunction with the CNC machine to assemble these lattices. With this configuration we are able to place voxels at a rate of 1.5 per minute. The scalability of digital material structures due to the incremental modular assembly is one of its key traits and an important metric of interest. We investigate the build times of a 5x5 beam structure on the scale of 1 meter (325 parts), 10 meters (3,250 parts), and 30 meters (9,750 parts). Utilizing the current configuration with a single end effector, performing serial assembly with a globally fixed feed station at the edge of the build volume, the build time increases according to a scaling law of n4, where n is the build scale. Build times can be reduced significantly by integrating feed systems into the gantry itself, resulting in a scaling law of n3. A completely serial assembly process will encounter time limitations as build scale increases. Automated assembly for digital materials can assemble high performance structures from discrete parts, and techniques such as built in feed systems, parallelization, and optimization of the fastening process will yield much higher throughput.

Manufacturing↗

DOE OSTI2021

Scalable HPC & AI infrastructure for COVID-19 therapeutics

COVID-19 has claimed more than 2.7 × 10^6 lives and resulted in over 124 × 10^6 infections. There is an urgent need to identify drugs that can inhibit SARS-CoV-2. We discuss innovations in computational infrastructure and methods that are accelerating and advancing drug design. Specifically, we describe several methods that integrate artificial intelligence and simulation-based approaches, and the design of computational infrastructure to support these methods at scale. We discuss their implementation, characterize their performance, and highlight science advances that these capabilities have enabled.

Lee, Hyungro↗

DOE OSTI2022

Development of scalable design optimization parameters for bi-component protective systems

Bi-component protection-systems need to withstand a variety of extreme loading conditions such as high-velocity projectile impact to shock and blast mitigation. The large number of parameters at each stage of bi-component protection system design process results in a near-infinite number of possible variables for optimization. The aim of this project is to identify the scaling laws linking material and geometric scales of the problem to the final impact performance, further including the effects of wave propagation/interaction and inter-component bonding. Specifically, the impact dynamics for different optimal projectile/target density ratios and relative component mass fractions are quantified for bonded ceramic/titanium alloy systems. The impact behavior such as wave mechanics and damage propagation within the target system is characterized and quantified via digital image correlation or via x-ray phase-contrast imaging.

36 MATERIALS SCIENCE↗

DOE OSTI2026

A hybrid Penman-Monteith and machine learning model for simulating evapotranspiration and its components

Integrating physical processes with machine learning has advanced evapotranspiration (ET) simulation, yet most hybrid models fail to partition total ET into its components: soil evaporation (E) and vegetation transpiration (T). This study introduces Residual Neural Network–Penman–Monteith (RNN-PM), a novel hybrid dual-source ET model designed to overcome this limitation. The model synergizes the physically-based Penman–Monteith framework with three specialized residual neural networks trained to estimate key conductance parameters (canopy conductance, soil surface conductance, and aerodynamic conductance). Furthermore this explicit parameterization allows for the direct partitioning of total ET. Validation at National Ecological Observatory Network (NEON) flux sites using high-frequency partitioned E and T shows that RNN-PM reliably reproduces ET and the transpiration fraction (T/ET). For ET, the model achieves an average Kling–Gupta efficiency (KGE) of 0.89 and a root-mean-square error (RMSE) of 0.55 mm/day; for T/ET, the KGE is 0.87 with an RMSE of 0.06. Furthermore, RNN-PM demonstrates robust generalization, accurately simulating ET and its components well beyond the initial training dataset, even under extreme climatic conditions. This study extended the analysis by comparing the RNN-PM model with seven established dual-source ET models. The results indicate that RNN-PM outperforms both conventional machine learning models and purely physical process-based models in simulating ET components in most cases. Among the purely physical process-based dual-source models, those based on surface temperature decomposition showed improved performance as the leaf area index (LAI) decreased when evaluated against high-frequency ET component datasets. In contrast, the performance of conductance-based dual-source models declined with decreasing LAI. Although purely machine learning-based models can produce relatively accurate simulations of ET components, they often exhibit limited generalization capability, an issue that the RNN-PM model effectively overcomes. Ultimately, the RNN-PM model represents a significant advance in simulating ET components, offering a novel and scalable approach for improving the representation of land–atmosphere interactions in Earth system models.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2025

HP-FLEX: Field demonstration of the semantics-driven configuration of a Model Predictive Control system to make heat pumps flexible

Model Predictive Control (MPC) has demonstrated significant potential for optimizing building operations and enabling demand flexibility. However, the widespread adoption of MPC is hindered by complex manual configuration and commissioning processes that must be conducted by control experts working alongside building operators. These challenges drive up costs and reduce scalability, particularly when technical human resources and building automation systems are limited, such as in small and medium commercial buildings (SMCBs). This paper demonstrates how semantic standards, specifically ASHRAE 223P, can accelerate the adoption of MPC applications for load flexibility in SMCBs. The authors present a replicable control framework, titled “HP-FLEX” that leverages a building’s semantic model to bootstrap the required data configuration for an MPC controller developed for optimizing heat pump systems as flexible grid resources. The semantic model helps streamline the deployment workflow, particularly for site setup, data/control commissioning, and model setup. This integration enhances portability, transferability, and scalability of the HP-FLEX MPC, which has been developed to support MPC-based supervisory HVAC controllers in SMCBs. Additionally, the paper details the required building and thermostat metadata information to enable the HP-FLEX MPC based on field demonstrations. The new workflow was tested in a small commercial building located in California, U.S., and demonstrated a load shifting performance of 9% based on a dynamic pricing signal that varies by the hour. This work provides a practical pathway for transitioning sophisticated building applications from custom to standardized semantic representations, supporting the broader adoption of advanced control strategies like MPC. The framework also establishes a foundation for evolving metadata requirements as applications mature while maintaining compatibility with industry standards.

Paul, Lazlo↗

DOE OSTI2024

Harnessing Quantum Capacitance in 2D Material/Molecular Layer Junctions for Novel Electronic Device Functionality

Two-dimensional (2D) materials promise advances in electronic devices beyond Moore’s scaling law through extended functionality, such as non-monotonic dependence of device parameters on input parameters. However, the robustness and performance of effects like negative differential resistance (NDR) and anti-ambipolar behavior have been limited in scale and robustness by relying on atomic defects and complex heterojunctions. In this paper, we introduce a novel device concept that utilizes the quantum capacitance of junctions between 2D materials and molecular layers. We realized a variable capacitance 2D molecular junction (vc2Dmj) diode through the scalable integration of graphene and single layers of stearic acid. The vc2Dmj exhibits NDR with a substantial peak-to-valley ratio even at room temperature and an active negative resistance region. The origin of this unique behavior was identified through thermoelectric measurements and ab initio calculations to be a hybridization effect between graphene and the molecular layer. The enhancement of device parameters through morphology optimization highlights the potential of our approach toward new functionalities that advance the landscape of future electronics.

2D materials↗

DOE OSTI2025

HP-FLEX: Field Demonstration of the Semantics-Driven Configuration of a Model Predictive Control System to Make Heat Pumps Flexible

Paul, Lazlo↗

NASA NTRS2005

Lean Middleware

This paper describes an approach to achieving data integration across multiple sources in an enterprise, in a manner that is cost efficient and economically scalable. We present an approach that does not rely on major investment in structured, heavy-weight database systems for data storage or heavy-weight middleware responsible for integrated access. The approach is centered around pushing any required data structure and semantics functionality (schema) to application clients, as well as pushing integration specification and functionality to clients where integration can be performed on-the-fly .

Maluf, David A.↗

NASA NTRSDate not supplied

Design Considerations for Practical Li-S Battery Components for Electric Aviaiton

The development of high-energy, high-safety, and high-power batteries beyond electric automobile requirements is vital for future electric aviation applications. The transition to non-volatile solid-state electrolytes (SSE) promises many advantages over traditional flammable liquid electrolytes, and may also be an enabling technology for next generation chemistries. However, significant manufacturing challenges must be overcome before the adoption of such technology. Electrolytes developed in this study were produced as sulfide-polymer composites with densified thicknesses between 20-30 microns using a tape-casting technique and elastomer binder [1]. The composites were determined to retain reasonable ionic conductivity with improved flexibility and scalability critical for practical manufacturing of such cells. Films were produced 10-15 times thinner than comparable bulk powder electrolytes and within the range of commercial polyolefin separators (25 micron) used in commercial liquid containing lithium-ion cells. Composite conductivities were maintained above 0.2mS/cm, which holds promise for future electric aviation applications. Processing techniques are investigated to further improve electrochemical performance.

battery↗

DOE OSTI2025

Multiphysics simulation of recent experiments on alkali‐silica reaction expansion in reinforced concrete members

Alkali‐silica reaction (ASR) is an important degradation process that causes volumetric expansion and damage in concrete, and is affected significantly by the local temperature, moisture and stress conditions that often vary across the regions of a structure. Numerical simulation is essential to predict the progression and effects of ASR on the performance of structures. Because of the interactions between thermal and moisture transport and mechanical deformation, it is important for numerical models to represent all these physical phenomena and the coupling between them. Simulations of ASR in reinforced concrete (RC) structures are further complicated by the need to capture interactions between concrete and embedded reinforcing bars. Here, this paper describes the implementation of a scalable, coupled‐physics ASR model for simulating RC structures and assesses the ability of that model to predict ASR‐induced expansion in recent laboratory tests on RC block and beam specimens. These laboratory tests and the simulation approach were selected because of their applicability to RC structural‐scale simulations. This validation study helps builds confidence the ability of this approach to model ASR expansion in large, complex RC structures, which is a current high‐priority need.

36 MATERIALS SCIENCE↗