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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 487 records · Page 27
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

The future of computing beyond Moore’s Law

Moore’s Law is a techno-economic model that has enabled the information technology industry to double the performance and functionality of digital electronics roughly every 2 years within a fixed cost, power and area. Advances in silicon lithography have enabled this exponential miniaturization of electronics, but, as transistors reach atomic scale and fabrication costs continue to rise, the classical technological driver that has underpinned Moore’s Law for 50 years is failing and is anticipated to flatten by 2025. Here, this article provides an updated view of what a post-exascale system will look like and the challenges ahead, based on our most recent understanding of technology roadmaps. It also discusses the tapering of historical improvements, and how it affects options available to continue scaling of successors to the first exascale machine. Lastly, this article covers the many different opportunities and strategies available to continue computing performance improvements in the absence of historical technology drivers. This article is part of a discussion meeting issue ‘Numerical algorithms for high-performance computational science’.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2026

High-Speed Layup and Forming of Automotive Composite Components

This Project is focused on the design and manufacture of automotive components that meet functional and environmental requirements of an existing automotive application at a cost of ≤ $\$$11.00 per kilogram weight reduction. This project fosters the development of composite material technologies suitable for high volume automotive processes and run rates as well as industry workforce development with these technologies. Current automotive manufacturing involves utilizing steel or aluminum in sheet form which is rapidly stamped into components at rates up to 3600 per hour. The metallic sheets are available in many different thicknesses, strength levels, and manufacturing rates are reasonable independent of part size. While composite materials are available for use in automotive applications, the material cost, labor to manufacture and the processing of the waste far exceed the cost compared to metallic designs. Typical composite layer by layer layup procedures don’t meet the desired 60 second layup time that current automotive processes require and are also restricted by part size. Due to these factors, composites have not yet made advances into today’s high volume automotive applications. Industry partners DURA, BASF, Ford, and IACMI core innovation partner MSU collaborated to develop a manufacturing process technology that is capable of manufacturing composite blanks at high volume and independent of part size. IACMI core innovation partner Purdue provided FEA analysis and cost modelling. The objective of this project was to demonstrate a composite sheet layup and consolidation process that can be commercialized for high volume requirements, identify potential layup equipment suppliers, and develop a process of 60 second layup, forming, and trimming of a continuous fiber automotive component for the mainstream market.

42 ENGINEERING↗
DOE OSTI2021

Inventory Management Improvement Project

The Lawrence Livermore National Laboratory (LLNL) conducts research and development for the National Nuclear Security Agency (NNSA) and its affiliates. The Polymer AM team at LLNL conducts research and development in 3D printing, specifically direct-ink-write, in accordance with LLNL and NNSA missions. The team is composed of machine operators, chemists, testing engineers, and project engineers across multiple lab spaces with one shared storage area at LLNL. The operations use a variety of different consumables and hardware to conduct research applications for LLNL and NNSA. These items are critical to performing operation tasks; if an item is out of stock, operations associated with the respective item could be suspended for weeks. As such, the Polymer AM team manages an inventory of spare consumables and hardware to ensure these items are always available. However, the current management system, an excel sheet managed by the project engineer group, is not intuitive in providing inventory information despite the high labor utilization needed to maintain the system. As such, the team is looking to improve their inventory management that can send notifications regarding inventory needs, accessible to other team members, provide all relevant information to a specific item, and store historical information for budget and operation planning. The proposed solution is a Computer Maintenance Management System (CMMS): a web-based management software that stores inventory information and provides automated notifications. The system will store information on each item including quantity in stock, technical information, supplier information, costs, lead times, and expiration date. The system has a notifications function that can send notifications to the respective team member’s when an item needs to be counted, item inventory is low, or an item is approaching their expiration date. Information for each item can be tracked over time creating data to be used for budget planning and process improvement purposes. Due to its web-based source, the system can be accessed by the respective team member viewing technical information, quantity, and purchasing information.

42 ENGINEERING↗
DOE OSTI2026

Operations and Maintenance for Electric Vehicle Charging Infrastructure in Indian Country

This fact sheet provides an overview of operations and maintenance (O&M) costs, responsibilities, and contracts to help ensure EV charging infrastructure remains functional and reliable in Indian Country. O&M planning is important for Tribes to consider at the beginning of EV charging infrastructure projects to help ensure charger uptime and longevity.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2022

MODULARITY-AT-SCALE FOR COST COMPETITIVE DEPLOYMENT OF NUCLEAR ENERGY

Modularity options have been limited for traditional nuclear energy deployment due to the conventional light water reactor (LWR) safety requirements, such as high pressure retaining heavy and robust containment structures. However, a relatively new regulatory approach called ‘Functional Containment’ has potential to allow less expensive and more flexible designs for non LWRs. Functional containment provides flexibility in design and deployment based on risk informed and performance-based criteria, so that reactors are not over-designed. Non-nuclear industry has successfully used modular design approaches in automotive, aerospace, chemical processing, building construction, and ship building. These industries have shown that modular construction reduces construction time by around 30% - 50% compared to the conventional stick built approach. The nuclear industry can use similar approaches to reduce construction time and costs, balanced with safety requirements, using the functional containment approach. This paper discussing the background of modularity in nuclear energy, examples of less learned, modularity approaches in non-nuclear industries and the potential of cost and schedule savings through the emerging regulatory design flexibilities potentially enabling combination of modular deployment at different scales.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2024

Evaluating consumer 3D printing nozzles as a low cost alternative for mesophase pitch-derived carbon fiber production

Synthetic fibers, such as Kevlar fibers, SiC fibers, and carbon fibers, are essential components for constructing high performance structures. Whether for engineering, sports, energy storage (batteries and supercapacitors), or aerospace applications, fiber microstructure plays a critical role in fiber properties and functionalities. However, studying fiber nozzle configurations and spinning parameters to achieve the desired microstructure remains challenging, costly, and time consuming. Here, mesophase pitch-derived fibers were used as an example to demonstrate that low cost, commercially available 3D printer nozzles can “print” fibers. Four different nozzles were used to “print” fibers and the effects of their features on fiber properties were observed and compared to other lab spun and commercial pitch- derived CF. A longer orifice length resulted in higher modulus fiber whereas a larger draw-down ratio yielded a stronger fiber. Furthermore, the findings provide a new opportunity for 3D printer hardware application and open up opportunities for developing low-cost fibers.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Variational Discrete Action Theory

In this work, we propose the variational discrete action theory (VDAT) to study the ground state properties of quantum many-body Hamiltonians. VDAT is a variational theory based on the sequential product density matrix (SPD) ansatz, characterized by an integer $\mathscr{N}$, which monotonically approaches the exact solution with increasing $\mathscr{N}$. To evaluate the SPD, we introduce a discrete action and a corresponding integer time Green’s function. We use VDAT to exactly evaluate the SPD in two canonical models of interacting electrons: the Anderson impurity model and the d = ∞ Hubbard model. For the latter, we evaluate $\mathscr{N}$ = 2 – 4, where $\mathscr{N}$ = 2 recovers the Gutzwiller approximation (GA), and we show that $\mathscr{N}$ = 3, which exactly evaluates the Gutzwiller-Baeriswyl wave function, provides a truly minimal yet precise description of Mott physics with a cost similar to that of the GA. VDAT is a flexible theory for studying quantum Hamiltonians, competing both with state-of-the-art methods and simple, efficient approaches all within a single framework.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2025

An end-to-end deep learning method for solving nonlocal Allen–Cahn and Cahn–Hilliard phase-field models

Here, we propose an efficient end-to-end deep learning method for solving nonlocal Allen–Cahn (AC) and Cahn–Hilliard (CH) phase-field models. One motivation for this effort emanates from the fact that discretized partial differential equation-based AC or CH phase-field models result in diffuse interfaces between phases, with the only recourse for remediation is to severely refine the spatial grids in the vicinity of the true moving sharp interface whose width is determined by a grid-independent parameter that is substantially larger than the local grid size. In this work, we introduce non-mass conserving nonlocal AC or CH phase-field models with regular, logarithmic, or obstacle double-well potentials. Because of non-locality, some of these models feature totally sharp interfaces separating phases. The discretization of such models can lead to a transition between phases whose width is only a single grid cell wide. Another motivation is to use deep learning approaches to ameliorate the otherwise high cost of solving discretized nonlocal phase-field models. To this end, loss functions of the customized neural networks are defined using the residual of the fully discrete approximations of the AC or CH models, which results from applying a Fourier collocation method and a temporal semi-implicit approximation. To address the long-range interactions in the models, we tailor the architecture of the neural network by incorporating a nonlocal kernel as an input channel to the neural network model. We then provide the results of extensive computational experiments to illustrate the accuracy, predictive capabilities, and cost reductions of the proposed method.

42 ENGINEERING↗
DOE OSTI2022

Formulation of PI Aerogels with 3-D Printing Applications

Polyimide-based films were produced with 3-D printing capability consisting of TDI and PMDA monomers with acrylate functionality. The polymer was synthesized using a standard condensation with diisocyanate and dianhydride heated to 95⁰C under water free conditions. This method is relatively fast, cost effective and far more synthetically simple compared to diamine condensations seen in literature. Considering how crucial proper grafting of functional groups was in hierarchy application, various formulation variables were screened. Lastly, films were produced with PI material and the physical and chemical properties were examined using microscope imaging and UV-Vis Spectroscopy. The material was proven to have relatively fast cure times (5-10 minutes) and the films were shown to have long term stability.

36 MATERIALS SCIENCE↗
DOE OSTI2026

ICE Calculator 2: Final Report for Phase 1 and 2 of the National Initiative to Update the Interruption Cost Estimate (ICE) Calculator

ICE 2.0 Phase 2 Final Report In 2021, Berkeley Lab and Resource Innovations, Inc. launched the “ICE Calculator 2 Initiative” – a national study to refresh the underlying data and enhance the functionality of the ICE Calculator. The Initiative involves Berkeley Lab contracting with sponsoring utilities to administer identical, updated and comprehensive interruption cost surveys to statistically representative samples of each utility’s customers. Berkeley Lab and Resource Innovations then pool the survey results across the utilities and use them to update the analytical engines that drive the ICE Calculator. The ICE Calculator 2 Initiative is being conducted in phases. Each phase involves the administration of interruption cost surveys to the customers of sponsoring utilities, followed by an update to the ICE Calculator based on analysis of the pooled survey results. This report describes the activities and findings from Phase 1 and 2 of the ICE Calculator 2 Initiative. Phase 1 was sponsored by eight utilities: American Electric Power, Commonwealth Edison, Dominion Energy, Duke Energy, DTE Electric, Exelon, National Grid, and Puget Sound Energy. Phase 2 was sponsored by six utilities: Empire District Electric Company, Evergy Missouri, Pacific Gas & Electric, San Diego Gas & Electric, Southern California Edison, and Union Electric. Phase 1 and 2 involved 15 customer interruption cost survey activities representing a total of 30 electricity distribution service territories. ICE Calculator Version 2.0 and 2.2 Comparison This memorandum describes–at a high-level–the improvements in interruption cost estimates for the version 2.2 of the ICE Calculator (released February 2026) compared to version 2.0 (released in April 2025). Version 2.2 of the ICE Calculator corresponds to Phase 2 of the initiative, while version 2.0 corresponds to Phase 1. The improvements in version 2.2 result from both a significant increase in the number of customer responses that have been collected and the identification of seven additional factors that help estimate customer interruption costs.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2024

A Theoretical Analysis on the Dynamic Behavior and Electromechanical Response of PZT

Piezoelectric materials are used as a power source in stockpile components for safety and reliability assurances. The objective of this project is to gain insights into the fundamental pressure-induced behavior and electromechanical response of lead zirconate titanate (PZT). Specifically, to establish the basis for an accurate, physics-based material model for robust model-based design to rapidly optimize PZT-based materials and components for performance studies. Established ab-initio methods are used to interrogate and understand the dynamic behavior of PZT as a function of composition (50/50, 65/35, 80/20, 95/5) and dopants (La, Nb) to overcome the costly and time-consuming experimental methodologies. New cold curves for pure and doped single-crystal PZT are obtained as the reference equation-of-state (EOS). A negligible change in the pressure responses was observed for the systems and strains studied. Dielectric and piezoelectric responses of pure and doped single-crystal PZT were also calculated as a function of pressure. For undoped PZT, there is a clear and orderly pressure and composition dependence. For doped PZT, there is a significant increase in the responses, but the behavior is very disordered and inconclusive.

36 MATERIALS SCIENCE↗
DOE OSTI2026

SERA: A Hydrogen Infrastructure Capacity Expansion Model

The Scenario Evaluation and Regionalization Analysis (SERA) model is an infrastructure planning optimization model that can guide hydrogen production, delivery, and end-use investment decisions and accelerate the adoption of low-cost hydrogen at scale, whether for fuel cell electric vehicles or non-transportation applications. In this talk, we will review the SERA model objective function as well as the data inputs and outputs. We will also look at a SERA case study identifying potential dispensed costs of hydrogen along major refueling corridors throughout the United States. In addition to the SERA model, Justin will also discuss his recent work for the Office of Manufacturing and Energy Supply Chains on electrolyzer supply chain readiness, and his work for the Hydrogen Fuel Cell Technologies Office and Environmental Protection Agency on the levelized cost of dispensed hydrogen for heavy-duty trucking.

30 DIRECT ENERGY CONVERSION↗
DOE OSTI2020

Electricity Pricing aware Deep Reinforcement Learning based Intelligent HVAC Control

Recently, deep reinforcement learning (DRL) based intelligent control of Heating, Ventilation, and Air Conditioning (HVAC) has gained a lot of attention due to DRL's ability to optimally control HVAC for minimizing operational cost while maintaining resident's comfort. The success of such DRL-based techniques largely depends on the articulation of the problem in terms of states, actions, and reward function. Inclusion of the electricity pricing information in the problem formulation can play an important role in saving the cost of HVAC operation. However, less attention has been given in the literature on formulating well-crafted state features based on electricity pricing. In this work, we propose an approach for training the DRL model with a specific focus on feature engineering based on electricity pricing. During training, we generate random but sufficiently realistic electricity price signals so that the pre-trained DRL model is robust and adaptive to the dynamic and variable electricity prices. The validation results are encouraging and show the potential of ≈12%-15% savings in the one day cost of HVAC operation, proving the usefulness of including electricity pricing related features as state features.

Kurte, Kuldeep↗
DOE OSTI2022

Feasibility of critical infrastructure protection using network functions for programmable and decoupled ICS policy enforcement over WAN

Industrial control systems (ICS) represent a major component of our critical infrastructure. With the increasing need for more control and monitoring of such systems, ICS have seen an increase in connectivity to wide area networks (WAN) exposing aging equipment to rapidly evolving cybersecurity threats. Furthermore, the ICS data requires a reliability measure from the networks for critical functions for infrastructure monitoring and control. Especially when remote plant sites are involved such as pipelines, energy distribution networks, and transportation, WAN transport impairments most often provide a best effort delivery with no strict reliability guarantees. Network functions can provide a vendor agnostic, programmable critical infrastructure protection with a single maintenance, policy determination, and reliability assurance surface. A network function (NF) can be utilized for policy enforcement over the communication between remote entities and the main control office. This paper presents the research on transparent integration with existing ICS without disrupting communications, resulting in minimal downtime while decoupling the fast paced evolution of defensive security measures from the upgrade cycle of expensive long term hardware. We report our measurements on the resource requirements and overhead in the network for successful NF insertion under a wide variety of network impairments (network packet delay, reordering, and loss). Our paired NF implementation provides a policy enforcement platform extensible to cover myriad cybersecurity-related communication goals, including packet signing for verification, encryption for data privacy, packet filtering and data diode operation (i.e. protecting against eavesdropping, packet injection, and denial-of-service). Furthermore, bundling communication specifications into packet flows allows for tunability in applying policies as coarse- or fine-grained as the needs of the operator. We report on network function resource requirements in the form of required queue depth and network utilization overhead to inform the decision making against hardware cost constraints.

42 ENGINEERING↗
DOE OSTI2025

Tunable structure and reinforcement of polyvinyl alcohol (PVA) hydrogels using fungal chitin particles

Polysaccharides, including chitin, are one of the most abundant biopolymers in nature and are increasingly recognized as a sustainable alternative to petroleum-derived plastics and synthetic fillers in polymer composites. Traditionally sourced from crustacean shells, chitin offers mechanical strength and biocompatibility with limitations also in processability and functionality. Fungal-derived chitin material represents a promising alternative, with advantages including scalable fermentation on low-cost substrates, absence of shellfish allergens, and tunable molecular architectures that vary by species, developmental stage, and growth environment. Here, in this study, we systematically examined chitinous materials obtained from taxonomically and functionally distinct fungi, Laccaria bicolor, Trichoderma reesei and Rhizopus oryzae, to assess their structural, chemical, and morphological properties as reinforcement agents in polymer composites. Mild alkaline pretreatment was employed to obtain mycelium chitin particles, thereby improving accessibility to chitin and co-occurring β-D-glucans while maintaining microparticle integrity. Comprehensive FTIR and solid-state NMR analyses revealed species-specific differences in chemical composition and microstructure, with R. oryzae exhibiting a unique spectral signature. These fungal-derived chitin were then incorporated into poly(vinyl alcohol) (PVA) hydrogels, where they acted as reinforcing fillers without the need for additional chemical crosslinkers. Comparative evaluation of hydrogel properties demonstrated that fungal chitin significantly enhanced mechanical performance, with all mycelium fillers mitigating the water weakening in PVA hydrogels. R. oryzae-derived composites tripled the hydrogel tensile strength while the submicron fibrous morphology in L. bicolor contributes to over 45 % tensile improvement in dry PVA composites. Our findings highlight the potential of fungal biomass as a tunable, sustainable platform for producing chitin-based reinforcing agents.

Chitin↗
DOE OSTI2024

Multitask methods for predicting molecular properties from heterogeneous data

Data generation remains a bottleneck in training surrogate models to predict molecular properties. We demonstrate that multitask Gaussian process regression overcomes this limitation by leveraging both expensive and cheap data sources. In particular, we consider training sets constructed from coupled-cluster (CC) and density functional theory (DFT) data. We report that multitask surrogates can predict at CC-level accuracy with a reduction in data generation cost by over an order of magnitude. Of note, our approach allows the training set to include DFT data generated by a heterogeneous mix of exchange–correlation functionals without imposing any artificial hierarchy on functional accuracy. More generally, the multitask framework can accommodate a wider range of training set structures—including the full disparity between the different levels of fidelity—than existing kernel approaches based on Δ-learning although we show that the accuracy of the two approaches can be similar. Consequently, multitask regression can be a tool for reducing data generation costs even further by opportunistically exploiting existing data sources.

Chemistry↗
DOE OSTI2026

Integrative Additive Design for Robust SEI Formation in NMC811||Silicon Batteries

Silicon (Si) is considered a promising replacement for graphite anodes in lithium-ion batteries (LIBs) due to its high abundance and exceptional specific capacity, but its widespread commercialization has been hindered by poor electrochemical performance. Among various strategies, the use of functional additives has emerged as one of the most effective and cost-efficient methods to enhance the electrochemical properties of LIBs. In this study, several additives—vinylene carbonate (VC), vinyl ethylene carbonate (VEC), lithium difluorophosphate (LiDFP), lithium difluoro(oxalato)borate (LiDFOB), lithium tetrafluorooxalatophosphate (LiTFOP), and lithium difluorobis(oxalato)phosphate (LiDFBOP)—were systematically investigated in LiNi₀.₈Mn₀.₁Co₀.₁O₂ (NMC811)||Si full cells. Notably, LiDFBOP, a lithium salt containing two oxalate groups, outperformed all other additives, delivering the best capacity retention after 300 cycles. Comprehensive characterizations, including FTIR, SEM, and XPS, revealed that LiDFBOP's superior performance stems from its ability to form a more stable solid electrolyte interphase (SEI) on the Si anode, owing to its favorable molecular structure that integrates the beneficial features of the other additives.

25 ENERGY STORAGE↗
DOE OSTI2021

The number of catalytic cycles in an enzyme’s lifetime and why it matters to metabolic engineering

Metabolic engineering uses enzymes as parts to build biosystems for specified tasks. Although a part’s working life and failure modes are key engineering performance indicators, this is not yet so in metabolic engineering because it is not known how long enzymes remain functional in vivo or whether cumulative deterioration (wear-out), sudden random failure, or other causes drive replacement. Consequently, enzymes cannot be engineered to extend life and cut the high energy costs of replacement. Guided by catalyst engineering, we adopted catalytic cycles until replacement (CCR) as a metric for enzyme functional life span in vivo. CCR is the number of catalytic cycles that an enzyme mediates in vivo before failure or replacement, i.e., metabolic flux rate/protein turnover rate. We used estimated fluxes and measured protein turnover rates to calculate CCRs for ~100–200 enzymes each from Lactococcus lactis, yeast, and Arabidopsis. CCRs in these organisms had similar ranges (<10 3 to >10 7 ) but different median values (3–4 × 10 4 in L. lactis and yeast versus 4 × 10 5 in Arabidopsis). In all organisms, enzymes whose substrates, products, or mechanisms can attack reactive amino acid residues had significantly lower median CCR values than other enzymes. Taken with literature on mechanism-based inactivation, the latter finding supports the proposal that 1) random active-site damage by reaction chemistry is an important cause of enzyme failure, and 2) reactive noncatalytic residues in the active-site region are likely contributors to damage susceptibility. Enzyme engineering to raise CCRs and lower replacement costs may thus be both beneficial and feasible.

54 ENVIRONMENTAL SCIENCES↗