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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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All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Quantifying policy gaps for achieving the net-zero GHG emissions target in the U.S. light-duty vehicle market through electrification

The U.S. light-duty vehicle (LDV) industry, a major greenhouse gas (GHG) emitting sector, is embracing decarbonization. Considering only electrification pathways, this study uses publicly-available tools – MA3T and VISION on vehicle market penetration, fleet accounting and life-cycle analysis to quantify the policy gaps for LDVs to achieve the net-zero GHG emissions target in nine vehicle penetration cases under two electricity mix scenarios, including the U.S. administration's decarbonization strategy – 100% clean electricity by 2035. The MA3T model is a multinomial discrete choice model for market share projection by vehicle technology, and the VISION is a vehicle stocks and GHG emissions projection model by using vehicle and travel characteristics. This study projects the impacts of technology and policy enforcement on shaping the dynamics and decarbonization of the LDV market. Additionally, achieving the expected improvement of battery technology and charging infrastructure is critical but can only reduce the 2050 GHG emissions to 48–54% of the 2020 level under the electricity renewable mix scenario. It is almost impossible to achieve a 100% battery electric vehicle stock by 2050 and the 2050 net-zero target in the LDV industry unless ban of internal combustion engine technology is implemented starting in 2035 and under the 2035 100% clean electricity scenario. These extreme conditions also sacrifice most from the consumer welfare perspective. A greater policy forcing intensity accelerates plug-in electric vehicle penetration, while with declining marginal effect and reduced consumer welfare. Among the investigated policy scenarios, the policy forcing intensity equivalent to a fuel tax of $1–2 per gasoline gallon reduces the most GHG emissions while keeping a positive consumer welfare.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2025

Datum: A Scientific Metadata Catalog

The data catalog market is currently flooded with a myriad of different products, but none serve the scientific community well. There are cloud-native tools like Databricks, Snowflake,to on-premise solutions like Collibra and Datahub. The common failing of all these tools however, is their inability to serve the scientific data community directly. Most catalogs are targeted towards financial, health, or user data - not sensor or scientific domain data. They also prioritize integrations that often don’t exist or are just starting to be used in the scientific realm - all while ignoring common scientific tools and file types. Datum is a catalog which targets the scientific data directly, including the tools and networks in which those tools are used. We work with the producers and consumers of the data where they are, targeting cloud and on-premise with a focus on classified networks. Datum is an Erlang/Elixir application. Technical Features Note: The features listed below are still under development and may change, slightly, upon final delivery of the product. File Formats - Datum has the ability to read additional metadata and provides processing pipelines for the following file formats: Plain Text, PDF, LaTeX, HTML, Open Document Format (.odt), XML, CSV/TSV (and other standard delimiters), OpenDocument Database and Spreadsheets, Geo-Referenced TIFF, Common Data Format, HDF/HDF5, LabView TDMS, Excel, DeltaTables, Parquet, Apache Iceberg, Apache Hudi and many others. Metadata Collection - Scanners for the local and networked file systems and cloud storage providers. Network integration with common databases such as MSSQL and MySQL. User Plugin System - Users are able to provide either file processing, metadata extraction, or sampling plugins in the programming language of their choice. Authentication/Authorization -: OIDC integration, SCIM provisioning and EntraID integration out of the box. Full user and group management system with a “least privilege” operating mode. Governance - Customizable data governance platform; dictate and enforce required metadata, enforce data embargos, and enforce user agreements and NDAs before data access. Ability to create health checks on data, rejecting abandoned or poorly curated data and automatically removing it from the search index. Ability for users to submit corrections. Search - Semantic search is a first class citizen. No licenses to expensive, external software required. Integrated use of vectors and vector-based search allows for AI agent integration at all levels of operation. Metadata Model - Display and control data’s lineage and connections to other data and data directories. Data is modeled after a filesystem - an organization instantly recognizable and navigable by most any user. CLI and SDK - Ships with a Command Line Interface (CLI) tool and with a fully-featured Python SDK. This allows for rapid and programmatic use of Datum by every level of user. Minimal Infrastructure - Datum ships as a single executable file and can be run on any operating system and most CPU architectures. Datum has no reliance on external databases, search indexing tools, or other outside services - and it runs equally well on edge computing devices, cloud services, or in a clustered HPC environment.

darrington, john↗
DOE OSTI2020

Quantifying the Tangible Value of Public Electric Vehicle Charging Infrastructure

The lack of an extensive public recharging infrastructure is an important barrier to the growth of the plug-in electric vehicle (PEV) market. Because charging infrastructure is likely to be underutilized during the early stages of market development, it is difficult for decision makers to decide how much to invest in public charging stations. Quantifying the value of public charging infrastructure to current and potential future owners of PEVs is essential for estimating the benefits of charging stations to current PEV owners and for predicting the impact on future PEV sales. This paper estimates consumers’ willingness to pay for public charging infrastructure in the context of utility maximization. The objective is to provide a method for valuing charging infrastructure that can inform investment decisions and be used in forecasting models to predict the impacts on future PEV sales. A basic theory of the tangible value of charging infrastructure is developed as a function of PEV type, range, recharging time and existing infrastructure. Existing simulation studies provide functional relationships that quantify the ability of charging infrastructure to enable additional miles of electrified travel. The enabled travel functions are used to predict impact of infrastructure deployment on incremental electrified travel for 1) plug-in hybrids and 2) intra-regional and 3) inter-regional travel by all-electric vehicles. The willingness to pay for increased electrified miles is derived from the willingness to pay for increased electric driving range, based on econometric studies of plug-in vehicle choice. The result is a set of three functions that can be used to calculate the marginal willingness-to-pay for public charging infrastructure as a function of vehicle attributes, existing charging infrastructure, energy prices and annual vehicle travel.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2023

Autonomous Inversion of In Situ Deformation Measurement Data for Injection-Induced Stress Change

Geologic carbon storage (GCS) is likely to play a key part of the global effort to dramatically reduce CO2 emissions and perhaps even reduce atmospheric CO2 concentrations through carbon negative operations. A critical part of effort to commercialize and widely deploy this technology is developing the capability to rapidly assimilate real-time monitoring data into a form that will enable site operators to make decisions to manage the safe and efficient operations. Two of the risks associate with GCS are the risk of inducing fractures in the sealing formations that can create leakage pathways and the risk of inducing earthquakes of sufficient magnitude to cause public concern, property damage, or safety risks. To properly manage these risks the site operator needs to know the initial state of stress, the change in stress induced by injection, and the relationship between operational parameters such as injection rate and pressure and the change in stress. Current methods of estimating the change in stress require choosing the type of constitutive model and the model parameters based on core, log, and geophysical data during the characterization phase, with little feedback from operational observations to validate or refine these choices. These characterization methods interrogate the geologic formations using length scales, loading rates or magnitudes that are quite different from those encountered by the actual storage system. It is shown that errors in the assumed constitutive response, even when informed by laboratory tests on core samples, are likely to be common, large, and underestimate the magnitude of stress change caused by injection. Recent advances in borehole-based strain instruments and borehole and surface-based tilt and displacement instruments have now enabled monitoring of the deformation of the storage system throughout its operational lifespan. This data can enable validation and refinement of the knowledge of the geomechanical properties and state of the system, but brings with it a challenge to transform the raw data into actionable knowledge. We demonstrate a method that uses automatic differentiation and a finite-element based geomechanical model perform a gradient-based deterministic inversion of geomechanical monitoring data. This approach allows autonomous integration of the instrument data without the need for time consuming manual interpretation and selection of updated model parameters. Furthermore, only isotropic linear elasticity is considered in this paper, the approach presented is very flexible as to what type of geomechanical constitutive response can be used. The approach is easily adaptable to nonlinear physics-based constitutive models to account for common rock behaviors such as creep and plasticity. The approach also enables training of machine learning-based constitutive models by allowing back propagation of errors through the finite element calculations. This enables strongly enforcing known physics, such as conservation of momentum and continuity, while allowing data-driven models to learn the truly unknown physics such as the constitutive or petrophysical responses.

Burghardt, Jeffrey A.↗
DOE OSTI2022

Multi-fidelity modeling to predict the rheological properties of a suspension of fibers using neural networks and Gaussian processes

Unveiling the rheological properties of fiber suspensions is of paramount interest to many industrial applications. There are multiple factors, such as fiber aspect ratio and volume fraction, that play a significant role in altering the rheological behavior of suspensions. Three-dimensional (3D) numerical simulations of coupled differential equations of the suspension of fibers are computationally expensive and time-consuming. Machine learning algorithms can be trained on the available data and make predictions for the cases where no numerical data are available. However, some widely used machine learning surrogates, such as neural networks, require a relatively large training dataset to produce accurate predictions. Multi-fidelity models, which combine high-fidelity data from numerical simulations and less expensive lower fidelity data from resources such as simplified constitutive equations, can pave the way for more accurate predictions. Here, we focus on neural networks and the Gaussian processes with two levels of fidelity, i.e., high and low fidelity networks, to predict the steady-state rheological properties, and compare them to the single-fidelity network. High-fidelity data are obtained from direct numerical simulations based on an immersed boundary method to couple the fluid and solid motion. The low-fidelity data are produced by using constitutive equations. Multiple neural networks and the Gaussian process structures are used for the hyperparameter tuning purpose. Results indicate that with the best choice of hyperparameters, both the multi-fidelity Gaussian processes and neural networks are capable of making predictions with a high level of accuracy with neural networks demonstrating marginally better performance.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2024

Autocalibration of the E3SM Version 2 Atmosphere Model Using a PCA-Based Surrogate for Spatial Fields

Global Climate Model tuning (calibration) is a tedious and time-consuming process, with high-dimensional input and output fields. Experts typically tune by iteratively running climate simulations with hand-picked values of tuning parameters. Many, in both the statistical and climate literature, have proposed alternative calibration methods, but most are impractical or difficult to implement. We present a practical, robust, and rigorous calibration approach on the atmosphere-only model of the Department of Energy's Energy Exascale Earth System Model (E3SM) version 2. Our approach can be summarized into two main parts: (a) the training of a surrogate that predicts E3SM output in a fraction of the time compared to running E3SM, and (b) gradient-based parameter optimization. To train the surrogate, we generate a set of designed ensemble runs that span our input parameter space and use polynomial chaos expansions on a reduced output space to fit the E3SM output. We use this surrogate in an optimization scheme to identify values of the input parameters for which our model best matches gridded spatial fields of climate observations. To validate our choice of parameters, we run E3SMv2 with the optimal parameter values and compare prediction results to expertly-tuned simulations across 45 different output fields. This flexible, robust, and automated approach is straightforward to implement, and we demonstrate that the resulting model output matches present day climate observations as well or better than the corresponding output from expert tuned parameter values, while considering high-dimensional output and operating in a fraction of the time.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Brilliant Execution of Smart Labs: Employing Best Practices to Improve Safety and Reduce Energy for Sustainable Labs: Preprint

Laboratory buildings often dominate the energy use on research campuses, consuming three to four times the energy of a typical office building. In most cases, these facilities are defined as “mission critical,” with excessive energy use as simply an unfortunate truth that cannot be avoided without compromising safety. However, case studies indicate cost savings opportunities of up to 40% in laboratories (compared to standard practices) by employing energy efficient design strategies that also improve the safety of lab spaces (Better Buildings 2018). To help stakeholders implement improvement measures, the Smart Labs Toolkit describes a proven approach to optimizing performance of laboratories and critical control environments in new or existing facilities. The Toolkit outlines distinct phases for building a successful Smart Labs program that include: Plan, Assess, Optimize, and Manage. Each phase has specific tasks, training videos, and resources to educate users, support the efforts of key stakeholders, and maximize benefits for the organization in implementing high-performance labs. The Toolkit also includes case studies from the 17 Better Buildings Smart Labs Accelerator partners, sharing lessons learned and supporting the Smart Labs approach to safe, efficient world class science. This paper reviews the technology choices for implementing Smart Labs ventilation strategies, controls, and other efficiency measures. Better Building Smart Lab Accelerator partners provide case study examples of safety and energy-savings results of using Smart Labs approaches.

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

Design and Manufacturing of High Performance, Reduced Charge Heat Exchangers

Air-to-fluid heat exchangers (HXs) play a critical role as the main heat transfer component in Heating, Ventilation, Air-Conditioning, and Refrigeration (HVAC&R) systems. However, their airside thermal resistance significantly inhibits their overall performance. Furthermore, these HXs must be continually more compact to meet the latest refrigerant charge limits to reduce emissions. Recent literature suggests that traditional HX geometries (e.g., round or flat tubes with fins), have reached their limits, and more sophisticated shape- and topology-optimized designs are required to achieve the next jump in performance. This research sheds light on the next generation of air-to-refrigerant HXs and aims to address several practical issues to commercialization such as novelty challenges (improved performance for significant charge reduction; modeling expertise & time investment), manufacturing challenges (non-round tube manufacturing; tube-header integration; product qualification, e.g., burst pressure testing, extreme operational environment, etc.), and operational challenges (flow maldistribution, fouling & wetting, noise & vibration). For example, a >20% improvement on one (or more) HX-level performance metrics (e.g., envelope volume, airside pressure drop, face area, capacity, refrigerant charge, weight, cost, etc.) must be achieved before a HX design is considered for commercialization. We present a new, comprehensive and experimentally validated air-to-refrigerant HX optimization framework with simultaneous thermal-hydraulic performance and mechanical strength considerations for novel, non-round, shape- and topology-optimized tubes capable of optimizing single and two-phase HX designs for any refrigerant choice and performance requirement with significant engineering time savings compared to conventional design practices. The framework was exercised for a wide range of applications and refrigerants, resulting in HXs which achieved greater than 20% improved performance, 20% reductions in size, and 25% reductions in refrigerant charge. To enable non-round tube bundle use in next generation HVAC&R equipment, novel manufacturing techniques were investigated, including the development of conventional manufacturing methods for small diameter, non-round tubes and novel tube-header integration strategies. In total, ten HX prototypes were manufactured, nine using conventional methods directly attributed to this project and one using advanced additive manufacturing methods. The five-year manufacturing feasibility of the proposed HXs was found to have a good outlook. The non-round tube HX simulated performance was validated through comprehensive experimental testing, including nine in-house component-level tests, one independent component-level test at an industry partner laboratory, and in-house system-level tests of using a commercially-available, residential packaged A/C unit which was retrofitted with a non-round tube prototype HX. It was found that HX designs proposed by the new framework can successfully predict experimental thermal-hydraulic performance within ±10-20% the first time with no manual design changes, eliminating the need for time-consuming and expensive prototyping efforts. This work will accelerate design and time to market for next generation HXs while simultaneously facilitating industry transition to new refrigerants at lower charge.

42 ENGINEERING↗
DOE OSTI2022

A Biomass Pyrolysis Oil as a Novel Insect Growth Regulator Mimic for a Variety of Stored Product Beetles

Abstract As fumigants face increasing regulatory restrictions, resistance, and consumer pushback, it is vital to expand the integrated pest management (IPM) chemical toolkit for stored products. The production of biomass derived insecticides (e.g., bio-oil fraction) from byproducts of biofuel production may be a promising alternative source of chemistries for controlling stored product insects. These potential insecticidal bio-oils were fractionated based on boiling points (ranging from 115 to 230°C in one series and 245–250°C in another). Fractions were analyzed using GC-MS, and were found to be unique in composition. The lethality of these fractions was tested on Tribolium castaneum, Tribolium confusum, and Oryzaephilus surinamensis (L.) (Coleoptera: Silvanidae). Fractions were tested at concentrations ranging from 5–260 mg/ml to screen for efficacy against adults for durations of 2–8 hr sprayed on concrete arenas. In addition, a separate assay evaluated adult emergence of larvae after 6 wk with supplemental food in arenas, while repellency was evaluated against four stored product insect species in a laminar wind tunnel. A greenhouse gas (GHG) emissions life cycle assessment was also performed, which found the use of the bio-oil fraction could reduce GHG emissions associated with the insecticide supply chain by 25–61% relative to a fossil-fuel based insecticide or pyrethroid. While adults were largely unaffected, we found that larval emergence was significantly suppressed compared to controls by roughly half or more. We also determined that there was minimal repellency to most fractions by most species. We conclude that the use of bio-oil fractions is a climate-friendly choice that may support IPM programs.

09 BIOMASS FUELS↗
DOE OSTI2020

Will Solar Panels Help When the Power Goes Out? Planning for PV Resilience

PV systems, like all energy systems, must chart a difficult course in order to improve resilience. They need to simultaneously withstand extreme conditions that cause grid power disruptions, be configured in a way that allows electricity to be delivered to meet the PV user's needs, and overcome disruptions arising from solar resource intermittency. Certain considerations reveal themselves as key to a PV system's resilience benefits. Choice of hardware is critical, as is site selection for the PV array. Voluminous evidence in a hurricane context demonstrates that durable hardware, properly maintained and sited, leads to PV systems surviving extreme weather with their functionality intact. Structurally weak or poorly maintained PV equipment, on the other hand, has demonstrated failures during extreme weather events. PV system stakeholders should ensure that best practices are followed for hardware procurement and siting. It is also important to understand that PV systems are not one-size-fits-all. While incorporating islanding capabilities and energy storage into the system's electrical topology will typically enhance resilience, the user's needs will determine the optimally resilient conformation of any given system. These factors - appropriate hardware, siting, maintenance, and system design - are the primary drivers of PV resilience. PV systems possess high potential for resilient energy generation due to their distributed, abundant, and free fuel supply, but the technology itself does not inherently lead to resilient outcomes. Realizing PV's potential for resilience requires consideration of these determinative factors.

14 SOLAR ENERGY↗
DOE OSTI2020

Subtask 1.5 – CO2 Injection Monitoring with an Optimized Scalable, Automated, Semipermanent Seismic Array

The scalable, automated, semipermanent seismic array (SASSA) method is a flexible and relatively cost-effective surface geophysical method for regular time-lapse monitoring of the movement of injected carbon dioxide (CO2) in a reservoir for CO2 enhanced oil recovery (EOR) or geologic CO2 storage operations. It has the advantages of a low-environmental-footprint while monitoring regions of a reservoir from the surface without the need for a regular grid distribution of receivers. Automated data collection is possible. As only time-lapse amplitude changes at the reservoir level due to CO2 movement within the reservoir are monitored, the turnaround time to deliver results from the SASSA method can be short, without the need for long, time-consuming data-processing workflows. As data is collected and processed, incremental information can be provided to the field operator. The Energy & Environmental Research Center (EERC) conducted a SASSA field test from September 2018 to November 2020 in a portion of the Bell Creek Field in Montana, which implemented new CO2 EOR field activities during the study period. Lessons learned from a proof-of-concept study were incorporated to improve the data quality of the SASSA method and demonstrate the viability of the technology. The EERC implemented several enhancements to improve data quality, including 1) an iterative survey design, which allowed placing the receivers in strategic locations where the movement of the CO2 in the reservoir could be tracked with minimum interference by the cultural noise in the study area; 2) the use of powerful seismic sources in the form of surface orbital vibrators, and 3) data acquisition during optimal periods. History-matched reservoir simulation was performed to predict gas saturation and pressure response induced by CO2 injection in the study area. The results were compared with the SASSA-measured responses to CO2 injection as a partial validation technique. A match between the two methods was observed for most of the SASSA points predicted to have intersected a CO2 saturation change. The validated results provide confidence that the SASSA method can be used independently as a CO2 saturation monitoring technique. As data are collected and processed, incremental information can be provided to the field operator. The critical components of the SASSA workflow for a successful application of the method are the following: Iterative survey design with information about CO2 injection activities from the oilfield operator. A detailed CO2 injection plan is the key driver to select the strategic monitoring location of the SASSA sensors. After this information is incorporated in the initial distribution of sources and receivers in the study area, high-resolution satellite images are used to identify ground locations not affected by cultural noise sources, such as power lines, pipelines/flow lines, or roadways. In the next iteration of the survey design, a scouting trip to the study area is needed to understand more details of the noise sources identified in the previous step and the intensity of the field activities that can also generate noise during the monitoring. Integrating the information from the scouting trip into the survey design to select the optimum source and receiver locations is the final step. Noise attenuation. The variety of noise types during seismic monitoring of an oil field is enormous. Tailored noise characterization and processing at a node-by-node level can enhance the performance and sensitivity of the SASSA technique. Future advancements that could improve the efficiency and application of the SASSA technology include: Gaining a better understanding of the noise field produced by the seismic source to aid the choice of receiver location. Surface noise from the source can overwhelm the small signal changes due to CO2 that the SASSA method measures. Improved data-processing workflow to automatically analyze and adapt to dynamic noise conditions associated with industrial settings. This subtask was funded through the EERC–DOE Joint Program on Research and Development for Fossil Energy-Related Resources Cooperative Agreement No. DE- FE0024233.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗