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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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DOE OSTI2026

A Simple Panel System to Overcome Interface Challenges for Retrofits: Preprint

Retrofitting buildings is usually an expensive and labor-intensive process. Weatherization measures can improve comfort and energy affordability to some extent, but deep energy retrofits are needed to optimize performance and comfort, and to achieve significant energy cost savings. Barriers to deep energy retrofits include a limited supply of skilled labor, different building types, planning complexity, split incentives, and a long or non-existent ROI horizon. The "Simple Panel System" (SPS) workflow developed and demonstrated in this effort streamlines deep energy retrofits by applying advanced site capture, machine learning, and mixed reality to panelized construction. The result is a one-stop, product-independent solution for rapidly scalable retrofits with the potential to reduce construction time and project costs by 50%. Soft costs are reduced by more than 66%, total costs by more than 50%, and field construction time by more than 50% - not to mention the reduction in construction waste, improvement in working conditions, and the ability to scale without an influx of skilled labor. This paper presents the SPS and the preliminary results and findings from the pilot project.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2024

ASGarD: Adaptive Sparse Grid Discretization

Many areas of science exhibit physical processes that are described by high dimensional partial differential equations (PDEs), e.g., the 4D, 5D and 6D models describing magnetized fusion plasmas, models describing quantum chemistry, or derivatives pricing. Such problems are affected by the so-called “curse of dimensionality” where the number of degrees of freedom (or unknowns) required to be solved for scales as N D where N is the number of grid points in any given dimension D. A simple, albeit naive, 6D example is demonstrated in the left panel of Figure 1. With N = 1000 grid points in each dimension, the memory required just to store the solution vector, not to mention forming the matrix required to advance such a system in time, would exceed an exabyte - and also the available memory on the largest of supercomputers available today. The right panel of Figure 1 demonstrates potential savings for a range of problem dimensionalities and grid resolution. While there are methods to simulate such high-dimensional systems, they are mostly based on Monte-Carlo methods, which rely on a statistical sampling such that the resulting solutions include noise. Since the noise in such methods can only be reduced at a rate proportional to $\sqrt{N_p}$ where N p is the number of Monte-Carlo samples, there is a need for continuum, or grid/mesh-based methods for high-dimensional problems, which both do not suffer from noise and bypass the curse of dimensionality. We present a simulation framework that provides such a method using adaptive sparse grids.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2024

Techno-Economic Analysis of Green Hydrogen Energy Storage in a Cryogenic Flux Capacitor

Abstract The Cryogenic Flux Capacitor (CFC) is a cold, dense energy storage core that is being studied in the cryo-compressed, about 300 bar and 80K, region of gaseous hydrogen (GH2) storage and liquid hydrogen (LH2) region near the normal boiling point. Hydrogen storage is improved by physically bonding the molecules within the nanoscale pores of the aerogel composite blanket material. The process of bonding or debonding is governed by principles of physical adsorption (physisorption) and thermodynamics. The large surface area afforded by the nanoporous aerogel (∼1,000 m2/g) allows its storage performance to easily exceed capacities of high-pressure GH2 storage for an equivalent volume. With the integrated aerogel, subscale tests have shown that storage is increased by about 36% over a simple tank filled with GH2 at the same operating temperature and pressure. For LH2 conditions, the CFC is shown to operate at improved densities, but testing is ongoing. For the techno-economic analysis (TEA), the source of hydrogen is compared between onsite steam methane reforming (SMR) and onsite solar photovoltaic (PV) panels providing power to electrolyzers to produce green GH2. The TEA compares pure hydrogen produced at a small scale for a 25 MW power system and at a large scale in a 500 MW power system. The system allowed for hydrogen imports and exports at a set price with a tank sized for 10 hours of power production. The two power producing technologies are a combined cycle gas turbine (CCGT) and hydrogen fuel cells. The SMR system uses natural gas as an input and includes a carbon capture and storage (CCS) system. The levelized cost of electricity (LCOE), levelized cost of hydrogen (LCOH), and levelized cost of storage (LCOS) are developed based on the capital cost and operating cost of the systems. The results are shown for current costs using a 2021 benchmark and DOE projections for cost improvements by 2030. The TEA showed that onsite hydrogen generation from SMR has an LCOH of about 1.4 to 2 USD per kg over the life of the plant and the PV hydrogen production LCOH is about 5.2 to 5.5 USD per kg. The LCOS of conventional GH2 systems is estimated to be $210/MWh and cost of storage for LH2 systems is $205/MWh for fuel cell systems and $249/MWh for CCGT systems. CFC improved the LCOS of all these systems to $198/MWh, $191/MWh and $233/MWh respectively. The LCOE also improved with conventional systems between $171/MWh and $228/MWh improved by CFC to between $167/MWh and $212/MWh. Using projections for improvement in costs following DOE’s goals by 2030, green hydrogen improved to as low as $78/MWh LCOS and LCOE for conventional cases. CFC improved over conventional storage with the lowest LCOS being $62/MWh and the lowest LCOE being $73/MWh. These results correspond to an LCOH of $2/kg. Finally, the TEA shows how LCOE is improved for hydrogen conditioning and storage over conventional systems and caverns in the 10 to 50 hour range.

08 HYDROGEN↗

DOE OSTI2026

Systematic Benchmarking of Climate Models: Methodologies, Applications, and New Directions

As climate models become increasingly complex, there is a growing need to comprehensively and systematically assess model performance with respect to observations. Given the increasing number and diversity of climate model simulations in use, the community has moved beyond simple model intercomparison and toward developing methods capable of benchmarking a large number of simulations against a suite of climate metrics. Here, we present a detailed review of evaluation and benchmarking methods and approaches developed in the last decade, focusing primarily on scientific implications for Coupled Model Intercomparison Project (CMIP) simulations and CMIP6 results that contributed to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6). Based on this review, we explain the resulting contemporary philosophy of model benchmarking, and provide clear distinctions and definitions of the terms model verification, process validation, evaluation, and benchmarking. While significant progress has been made in model development based on systematic evaluation and benchmarking efforts, some climate system biases still remain. The development of open‐source community software packages has played a fundamental role in identifying areas of significant model improvement and bias reduction. We review the key features of several software packages that have been commonly used over the past decade to evaluate and benchmark global and regional climate models. Additionally, we discuss best practices for the selection of evaluation and benchmarking metrics and for interpreting the obtained results, the importance of selecting suitable sources of reference data and accurate uncertainty quantification.

Environmental sciences↗