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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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This is an example of how to file for alternative compliance annual report under the US DOE's Energy Policy Act (EPAct).
This is a guidance document on how to use the compliance reporting tool under the US DOE's Energy Policy Act (EPAct).
This is a guidance document on submitting the standard compliance annual report under the US DOE's Energy Policy Act (EPAct).
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This Equipment Self-Assessment Checklist is designed for asset owners and operators (AOOs) responsible for the deployment, operation, maintenance, or cybersecurity oversight of grid systems and digital energy technologies. It provides a structured inspection checklist for evaluating the security, integrity, and operational trustworthiness of equipment across substations, generation sites, distributed energy resources (DERs), and control environments.
Battery energy storage systems (BESS) deployed behind the meter at resilience hubs and other critical infrastructure can provide economic and operational value during normal operations—such as lower and more predictable energy costs—as well as resilience and security benefits during power disruptions by maintaining essential services. However, existing evaluation approaches tend to focus narrowly on engineering performance or rely on broad socio-economic frameworks that are not well suited to behind-the-meter storage. As a result, developers, utilities, and funders often lack consistent methods for defining success, quantifying benefits, and comparing outcomes across projects. This report presents a practitioner-oriented impact assessment framework for evaluating behind-the-meter BESS at resilience hubs and critical infrastructure facilities. The framework is organized into five iterative components—developing an action plan, defining project goals, identifying metrics, collecting data and measuring outcomes, and reporting and using results—and includes a structured metric architecture spanning six impact categories. Designed for real-world constraints such as limited staffing and uneven data availability, the framework was developed, applied, and refined through real projects, and is illustrated with case studies across diverse deployment contexts.
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Vaccines have historically played a pivotal role in controlling epidemics. Effective vaccines for viruses causing significant human disease, e.g., Ebola, Lassa fever, or Crimean Congo hemorrhagic fever virus, would be invaluable to public health strategies and counter-measure development missions. Here, we propose coverage metrics to quantify vaccine-induced CD8 + T cell-mediated immune protection, as well as metrics to characterize immuno-dominant epitopes, in light of human genetic heterogeneity and viral evolution. Proof-of-principle of our approach and methods are demonstrated for Ebola virus, SARS-CoV-2, and Burkholderia pseudomallei (vaccine) proteins.
The process of time-series forecasting such as predicting trajectories of silicon content in blast furnaces is a difficult task. Most time-series approaches today focus on scalar-type MSE loss optimization. This optimization approach, while widely common, could benefit from the use of human expert or process-level preferences. In this paper, we introduce a novel alignment and fine-tuning approach that involves learning from a corpus of preferred and dis-preferred time-series prediction trajectories. Our contributions include (1) a preference annotation pipeline for time-series forecasts, (2) the application of Score-based Preference Optimization (SPO) to train decoder-only transformers from preferences, and (3) results showing improvements in forecast quality. The approach is validated on both proprietary blast furnace data and the UCI Appliances Energy dataset. The proposed preference corpus and training strategy offer a new option for fine-tuning sequence models in industrial settings.
Accurate short-term wave forecasting is critical for the safe and efficient operation of marine structures that rely on real-time, phase-resolved ocean wave information for control and monitoring purposes (e.g., digital twins). These systems often depend on environmental sensors (e.g., waverider buoys, wave-sensing LIDAR). Challenges arise when upstream sensor data are missing, sparse, or phase-shifted due to drift. This study investigates the performance of two machine learning models, time-series dense encoder (TiDE) and long short-term memory (LSTM), for forecasting phase-resolved ocean surface elevations under varying degrees of data degradation. We introduce the τ-trimming algorithm, which adapts the prediction horizon based on uncertainty thresholds derived from historical forecasts. Numerical wave tank (NWT) and wave basin experiments are used to benchmark model performance under short- and long-term data masking, spatially coarse sensor grids, and upstream phase shifts. Results show under a 50% probability of upstream data loss, the τ-trimmed TiDE model achieves a 46% reduction in error at the most upstream target, compared to 22% for LSTM. Furthermore, phase misalignment in upstream data introduces a near-linear increase in forecast error. Under moderate model settings, a ±3 s misalignment increases the mean absolute error by approximately 0.5 m, while the same error is accumulated at ±4 s using the more conservative approach. These findings inform the design of resilient, uncertainty-aware wave forecasting systems suited for realistic offshore sensing environments.
In the identification of differential equations from data, significant progresses have been made with the weak/integral formulation. In this paper, we explore the direction of finding more efficient and robust test functions adaptively given the observed data. While this is a difficult task, we propose weighting a collection of localized test functions for better identification of differential equations from a single trajectory of noisy observations on the differential equation. We find that using high dynamic regions is effective in finding the equation as well as the coefficients, and propose a dynamics indicator per differential term and weight the weak form accordingly. For stable identification against noise, we further introduce a voting strategy to identify the active features from an ensemble of recovered results by selecting the features that frequently occur in different weighting of test functions. Systematic numerical experiments are provided to demonstrate the robustness of our method.
The Agile BioFoundry (ABF) is a consortium of national laboratories dedicated to accelerating biomanufacturing and enabling the bioeconomy. The ABF operates a flexible biotechnology platform that can adjust to the needs of numerous government, academic, and industrial partners, thus enabling them to rapidly develop and optimize the production of a wide range of bioproducts. In this project, the ABF partnered with Birch Biosciences, Inc. to use ABF technology to assist in the development of high-performance enzymes that enable circular, sustainable recycling of poly(ethylene terephthalate) (PET) plastics. A primary goal of this project is to enable cost-effective, high yield, sustainable recycling of PET plastic packaging products.
Many engineering applications require the simultaneous optimization of multiple conflicting objective functions. Often, these objective functions are evaluated using highly accurate computer simulations that are computationally too expensive to be evaluated hundreds or thousands of times during optimization. Thus, the goal is to find good approximations of the Pareto front using as few of these expensive simulations as possible. Here, we describe an optimization approach based on surrogate models and diverse sampling strategies to accelerate the search for the Pareto solutions. We use a separate surrogate model for approximating each objective function and then we use the surrogate models to inform where additional expensive simulations should be run. The surrogate models are updated in an active learning framework whenever new information from the expensive simulations becomes available. The sampling strategies aim at balancing local improvements of the approximate Pareto front and global exploration to identify the extrema and fill in large gaps of the approximate Pareto front. We demonstrate on a large set of benchmark problems the effectiveness of the method for finding good approximations of the Pareto front.