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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 OSTI2025

Full Modeling and parameter compression methods in configuration space for DESI 2024 and beyond

In the contemporary era of high-precision spectroscopic surveys, led by projects like DESI, there is an increasing demand for optimizing the extraction of cosmological information from clustering data. This work conducts a thorough comparison of various methodologies for modeling the full shape of the two-point statistics in configuration space. We investigate the performance of both direct fits (Full Modeling) and the parameter compression approaches (ShapeFit and Standard). We utilize the ABACUS-SUMMIT simulations, tailored to exceed DESI's precision requirements. Particularly, we fit the two-point statistics of three distinct tracers (LRG, ELG, and QSO), by employing a Gaussian Streaming Model in tandem with Convolution Lagrangian Perturbation Theory and Effective Field Theory. We explore methodological setup variations, including the range of scales, the set of galaxy bias parameters, the inclusion of the hexadecapole, as well as model extensions encompassing varying ns and allowing for w 0 w a CDM dark energy model. Throughout these varied explorations, while precision levels fluctuate and certain configurations exhibit tighter parameter constraints, our pipeline consistently recovers the parameter values of the mocks within 1σ in all cases for a 1-year DESI volume. Additionally, we compare the performance of configuration space analysis with its Fourier space counterpart using three models: PyBird, FOLPS and velocileptors, presented in companion papers. We find good agreement with the results from all these models.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2022

SolarPlus Optimizer: Integrated Control of Solar, Batteries, and Flexible Loads for Small Commercial Buildings

Building-level microgrids may be a key strategy to unlock the combined potential of flexible loads, renewable generation, and energy storage. However, few software options exist for integrated control of building loads and other distributed energy resources at this scale. The commercial software solutions on the market can force customers to adopt one particular ecosystem of products, thus limiting consumer choice. The SolarPlus Optimizer (SPO) is an open-source building-level microgrid control platform that uses Model Predictive Control to optimize both building loads and behind-the-meter energy storage to reduce energy bills and increase demand flexibility. This paper evaluates the capabilities of SPO in a small commercial building in Northern California under multiple electricity tariffs and demand response scenarios. Comparing SPO operation with an emulated battery and baseline operation employing a commercial optimization service, SPO reduced electricity bills by an estimated 7.3% in summer, 3.2% in spring, and 3.7% in winter. In a “load shape” scenario meant to counter the “duck curve”, SPO achieved 71% fewer violations from the load signal than the baseline control method. During a three hour long load shed event, SPO reduced cooling and refrigeration load by 38%. This research shows significant potential to provide load flexibility for building-level microgrids for this type of control systems. Finally, the paper discusses the future direction of research on open-source control systems.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2021

Rossi Alpha Measurements – Rapid Organic (n, $\gamma$) Discrimination Detector (RAM-RODD) system capabilities

The Rossi Alpha Measurements–Rapid Organic (n, $\gamma$) Discrimination Detector (RAM-RODD) is an array organic scintillation detectors that has been optimized for use in neutron noise measurements. This report details the capability of the system including the method of energy calibration, pulse shape discrimination, and time resolution.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2023

Pragmatic Uncertainty Quantification and Propagation in Inverse Estimation of Structural Dynamics Parameters given Material Property Uncertainties and Limited Sensor Data

In this report we demonstrate some relatively simple and inexpensive methods to effectively account for various sources of epistemic lack-of-knowledge type uncertainty in inverse problems. The demonstration problem involves inverse estimation of six parameters of a bolted joint that attaches a kettlebell shaped object to a thick plate. The parameters are efficiently inverted in a modal-based model calibration using gradient-based optimization. Two material properties of the kettlebell are treated as uncertain to within given epistemic uncertainty bounds. We apply and test interval and sparse-sample probabilistic approaches to account for uncertainty in the estimated parameters (and various scalar functionals of the parameters as generic quantities of interest, QOIs) due to uncertainties in the material properties. We also investigate the error effects of limited numbers of vibration sensors (accelerometers) on the kettlebell and plate, and therefore abbreviated excitation/response information in the parameter inversions. We propose and demonstrate a Leave-K-Sensors-Out “cross-prediction” UQ approach to estimate related uncertainties on the parameters and QOI functionals. We indicate how uncertainties from material properties and limited sensors are treated in a combined manner. The economical combined UQ approach involves just three to five samples (i.e. three to five inverse simulations), with no added complication or error/uncertainty from use of surrogate models for affordability. Finally, we describe a related economical UQ approach for handling potential parameter solution non-uniqueness and numerical optimization related precision uncertainties in the estimated parameter values. Indicated further research is identified.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Regularizing Invertible Neural Networks for Airfoil Design Through Dimension Reduction

This work examines how dimension reduction can improve the performance of invertible neural networks (INN) for airfoil design. Design workflows are typically expensive, relying on many evaluations of high fidelity computational fluid dynamics (CFD) models. Furthermore, the inverse design problem is typically ill-posed. That is, multiple valid solutions exist that satisfy the design criteria. Regularization can reduce this inverse design space and simplify the problem. We study the use of subspace-based input dimension reduction to act as a regularizer for the INN model and improve the recovery of new airfoil shapes with desired performance characteristics. We find that the dimension reduction identifies two dominant modes, relating to airfoil thickness and camber, that optimally determine the airfoil's aerodynamics. We demonstrate the capability of the proposed INN model to generate 100 airfoils that satisfy the specific aerodynamic and structural characteristics.

airfoil design↗
DOE OSTI2021

Coordinated resource allocation to plant growth–defense tradeoffs

Summary Plant resource allocation patterns often reveal tradeoffs that favor growth (G) over defense (D), or vice versa. Ecologists most often explain G–D tradeoffs through principles of economic optimality, in which negative trait correlations are attributed to the reconciliation of fitness costs. Recently, researchers in molecular biology have developed ‘big data’ resources including multi‐omic (e.g. transcriptomic, proteomic and metabolomic) studies that describe the cellular processes controlling gene expression in model species. In this synthesis, we bridge ecological theory with discoveries in multi‐omics biology to better understand how selection has shaped the mechanisms of G–D tradeoffs. Multi‐omic studies reveal strategically coordinated patterns in resource allocation that are enabled by phytohormone crosstalk and transcriptional signal cascades. Coordinated resource allocation justifies the framework of optimality theory, while providing mechanistic insight into the feedbacks and control hubs that calibrate G–D tradeoff commitments. We use the existing literature to describe the coordinated resource allocation hypothesis (CoRAH) that accounts for balanced cellular controls during the expression of G–D tradeoffs, while sustaining stored resource pools to buffer the impacts of future stresses. The integrative mechanisms of the CoRAH unify the supply‐ and demand‐side perspectives of previous G–D tradeoff theories.

Monson, Russell K.↗
DOE OSTI2026

Intelligent Experiments through Real-Time AI: Fast Data Processing and Autonomous Detector Control for High-Energy Nuclear Experiments

The aim of this project is to develop software and hardware for fast real-time data processing and autonomous detector control and calibration for the sPHENIX and the future EIC experiments. Below summarizes Georgia Tech team efforts in the past year: 1. We developed a real-time clustering algorithm and FPGA-based pipeline architecture for processing fired pixel data from ALPIDE sensors in sPHENIX experiments. Our Columnar Clustering Co-Design introduces a hardware-aware, stream-friendly approach that segments pixel data by column pairs using a Column Pair Clustering (CPC) strategy, followed by Cluster Stitching to merge adjacent subclusters. Implemented in Vitis HLS, the pipeline comprises five stages—read-in, subclustering, stitching, analysis, and write-out—connected by tagged HLS streams with custom end-of-event signaling for robust synchronization. We designed a pipelined dataflow model optimized for throughput, low latency, and minimal buffering, enabling scalable clustering across events of arbitrary size. Our system maintains spatial precision via center-of-mass and shape key extraction and efficiently handles edge cases such as fragmented or nested clusters. Compared against DBSCAN in both software and hardware, our approach demonstrates competitive performance under FPGA constraints. 2. We also conducted a comprehensive algorithm-to-hardware co-design of connected component analysis tailored for sPHENIX experiments, focusing on real-time, low-latency processing using FPGAs and High-Level Synthesis (HLS). Starting from a Python-based particle tracking pipeline, the team translated the core logic—graph traversal via DFS and Union-Find—into an HLS-compatible C++ model, replacing dynamic memory and recursion with static arrays and pipelined control flow. The final design includes a fully streamed and dataflow-compatible Union-Find kernel optimized across five iterations, incorporating loop pipelining, array partitioning, AXI/FIFO interface tuning, and function flattening. Experimental results show up to 14.8× speedup over the CPU baseline, reducing per-graph latency to 1.58 μs and demonstrating strong resource efficiency with only ~7k LUTs and zero BRAM usage. The design maintains functional correctness against the Python reference using a Python-based C-simulation framework and Mean Squared Error metrics. This work validates the potential of HLS-driven FPGA designs for edge-level HEP data acquisition, laying a scalable foundation for future integration with real-time detector pipelines and multi-graph processing systems.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2025

The transition from resistance to acceptance: Managing a marine invasive species in a changing world

Abstract Marine invasive species can transform coastal ecosystems, yet mitigating their effects can be difficult, and even impractical. Often, marine invasive species are managed at poorly matched spatial scales, and at the same time, rates of spread and establishment are increasing under climate change and can outpace resources available for population suppression. These circumstances challenge traditional conservation goals of maintaining a historic environmental state, especially for a species like the European green crab ( Carcinus maenas ), a formidable invader with few examples of successful long‐term removal programs. A management paradigm where decision alternatives include resisting or accepting a new ecological trajectory may be needed. We apply mathematical concepts from decision theory to develop a quantitative framework for navigating management decisions in this new resist‐accept paradigm. We develop a model of European green crab growth, removal and colonization, and we find optimal levels of removal effort that minimize both ecological change and removal cost. We establish a benchmark of colonization pressure at which green crab density becomes decoupled from a decision maker's actions, such that population control can no longer shape the invasion trajectory. For informing the decision boundary between resistance and acceptance, our results highlight that a decision maker's understanding of how removal cost scales with removal effort is more important than understanding the density‐impact relationship. We show that assuming stationary system dynamics can result in sub‐optimal levels of species removal effort, highlighting the importance of developing anticipatory management strategies by accounting for non‐stationary dynamics. Policy implications . For marine invasive species that can disperse across long distances and recolonize rapidly after removal, the focus of conservation policy should shift away from understanding how to resist change to understanding when to stop resisting change. Navigating this decision problem involves trade‐offs among competing objectives, highlighting the need for structured approaches to elicit objective weights that reflect the values of the decision maker. For natural resource managers facing possible ecosystem transformation, this decision framework can enable proactive and strategic decisions made under uncertainty in a changing world.

Keller, Abigail G. [Department of Environment Scie↗
DOE OSTI2025

Beam Shaping and Oscillation for Metals Additive Manufacturing

Additive Manufacturing (AM) is pivotal for next-generation industrial manufacturing but is hampered by process control challenges leading to unwanted defects and sub-optimal material properties. Our research builds on prior work that utilized beam oscillation techniques to mitigate these issues (Wu et al., 2023). We focus on investigating the influence of beam oscillation frequency on melt pool dynamics, utilizing in-situ synchrotron material characterization from the ESRF (Chen et al., 2026). Preliminary findings indicated that beam oscillation results in unique fluid flow patterns and enhanced melting stability. These findings suggested the possibility of optimized AM process control, including minimized defects and tailored thermal histories. We proposed further research to explore the effects of beam oscillation frequencies comprehensively, using the in-situ characterization capabilities at the ESRF. The goal was to quantify this relationship, offering a pathway to more precise AM process control and optimization. Our research is expected to offer new scientific insights into the fluid mechanics and thermal behaviour in AM, contributing to its broader applicability and commercial viability.

36 MATERIALS SCIENCE↗
DOE OSTI2022

CONCEPT OF A POLARIZED POSITRON SOURCE FOR CEBAF

Positron beams would provide new and meaningful probes for the experimental program at the Thomas Jefferson National Accelerator Facility (JLab), including but not limited to future hadronic physics and dark matter experiments. Critical requirements involve generating positron beams with a high degree of spin polarization, sufficient intensity and a continuous-wave (CW) bunch train compatible with acceleration to 12 GeV at the Continuous Electron Beam Accelerator Facility (CEBAF). To address these requirements, a polarized positron injector based upon the bremsstrahlung of an intense CW spin polarized electron beam is considered*. First a polarized electron beam line provides >1 mA of polarized electrons at ~120 MeV to a high-power target for positron production. Next, a second beam line collects, shapes and aligns the spin of positrons for users. Finally, the positron beam is matched into the CEBAF acceptance for acceleration and transport to the end stations with energies up to 12 GeV. An optimized layout to provide positrons beams with intensity >100 nA (polarized) or intensity >3 µA (unpolarized) will be discussed in this poster.

Habet, S. H.↗
DOE OSTI2021

Optimum Multinozzle Configuration for Minimizing the Rayleigh Integral During High-Frequency Transverse Instabilities

This paper develops a formalism for optimizing nozzle location/configuration with respect to combustion stability of high-frequency transverse modes in a can combustor. The stability of these acoustically noncompact flames was assessed using the Rayleigh integral (RI). Several key control parameters influence RI—flame angle, swirling strength, nozzle location, as well as nozzle location with respect to the acoustic mode shape. In this study, we consider a N-around-1 configuration such as typically used in a multi-nozzle can system and study the overall stability of this system for different natural transverse modes. Typically, such nozzles are distributed in a uniformly circular manner for which we study the overall RI, and for cases where RI > 0, we optimize the nozzle distribution that can reduce and minimize RI. For a fixed geometry such as a circular configuration, the analysis shows how the flame's parameters must vary across the different nozzles, to result in a relatively stable system. Additionally, for a fixed set of flame parameters, the analysis also indicates the noncircular distribution of the N nozzles that minimizes RI. Overall, the analysis aims to provide insights on designing nozzle locations around the center nozzle for minimal amplification of a given transverse mode.

42 ENGINEERING↗
DOE OSTI2018

Analytical Approach for Detection of Ergosterol in Mushrooms Based on Modification Free Electrochemical Sensor in Organic Solvents

Ergosterol (Erg) is the major sterol component in the plasma membranes of fungi. The main goal of this work was to develop rapid, reliable, and cost-effective method toward Erg detection based on application of unmodified boron-doped diamond electrode. Several organic solvents were tested (acetonitrile, dimethyformamide, dimethylsulfoxide, dichloromethane), and it was found that best analytical characteristics were achieved using acetonitrile containing 0.1 M tetrabutylammonium hexa-fluorophosphate as supporting electrolyte. Oval shaped and well-defined oxidation peak was recorded at potential of around 1.17 V versus Ag/Ag+ non-aqueous reference electrode. Under optimized experimental conditions, linear working range from 2 to 200 mu M was estimated, with limit of detection of 0.7 mu M, using square wave voltammetry. Negligible effect of possible interfering compound was observed. Proposed methodology was successfully applied for estimation of Erg content in mushrooms extract with satisfactory results in comparison with spectrophotometric procedure. Obtained results clearly show that developed analytical methodology can be adequate replacement for the, up to date, used methods and open-novel approach for ergosterol detection.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Designing robust energy policy packages under deep uncertainty: A multi-metric decision support framework

The complexity of transitioning to sustainable energy systems requires policy frameworks capable of balancing multiple objectives while addressing deep uncertainty. However, existing approaches often lack systematic methods to identify combinations of policy levers that remain effective across a wide range of uncertain futures. This paper presents a novel decision support framework that guides the selection of robust policy packages based on their performance across multiple objectives under uncertainty. Our method leverages a large ensemble of scenarios and applies scenario discovery techniques to identify influential policy levers. Here, we introduce new indicators to assess the robustness of policies by evaluating their ability to mitigate adverse outcomes across metrics. These indicators support an iterative process to build a robust policy package. Finally, we map the technological and energy pathways associated with the robust policy package by leveraging an energy system optimization model. We illustrate the application of this framework to the Spanish energy system, providing insights into how specific combinations of policy levers shape decarbonization pathways under uncertainty.

Decision-support method↗
DOE OSTI2024

Microbe of the month: Methanococcus maripaludis

Methanococcus maripaludis was first isolated in 1983 from the anoxic sediment of a salt marsh in South Carolina, USA. M. maripaludis grows by using H 2 or formate to reduce CO 2 to CH 4 and is an autotroph that assimilates CO 2 via the archaeal version of the Wood–Ljungdahl pathway. In some strains, supplementation of growth medium with acetate or propionate can increase biomass yield. Furthermore, this microorganism is strictly anaerobic, has a pleomorphic coccoid shape, and forms translucent, pale yellow, smooth colonies within 3 days on agar plates. The optimum temperature for growth is 38°C with optimal pH between 6.8 and 7.2.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

Molecular Lignin Solubility and Structure in Organic Solvents

Lignin, a polymer found in the secondary plant cell wall of terrestrial plants, is the single largest source of renewable aromatics and has attracted considerable attention as a feedstock for potential industrial use. However, the secondary plant cell wall is a crowded environment, and lignin in its native form interacts with other biomass components within a larger network. Application of some organic solvents is known to liberate lignin from this network and creates lignin-rich streams suitable for conversion into target products. Through molecular-scale lignin simulation, we analyze how diverse lignin polymers change their structure in response to varying organic solvent environments. We quantify the relationship between solvent polarity and lignin polymer extension, observing maximum polymer expansion and solvation for solvents with polarity near those of dimethyl sulfoxide. From our observations at the nanoscale, increasing syringyl content within lignin polymers reduces the expansion of the polymer in organic solvent environments and decreases the free energy difference compared to aqueous solvent environments, thereby reducing solubility for high syringyl lignin polymers. The conformational transition rates between lignin polymer shapes increased through a combination of the solvent diffusion constant and polymer extension. The molecular simulations indicate that there is likely no single optimal organic solvent for lignin. Different solvent mixtures have optimal or near-optimal properties in solubilizing lignin polymers, thereby disrupting interactions with other biopolymers.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Design of protein-binding proteins from the target structure alone

The design of proteins that bind to a specific site on the surface of a target protein using no information other than the three-dimensional structure of the target remains a challenge. Here we describe a general solution to this problem that starts with a broad exploration of the vast space of possible binding modes to a selected region of a protein surface, and then intensifies the search in the vicinity of the most promising binding modes. We demonstrate the broad applicability of this approach through the de novo design of binding proteins to 12 diverse protein targets with different shapes and surface properties. Biophysical characterization shows that the binders, which are all smaller than 65 amino acids, are hyperstable and, following experimental optimization, bind their targets with nanomolar to picomolar affinities. We succeeded in solving crystal structures of five of the binder–target complexes, and all five closely match the corresponding computational design models. Experimental data on nearly half a million computational designs and hundreds of thousands of point mutants provide detailed feedback on the strengths and limitations of the method and of our current understanding of protein–protein interactions, and should guide improvements of both. Our approach enables the targeted design of binders to sites of interest on a wide variety of proteins for therapeutic and diagnostic applications.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Effects of tensile loading during annealing of alnico melt spun ribbons

Conventional magnetic annealing (MA) of the permanent magnet alloy alnico involves application of an external magnetic field at temperatures within the spinodal decomposition range. This field biases the growth of the Fe-Co rich, ferromagnetic α 1 -phase in an energetically favorable 〈001〉 direction in alignment with the applied field within an Al-Ni rich, paramagnetic α 2 -phase. Utilizing a magnetic field to bias the α 1 -phase may limit alnico from reaching theoretical coercivity due to (1) the field having maximum biasing ability at temperatures near the Curie temperature where large α 1 -phase nanorods form and (2) connectivity of the α 1 -phase occurs unavoidably during MA. Both decrease the effective shape anisotropy of the α 1 -phase, thereby reducing coercivity. Herein, we explore tensile-loading as a biasing mechanism to control and optimize the final alnico nanostructure beyond that achieved by MA. Two samples of melt-spun alnico were heat-treated at 860 °C for 5 minutes: one sample was subjected to 10 MPa tensile stress for comparison with a stress-free control sample. Structural and magnetic characterization revealed that the stress-annealed ribbon sample possessed expected phase assemblages, but was distinguished by a ∼2× larger grain diameter and an elongated anisotropic α 1 -phase within grains that were oriented to a shear stress along 〈001〉 directions at an angle of ∼45° relative to the loading direction. Both types of annealing produced a similar increase in the coercivity and remanence, but a decrease in saturation magnetization.

Rinko, E. A.↗
DOE OSTI2019

ERAP1 enzyme-mediated trimming and structural analyses of MHC I–bound precursor peptides yield novel insights into antigen processing and presentation

Endoplasmic reticulum aminopeptidase 1 (ERAP1) and ERAP2 critically shape the major histocompatibility complex I (MHC I) immunopeptidome. The ERAPs remove N-terminal residues from antigenic precursor peptides and generate optimal-length peptides (i.e. 8–10-mers) to fit into the MHC class I groove. It is therefore intriguing that MHC class I molecules can present N-terminally extended peptides on the cell surface that can elicit CD8+ T-cell responses. This observation likely reflects gaps in our understanding of how antigens are processed by the ERAP enzymes. To better understand ERAPs' function in antigen processing, here we generated a nested set of N-terminally extended 10–20-mer peptides (RA)nAAKKKYCL covalently bound to the human leukocyte antigen (HLA)-B*0801. We used X-ray crystallography, thermostability assessments, and an ERAP1-trimming assay to characterize these complexes. The X-ray structures determined at 1.40–1.65 Å resolutions revealed that the residue extensions (RA)n unexpectedly protrude out of the A pocket of HLA-B*0801, whereas the AAKKKYCL core of all peptides adopts similar, bound conformations. HLA-B*0801 residue 62 was critical to open the A pocket. We also show that HLA-B*0801 and antigenic precursor peptides form stable complexes. Finally, ERAP1-mediated trimming of the MHC I–bound peptides required a minimal length of 14 amino acids. We propose a mechanistic model explaining how ERAP1-mediated trimming of MHC I–bound peptides in cells can generate peptides of canonical as well as noncanonical lengths that still serve as stable MHC I ligands. Our results provide a framework to better understand how the ERAP enzymes influence the MHC I immunopeptidome.

59 BASIC BIOLOGICAL SCIENCES↗