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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 1,585 records · Page 88
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Enhancing Early Systems R&D Capabilities with Systems —Theoretic Process Analysis

Systems engineering today faces a wide array of challenges, ranging from new operational environments to disruptive technological — necessitating approaches to improve research and development (R&D) efforts. Yet, emphasizing the Aristotelian argument that the “whole is greater than the sum of its parts” seems to offer a conceptual foundation creating new R&D solutions. Invoking systems theoretic concepts of emergence and hierarchy and analytic characteristics of traceability, rigor, and comprehensiveness is potentially beneficial for guiding R&D strategy and development to bridge the gap between theoretical problem spaces and engineering-based solutions. In response, this article describes systems–theoretic process analysis (STPA) as an example of one such approach to aid in early-systems R&D discussions. STPA—a ‘top-down’ process that abstracts real complex system operations into hierarchical control structures, functional control loops, and control actions—uses control loop logic to analyze how control actions (designed for desired system behaviors) may become violated and drive the complex system toward states of higher risk. By analyzing how needed controls are not provided (or out of sequence or stopped too soon) and unneeded controls are provided (or engaged too long), STPA can help early-system R&D discussions by exploring how requirements and desired actions interact to either mitigate or potentially increase states of risk that can lead to unacceptable losses. This article will demonstrate STPA's benefit for early-system R&D strategy and development discussion by describing such diverse use cases as cyber security, nuclear fuel transportation, and US electric grid performance. Together, the traceability, rigor, and comprehensiveness of STPA serve as useful tools for improving R&D strategy and development discussions. In conclusion, leveraging STPA as well as related systems engineering techniques can be helpful in early R&D planning and strategy development to better triangulate deeper theoretical meaning or evaluate empirical results to better inform systems engineering solutions.

42 ENGINEERING↗
DOE OSTI2024

A Semi‐Automated, High‐Throughput Approach for the Synthesis and Identification of Highly Photo‐Cytotoxic Iridium Complexes

Abstract The discovery of new compounds with pharmacological properties is usually a lengthy, laborious and expensive process. Thus, there is increasing interest in developing workflows that allow for the rapid synthesis and evaluation of libraries of compounds with the aim of identifying leads for further drug development. Herein, we apply combinatorial synthesis to build a library of 90 iridium(III) complexes (81 of which are new) over two synthesise‐and‐test cycles, with the aim of identifying potential agents for photodynamic therapy. We demonstrate the power of this approach by identifying highly active complexes that are well‐tolerated in the dark but display very low nM phototoxicity against cancer cells. To build a detailed structure–activity relationship for this class of compounds we have used density functional theory (DFT) calculations to determine some key electronic parameters and study correlations with the experimental data. Finally, we present an optimised semi‐automated synthesise‐and‐test protocol to obtain multiplex data within 72 hours.

Kench, Timothy↗
DOE OSTI2024

A Semi‐Automated, High‐Throughput Approach for the Synthesis and Identification of Highly Photo‐Cytotoxic Iridium Complexes

Abstract The discovery of new compounds with pharmacological properties is usually a lengthy, laborious and expensive process. Thus, there is increasing interest in developing workflows that allow for the rapid synthesis and evaluation of libraries of compounds with the aim of identifying leads for further drug development. Herein, we apply combinatorial synthesis to build a library of 90 iridium(III) complexes (81 of which are new) over two synthesise‐and‐test cycles, with the aim of identifying potential agents for photodynamic therapy. We demonstrate the power of this approach by identifying highly active complexes that are well‐tolerated in the dark but display very low nM phototoxicity against cancer cells. To build a detailed structure–activity relationship for this class of compounds we have used density functional theory (DFT) calculations to determine some key electronic parameters and study correlations with the experimental data. Finally, we present an optimised semi‐automated synthesise‐and‐test protocol to obtain multiplex data within 72 hours.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

Ion separations with membranes

Ion separations are important for resource recovery, water treatment, and energy production and storage. Techniques such as chemical precipitation, selective adsorption, and solvent extraction are effective, but membranes may separate ions continuously with less waste and lower energy costs. Separation of monovalent and multivalent ions with nanofiltration or electrodialysis membranes already enables water softening and edible salt purification. Similar membranes are attractive as separators in vanadium redox flow batteries. Selective partitioning of divalent counter-ions into ion-exchange membranes even allows transport of these ions against their concentration gradients in salt mixtures. However, separations of ions with the same charge is more challenging. Recent research demonstrated highly selective ion “sieving” at small scales. Here, separations using electrical potentials and differences in ion electrophoretic mobilities are promising, but relatively unexplored. Carrier-mediated transport affords high selectivity in liquid membranes, but these systems are not very stable, and selective transport via hopping between anchored carriers has proven elusive. Finally, this paper discusses how concentration polarization decreases selectivities in many membrane processes. Although development of selective, inexpensive ion-separation membranes is a work in progress, successes in water softening and edible salt purification suggests that future selective membranes will serve as complementary methods to traditional purification techniques.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

Characterization of flow and transport in a fracture network at the EGS Collab field experiment through stochastic modeling of tracer recovery

Energy extraction from subsurface reservoirs is important for addressing the increasing energy demand and environmental concerns such as global warming. However, the characterization of subsurface reservoirs, particularly reservoirs dominated by fracture networks remains a challenge due to the lack of means to directly observe subsurface processes. This study explores the feasibility and efficacy of characterizing fracture flow and transport processes in an enhanced geothermal system (EGS) testbed through stochastic tracer modeling. There are two enabling factors that allow application of stochastic modeling to characterize a subsurface reservoir. First, an abundance of geological and geophysical measurements enables the development of a high-fidelity and well-constrained fracture network model. Second, high-performance computing (HPC) allows running massive realizations efficiently. Six conservative tracer tests were stochastically modeled and produced satisfactory realizations that successfully reproduce field tracer recovery data from each tracer test. The evolution of flow and transport processes in the fracture network was then analyzed from these satisfactory realizations. This work demonstrates that stochastic tracer modeling on a high-fidelity fracture network model is feasible and can provide important insights regarding flow and transport characteristics in subsurface fractured reservoirs.

58 GEOSCIENCES↗
DOE OSTI2024

A novel conditional generative model for efficient ensemble forecasts of state variables in large-scale geological carbon storage

Integrating monitoring data to efficiently update reservoir pressure and CO 2 plume distribution forecasts presents a significant challenge in geological carbon storage (GCS) applications. Inverse modeling techniques are commonly used to fuse observational data and refine reservoir model parameters, thereby improving state variable forecasts. However, these techniques often rely on linear or Gaussian assumptions, which can limit their effectiveness in accurately predicting state variables. Moreover, simulating large-scale three-dimensional (3D) GCS problems is computationally expensive, making iterative runs in inverse problems prohibitive. To address these challenges, we propose a conditional generative model utilizing the score-based diffusion method for real-time 3D pressure and saturation field distribution predictions. Our approach involves solving the score function with a mini-batch-based Monte Carlo estimator to generate labeled data. This data is subsequently employed to train a fully connected neural network, enabling it to learn the conditional sample generator within a supervised learning framework. This method enables the rapid generation of a large ensemble of predictions, facilitating comprehensive uncertainty quantification of state variables. Here we applied our method to forecast the dynamic 3D distributions of pressure and saturation fields over a 30-year injection period. The statistical assessment with low root mean square error (RMSE) values demonstrates that our method can accurately predict the spatiotemporal distributions of both pressure and saturation fields. Moreover, the developed conditional generative model shows high computational efficiency by generating 100 ensemble forecasts of 3D state variables in less than 10 min. The consistency between ensemble averages and ground truth values further illustrates the model’s capability to capture state variable dynamics during the CO 2 plume injection process. Notably, the ground truth values fall within the ensemble forecasts, indicating that our uncertainty quantification effectively captures variability and potential noise in the observations. Thus, the developed conditional generative model proves to be a more efficient, accurate, and practical tool for GCS applications, facilitating timely risk analysis and informed decision-making.

58 GEOSCIENCES↗
DOE OSTI2021

Polysiloxane coatings on microspheres based on Multiphase Flow with Interface Exchange-Discrete Element Modelling

Polysiloxane coatings on yttria stabilized zirconia (YSZ) microspheres of 500 μm were simulated in a spout fluidized bed coater using Multiphase Flow with Interface Exchange-Discrete Element Modelling (MFiX-DEM). In this work, two different coater configurations were developed to study the influence of gas velocity and its distribution on particle dynamics. The presence of the Wurster tube not only enhances the distribution but also increases the overall residence time of the particles. Investigations were also carried out with different Wurster tube positions (normal, 10% and 20% lowered from its initial position). Among these, 20% lowered Wurster tube position demonstrated the most effective coating process. The effects of gas inlet pressure on the average gas velocity and the distribution of particles were analyzed. More than 97% of the particles can be retained. The derived results, including average gas velocity, particle retention percentage, and distribution of particles with gas velocity, are being used to guide the experimental work in obtaining defect-free coatings for YSZ microspheres.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Redox-Active Polymers Designed for the Circular Economy of Energy Storage Devices

Electrochemical energy storage is a keystone to support the rapid transition to a low-carbon-emission future for grid storage and transportation. While research on electrochemical energy storage devices has mostly dealt with performance improvements (energy density and power density), little attention has been paid to designing devices that can be recycled with low cost and low environmental impact. Thus, next-generation energy storage devices should also address the integration of recyclability into the device design. In this study, we demonstrate recyclable energy storage devices based on solution-processable redox-active conjugated polymers. The high electronic and ionic charge transport in these polymers enables the operation of single-phase electrodes in aqueous electrolytes with C-rates >100 with good electrochemical stability when the cell is charged to 1.2 V. Finally, we demonstrate the recyclability of these devices, achieving >85% capacity retention in each recycling step. Our work provides a framework for developing recyclable devices for sustainable energy storage technologies.

25 ENERGY STORAGE↗
DOE OSTI2021

Critical Assessment of MetaProteome Investigation (CAMPI): a multi-laboratory comparison of established workflows

Metaproteomics has matured into a powerful tool to assess functional interactions in microbial communities. While many metaproteomic workflows are available, the impact of method choice on results remains unclear. Here, we carry out a community-driven, multi-laboratory comparison in metaproteomics: the critical assessment of metaproteome investigation study (CAMPI). Based on well-established workflows, we evaluate the effect of sample preparation, mass spectrometry, and bioinformatic analysis using two samples: a simplified, laboratory-assembled human intestinal model and a human fecal sample. We observe that variability at the peptide level is predominantly due to sample processing workflows, with a smaller contribution of bioinformatic pipelines. These peptide-level differences largely disappear at the protein group level. While differences are observed for predicted community composition, similar functional profiles are obtained across workflows. CAMPI demonstrates the robustness of present-day metaproteomics research, serves as a template for multi-laboratory studies in metaproteomics, and provides publicly available data sets for benchmarking future developments.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

PySAGES: flexible, advanced sampling methods accelerated with GPUs

Abstract Molecular simulations are an important tool for research in physics, chemistry, and biology. The capabilities of simulations can be greatly expanded by providing access to advanced sampling methods and techniques that permit calculation of the relevant underlying free energy landscapes. In this sense, software that can be seamlessly adapted to a broad range of complex systems is essential. Building on past efforts to provide open-source community-supported software for advanced sampling, we introduce PySAGES, a Python implementation of the Software Suite for Advanced General Ensemble Simulations (SSAGES) that provides full GPU support for massively parallel applications of enhanced sampling methods such as adaptive biasing forces, harmonic bias, or forward flux sampling in the context of molecular dynamics simulations. By providing an intuitive interface that facilitates the management of a system’s configuration, the inclusion of new collective variables, and the implementation of sophisticated free energy-based sampling methods, the PySAGES library serves as a general platform for the development and implementation of emerging simulation techniques. The capabilities, core features, and computational performance of this tool are demonstrated with clear and concise examples pertaining to different classes of molecular systems. We anticipate that PySAGES will provide the scientific community with a robust and easily accessible platform to accelerate simulations, improve sampling, and enable facile estimation of free energies for a wide range of materials and processes.

Chemistry↗
DOE OSTI2025

Evolution at the Edge: Real-Time Evolution for Neuromorphic Engine Control

Neuromorphic computing systems are attractive for real-time control at the edge because of their low power operation, real-time processing capabilities and their potential ability to do online learning. In this work, we describe an approach for performing real-time evolution of spiking neural networks for neuromorphic systems at the edge called Neuromorphic Optimization using Dynamic Evolutionary Systems or NODES. We apply this approach to real-time combustion engine control and develop an engine-specific hardware platform for NODES called FireBox. We demonstrate how the real-time evolution approach works in simulation and the performance of networks trained in simulation on the physical engine.

Maldonado Puente, Bryan [ORNL] (ORCID:000000033880↗
DOE OSTI2020

ADC Nonlinearity Correction for the Majorana Demonstrator

Imperfections in analog-to-digital conversion cannot be ignored when signal digitization requirements demand both wide dynamic range and high resolution, as is the case for the MAJORANA DEMONSTRATOR 76 Ge neutrinoless double beta decay search. Enabling the experiment's high-resolution spectral analysis and efficient pulse shape discrimination required careful measurement and correction of ADC nonlinearites. Here, a simple measurement protocol was developed that did not require sophisticated equipment or lengthy data taking campaigns. A slope-dependent hysteresis was observed and characterized. A correction applied to digitized waveforms prior to signal processing reduced the differential and integral nonlinearites by an order of magnitude, eliminating these as dominant contributions to the systematic energy uncertainty at the double-beta decay Q value.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2025

Yeast Transformation on Hamilton Vantage (YT Vantage) v1

Our software program is designed for the Hamilton Vantage liquid handling robot, automating the Build step in the Design-Build-Test-Learn (DBTL) cycle for Saccharomyces cerevisiae. This program minimizes human intervention, enabling rapid identification of pathway bottlenecks and genes that enhance verazine production. The program takes competent yeast and plasmid DNA as input and generates an output library of engineered strains compatible with automated colony picking, high-throughput culturing, and chemical extraction for downstream LC-MS analysis. A user-friendly interface, developed using the Hamilton Method Editor software, allows for on-demand parameter customization. By automating this process, our program streamlines the construction of Saccharomyces cerevisiae, reducing manual labor and increasing efficiency. While the manual process is well-documented, integration with robotic automation is less common, making our program a valuable tool for researchers. With this software, we achieved 2-5 fold increases in verazine production, demonstrating its potential to accelerate research in this field.

Louie, Randy [Lawrence Berkeley National Laborator↗
DOE OSTI2024

ECP libraries and tools: An overview

The Exascale Computing Project (ECP) Software Technology and Co-Design teams addressed the growing complexities in high-performance computing (HPC) by developing scalable software libraries and tools that leverage exascale system capabilities. As we enter the exascale era, the need for reusable, optimized software solutions that can handle the unique challenges posed by these systems becomes increasingly important. The primary challenges the ECP teams faced were to create software libraries and tools that are performant on exascale architectures and portable and usable across diverse hardware platforms. Efforts addressed issues related to concurrent execution, memory management, and the integration of heterogeneous computing resources, such as GPUs from multiple vendors. The ECP’s strategy involved a structured development process encompassing the creation, optimization, and deployment of software in collaboration with industry, academia, and national laboratories. The project was organized into several technical areas: co-design of domain-specific suites with target applications, programming models and runtimes, development tools, mathematical libraries, data and visualization tools, and software ecosystem and delivery mechanisms. ECP has successfully developed a large portfolio of software libraries and tools that demonstrate significant improvements in performance and scalability on exascale systems. These products have been integrated into the Department of Energy’s computing facilities, supporting various scientific applications and ensuring robust performance across different hardware setups. ECP advancements in software development for exascale computing highlight the importance of a collaborative and adaptive approach to handling next-generation HPC systems complexities. The lessons learned emphasize the need for continuous engagement with end-users and vendors, and the importance of maintaining a balance between innovation and practical implementation. Future efforts will focus on ensuring scalability, keeping pace with rapid hardware advancements, and further enhancing the interoperability and usability of the software ecosystem. In conclusion, subsequent articles in this special issue provide in-depth discussions and case studies into specific library and tool efforts.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Advanced Building Construction (ABC) Research Opportunities Report: Industrializing Construction to Decarbonize Buildings

The DOE Building Technologies Office generally seeks to develop, demonstrate, and accelerate the adoption of cost-effective technologies, techniques, and tools in support of an equitable transition to a decarbonized building stock and energy system by 2050. This ABC Innovations Roadmap specifically focuses on and prioritizes innovations that support the industrialization of whole building retrofits and rapid growth of efficient new construction. The content relates to the integration of technologies and industrialization of processes associated with building construction and renovation. The ABC Innovations Roadmap cross-applies innovations in both the new and existing building sectors with a focus on widescale applicability. Installation flexibility is key to commoditizing solutions that are applicable for a wide variety of buildings (e.g., different building types, vintages, architectural details, and system configurations) and to help simplify decarbonization processes for the workforce.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2026

Shower Reconstruction for Cross-Section Measurements with Photon(s) in the Final State at SBND

The Short-Baseline Near Detector (SBND) is a 112-ton liquid argon time projection chamber operating in the Booster Neutrino Beam at Fermilab as part of the Short-Baseline Neutrino Program. Precise reconstruction of electromagnetic showers from photons and electrons is essential for searches for neutrino oscillations, cross-section measurements, and rare-process studies in liquid argon. In this poster, we present a unified set of SBND analyses enabled by the SPINE (Scalable Particle Imaging with Neural Embeddings) end-to-end machine-learning–based reconstruction framework, highlighting charged-current neutral pion (CC π⁰) production and neutral-current (NC) Δ radiative decay as complementary physics cases. Charged-current π⁰ interactions provide a high-statistics benchmark for validating photon reconstruction, constraining the electromagnetic energy scale, and modeling backgrounds to other cross-section measurements and BSM searches with one or more photons in the final state. We summarize progress toward the ongoing development of a CC π⁰ selection in SBND and, report on the current status of a search for NC resonant Δ production followed by radiative decay (Δ→Nγ) in SBND. This rare process, previously investigated in MicroBooNE, provides a stringent test of photon-only final-state reconstruction and is of interest in the context of the MiniBooNE low-energy excess. Together, these analyses demonstrate how modern machine-learning–based shower reconstruction enables a diverse and scalable physics program at SBND.

Chung, Seokju [Nevis Labs, Columbia U.]↗
DOE OSTI2025

Evaluating Pretreatment Strategies with Modeling for Reducing Scaling Potential of Reverse Osmosis Concentrate: Insights from Ion Exchange and Activated Alumina

Reverse osmosis concentrate (ROC) treatment is critical for enhancing water recovery and minimizing concentrate volume for disposal, especially in regions facing water scarcity. This study investigates the application of ion exchange (IX) resins and activated alumina (AA) as pretreatment strategies to mitigate scaling in ROC due to high concentrations of total dissolved solids, hardness (Ca 2+ and Mg 2+ ), and silica. Through a series of Langmuir isotherms, continuous column experiments, and model simulation, two types of strong acid cation IX resins and three types of strong base anion IX resins alongside three types of AA were evaluated. Results indicate that AA exhibits superior performance in silica removal, achieving up to a 65% reduction and maintaining performance for up to 800 bed volume without reaching saturation. Model simulation of a secondary reverse osmosis treating ROC after the IX and AA pretreatment indicated an additional water recovery of ~70% using antiscalants. This study demonstrates the potential for achieving higher water recovery while also identifying opportunities for pretreatment improvement. Challenges such as the limited IX capacity treating ROC, which requires frequent regeneration and increases operational costs, along with the restricted regeneration capacity of AA, underscore the importance of innovation. These findings emphasize the critical need for developing advanced materials and optimized strategies to further enhance the efficiency of ROC treatment processes.

activated alumina↗