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At least 955 records · Page 53

All sources NASA NTRS DOE OSTI

NASA NTRS2017

Implicit Formulation of Muscle Dynamics in OpenSim

Astronauts lose bone and muscle mass during spaceflight. Exercise countermeasure is the primary method for counteracting bone and muscle mass loss in space. New spacecraft exercise device concepts are currently being developed for the NASAs new crew exploration vehicle. The NASA Digital Astronaut Project (DAP) uses computational modeling to help determine if the new exercise devices will be effective as countermeasures. The NASA Digital Astronaut Project is developing the ability to utilize predictive simulation to provide insight into the change in kinematics and kinetics with a change in device and gravitational environment (1-g versus 0-g). For example, in space exercise the subject's body weight is applied in addition to the loads prescribed for musculoskeletal maintenance. How and where these loads are applied obviously directly impacts bone and tissue loads. Additionally, due to space vehicle structural requirements, exercise devices are often placed on vibration isolation systems. This changes the apparent impedance or stiffness of the device as seen by the user. Data collection under these conditions is often impractical and limited. Predictive modeling provides a means to have a virtual subject to test hypotheses. Predictive simulation provides a virtual subject for which we are able to perform studies such as sensitivity to device loading and vibration isolation without the need for laboratory kinematic or kinetic test data.Direct Collocation optimization provides an efficient means to perform task based optimization and predictive modeling. It is relatively straight forward to structure a physical exercise task in a Direct Collocation mathematical formulation: perform a motion such that you start at an initial pose, achieve a given amount of deflection i.e a squat, return to the initial pose, and minimize muscle activation cost. Direct Collocation is advantageous in that it does not require numerical integration to evaluate the objective function. Instead, the system dynamics are transformed to discrete time and the optimizer is constrained such that the solution is not considered to be a valid unless the dynamic equations are satisfied at all time points. The simulation and optimization are effectively done simultaneously. Due to the implicit integration, time steps can be more coarse than in a differential equation solver. In a gait scenario this means that that the model constraints and cost function are evaluated at 100 nodes in the gait cycle versus 10,000 integration steps in a variable-step forward dynamic simulation. Furthermore, no time is wasted on accurate simulations of movements that are far from the optimum. Constrained optimization algorithms require a Jacobian matrix that contains the partial derivatives of each of the dynamic constraints with respect to of each of the state and control variables at all time points. This is a large but sparse matrix. An implicit dynamics formulation requires computation of the dynamic residuals f as a function of the states x and their derivatives, and controls u:f(x, dxdt, u) 0If the dynamics of musculoskeletal system are formulated implicitly, the Jacobian elements are often available analytically, eliminating the need for numerical differentiation; this is obviously computationally advantageous. Additionally, implicit formulation of musculoskeletal dynamics do not suffer from singularities from low mass bodies, zero muscle activation, or other stiff system or

physical exercise↗

DOE OSTI2022

LANL Activities on Mechanistic Approach to Analyzing and Improving Unconventional Hydrocarbon Production

Hydrocarbon production from shale reservoirs is inherently inefficient and challenging since these are low permeability plays. In addition, there is a limited understanding of the fundamentals and the controlling mechanisms, further complicating how to optimize these plays. Herein, we summarize our experimental and computational efforts fully and partially supported by the fundamental shale portfolio to reveal unconventional shale fundamentals and devise development strategies to enhance extraction efficiency with a minimal environmental footprint. Integrating these fundamentals with machine learning, we outline a pathway to improve the predictive power of our models, which enhances the forecast quality of production, thereby improving the economics of operations in unconventional reservoirs. For instance, we have developed science informed workflows and platforms for optimizing pressure-drawdown at a site, which allow operators to make reservoir-management decisions that optimize recovery in consideration of future production. Recently, our work relies on the hybridization of physics-based prediction and machine learning, whereby accurate synthetic data (combined with available site data) can enable the application of machine learning methods for rapid forecasting and optimization. Consequently, the workflow and platform are readily extendable to operations at other sites, plays, and basins.

04 OIL SHALES AND TAR SANDS↗

DOE OSTI2020

Optimal economic operation of liquid petroleum products pipeline systems

The majority of overland transport needs for crude petroleum and refined petroleum products are met using pipelines. Numerous studies have developed optimization methods for design of these systems in order to minimize construction costs while meeting capacity requirements. Here, we formulate problems to optimize the operations of existing single liquid commodity pipeline systems subject to physical flow and pump engineering constraints. The objectives are to maximize the economic value created for users of the system and to minimize operating costs. We present a general computational method for this class of continuous, non-convex nonlinear programs, and examine the use of pump operating settings and flow allocations as decision variables. Furthermore, the approach is applied to compute optimal operating regimes and perform engineering economic sensitivity analyses for a case study of a crude oil pipeline developed using publicly available data.

02 PETROLEUM↗

DOE OSTI2024

Optimization strategies for produced water networks with integrated desalination facilities

Optimal management and desalination of produced water is a major challenge for U.S. oil and gas development. Integrating rigorous desalination models into multi-period produced water network optimization problems presents several hurdles, which need to be tackled using advanced optimization strategies. Here, in this work, a novel multi-period produced water network formulation with separate solid and liquid flows is introduced to avoid singularities at zero flows. Rigorous steady state desalination models based on mechanical vapor recompression are embedded at the desalination sites in the network model. An integrated optimization formulation is developed to co-optimize the design of desalination units along with the operation of the network. Furthermore, a more robust approach based on the trust region filter method is developed to efficiently integrate complex desalination models into the multi-period planning problem. Both optimization approaches are demonstrated on a produced water network from the PARETO library (Drouven et al., 2022) using thermal desalination units. Our results show that while the TRF and integrated approaches have comparable solve times, the TRF approach has better performance reliability in terms of solver convergence. Furthermore, the optimal solution obtained by embedding rigorous models into the network is significantly different than when desalination costs are approximated using simple cost models, which motivates further research in this field.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2026

Feedback, physics, and forecasts: The emerging paradigm of machine learning-driven battery research

Machine learning (ML) is reshaping how we understand, predict, and optimize electrochemical systems. In batteries, ML accelerates discovery across chemistry, design, and operation by transforming massive experimental and simulated datasets into predictive, interpretable models. This review consolidates a decade of progress in ML-driven battery innovation, from early-cycle feature extraction to operando image analysis and physics-informed modeling. We categorize approaches by data domain and physical fidelity, emphasizing interpretable ML for diagnostics, reinforcement learning for control, and multi-objective optimization for lifetime extension strategies. Additionally, we demonstrate how integrated models accelerate discovery, reduce testing time, and guide sustainable design. Economic analyses furthermore illustrate how these advances can lower cost per cycle and improve circularity. Together, these developments chart a path toward self-optimizing, sustainable battery technologies.

artificial intelligence↗

DOE OSTI2022

High Yield, Economical and Environmentally Benign Production of Rare Earth Elements from Coal Ash (Phase II Final Summary Report)

Fly ash stored in landfills and ponds across the United States is an attractive, abundant domestic resource for the cost-effective recovery of rare earth elements (REE) and other critical minerals (CM). Physical Sciences Inc. (PSI) and its team members, Winner Water Services (WWS) and University of Kentucky/Center for Applied Energy Research (UK/CAER) successfully executed a multiphase program that developed technologies and their implementation in a pilot plant. We demonstrated plant operations for cost-effective and environmentally-friendly production of rare earth element oxide (REO) concentrates, and the critical minerals scandium and aluminum (in the forms of salts or oxide products), from coal ash. We also constructed and demonstrated a research-scale (0.5 kg/day) micropilot facility to validate the key physical and chemical processing operations, predict yields, and troubleshoot process bottlenecks. The project team then designed, constructed and operated two decoupled pilot plants: (1) an operational pilot plant for physical separation processes with capacity of 0.4 metric tons per day (tpd), where we optimized processes to produce selected ash fractions as the feedstock for chemical processing and as valuable byproducts such as cenospheres, magnetic ash, and secondary fuel carbon, and (2) an operational pilot plant for chemical ash processing with a capacity of 0.5 tpd that developed optimized processes for the production of: (a) REO concentrates, (b) critical minerals (Sc, Al), and (c) beneficiated ash as a valuable byproduct suitable for cement applications. In Phase I, the project team (with Equinox Chemicals in place of WWS) developed and demonstrated the feasibility of the physical and chemical separation processes, developed the design of a pilot plant, and began the development of a preliminary techno-economic model. In the baseline (initial) Phase II program, the project team developed and demonstrated the above pilot scale plant, producing salable REE concentrates, including Y and Sc (REYSc), plus commercially viable byproducts, using environmentally safe and high-yield physical and chemical enrichment processes. The team successfully demonstrated chemical pilot design, construction, shakedown, and operations of the plant. We produced the Phase II deliverable REYSc concentrate ((50 g of >60 wt.% purity REYSc salts on elemental basis), generated the feed for the Phase II follow-on program, identified processing challenges for future optimizations, and refined the techno-economic model. In the Phase II follow-on program, the project team: (1) developed and demonstrated processes to increase the REE amount by 3X (content basis) and convert the Phase II REE salt mixture to an oxide mixture, (2) produced/delivered >38 g of REO mixture with >85 wt.% purity (elemental basis); (3) developed processes to recover critical minerals scandium and aluminum from intermediate streams; (4) produced/delivered > 1 g of scandium salt mixture with >85 wt. % purity (elemental basis); (5) produced/delivered > 100 g of aluminum oxide type material with >70% wt. purity (elemental basis); and (6) updated the techno-economic model from the baseline Phase II program to assess CAPEX and OPEX of a commercial operation. This program has developed extensive databases on process chemistry, unit operations, plant engineering, and techno-economics that will enable further scale-up toward commercial plant design. Specific future developments will be focused on achieving dramatic savings in energy, reagent usage, and operating costs. The combined results will contribute significantly for maturing the technologies of REE recovery from coal byproducts and promote the establishment of domestic REE and CM supply chains.

01 COAL, LIGNITE, AND PEAT↗

DOE OSTI2024

Autonomous Synthesis of Thin Film Materials with Pulsed Laser Deposition Enabled by In Situ Spectroscopy and Automation

Autonomous systems that combine synthesis, characterization, and artificial intelligence can greatly accelerate the discovery and optimization of materials, however platforms for growth of macroscale thin films by physical vapor deposition techniques have lagged far behind others. Here this study demonstrates autonomous synthesis by pulsed laser deposition (PLD), a highly versatile synthesis technique, in the growth of ultrathin WSe 2 films. Further, by combing the automation of PLD synthesis and in situ diagnostic feedback with a high-throughput methodology, this study demonstrates a workflow and platform which uses Gaussian process regression and Bayesian optimization to autonomously identify growth regimes for WSe 2 films based on Raman spectral criteria by efficiently sampling 0.25% of the chosen 4D parameter space. With throughputs at least 10x faster than traditional PLD workflows, this platform and workflow enables the accelerated discovery and autonomous optimization of the vast number of materials that can be synthesized by PLD.

36 MATERIALS SCIENCE↗

DOE OSTI2021

Control of the Plasma-Material Interface for Long Pulse Optimization in the Experimental Advanced Superconducting Tokamak (EAST) (Final Report)

This project involved a collaborative effort, led by Dr. Rajesh Maingi of Princeton Plasma Physics Laboratory, to understand and control the plasma-material interface to improve long pulse discharge control and performance in the EAST devices. The focus is on long pulse recycling control and optimization, which can be challenging because of their broad range of materials used for plasma-facing components (PFC) and wall conditioning techniques. The purpose of the EAST device is to demonstrate long-pulse, stable, high performance plasma operation, providing a test-bed for key physics and technology issues for next step devices. As part of these tests, EAST has deployed carbon for the lower divertor PFC, tungsten for the upper divertor, and molybdenum for the main wall. In addition wall coatings are deployed for long pulse recycling control: lithium (Li) via several delivery tools, boronization, and recently also siliconization; for example, in 2013, a layer of SiC was deposited on all of the graphite tiles. Oxygen is also present at trace levels, making for a complex mix of materials and plasma-materials interactions (PMI). This mix of materials and the limited durability of wall coatings, coupled to partially optimized cryo-pumping, lead to evolution of the recycling off the PFCs, which restricts the duration of stationary, high performance, long pulse discharges. Comparing the wall conditioning techniques, Li enables the highest energy confinement and lowest recycling for the longest duration, while also suppressing ELMs in certain discharges. More specifically, research activities at the University of Tennessee focused on heat flux profile measurements in EAST, modeling the surface response, including the bonding energetics and chemical interactions between Li with carbon, tungsten, hydrogen and impurities, in addition to post situ experimental characterization and analysis of the divertor plasma facing components in EAST. This final report describes the overall progress within each of these tasks at the University of Tennessee within the project.

36 MATERIALS SCIENCE↗

DOE OSTI2024

Factors influencing quantum evaporation of helium from polar semiconductors from first principles

While there is much indirect evidence for the existence of dark matter (DM), to date it has evaded detection. Current efforts focus on DM masses over ~GeV—to push the sensitivity of DM searches to lower masses, new DM targets and detection schemes are needed. In this work, we focus on the latter—a novel detection scheme recently proposed to detect 10–100 meV phonons in polar target materials. Previous work showed that well-motivated models of DM can interact with polar semiconductors to produce an athermal population of phonons. This new sensing scheme proposes that these phonons then facilitate quantum evaporation of 3 He from a van der Waals film deposited on the target material. However, a fundamental understanding of the underlying process is still unclear, with several uncertainties related to the precise rate of evaporation and how it can be controlled. In this work, we use ab initio density functional theory calculations to compare the adsorption energies of helium atoms on a polar target material, sodium iodide, to understand the underlying evaporation physics. We explore the role of surface termination, monolayer coverage, and elemental species on the rate of He evaporation from the target material. Using this, we discuss the optimal target features for He-evaporation experiments and their range of tunability through chemical and physical modifications such as applied field and surface termination.

36 MATERIALS SCIENCE↗

DOE OSTI2024

Stochastic machine learning via sigma profiles to build a digital chemical space

This work establishes a different paradigm on digital molecular spaces and their efficient navigation by exploiting sigma profiles. To do so, the remarkable capability of Gaussian processes (GPs), a type of stochastic machine learning model, to correlate and predict physicochemical properties from sigma profiles is demonstrated, outperforming state-of-the-art neural networks previously published. The amount of chemical information encoded in sigma profiles eases the learning burden of machine learning models, permitting the training of GPs on small datasets which, due to their negligible computational cost and ease of implementation, are ideal models to be combined with optimization tools such as gradient search or Bayesian optimization (BO). Gradient search is used to efficiently navigate the sigma profile digital space, quickly converging to local extrema of target physicochemical properties. While this requires the availability of pretrained GP models on existing datasets, such limitations are eliminated with the implementation of BO, which can find global extrema with a limited number of iterations. A remarkable example of this is that of BO toward boiling temperature optimization. Holding no knowledge of chemistry except for the sigma profile and boiling temperature of carbon monoxide (the worst possible initial guess), BO finds the global maximum of the available boiling temperature dataset (over 1,000 molecules encompassing more than 40 families of organic and inorganic compounds) in just 15 iterations (i.e., 15 property measurements), cementing sigma profiles as a powerful digital chemical space for molecular optimization and discovery, particularly when little to no experimental data is initially available.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2024

AI-Driven Detector Design for the EIC (Final Technical Report)

We developed an optimization workflow based on DNN-based fast-simulation and reconstruction algorithms. We used these methods to advance the design of calorimeter systems for the Electron-Ion Collider (EIC). This DNN-driven optimization provides a blueprint for integrating gradient-based methods into detector-design workflows. All software pipelines and methods have been released publicly and incorporated into the EIC collaboration’s physics studies, broadening their impact. Three journal articles detailing the methods developed here serve as a reference for the design and optimal use of next generation high-granularity calorimeter systems in nuclear and particle physics.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

DOE OSTI2022

Wire-coil insert optimization for high-heat-load/flux synchrotron components

Many synchrotron components require high levels of internal-flow, forced-convection heat transfer to minimize surface temperatures, thermal gradients, and thermally induced stress on high-power beam-interacting surfaces. Wire-coil inserts, physically similar to a common spring, are mechanically fitted inside of component cooling passages to optimize heat transfer performance. They are routinely used in Advanced Photon Source (APS) front end and beamline high-heat-load/flux components to significantly enhance convection heat transfer-up to 400% compared to plain open passages. This has the additional benefit of greatly reducing coolant flow requirements for these components. Using several cooling passage sizes, five different wire sizes, and a range of pitch values, an experimental investigation conducted at the APS has determined the average heat transfer coefficient and resulting pressure loss as a function of water flow rate for 65 different wire-coil inserts. Data from this study have been non-dimensionalized and generalized to yield relationships that can be used to determine the heat transfer performance and resulting pressure loss for any given wire-coil insert that may be used at the APS. Through data reduction, the wire-coil insert characteristic dimensions have also been optimized to yield the highest heat transfer enhancement while minimizing the coolant flow requirements. These generalized expressions for wirecoil inserts will be presented, and they can be used by scientists and engineers during the component design process to evaluate achievable heat transfer performance and associated pressure loss, aiding in the establishment of optimized operating parameters and cooling passage flow distribution schemes.

43 PARTICLE ACCELERATORS↗

DOE OSTI2020

Automated Fitting of a Semi-empirical Multiphase Equation of State for Carbon

The equation of state (EOS) of carbon is important in high explosive, geophysical, and inertial confinement fusion applications. Within the semi-empirical Sesame framework, the EOS of each phase is represented by a sum of cold, vibrational, and thermal electronic Helmholtz free energy contributions. Each phase has ~5-10 independent parameters that are adjusted to reproduce single-phase data (e.g., thermal expansion, isothermal compression) as well as experimentally- and computationally- derived phase boundaries. Manual calibration of the full multiphase EOS is arduous. We present our progress in development of automated EOS parameterization based on minimization of an objective function. Here, this function encodes deviation of model EOS results from experimental/computational benchmarks. Optimization is implemented as a combination of global (particle swarm) and local optimization techniques.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

DOE OSTI2022

Application-specific optimal model weighting of global climate models: A red tide example

Global climate models (GCMs) and Earth system models (ESMs) provide many climate services with environmental relevance. The High Resolution Model Inter-comparison Project (HighResMIP) of the Coupled Model Intercomparison Project Phase 6 (CMIP6) provides model runs of GCMs and ESMs to address regional phenomena. Developing a parsimonious ensemble of CMIP6 requires multiple ensemble methods such as independent-model subset selection, prescreening-based subset selection, and model weighting. The work presented here focuses on application-specific optimal model weighting, with prescreening-based subset selection. As such, independent ensemble members are categorized, selected, and weighted based on their ability to reproduce physically-interpretable features of interest that are problem-specific. We discuss the strengths and caveats of optimal model weighting using a case study of red tide prediction in the Gulf of Mexico along the West Florida Shelf. Red tide is a common name of specific harmful algal blooms that occur worldwide, causing adverse socioeconomic and environmental impacts. Our results indicate the importance of prescreening-based subset selection as optimal model weighting can underplay robust ensemble members by optimizing error cancellation. Prescreening-based subset selection also provides insights about the validity of the model weights. By illustrating the caveats of using non-representative models when optimal model weighting is used, the findings and discussion of this study are pertinent to many other climate services.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2024

Toward digital design at the exascale: An overview of project ICECap

High performance computing has entered the Exascale Age. Capable of performing over 1018 floating point operations per second, exascale computers, such as El Capitan, the National Nuclear Security Administration's first, have the potential to revolutionize the detailed in-depth study of highly complex science and engineering systems. However, in addition to these kind of whole machine “hero” simulations, exascale systems could also enable new paradigms in digital design by making petascale hero runs routine. Currently, untenable problems in complex system design, optimization, model exploration, and scientific discovery could all become possible. Motivated by the challenge of uncovering the next generation of robust high-yield inertial confinement fusion (ICF) designs, project ICECap (Inertial Confinement on El Capitan) attempts to integrate multiple advances in machine learning (ML), scientific workflows, high performance computing, GPU-acceleration, and numerical optimization to prototype such a future. Built on a general framework, ICECap is exploring how these technologies could broadly accelerate scientific discovery on El Capitan. In addition to our requirements, system-level design, and challenges, we describe some of the key technologies in ICECap, including ML replacements for multiphysics packages, tools for human-machine teaming, and algorithms for multifidelity design optimization under uncertainty. As a test of our prototype pre-El Capitan system, we advance the state-of-the art for ICF hohlraum design by demonstrating the optimization of a 17-parameter National Ignition Facility experiment and show that our ML-assisted workflow makes design choices that are consistent with physics intuition, but in an automated, efficient, and mathematically rigorous fashion.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

NASA NTRS1972

Near-earth orbital guidance and remote sensing

The curriculum of a short course in remote sensing and parameter optimization is presented. The subjects discussed are: (1) basics of remote sensing and the user community, (2) multivariant spectral analysis, (3) advanced mathematics and physics of remote sensing, (4) the atmospheric environment, (5) imaging sensing, and (6)nonimaging sensing. Mathematical models of optimization techniques are developed.

Powers, W. F.↗

NASA NTRSDate not supplied

OSMA Digital Evolution: R&M Physics of Failure (PoF) Handbook

At the intersection of mission, technology, and place is NASA’s need to modernize for a digital-forward future. Therefore, NASA’s Reliability & Maintainability (R&M) Enterprise Data Sharing team is working to leverage both Digitization and Digital Transformation to develop and share best practices and methodologies across NASA. One such cutting edge methodology is Physics of Failure. Therefore NASA OSMA's R&M office is developing a Reliability Physics of Failure Handbook in order to keep our engineers current and on the cutting edge of anlayis methodologies to help ensure NASA’s optimize and achieve continued success.

Digital Transformation↗

NASA NTRSDate not supplied

NASA Physics of Failure (PoF) Handbook Update

Digital transformation↗