Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “Optimal Experiment Design”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 1,495 records · Page 83
All sourcesNASA NTRSDOE OSTI
DOE OSTI2018

Prospects for a Muon Spin Resonance Facility in the Fermilab MuCool Test Area

This paper investigates the feasibility of re-purposing the MuCool Test Area (MTA) beamline and experimental hall to support a Muon Spin Resonance (MuSR) facility, which would make it the only such facility in the US. This report reviews the basic muon production concepts studied and operationally implemented at TRIUMF, PSI, and RAL and their application in the context of the MTA facility. Two scenarios were determined feasible. One, an initial minimal-shielding and capital-cost investment stage with a single secondary muon beamline that utilizes an existing high- intensity beam absorber and, another, upgraded stage, that implements an optimized production target pile, a proximate high-intensity absorber, and optimized secondary muon lines. A unique approach is proposed which chops or strips a macropulse of H$^-$ beam into a micropulse substructure – a muon creation timing scheme – which allows Muon Spin Resonance experiments in a linac environment. With this timing scheme, and attention to target design and secondary beam collection, the MTA can host enabling and competitive Muon Spin Resonance experiments.

43 PARTICLE ACCELERATORS↗
DOE OSTI2020

Kinetics Modeling for Design of Continuous Enzymatic Hydrolysis

Enzymatic hydrolysis of cellulose to monomeric sugars continues to be a limiting step in cost-effectively producing sugar and fermentation-based biofuels from biomass. In particular, the high cost of enzymes coupled with the long time-scale of reaction pose challenges to economic viability. Performing enzymatic hydrolysis in a continuous mode with enzyme recycle may provide a path towards substantially reduced costs for sugar production, but design and analysis are complicated by a lack of suitable kinetics models. Computationally attainable models, such as fractal-based models, require knowledge of the reaction-history of the biomass, and are thus only suitable for describing the batch reactions from which they are derived. Fundamental models, while potentially more generalizable, are often too computationally intensive to use in process or reactor modeling. In this work, a phenomenological rate model is proposed based on a two-phase substrate representation. Good agreement is seen between batch and continuous enzymatic hydrolysis (CEH) experiment data, which validates the model and enables us to solve for reactor design parameters, such as CEH reactor size and stream flow rates, based on process variables like yield. This model is integrated with techno-economic analysis software to explore economic sensitivities. Important design optimizations and tradeoffs are identified and quantified, including the relative cost imposed by rate slowdown from sugar inhibition versus the cost to remove and concentrate sugars at a lower concentration. It also identifies, high-leverage avenues for further exploration, such as increasing the maximum feasible solids concentration, and sustaining high membrane flux and reliability.

09 BIOMASS FUELS↗
DOE OSTI2022

Open-source FPGA-ML codesign for the MLPerf Tiny Benchmark

We present our development experience and recent results for the MLPerf Tiny Inference Benchmark on field-programmable gate array (FPGA) platforms. We use the open-source hls4ml and FINN workflows, which aim to democratize AI-hardware codesign of optimized neural networks on FPGAs. We present the design and implementation process for the keyword spotting, anomaly detection, and image classification benchmark tasks. The resulting hardware implementations are quantized, configurable, spatial dataflow architectures tailored for speed and efficiency and introduce new generic optimizations and common workflows developed as a part of this work. The full workflow is presented from quantization-aware training to FPGA implementation. The solutions are deployed on system-on-chip (Pynq-Z2) and pure FPGA (Arty A7-100T) platforms. The resulting submissions achieve latencies as low as 20 $\mu$s and energy consumption as low as 30 $\mu$J per inference. We demonstrate how emerging ML benchmarks on heterogeneous hardware platforms can catalyze collaboration and the development of new techniques and more accessible tools.

Borras, Hendrik↗
DOE OSTI2016

Remote temperature distribution sensing using permanent magnets

Remote temperature sensing is essential for applications in enclosed vessels where feedthroughs or optical access points are not possible. A unique sensing method for measuring the temperature of multiple closely-spaced points is proposed using permanent magnets and several three-axis magnetic field sensors. The magnetic field theory for multiple magnets is discussed and a solution technique is presented. Experimental calibration procedures, solution inversion considerations and methods for optimizing the magnet orientations are described in order to obtain low-noise temperature estimates. The experimental setup and the properties of permanent magnets are shown. Finally, experiments were conducted to determine the temperature of nine magnets in different configurations over a temperature range of 5 to 60 degrees Celsius and for a sensor-to-magnet distance of up to 35 mm. Furthermore, to show the possible applications of this sensing system for measuring temperatures through metal walls, additional experiments were conducted inside an opaque 304 stainless steel cylinder.

47 OTHER INSTRUMENTATION↗
DOE OSTI2024

Control And Optimization Modular Modeling Application For Nuclear Deployment

The purpose of the COMMAND code is to provide a flexible, scalable tool for use in developing, integrating, and testing the technologies necessary for achieving autonomous operations of advanced nuclear reactors. The code enables users to efficiently implement custom simulations and experiments by combining key methods from different software modules. These modules are focused on: modeling and simulation tools, such as nuclear simulation tools used for high-fidelity modeling (e.g., Reactor Excursion and Leak Analysis Program [RELAP5-3D] and Monte Carlo N-Particle [MCNP]); machine learning and optimization tools (e.g., anomaly detection and data-driven modeling techniques); advanced control in its digital, high-performance, and supervisory control forms (e.g., proportional integral derivative (PID) control and model predictive control (MPC); and integration with hardware through industrial communication protocols. To ensure flexibility and scalability, COMMAND was designed to be both modular—the software “pieces” all inherit from generic building blocks and can be combined and connected to create complicated simulations—and high performing—designed for parallel processing, enabling simulations and experiments to take advantage of multi-core computers, servers, and nodes. The code is written in the Python programming language due to the language's popularity, active community, and open-source and cross-platform nature. Maintaining consistency with other simulation tools used within the nuclear energy community, users implement simulations and experiments through text input files, which define components, parameters, connections, etc., through lines of text. Given that COMMAND is written in Python, these input files are native Python scripts, and so use the standard Python structure and formatting. This also enables users to take advantage of Python's extensive package library to develop custom capabilities for their specific use cases.

Faber, Jacob [Idaho National Laboratory (INL), Ida↗
DOE OSTI2025

Measurements of source emittance and beam coherence properties of the upgraded Advanced Photon Source

The Advanced Photon Source (APS) has been upgraded with a multi-bend achromat lattice, achieving significantly reduced electron beam emittance and enhanced X-ray coherence. Precise characterization of these properties is essential for optimizing beamline performance and enabling new experiments that take full advantage of the upgraded source. We report on measurements of source size and transverse coherence properties using grating interferometry at two beamlines: the APS 3-ID-B undulator beamline and the 1-BM-B bending magnet beamline. The results confirm the world-record horizontal emittance of the upgraded APS below 30 pm rad and validate the theoretical design parameters. We further investigate the impact of optical aberrations and mechanical vibrations at the 12-ID-C beamline on coherence preservation. These measurements establish a benchmark for future beamline enhancements and demonstrate the effectiveness of grating interferometry for high-precision beam characterization. Our findings provide critical insights into synchrotron beam dynamics, coherence degradation mechanisms, and strategies for optimizing beamline X-ray optics at next-generation light sources.

Advanced Photon Source↗
NASA NTRS1993

Space transportation vehicle design evaluation using saturated designs

An important objective in the preliminary design and evaluation of space transportation vehicles is to find the best values of design variables that optimize the performance characteristic (e.g. dry weight). For a given configuration, the vehicle performance can be determined by the use of complex sizing and performance evaluation computer programs. These complex computer programs utilize iterative algorithms and they are generally too expensive and/or difficult to use directly in multidisciplinary design optimization. An alternative is to use response surface methodology (RSM) and obtain quadratic polynomial approximations to the functional relationships between performance characteristics and design variables. In RSM, these approximation models are then used to determine optimum design parameter values and for rapid sensitivity studies. Constructing a second-order model requires that 'n' design parameters be studied at least at 3 levels (values) so that the coefficients in the model can be estimated. There, 3(n) factorial experiments (point designs or observations) may be necessary. For small values of 'n' such as two or three, this design works well. However, when a large number of design parameters are under study, the number of design points required for a full-factorial design may become excessive. Fortunately, these quadratic polynomial approximations can be obtained by selecting an efficient design matrix using central composite designs (CCD) from design of experiments theory. Each unique point design from the CCD matrix is then conducted using computerized analysis tools (e.g. POST, CONSIZ, etc.). In the next step, least squares regression analysis is used to calculate the quadratic polynomial coefficients from the data. However, in some multidisciplinary applications involving a large number of design variables and several disciplines, the computerized performance synthesis programs may get too time consuming and expensive to run even with the use of efficient central composite designs. In such cases, it may be preferable to keep the number of design points to an absolute minimum and trade some model accuracy with cost. For this purpose, another class of experimental designs, called saturated D-optimal designs may be utilized for generating a matrix of vehicle designs. A design is called saturated when the number of design points is exactly equal to the number of terms in the model to be fitted. As a result, saturated designs require the absolute minimum number of design points ((n+1)(n+2)/s) to estimate the quadratic polynomial model coefficients. Saturated designs can be generated using the D-optimality criterion. A good saturated design should give rise to least squares estimates with minimum generalized variance.

Unal, Resit↗
DOE OSTI2017

Design and performance of SiPM-based readout of PbF 2 crystals for high-rate, precision timing applications

Here, we have developed a custom amplifier board coupled to a large-format 16-channel Hamamatsu silicon photomultiplier device for use as the light sensor for the electromagnetic calorimeters in the Muon g-2 experiment at Fermilab. The calorimeter absorber is an array of lead-fluoride crystals, which produces short-duration Cherenkov light. The detector sits in the high magnetic field of the muon storage ring. The SiPMs selected, and their accompanying custom electronics, must preserve the short pulse shape, have high quantum efficiency, be non-magnetic, exhibit gain stability under varying rate conditions, and cover a fairly large fraction of the crystal exit surface area. We describe an optimized design that employs the new-generation of thru-silicon via devices. As a result, the performance is documented in a series of bench and beam tests.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2022

Calibrating hypersonic turbulence flow models with the HIFiRE-1 experiment using data-driven machine-learned models.

In this paper we study the efficacy of combining machine-learning methods with projection-based model reduction techniques for creating data-driven surrogate models of computationally expensive, high-fidelity physics models. Such surrogate models are essential for many-query applications e.g., engineering design optimization and parameter estimation, where it is necessary to invoke the high-fidelity model sequentially, many times. Surrogate models are usually constructed for individual scalar quantities. However there are scenarios where a spatially varying field needs to be modeled as a function of the model’s input parameters. Here we develop a method to do so, using projections to represent spatial variability while a machine-learned model captures the dependence of the model’s response on the inputs. The method is demonstrated on modeling the heat flux and pressure on the surface of the HIFiRE-1 geometry in a Mach 7.16 turbulent flow. The surrogate model is then used to perform Bayesian estimation of freestream conditions and parameters of the SST (Shear Stress Transport) turbulence model embedded in the high-fidelity (Reynolds-Averaged Navier–Stokes) flow simulator, using shock-tunnel data. The paper provides the first-ever Bayesian calibration of a turbulence model for complex hypersonic turbulent flows. We find that the primary issues in estimating the SST model parameters are the limited information content of the heat flux and pressure measurements and the large model-form error encountered in a certain part of the flow.

42 ENGINEERING↗
DOE OSTI2015

Interplay between experiments and calculations for organometallic clusters and caged clusters

Clusters consisting of 10-1000 atoms exhibit size-dependent electronic and geometric properties. In particular, composite clusters consisting of several elements and/or components provide a promising way for a bottom-up approach for designing functional advanced materials, because the functionality of the composite clusters can be optimized not only by the cluster size but also by their compositions. In the formation of composite clusters, their geometric symmetry and dimensionality are emphasized to control the physical and chemical properties, because selective and anisotropic enhancements for optical, chemical, and magnetic properties can be expected. Organometallic clusters and caged clusters are demonstrated as a representative example of designing the functionality of the composite clusters. Organometallic vanadium-benzene forms a one dimensional sandwich structure showing ferromagnetic behaviors and anomalously large HOMO-LUMO gap differences of two spin orbitals, which can be regarded as spin-filter components for cluster-based spintronic devices. Caged clusters of aluminum (Al) are well stabilized both geometrically and electronically at Al{sub 12}X, behaving as a “superatom”.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2026

Bayesian Optimization of Catalysis with In-Context Learning

Large language models (LLMs) can perform accurate classification with zero or few examples through in-context learning (ICL), allowing the model to observe query-relevant examples at inference time and eliminating the need for additional weight updates to generalize beyond its original training data. We extend this capability to regression with uncertainty estimation using frozen LLMs (e.g., GPT-4o, Gemini), enabling Bayesian optimization (BO) in natural language without explicit model training or feature engineering. We apply this to materials discovery by representing materials as synthesis and testing procedures for use in natural language prompts. This Bayesian, design-first approach prioritizes optimization toward target material properties before detailed characterization, in contrast to conventional experimental workflows that often emphasize characterization of suboptimal materials. On benchmarks like aqueous solubility and oxidative coupling of methane (OCM), BO-ICL matches or outperforms Gaussian processes. In live experiments on the reverse water–gas shift (RWGS) reaction, BO-ICL identifies multimetallic catalysts that approach equilibrium CO yield within 6 and 10 iterations from a pool of 3,700 and 360,000 candidates, respectively. Our method redefines materials representation and accelerates discovery, with broad applications across catalysis, materials science, and AI.

Calibration↗
DOE OSTI2023

Coupled Lattice Boltzmann Modeling Framework for Pore-Scale Fluid Flow and Reactive Transport

In this paper, we propose a modeling framework for pore-scale fluid flow and reactive transport based on a coupled lattice Boltzmann model (LBM). We develop a modeling interface to integrate the LBM modeling code parallel lattice Boltzmann solver and the PHREEQC reaction solver using multiple flow and reaction cell mapping schemes. The major advantage of the proposed workflow is the high modeling flexibility obtained by coupling the geochemical model with the LBM fluid flow model. Consequently, the model is capable of executing one or more complex reactions within desired cells while preserving the high data communication efficiency between the two codes. Meanwhile, the developed mapping mechanism enables the flow, diffusion, and reactions in complex pore-scale geometries. We validate the coupled code in a series of benchmark numerical experiments, including 2D single-phase Poiseuille flow and diffusion, 2D reactive transport with calcite dissolution, as well as surface complexation reactions. The simulation results show good agreement with analytical solutions, experimental data, and multiple other simulation codes. In addition, we design an AI-based optimization workflow and implement it on the surface complexation model to enable increased capacity of the coupled modeling framework. Compared to the manual tuning results proposed in the literature, our workflow demonstrates fast and reliable model optimization results without incorporating pre-existing domain knowledge.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2011

Short-wavelength and three-dimensional instability evolution in National Ignition Facility ignition capsule designs

Ignition capsule designs for the National Ignition Facility (NIF) [G. H. Miller, E. I. Moses, and C. R. Wuest, Opt. Eng. 443, 2841 (2004)] have continued to evolve in light of improved physical data inputs, improving simulation techniques, and, most recently, experimental data from a growing number of NIF sub-ignition experiments. This paper summarizes a number of recent changes to the cryogenic capsule design and some of our latest techniques in simulating its performance. Specifically, recent experimental results indicated harder x-ray drive spectra in NIF hohlraums than were predicted and used in previous capsule optimization studies. To accommodate this harder drive spectrum, a series of high-resolution 2-D simulations, resolving Legendre mode numbers as high as 2000, were run and the germanium dopant concentration and ablator shell thicknesses re-optimized accordingly. Simultaneously, the possibility of cooperative or nonlinear interaction between neighboring ablator surface defects has motivated a series of fully 3-D simulations run with the massively parallel HYDRA code. These last simulations include perturbations seeded on all capsule interfaces and can use actual measured shell surfaces as initial conditions. 3-D simulations resolving Legendre modes up to 200 on large capsule sectors have run through ignition and burn, and higher resolution simulations resolving as high as mode 1200 have been run to benchmark high-resolution 2-D runs. Finally, highly resolved 3-D simulations have also been run of the jet-type perturbation caused by the fill tube fitted to the capsule. These 3-D simulations compare well with the more typical 2-D simulations used in assessing the fill tube's impact on ignition. Coupled with the latest experimental inputs from NIF, our improving simulation capability yields a fuller and more accurate picture of NIF ignition capsule performance.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2022

LigninGraphs: lignin structure determination with multiscale graph modeling

Lignin is an aromatic biopolymer found in ubiquitous sources of woody biomass. Designing and optimizing lignin valorization processes requires a fundamental understanding of lignin structures. Experimental characterization techniques, such as 2D-heteronuclear single quantum coherence (HSQC) nuclear magnetic resonance (NMR) spectra, could elucidate the global properties of the polymer molecules. Computer models could extend the resolution of experiments by representing structures at the molecular and atomistic scales. We introduce a graph-based multiscale modeling framework for lignin structure generation and visualization. The framework employs accelerated rejection-free polymerization and hierarchical Metropolis Monte Carlo optimization algorithms. We obtain structure libraries for various lignin feedstocks based on literature and new experimental NMR data for poplar wood, pinewood, and herbaceous lignin. The framework could guide researchers towards feasible lignin structures, efficient space exploration, and future kinetics modeling. Its software implementation in Python, LigninGraphs, is open-source and available on GitHub.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
NASA NTRS1985

Evaluation of results of cell electrophoresis experiments on space shuttle STS-3 including pre-flight and post-flight laboratory experiments

The objectives of the red blood cell experiments were to provide a visual check on the electrophoretic process and especially electroosmotic flow in space as well as to provide test separations of non-degradable standard particles for comparison with the separations of the three viable cell types studied on the Apollo-Soyuz Test Project. Determination of the maximum concentrations of cells that can be separated in column electrophore was a significant goal. Two of the eight columns were available for red cell experiments, so two concentrations of human and rabbit RBC mixtures were used. The objectives of another experiment were to evaluate the reproducibility of microgravity electrophoretic separation of living kidney cells, to separate cells with highly viability despite two freeze-thaw cycles, and to optimize the physical conditions of cell separation. Owing to the uncertain heterogeneity of the starting material, the experimental design does not assess resolution in microgravity, but improved separability was sought in comparison to density-gradient electrophoresis or continuous-flow electrophoresis. Efforts were made to increase cell yield and cell viability and to assess reproducibility directly.

Todd, P. W.↗
NASA NTRS1989

Direct electron-pair production by high energy heavy charged particles

Direct electron pain production via virtual photons by moving charged particles is a unique electro-magnetic process having a substantial dependence on energy. Most electro-magnetic processes, including transition radiation, cease to be sensitive to the incident energy above 10 TeV/AMU. Thus, it is expected, that upon establishment of cross section and detection efficiency of this process, it may provide a new energy measuring technique above 10 TeV/AMU. Three accelerator exposures of emulsion chambers designed for measurements of direct electron-pains were performed. The objectives of the investigation were to provide the fundamental cross-section data in emulsion stacks to find the best-fit theoretical model, and to provide a calibration of measurements of direct electron-pairs in emulsion chamber configurations. This paper reports the design of the emulsion chambers, accelerator experiments, microscope measurements, and related considerations for future improvements of the measurements, and for possible applications to high energy cosmic ray experiments. Also discussed are the results from scanning 56m of emulsion tracks at 1200x magnification so that scanning efficiency is optimized. Measurements of the delta-ray range spectrum were also performed for much shorter track lengths, but with sufficiently large statistics in the number of measured delta-rays.

Takahashi, Y.↗
NASA NTRS1994

Status of Diffused Junction p(+)n InP Solar Cells for Space Applications

Recently, we have succeeded in fabricating diffused junction p(+)n(Cd,S) InP solar cells with measured AMO, 25 C open circuit voltage (V(sub OC)) of 887.6 mV, which, to the best of our knowledge, is higher than previously reported V(sub OC) values for any InP homojunction solar cells. The experiment-based projected achievable efficiency of these cells using LEC grown substrates is 21.3%. The maximum AMO, 25 C efficiency recorded to date on bare cells is, however, only 13.2%. This is because of large external and internal losses due to non-optimized front grid design, antireflection (AR) coating and emitter thickness. This paper summarizes recent advances in the technology of fabrication of p(+)n InP diffused structures and solar cells, resulted from a study undertaken in an effort to increase the cell efficiency. The topics discussed in this paper include advances in: 1) the formation of thin p(+) InP:Cd emitter layers, 2) electroplated front contacts, 3) surface passivation and 4) the design of a new native oxide/AI203/MgF2 three layer AR coating using a chemically-grown P-rich passivating oxide as a first layer. Based on the high radiation resistance and the excellent post-irradiation annealing and recovery demonstrated in the early tests done to date, as well as the projected high efficiency and low-cost high-volume fabricability, these cells show a very good potential for space photovoltaic applications.

Faur, Mircea↗
NASA NTRS2000

Aerospace Systems Design in NASA's Collaborative Engineering Environment

Past designs of complex aerospace systems involved an environment consisting of collocated design teams with project managers, technical discipline experts, and other experts (e.g., manufacturing and systems operation). These experts were generally qualified only on the basis of past design experience and typically had access to a limited set of integrated analysis tools. These environments provided less than desirable design fidelity, often lead to the inability of assessing critical programmatic and technical issues (e.g., cost, risk, technical impacts), and generally derived a design that was not necessarily optimized across the entire system. The continually changing, modern aerospace industry demands systems design processes that involve the best talent available (no matter where it resides) and access to the the best design and analysis tools. A solution to these demands involves a design environment referred to as collaborative engineering. The collaborative engineering environment evolving within the National Aeronautics and Space Administration (NASA) is a capability that enables the Agency's engineering infrastructure to interact and use the best state-of-the-art tools and data across organizational boundaries. Using collaborative engineering, the collocated team is replaced with an interactive team structure where the team members are geographical distributed and the best engineering talent can be applied to the design effort regardless of physical location. In addition, a more efficient, higher quality design product is delivered by bringing together the best engineering talent with more up-to-date design and analysis tools. These tools are focused on interactive, multidisciplinary design and analysis with emphasis on the complete life cycle of the system, and they include nontraditional, integrated tools for life cycle cost estimation and risk assessment. NASA has made substantial progress during the last two years in developing a collaborative engineering environment. NASA is planning to use this collaborative engineering engineering infrastructure to provide better aerospace systems life cycle design and analysis, which includes analytical assessment of the technical and programmatic aspects of a system from "cradle to grave." This paper describes the recent NASA developments in the area of collaborative engineering, the benefits (realized and anticipated) of using the developed capability, and the long-term plans for implementing this capability across Agency.

Monell, Donald W.↗