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Results for “sequential experimental 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.

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At least 19 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Bayesian sequential optimal experimental design for nonlinear models using policy gradient reinforcement learning

We present a mathematical framework and computational methods for optimally designing a finite sequence of experiments. This sequential optimal experimental design (sOED) problem is formulated as a finite-horizon partially observable Markov decision process (POMDP) under a Bayesian setting and with information-theoretic utilities. The formulation is general and may accommodate continuous random variables, non-Gaussian posteriors, and nonlinear forward models. The sOED design policy incorporates elements of feedback and lookahead simultaneously, and we show it to generalize the commonly-used batch and greedy design strategies. We solve for the sOED policy using the policy gradient (PG) method from reinforcement learning, and provide a derivation for the PG expression in the sOED context. Adopting an actor-critic approach, the policy and value functions are parameterized using deep neural networks and improved via PG estimates produced from simulated episodes of designs and observations. The new PG-sOED algorithm is first validated on a linear-Gaussian benchmark, and then compared against other design baselines on a sensor movement problem for contaminant source inversion in a convection-diffusion field. As a result, we provide explanation for the policy behaviors using knowledge of the underlying physical process.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Practical and Optimal Sequential Bayesian Experimental Design for Complex Systems Incorporating Human Experimenter Preferences (Final Scientific/Technical Report)

Experiments are indispensable for developing models of complex systems. Carefully designed experiments can provide substantial savings for these expensive data-acquisition opportunities. However, designs based on heuristics are often suboptimal for systems with multiphysics, nonlinear dynamics, and uncertain and noisy environments. Optimal experimental design, while leveraging predictive models, seeks to systematically quantify and maximize the value of experiments. In this project, we focused on the design of multiple experiments, where current approaches are largely suboptimal: batch-design does not adapt to new data acquired during the experiment campaign (no feedback), and greedy/myopic design ignores future dynamics and consequences (no lookahead). We developed the mathematical framework and computational methods for sequential optimal experimental design (sOED) for complex systems. We enabled tractable model-based sOED in a rigorous manner through novel algorithms based on reinforcement learning, and investigated the effects of human experimenters on the design process. Our methods are fully Bayesian, able to quantify and update uncertainty in a principled manner. The traits aimed by our approach—mathematical rigor and optimality, human effects and uncertainty quantification, computational practicality—are crucial for elevating the standards of artificial intelligence (AI) to support decision-making in scientific domains, and contribute toward trust and realistic adoption of AI in experimental design practice.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Sequential Bayesian Experimental Design for Calibration of Expensive Simulation Models

Simulation models of critical systems often have parameters that need to be calibrated using observed data. For expensive simulation models, calibration is done using an emulator of the simulation model built on simulation output at different parameter settings. Using intelligent and adaptive selection of parameters to build the emulator can drastically improve the efficiency of the calibration process. The article proposes a sequential framework with a novel criterion for parameter selection that targets learning the posterior density of the parameters. The emergent behavior from this criterion is that exploration happens by selecting parameters in uncertain posterior regions while simultaneously exploitation happens by selecting parameters in regions of high posterior density. Furthermore, the advantages of the proposed method are illustrated using several simulation experiments and a nuclear physics reaction model.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Towards informatics-driven design of nuclear waste forms

Informatics-driven approaches, such as machine learning and sequential experimental design, have shown the potential to drastically impact next-generation materials discovery and design.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2025

Dial

A key step in almost all scientific endeavors is answering the question: Given this data I already collected, what new data do I expect will yield the most useful information toward my scientific objective? The area of (sequential) experimental design has long been investigating answers to this question, but in recent years techniques from the machine learning subfield of active learning are increasingly applied. Researchers need a simple software tool for active learning applied to experimental design that can easily integrate into their existing workflows. This computer code, Dial, provides a microservice in ORNL's INTERSECT ecosystem for active learning applied to experimental design. By being part of the INTERSECT ecosystem, Dial is simple to integrate into any INTERSECT-based workflow. Dial provides multiple backend options, where a backend is an implementation of a specific active learning method. Users can select the backend that performs best for their application. Developers can also add new backends as needed. At its core, Dial receives a set of pre-existing measurements and input parameter bounds and then recommends one or more new sets of parameters to measure. Dial also includes interfaces to other microservices in the INTERSECT ecosystem so that it can be incorporated into INTERSECT campaigns. Dial provides a simple, yet powerful interface to convert automated INTERSECT workflows into autonomous workflows that adapt based on the results that are obtained. A shared microservice for active learning prevents duplicated effort by each application team implementing its own adaptive design of experiments tool.

Drane, Lance [Oak Ridge National Laboratory (ORNL)↗
NASA NTRS1972

Kullback-Leibler information function and the sequential selection of experiments to discriminate among several linear models

A sequential adaptive experimental design procedure for a related problem is studied. It is assumed that a finite set of potential linear models relating certain controlled variables to an observed variable is postulated, and that exactly one of these models is correct. The problem is to sequentially design most informative experiments so that the correct model equation can be determined with as little experimentation as possible. Discussion includes: structure of the linear models; prerequisite distribution theory; entropy functions and the Kullback-Leibler information function; the sequential decision procedure; and computer simulation results. An example of application is given.

Sidik, S. M.↗
NASA NTRS2002

Structural Optimization of a Force Balance Using a Computational Experiment Design

This paper proposes a new approach to force balance structural optimization featuring a computational experiment design. Currently, this multi-dimensional design process requires the designer to perform a simplification by executing parameter studies on a small subset of design variables. This one-factor-at-a-time approach varies a single variable while holding all others at a constant level. Consequently, subtle interactions among the design variables, which can be exploited to achieve the design objectives, are undetected. The proposed method combines Modern Design of Experiments techniques to direct the exploration of the multi-dimensional design space, and a finite element analysis code to generate the experimental data. To efficiently search for an optimum combination of design variables and minimize the computational resources, a sequential design strategy was employed. Experimental results from the optimization of a non-traditional force balance measurement section are presented. An approach to overcome the unique problems associated with the simultaneous optimization of multiple response criteria is described. A quantitative single-point design procedure that reflects the designer's subjective impression of the relative importance of various design objectives, and a graphical multi-response optimization procedure that provides further insights into available tradeoffs among competing design objectives are illustrated. The proposed method enhances the intuition and experience of the designer by providing new perspectives on the relationships between the design variables and the competing design objectives providing a systematic foundation for advancements in structural design.

Parker, P. A.↗
NASA NTRS1992

Efficient use of high performance computers for integrated controls and structures design

Simply transporting design codes from sequential-scalar computers to parallel-vector computers does not fully utilize the computational benefits offered by high performance computers. By performing integrated controls and structures design on an experimental truss platform with both sequential-scalar and parallel-vector design codes, conclusive results are presented to substantiate this claim. The efficiency of a Cholesky factorization scheme in conjunction with a variable-band row data structure is presented. In addition, the Lanczos eigensolution algorithm has been incorporated in the design code for both parallel and vector computations. Comparisons of computational efficiency between the initial design code and the parallel-vector design code are presented. It is shown that the Lanczos algorithm with the Cholesky factorization scheme is far superior to the sub-space iteration method of eigensolution when substantial numbers of eigenvectors are required for control design and/or performance optimization. Integrated design results show the need for continued efficiency studies in the area of element computations and matrix assembly.

Belvin, W. K.↗
NASA NTRS2015

Statistical Engineering in Air Traffic Management Research

NASA is working to develop an integrated set of advanced technologies to enable efficient arrival operations in high-density terminal airspace for the Next Generation Air Transportation System. This integrated arrival solution is being validated and verified in laboratories and transitioned to a field prototype for an operational demonstration at a major U.S. airport. Within NASA, this is a collaborative effort between Ames and Langley Research Centers involving a multi-year iterative experimentation process. Designing and analyzing a series of sequential batch computer simulations and human-in-the-loop experiments across multiple facilities and simulation environments involves a number of statistical challenges. Experiments conducted in separate laboratories typically have different limitations and constraints, and can take different approaches with respect to the fundamental principles of statistical design of experiments. This often makes it difficult to compare results from multiple experiments and incorporate findings into the next experiment in the series. A statistical engineering approach is being employed within this project to support risk-informed decision making and maximize the knowledge gained within the available resources. This presentation describes a statistical engineering case study from NASA, highlights statistical challenges, and discusses areas where existing statistical methodology is adapted and extended.

Wilson, Sara R.↗
DOE OSTI2025

Efficient and Selective Chemical Transformations in Highly Charged and Confined Nanodroplets

The acceleration of chemical reaction rates and the increased product selectivity in microdroplets compared to that in bulk solutions has become a topic of increasing interest that has been extensively characterized by electrospray ionization mass spectrometry (ESI-MS). However, the sources of this acceleration and the detailed relationships between droplet properties and resulting reaction rate acceleration are still under debate. Moreover, droplet properties are governed by multiple interrelated experimental parameters, i.e., electrospray voltage, solution flow rate, etc., which makes it difficult and time-consuming to explore this diverse parameter space using traditional manual experimental or computational approaches. In this work, we developed an automated experimental platform integrating reactions in controlled charged microdroplet environments with ESI-MS characterization and sequential hybrid Bayesian modeling, as well as an optimal experimental design framework, to achieve multidimensional parameter optimization for higher reaction turnover rates, based on a model reaction of tetraethylenepentamine (TEPA) with carbon dioxide. With the current platform, we have achieved automated scans with a range of electrospray voltages and solution flow rates, and determined and optimized parameter settings to achieve increased reaction turnovers. We have also linked this platform to the underlying properties of droplets via a hybrid model incorporating physics, high-level theoretical calculations, and machine learning (ML) approaches. The autonomous platform is broadly applicable to a range of chemical reactions relevant to DOE’s mission in chemical separations, catalysis, and materials synthesis.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
NASA NTRS1973

Ion time-of-flight determinations of doubly to singly ionized mercury ion ratios from a mercury electron bombardment discharge

Doubly to singly charged mercury ion ratios in electron bombardment ion thruster exhaust beams have been determined as functions of bombardment discharge potential, thrust beam current, thrust beam radial position, acceleration-deceleration voltage ratio, and propellant utilization fraction. A mathematical model for two-step ionization processes has been derived, and calculated ion ratios are compared to observed ratios. Production of Hg(++) appears to result primarily from sequential ionization of Hg(+) in the discharge. Experimental and analytical results are presented, and design, construction, and operation features of an electrostatic deflection ion time-of-flight analyzer for the determination of the above-mentioned ratios are reviewed.

Sellen, J. M., Jr.↗
DOE OSTI2025

Spatially Patterned Architectures to Modulate CO 2 Reduction Cascade Catalysis Kinetics

Electrochemical CO 2 reduction using renewable sources of electrical energy holds promise for converting CO 2 into fuels and chemicals. The complex interactions among chemical/electrochemical reactions and mass transport make it difficult to analyze the effect of an individual process on electrode performance based only on experimental methods. Here, we developed a generalized steady-state simulation to describe an electrode surface in which sequential cascade catalysts are patterned in a periodic trench design. If appropriately constructed, this trench geometry is hypothesized to be able to yield a higher net current density for a CO 2 reduction (CO 2 R) cascade reaction. We have used realistic experimental reaction kinetics to investigate the role of trench geometry in mass transport, local microenvironments, and selectivity for a model CO 2 R cascade reaction. The model considers local concentration gradients of bicarbonate species at quasi-equilibrium and catalytic surface reactions based on concentration-dependent Butler–Volmer kinetics. Our results suggest that varying the spatial distribution of active sites plays a significant role in facilitating effective mass transport between active sites, modulating selectivity for the cascade reaction, and enhancing the yield of desirable cascade products. Moreover, we observe that this trench geometry significantly alters the cascade reaction rate by affecting the local pH, which can cause inadvertent depletion of available aqueous CO 2 to limit the CO 2 R cascade kinetics and modest suppression of the hydrogen evolution reaction (HER). The results highlight the trade-offs between mass transport, pH, and reaction kinetics that become apparent only when considering the coupled physics of all processes at the electrode surface. Here, this model can thus serve as a primary tool to build more selective and efficient patterned architectures for the CO 2 R cascade catalysis.

CO2 reduction↗
NASA NTRS1993

Experimental validation of optimization-based integrated controls-structures design methodology for flexible space structures

An optimization-based integrated design approach for flexible space structures is experimentally validated using three types of dissipative controllers, including static, dynamic, and LQG dissipative controllers. The nominal phase-0 of the controls structure interaction evolutional model (CEM) structure is redesigned to minimize the average control power required to maintain specified root-mean-square line-of-sight pointing error under persistent disturbances. The redesign structure, phase-1 CEM, was assembled and tested against phase-0 CEM. It is analytically and experimentally demonstrated that integrated controls-structures design is substantially superior to that obtained through the traditional sequential approach. The capability of a software design tool based on an automated design procedure in a unified environment for structural and control designs is demonstrated.

Maghami, Peiman G.↗
NASA NTRS1993

Integrated design and experimental validation of the CSI evolutionary structure

An experimental validation of the optimization-based integrated design methodology performed under the Controls-Structures Interaction (CSI) program for a class of flexible spacecraft is reviewed. The studies have been performed using an integrated design software tool which is under development at the NASA-Langley Research Center. It is analytically and experimentally demonstrated that integrated controls-structures design can yield designs which are substantially superior to those obtained through the traditional sequential approach.

Maghami, P. G.↗
NASA NTRS2008

RFID in Space: Exploring the Feasibility and Performance of Gen 2 Tags as a Means of Tracking Equipment, Supplies, and Consumable Products in Cargo Transport Bags onboard a Space Vehicle or Habitat

Current inventory management techniques for consumables and supplies aboard space vehicles are burdensome and time consuming. Inventory of food, clothing, and supplies are taken periodically by manually scanning the barcodes on each item. The inaccuracy of reading barcodes and the excessive amount of time it takes for the astronauts to perform this function would be better spent doing scientific experiments. Therefore, there is a need for an alternative method of inventory control by NASA astronauts. Radio Frequency Identification (RFID) is an automatic data capture technology that has potential to create a more effective and user-friendly inventory management system (IMS). In this paper we introduce a Design for Six Sigma Research (DFSS-R) methodology that allows for reliability testing of RFID systems. The research methodology uses a modified sequential design of experiments process to test and evaluate the quality of commercially available RFID technology. The results from the experimentation are compared to the requirements provided by NASA to evaluate the feasibility of using passive Generation 2 RFID technology to improve inventory control aboard crew exploration vehicles.

Jones, Erick C.↗
DOE OSTI2024

Targeted materials discovery using Bayesian algorithm execution

Rapid discovery and synthesis of future materials requires intelligent data acquisition strategies to navigate large design spaces. A popular strategy is Bayesian optimization, which aims to find candidates that maximize material properties; however, materials design often requires finding specific subsets of the design space which meet more complex or specialized goals. We present a framework that captures experimental goals through straightforward user-defined filtering algorithms. These algorithms are automatically translated into one of three intelligent, parameter-free, sequential data collection strategies (SwitchBAX, InfoBAX, and MeanBAX), bypassing the time-consuming and difficult process of task-specific acquisition function design. Our framework is tailored for typical discrete search spaces involving multiple measured physical properties and short time-horizon decision making. We demonstrate this approach on datasets for TiO 2 nanoparticle synthesis and magnetic materials characterization, and show that our methods are significantly more efficient than state-of-the-art approaches. Overall, our framework provides a practical solution for navigating the complexities of materials design, and helps lay groundwork for the accelerated development of advanced materials.

42 ENGINEERING↗