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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 1,693 records · Page 94
All sourcesNASA NTRSDOE OSTI
NASA NTRS2010

Application of Ensemble Detection and Analysis to Modeling Uncertainty in Non Stationary Process

Characterization of non stationary and nonlinear processes is a challenge in many engineering and scientific disciplines. Climate change modeling and projection, retrieving information from Doppler measurements of hydrometeors, and modeling calibration architectures and algorithms in microwave radiometers are example applications that can benefit from improvements in the modeling and analysis of non stationary processes. Analyses of measured signals have traditionally been limited to a single measurement series. Ensemble Detection is a technique whereby mixing calibrated noise produces an ensemble measurement set. The collection of ensemble data sets enables new methods for analyzing random signals and offers powerful new approaches to studying and analyzing non stationary processes. Derived information contained in the dynamic stochastic moments of a process will enable many novel applications.

Racette, Paul↗
NASA NTRS2014

Maiden Voyage of the Rodent Habitat on ISS: Opportunities for Investigating Molecular Mechanisms and Biomedical Consequences of Long Duration Spaceflight

Research using rodents is an essential tool for advancing biomedical research on Earth and in space. The National Research Counsel’s Decadal survey (1) emphasized the importance of expanding NASAs life sciences research to perform long duration, rodent experiments on the International Space Station (ISS). To accomplish this objective, flight hardware, operations, and science capabilities were developed at NASA ARC to support both commercial and government-sponsored research. In preparation for the maiden voyage of the Rodent Habitat hardware and operations system (Rodent Research-1), and in close consultation with a Science Working Group comprised of veterinarians and experienced spaceflight investigators, we modified existing Animal Enclosure Module hardware, developed new hardware, operations, and science activities, and performed a series of ground-based verification tests. Preflight, ground based hardware tests included a simulation of SpaceX Dragon launch conditions (vibration and hypergravity) using the Transporter, and also two long-term biocompatibility tests (32 and 92 days) using the Habitat developed for long term housing on the ISS. The launch simulation test showed that adult mice housed in Transporter hardware adapted well, even if launch simulation was followed by a period of simulated weightlessness (via hind limb unloading). The biocompatibility tests demonstrated that the Habitat successfully supported animal health and also provided a useful video imaging system that enables frequent monitoring of animal health and behavior by veterinary and scientific experts on the ground, independent of ISS crew intervention. At the conclusion of all tests, mice were deemed healthy and suitable for conducting biological research. Additional preflight analyses of tissues preserved by freezing or fixation for gene expression analyses revealed that spleen and liver tissues recovered under conditions that simulated on-orbit activities yielded high quality RNA (RIN values 8-10) and liver enzyme activities and protein content (e.g. catalase). In addition, new methods were developed to optimize future science return by dissecting tissues post-euthanasia and storage. Various tissues were harvested from either intact or partially dissected, frozen carcasses after storage for ~2-6 months; most of the tissues (brain, heart, kidney, eye, adrenal glands and skeletal muscle) were of high RNA quality for science return, whereas some tissues (small intestine, bone marrow and bones) were not. These data demonstrated the protocols developed for future flight experiments supported science return despite delayed preservation post-euthanasia or prolonged storage, and furthermore, that high-quality RNA samples from many different tissues can be recovered by dissection following prolonged storage of the tissue in situ at -80˚C. The first flight experiments carrying 20 mice were launched on Sept 21, 2014 in an unmanned Dragon Capsule, SpaceX4; Rodent Research-1 is dedicated to achieving both NASA validation and CASIS science objectives. Ground based control groups (housed in flight hardware or standard cages) were maintained in environmental chambers at Kennedy Space Center. Crewmembers previously trained in animal handling transferred mice from the Transporter into Habitats under simultaneous veterinary supervision by video streaming and were deemed healthy. Health and behavior of all mice on the ISS was monitored by video feed on a daily basis. The 10 mice for validation (16wk old, female C57Bl6/J) ambulated freely and actively throughout the Habitat, relying heavily on their forelimbs for locomotion. The first on-orbit dissections of mice were performed successfully on Oct 12 and 13, 2014, and the validation mice will reside on ISS for up to 30 days. In conclusion, new capability for long duration rodent research is under development, including in-flight sample collection (which avoids the complication of reentry); results obtained to date will be described. This new Rodent Research system enables achievement of both basic science and translational research objectives to advance human exploration of space.

maiden voyage↗
NASA NTRS2016

Determination of Celestial Body Principal Axes via Gravity Field Estimation

The determination of physical parameters of celestial bodies provides critical navigational and scientific information. Determining the mass, rotation state, and density distribution is an important task of the navigation team on an operational mission. One representation of the internal density distribution, spherical harmonics, may be leveraged to compute the orientation of the principal axis frame of the celestial body, which in turn informs the dynamics of the body’s motion. Using data from NASA’s Dawn mission to the massive asteroids (1) Ceres and (4) Vesta, a method is proposed that refines the knowledge of the principal axis frame by utilizing estimated spherical harmonic coefficients. Applying an iterative rotation scheme to the solved-for body-fixed frame leads to convergence on a frame that is very close to the actual dynamical principal axis frame.

Takahashi, Yu↗
DOE OSTI2021

Hierarchical Analysis of Halo Center in Cosmology

Ever-increasing data size raises many challenges for scientific data analysis. Particularly in cosmological N-body simulation, finding the center of a dark matter halo suffers heavily from the large computational cost associated with the large number of particles (up to 20 million). In this work, we exploit the latent structure embed in a halo, and we propose a hierarchical approach to approximate the exact gravitational potential calculation for each particle in order to more efficiently find the halo center. Tests of our method on data from N-body simulations show that in many cases the hierarchical algorithm performs significantly faster than existing methods with a desirable accuracy.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

CONFLUX: A standardized framework to calculate reactor antineutrino flux

Nuclear fission reactors are abundant sources of antineutrinos for neutrino physics experiments. The flux and spectrum of antineutrinos emitted by a reactor can indicate its activity and composition, suggesting potential applications of neutrino measurements beyond fundamental scientific studies that may be valuable to society. The utility of reactor antineutrinos for applications and fundamental science is dependent on the availability of precise predictions of these emissions. For example, in the last decade, disagreements between reactor antineutrino measurements and models have inspired revision of reactor antineutrino calculations and standard nuclear databases as well as searches for new fundamental particles not predicted by the Standard Model of particle physics. Past predictions and descriptions of the methods used to generate them are documented to varying degrees in the literature, with different modeling teams incorporating a range of methods, input data, and assumptions. The resulting difficulty in accessing or reproducing past models and reconciling results from differing approaches complicates the future study and application of reactor antineutrinos. The CONFLUX (Calculation Of Neutrino FLUX) software framework is a neutrino prediction tool built with the goal of simplifying, standardizing, and democratizing the process of reactor antineutrino flux calculations. CONFLUX includes three primary methods for calculating the antineutrino emissions of nuclear reactors or individual beta decays that incorporate common nuclear data and beta decay theory. The software is prepackaged with the current nuclear databases, including ENDF.B/VIII, JEFF-3.3, and ENSDF, and it includes the capability to predict time-dependent reactor emissions, adjust nuclear database or beta decay inputs/assumptions, and propagate related sources of uncertainty. Here, this paper describes the CONFLUX software structure, details the methods used for flux and spectrum calculations, and provides examples of potential use cases.

Zhang, Xianyi [Lawrence Livermore National Laborat↗
DOE OSTI2023

Modeling the 4D discharge of lithium-ion batteries with a multiscale time-dependent deep learning framework

The lithium-ion battery (LIB) field is moving towards the direction of investigating spatially resolved physical phenomena in the 3D porous microstructure of electrodes. These pore-scale simulations give new insights into the local dynamics of lithiation/de-lithiation and charge transport, Nevertheless, the computational time of these simulations limits the integration of these models in optimization workflows of cycling conditions or electrode manufacturing processes. Machine learning models present a way of assessing in real-time the performance of materials. While several successful techniques for replicating simulations with machine learning have been proposed, this case study presents a more demanding problem, due to the necessity of understanding the behavior of heterogeneous 3D local data, as it evolves in time: this poses both a scientific and a technical challenge. To this end, we propose an autoregressive multiscale convolutional neural network model to predict relevant quantities at the pore-scale in the solid phase: the lithium concentration (in the active material) and potential (in the active material and carbon binder). Here, these are ultimately used to reconstruct the battery discharge curve. 3D images of the electrode microstructures are the input to the network, trained with a dataset of finite element method simulations to predict the discharge behavior of the cathode side in lithium ion batteries. We propose this machine learning model as a proof-of-concept of the applicability of multiscale networks for time-dependent physics problems. The trained model exhibits very high accuracy (with errors lower than 2 %) in forecasting the discharge behavior of new unseen cathodes.

25 ENERGY STORAGE↗
DOE OSTI2021

Nomenclatural issues concerning cultured yeasts and other fungi: why it is important to avoid unneeded name changes

The unambiguous application of fungal names is important to communicate scientific findings. Names are critical for (clinical) diagnostics, legal compliance, and regulatory controls, such as biosafety, food security, quarantine regulations, and industrial applications. Consequently, the stability of the taxonomic system and the traceability of nomenclatural changes is crucial for a broad range of users and taxonomists. The unambiguous application of names is assured by the preservation of nomenclatural history and the physical organisms representing a name. Fungi are extremely diverse in terms of ecology, lifestyle, and methods of study. Predominantly unicellular fungi known as yeasts are usually investigated as living cultures. Methods to characterize yeasts include physiological (growth) tests and experiments to induce a sexual morph; both methods require viable cultures. Thus, the preservation and availability of viable reference cultures are important, and cultures representing reference material are cited in species descriptions. Historical surveys revealed drawbacks and inconsistencies between past practices and modern requirements as stated in the International Code of Nomenclature for Algae, Fungi, and Plants (ICNafp). Improper typification of yeasts is a common problem, resulting in a large number invalid yeast species names. With this opinion letter, we address the problem that culturable microorganisms, notably some fungi and algae, require specific provisions under the ICNafp. We use yeasts as a prominent example of fungi known from cultures. But viable type material is important not only for yeasts, but also for other cultivable Fungi that are characterized by particular morphological structures (a specific type of spores), growth properties, and secondary metabolites. We summarize potential proposals which, in our opinion, will improve the stability of fungal names, in particular by protecting those names for which the reference material can be traced back to the original isolate.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2026

Detecting and Characterizing Fracture Zones Using a Convolutional Neural Network

This project directly supports the Geothermal Technologies Office (GTO) objectives outlined in the Multi-Year Program Plan (MYPP) by advancing two key research areas: “Exploration and Characterization” and “Data, Modeling, and Analysis.” This project has successfully demonstrated a pre-drilling ability to image and characterize the distribution and connectivity of subsurface faults and fractures, key parameters for identifying permeable pathways that enable geothermal fluids to circulate and produce energy. Specifically, we developed and implemented innovative machine learning methodologies to enhance geothermal exploration. Large-scale faults were detected using a Convolutional Neural Network (CNN), while small-scale fractures were characterized using a novel Double-Beam Neural Network (DBNN). These tools have proven both technically effective and cost-efficient by reducing reliance on expensive exploratory drilling. Through collaboration with our geothermal industry partner, this research has significantly advanced techniques for identifying hidden geothermal systems and extending the productive lifespan of existing geothermal fields. We applied our methods to two geothermal fields—Soda Lake (Nevada) and Lightning Dock (New Mexico)—to identify shallow steam-charged fracture zones and characterize deep faults at depths of 1.5-2 km. The steam zone identified at the Soda Lake geothermal field showed excellent agreement with prior drilling data, validating the effectiveness of our approaches. In addition, the analysis revealed three new prospective drilling targets for further development and verification. The outcomes of this project improve our scientific understanding of geothermal reservoir behavior, enhance exploration efficiency, extend the economic life of existing geothermal plants. Ultimately, these advancements contribute to GTO’s goal of achieving more sustainable, affordable, and data-driven geothermal energy development across the United States.

15 GEOTHERMAL ENERGY↗
NASA NTRS2009

The Legacy of the Space Shuttle Program: Scientific and Engineering Accomplishments

The goal of this project was to assist in the creation of the appendix for the book being written about the Space Shuttle that is titled The Legacy of the Space Shuttle Program: Scientific and Engineering Accomplishments. The specific responsibility of the intern was the creation of the human health and performance (life sciences) and space biology sections of the appendix. This included examining and finalizing the list of flights with life sciences and space biology experiments flown aboard them, researching the experiments performed, synopsizing each experiment into two sentences, and placing the synopses into an appendix template. Overall, approximately 70 flights had their experiments synopsized and a good method for researching and construction of the template was established this summer.

Torrez, Jonathan↗
NASA NTRS2018

Evacuated Airship for Mars Missions: NIAC Phase I, 2017

An evacuated or vacuum airship relies on the same principle of buoyancy used by standard balloons. However, unlike a balloon which uses a lighter than air gas to displace air and provide lift, the vacuum airship leverages a rigid structure to maintain a vacuum and displace air, thereby providing buoyancy. This method is similar to how a ship uses a rigid structure to displace water and fill the space with air; an evacuated airship uses the same mechanism, except air is displaced and the space remains vacant. Using this method, the evacuated airship is capable of utilizing the full potential of the displaced mass of air, which has interesting implications in the Martian atmosphere. Unlike other aerial vehicles, which are at a disadvantage in Martian atmospheric conditions, the evacuated airship benefits from the Martian atmosphere by virtue of the temperature and molecular composition. As a result, the evacuated airship offers an unprecedented payload capacity and, if implemented, may be used to transport current and future scientific instruments, other vehicles, rovers, and possibly even human habitations. A standard dirigible or balloon for Mars would have a severely limited span of operation and a very narrow field of study, nearly exclusively the atmosphere, but a vacuum airship can be used as a long term tool for many different missions: transportation, ground study, communications, atmospheric study, etcetera, thereby making it a far more economically sensible choice

Clarke, John-Paul↗
NASA NTRSDate not supplied

Feasibility and Performance Analysis of Magnetohydrodynamics Control for Aerocapture at Neptune

NASA’s most recent Decadal Survey posed a mission to one of the Ice Giants as the top priority for flagship missions in the organization’s future. However, current technologies limit the amount of scientific payload available for future Uranian and Neptunian missions due to the need for fuel for orbit insertion maneuvers. Thus, to maximize the scientific potential of future missions, atmospheric aerocapture has been heavily researched. While atmospheric aerocapture simulations have proven enabling for capturing around Neptune, its deep atmospheric pass requires an aeroshell with thermal protection systems(TPS). Magnetohydrodynamically-controlled aerocapture serves as a potential solution to both fully-propulsive orbit insertion and atmospheric aerocapture. Through NASA Langley’s high-fidelity flight dynamics simulation, the Program to Optimize Simulated Trajectories II, both the atmospheric and magnetohydrodynamic aerocapture methods were simulated and compared for identical missions to Neptune. After applying a guidance algorithm for both methods, the results showed that magnetohydrodynamics has not only the control authority to successfully capture around Neptune, but also the unique advantage of a shallower atmospheric pass which decreases the maximum heat load and the required TPS mass.

Danny Nguyen↗
NASA NTRSDate not supplied

Developing Satellite-Assisted Particulate Matter (SAPM) Estimates over India for the MIRA Working Group

The Models, In Situ, and Remote Sensing of Aerosols (MIRA) Working Group is an international collective that encourages collaboration among researchers from these three atmospheric aerosol communities. MIRA currently comprises five interdisciplinary and independently funded Topic Groups, each with specific goals, and involves requests for additional scientific datasets. The Satellite-Assisted Particulate Matter (SAPM) Topic Group, as part of MIRA, focuses on studying particulate matter smaller than 2.5 microns in diameter (PM2.5) due to its significant contribution to air pollution and its harmful effects on human health. While the annual mean PM2.5 levels are typically low (~5-15 μg/m³) across most of the contiguous United States (CONUS), other countries experience much higher concentrations (e.g., India). SAPM aims to compare different methods and techniques for obtaining surface PM2.5 proxies using spaceborne passive and active remote sensors, aerosol models, and in situ measurements. Ultimately, SAPM aims to provide more extensive coverage of PM2.5 concentrations than what is currently available from in situ ground stations, which are limited in some parts of the CONUS and large regions worldwide. Current SAPM members are exploring PM2.5 estimation techniques using active sensors. This presentation offers an overview of these techniques and highlights the strengths and limitations of each approach. These techniques include 1) spaceborne lidar (CALIOP: Cloud-Aerosol Lidar with Orthogonal Polarization) alone, and 2) a combination of spaceborne lidar (CATS: Cloud Aerosol Transport System) and a global aerosol transport model (GEOS: Goddard Earth Observing System). Additionally, we present a case study featuring our SAPM research in India, a country with high levels of PM2.5 concentrations (i.e., state-level annual means of ~100-200 μg/m³). Consistent spatial patterns in PM2.5 over India are found from the in situ data, CALIOP-based, and CATS/model-based methods, with the highest concentrations found in northern India near New Delhi. The gridded PM2.5 analysis yields high R values between in situ and CATS/model (~0.7) and between in situ and CALIOP nighttime (~0.9), as well as good agreement between CATS/model and CALIOP nighttime PM2.5 estimates (R = ~0.8 and slope = ~0.9). For current and future efforts, the SAPM Topic Group is actively seeking new collaborators, especially those working with in situ aerosol measurements, and is interested in acquiring additional aerosol datasets to improve and validate the PM2.5 proxies.

Travis D Toth↗
DOE OSTI2021

Evaluating Probability of Containment Effectiveness at a GCS Sites using integrated assessment modeling approach with Bayesian decision Networks

Improved scientific and engineering understanding of the behavior of geologic CO2 storage together with established regulatory framework and incentive structures raise the prospects for accelerated, large-scale deployment of this greenhouse gas emissions reduction approach. Incentive structures call for the establishment of appropriate verification and accounting approaches to support claims of the integrity of a geologic storage complex and to justify taking credit for long-term storage. In this study, we present a framework for assessing the probability of containment effectiveness over the lifetime of a geologic carbon storage site (e.g., after 70 years of injection and post-injection site performance) using forward stochastic model realizations based on site characterization data and using a monitoring-informed Bayesian network based on hypothetical detectability from surface seismic surveys over the site injection and post-injection phases. The National Risk Assessment Partnership’s open-source Integrated Assessment Model (NRAP-Open-IAM) was utilized to develop an ensemble of 10,000 a priori stochastic forecasts of CO2 containment. Those simulations were used to train the Bayesian network model to estimate the prior probabilities of the CO2 leakage mass into overlying, monitorable aquifers considering the uncertainties in the reservoir properties, permeability of potentially leaky wells and the overlying aquifers. The conditional probabilities in the Bayesian network were either learned from the NRAP-Open-IAM simulations or derived from the predefined detection thresholds for the monitoring method. Observations obtained from monitoring, over time during the site operation phases were then used to generate updated posterior probabilities of containment (and any loss from containment) in the Bayesian network by propagating the prior probabilities through the conditional probabilities. We demonstrate how to construct and use the Bayesian network for verifying the long-term storage complex effectiveness informed by monitoring based on the NRAP-Open-IAM simulations previously developed for the FutureGen 2.0 site. This approach may have relevance for stake holders to demonstrate secure geologic storage, provide a defensible, probabilistic approach to claim credit for geologic storage, and to estimate the likelihood that any fraction of the claimed credit may need to be refunded to the creditor based on available monitoring information.

Bayesian network, Risk assessment, Monitoring, car↗
DOE OSTI2022

How to Obtain the Redshift Distribution from Probabilistic Redshift Estimates

Abstract A reliable estimate of the redshift distribution n ( z ) is crucial for using weak gravitational lensing and large-scale structures of galaxy catalogs to study cosmology. Spectroscopic redshifts for the dim and numerous galaxies of next-generation weak-lensing surveys are expected to be unavailable, making photometric redshift (photo- z ) probability density functions (PDFs) the next best alternative for comprehensively encapsulating the nontrivial systematics affecting photo- z point estimation. The established stacked estimator of n ( z ) avoids reducing photo- z PDFs to point estimates but yields a systematically biased estimate of n ( z ) that worsens with a decreasing signal-to-noise ratio, the very regime where photo- z PDFs are most necessary. We introduce Cosmological Hierarchical Inference with Probabilistic Photometric Redshifts ( CHIPPR ), a statistically rigorous probabilistic graphical model of redshift-dependent photometry that correctly propagates the redshift uncertainty information beyond the best-fit estimator of n ( z ) produced by traditional procedures and is provably the only self-consistent way to recover n ( z ) from photo- z PDFs. We present the chippr prototype code, noting that the mathematically justifiable approach incurs computational cost. The CHIPPR approach is applicable to any one-point statistic of any random variable, provided the prior probability density used to produce the posteriors is explicitly known; if the prior is implicit, as may be the case for popular photo- z techniques, then the resulting posterior PDFs cannot be used for scientific inference. We therefore recommend that the photo- z community focus on developing methodologies that enable the recovery of photo- z likelihoods with support over all redshifts, either directly or via a known prior probability density.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2021

A Segregated Approach for Modeling the Electrochemistry in the 3-D Microstructure of Li-Ion Batteries and Its Acceleration Using Block Preconditioners

Abstract Battery performance is strongly correlated with electrode microstructure. Electrode materials for lithium-ion batteries have complex microstructure geometries that require millions of degrees of freedom to solve the electrochemical system at the microstructure scale. A fast-iterative solver with an appropriate preconditioner is then required to simulate large representative volume in a reasonable time. In this work, a finite element electrochemical model is developed to resolve the concentration and potential within the electrode active materials and the electrolyte domains at the microstructure scale, with an emphasis on numerical stability and scaling performances. The block Gauss-Seidel (BGS) numerical method is implemented because the system of equations within the electrodes is coupled only through the nonlinear Butler–Volmer equation, which governs the electrochemical reaction at the interface between the domains. The best solution strategy found in this work consists of splitting the system into two blocks—one for the concentration and one for the potential field—and then performing block generalized minimal residual preconditioned with algebraic multigrid, using the FEniCS and the Portable, Extensible Toolkit for Scientific Computation libraries. Significant improvements in terms of time to solution (six times faster) and memory usage (halving) are achieved compared with the MUltifrontal Massively Parallel sparse direct Solver. Additionally, BGS experiences decent strong parallel scaling within the electrode domains. Last, the system of equations is modified to specifically address numerical instability induced by electrolyte depletion, which is particularly valuable for simulating fast-charge scenarios relevant for automotive application.

25 ENERGY STORAGE↗
DOE OSTI2023

Recent advancements in high performance polymer electrolyte fuel cell electrode fabrication – Novel materials and manufacturing processes

The global effort to introduce polymer electrolyte fuel cells for clean and renewable energy to the market is increasing the demand for high performance, robust and affordable membrane electrode assemblies (MEAs). There is not yet a standard method for large scale production of MEAs, or the methods employed are generally unsatisfactory in terms of quality and performance. A large number of published data of newly developed catalyst and electrolyte materials, claim to improve the state of the art, but are often not fully comparable due to different experimental studies and experimental designs. This article summarizes the trends in material developments and emerging MEA-manufacturing techniques. The materials and techniques are systematically compared in terms of cell performance and scalability. Current and future scientific challenges are identified and analysed based on published findings over the past five years. Finally, the results of the cited papers have been quantitatively compared to each other and to the internal benchmarks used in each cited work to provide a complete picture of the state of the art in PEFC MEA manufacturing.

25 ENERGY STORAGE↗
DOE OSTI2022

Multimodal parameter spaces of a complex multi-channel neuron model

One of the most common types of models that helps us to understand neuron behavior is based on the Hodgkin–Huxley ion channel formulation (HH model). A major challenge with inferring parameters in HH models is non-uniqueness: many different sets of ion channel parameter values produce similar outputs for the same input stimulus. Such phenomena result in an objective function that exhibits multiple modes (i.e., multiple local minima). This non-uniqueness of local optimality poses challenges for parameter estimation with many algorithmic optimization techniques. HH models additionally have severe non-linearities resulting in further challenges for inferring parameters in an algorithmic fashion. To address these challenges with a tractable method in high-dimensional parameter spaces, we propose using a particular Markov chain Monte Carlo (MCMC) algorithm, which has the advantage of inferring parameters in a Bayesian framework. The Bayesian approach is designed to be suitable for multimodal solutions to inverse problems. We introduce and demonstrate the method using a three-channel HH model. We then focus on the inference of nine parameters in an eight-channel HH model, which we analyze in detail. We explore how the MCMC algorithm can uncover complex relationships between inferred parameters using five injected current levels. The MCMC method provides as a result a nine-dimensional posterior distribution, which we analyze visually with solution maps or landscapes of the possible parameter sets. The visualized solution maps show new complex structures of the multimodal posteriors, and they allow for selection of locally and globally optimal value sets, and they visually expose parameter sensitivities and regions of higher model robustness. We envision these solution maps as enabling experimentalists to improve the design of future experiments, increase scientific productivity and improve on model structure and ideation when the MCMC algorithm is applied to experimental data.

97 MATHEMATICS AND COMPUTING↗